dataesr/scientific-paragraphs-categorization huggingface dataset:
The poorest performance (between 13 to 39%) with existing IS was for the S/U/S data subset. The three methods were applied to the four country-based data subsets providing four country-based scores for each method. Intercepts and weights specific to each country-based subsample are shown in Table  S1. Scores were applied to all country-based data subsets: one subset was used to build the score which was then applied to the three other subsets (Table  5 ). Considering four blocks representing the four data sets, the set used for both training and validating the score is displayed in the first row of each block. For each method, the precision obtained from country-based data subsets was better than that with the global data set (the weighted mean of precision computed from the four country-based data sets is higher than the precision obtained from the global data set).
Logistic regression is a simple method, available in many statistical software packages, and has been successfully used in the context of models of resistance to antiretrovirals  [32, 33]. For the entire sample, ANRS, LDVD and REGA II provided better precision than HIV-db, contradicting previous results for didanosine resistance based on the FORUM database  [9]. However, our criterion was very different to those used previously in predictive models: we focused on the precision measures as the ability to predict resistance from the genotype and then to predict virological failure among patients classified as resistant. It appears that there is a negative correlation between the number of patients classified resistant and precision. The HIV-db IS gives the largest number of patients classified as 'resistant' because it considers mutations at 21 positions to assess didanosine resistance, the ARNS and REGA systems include only 9 and 11 positions, respectively.
Scores defined from the entire sample and applied to countrybased data subsets performed better than already existing IS. As each country-based sample is involved in building the global scores, the global scores could be considered not to be independent of the country-based data subsets. This is also true, however, for existing IS. Indeed, the FORUM database on didanosine contains some of the data used to build these IS. For example, the didanosine score defined in the Jaguar study has been included in the ANRS IS since 2007. The studies included in the FORUM database represent a part of the knowledge about didanosine resistance that was used to define some of the rules in existing IS. We provide a comparison of different rules applied to four distinct samples in Table  4. The poor performance of the S/U/S subset is explained because data from three different countries are included in this subset.
This limitation is inherent to scores defined on one particular population and applied to completely independent samples. There were substantial differences in the prevalence of the various mutations between the four country-based data sets (Figure  1 ). These epidemiological differences are probably consequences of differences in treatment histories between populations in different countries  [13]  although there may be other causes  [17]. The precision was up to 45% lower when the USA/Canada and S/U/S data sets were used for validation. The smallest loss was when the score was trained on the Italian data subset and applied to the French data subset; we have no clear explanation for this. Despite difference in weights, especially between SVM and the two others methods, there was no significant difference in precision between the three statistical methods investigated.
Overall, seven mutations (L74I, F77L, D123S, Q207K, H208Y, L210W and K223Q) have a major impact on the resistance by at least one of the three scoring methods used. The L74I, and to a lesser extent the L210W and F77L mutations, are used by one or more existing IS to predict resistance. These IS do not use the four other mutations to predict didanosine resistance. The prevalence  of these mutations is low, and this may explain why they were not found to be associated with didanosine resistance in previous studies based on small sample size. In the Stanford HIV Drug Resistance Database (http://hivdb. stanford. edu/cgi-bin/ MutPrevBySubtypeRx. cgi), the percentages of viruses with the D123S, Q207K, H208Y and K223Q mutations in antiretroviralexperienced patients infected with subtype B are 0. 8, 1.
1, 3. 6 and 0. 7%, respectively. The D123S and Q207K mutations were already identified as being associated with didanosine resistance in the LDVD algorithm developed on the FORUM database  [24]. Some mutations (insertion at codons 66, 67, 68, 69, 70 or 71, Q151M) used by existing IS as strong predictors of resistance to didanosine were not retained in our analysis because their prevalence is lower than 1%, and consequently they have no impact on resistance in our analysis. This emphasizes an apparent discordance between weights obtained from methods investigated here and weights in existing IS. Such a difficulty has been sometimes circumvented in including well known resistance mutations in a score even if there were not present in the sample  [24, 30]. Few mutations had positive impact on virological response in our analysis and had negative impact in existing IS.
Discordant findings for the M184I/V mutation, even between existing IS, have been reported previously and discussed  [28]. One limitation of this work is that the scoring methods investigated use a binary outcome. Moving from a continuous outcome, the HIV-1 RNA reduction, to a binary outcome, response versus no response, generally leads to a loss of information. The threshold of 0. 6 log 10 copies/mL used to define a virological response was chosen due to the moderate potency of didanosine. Sensitivity analyses, however, using 0. 5 and 0. 7 log 10 copies/mL provided similar results (data not shown). Another limitation is the absence or low prevalence of certain mutations involved in resistance to NRTI-class. Some relevant mutations and insertions were absent from our database even though it is large.
Acknowledgments Members of the  Standardization and Clinical Relevance of HIV Drug Resistance Testing Project for the Forum for Collaborative HIV  Research are listed below. The data providers included  H. Ribaudo ,  P. Bohlin ,  M. Wantman ,  ACTG Trials, Statistical and Data Analysis Center, Boston, Massachusetts ;  B. Masquelier ,  Aquitaine Cohort , France;  M. Zazzi ,  ARCA, University of Siena, Italy ;  B. Hogg ,  R. Harrigan ,  Homer Cohort ,  British Columbia ,  Canada ;  J. Lundgren ,  J. Kjaer ,  EuroSIDA Cohort , Europe;  A. d'Arminio Monforte, ICoNA, Italy ;  J. Gatell ,  IDIBAPS Barcelona Hospital Clinic Cohort ,  Spain; MJ Perez ,  Ramon y Cajal Madrid Hospital clinical database , Spain;  D. Seekins ,  Jaguar Trial BMS, France ;  D. Costagliola ,  F. Brun-Ve  ´zinet, NARVAL ANRS 88 Trial, France;  B.
AH is grateful for the financial support of SIDACTION in the form of a PhD fellowship (Grant  FJC-1/AO20-2/09 ). This work was supported by the  European Community Seventh Framework Program  ( FP7/2007-2013 ) under the project '' Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN) '' (Grant agreement no. 223131 ), the  French Agence National de Recherches sur le Sida et les hepatites virales (ANRS)  and the  Association de Recherche en Virologie en Dermatologie (ARVD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The transcripts differentially expressed by GFP  and GFP + CD4 + T cells from five independent cell preparations are displayed. Up-and down-regulated transcripts are indicated in red and green, respectively. The magnitude of expression is depicted by the color bar. Full raw data is available from the NCBI Gene Expression Omnibus under accession no. GSE50149. population can control the number of cells of the IL-2p subset. The existence of a mutually dependent regulation of T reg and IL-2p cell populations has been previously suggested  (Almeida et al. , 2006). These findings support the notion that the regulation of the two cell populations is tightly linked  (Almeida et al. , 2006 (Almeida et al. , , 2012))   in the absence of IL-2 (Fig. 7 g ). This in vitro effect was not caused by the suppressive effects of the augmented representation of T reg cells in the populations studied, as it was also observed when the stimulated CD4 + T cells were previously depleted of the CD25 + T reg cell fraction (Fig.
Our results revealed a sequence error in the reported sequence of the light chain in databases and in publications, thus highlighting the potency of mass spectrometry to establish correct antibody sequences. We were also able to achieve a comprehensive identification of cetuximab's glycoforms and glycosylation profile assessment on both Fab and Fc domains. Taken together, the reported approaches and data form a solid framework for the comparability of antibodies and their biosimilar candidates that could be further applied to routine structural assessments of these and other antibody-based products.
The amino acid sequence for both the light and heavy chains of cetuximab are reported in the IMGT database (www. imgt. org) and the drug bank (www. drugbank. ca). The crystal structure of the antigen binding fragment (Fab) has been reported by Li et al. 16  and is referenced in the RCSB Protein Data Bank. Cetuximab's primary sequence has also been reported by Dubois et al. who used liquid chromatography (LC)-MS/MS and matrix assisted laser desorption ionization-time of flight-MS (MALDI-TOF-MS) for PK studies. 17 etuximab is produced by SP2/0 murine myeloma cells and is N-glycosylated both in the Fc and the Fab domains. A high prevalence of hypersensitivity reactions to cetuximab has been reported in some areas of the US. Some of the glycoforms were demonstrated to be responsible for these hypersensitivity reactions and anaphylaxis.
Using a proprietary search engine to query carbohydrate structure databases, glycopeptide and glycan identifications and profiles were automatically generated. From the LC-ESI-MS mass spectra of cetuximab subunits (middle-up) we derived glycosylation site-specific accurate masses of the various antibody glycoforms. Our results confirmed an unexpected modification revealing a sequence error in the light chain terminal region. The methodologies we describe here form a solid framework for routine biosimilar structure verification.
The IMGT provides a common access to sequence, genome and structure immunogenetics data. These data are based on the scientific information available in other databases or published in the scientific literature and patents and, as such, are an extremely useful and helpful resource in the development of biosimilar products. However, sequence errors do exist in the scientific data and databases. Sequence errors have been reported for trastuzumab, 9 rituximab 10 and etanercept. 11 Another major quality attribute of mAbs is their N-glycosylation profile,  12  which has important effects on effector functions such as antibody-dependent cell-mediated cytotoxicity (ADCC) and complement dependent cytotoxicity (CDC). 13, 14 he N-glycosylations of mAbs can also affect safety and PK/PD. It is evident that extensive structural and functional comparison of the biosimilar and the reference product is the foundation of biosimilar development and that assessment of the amino acid sequences and N-glycosylation patterns are among the most important criteria.
69 Da (average) and 23354. 512 Da (monoisotopic). The measured monoisotopic mass of the light chain is 23,412. 518 Da, which corresponds to a +58. 006 Da mass difference (Fig. 4B ). Thus, we can speculate that there is an unidentified modification/variation in the light chain sequence. The third chromatographic peak corresponds to the Fd fragment. The monoisotopic MW derived from the IMGT sequence translational modifications (PTMs). 2] [23]  To interpret the intact measurements, the observed heterogeneity needs to be reduced and the theoretical MWs of the expected isoforms calculated. The sequences of the light and heavy chain of cetuximab reported in the IMGT database and the previously cited references  16, 17  are shown in Figure  2. Intact IgG1 contain 32 cysteines and thus 16 disulfide bonds (-32 Da when calculating the intact antibody mass).
24 IdeS or Immunoglobulin degrading enzyme of Streptococcus pyogenes has the advantage of being more specific than the other described IgG hinge cleaving enzymes. 25 IdeS cleavage followed by the readout length is 60-90 residues from N-and C-terminus) and even permits de novo sequencing of medium-sized proteins. 29 iddle-down MALDI-ISD enables fast sequencing of terminal domains of mAbs in a targeted way. The cetuximab fragments after IdeS digest and chromatographic separation were collected for further MALDI-ISD analysis. The results confirm the Nand C-terminal sequences of the heavy chain, including the heterogeneities discussed above (N-terminal pyroglutamic acid presence/absence of the C-terminal lysine) (Fig. S1 ). The ISD sequencing of the light chain confirmed the first 86 N-terminal residues of the IMGT database sequence.
S4-6 ). In this approach, MS/MS spectra of glycopeptides are acquired during standard bottom-up LC-MS/MS experiments and classified as glycopeptide spectra. For this purpose, the ProteinScape software we used searches for specific fragmentation patterns and oxonium ions to classify the potential glycopeptide spectra (Fig. S7 ). In the case of MALDI-TOF/TOF-MS/MS data, the fragmentation pattern described by Wuhrer el al. 31  and Rapp et al. 32  is used for classification. From the fragmentation pattern of the glycopeptide (Fig. S8A ), ProteinScape determines the m/z value of the peptide and the glycan moieties. These are used for their identification through database searches. Glycans were identified through the search engine GlycoQuest (integrated in ProteinScape) and peptides through Mascot (Matrix Science, USA) (Fig.
S8B ). Initially, GlycoQuest searches public or userdefined databases for glycan structures that match each experimental parent MW within a given tolerance. From the candidate glycan structures, fragments are calculated and matched with the respective MS/MS spectra. As a result of this search, a list of glycan structures is obtained, which is ranked by a score. The score is based on the number of identified fragments and the intensity coverage of the MS/MS spectrum, i. e. , the fraction of the sum of the intensities of the peaks assigned to a glycan structure vs. the sum of the intensities of all MS/MS fragment ions. A spectrum viewer provides the annotation of both glycan and peptide fragments for the interactive validation of GlycoQuest and Mascot database search results (Fig. S9-10 ). Glycan identification with the described mass spectrometric methods relates to the assignment of glycan composition and to some extent of the glycan structure.
The isotopic patterns calculated from the sequence of the antibody as specified in the IMGT database were also displayed for the three subunits (blue). The position of the average mass is marked with an arrow. For Fc/2, the isotopic pattern was calculated taking into account lysine clipping at the C-terminus and G0F glycosylation. For the Fd subunit N-terminal pyroglutamic acid formation and G2FGal2 glycosylation was assumed. While the agreement between the measured and expected monoisotopic mass is well below 1 ppm for the Fc/2 and Fd fragments [-1. 5 mDa for Fc/2 (A) and 0. 4 mDa for Fd (C)], a significant MW difference of 58. 006 Da was observed for the LC (B) clearly indicating a sequence or structural variation. The SNAP algorithm fits a theoretical peak pattern derived from the average atomic composition found in proteins  33  to the measured isotopic peak patterns.
34, 35 Raw data were inspected and sporadic peak mis-assignments were manually corrected. MALDI-ISD middle-down analysis of cetuximab. For middle-down analysis, the cetuximab Ides digest was desalted using Micro Spin G 25 columns (GE Healthcare) and LC-separated using Agilent 1200 and Zorbax 300SB-C8 column with water/0. 1% TFA as solvent A and acetonitrile/0. 1% TFA as solvent B. Fractions were spotted directly to MTP BigAnchor plates. The dry sample spots were then covered with sDHB (9+1 mixture of 2,5-dihydroxybenzoic acid and 2-hydroxy-5-methoxybenzoic acid, 25 g/l in 50% acetonitrile/water/0. 1% TFA) and intact protein spectra and ISD spectra were acquired using WARP-LC 1. 3 and compass 1. 4 flexSeries. ISD spectra were acquired in reflector mode and externally calibrated using bovine ubiquitin ISD fragments.
The whole LC-MS system and analysis was controlled by BioPharma Compass 1. 1 (Bruker Daltonik). Monoisotopic molecular weights (MWs) of the IdeS-generated cetuximab subunits were determined by the SNAP algorithm. (Analytical column). Five pmol of digest were loaded and eluted using a linear gradient starting from 2% acetonitrile/0. 1% TFA/ water to 35% acetonitrile in 64 min. Fractions of 10 sec were spotted using Proteineer FcII (Bruker Daltonik) with a matrix sheath flow (α-cyano-4-hydroxycinnamic acid) providing co-crystallization of sample and matrix on the MALDI sample holder. MALDI-TOF/ TOF-MS and MS/MS spectra were acquired using ultra-fleXtreme (Bruker Daltonik) and further analyzed using the ProteinScape 3. 1 software platform. For glycan and peptide identification based on glycopeptide MS/MS spectra, an automated four-step workflow was executed in ProteinScape.
In step 1, each MS/MS spectrum was screened for the presence of characteristic N-linked glycopeptide fragmentation patterns (Fig. S8A ). In step 2, the peptide [M+H] + was extracted from that pattern, yielding both the precise glycan and the peptide MW from a single glycopeptides MS/MS spectrum. In step 3, Bruker's GlycoQuest glycan search engine in ProteinScape obtained glycan identifications via GlycomeDB 36 database searches using the glycan fragments of the glycopeptide MS/MS spectra. Several input parameters were specified for the search: the glycan type was restricted to N-glycan; taxonomy and composition were not restricted. Only singly charged, protonated ions and a fragmentation type containing b, c, y and z-ions were used. MS tolerance was set to 0. 5 Da and MS/MS tolerance to 0.
Conclusion To meet the expectations of regulatory agencies, marketing applications for biosimilar antibodies must include data from Pepmap 100, 5 μ, 0. 1 × 20 mm) at a flow rate of 5 μL/min, elution was performed at 0. 300 μL/min with a 5-40% linear gradient of solvent B in 55 min followed by 15 min at 97% solvent B before reconditioning of the column at 5% solvent B. The maXis impact was operated in the positive ion mode. The TOF was mass calibrated prior to analysis using sodium TFA in the m/z 430-2100 range. For tandem MS, the system automatically switched between MS and MS/MS modes. MS spectra were recorded over an m/z range of 50-2200 at 2 spectra/sec. The MS/MS accumulation time was regulated by the intensity of the selected peak on the MS spectrum. The 3 most abundant peptide ions, preferably doubly or triply charged, were selected in each MS spectrum for further isolation and collision induced dissociation using optimized collision energies depending on charge state and m/z of the ion.
The analyzed peptides were subsequently excluded from re-selection for 60 sec. The acquisition process was controlled by the Compass software (Bruker Daltonik). Mass spectra were processed and the resulting peak lists transferred to ProteinScape 3. 1 (Bruker Daltonik). The Mascot search engine (Matrix Sciences) was used for MS/MS searches against a custom database containing the IMGT light and heavy chain sequences of cetuximab and common likely contaminants (trypsin, human keratins). Carbamidomethylation of cysteine residues was set as a fixed modification while methionine oxidation, N-terminal cyclization of glutamine, deamidation of glutamine and asparagine where set as variable modifications. The peptide mass tolerance was set at 7 ppm and fragment mass tolerance was set to 0. 05 Da. Up to two trypsin or GluC mis-cleavages were allowed.
Acknowledgments We thank  Stephanie Kaspar  and  Peter Brechlin  for help with the bottom-up measurements and  Ulrike Schweiger-Hufnagel  (all  Bruker-Daltonik, Bremen)  for critically reading the manuscript. We further thank  Jason Rouse 's team ( Pfizer , Andover, MA) and  Fredrik Olssen  ( Genovis, Lund ) for many helpful discussions on subunit analysis of antibodies.
DNA sequencing Second-round positive PCR products were first purified by ExoSAP-IT Ò (USB Ò Products Affymetrix, Inc. , Ohio, USA) and bidirectional sequencing analysis was carried out with a 3500xL Dx Genetic Analyser (Applied Biosystems Ò , Life Technologies) using Big Dye Terminator Cycle Sequencing and Purification Kit, according to the manufacturer's instructions (Applied Biosystems Ò , Life Technologies). Electropherograms were analysed and consensus sequences were constructed using DNA Baser Software. BLAST software was used to determine homologies of these consensus sequences with those deposited in GenBank.
However, positive PCR results were obtained for four samples (No. 13, 14, 15, 16) when using 2, 1 and 0. 5 ll samples of DNA extracts. Species identification was confirmed by direct DNA sequencing, as analysis of electropherograms failed to demonstrate any species diversity in the samples studied. BLAST results showed a 100% identity of our sequences with C. parvum 18S ribosomal rRNA gene sequences available in GenBank.
Participants and Follow-Up We used data from the Antiretroviral Research for Watoto (ARROW) trial, an open-label, randomised 5-y paediatric trial comparing two monitoring strategies during first-line ART (http://www. arrowtrial. org/; ISRCTN 24791884  [20] ). 1,206 HIV-infected, ART-naı ¨ve children aged 3 mo to 18 y and meeting WHO criteria for ART were enrolled at three centres in Uganda (Joint Clinical Research Centre, Paediatric Infectious Diseases Centre, and Medical Research Council/Uganda Virus Research Institute Uganda Research Unit on AIDS) and one in Zimbabwe (University of Zimbabwe, Harare). The trial was approved by research ethics committees in Uganda, Zimbabwe, and the United Kingdom. Written informed consent was obtained from children's parents/guardians, and written assent from the children as appropriate.
The ARROW trial's primary aim was to compare two monitoring strategies in children starting ART. Accordingly, children were randomised to either the ''clinically driven monitoring'' (CDM) or ''laboratory and clinical monitoring'' (LCM) group. In both groups, children had a routine full blood count, lymphocyte subsets (CD4, CD8), and liver and renal function tests (bilirubin, urea, creatinine, aspartate transaminase, alanine transaminase) performed every 12 wk. Demographic (age, sex) and clinical (WHO staging, height, weight) data were also collected. An additional week 4 lymphocyte subset measurement was available in children participating in an immunology substudy. In the CDM group, test results were returned to the clinician only if he or she requested them for clinical reasons, or for a grade 4 adverse event as defined in the trial protocol  [21].