dataesr/scientific-paragraphs-categorization huggingface dataset:
efficacy analyses, pre-and postmatching In the sensitivity analysis, patients with no cytogenetic test done were excluded from the dataset. Matched patient population Unmatched patient population Primary analysis Sensitivity analysis Absolute Absolute Absolute standard- standard- standard- VTd-label, ized mean VTd-label, ized mean VTd-label, ized mean D-VTd, PETHEMA/ difference D-VTd, PETHEMA/ difference D-VTd, PETHEMA/ difference Variables CASSIOPEIA GEM prematch CASSIOPEIA GEM postmatch CASSIOPEIA GEM postmatch Sample size, n 543 130 NA 250 125 NA 156 78 NA Age, mean, y 56. 8 55. 6 0. 162 55. 3 55. 4 0. 025 55. 0 54. 9 0. 004 Male sex, % 58. 2 58. 5 0. 005 56. 8 57. 6 0. 016 61. 5 59. 0 0. 052 ECOG PS ≥1, % 51. 2 56. 2 0. 100 54. 0 54. 4 0. 008 56. 4 57. 7 0. 026 IgG myeloma, % 64. 6 66.
2e, f ). Cross-linked residues were identified by MS of tryptic peptides. The most abundant cross-link by far was between residue 283 in domain 5 of FH and residue 24 near the membrane-distal N terminus of FHR (Supplementary Fig. 2g ). Other less abundant cross-links from FHR to FH domains 8 and 20 were also detected (Supplementary Fig. 2g ). These findings were used to guide the selection of FH domains, expressed as single or tri-domains, for the investigation of FHR binding specificity and kinetics (Fig. 2  and Supplementary Fig. 3a-c ). The highest affinity interaction, with a similar K D to full-length FH, was between FHR and FH domains 4-5-6 with a K D of 167 nM and the K D for binding to domain 5 alone was 562 nM (Supplementary Fig. 3b, c ). Binding of FHR to domains 7-8-9 and 18-19-20 were too weak to allow calculation of K D values from the data obtained.
c Tsetse flies were fed on infected mice and transmission of the infection was evaluated by the dissection of >100 midguts per cell line on days 6-16 post infection. The total number is derived from seven replicates per cell line. The number of infected midguts over the total dissected midguts is plotted, whereby 31/114 were infected for FHR +/+ , 12/119 for FHR -/-clone 1, and 11/106 for FHR -/-clone 2. The observed difference between the FHR +/+ and each of the FHR -/-clones was significant (P < 0. 002 for each cell line by χ 2 -test). Source data are provided as a Source Data file. The parasitaemia in animals describes the total number of both slender and stumpy BSFs, and is influenced by the proliferation of slender BSFs, the lifespan of each life stage and the clearance of parasites by the host immune system.
A mouse moAb (L13D6, 1:100 dilution) to a paraflagellar rod protein was used as loading control (kind gift of Keith Gull)  53. Donkey or goat anti-rabbit and donkey anti-mouse horseradish peroxidases (HRP; 1:5000 dilutions) were used as secondary antibody. Streptavidin HRP (strep POD) was used at varying dilutions as specified. MS for protein identification. Pulldown samples were fractionated by SDS-PAGE (Criterion XT-PreCast Gel, 12 % Bis-Tris, Bio-Rad) and protein bands were excised, trypsin-digested and run on an liquid chromatography-tandem MS (LC-MS/MS) system by electrospray ionization-quadropole-time of flight (ESI-QUAD-TOF) in the Advanced Proteomics Facility (University of Oxford) using standard procedures. Results were searched against the UniProtKB/Swiss-Prot database and analysed by Mascot.
All analyses were performed at 20 °C in 10 mM HEPES, 150 mM sodium chloride, and 0. 005% Tween-20 pH 7. 5. Biotinylated FHR or mutants were immobilized on a streptavidin-coated chip (Series S Sensor Chip SA). Ligand was serially diluted twofold into running buffer and flowed over the chip surface, followed by running buffer. Regeneration used 0. 1 M glycine-HCl pH 2. 5. Binding responses were obtained by subtracting a blank response from the FHR/mutant responses. A two-state reaction model (bovine FH) or a 1 : 1 interaction model (bovine FH domains 4-5-6 and 5) were fit to blank subtracted kinetic sensorgrams using the BIAevaluation software, enabling the determination of the affinity (K D ) and kinetics of the interaction (k on , k off ). For interactions with a fast dissociation rate (k off ) (mouse and human FH domains 4-5-6), steady-state levels as a function of ligand concentration were analysed to obtain K D only.
Charge states 1+ and 2+ were excluded from HCD fragmentation. MS data were converted into mgf format using pParse and searched using the pLink software  55. The database contained the target proteins only. Search parameters were as follows: maximum number of missed cleavages = 2, fixed modification = carbamidomethyl-Cys, variable modification 1 = Oxidation-Met, variable modification 2 = Glu to pyro-Glu. Data were initially filtered by E-value < 1. 0 -8. Cross-links were further filtered/inspected with specific emphasis on fragmentation patterns. CD and thermal melts. Far ultraviolet CD spectroscopy was performed on WT, mutant 2 and mutant 3 FHR using a J-815 Spectropolarimeter (Jasco) and a Peltier temperature control unit. Proteins were analysed at 0. 2-0. 25 mg/ml in 50 mM sodium phosphate pH 7.
Mathematical modelling of BSF infections in mice. Mathematical modelling of T. brucei infection dynamics in immunocompetent mice  45, 46  has been performed here with minor modification. The model, as first described  46  , was fitted to the parasitaemia of individual immunocompetent mice in this study, but the number of antigenic variants was limited to three and a variant 3-induced immune response was removed. PAD1 expression and slender, intermediate and stumpy cell proportions were not measured and therefore not fitted. However, the dynamics of slender and stumpy forms were modelled. The full details of the model are available in Supplementary Data 1. Tsetse fly infections with BSFs from infected mice. Female Glossina palpalis gambiensis were fed on the bellies of infected, anaesthetized mice  65  and subsequently on sheep blood by in vitro membrane feeding.
Microscopy was performed using a Zeiss Axioimager M1; images were recorded using the Axiovision software (Zeiss) and the same settings were used for all samples within a set of experiments. Images were imported into Adobe Photoshop for figure preparation. The midgut and proventriculus from tsetse flies infected with WT and FHR -/-PCF cells as described above were dissected  61. The organs were transferred directly to slides in vPBS and fixed in an equal volume of 4% or 8% paraformaldehyde. The remaining steps of the IFA were as described for cultured BSF cells. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was funded by  MRC  grant  MR/P001424/1  to M. C. and M. K. H. , and  Magdalene College, University of Cambridge , scholarship to  O. J. S. M. This  project has received resources funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  731060 (Infravec2 ) to O. J. S. M. ,  Labex Parafrap   ANR-11-LABX-0024  to  J. -M. B  and  BBSRC  grant  BB/M008924/1  to W. G. M. C. ( 217138/Z/19/Z ) and M. K. H. ( 101020/Z/13/Z ) are  Wellcome Trust Investigators. We thank  Ed Lowe  and the beamline scientists at I03 at  Diamond Light Source ; the  Oxford Protein Production Facility (OPPF) ;  Federico Rojas  and  Keith Matthews ,  University of Edinburgh , for stumpy trypanosome lysate;  Raymond Stevens  for  PreScission expression plasmid (NIH grant   P50 GM073197 );  Katherine Stott  and the  BioPhysics Facility, University of Cambridge ; the  Advanced Proteomics Facility, University of Oxford ;  InterTryp IRD-CIRAD  and  Vectopole Sud , Montpellier, France insect facilities;  Andrew Herbert  and  Paul Barlow ,  University of Edinburgh  for advice and discussions; and  The Sanger Mouse Genetics Programme (Sanger MGP)  for the kind gift of reagents.
In a more recent work, Ramirez et al. [29]  used temporal expression profiles of in vitro macrophage cytokines to infer logical models of macrophage polarization (M1 and 3 subcategories of M2: M2a, M2b and M2c) in the presence of different stimuli. Nevertheless, many important questions remain to be explored regarding the polarization states, especially in a tumor setting. More specifically, it is important to identify the pathways involved in TAM formation and to understand to what extent the macrophage plasticity facilitates this process inside a tumor. On the other hand, despite the wealth of quantitative information from bulk and single-cell sequencing datasets, the inference of regulatory networks based on experimental data remains a difficult challenge, with most approaches proposing a combination of both literature-and data-driven methods  [29] [30] [31] [32].
We then used public transcriptomics data for monocytes and M1, M2 and NLCs to calculate the TF activities in each phenotype. In these experiments, monocytes were either not induced, induced to mature to macrophages with M-CSF and further activated with Interferon gamma and LPS (M1) or IL-4 (M2)  [51, 52]. The complete analysis of TF estimation on microarray gene expression datasets was performed using the Dorothea R package and Viper  [53]  and ISMARA  [54]  (see Methods, Section 5. 3). The list of most active TFs for each phenotype (M1, M2 and NLC) from both methods can be accessed in Tables S2a-d and S3a-d. For NLCs, we identified specific TFs using a set of 19 microarray expression profiles we have generated using an in vitro co-culture experiment in which NLCs are differentiated from monocytes upon contact with B cells from a CLL patient  [55].
Looking at public transcriptomics and proteomics datasets we were surprised to see relatively little overlap between the well-accepted biomarkers for M1 and M2 and the genes and proteins that are highly expressed in each specific cell type. This could be explained by the important regulatory mechanisms that lead from expression of a gene to the appearance of a protein on the cell membrane (which could be typically detected by FACS), on which most cell type descriptions in immunology are based. Nevertheless, we think that the integration of data in such a model would greatly improve both the usefulness of this model and our understanding of the different phenotypes and we will consider this approach in further work  [29, 79, 80]. For example, Ramirez et al. [29]  employed transcriptomic time courses to arrive at Boolean models of macrophages in different polarization states, showcasing the potential of integrating experimental data in our mostly literature-based approach.
Importantly, the state space is characterized by non-overlapping basins of attractions. • asynchronous updating method: at each time step, the updated nodes are chosen randomly (General Asynchronous, Random Asynchronous) or according to their characteristic updating time, while the system's state will be characterized by overlapping basins of attractions. It is important to note that fixed point attractors are time invariant, i. e. , do not depend on the updating method. Choosing the synchronous update method we obtain all transitions between them and consider the final attractors. Our network is composed of N = 40 components (excluding the 3 output nodes which are added for clarity in Figure  1 ) and has 2 40 possible states. The model was implemented using the BoolNet [69] R package  [90].
Calculating the Transcription Factor Activities Since gene expression of a TF is not representative of its activity level and many of the nodes in our network are TFs, we sought to estimate activity of these components using bioinformatic methods that consider the expression of a TF's targets taken from a dataset in combination with a regulatory network database. We extracted gene expression data from different public sources. Microarray data used in this publication were downloaded from the NCBI repository Gene Expression Omnibus (GEO) database. M1 and M2 Macrophages microarray data accession number is GSE5099  [51, 52]. Our previously published NLC microarray dataset can be found under accession number GSE87813 and was processed as described in  [55]. Raw microarray datasets were then normalized using the RMA (Robust Multi-arrays Average) normalization method and batch corrected.
Transcription factors activities were estimated using the Dorothea R package and ISMARA. Dorothea is a TF-regulon interaction database giving each interaction a confidence level. Here, levels of confidence of interactions from A to E were taken into account. The VIPER algorithm was used to estimate TF activities based on Dorothea interactions and our expression data  [53]. ISMARA is a web-based tool to identify the key TFs and miRNAs driving expression/chromatin changes and to predict activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators  [54]. For both methods, the comparison between TF activities across the phoneotypes (M1, M2, and NLC) is performed using the rank method. The complete analysis of TF estimation from both packages, as well as TF activity comparison between phenotypes, can be accessed in Material, Tables S2a-d and S3a-d.
An anti-GAPDH antibody (1:10000, Imgenex) was also used as a loading control. Detection was performed using either a secondary anti-mouse IgG HRP-conjugated antibody P0447 (1:5000, Dako) for dystrophin primary antibodies or secondary anti-goat IgG HRP-conjugated antibody P0449 (1:2000, Dako) for GAPDH primary antibody. Immunoblots were revealed with ECL Western blotting substrate (Pierce) and exposed to ECL-Hyperfilm (Amersham). For the semi-quantification analysis of the data, levels of dystrophin isoforms in each central nervous system region (n = 4 per genotype) were first normalized to GAPDH levels using ImageJ software, and then values were normalized to dystrophin Dp427 control muscle levels or to total brain Dp71 and Dp140 levels. A schematic representation of the dystrophin epitopes recognized by all antibodies used in this study is depicted in.
Data analysis All values are expressed as mean ± SEM, with a significance level set at p < 0. 05. Statistical evaluation has been performed by using GraphPad Prism 5 (GraphPad Software, Inc. , La Jolla, CA, USA). Data distribution was first evaluated with a D'Agostino & Pearson omnibus normality test, for each group, at each tested timepoint, for each measured parameter. We then used either Kruskal Wallis test or ordinary two-ANOVA analysis and if a significant interaction was found, we further performed multiple comparison tests.
Data The data set used for our modelling is compiled from ground observatory and satellite data, and is made up exclusively of vector magnetic data. It covers a period of 20 years, from 2000. 0 to 2020. 0. The observatory data set is built using hourly means from all available ground observatories from 2000. 0 to 2020. 0, reprocessed according to  Macmillan and Olsen (2013) , so that it covers this time period continuously. Satellite data are compiled from the Champ and Swarm missions data sets. The Champ data covers a time period ranging from Sept. 2000 to Aug. 2010. A gap of about 3 years in satellite data separates the Champ and Swarm missions. The Swarm data set spans Nov. 2013 to the end of 2020. It includes the latest available versions (0505, 0506 and 0507) of level-1b vectorial data files in December 2019 from the Swarm A (Alpha) and B (Bravo) satellites.
Data selection We apply an overall light selection on the data set [see  Thomson and Lesur (2007) ]. The selection criteria for all types of data are detailed in Table  1. Different criteria apply for HL and ML data. ML data are taken only inside the 23:00-5:00 local time window, while HL data are selected for all local times. Different time samplings are set for ML and HL data, to compensate for the higher data density at high latitudes due to the nearly sunsynchronous satellite orbits (see Table  1 ). Data are also selected for a limited range of values of the D st index, and for positive values of the z component of the interplanetary magnetic field (IMF). Finally, observatory and ML satellite data are selected only if they are located in a nonsunlit area. We define N v and N d , the number of selected vector data, and the total number of selected data, respectively ( N d = 3 × N v ).
Data weights The variances attributed to each type of data are given in Table  2. X,Y and Z are the coordinates in the NEC reference frame for HL data, and the SM frame for the ML data. In this study, data errors are assumed to be uncorrelated, so the time and spatial covariance is set to zero between different data samples. Similarly, the errors of the different data vector components are assumed to be uncorrelated. The inverse of the variances are used to weight the data.
Candidate models to the IGRF The models presented here have contributed to set two independent contributions to the IGRF-13: the IPGP candidate to the IGRF main field 2020, and the Japanese team candidate to the IGRF predictive secular variation  (Minami et al. 2020). In both cases, the data selection criteria were those described in the first section, but Swarm-B satellite data were not used. Regarding the contribution of the Japanese candidate, the model is nearly the same as the HS model presented here with a data set including data only up to 2019. 5. The scaling and reference radius used for the different covariance matrices were those of Table  3. The obtained core field and secular variation components of the model were then used as input data to the En4dVar assimilation process that led ultimately to a prediction of the mean SV over 2020-2025-see  Minami et al.
Acknowledgements The authors acknowledge  ESA  for the provision of Swarm data and the  GFZ  for the provision of Champ data. S. Macmillan, the  World Data Center for Geomagnetism (Edinburgh) ,  INTERMAGNET , and all the staff operating magnetic observatories around the world are acknowledged for the preparation and provision of the observatory data sets. J. Aubert is ackowledged for the provision of Coupled Earth snapshot models. This is IPGP contribution:  4156.
Funding This research was supported by  CNES  in the context of the " Suivi et exploitation de la mission Swarm " project and by  ESA  (contract  4000109587/13/I-NB Swarm ESL/SW-CO-DTU-GS-010 ). The study was also partly supported by the  PRC JSPS CNRS Bilateral Joint Research Project  " Forecasting the geomagnetic secular variation based on data assimilation ". The contribution from MH and JB was supported by the  DFG  in the context of the  SPP 1788 "Dynamic Earth ". The DAAD supported GR for a 2 months stay at the  University of Potsdam  (Research Grants-Short-Term Grants, 2019 (57442045)).
Data availability statement Observatory data are available at ftp://ftp. nerc-murch ison. ac. uk/geoma g/ Swarm /AUX_OBS/. Champ data are available on the GFZ data center at https ://isdc. gfz-potsd am. de/champ -isdc/acces s-to-the-champ -data/. Swarm data are available on the ESA data center at https ://earth. esa. int/web/guest /swarm /data-acces s. The coefficients of the models presented are available upon request.
The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data  were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency.
Data The phytoplankton biomass is commonly indexed by Chl-a, estimated from ocean color remote sensing data  [35]. In this study, we used satellite-retrieved Chl-a data including Sea-Viewing Wide Field-of-View sensor  (SeaWiFS, 1997 (SeaWiFS, -2010)) , Moderate Resolution Imaging Spectroradiometer on Terra (MODIST, 1999-present) and Aqua (MODISA, 2002-present), Medium Resolution Imaging Spectrometer  (MERIS, 2002 (MERIS, -2012)) , and Visible Infrared Imaging Radiometer Suite (VIIRS, 2011-present). These data were downloaded from the website (http://oceancolor. gsfc. nasa. gov), as listed in Table  1. The data quality has been evaluated by lots of regional and global in-situ measurements  [6, 22, 36]. The data are the global daily Chl-a of level-3 products and binned into the same pixel resolution of approximately 9 km × 9 km.
From Table  1 , the length of satellite observation varies from eight years for VIIRS to almost twenty years for MODIST. The MODISA, MODIST, and VIIRS satellites are still operating. The end dates for these satellites in Table  1  are the periods of data downloaded from the web. There is a total of 24,089 files which are merged into 7956 daily files. These five satellites together provide continuous measurements of Chl-a from September 1997 to June 2019. As they cannot measure Chl-a in the presence of clouds and other factors, there are many empty values in the daily images of Chl-a. The percentages of valid pixels are relatively low, as shown in Figure  1 , where the percentages are computed from the ratio of the number of valid pixels to the number of the global ocean pixels. From Figure  1 , the daily percentages of valid pixels of each satellite exhibit seasonal changes which are mainly due to seasonal variability of the coverage of the incident solar irradiance on the sea surface.
The numbers of valid satellite-retrieved Chl-a are also affected by the satellite swath, data processing algorithms, the cloud coverage, thick aerosols, sun glint, and other factors. The percentage of valid pixels of each satellite ranges from 10% to 20% with the average of 14. 5% (SeaWiFS), 15. 4% (MODISA), 13. 8% (MODIST), 13. 8% (MERIS), and 14. 0% (VIIRS), respectively. The valid pixel coverage of Chl-a images of Level 3 products between MERIS and MODIS are similar, even though there is a large difference of satellite swath between about 1000 km of MERIS and about 2000 km of MODIS. Figure  1  shows that more than two satellites simultaneously have measured the ocean since 2000 and four satellites together between 2002 and 2010. The percentage of merged data shows that the numbers of valid pixels increase with the increasing numbers of satellites.
The percentage of valid pixels of each satellite ranges from 10% to 20% with the average of 14. 5% (SeaWiFS), 15. 4% (MODISA), 13. 8% (MODIST), 13. 8% (MERIS), and 14. 0% (VIIRS), respectively. The valid pixel coverage of Chl-a images of Level 3 products between MERIS and MODIS are similar, even though there is a large difference of satellite swath between about 1000 km of MERIS and about 2000 km of MODIS. Figure  1  shows that more than two satellites simultaneously have measured the ocean since 2000 and four satellites together between 2002 and 2010. The percentage of merged data shows that the.
The Effects of Climatology on Seasonal Cycles We have established a climatological dataset from the five satellite-retrieved Chl-a and used sine equations to fit the seasonal variability. To examine the relationship between the climatology and satellite-retrieved Chl-a, the climatological values are compared to all satellite-retrieved data at four sites (E1-E4 marked in Figure  8b ) against the day of year, as shown in Figure  10. In Figure  10a , there are 611, 734, 463, 772, and 300 dots for SeaWiFS, MODIST, MERIS, MODISA, and VIIRS, respectively. The mean numbers of dots on one day of year are very small with the average of 1. 7, 2. 0, 1. 3, 2. 1, and 0. 8 for these five satellites. The numbers become smaller for site E3 (Figure  10c ) with the average of 0. 5, 1. 1, 0. 7, 1. 1, and 0. 4, respectively.