dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to thank former visiting Fulbright U. S. researcher  James Jung  for his English editing. This work was supported by grants from  Polish National Science Center   UMO-2011/02/A/NZ5/00337  and  UMO-2017/25/B/NZ6/01003  (to JC). This work was also supported by  European COST Action BM1404 Mye-EUNITER  (www. mye-euniter. eu).
Data are shown as the MFI among indicated antigen expression in autologous pairs of LDGs versus PMNs. Lines indicate the mean value for each data set; n ¼ 10 donors in each group. ***P < 0. 001, ****P < 0. 0001; two-way matched ANOVA; Sidak post hoc test. (d) Cell-sorted PMNs and LDGs were harvested on poly-L-lysine coated slides and incubated for 3 hours. Cells were then stained for NE (red), counterstained with Hoechst to detect DNA (blue), and examined by fluorescence microscopy. One representative dataset of at least three experiments is shown. Bar ¼ 10 mm. ANOVA, analysis of variance; LDG, low-density granulocyte; MFI, mean fluorescence intensity; PBMC, peripheral blood mononuclear cell; PMN, polymorphonuclear neutrophil; SLE, systemic lupus erythematosus.
Compound 33 likely inhibits SmLSD1 activity by covalently reacting with the FAD cofactor Irreversible inhibitors of LSD1 (tranylcypromine and its derivatives) have been shown to modify the FAD cofactor by covalent bonding of their cyclopropylamine group to the N5 atom of the cofactor flavin ring. Hence, the resulting N5 adduct inhibits the demethylase activity of the enzyme  [24]. Due to structural similarity of compound 33 with these tranylcypromine derivatives  [64] [65] [66] , a similar molecular mechanism of action was expected upon interaction of this compound with SmLSD1. To explore this, homology modelling and compound docking were performed (Fig  8 ). As previously published  [19] , the homology model of the parasite enzyme was generated using HsLSD1 (PDB entry: 2V1D, Fig 8A)  [67].
Ligand preparation, protein preparation and molecular docking From the chemical structure of compound 33 (an N-alkylated tranylcypromine derivative) and the cofactor FAD (S5A Fig and  S5B Fig, respectively ), the covalent adduct was generated using the builder tool in the software MOE (Molecular Operating Environment) 2015. 10 [82]. In summary, the chemical structure of FAD was firstly obtained from a selected crystal structure of HsLSD1 (PDB entry: 6NQU, 41 % sequence identity) and then its structure was simplified since only the flavin ring of the cofactor is involved in the mechanism of action of this class of LSD1 irreversible inhibitors. Thereafter, the desired ligand was obtained by forming a covalent adduct between the amino group of the cyclopropyl core of compound 33 and the flavin ring of the cofactor.
The generated model was prepared with the Schrodinger Protein Preparation Wizard tool using the OPLS_2005 force field where hydrogens atoms were added, partial charges were assigned and energy minimisation was performed. To facilitate docking, the flavin ring of FAD was selected as the centroid to computationally prepare a 12 Å docking grid. Docking simulations were performed using the Glide docking software and the in-built Extra Precision (XP) scoring function in order to estimate the target-compound binding affinity (as expressed as XP score). Selecting default parameters, only ten output poses (conformations) for ligand conformer were generated in the final step.
05% Tween 20 (TBST). Following that, the membranes were probed with 1:2000 dilution of anti-H3K4me2 (Abcam, ab32356; Lot GR84714-4) overnight (at 4°C) in 5% fat-free dry milk in TBST. The blot was washed, incubated overnight at 4°C with 1:500 dilution of the secondary antibody (goat anti-rabbit Horseradish Peroxidase coupled antibody -Pierce #31460, Lot HB987318) in 5% fat-free dry milk in TBST. The blot was then developed by incubating with an enhanced chemiluminescence (ECL) substrate  (Pierce)  followed by CCD camera image acquisition. Acquisition time was adjusted to have maximum exposure without saturation. The detected bands were analysed with ChemiDoc software v4. 0. 1 with high sensitivity settings. For sample normalisation, the membranes were stripped by incubation at 50°C for 1 h in 62 mM TRIS-HCl pH 6.
Acknowledgments KFH, GP and AB thank the  Welsh Government, Life Sciences Research Network Wales  scheme and the  Wellcome Trust  ( 107475/Z/15/Z ) for financially supporting this project. JHK and PAC thank the  NIH  ( GM62437 ). DL and CG acknowledge the support provided by the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). We thank past and current members of the Hoffmann group and  Miss Julie Hirst  for contributions to S. mansoni lifecycle maintenance. We thank  Mr Alan Cookson  from  IBERS Advanced Microscopy and Bioimaging Suite  for assisting with the use of the confocal microscopy facility. We also thank  Dr Dylan Phillips  and  Mr Benjamin J. Hulme  for the help during the acquisition and quantification of confocal microscopy images.
Study population and design The study population included 4,822 patients who received a single-unit unrelated cord blood transplant in an Eurocord/European Blood and Marrow Transplant (EBMT) center for the treatment of a blood disorder between 1990 and 2018 (Table  1 ; Online Supplementary Table  S1 ). Data were collected by Table  1. Demographics of the study population. The effect of the HLA-B leader in clinical outcome was assessed among the base population of 4,822 single cord blood unit transplants, the subset of 2,178 transplants with one HLA-B mismatch, and the subset of 1,013 HLA-A and -DRB1-matched transplants with one HLA-B mismatch.
Various interactions, as detailed in the Results, were examined by including appropriate factors in these regression models. Covariates with missing data were included in models by creating an additional category to reflect the missing value of the appropriate covariate. If outcome data were missing for a particular patient, such a patient was excluded from the appropriate regression analysis. Two-sided P-values from Cox regression models were obtained from the Wald test. Several comparisons were made, all focused on refinements of the concept of leader matching versus leader mismatching. No adjustments were made to the P-values associated with the fitted regression models, although the impact of multiple comparisons is reduced because the examined endpoints are correlated with one another.
Study population The study was performed in the infectious diseases ICU of Bichat-Claude Bernard University hospital in Paris. Every patient admitted with a diagnosis of severe malaria between January 2004 and December 2017 was included. Hospitalization report database and biological data were retrospectively collected. According to the French recommendations for the management of severe imported malaria, adapted from the 2014 WHO's definition  [9] , severe imported malaria cases were defined by the presence of Plasmodium falciparum parasites in peripheral blood (or, more rarely, of one of the four other Plasmodium species), associated with one or more defined severe clinical conditions or biological findings. Clinical criteria included neurological failure (with obnubilation, confusion or prostration, Glasgow Coma Scale < 11, or multiple seizures), respiratory failure (requirement for mechanical ventilation with PaO 2 /FiO 2 < 300 mmHg or spontaneous breathing with PaO 2 < 60 mmHg and/or respiratory rate > 30 per min), cardio-circulatory failure (systolic blood pressure < 80 mmHg despite adequate volume repletion or need for vasoactive drugs), hemorrhage, or jaundice.
Data collection Patients' characteristics and outcomes were compared across two periods, namely from 2004 to end 2012 (1st study period) and from 2013 to 2017 (2nd study period), i. e. , before and after artesunate became the first-choice treatment. The demographic variables such as age, sex, ethnicity (Caucasian, African living in France or in Africa), visited endemic area, length of stay in endemic areas, chemoprophylaxis strategy applied, cause of travel, time between symptoms onset and ICU admission, previous therapy before ICU admission, medical history of prior malaria infection clinical and biological parameters during the first hour following ICU admission, curative treatment used, and ICU and hospital lengths of stay were collected. Malaria serology was also performed for all patients whom diagnosis was confirmed in our laboratory.
Statistical analysis Univariate analysis (Mann-Whitney or Chi-square tests as appropriate) was performed to unveil differences between both study periods. A similar analysis was used to select variables associated with poor outcome. Quantitative clinical and biological variables were transformed into dummy variables according to WHO criteria for severity. Variables with a p value of 0. 1 or less were proposed for selection in a logistic regression model with stepwise selection stratified by period. Data are presented as median (interquartile range) or numbers (%). We used the SAS 9. 4 software for all statistical analyses.
Competence induction was achieved by injecting CSP diluted at a concentration of 500 ng/µL in C+Y medium and catalase under pressure (2 min at 6 psi, followed by 6 min at 3 psi). Images were captured and processed using the Nis-Elements AR software (Nikon Instruments Europe BV, Amsterdam, The Netherlands). Phase contrast and fluorescence microscopy were performed with an automated inverted epifluorescence microscope Nikon Ti-E/B equipped with the "perfect focus system" (PFS, Nikon), a phase contrast objective (CFI Plan Apo Lambda DM 100X, NA1. 45), Semrock filters sets for Green Fluorescent Protein (GFP) (Ex: 482BP35; DM: 506; Em: 536BP40), CFP (Ex: 438BP24; DM: 458; Em: 483BP32) and mCherry (Ex: 562BP40; DM: 593; Em: 641BP75), a LED light source (Spectra X Light Engine, Lumencor, Beaverton, OR, USA), and sCMOS camera (Neo sCMOS, Andor, Belfast, UK), and a thermostated chamber at 37 • C.
Gene sequencing Genomic DNA was extracted with the Qiamp DNA minikit (Qiagen) and tested with a multilocus sequence typing (MLST) scheme designed for C. psittaci  [6]. Multiple alignments of the seven concatenated MLST gene fragments were conducted using Mega6 software and a dendrogram was constructed using NJ. For dendrogram construction, sequences were trimmed to 3098 bp. DNA libraries were prepared using the Nextera XT kit (Illumina). Whole-genome shotgun sequencing of the C. psittaci isolate was performed using an Illumina MiSeq platform (Illumina) according to the manufacturer's instructions. The MiSeq run was carried out on the DNA preparation, with paired-end reads of 150 bp using MiSeq V2 reagents. Raw reads were trimmed and assembled de novo using SPAdes 3. 13. 1  [7]. Contigs from SPAdes were subsequently blasted on the nucleotide database of NCBI to remove residual mammalian sequences from cell culture constituents.
Results Chlamydiaceae DNA was detected in two of 474 birds (0. 4%), and confirmed as C. psittaci by the specific PCR assay. One isolate could be propagated onto BGM cells from the Fulmar sample 127 (hereafter referred to as Ful127). Genome sequencing of Ful127 resulted in a single, circular, 1. 171-Mb chromosome (accession number CP033059) and a circular 7. 5-kb plasmid (accession number CP033060). The obtained phylogenetic tree generated by multilocus sequence typing (MLST) and based on all available C. psittaci sequence types (STs) reveals that Ful127 belongs to a new sequence type (ST197), with close relation with the sequence type ST24, mostly isolated from psittacine birds (Fig. 2A ). The only differences between ST197 and ST24 were two substitutions in the hflX gene, while the other six genes were identical.
Sequence quality control and microbiota analyses The quality of MiSeq raw sequences was assessed with the FastQC software  [35]  (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Sequences were trimmed with Trimmommatic [36], with a cutoff value of Q30 for both ends, a minimum mean threshold of Q20 for 30bp-sliding window across sequences, and a minimum read length of 250 bp (Supplemental Figs. 1a  and b ). After quality control, 28 samples including controls (n = 8) and volunteers (n = 10, 5 women and 5 men) for skin and stools, were further analyzed. Mothur pipeline  [37]  was used to bin 16S rDNA sequences into operational taxonomic units (OTUs) with a threshold of 97% sequence similarity. OTUs present in only a single sample were discarded. Rarefaction curves were represented by de ning the maximum subsampling size as the number of sequences of the sample with the fewest sequences (2751 sequences for skin samples, and 1059 sequences for stool samples) (Supplemental Fig.
RNA extraction and data preprocessing for transcriptomic analysis Whole blood samples of 2. 5 mL were collected in PAXgene RNA tubes (PreAnalytix) twice from each volunteer two weeks before (w-2) and the day of the vaccination (w0). These tubes enable the preservation and stabilization of RNA (storage at -80 °C). Total RNA was extracted from whole blood according to the instructions in the handbook accompanying the PAXgene blood RNA Kit (PreAnalytiX, Hombrechtikon, Switzerland). RNA purity and integrity were assessed on the Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip reagent set (Agilent, Palo Alto, CA, USA). Samples for microarray hybridization were prepared as described in the Affymetrix GeneChip WT PLUS Reagent Kit User Manual (Affymetrix, Inc. , Santa Clara, CA, USA). For hybridization (to Affymetrix Human Gene 2.
1 ST Array Plates), washing, staining, and scanning took place in an Affymetrix GeneTitan system, controlled by the Affymetrix GeneChip Command Console software w4. 2. Background signal correction was performed by applying the backgroundCorrect function from the limma package on the perfect match (PM) signals with R Software 3. 3. 1. The underlying model is the normal-exponential convolution model from RMA (chip intensity: addition of a signal exponentially distributed, chip noise: follows Gaussian distribution)  [42]. The variance stabilizing transformation algorithm (justvsn function from the vsn package  [43]  was applied to the background corrected signal (monotonic transformation), and the signal then transformed back to its usual scale by exponentiation (base 2). To make the chips comparable, a quantile normalization  [44]  (normalize function from the affy package) was then applied to the variance-stabilized signal.
097) (Supplemental Fig. 2a ). The predominant microbial families relatively abundant in skin samples were Moraxellaceae, Staphylococcaceae and Pseudomonadaceae, whereas Ruminococcaceae, Lachnospiraceae, Prevotellaceae, and Bacteroidaceae were predominant in stool samples (Supplemental Fig. 2c ). The 16S RNA sequencing generated several metrics: richness (sobs: number of observed OTUs; chao: Chao1 richness estimate; ace: Abundance-based coverage estimation) and diversity (Shannon: Shannon diversity index; sd_invsimpson: inverse Simpson diversity index). The amplitude of the humoral response was de ned by the MVA-speci c IgG neutralizing antibodies measured in serum at w8 post-vaccination. We observed no correlation between the MVA-Nab response and the baseline indexes of diversity and richness in either skin or stool (data not shown).
Whole blood gene expression and host microbiota before vaccination are associated with post-vaccination humoral responses To improve our understanding of host molecular mechanisms potentially associated with skin and gut microbiota that may be involved in vaccine immunogenicity, we counted the number of genes at baselines that were correlated with the MVA-Nab response at w8. We con rmed that gene expression of the baseline samples did not differ between w-2 and w0 using hierarchal clustering analysis (data not shown). Out of all samples, we found 154 signi cant genes correlated with the MVA-Nab response (P < 0. 05) (Fig. 2a ). However, no correlation was observed between genus diversity and MVA-Nab response. Next, we looked for a correlation between the microbiota diversity index and the genes (n = 154) correlated at baseline with MVA-Nab responses.
Acknowledgment We thank members of the  CUTHIVAC-EU consortium and Cuthivac Clinical study group  :  Pedro Gonzáles ,  María Fernández-Maldonado ,  Susana Perez ,  Samandhy Cedeño ,  Annika Vogt ,  Marco Antonio Fernández ,  Olivia bonduelle ,  Eric pedruzzi ,  Mariona Parera  and  Marc Noguera-Julián ,  Carmela Ganoza ,  Beatriz Mothe ,  Carmen E. Gòmez ,  Mariano Esteban ,  Felipe Garcia. We also thank  Julien Nourikyan ,  Simon de Bernard  from  Altrabio, Lyon, France  and  Jo Ann Cahn  for editing the English of the manuscript.
26 In our study, WhatsApp was the second most preferred ICT used for remote consultations by UCAREs, after phone calls. The use of WhatsApp comes at low or no costs, but there are concerns of patient data protection and confidentiality. Clearly, there is a need for the improvement of remote care of patients and the use of inexpensive, safe, and accessible tools for this. The reduced use of patient-reported outcome measures (PROMs) during the pandemic is a major cause of concern. PROMs guide treatment decisions and help to monitor treatment outcomes. Current guidelines recommend the use of PROMs to assess disease activity, impact, and control at every CU patient visit. PROMs may be especially helpful during the current pandemic, with disease activity, impact, and loss of control expected to increase in many patients with CU.
The SR subtype is defined by the infiltration of CD8 + T cells in the stroma and low IFN signature. Only the FI TNBC subtype has a proinflammatory microenvironment defined by a type I IFN gene signature, CD8 + T-cell infiltration in the tumor epithelium, and good outcomes  (19). We compared gene expression in FI versus non-fully inflamed tumors (MR+SR+ID). In 38 TNBC patient specimens from the Gene Expression Omnibus (GEO) database (accession number: GSE88847), the mRNAs coding for the aforementioned immune-relevant proteins' upregulation of B7-H4 expression was associated with the non-fully inflamed phenotype (Fig. 1B ), contrasting with high expression of CD274 (best known as PD-L1) and IDO1 in hot tumors, consistent with another report  (19). We then systematically measured the protein expression levels of PD-L1, PD-L2, B7-H3, and B7-H4 in a panel of 45 breast and 4 ovarian cell lines (Fig.
Molecular Analysis of the Interaction between B7-H4 and the E3 Ligase AMFR To identify the domains of AMFR and B7-H4 responsible for their interaction, we constructed a series of FLAG-tagged B7-H4 and MYC-tagged AMFR truncation mutants (Fig. 4A-D ), cotransfected them into cells, and performed co-IP experiments. This approach led to the prediction that the domain comprising amino acid residues 153-241 (corresponding to the Ig-like C2-type domain) in B7-H4 (Fig. 4B ) and the RING domain comprising residues 302-379 on AMFR are implicated in the AMFR-B7-H4 interaction (Fig. 4D ). Next, we conducted a series of docking and molecular dynamics (MD) simulations. The X-ray structure of the B7-H4 Ig-like V-type domain (PDB: 4GOS; ref. 34)  and PD-L1 (PDB: 5JDR; ref. 35) , which shares 23. 9% sequence identity with B7-H4, were used as templates in homology modeling.
Bioinformatics Analysis The proteomic data are provided by TCGA BRCA samples  (18). The samples are grouped by their PAM50 subtypes. The genes are sorted according to the difference between the average proteomic level in the basal subtype and the average proteomic level across the other samples. The heat map was generated with R (version 3. 3. 3). The accession numbers GSE88847 (for bulk tumor gene expression) in the GEO database were retrieved and processed using the GEOquery package which included 38 patients with therapy-naïve TNBC. The TNBC subgroups (referred to as TIME subtypes) based on differential localization of CD8 + T cells were requested from the author  (19). Gene expression from the listed immune-related proteins was obtained with R (version 3. 3. 3). All software dependencies are available in the Comprehensive Repository R Archive Network (CRAN; https://cran.
Chemicals and Antibodies Camsirubicin was kindly provided by Monopar therapeutics Corporation. PNGase F was purchased from New England Biolabs Inc. Cycloheximide, doxorubicin, MG132, tunicamycin, Brefeldin A, puromycin dihydrochloride, and polyethyleneimine (PEI; branched -average Mw ∼800, average Mn ∼600) were purchased from Sigma Aldrich. NGI-1 and durvalumab were purchased from MedChem Express. PEG (5000)-b-PLA (10,000; Diblock Polymer) was purchased from Polysciences, Inc. The following antibodies were used: B7-H4 (Cell Signaling Technology, 14572), FLAG (Cell Signaling Technology, 8146 or 14793), MYC (Cell Signaling Technology, 2272), eIF2a (Cell Signaling Technology, 5324), p-eIF2a (Cell Signaling Technology, 3398), HSP70 (Cell Signaling Technology, 4873), Hsp90 (Cell Signaling Technology, 4877), CALR (Cell Signaling Technology, 12238), normal IgG (Cell Signaling Technology, 2729), ubiquitin (Cell Signaling Technology, 43124), AMFR (Cell Signaling Technology, 9590), PERK (Cell Signaling Technology, 5683), eIF2a (Sigma, HPA084885), AMFR (Sigma, HPA077835), B7-H4 (Sigma, HPA054200), STT3A (Protein-Tech, 12034), mouse B7-H4 (R&D Systems, AF2154), RPN1 (Bethyl Laboratories, A302-026A), RPN2 (Bethyl Laboratories, A304-265A), STT3A (Bethyl Laboratories, A305-531A), and UGGG1 (Bethyl Laboratories, A305-530A), CD45 rat anti-mouse, Brilliant Violet 711 (BD, Thermo Fisher Scientific, 563709), IFNγ rat anti-mouse, Brilliant Violet 711 (BD, Thermo Fisher Scientific, 564336), CD8a rat antimouse, APC (BD, Thermo Fisher Scientific, 553035), I-A/I-E rat antimouse, Brilliant Violet 711 (BD, Thermo Fisher Scientific, 563414), CD86 rat anti-mouse, PE-Cy7 (BD, Thermo Fisher Scientific, 560582), CD11c hamster anti-mouse, APC (Thermo Fisher Scientific, 550261), PE mouse anti-human B7-H4 (BD Biosciences, 562507), FITC rat antimouse CD4 (BD Biosciences, 553046), APC-Cy7-labeled anti-mouse CD11b (BD Biosciences, 557657), PE-Cy7-labeled anti-mouse Ly-6G and Ly-6C (Gr-1; BD Biosciences, 552985), anti-rat IgG (H+L; Alexa Fluor(R) 647 Conjugate; Cell Signaling Technology, 4418), anti-rabbit IgG (H+L), F(ab')2 Fragment [Alexa Fluor(R) 647 Conjugate; Cell Signaling Technology, 4414], rabbit IgG isotype control (Cell Signaling Technology, 3452), Alexa Fluor(R) 488 Conjugate (Cell Signaling Technology, 4340S), anti-rabbit IgG (H+L), F(ab')2 Fragment (Alexa Fluor(R) 488 Conjugate; Cell Signaling Technology, 4412S).
Tissue Microarray, IHC, and Image Analysis The breast cancer tissue array with adjacent normal breast tissue BC081116d, which contains 110 cases (15-mm tissue cores for each tissue) were purchased from US Biomax. IHC staining was carried out following standard streptavidin-biotin-peroxidase complex method. Briefly, a section was deparaffinized, and nonspecific bindings were blocked with 10% normal goat serum for 30 minutes. The section was then incubated with antibody overnight at 4°C. Antibodies including B7-H4 (Sigma-Aldrich HPA054200), PD-L1 (E1L3N; Cell Signaling Technology 14772S), STT3A (Thermo Fisher Scientific PIPA556550), AMFR (Thermo Fisher Scientific PIPA512051), Sambucus Nigra Lectin (SNA, Vector Laboratories, B-1305-2), Maackia Amurensis Lectin II (MAL II, Vector Laboratories, B-1265-1), hCD8α (Cell Signaling Technology, 70306), mCD8α (Cell Signaling Technology, 98941), and phospho eIF2α (Ser51; D9G8; 1:50; Cell Signaling Technology 3398S).