dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: 13 C NMR analyses were performed at the  Centre d'Etude Structural et d'Analyse des Molécules Organiques (CESAMO) ,  ISM ,  Université de Bordeaux ,  Département de Chimie , and the authors thank the CESAMO team, and especially  Cybille Rossy , for her help. 43 Ca and 25 Mg NMR spectra were recorded thanks to the  Infrastructure de Recherche of the CNRS  and the authors want to thank the  IR INFRANALYTICS FR2054  for conducting the research and for financial support.
The data in this paper were recorded using FairFieldNodal Zland three-component geophones. These all-in-one sensors have an internal battery, a built-in GPS and flash memory to store the data, allowing for several weeks of autonomous and continuous recording, without the need of external cables. They have a cylindrical geometry of about 17 cm in height and 12 cm in diameter and are mounted on a detachable spike. They were installed directly in the ice without their spike. To maximize the coupling, a milling tool was specifically designed to drill the ice at the diameter of the nodes. The snow was removed prior to drilling holes, and geophones were installed in the holes at about half their height. We covered them back with snow to insulate them in view of preserving their battery life. At the time of the deployment, the internal temperature of several nodes was measured, before and after covering them with snow, showing an increase from -21 to -16 • C.
FairFieldNodal Zland three-component geophones have a flat frequency response down to their eigenfrequency of 5 Hz. We recorded the data with a sampling frequency of 1000 Hz  (Moreau and RESIF, 2019) , but in order to reduce the computational cost of the present study, data were downsampled at 250 Hz. Conversion of the raw data into a miniSEED (Standard for the Exchange of Earthquake Data) format was obtained using the Fairfield software. Instrument response deconvolution is not necessary for our methodology and was therefore not applied. Data are expressed in millivolts but could be converted to velocity by dividing by the proportionality factor of 89 V m -1 s -1 and further converted to displacement by integration with respect to time.
A smaller distance implies a larger number of clusters. In the present case, apart from the transient waveforms lasting several minutes, the typical duration of the waveforms in our recording is of a few seconds. Since this study does not focus on the transient signals, we use a 40 s long sliding window with a 20 s overlap. We represent the hierarchical clustering output in the form of a dendrogram as in Fig. 3a , for data collected at station S 1. This clustering would not change if applied to another stations, which is one of the advantages of using a deep scattering network. With the threshold distance indicated in the The family with clusters referenced 0 to 7 (Fig. 3a ) accounts for about 30 % of the dataset (Fig. 3b ). Figure  4a  shows 10 waveforms randomly sampled from cluster 0. We see that this cluster contains clean icequakes with a very good signal-to-noise ratio (SNR).
On the horizontal channels, where the QS 0 and SH 0 modes are dominant, the spectrum remains over -30 dB up to 50 Hz. Although some icequakes may be the consequence of thermomechanical forcing  (Olinger et al. , 2019) , it is likely that the majority are tidal icequakes. The temperature log can be extracted from the Sveagruva weather station (SN99760), located 2 km west of the experiment location. Data for this station are available at https://seklima. met. no/hours/air_ temperature/custom_period/SN99760/en/2019-02-27T00: 00:00+01:00;2019-03-26T23:59:59+01:00 (last access: 21 March 2023). These temperatures are shown in Fig. 5. The absence of a periodic pattern in temperature variations suggests that tides have more effect on the generation of icequakes than changes in temperature. The majority of icequakes occur within a period of 24 h (Fig.
6 ). This periodicity can also be seen in Fig. 3c , especially between 1 and 15 March. One would expect the semidiurnal tide (10-20 cm) to reflect in the periodicity of the icequakes, but the specific geometry of the moraines around the experiment, together with the small channel that connects it to the fjord, generates some nonlinear effects that cause the tide in Lake Vallunden to be asymmetric  (Marchenko and Morozov, 2013). This could explain why occurrences are dominated by a period of 24 h instead of 12 h. The family with clusters referenced 26 to 29 accounts for about 13 % of the dataset. Figure  4b  shows 10 waveforms randomly sampled from cluster 27. The waveforms are more complex and include many impulsive events, some noise, repeating events and events that last up to 15-20 s. The waveforms in this family occur mostly during three sequences.
Code availability. Codes are available at https://doi. org/10. 5281/zenodo. 7755633 (Moreau, 2023a). Data availability. All metadata for this experiment are available online (https://doi. org/10. 15778/RESIF. XG2019; Moreau and RE-SIF, 2019). A subset can be accessed instantly via a direct download link. To request this link, please send an email to resif-dc@ujf-grenoble. fr with your affiliation and IP address. The full dataset (total size of 3. 2 TB) is available upon request to the corresponding author, but we do not have the possibility of having such a large dataset made available with a download link. Catalogs of extracted icequakes can be downloaded at https://doi. org/10. 5281/zenodo. 7755650 (Moreau, 2023b).
The next step is a comparison in turbulent inflow conditions, which is featured in IEA Wind Task 47. Doing this analysis in cooperation under the auspices of the IEA Wind Technology Collaboration Program (TCP) has led to many mutual benefits for the participants. The large size of the consortium brought ample manpower for the analysis where the learning process by combining several complementary experiences and modeling techniques gave valuable insights that could not be found when the analysis is carried out individually.
, 2010; Madsen et al. , 2010b). The unique data from this experiment, including a description of the turbine, were made available to the participants of IEA Task 29 Phase IV so that it could form a basis for a thorough analysis. This paper presents the progress of this task, where many participants from different countries simulated the same experiment. The studies may serve as a benchmark for performing code-to-code comparison involving many participants. Suggestions will be given to improve the agreement between codes. Section 2 presents the methodology of the comparison round, including a description of the measurements and the setup of the comparison. Sections 3 and 4 give the results of the comparison for the two cases under investigation together with a discussion, which is followed by conclusions.
DanAero field measurement campaign Detailed aerodynamic measurements on MW-scale wind turbines are scarce and open publications about them even more so. An exception lies in the DanAero experiment  (Bak et al. , 2010; Madsen et al. , 2010b) , which was carried out in atmospheric field conditions on an NM80 2. 3 MW turbine in a Danish project by the Danish Technical University (DTU) and four industrial partners (LM Glasfiber, Siemens Wind-Power, Vestas and Dong Energy) in two periods from 2007 until 2010 and from 2010 until 2013. At the initiation of IEA Task 29 Phase IV in 2018, the DanAero partners agreed that the measurements, as well as the model data for aerodynamic and aeroelastic modeling of the NM80 turbine, could be shared with the partners participating in this task. The pitch-controlled turbine features three LM38.
Acknowledging the earlier observed variations in axial-induced velocities, this discrepancy seems to be in contradiction with momentum theory, where a higher loading is accompanied with more induction. However this could also be attributed to uncertainties in the engineering extensions used for the turbulent wake state in which the turbine is operating for this case. Grouping the results by code type, as will be shown in Sect. 3. 3, allows us to better observe the loading differences. The comparison with measured values (obtained from the integration of the measured pressure distributions) shows the normal force to be consistently overpredicted by approxi-mately 10 % for all radial positions. It could then be expected that this overprediction is reflected in the measured flatwise moments from Fig.
Acknowledging the fact that most CFD codes model the boundary layer fully turbulent (whereas the experiment features free transition), this is contrary to expectation. At r = 30. 20 m (Fig. 4e ), the linear shape of the measured pressure distribution on the pressure side just before the chordwise position of maximum thickness differs from the rounded shape, as predicted by all simulations. A separate sectional study was performed by DTU to compare the performance between design and measured geometries, of which the first was used for this comparison round. This however revealed very small differences in comparison to the differences shown here. Besides these small deviations, one can conclude that generally speaking the pressure distributions are in good agreement between simulations and between simulations and experiment.
Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The views expressed in the article do not necessarily represent the views of the DOE or the U. S. Government. Acknowledgements. The authors would like to thank IEA TCP Wind for facilitating the IEA Task 29 Phase IV project in their framework. The participants of the Danish DanAero project are acknowledged for providing the field measurement database. Financial support. The contributions of the participants to IEA Task 29 Phase IV have been funded in various national programs, which are detailed in the corresponding final report  (Schepers et al.
Acknowledgements. The authors thank the referee for her/his comments, which have allowed us to improve our manuscript. The authors acknowledge support from the  European Research Council  through  ERC  grant  SPIRE 647383  and from  GOLF  and  PLATO CNES  grants at the  Department of Astrophysics of CEA. J. Park acknowledges support from the  Royal Astronomical Society and Office of Astronomy for Development  through the RAS-OAD astro4dev grant and from the  Engineering and Physical Sciences Research Council (EPSRC)  through the  EPSRC mathematical sciences  small grant ( EP/W019558/1 ). A. Astoul acknowledges support from the  Science and Technology Facilities Council (STFC)  grant  ST/S000275/1 , as well as the  Leverhulme Trust   for early career grant.
Predicting bNAb probabilities for V(D)J recombination and somatic hypermutation To predict the probabilities of developing HIV-1 specific bNAbs, we retrieved sequence and neutralization data for 70 HIV-1 neutralizing antibodies with varying neutralization breadths and potencies against a panel of 56 different HIV-1 strains (Supplementary Fig. 4  and Supplementary Data 3) from the CATNAP database  59. The 70 antibodies target various sites on the envelope spikeprotein (Fig. 2a , adapted from Klein et al. 11  , as well as Sok and Burton  60  ) and cover a broad range of potencies with geometric mean IC 50 values from 0. 005 to 16. 991 µg/ml as well as neutralization breadths between 10. 7 and 98. 2% (Supplementary Data 4, Fig. 2b ). Breadth and potency are the most critical parameters of HIV-1 neutralizing antibody activity and can diverge substantially among bNAbs.
2b ). 1  | Overall approach and unbiased repertoire sequencing. a B cells are purified from peripheral blood and mRNA is isolated. Unique molecular identifiers (UMI) are added by template switching reverse transcription (TS-RT) and variable regions are amplified by PCR. Amplicons are sequenced by 2x300 bp Illumina sequencing and UMIs are exploited for error correction. High-quality sequences are used to learn probabilistic models and predict probabilities of bNAb sequences from the CATNAP database. Correlation of probabilities and neutralization allows for identifying highly probable and potent bNAbs. b Biological replicates of 100,000 IgG + B cells were isolated from three uninfected donors for pipeline validation. A 2 represents a technical replicate of A (i. e. , the same PCR product, but independent library preparation and sequencing).
c Overlap of unique CDRH3s between replicates from samples in b. Upper panel shows the total number of overlapping CDRH3s between biological and technical replicates. Lower panel shows CDRH3 overlap within and between donors as the mean overlap of n = 3623 randomly drawn CDRH3s from each dataset after 100 iterations (see methods for details). g Total and mean number of raw reads, annotated reads, and identical sequences (i. e. , the same UMI) that were found at least three times in n = 57 uninfected individuals. h V gene segment distributions, mean CDR3 lengths, and mean V gene mutation frequencies for heavy, kappa, and lambda chains of n = 57 uninfected individuals. V gene segment distributions are depicted as mean values ± SD. Boxplots in f and h depict the 25% and 75% percentiles with the median as average lines and minimum/maximum values as whiskers.
) and its immediate descendants (ω D ). Distributions of their ratios (ω D /ω anc. ) were plotted (left panel) to illustrate the clone tree skewness (dashed line = neutral, see methods for details). The right panel shows the mean terminal/mean branch length distribution for clone trees. g Clone size distribution for the cohorts. h Clone diversity determined by Gini-Simpson index and entropy (n = 57 for CTRL, n = 34 for HIV-1, and n = 12 for HCV). Data is presented as mean values ± SD. Box-plots in panels b-d depict 25% and 75% percentiles with medians as average lines and minimum/maximum values as whiskers. Source data are provided as a Source Data file. tools for HIV-1 prevention and therapy in humans  67, 68. However, bNAbs have only been identified in a minor fraction of HIV-1-infected individuals and to elicit highly broad and potent bNAbs by vaccination remains a yet unreached goal  11, 69, 70.
Discussion Notably, MPER antibody precursors have recently been successfully induced in the human HVTN133 immunization trial (NCT03934541)  86. In addition to uninfected individuals, we also sequenced the repertoires of 34 HIV-1-and 12 HCV-infected individuals. In concordance with previous studies  49, 87  , we detected marginally longer mean CDRH3 lengths in HCV-infected as well as HIV-1-infected individuals that do not receive antiretroviral therapy. Similar to the findings from Roskin et al. 87  , we also detected an increase of the inherently auto-reactive gene segment V H 4-34 in our ART-naive HIV-1-infected subgroup. However, we do not see substantial differences in bNAb sequence probability scores in the IgG + memory compartment when averaging over individuals in the cohorts. This suggests that chronic infection itself does not alter the probabilities to develop specific bNAbs substantially in any direction, even if it leaves traces of particular sequence features within the whole IgG repertoire.
As a consequence, long CDRH3 antibodies such as the V2-apex or MPER class might be more likely generated than proposed by our current models. Third, we relied on the IMGT database for allelic variant calling and did not determine subject specific alleles de novo  89. Novel allelic variants will therefore be counted as mutations and the total number of somatic mutations in donors with novel allelic variants might be slightly overestimated. In terms of P SHM , this would translate to a higher probability of finding a particular mutation at a certain position. Finally, by performing bulk sequencing, we lose heavy-light chain pairing information, which could have an influence on the total probability of bNAbs and differ across cohorts. It will therefore be interesting to adapt the presented approach to paired sequencing techniques (e.
g. , 10X genomics) in the future. Taken together, we present a framework for assessing the overall probability to develop an antibody with a specific CDR3 motif and pattern of point mutations. Our modeling approach on HIV-1neutralizing antibodies quantitatively confirms that bNAbs are unlikely outcomes of the B cell evolution within a host due to individual or a combination of improbable sequence features. Using neutralization data and probability scores, we demonstrate that the more unlikely bNAbs tend to have a higher neutralization efficacy. Moreover, this approach allows us to identify potent antibodies with higher chances to be elicited by vaccination. Finally, the data suggest that chronic infection has no impact on the generation of bNAb sequences within the IgG + B cell memory compartment, fostering the hope that a potent vaccine should be able to elicit bNAbs in uninfected individuals.
Sample collection Samples were obtained under study protocols approved by the Ethics Committees of the Medical Faculties of the University of Cologne and University of Bonn (study protocols 16-054 and 017/16, respectively; Clinical trial registration DRKS00010169). Recruitment and sample collection of uninfected control and HIV-1 cohorts was conducted in cologne, HCV cohort recruitment and sample collection was conducted in Bonn. All participants provided written informed consent and received a financial compensation for their participation. Sex/ gender was not considered in the design of the biosample collection protocol and samples were collected irrespective of sex and gender. Sex was assigned based on data recorded in the hospital system (male: 71; female: 31; n/a: 1).
Serum IgG neutralization test Neutralization assays with serum IgGs against the 12-strain global virus panel, were performed in 96-well plates following published protocols  9, 90. To this end, 12 HIV-1 pseudovirus strains were each mixed with 1:2 serial dilutions of purified IgG (1 mg/ml starting concentration, 8 dilutions) and incubated for 1 h at 37 °C. TZM-bl cells (RRID:CVCL_B478; ordered from the HIV Reagent Program, Cat-No. ARP-8129) were added (10 4 per well) in growth medium (DMEM, Gibco; 10% heat-inactivated FBS, Sigma-Aldrich; 2 mM Lglutamine, Thermo Fisher; 1 mM sodium pyruvate, Gibco; 50 µg/ml gentamicin, Sigma-Aldrich; 25 mM HEPES, Biochrom) with DEAEdextran at a final concentration of 10 µg/ml and incubated for 2 days. Equal amounts of Luciferin-containing lysis buffer (10 mM MgCl 2 , 0.
Isolation of B cells and RNA Isolation PBMCs were isolated by standard density gradient centrifugation using Histopaque (Sigma Aldrich) and LeucoSep tubes (Greiner Bio-one). Cells were stored at -150 °C in 90% (v/v) FBS (Sigma Aldrich) and 10% (v/v) DMSO (Sigma Aldrich). Plasma was collected and stored separately at -80 °C. B cells were enriched from PBMCs with CD19 microbeads (Miltenyi Biotec). B cells were stained with anti-human AF700-CD20 (clone 2H7, BD Biosciences Cat-No. 560631, RRID: AB_1727447 All antibodies are routinely tested by the vendor (BD Biosciences). CD20 + IgG + or CD20 + IgD + IgM + CD27 -IgG -B cells were sorted into FBS (Sigma-Aldrich) using a FACSAria Fusion cell sorter (BD Biosciences). Spike-in experiments (Supplementary Fig. S3 ) were performed with the human cell lines RAMOS (RRID: CVCL_0597; DSMZ, Cat-No.
ACC 603), MEC-1 (RRID: CVCL_1870; DSMZ, Cat-No. ACC 497), SuDHL-5 (RRID: CVCL_1735; DSMZ, Cat-No. ACC 571), and RI-1 (RRID: CVCL_1885; DSMZ, Cat-No. ACC 585). RNA-Isolation was performed using the RNeasy Micro Kit (Qiagen) on a QiaCube (Qiagen) instrument. RT-PCR and next generation sequencing BCR repertoire sequence data was generated by template-switch RT-PCR. cDNA was generated from 10 µl RNA according to the SMARTer RACE 5'/3' manual using SMARTScribe Reverse Transcriptase (Takara) and a self-designed template-switch oligo (AGGGCAGTCAGTCG-CAGNNNNWSNNNNWSNNNNWSGCrGrGrG). cDNA was diluted with 10 µl Tricine-EDTA buffer (Takara) according to the manual. Heavy and light chain variable regions were pre-amplified from 5 µl cDNA each by PCR with 1 µM forward primer (CTGATACGATTCACGCTAGGG CAGTCAGTCGCAG) and 0.
NGS data processing and sequence annotation Data pre-processing was performed with a Python (v. 3. 6)-based pipeline, including python packages Biopython (v. 1. 78), pandas (v. 0. 23. 4), NumPy (v. 1. 19. 2), Matplotlib (v. 3. 3. 4), and python-Levenshtein (v. 0. 12. 2). Raw NGS reads were filtered for a mean Phred score of 25 and read-lengths of at least 250 bp. Reads were annotated with IgBLAST 91 to identify read-orientations and CDR3 sequences. Reads were then grouped by the UMI and wrongly assigned reads (collisions) were identified by comparison of V gene segment calls and a second 18-nucleotide molecular identifier within the CDR3. Next, reads were aligned with Clustal Omega  92, 93  and consensus sequences were generated by taking into account the quality score-weighted base-calls for each position.
The 2x300 bp consensus read pairs were then aligned and combined using the AssemblePairs. py script form the pRESTO toolkit with a minimal overlap of 6 nucleotides  94. Annotation of pre-processed reads was performed by using BLAST (v. 2. 9) and IgBLAST(v. 1. 13)  91. Reference templates for all functional (F) heavy and light chain V(D) J genes were retrieved from the IMGT database  95, 96  (265 IGHV, 30 IGHD, 13 IGHJ, 66 IGKV, 9 IGKJ, 70 IGLV, and 7 IGLJ at the time point of this study). After sequence annotation with IgBLAST, sequences were filtered to include complete V/J annotations covering at least 250 nucleotides of the V gene as well as productive sequences (i. e. , no STOP codon and in-frame CDR3 recombination, as determined by IgBLAST) only. To minimize the influence of sequencing and PCR errors, annotated sequences were only used for downstream analyses, when their UMIs were initially found in at least three reads (high-quality reads).
Antibody selection Antibody information was retrieved from the CATNAP database  59. At the time of this study, the available antibody dataset comprised 507 entries, from which non-human antibodies, polyclonal antibodies, antibody mixtures, as well as mutants/chimeras and non-antibody proteins were removed to yield a final set of 291 antibodies. The 291 antibodies were tested against different or only partially overlapping viral panels (such as the 12-strain global panel  65  or the 118-strain multiclade panel  61  ), making it difficult to compare them in terms of breadth and potency  97. Of the 291 antibodies, 50 have been tested against the global panel and 19 against the 118 multi-clade panel. As a trade-off between number of antibodies and accuracy of breadth and potency, we selected a subset of 56 strains from the 118 multi-clade panel, which resembles its clade distribution, its tiered categorization, and yields similar values for breadth and potency (Supplementary Fig.
Determination of antibody breadth, mean potency and neutralization score IC 50 values for individual antibody-virus combinations were derived from the CATNAP database  59  as the geometric mean of all published IC 50 values (i. e. from different studies). IC 50 values below the detection limit were set to an arbitrary threshold of 100 µg/ml. The mean potency of an antibody against a viral panel was determined by the geometric mean of all geometric mean IC 50 values that were below the arbitrary threshold of 100 µg/ml (also known as the mean potency of all neutralized strains). Antibody breadth was defined as the percentage of neutralized strains (i. e. geometric mean IC 50 above the arbitrary threshold of 100 µg/ml) to the total number of strains tested and is given as percentage. To determine the combined neutralization score, we took all IC 50 values that were below the threshold of 100 µg/ml for each antibody and sorted them from lowest to highest.