dataesr/scientific-paragraphs-categorization huggingface dataset:
We show remarkable variation in the treatment of bronchiectasis between countries illustrating absence of clear evidence for many interventions. These data should inform future guidelines, stimulate efforts to better implement existing evidence-based treatment such as airway clearance and macrolides, and encourage future randomised trials. Inequality in access to evidence-based treatment can lead to worse outcomes and we show lower receipt of treatments as well as greater disease severity and increased exacerbations in central and eastern European countries, which emphasise the importance of developing strategies to improve outcomes in these regions where historically bronchiectasis has received little attention. This is the first report from the EMBARC registry which has the capacity to provide extensive insights into the epidemiology of bronchiectasis.
Study design The EMBARC registry is an ongoing multicentre, prospective, observational cohort study enrolling consecutive adult patients with bronchiectasis across Europe and Israel. The study is open to both primary and secondary care, and from specialised and nonspecialised centres; with the majority of patients being recruited from secondary care. Patients give informed consent to participate and data are collected at baseline (recruitment) and up to 5 years at annual followup visits (±3 months). Data are collected across a series of domains including: demographics; comorbidities; aetiological testing; lung function; exacerbations; disease impact; quality of life (QoL); microbiology; radiology; and treatment. The protocol for the registry has been previously published. 1 he study is noninterventional.
Patients are managed according to local practice with no interference from the registry team. The study received central ethical approval from the Multicentre Research Ethics Committee in the UK on Jan 8, 2015 (14/SS/1101) and the study is sponsored by the University of Dundee, Dundee, UK. Recruitment began on Jan 12, 2015, and is ongoing. The database was locked on May 1, 2022, for this analysis. The registry was developed in accordance with recommendations on the design, implementation, governance, and longterm sustainability of disease registries in the EU, as proposed by the European Platform for Rare Disease Registries consortium and EU Committee of Experts on Rare Diseases. 19.
Data collection Data were collected by site staff for patients during stability, defined as the absence of exacerbation treated with antibiotics, with additional data collection during exacerbations. The causes of bronchiectasis were determined and reported by the physician caring for the patient. Extensive data were collected on the aetiological testing performed by the treating clinician, based on the testing recommended by consensus guidelines and, therefore, the underlying basis for an aetiological diagnosis was collected and could be validated. Exacerbations were defined as an acute deterioration in symptoms requiring antibiotic therapy as determined by the treating clinician. Spirometry was recorded with % predicted values calculated using European Community of Coal and Steel equations  20  implemented centrally.
The registry recorded data on spirometry before and after bronchodilator. Breathlessness was evaluated using the modified Medical Research Council Dyspnoea scale. QoL was assessed using the QoLBronchiectasis Questionnaire (version 3•1). 21 Sputum microbiology was recorded for samples sent in stable state and during exacerbation in the previous 12 months with results from local laboratories. Severity of bronchiectasis on CT scans were scored using the modified Reiff score. 22 Disease severity was evaluated using the Multidimensional Bronchiectasis Severity Index (BSI) and the FACED score. 23 Patients who are still under followup with their respective clinical teams are followed up annually with data collection including changes in medication, exacerbation frequency, and exacerbation leading to hospitalisations.
Analyses were conducted using SPSS (version 22). Due to the large sample size of the EMBARC registry, significant differences might be observed at the conventional p<0•05 threshold even where clinically meaningful differences are not observed. We therefore took a conservative approach to hypothesis testing. Data are presented descriptively for the majority of outcomes with pvalues presented only where they aid interpretation of the data. Results of the negative binomial regression analysis are presented as unadjusted and adjusted rate ratios (RR) with 95% CIs without pvalues.
All treatments were less common in central and eastern Europe. In patients without a history of P aeruginosa, inhaled antibiotic use was uncommon but macrolide use was similarly high in UK and northern and western Europe (appendix 9 p 17). Having observed that patients in central and eastern Europe were more likely to have severe cystic bronchiectasis and were less likely to receive evidence based treatments, we investigated whether this translated into more exacerbations during followup. For this analysis of regional differences in exacerbations, we included 9978 patients who had at least 1 year of follow up data. Exacerbation data were available for 652 patients from central and eastern Europe (median of 1168 days of observation per participant), 1831 patients from north and western Europe (median 1095 days of followup), 1838 patients from southern Europe (1096 days of follow up) and 5657 patients from the UK (median 1172 days of followup).
Discussion The EMBARC registry is, to the best of the our knowledge, the largest and most comprehensive prospective dataset of patients with bronchiectasis globally. This first report of data from the registry provides a comprehensive description of the disease from 28 countries. The most frequent causes of bronchiectasis were idiopathic and postinfective and combined these causes accounted for 59•3% of all cases. Postinfective is a poorly defined clinical entity often combined with idiopathic for analysis purposes. Asthma and COPD were frequently reported as the cause of bronchiectasis reflecting the overlap syndromes that have been previously shown to be associated with worse outcomes. 28, 29 It should be noted that unlike past studies in tertiary referral centres where all patients underwent standardised testing our results reflect the causes of bronchiectasis assigned by clinicians in reallife clinical practice.
All other authors declare no competing interests. Acknowledgments This work was supported by the  Innovative Medicines Initiative  and  EFPIA  companies under the  European Commission  funded project, iABC (grant  115721 ) and by the  European Respiratory Society (ERS)  through the  EMBARC2 consortium. EMBARC2  is supported by project partners  AstraZeneca ,  Chiesi ,  Grifols ,  Insmed ,  Janssen ,  Novartis and Zambon. JDC is supported by the  Asthma and Lung UK Chair of Respiratory Research  and a  Scottish Senior Fellowship  from the  Chief Scientist Office. The EMBARC registry is hosted by the  Health Informatics Centre, Dundee, UK. EMBARC acknowledges the collaboration of the partners who contribute to the registry including the ERS, The  US COPD Foundation, BronchUK, IRIDE-the Italian Bronchiectasis registry, The Group for Research and Education in Pneumoinfectious Diseases France  and  The Prospective German NonCF Bronchiectasis registry.
We find that the Stackelberg strategy coincides with the Nash strategy, which goes in agreement with theorem 4. 1 as du Ã (m)/dm = 0 when m < m c. When m Ã (0) > m c , the best response m Ã (u) intersects with both u Ã þ and u Ã À such that m N > m c and u N ≠ 0. This leads to a change in trait u and potentially speciation of the fish. In contrast to the previous case, the Stackelberg equilibrium does not coincide anymore with the Nash equilibrium. Rather, the Stackelberg strategy m S is equal to the critical harvesting effort m c , thus harvesting as much as possible while keeping the carrying capacity maximized with u S = 0, as illustrated in figure  3b. Mathematical details and calculations can be found in the electronic supplementary material, and Mathematica code is provided online (see Data accessibility).
RESULTS Expression of the three AKTs in normal myeloid differentiation. To generate a picture of how the three AKT genes are expressed in the different myeloid lineages, analyses of micro array data from the public Bloodspot-HemaExplorer 8 cohort were performed on bone marrow cells sorted by FACS with the help of speci c markers distinguishing different steps of myeloid differentiation, staging from the hematopoietic stem cell (HSC) to the granulocyte and the monocyte. The data revealed that AKT1 expression shows a bi-phasic but signi cant increase along myeloid differentiation, AKT2 expression remains constant, while the initial higher expression of AKT3 detected in HSCs decreases gradually but strongly as cells progress toward the terminal granulocyte and monocyte stages (Fig. 1 ). Expression of the three AKTs in AML samples.
Indeed, the ELN-2017 favorable group expresses a lower amount of AKT3. Also, the level of AKT3 expression is lower in leukemic blasts harboring NPM1 mutations or the RUNX1-RUNX1T1 translocation (which are both of better prognosis), but higher in samples harboring RUNX1 mutations (which are of bad prognosis). To assess the prognostic impact of the expressions of the three AKTs, the patient overall survivals were rst evaluated by Kaplan-Meier curves using the BEAT-AML dataset which contains 660 AML samples. To avoid potential bias in data interpretation, we removed here the samples that were not collected at diagnosis but later after the rst line of therapy, those presenting with a myelodysplastic syndrome (MDS) or a myeloproliferative neoplasm (MPN) and the samples from patients who could bene t from a transplantation (either bone marrow or cord blood cells engraftment).
The Kaplan-Meier curves were nally plotted with such a homogeneous set of n = 239 patients (Fig. 6A ). Once more, AKT3 behaved distinctly. Its expression level is highly correlated to a poor outcome (p = 0. 0000), while AKT2 expression cannot distinguish two groups with a signi cant difference (p = 0. 4550). The data obtained with AKT1 were very surprising. As opposed to AKT3, its expression level correlates signi cantly to a better survival (p = 0. 004). Given these strong, yet opposite, predictive values of AKT1 and AKT3 expression levels, it was important to verify whether similar counterintuitive observations could be made in another consistent cohort. We therefore looked at the Leucegene public dataset encompassing 373 AML samples. As no clinical information can be obtained with the Leucegene cohort, were here downloaded the Kaplan-Meier overall survival curves as they appear online.
This warrants further investigation to experimentally determine whether the RUNX1 transcription factor or its mutant or translocated variants interact with the AKT3 gene locus to directly regulate its transcription. In a clinical perspective, the very low expression of AKT3 in RUNX1-RUNX1T1 AML suggests it would be preferable to target AKT1 or AKT2 kinase in this leukemic entity. This is consistent with an earlier report which indicated that RUNX1-RUNX1T1 blasts harbor an activated AKT1 kinase and are speci cally sensitive to AKT1 inhibition both in vitro and in vivo settings  15. Normal hematopoiesis and validation AML cohorts. As cohorts for normal hematopoiesis and for validation in AML, we used three different public sources of data available on-line: https://www. bloodspot. eu 8. In this cohort, we used the data appearing in the tab "Normal human hematopoiesis (HemaExplorer)".
RNA-seq RNA sequencing of the 40 CN-AML samples was performed at the BGI (Hong Kong). Paired-end, strandspeci c reads of ~ 100 nucleotides were generated on an Illumina HiSeqTM2000. Alignment and mapping were performed using Tophat1 against the hg19 genome and the mapped reads were assembled by Cu inks 2. 0. 22. The Cuffcompare program was used to merge the RefSeq, ENCODE and UCSC human known genes freeze January 2013 into one gene annotation set for comparison with the assembled transcripts 16.
K E Y W O R D S HLA, HLA-A*24:585, novel allele, sequencing-based typing We report here a novel HLA-A*24 allele, now named HLA-A*24:585, that carries one nucleotide substitution in exon 1 when compared with the HLA-A*24:02:01:01 allele, identified in a volunteer bone marrow donor. The HLA typing was performed using Next Generation Sequencing (AllType NGS, One Lambda, Canoga Park, CA) on the Ion S5 system platform (ThermoFisher Scientific, Waltham, MA),  1   1 ). This nucleotide change was confirmed using other NGS reagents provided by GenDX NGSgo-MX6-1 (Utrecht, Netherlands) run on the Illumina MiSeq system (San Diego, CA) and analyzed with the NGSEngine software (GenDX, version 2. 26). We were very confident in the phasing as the sample displayed a mean read length of 302 base pairs over all the loci, the mismatched C base was attributed 157 times to the new HLA-A*24 allele, and can be only attributed to this allele because it was possible to discriminate from the associated HLA-A*11:01:01:01 allele by virtue of 6 variant positions each distant by less than 100 base pairs.
The coding nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OP807951) and to the IPD-IMGT/HLA Database (Submission No. HWS10064313). The name A*24:585 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in November 2022. This follows the agreed policy that, subject to the conditions stated in the most recent Nomenclature Report,  3  names will be assigned to new sequences as they are identified. Lists of such new names will be published in the following WHO Nomenclature Report. performance of the research. Marine Cargou, Vincent Elsermans, Isabelle Top, Elodie Wojciechowski, and Jonathan Visentin participated in data analysis. Vincent Elsermans, Isabelle Top, and Elodie Wojciechowski were involved in critical revision of the manuscript.
from exons 1 to 8. The reads were analyzed using the TypeStream Visual Software version 2. 1 (One Lambda). This recipient was found to have a new A*24 allele and was consequently typed A*11:01, 24:585; B*35:01, 51:01; C*04:01, 16:02; DRB1*01:03, 11:01; DRB3*02:02; DQA1*01:01, 05:05; DQB1*03:01P, 05:01P; DPA1*01:03, 02:01; DPB1*02:01, 09:01. Using the IPD-IMGT/HLA Database, 2 nucleotide sequence alignment with HLA-A alleles shows that this new allele has one nucleotide change from A*24:02:01:01 in codon -15 in exon 1, where G ! C resulting in a new protein (GTC ! CTC, Valine ! Leucine, Figure.
Data collection For each patient, data were collected from the medical records and inserted into our study database. Variables comprised demographics, major comorbidities, dates of onset, diagnosis, hospital admission and discharge. At admission and during hospitalization, daily information on the disease stage, need of respiratory support, treatments used, diagnosis of pulmonary embolism if present, tomography data, blood count, quantification of C-reactive protein (CRP), ferritin, d-dimer, and procalcitonin, and clinical observation of patients' evolution were collected. At the outpatient setting, and according to clinical protocol, patients were asked about the existence or persistence of symptoms attributable to PASC and an extensive physical examination was performed. Additional exams directed to patients' complaints were requested when necessary.
Production of the nucleoprotein of SARS-CoV-2 The full gene coding for the N protein (accession number YP_009724397, amino acids 1 to 419) was cloned into a pET24d vector (Eurogentec). The protein was expressed in E. coli bacteria were transformed with this plasmid and the N protein production was induced by the addition of 0. 5 mM IPTG. After four hours at 37 °C with shaking at 130 rpm, the cells were lysed (buffer A, 50 mM Tris, 500 mM NaCl and 5% glycerol, pH = 7. 5) and sonicated for 4 minutes. The supernatant was passed through a 1 mL nickel-NTA resin (GE, 17-5318-02). The protein was diluted in buffer B (50 mM Hepes, 150 mM NaCl, 10 % glycerol, pH = 7) and further purified on a 1 mL Source 15 S (GE, 17-0944-10) cationic exchange chromatography. The protein N was eluted by applying a gradient with buffer C (50 mM Hepes, 1 M NaCl, 10 % glycerol, pH = 7).
Statistical analysis Statistical analysis were performed using SPSS version 28 software (IBM, New York, USA) and data was plotted using GraphPad Prism version 8 software (San Diego, CA) For each variable, a two-way ANOVA was used to assess the interaction between disease severity during acute disease and the occurrence or absence of PASC. Only variables where the interaction terms were significant were divided according to these factors; if these significant differences were not observed, only the factor occurrence or absence of PASC was evaluated. Regarding the small sample size and the non-normality observed in our variables, Kruskal-Wallis test was applied to identify statistical differences. For variables that attained global significance, pairwise comparisons were performed using Dunn's multiple comparisons test.
Acknowledgements This work has been funded by National funds, through the  Foundation for Science and Technology (FCT)  -project  UIDB/50026/2020  and  UIDP/ 50026/2020  and by the project  NORTE-01-0145-FEDER-000039 , supported by  Norte Portugal Regional Operational Programme  ( NORTE 2020 ), under the  PORTUGAL 2020 Partnership Agreement , through the  European Regional Development Fund (ERDF)  to R. S. , the FCT contracts 2021. 07836. BD to AMF and  CEECIND/00185/2020  to RS, and the  Clinical Academic Center  ( 2CA-Braga) grant  to  A. S. C. Marne Azarias  thanks the  ANRS | Maladies infectieuses emergentes  for his fellowship. This work was also supported by a grant to J. E. from the  Fondation Recherche Médicale  and the  Agence Nationale de la Recherche  ( COVID-I²A ). J. E. is supported by  Canadian Institute of Health Research  ( FRN-177760 ) and the  Canada Research Chair program.
We also confirmed that PARylation was induced in PARGi-treated S2R+ cells following DNA damage (Fig. 2C ). By contrast, we detected only negligible differences in pan-ADPr (combination of both MAR-and PARylation) between DMSO and PARGi-treated S2R+ cells under the unstimulated condition (Fig. 2C ). However, we observed that ADPr was dramatically stimulated in PARGi-treated S2R+ cells upon DNA damage in comparison to DMSO-treated S2R+ cells (Fig. 2C ). These data suggest that the PARGi developed against human PARG (hPARG) efficiently inhibited dParg activity, thereby blocking degradation of ADPr in Drosophila and allowing enrichment of ADPr sites. Next, we aimed to identify the specific proteins that are ADPribosylated in Drosophila S2R+ cells by mass spectrometric analysis. To this end, we employed the well-established Af1521 enrichment approach  [50] [51] [52]  , and analysed protein extracts from DMSO-and PARGitreated S2R+ cells in the absence or presence of DNA damage (Fig.
Mass spectrometric analysis and data analysis. All MS experiments were analysed on an EASY-nLC 1200 HPLC system (Thermo) connected to a Fusion Lumos Orbitrap mass spectrometer (Thermo). Each sample  was separated on a 15 cm analytical column, with an internal diameter of 75 μm, packed in-house with 1. 9 μm C18 beads (ReproSil-Pur-AQ, Dr. Maisch), and heated to 40 °C using a column oven. Peptide separation was performed using a 60 min gradient at a flow rate of 250 nL/min, utilising buffer A consisting of 0. 1% FA, and buffer B consisting of 80% ACN in 0. 1% FA. The mass spectrometer was operated in datadependent acquisition mode, with full scans performed at a resolution of 120,000 and a maximum injection time of 250 ms. Precursor fragmentation was accomplished using electron transfer disassociation with supplemental higher-collisional disassociation (EThcD), with supplemental activation energy of 20.
Precursors with charge state 3-5 were included and prioritised from charge 3 (highest) to charge 5 (lowest), using the decision tree algorithm. Selected precursors were excluded from repeated sequencing by setting a dynamic exclusion of 45 s. MS/MS spectra were measured in the Orbitrap, with a maximum precursor injection time of 500 ms, and a scan resolution of 60,000. All MS raw data were analysed using the MaxQuant software suite version 1. 5. 3. 30  76 , and searched against the Drosophila proteome in FASTA file format, as downloaded from UniProt on the 11 th of November 2020. Default MaxQuant settings were used except the following: cysteine carbamidomethylation, and ADP-ribosylation on cysteine, aspartic acid, glutamic acid, histidine, lysine, arginine, serine, threonine, and tyrosine residues were included as variable modifications.
RT-qPCR The total RNAs from S2R+ cells were purified with RNeasy Plus Mini kit (QIAGEN) and then 0. 5 μg total RNA was used for cDNA synthesis with QuantiTect Reverse Transcription Kit according to the manufacturer's instructions. The cDNAs were detected by quantitative real-time PCR using the Rotor-Gene SYBR Green PCR Kit and the Rotor-Gene Q (QIAGEN). Primer pairs for RT-qPCR are given in Supplementary Data 3. The relative gene expression analysis of dParg gene was performed using the ddCt method. Quantification and statistical analysis Prism 9. 1 (GraphPad) was used for statistical analysis, where ****p < 0. 0001. Details of statistical analyses are described in the Fig. 4  legend.
Cloning, expression, and purification Expression vectors for hARH3, hTARG1, hHPF1, hPARP1 and hPARG were described earlier  5, 15, 54, 63. The coding sequence of dParp, dParg, dTarg1-3 and dHpf1 were amplified from cDNA prepared from S2R+ cells using oligonucleotides listed in Supplementary Data 3 and cloned into pET28a expression plasmids with an N-terminal His-tag. All indicated mutations were introduced via PCR based site-directed mutagenesis (Supplementary Data 3). Expression was carried out in E. coli Rosetta (DE3) cells (Novagen), and Terrific Broth media supplemented with 30 μg/ml kanamycin and 30 μg/ml chloramphenicol. Cells were grown at 37 °C and growth stopped when cultured reached OD 600 ~0. 6. Cultures were then induced with 1 mM IPTG and incubated at 18 °C overnight. Cells were centrifuged for 10 min at 3000 x g, and the pellets resuspended in buffer A (50 mM TrisHCl (pH 8.
Inference of phylogenetic relationships and sequence similarities Alignments of HPF1 sequences from metazoan and protozoan species (Supplementary Data 1) were generated using JalView v. 2. 11  86  and the HPF domain extracted from their sequential context based on Mafft L-INS-i alignment  87  using crystallographic data to determine domain boundaries. Extracted sequences were re-aligned using Mafft L-INS-i algorithm. The evolutionary histories of the HPF domain was inferred by using the maximum-likelihood method and Le_Gascuel_2008 model  88  with an automatically obtained initial tree for the heuristic search by applying the maximum-parsimony method. The analysis was carried out using a site coverage of 95% with partial-deletion option. Confidence levels were estimated using 1000 cycles of the bootstrap method.
5 mM prior to crystallisation. All crystals were cryoprotected in 15% (v/v) glycerol in the mother liquor before being vitrified by submersion in liquid nitrogen. Data collection was performed at beamlines I04 and I24 of the Diamond Light Source (Rutherford Appleton Laboratory, Harwell, UK). X-ray data were processed using Xia2  90. PHASER  91  was used for molecular replacement trials with hPARG (PDB: 6HMK) as molecular replacement model. Density modification was performed with PARROT  92  and initial models were built using the automated model building program BUCCANEER  93. Model building for all structures were carried out with COOT  94  and real space refinement with REFMAC5  95  , coupled with automatically generated local noncrystallographic symmetry restraints and TLS refinement. Statistics for dParg and dParg:PDD00017273 complex are shown in Supplementary Table  2.