dataesr/scientific-paragraphs-categorization huggingface dataset:
Data In a retrospective study between January 2008 and December 2017, we included 32 consecutive patients with SCID having received a primary HLA-haploidentical CD34+ selected graft from a family member. Patients with a diagnosis of adenosine deaminase deficiency and intrathymic deficiency were excluded. The dataset used for model building and covariate analysis was collected during routine clinical practice in the Immunohematology and Rheumatology Department at Necker Children's Hospital (Paris, France). The myeloablative conditioning regimen (CR, when used) was based on a combination of busulfan, fludarabine (160 mg/m 2 ), and serotherapy [5 or 10 mg/kg anti-thymocyte globulin (ATG) or 1 mg/kg alemtuzumab] (9). Only four patients treated before 2010 underwent myeloablative CR with a CY-BU myeloablative conditioning protocol  (10).
The parameter d stands for the cell loss rate and was coded as d (X, t, τ ) = y (τ ) d 0 e ( X(t) N(t) -1) , where d 0 stands for the theoretical cell loss rate (/days). This mechanistic nonlinear mixed-effect model was applied to our data, and model parameters were estimated using Monolix software (version 2019, Lixoft, Antony, France)  (13). The Monolix code is provided in the Supplementary Data. We chose eight specific pre-HSCT covariate parameters that putatively influence CD4+ T-cell reconstitution after CD34+ selected stem cell transplantation: age at transplantation, the type of molecular defect, Omen syndrome, pre-HSCT viral diseases, the ATG dose level (5 or 10 mg/kg), the presence or absence of a CR, and the CD34+ or CD3+ count of the stem cell graft. Model parameters were estimated using the stochastic approximation expectation-maximization and Markov chain Monte Carlo algorithm.
Characteristics of Patients A total of 231 data points for the T-cell phenotype of 32 consecutive patients were available for the 2 years following HSCT and were included in the immune reconstitution model (Figure  1 ). We obtained a median of seven samples per patient (range: 3-23 samples) over a median follow-up period of 20 months (range: 3-24). The patients' characteristics and outcomes are summarized in Table  1. Five of the patients (15. 6%) died during the study period: four from infectious diseases and one from GvHD, followed by an infection. Two patients did not engraft, and the cumulative incidence of acute grade ≥II GvHD was 20. 5%.
Unfortunately, we could not recover the data for BU exposure for all patients to implement our model. In addition, ATG pharmacokinetic studies were not performed in routine during this time of the study. Other modeling strategies, based on multivariate immune reconstitution patterns, identified variables and described the correlation between immune system dysfunction and the occurrence of complications. Principal component analysis is a performant tool and is valuable for the simultaneous interpretation of different parameters  (20, 21). However, those models need an exhaustive post-transplant multiparameter database, which is not adapted to rare diseases. Furthermore, they do not use single factors, and neither elaborate prediction adjusts to clinical practice. A low number of patients, large dataset, and multiparameter studies do not also fit with conventional statistical methods, in which problems with mass significance can quickly occur.
In contrast to a comparative statistical test at a single time point, the model provides a dynamic assessment of immune reconstitution while increasing substantially (by up to 10-fold) the ability to detect covariate effects. Our model also counts the variability in the number of data points per patient. In addition, the representation and use of the probability theory make Bayesian computation suitable for combining domain knowledge and data, avoiding to overfit a model to training data, and learning from incomplete datasets. The establishment of the model and parameter allowed us to predict a probability of distribution of T-CD4+ for each subpopulation. For example, in the T-Bgroup, nearly everyone received conditioning. However, only 1/11 patients were unconditioned due to pre-transplant severe comorbidities and a high risk of toxicity.
Therefore, the Bayesian method did not consider only one patient but interpreted all data points with model and covariate adjustments to compute the curve of IR for the T-B-group. However, a larger dataset may have also allowed us to point out other covariates. It could explain the remaining between individual variability and substantially. A larger data set may also increase the model's ability to predict personal CD4+ reconstitution very soon after transplantation. Posttransplant clinical events had a low incidence in our cohort. Contrary to other studies, we were not able to report the different immune reconstitution of T-CD4+ before the onset of GvHD, post-transplant viral infectious disease, or BCGitis (22). The Bayesian inference is a data-driven technique; the prior probabilities of an event are updated when the new data are gathered.
Finally, cells were washed and mounted as described before for non-infected cells. For cholesterol labeling, T. gondii infected (TgRH YFP SAG1-OVA at MOI 1) or non-infected BMDCs were incubated with 1 mg/ml of filipin for 1 h at RT, prior to fixing, and mounted with Dako Omnis mounting medium without DAPI. Image acquisition was performed on an Olympus FV-1000 confocal microscope with a 63×/1. 4 NA oil immersion objective. One z-stack plane is shown from the acquired images and they were processed with the ImageJ software (Wayne Rasband, National Institutes of Health). Image deconvolution was performed with the Parallel Spectral Deconvolution plugin (Piotr Wendykier) using a theoretical PSF generated by the Diffraction PSF 3D plugin (Robert Dougherty).
ACKNOWLEDGMENTS We thank  S. Janssens ,  S. Lippens  and the  VIB Bioimaging core, University of Gent, Belgium , for FIB-SEM acquisitions;  S. Allart  and  A. Canivet-Laffitte  for technical assistance at  Infinity-Inserm U1291 Imaging Facility (France). We also thank the  Flow Cytometry Facility of the "Facultad de Ciencias Médicas ,  Universidad Nacional de Cuyo" (Argentina) , and the  Confocal and Electron Microscopy Facilities of IHEM -UNCuyo -CONICET (Argentina)  for technical assistance.
FUNDING This work was supported by the  "Agencia Nacional de Promoción Científica y Tecnológica " ( PICT 2016-0013  to IC,  PICT 2016-0894  and  PICT 2018-4451  to LM), the  "Centre National de la Recherche Scientifique" (CNRS)  with the  PICS  ( Projet International de Coopération Scientifique) collaborative program  ( TOXORAB 2017  to NB and IC), the  PIA   PARAFRAP Consortium  ( ANR-11-LABX0024  to NB and SM), ' Agence Nationale pour la Recherche'  ( ANR-18-CE15-0015  to NB).
Although expressed in many healthy tissues, GSDME can be effectively silenced in the context of cancer by promoter methylation, and expression can be restored through a methyltransferase inhibitor  (161). Mutation of gasdermin proteins is also observed in the context of tumorigenesis. For instance, interrogation of the TCGA database demonstrated that GSDME had a high prevalence of mutations, which were especially concentrated around the caspase-3 cleavage site; 20 of 22 cancer-associated GSDME mutations tested were shown to inhibit its function  (81). Clearly, such repression and mutational burdens present a f o r m i d a b le b a r r i e r t o t h e s u c c e s s o f C TL -d r i v e n immunotherapies that rely upon pyroptosis in target cells; however, they also provide opportunities to increase susceptibility to killing through upregulation of gasdermins through strategies such as inhibition of promoter methylation (e.
Statistical analysisStatistical analyses were performed with Prism software version 8 (GraphPad Software, Inc. , La Jolla, CA, USA) and with Stata (StataCorp, College Station, TX, USA) for Passing-Bablok regression analysis. For HIL interference, the results of the non-HIL sample and those of the HIL samples were compared with the Friedman test (normality and homoscedasticity verified with the Bartlett test), followed by the appropriate multiple comparison post hoc tests (Dunn test). For HAMA and RF interference, the results of the samples with and without HAMA/RF were compared with the Wilcoxon test for paired data. P values <. 005 were considered statistically significant. For all interferences, the calculated mean bias was compared with the desirable bias obtained from the online database created and managed by Ricos et al.
2. 6 | 6 Statistical analysisStatistical analyses were performed with Prism software version 8 (GraphPad Software, Inc. , La Jolla, CA, USA) and with Stata (StataCorp, College Station, TX, USA) for Passing-Bablok regression analysis. For HIL interference, the results of the non-HIL sample and those of the HIL samples were compared with the Friedman test (normality and homoscedasticity verified with the Bartlett test), followed by the appropriate multiple comparison post hoc tests (Dunn test). For HAMA and RF interference, the results of the samples with and without HAMA/RF were compared with the Wilcoxon test for paired data. P values <. 005 were considered statistically significant. For all interferences, the calculated mean bias was compared with the desirable bias obtained from the online database created and managed by Ricos et al.
5 µg/mL, JIR 711-175-152). Rabbit anti-pimonidazole antisera (1:100, Hypoxyprobe Omni Kit Pab2627) associated with Cy5 antirabbit antibody (5 µg/mL, JIR 711-175-152) and rat anti-CD8 primary antibody (2 µg/mL, eBioscience 14-0081-86) associated with AF594 anti-rat IgG (3. 75 µg/mL, JIR 112-585-167) were used to analyze tumor hypoxia and CD8 + T cells respectively. Nuclei were labeled by DAPI staining (1 µg/mL). Stained tumor sections were scanned with a Vectra® automated microscope with 8-10 fields examined with InForm® Software (Akoya Biosciences). A tissue segmentation algorithm was used to determine either hypoxic areas, endothelial cells or pericytes by analyzing the percentage of positive areas for the associated markers. CD8 + cell counts were determined using a cell segmentation algorithm combined with phenotyping.
Luminex CD8 + T cells isolated from tumors were enriched by negative selection using EasySep TM Mouse CD8 + T cell Isolation Kit (STEMCELL Technologies) and 5 × 10 4 cells/well were in vitro restimulated with anti-CD3/anti-CD28 beads (Thermo Fisher Scientific) following manufacturer's protocol. At 24 h, supernatants were collected and a bead-based multiplex immunoassay (Luminex) was performed according to manufacturer's indications (Bio-Rad) and analyzed on Bio-Plex 200 (Bio-Rad) to measure the levels of cytokines produced. The analytes concentration was calculated using a standard curve (5-PL regression) with Bio-Plex manager software. Non-detectable or non-relevant cytokines were not presented.
Flow cytometry After performing cell viability labeling using the Live/Dead Fixable Aqua Dead Cell Stain Kit (Invitrogen) and FcR blocking with anti-CD16/32 antibody (clone 93), the following antibodies were used for surface staining: anti-mouse CD3 (145-2C11), CD45 (30-F11), MHC-II (M5/114. 15. 2), CD11c (N418), Ly-6C (HK1. 4), CD8 (53-6. 7), NKp46 (29A1. 4), CD4 (RM4-5), CD11b (M1/70), Ly-6 G (1A8), F4/80 (T45-2342), and PD-1 (J43). For intracellular staining, samples were fixed/permeabilized using the FoxP3/Transcription Factor Staining Buffer Set (Invitrogen) and stained with anti-mouse FoxP3 (FJK-16s), anti-mouse Ki-67 (B56) and anti-human/mouse Granzyme B (GB11). All antibodies were purchased from BD Bioscience, Biolegend and Invitrogen. Acquisitions were performed on BD Fortessa X20 (Becton Dickinson), and data were analyzed with FlowJo Software (BD Biosciences).
Strong increase in the proportion and function of CD8 + T cells in the tumor after sunitinib treatment Finally, we studied the impact of the anti-angiogenic treatment on CD8 + T cells. While little impact was observed on the proportion of CD8 + T cells in the spleen (Figure  6d  top), a marked increase in CD8 + T cells infiltrating the tumor was detected one week after sunitinib treatment was stopped (d26) in treated mice compared to controls (Figure  6d  bottom), in line with the data obtained by immunofluorescence in situ (Figure  3f ). To further analyze the modification on CD8 + infiltrating T cells at this time point, we assessed their phenotype and functionality at d26. Sunitinib led to a reduced expression of the immune checkpoint molecule PD-1 by CD8 + T cells in mice treated with sunitinib compared to controls (Figure  6e ).
Also, on site observations were conducted to estimate donated goods/services such as personnel in the field or some equipment costs (bottomup costing) to estimate economic costs (Appendix 2). Finally, a time and motion study was conducted to allocate fieldbased personnel costs based on the proportion of time spent on HIVST distribution and HTS, respectively  (18, 20). HIVST program data were obtained from the IPO. HTS cost and program data were collected through bottom-up costing only. We conducted convenience sampling of sites, based on the type of distribution channel, the cohort size estimated of each distribution channel, and contextual factors such as accessibility to the HIV testing site. Because HIVST implementation was progressive over time, the analysis period was 21 months in Senegal and Mali, and 18 months in Côte d'Ivoire.
These costs are distributed according to the three intervention phases of the project (Development, Start-up (start-up and other costs), Implementation) (Figure  1 ). The development phase (June 2018-March 2019) identified sustainable distribution models in each country; the start-up phase [April 2019-July 2019 (Senegal/Mali), October 2019 (Côte d'Ivoire)] consisted of the validation, adaptation and implementation of the training strategy for HIVST providers; and the implementation phase (until March 2021) corresponded to the effective distribution of HIVST kits, supervision and data validation activities. For HIVST, we also estimated site level costs at health facilities, therefore excluding central costs (IPO costs). For HTS, above site-level costs were not estimated because we did not have data access, therefore, only site level costs were estimated.
For example, there remain uncertainties related to the true percentage of kits used in the secondary distribution model. Second, total and average costs are estimated across a diverse range of fixed health facilities for each country leading to inevitable cost variation by distribution channel. Third, reduced sample sizes from original targets were due to COVID restrictions for data collection and challenging partnerships with public sites, particularly in Mali. Therefore, results for this country should be interpreted with caution. Finally, cost analyses of small-scale interventions often generate extreme average costs per output without much application for program planning. Our suggestion to consider site level costs for financial planning in this specific study assumes that HIVST programs in these countries would always have largerscale mobile HIVST distribution strategies in place where central costs would be accounted for, which is the case of the ATLAS project.
Background: Mycoplasma pneumoniae respiratory infections are transmitted by aerosol and droplets in close contact. Aim: We investigated global M. pneumoniae incidence after implementation of nonpharmaceutical interventions (NPIs) against COVID-19 in March 2020. Methods: We surveyed M. pneumoniae detections from laboratories and surveillance systems (national or regional) across the world from 1 April 2020 to 31 March 2021 and compared them with cases from corresponding months between 2017 and 2020. Macrolide-resistant M. pneumoniae (MRMp) data were collected from 1 April 2017 to 31 March 2021. Results: Thirty-seven sites from 21 countries in Europe, Asia, America and Oceania submitted valid datasets (631,104 tests). Among the 30,617 M. pneumoniae detections, 62. 39% were based on direct test methods (predominantly PCR), 34.
j Exclusively positive test numbers (and no total test numbers) available and/or reported. k Data from the federal state of Saxony detected by the Landesuntersuchungsanstalt Sachsen based on combined direct and indirect test methods, but predominantly on serology (no information on isotypes)  [12]. l National reference laboratory data from the two related hospitals (Antwerp, Leuven; 86-98%) and across the country (2-14%). m Data collected through the Belgian Sentinel Network of Laboratories (SNL), a network of ca 95 microbiology laboratories (i. e. 60% of all Belgian microbiology laboratories)  [53] , based on direct test methods such as NAAT, antigen test, culture, microscopy, 'unknown' or 'other' (cases based on serology were excluded). n Period of enhanced surveillance from 1 October 2019 to 30 March 2020.
Quality control Entries were included if they met the following quality control criteria for valid datasets: (i) verification of the participant, laboratory and institution via provided link and/or references in PubMed, (ii) validation of the information and/or references about the test method, and (iii) data check for multiple entries from the same institutions (double reporting), invalid or incomplete data, and inconsistent entries. In case of inconsistency or multiple entries from the same institutions, participants were contacted by email to request clarification and/or adapt entries to exclude double reporting. Criteria for de-duplication and exclusion criteria are listed in Supplementary Table  S2.
Stay-at-home order and school closure periods Periods of stay-at-home orders for the general population (referred to as lockdowns) in Europe were obtained from the Response Measures Database (RMD) of the European Centre for Disease Prevention and Control (ECDC)  [25]  and those in other UN regions from a collection of pandemic lockdown dates in Wikipedia  [26] , with adjustments made by the participants. The total duration in days until the end of the study period was calculated for each site. School closure duration in days (full and partial closure in total) was determined according to the United Nations Children's Fund (UNICEF) global school closures database until 2 March 2021 (last update before the end of the study period)  [27].
Survey entries and detection methods We received entries from 48 sites, of which 29 were entered via the online survey and 19 via email to authors. Of the 12 experts collating laboratory detections of M. pneumoniae in Europe and Israel for the ESGMAC in a previous study (January 2011-April 2016)  [14] , eight provided information for this survey. An overall response rate could not be calculated because the survey was widely disseminated through societies, social media and further dissemination among participants themselves. We excluded 11 entries because of invalid or incomplete data (n = 7), inconsistent data (n = 2; positive test numbers by month did not match with total numbers per year) or double reporting (n = 2; congruent data from same institutions). Thus, 37 valid datasets from separate sites in 21 countries from four UN regions were eligible for inclusion (Europe: n = 12; Asia: n = 5; America: n = 2; Oceania: n = 2), 29 from hospital laboratories, two from national reference laboratories and six from national and/or regional surveillance systems (Figure  1 ).
Acknowledgements We are very grateful to all those who helped with the study:  Laure F. Pittet  ( Department of Pediatrics, Division of General Pediatrics, Children's Hospital, Faculty of Medicine, University of Geneva Hospitals, Geneva, Switzerland );  Petra Zimmermann  ( Faculty of Science and Medicine, University of Fribourg, Fribourg, Switzerland );  Jan Fehr  ( Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland );  Lucas M. Bachmann  ( Medignition Inc. Research Consultants, Zurich, Switzerland );  Semjon Sidorov  ( Division of Infectious Diseases and Hospital Epidemiology, University Children's Hospital Zurich, Zurich, Switzerland );  Wendy W. J. Unger  ( Division of Infectious Diseases and Immunology, Department of Pediatrics, Erasmus MC University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands );  Samuel Rhedin  ( Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden );  Todd A.
Study profile, global survey of Mycoplasma pneumoniae detections, April 2017-March 2021 Figure 1 Preparation (24 February to 29 March 2021) Survey development and test runs Dissemination (30 March to 31 May 2021) Societies Social media Direct approach to potential participants 48 Entries 29 via online survey 19 via email to authors 11 Entries excluded 7 invalid / incomplete 2 inconsistent 2 multiple entries / data from same institutions 37 Valid datasets from separate sites 4 UN regions (Europe, Asia, America, Oceania) 21 Countries 29 Hospital / clinical 2 National reference 6 National / regional laboratory laboratory surveillance Detection method: Detection method: Detection method: 24 PCR 2 PCR 3 PCR 1 Antigen test 1 PCR, antigen test, 6 Serology a or culture 1 Serology or PCR b 2 Serology or PCR b.
The PCR reactions were performed in an XP Cycler (Bioer Technology, Hangzhou, PR China) thermocycler. PCR products were resolved on 2% agarose gels. The desired size band was cut from the gel and purified with an EZNA Gel Extraction Kit (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer's recommendations. The purified PCR products were sequenced at Macrogen Europe (Madrid, Spain) with primers V1f, 530r, 530f, 1061f, and 1492r  [23, 26]. The obtained nucleotide sequences were edited with the MEGA X program  [8]  and subsequently aligned with other microsporidian sequences using the ClustalW program included in MEGA X. Minor corrections, to increase the aligned sequence similarity and improve the inferences on any positional homology, were then made by hand. A BLAST search was carried out in order to elucidate any homologies or similarities with the sequences previously published in the GenBank database (Supplementary material Table  S1 ).
The molecular identification was achieved by phylogenetic analysis through the Neighbour-Joining distance method with the Kimura 2-parameter estimate  [9]  with at least 1000 bootstrap replications (Supplementary material Figure  S1 ); and the Maximum Likelihood method with the Tamura-Nei model  [20]  in MEGA X, using the sequence of Trichonosema pectinatellae Canning, Refardt, Vossbrinck, Okamura & Curry, 2002 (AF484695. 1) as the outgroup (Fig. 1 ). Distance estimation was carried out in MEGA X using the p-distance model distance matrix for transitions and transversions (Kimura 2-parameter), with partial deletion of position with gaps or missing data (Supplementary material Table  S2 ). The nucleotide sequence obtained in this work was submitted to the GenBank database under accession no.
Molecular analyses One consensus sequence of 1228 bp was obtained (MicroBac 01) for T. baccigeri n. gen. , n. sp. The results of the BLAST (Supplementary material Table  S1 ) show the highest homologies for Cucumispora sp. (MG063425. 1) (94. 89% sequence identity), Microsporidium sp. (HM800853. 2) (94. 86% sequence identity), and H. aquatica (KX364284. 1) (94. 72% sequence identity). A final alignment of 1492 bp, including sequences from GenBank, was used for the phylogenetic studies. The results of the Neighbour-Joining (Supplementary material Figure  S1 ) and the Maximum Likelihood (Fig. 1 ) analyses based on SSU rDNA showed similar results. In both phylogenetic analyses, the novel sequence fell in a well-supported branch together with Cucumispora Ovcharenko, Bacela, Wilkinson, Ironside, Rigaud & Wattier, 2010 and several unidentified sequences of crustacean-infecting microsporidia (Microsporidium spp.