dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets The RNA sequencing (RNAseq) datasets of differential expression of I. scapularis transcription factors (TF) and target genes (TG) in response to A. phagocytophilum infection was obtained from previously published transcriptomics analyses in ISE6 cells, and fed adult female midguts and salivary glands  (Ayllón et al. , 2015; Villar et al. , 2015). Gene ontology (GO) level-3 annotations for biological processes (BP) were conducted using Blast2GO software (version 3. 0) 1   (Villar et al. , 2014 ; Supplementary Dataset 1). The RNAseq data is available at http://dx. doi. org/10. 5061/dryad. 50kt0 and http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE68881.
Networks were built separately for infected and uninfected I. scapularis ISE6 cells, salivary glands and midguts. Only TF and TG with GO functional annotations were included in the networks (Supplementary Dataset 1). Centrality is a fundamental property of a network because it refers to nodes that connect high score nodes  (Opsahl et al. , 2010; Estrada-Peña et al. , 2018). In this context, "high score" applies to other nodes with high importance in the network. We calculated the importance of a node in the "traffic" between different nodes of the network using Betweenness Centrality (BNC), giving a higher score to a node that sits on many shortest paths of other node pairs  (Barthelemy, 2004; Estrada-Peña et al. , 2018). In our context, it is an indicator of the relative importance of a TF/TG in the links between two or more processes.
Other calculated indexes included the PageRank (PR), a measure of the importance of the nodes linking with a given node, and the Weighted Degree (WD), which was calculated from the expression profile of each TF/TG linking to a cell process  (Estrada-Peña et al. , 2018). The interactions between TF and TG were demonstrated using CoCA. Only TF/TG with values of BNC or PR higher than zero were included. We did the CoCA using the indexes of centrality obtained from the network explained above. The function "coca" of the package "cocorresp" was used for the R programming environment  (Simpson, 2016). Data on BNC, PR and WD of each infected and uninfected datasets from ISE6 cells, salivary glands and midguts were entered into separate CoCAs. The analysis aimed to relate two different datasets from uninfected and infected samples to find patterns that are common to both and associating the TF and TG that are close in the reduced multivariate space and establishing correspondences.
In silico Prediction of TF-TG Interactions Putative DNA binding sites in TG for TF present only in infected tick ISE6 cells were predicted based on published information for TF-interacting sequences in other species, and the I. scapularis genomic scaffold whole genome shotgun sequence using cisTargetX 2  and direct search for TF binding sequences in the predicted 5 gene regulatory regions of the I. scapularis genome (Supplementary Dataset 2;  Potier et al. , 2012; Rougemont and Naef, 2012; Vaquerizas et al. , 2012). The BP with higher representation in the upregulated than in downregulated regulome (peptidase inhibitor and stress response) in response to infection were selected for further characterization of TF-TG interactions.
RNA Interference (RNAi) for Gene Knockdown in Tick ISE6 Cells The I. scapularis ISE6 cells (provided by U. G. Munderloh, University of Minnesota, United States) was maintained in L-15B300 medium as described previously  (Munderloh et al. , 1994). Four different TF (HSF, Ap-2, Arx and Hox; Supplementary Dataset 2) were silenced using two siRNAs for each TF (HSF: 5 GCA CUC AGG GCC AGG AUU A 3 and 5 CCU CGG AAG CAG ACA GGA A 3 ; Ap-2: 5 AGA AAG AGG ACA CGA AGA A 3 and 5 CCA AGA AAG AGG ACA CGA A 3 ; Arx: 5 CCA AGA AAG AGG ACA CGA A 3 and 5 GAC CGA AGC CAG AGU GCA A 3 ; Hox: 5 CCU CCA GCU UCA ACA CAU A 3 and 5 ACG CCA CGG CCG AGC UUA A 3 ) provided by Dharmacon (GE Healthcare Dharmacon Inc. , Lafayette, CO, United States). As control, two Rs86 siRNAs (5 CGG UAA AUG UCG AAG CAA A 3 and 5 GCG AAU AUG AAG UCG GUA A 3 ) were used.
The results of the network analysis were then plotted with TF and TG together in the reduced space to demonstrate that the position of the TF correlates with the TG that are near to these TF after the CoCA (Supplementary Figure  1D ). Finally, the results of both approaches were compared, and the TF-TG interactions predicted by both methods were functionally characterized by RNAi in A. phagocytophilum-infected and uninfected tick ISE6 cells (Supplementary Figure  1E ). The I. scapularis Regulome Shows Tissue-Specific Signatures in Response to A. phagocytophilum Infection For the construction of networks, a total of 144, 86, and 93 TF (Supplementary Dataset 3), and 5225, 3919, and 4341 TG (Supplementary Dataset 4) were used derived from tick ISE6 cells, salivary glands and midguts, respectively.
, 2015; Villar et al. , 2015; reviewed by de la Fuente et al. , 2017). These results evidenced that the regulome regulates various BP involved in tick-A. phagocytophilum interactions (Figures  1A, 3A  and Supplementary Dataset 2), a finding previously reported in other organisms  (Shih et al. , 2016; Casella et al. , 2017).
A. phagocytophilum Modulates the Tick Regulome to Upregulate Biological Processes That Facilitate Pathogen Infection To complement the network analysis approach to tick regulome study, the putative DNA binding sites were characterized in silico for TF and TG in the upregulated regulome in response to infection in tick vector ISE6 cells (Figures  3A, B  and Supplementary Dataset 2). In particular, the peptidase inhibitor and stress response BP with higher representation in the upregulated than in downregulated regulome (Figure  3A ) were selected for further characterization of TF-TG interactions (Figure  3B ). The results showed a correlation between complementary in silico approaches (Figure  4A ), therefore providing support for the network analysis of the regulome to predict at the transcriptomics level the most significant TF-TG interactions in response to stimuli such as pathogen infection.
and stress response (Figure  3A  and Supplementary Dataset 2). The stress response upregulated TG included genes coding for peroxinectin and uncharacterized protein with heme binding and peroxidase activity (n = 2), and glutathione peroxidase with glutathione peroxidase activity. The peptidase inhibitor TG encoded for a carboxypeptidase inhibitor precursor with carboxipeptidase and metalloendopeptidase inhibitor activity, uncharacterized protein, Kunitz-type proteinase inhibitor 5 II, serpin-2 precursors and secreted salivary gland peptides with serine-type endopeptidase inhibitor activity (n = 6), and cystatin and salivary cystatin-L with cysteine-type endopeptidase inhibitor activity (n = 2). These proteins are involved in key physiological processes during tick life cycle such as heme/iron metabolism and detoxification and tick-host interactions  (Chmelar et al.
2/1. 3 grids  (Russo and Passmore, 2014) , and a dataset was collected using a Titan Krios equipped with a K2 Summit detector (Figure  S1 ; Table  1 ). After motion correction, contrast transfer function (CTF) estimation, 2D classification, and iterative rounds of heterogeneous 3D refinement, a total of 241,669 particles remained. Homogeneous 3D refinement of this particle stack resulted in a 3. 4-A map. After initial building of the L model and docking of the closely related human metapneumovirus (hMPV) P oligomerization domain structure into the map  (Leyrat et al. , 2013) , it was possible to identify the portions of the density that corresponded to the P monomers. We then generated a map in which these regions were deleted. A low-pass filter was applied to this new map as well as to the original map, and both were used as input volumes for a final round of heterogenous 3D refinement to remove particles in which the tetrameric P had dissociated.
Cryo-EM data collection A cryo-EM dataset of the RSV L-P complex was collected on a Titan Krios operating at 300kV and equipped with a K2 Summit detector. An initial dataset was collected on samples frozen in 1. 2/1. 3 holey carbon grids, but the resolution was limited to 3. 7 A ˚. In an effort to improve the resolution, a second dataset was collected using UltrAuFoil 1. 2/1. 3 grids  (Russo and Passmore, 2014). UltrAuFoil 1. 2/1. 3 grids (Electron Microscopy Sciences) were plasma cleaned for 30 s using a Gatan Solarus 950 with a 4:1 O 2 :H 2 ratio. A 0. 57 mg/mL solution of L-P complex in 50 mM Tris-HCl pH 8. 0, 500 mM NaCl, 1 mM TCEP and 10% glycerol was diluted with an equal volume of 20 mM Tris-HCl pH 8. 0, 200 mM NaCl immediately before 3 mL was deposited onto grids and plunge-frozen in liquid ethane using a Vitrobot Mark IV (Thermo Scientific) set to 100% humidity and 4 C, with a wait time of 0.
Cryo-EM data processing Motion correction, CTF estimation, template-based picking, 2D classification, heterogeneous 3D refinement, local motion correction, homogeneous 3D refinement, and non-uniform 3D refinement were performed in cryoSPARC v2  (Punjani et al. , 2017; Rohou and Grigorieff, 2015; Rubinstein and Brubaker, 2015; Vilas et al. , 2018). After motion correction and CTF estimation, a total of 3,144 micrographs were selected for subsequent processing. Template-based picking identified 1,455,759 particles, which was reduced to 622,521 particles after 2D classification and 241,669 particles after two iterative rounds of heterogeneous 3D refinement. Local motion correction was performed on the final particle stack  (Rubinstein and Brubaker, 2015) , followed by homogeneous 3D refinement, which resulted in a 3.
ACKNOWLEDGMENTS We would like to thank  Dr. Aguang Dai  at the  Sauer Structural Biology Laboratory at UT Austin  for assistance with cryo-EM data collection. We acknowledge the  University of Texas College of Natural Sciences  and award  RR160023  of the  Cancer Prevention and Research Institute of Texas  for support of the EM facility at the  University of Texas at Austin. We acknowledge  Dr. Jerome Deval  for initial contributions to this project. We thank  Francoise Debart  and  Jean-Jacques Vasseur  for preparation of RNA molecules used in the methyltransferase assay. We also thank  Johannes Langedijk ,  George Kukolj , and members of the  McLellan Laboratory  for providing helpful comments on the manuscript. Support for this work was provided by  Janssen Pharmaceutical Companies of Johnson & Johnson.
Isolation and growth conditions In September 2016, we isolated, from a fresh stool sample of a 26-year-old healthy French woman, a bacterial strain (Marseille-P3369 T ). This strain was not identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The analysis was performed on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany), as previously described  [6]. Spectra obtained (Fig. 1 ) were imported and analysed using the BIOTYPER 3. 0 software against the Bruker database, which was constantly updated with the MEPHI database  [1]. Growth of colonies of the strain Marseille-P3369 T was observed after 48 hours of incubation at 37°C on 5% sheep's blood agar (bioMérieux, Marcy l'Etoile, France) under strict anaerobic conditions generated by anaeroGEN (Oxoid, Dardilly, France)  [7].
Strain identification To classify this bacterium, the 16S rRNA gene was amplified using the primer pair fD1 and rP2 (Eurogentec, Angers, France) and sequenced using the Dye ® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyzer capillary sequencer (Thermofisher, Saint-Aubin, France), as previously reported  [8]. The 16S rRNA nucleotide sequences were assembled and corrected using CODONCODE ALIGNER software (http://www. codoncode. com). Strain Marseille-P3369 T exhibited 98. 32% similarity with Varibaculum cambriense strain CCUG 44998 (GenBank Accession no. NR_114873. 1), its phylogenetically closest species with standing in nomenclature (Fig. 3 ). consequently proposed classifying strain Marseille-P3369 T as a new species within the genus Varibaculum belonging to the phylum Actinobacteria.
Genome sequencing Genomic DNA was extracted using the EZ1 biorobot with the EZ1 DNA tissue kit (Qiagen, Hilden, Germany) and then sequenced on a MiSeq sequencer (Illumina Inc. , San Diego, CA, USA) with the Nextera Mate Pair sample prep kit and Nextera XT Paired End (Illumina), as previously described  [9]. The assembly was performed using a pipeline containing several softwares (VELVET  [10] , SPADES  [11]  and SOAP DENOVO  [12] ), and trimmed data (MISEQ and TRIMMOMATIC  [13]  softwares) or untrimmed data (only MISEQ software). GAPCLOSER was used to reduce assembly gaps. Scaffolds <800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The best assembly was selected by using different criteria (number of scaffolds, N50, number of N). The genome of Strain Marseille-P3369 T was 2.
73 Mb with 33. 2% G + C content. The degree of genomic similarity of the strain with closely related species was calculated using ORTHOANI software  [14]. OrthoANI values among closely related species (Fig. 4 ) ranged from 58. 83% between Actinomyces neuii and Varibaculum timonense to 77. 54% between Actinomyces odontolyticus and Actinomyces georgiae. When Varibaculum timonense was compared with these closely related species values ranged from 58. 83% with Actinomyces neuii to 66% with Actinomyces georgiae.
Funding sources This study was supported by the  Institut Hospitalo-Universitaire Méditerranée Infection , the  National Research Agency the Investissements d'avenir , reference  ANR-10-IAHU-03 ,  Alpes d'Azur funding FEDER The study was approved by committee of the  Institut Federatif de Recherche 48  under reference  2016-010. The woman provided written consent.
Background Comparative, real-life and long-term evidence on the effectiveness and safety of phototherapy and systemic therapy in moderate-to-severe atopic eczema (AE) is limited. Such data must come from well-designed prospective patient registries. Standardization of data collection is needed for direct comparisons and data pooling. Objectives To reach a consensus on how and when to measure the previously defined domain items of the TREatment of ATopic eczema (TREAT) Registry Taskforce core dataset for research registries for paediatric and adult patients with AE. Methods Proposals for the measurement instruments were based on recommendations of the Harmonising Outcome Measures for Eczema (HOME) initiative, the existing AE database of TREATgermany, systematic reviews of the literature and expert opinions.
The proposals were discussed at three face-to-face consensus meetings, one teleconference and via e-mail. The frequency of follow-up visits was determined by an expert survey. Results A total of 16 experts from seven countries participated in the 'how to measure' consensus process and 12 external experts were consulted. A consensus was reached for all domain items on how they should be measured by assigning measurement instruments. A minimum follow-up frequency of initially 4 weeks after commencing treatment, then every 3 months while on treatment and every 6 months while off treatment was defined. Conclusions This core dataset for national AE research registries will aid in the comparability and pooling of data across centres and country borders, and enables international collaboration to assess the long-term effectiveness and safety of phototherapy and systemic therapy used in patients with AE.
What's already known about this topic? • Comparable, real-life and long-term data on the effectiveness and safety of pho- totherapy and systemic therapy in patients with atopic eczema (AE) are needed. • There is a high diversity of outcomes and instruments used in AE research, which require harmonization to enhance comparability and allow data pooling. What does this study add? • Our taskforce has reached international consensus on how and when to measure core domain items for national AE research registries. • This core dataset is now available for use by researchers worldwide and will aid in the collection of unified data. What are the clinical implications of this work? • The data collected through this core dataset will help to gain better insights into the long-term effectiveness and safety of phototherapy and systemic therapy in AE and will provide important information for clinical practice.
• Standardization of such data collection at the national level will also allow direct data comparisons and pooling across country borders (e. g. in the analysis of treatment-related adverse events that require large patient numbers). A significant number of paediatric and adult patients with moderate-to-severe atopic eczema (AE) may require phototherapy or systemic immunomodulatory therapy at some point during their life. For adults, ciclosporin, and recently dupilumab, are currently the only systemic therapies that are approved by the European Medicines Agency,  1, 2  while only dupilumab has been approved by the U. S. Food and Drug Administration. 3 For children, there are no approved systemic therapies, although our European and North American treatment surveys show that they are regularly prescribed.
4, 5 While there is some evidence on the short-term effectiveness of phototherapy and systemic immunomodulatory therapy, there is a clear lack of head-to-head comparison trials and a paucity of data on the long-term effectiveness and safety of such treatments. 6, 7 As randomized controlled trials have very strict inclusion criteria, important subgroups of patients (e. g. those with comorbidities) are commonly excluded and therefore evidence in real-life populations is missing. [10]  To harmonize data collection for such observational cohorts, the TREatment of ATopic eczema (TREAT) Registry Taskforce initiated a consensus exercise to develop a core set of domains and domain items for AE treatment research registries. After an international Delphi study and consensus meeting, the core dataset ('what to measure') was agreed on, consisting of 19 domains with 69 corresponding domain items (49 at baseline and 20 at follow-up).
Study design To establish a core set of measurement instruments ('how to measure'), three face-to-face expert consensus meetings, one teleconference and final discussions via e-mail were arranged. For this process we used the following sources to guide decision making: (i) recommendations from the Harmonising Outcome Measures for Eczema (HOME) initiative were used where possible (e. g. regarding the capture of clinical signs, patient-reported outcomes and quality of life);  [15] [16] [17]  (ii) relevant literature, in particular systematic reviews considering measurement instruments in AE;  7, 13, 14, [18] [19] [20]  (iii) the existing AE database of TREATgermany,  21  which already included over 100 patients at the start of this study; (iv) personal communications with experts in the field of measurement instruments for specific domain items [e.
How to measure In March, May and June 2017 four consensus meetings were held. The first was done by teleconference and the other three were by face-to-face meetings in London, Amsterdam and Nantes. A total of 16 participants met, all members of the TREAT Registry Taskforce, including 11 academic dermatologists, one dermatology resident, one dermatology PhD student, one patient/patient representative, one epidemiologist/ methodologist, and one health economist (Fig. S2 ; see Supporting Information). A total of 12 experts were consulted for specific items. During the face-to-face meetings, slight alterations were made to the 'what to measure' core dataset. To make the core dataset as feasible as possible some domain items were merged with others. The items 'medical history', 'follow-up (FU) safety bloods', 'adverse events that cause stop or switch of therapy or change in dosage' and 'probability of relationship with treatment' are now captured as part of the other items (for details see Table  1 ).
Use of validated diagnostic criteria Both the quality of the gathered data and the feasibility of the registry were considered. Many lists of diagnostic criteria for AE are available and reviewed (e. g. the U. K. Working Party criteria, Hanifin and Rajka criteria, refined Millennium Criteria). 23 Although in clinical practice a diagnosis of AE is often made without the use of specific diagnostic criteria, nevertheless the use of validated diagnostic criteria is desirable within the context of national AE treatment research registries. During the consensus exercise, we decided to give national registries the option to decide which validated diagnostic criteria they would like to use. In addition, the option 'physician diagnosed' was added in case no diagnostic criteria were used.
Reporting of disease control For this item, which is analogous to the long-term control domain as defined by HOME, HOME V has recommended the use of repeated measurements of the long-term control subdomains: clinical signs, symptoms, quality of life and a patient global instrument. 17 vestigator/Physician Global Assessment Futamura et al. concluded that global assessments are often used in AE research but comparisons are hard because there are no standardized definitions. 29 Therefore, the International Eczema Council and Eli Lilly and Company have worked on a validated five-point Investigator Global Assessment (IGA) scale, which was incorporated in to the core dataset for this item. 30, 31 tient global assessment Little research has been done towards the patient global assessment (PGA). This subdomain of the long-term control domain has been discussed during HOME V, but as yet stays undefined.
Discussion This consensus study identified measurement instruments for all domain items previously agreed on during our Delphi study for AE research registries that capture data on adults and children with moderate-to-severe AE on phototherapy and systemic immunomodulatory therapy. By doing so, a complete core dataset is now available for usage by researchers worldwide. Our recommendations for core domains and domain items for data collection were based on a carefully conducted international Delphi process, in which over 400 stakeholders (physicians, nurses, patients, methodologists, regulatory body and industry representatives) from over 30 countries contributed. 11, 12 The results of this Delphi directly fed into the 'how to measure' recommendations presented here. 12 In addition, proposals for the measurement instruments were based on the recommendations from the HOME initiative.
Although primarily meant for clinical trials and not specifically for research registries, the HOME recommendations represent an international consensus on core outcomes based on validation studies and systematic reviews. The experts who participated in the HOME initiative participated in this study as well, allowing us to benefit from their expertise. Further, a patient and experts in the field of AE and/or AE measurement instruments were involved, which strengthened our recommendations and provided insight into important aspects that will play a role during implementation of the core dataset. As for potential limitations, the final decisions on the 'how to measure' were made by a relatively small group for feasibility reasons, which did not include representatives from the regulatory bodies or pharmaceutical industry.