dataesr/scientific-paragraphs-categorization huggingface dataset:
Family analyzer The "Family Analyzer" page provides an overview of the properties of the SR families originated by the "Family Generator. " This tool provides both a summary of the SR family properties (Fig 2  , top ) and a full output  (Fig 2, bottom). The summary page displays statistical analyses of the properties of the SR family and lists each member's sequence with links to the ADAPTABLE and source database entries. The full output contains an interface, allowing rapid and effective navigation of a large amount of data: for example, it is possible to visualize sequence alignment with color codes for residue conservation (frequency), polarity, amino acid types, or secondary structure. Alternatively, one may choose to visualize only selected information such as the source and the gene of origin of each member of the SR family.
ADAPTABLE browsing tool The ADAPTABLE browsing tool ("Browse AMPs & Families") is composed of three subsections: A calculation label and username are provided to allow private access to the results. The user can optionally append new sequences to the calculation. The "Character picker" (insert above) allows the insertion of special characters used for non-proteinogenic amino acids into the sequence fields. The picker offers a direct link to the PubChem  (Kim et al, 2019)  database for each entry. Expanding the "Advanced" and "Peptide properties" sections (see inserts in the bottom) offers the possibility to choose from among over more than 60 parameters. In the last part of the page, the user can select the desired alignment method, the minimum similarity threshold for the creation of SR families, and the minimum percent overlap among SR families for their clustering.
Additional graphical analysis is optionally offered. The first one, "Browse AMPs Database," offers the possibility to view single entries by typing part of a sequence or name; the ADAPTABLE entry consists of a table summarizing all available data on the selected peptide and providing links to the related SR families obtained in the "all_families" built-in experiment described below (see Screenshot 2 in the Tutorial, Supplemental Data 1). In particular, the first field is a link to the SR family generated by the peptide, whereas the second field contains links to all SR families where the peptide can be found. The field "External database ID" can be used to visit the peptide entries in other databases. When available, biochemical parameters (provided by ExPASy ProtParam tool  [Gasteiger et al, 2005] ) and the relevant literature are accessible by links.
Direct access to the Protein Data Bank is provided for the experimental tridimensional structure, whereas integration with I-TASSER  (Roy et al, 2010; Yang et al, 2015; Yang & Zhang, 2015)  allows structural prediction even when the experimental one is not available. The second subsection, "Family overview," allows finding information regarding the built-in "all_families" experiment, created by sequence alignment of all the peptides in the database. It constitutes an unrestrained version of the general procedure for the creation of any ADAPTABLE SR family, where no restrictions are applied by the user in terms of peptide properties. As a consequence, each peptide acts as a father for the formation of a family. The third subsection allows generation of a FASTA file with the sequences of peptides featuring user-defined properties.
Future Directions With ADAPTABLE, we want to provide the researcher with a tool to study the mechanism of action of AMPs. Its completeness in terms of database sources permits its applications in various research fields, spanning from medicine to agriculture, food preservation, and antiseptic materials. We have shown how the generation of SR families is able to group peptides based on user-defined characteristics. ADAPTABLE also has built-in functions to localize conserved residues, thus highlighting motifs (well-definite arrangements of amino acids likely to be responsible for the different activities). The motifs are created by taking into account intrasequence correlations to avoid nonfunctional chimeras. This function is designed to provide optimal scaffolds for drug design. Recent studies suggest that this approach could be very useful for scientists working in this area  (Schmitt et al, 2016; Almaaytah et al, 2017).
We believe that the architecture of ADAPTABLE, besides offering easy access to all available information on a specific peptide described in many different databases, can also be used by the scientific community to (i) design new peptides using motifs responsible for the specificity towards a specific organism, (ii) predict several properties of a generic sequence, (iii) discover experimentally untested activities for a given peptide by retrieving information on similar sequences in its SR family, and (iv) generate optimal scaffold for drug design, thanks to the generation of the SR family representing sequence. ADAPTABLE has been designed as a self-updating platform, thanks to its automated tools that aggregate data from upstream sources. This is a fundamental characteristic because we expect that the number of peptides will continue to increase in the following years.
ADAPTABLE AMPs database ADAPTABLE incorporates automated tools to periodically download, process, and merge data to keep synched with data sources: ADAM  (Lee et al, 2015) , ANTISTAPHYBASE  (Zouhir et al, 2017) , APD  (Wang & Wang, 2004; Wang et al, 2009; Wang et al, 2016) , AVPdb  (Qureshi et al, 2014) , BaAMPs  (Di Luca et al, 2015) , BACTIBASE  (Hammami et al, 2007 (Hammami et al, , 2010)) , CAMPR3  (Waghu et al, 2016) , CancerPPD  (Tyagi et al, 2015) , ConoServer  (Kaas et al, 2008 (Kaas et al, , 2012)) , CPPsite  (Gautam et al, 2012; Agrawal et al, 2016) , DADP  (Novković et al, 2012) , DBAASP  (Gogoladze et al, 2014; Pirtskhalava et al, 2016) , Defensins  (Seebah et al, 2007) , DRAMP  (Fan et al, 2016; Kang et al, 2019) , Hemolytik  (Gautam et al, 2014) , HIPdb  (Qureshi et al, 2013) , InverPep  (Gómez et al, 2017) , LAMP  (Zhao et al, 2013 ), MilkAMP (Th éolier et al, 2014) , ParaPep  (Mehta et al, 2014) , Peptaibol  (Whitmore & Wallace, 2004) , PhytAMP  (Hammami et al, 2009) , SATPdb  (Singh et al, 2016) , UniProt (The UniProt Consortium, 2018), YADAMP  (Piotto et al, 2012) , PubChem  (Kim et al, 2019) , The Microbe Directory  (Shaaban et al, 2018) , and the Protein Data Bank  (Berman et al, 2000).
Data are merged in a single database of more than 40,000 unambiguous sequence entries. In the future, even more databases and resources will be implemented to enrich the information available for each sequence. Third-party contributors can develop their own tools to generate a final output adhering to our standardized simple text format (see "FAQ and Tutorial" section of the Supplemental Data 1). Figure 2. Screenshots of "Summary" (top) and "Full output" (bottom) for each SR family. The Summary (top) provides the minimal percentage of members displaying each property and, for each member, the links to their ADAPTABLE single entry, and to external databases. The full output (bottom) shows the properties in detail for each member and the peptide best representing each family. The SR family members can be visualized sorted by similarity to the father and colored by amino acid frequency, residue type, polarity, predicted secondary structure, or DSSP prediction (see inserts).
SR family generator algorithm After the user has selected interesting peptides based on properties and activity, ADAPTABLE creates SR families by comparing each sequence with all others by pairwise alignment  (Fig 3). By default, the sequence similarity score is computed by applying mutation data matrices  (Oren & Shai, 1998; Huang et al, 2010). The choice of BLO-SUM45 ("BLOcks of Amino Acid SUbstitution Matrix") as a default value was mandated by the fact that the database contains AMPs from very different sources, which are, thus, evolutionarily distant. In addition, other BLOSUM, "point accepted mutation" or unitary  (Dayhoff et al, 1983)  (1 or 0 score based on identity) matrices can optionally be chosen. Alignment in "simple" or "DSSP" modes (see the Results and Discussion section) uses the standard unitary scoring matrix.
Implementation ADAPTABLE is developed using mainly GAWK (version 4. 1 or newer at the time of the writing)  (Robbins, 2003)  and Bash (4. 3 or newer)  (Ramey & Fox, 2015). Graphics generation relies on Matplotlib (2. 2 or newer)  (Hunter, 2007)  and Python (3. 5 or newer)  (Van Rossum & Drake, 2011). Parallelization is achieved using GNU parallel  (Tange, 2011)  and Python Joblib. The web interface relies on Javascript and CSS3 and adheres to HTML5 web standard, running Apache 2. 4 server on a Linux system.
Figure1. Screenshot of the ADAPTABLE "Family Generator" page. A calculation label and username are provided to allow private access to the results. The user can optionally append new sequences to the calculation. The "Character picker" (insert above) allows the insertion of special characters used for non-proteinogenic amino acids into the sequence fields. The picker offers a direct link to the PubChem (Kim et al, 2019)  database for each entry. Expanding the "Advanced" and "Peptide properties" sections (see inserts in the bottom) offers the possibility to choose from among over more than 60 parameters. In the last part of the page, the user can select the desired alignment method, the minimum similarity threshold for the creation of SR families, and the minimum percent overlap among SR families for their clustering.
Acknowledgements We would like to thank  Professor Manuel Dauchez ,  University of Reims Champagne-Ardenne ,  Matrice Extra-cellulaire et Dynamique Cellulaire (MEDyC), Unit é mixte de recherche 7369 ,  Centre national de la recherche scientifique  for useful discussion, and the  Matrics platform at the University "Picardie Jules Verne"  for providing computing resources. We would also like to thank  Graham Bentham  for English language editing. Funding: This work was partly supported by the  Universit é de Picardie Jules Verne ,  S2R 2018 -Action 1 : " Incitations au d épôt de projets de recherche ";  Francisco Ramos-Martín 's PhD scholarship was co-funded by  Conseil r égional des Hauts-de-France  and by  European Fund for Economic and Regional Development (FEDER). The authors are grateful to the  Universit é de Picardie Jules Verne  for its financial support for publication through its S2R action.
Availability of Supporting Source Code and Requirements • Project name: ADAPTABLE. • Project home page: http://gec. u-picardie. fr/adaptable/. s https://gitlab. com/pachoramos/ADAPTABLE. • Operating system(s): Platform independent (client runs in the web browser). • Programming language: AWK, Bash, Python, HTML5, and Javascript. • Other requirements: Web browser adhering to HTML5 standards (i. e. , Chrome, Firefox, Safari, and Edge). • License: EUPL-1. 2.
CMV infection was prevalent, affecting 4 of 11 patients who died, raising the question of how to prevent CMV acquisition e. g. , via breastfeeding  (34). Analysis of a larger cohort and of similar studies in other countries will be key to fulfilling the promise of NBS. The optimal HSCT approach for these patients remains unclear, and likely should be tailored to the genotype of SCID, which is associated with survival, the likelihood of T-lymphocyte reconstitution, as well as humoral immune function after HSCT  (31). A PIDTC trial randomizing NBS SCID patients of certain genotypes to 2 different busulfan-based regimens is one way that optimal approaches can be identified (NCT03619551) (see review on "HSCT in Severe Combined Immunodeficiency").
Analysis of the sequenced oprD gene was compared against the reference strain P. aeruginosa PA01 using Multalin alignment software (http:// multalin. toulouse. inra. fr/multalin/). Multilocus sequence typing (MLST) was performed to determine the genetic relationship among the clinical isolate as described on the Institute Pasteur's MLST website (https://pubmlst. org/paeruginosa/). The results indicated that P. aeruginosa isolates were resistant to all antibiotics tested except to colistin and fosfomycin, with MICs for IMP >32 μg/mL. All isolates harboured the bla VIM- 2 gene, except P. aeruginosa (PA-4) (Table  1 ). In addition, all isolates had mutations in the oprD gene (Fig. 1 ). MLST analysis revealed that three P. aeruginosa (PA-3, PA-6, PA-16), and one P. aeruginosa (PA-4) isolates harboured sequence types (ST) 357 and ST233 respectively (Table  1 ).
500 individual plasma collections or more, depending on the manufacturer, with pathogen inactivation by solvent and detergent treatment; and (ii) Quarantine-(Q) FFP, that has not been modified apart from freezing, thawing and retesting for a limited number of pathogens  [19, 24]. More recently, new forms of FPP have been made available, including individual or mini-pools of therapeutic plasma pathogen inactivated by Amotosalen-UVA  [25]. Uses of different plasma categories in France has varied over time as recentmly discussed in  [26]. We thus aimed to evaluate the efficacy and tolerance of Amotosalenpathogen inactivated FFP in iTTP patients undergoing TPE, in comparison with SD-plasma which has been used previously as a standard of care in France, and in many other countries. Due to the low frequency of iTTP, we designed a retrospective study using data acquired from routine practice in multiple centers prospectively entered into a national reference center database (French National Network for TMAs/French National Reference Center Registry: CNR-MAT, Paris: http://www.
Patients, study design and objectives This non-randomized, retrospective, multi-center, cohort study compared the clinical outcomes in adult patients supported with AI-or SD-FFP for TPE in iTTP cases prospectively reported to the CNR-MAT over a period of 4 years (2010-2013); subsequent cases were disregarded as TPE were principally processed with one type of plasma only (AI-plasma up to 2017, or Quarantine plasma or imported SD plasma after that period). The considered period was the only one when either type of plasma (AI or SD) was used to treat [similar] patients, allowing comparisons as adjunctive therapies were also similar  [7]. Patient cohorts were identified in the national reference center database for TMA study centers within the collaborative network. Inclusion criteria were those of the French Register  [7] : a diagnosis of iTTP and TPE treatment with ≥70% of either AI-or SD-FFP, both licensed plasma products, during this period.
Statistical comparisons The data from the CNR-MAT database were extracted and transferred in an Excel file format to Cerus, Concord, CA for data analysis. Patients' characteristic and plasma usage data were summarized descriptively by patient cohort (AI plasma vs. SD plasma). Comparisons between the two patient cohorts (AI plasma vs. SD plasma) were performed using 2-sample t test with unequal variances for continuous variables, Fisher's exact test for binary outcome, and Cochran-Mantel-Haenszel Row Mean Scores Differ test for ordinal categorical variables. For continuous variables with skewed distributions (e. g. , baseline platelet count), the Wilcoxon rank-sum test was also conducted. For time-to-event variables (e. g. time from the first AI or SD plasma transfusion to platelet count recovery), the median time was estimated by Kaplan-Meier curves, and the log-rank test (with or without adjusting for patient age at admission and baseline platelet count as covariates) was used to assess the differences between the two patient cohorts.
Declaration of Competing Interest The French Reference Center for Thrombotic Microangiopathies (www. cnr-mat. fr) is in part supported by the French Ministry of Health (Plan National Maladies Rares, Direction Générale de l'Offre de Soin) and the Programme Hospitalier de Recherche Clinique (PHRC 2012 P120118, Clinicaltrials. gov identifier NCT02134172). OG has received invitations from Cerus-Europe, Terumo-BCT-Europe, MacoPharma-France and Octapharma-France to attend key opinion leader meetings; he declares no further link of interest. PC is member of the Advisory Boards of Ablynx for the development of caplacizumab, Alexion for the development of eculizumab and Shire for the development of Bax930. He has received funds from Ablynx, Alexion, Octapharma and Roche. AV i mber of the Advisory Boards of Abl s member of the Advisory Boards of Ablynx for the development of caplacizumab.
Acknowledgements The authors thank  Norman Huang  for initial data analysis; and members of the Reference Center for Thrombotic Microangiopathies (CNR-MAT):  Augusto Jean-François  ( Service de Néphrologie , dialyse et transplantation ;  CHU Larrey, Angers );  Azoulay Elie  ( Service de Réanimation Médicale, Hôpital Saint-Louis, Paris );  Barbay Virginie Amélie (Service de Réanimation Médicale , centre hospitalier de Vendée);  Servais Aude  ( Service de Néphrologie, CHU Necker-Enfants Malades );  Stépanian Alain  ( Laboratoire d'Hématologie, Hôpital Lariboisière, Paris );  Vernant Jean-Paul  ( Service d'Hématologie, Hôpital la Pitié-Salpétrière, Paris );  Veyradier Agnès  ( Service d'Hématologie Biologique, Hôpital Lariboisière, Paris );  Vigneau Cécile  ( Service de Néphrologie, Hôpital Pontchaillou, Rennes );  Wynckel Alain  ( Service de Néphrologie, Hôpital Maison Blanche, Reims );  Zunic Patricia  ( Service d'Hématologie, Groupe Hospitalier Sud-Réunion, la Réunion ).
Next generation sequencing allows us to gain access to repertoire-wide data supporting more comprehensive repertoire analysis and more robust vaccine design  [1]. Despite large-scale efforts  [2] , how repertoire statistics respond to such acute perturbations is unknown. Longitudinal repertoire sequencing (RepSeq) makes possible the characterization of repertoire dynamics. Despite the large number of samples (clones) in these datasets lending it to model-based inference, there are few existing model-based approaches to this analysis. Most current approaches (e. g. [3] ) quantify repertoire response properties using measurement statistics that are limited to what is observed in the sample, rather than what transpires in the individual. Model-based approaches, in contrast, can in principle capture features of the actual repertoire response to, for instance ongoing, natural stimuli, modeled as a point process of infections, and giving rise to diffusion-like response dynamics.
) and technical problems have been formulated and partly addressed. The differences between RNAseq and RepSeq data, however, means that direct translation of these methods is questionable. Moreover, the known structure of clonal populations may be leveraged for model-based inference using RepSeq, potentially providing advantages over existing RNAseq-based approaches. Here, we take a generative modeling approach to repertoire dynamics. Our model incorporates known features of clonal frequency statistics and the statistics of the sequencing process. The models we consider are designed to be learnable using RepSeq data, and then used to infer properties of the repertoires of the individuals providing the samples. To guide its development, we have analyzed a longitudinal dataset around yellow fever vaccination (some results of this analysis are published  [6] ).
As a byproduct, our method learned the properties of the clone size distribution, which is consistent with a power law of exponent ≈ -2. 1 robust across individuals and timepoints, consistent with previous reports  [8] [9] [10]. Using these parameters, various diversity mea- sures could be computed, such as the species richness 8 -10 9 ), which agrees with previous bounds  [14, 15] , or the "true diversity" (the exponential of the Shannon entropy), found to range between 10 6 and 10 8. The inferred null models were found to be conserved across donors and time, indicating that they should be valid for other datasets obtained with the same protocol. This implies that our method could be applicable to situations where replicate experiments are not available, as is often the case. On the other hand, the procedure for learning the null model should be repeated for each distinct protocol using different technologies, using replicate experiments.
We applied our method to data from mRNA sequencing experiments, which has the advantage over current DNA immune repertoire sequencing methods of being able to incoorporate unique molecular barcodes. Genomic DNA-based sequencing does not suffer from expression noise, however the technology is prone to PCR and statistical noise and primer biases. Given that our ultimate choice of noise distributions is often empirically motivated, different modeling choices may be applicable to gDNA datasets. The proposed probabilistic model of clonal expansion is described by two parameters: the fraction of clones that respond to the immune challenge, and the typical effect size (log fold-change). While these two parameters were difficult to infer precisely individually, a combination of them could be robustly learned.
There are thus three levels of noise in RepSeq: cell sampling noise, expression noise, and mRNA capture noise. By constrast in differential expression there is expression noise, cell-to-cell variability, and capture noise. These sources of noises combine in a different manner than in RepSeq. edgeR  [5] , a classical differential expression analysis software, proceeds by learning a noise model using a negative binomial model for expression noise from two identical conditions. Then, comparing RNA-seq data from two datasets, it evaluates a p-value corresponding to the probability that the observed difference in expression between the two datasets has occured just because of noise. We applied edgeR treating each clonotype as a separate gene.
In July 2015, MedImmune launched a first in-human trial (NCT02503774) evaluating the human anti-CD73 mAb Oleclumab, which allosterically prevents CD73 from assuming its catalytically active conformation  (413). In June 2016, Bristol-Myers Squibb (BMS) launched a Phase I/IIa trial (NCT02754141) to assess the efficacy of BMS-986179, a human IgG2-IgG1 hybrid mAb that not only inhibits CD73-exerted AMP hydrolysis but also induces CD73 internalization  (414). In April 2018, Corvus Pharmaceuticals.
CD39 ¢ mAb:  a Patient-derived tumor cell lines, NSCLC, Non-Small-Cell LungCancer. HNSCC, Head and neck squamous cellcarcinoma. X > Y: X contributes more than Y to the anti-tumor effect of adenosine axis modulation. initiated clinical evaluation (NCT03454451) of their humanized anti-CD73 mAb, CPI-006, which directly competes with AMP for the CD73 active site  (415). Finally, in July 2018, Novartis listed a Phase I/Ib trial (NCT03549000) evaluating the efficacy of SRF373/NZV930, a human mAb that impedes CD73 activity via a currently undisclosed mechanism, and was pre-clinically developed by Surface Oncology before being exclusively licensed to Novartis for further clinical development. CD39 also critically contributes to the generation of extracellular adenosine from ATP as evidenced by the fact that deficiency of this enzyme results in significantly decreased adenosine content in tissues, not only at steady state, but also upon ischemia induction (80).
Indeed, administration of CD38 mAbs retards tumor growth  (96, 420). Interestingly, tumors derived from anti-CD38 mAb-treated mice encompass more CD8 + T cells and less Tregs and MDSCs  (96). Moreover, increased fraction of CD8 + T cells infiltrating these tumors display an effector memory phenotype while less of these cells are double positive for the exhaustion markers PD-1 and TIM3  (96). Three anti-CD38 mAbs, Daratumumab (Janssen Biotech), Isatuximab (Sanofi), and MOR202 (Morphosys) are being clinically evaluated. Daratumumab was FDA-approved in 2015 for treating multiple myeloma patients, while to date the most advanced testing of Isatuximab and MOR202 as monotherapies are respectively the Phase II trials NCT01084252, NCT02960555, and NCT02812706, as well as the Phase I/IIa trial NCT01421186.