dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a grant  DPM 20121125559  from the  Fondation pour la recherche m edicale (FRM) , and a grant from the  Soci et e d'Acc el eration de Transfert de Technologie (ValorPACA/SATT Sud-Est ) from France.
For morphological and morphometric characterization, the mature myxospores were fixed in 10% formalin and transported to Department of Zoology, University of São Paulo, Brazil. The analyses were performed based on the criteria outlined by  Lom and Arthur (1989). Measurements and photographs were taken from 30 myxospores using a compound microscope Leica DM1000 LED equipped with Leica Application Suite version 1. 6. 0 image capture software. Smears containing free myxospores were air dried, fixed with methanol and stained with Giemsa solution to mount on permanent slides that were deposited in the collections of the Museum of Zoology of the University of São Paulo -USP, São Paulo, Brazil. For molecular characterization, plasmodia were removed from the infected tissue and preserved in absolute ethanol.
For sizing and approximate quantification of fragments, 1 Kb Plus DNA Ladder (Invitrogen by Life Technologies, USA) was used. PCR products were purified using USB® ExoSap-IT® (Thermo Fisher Scientific, Massachusetts, USA) in accordance with the manufacturer's instructions. PCR products were sequenced using the same PCR primers. Additional primers MC5, CCTGAGAAACGGCTACCACATCCA and MC3, GATTAGCCTGACAGATC ACTCCACGA  (Molnár et al. , 2002)  were used to connect overlapping fragments. Sequencing was performed with a BigDye® Terminator v3. 1 cycle sequencing kit (Applied Biosystems Inc. , California, USA) in an ABI 3730 DNA sequencing analyzer. The obtained sequences were visualized, assembled and edited using BioEdit 7. 1. 3. 0 software  (Hall, 1999). A basic local alignment search was conducted (BLASTn) to verify the similarity of our sequence with other sequences of myxozoans available in the National Center for Biotechnology non-redundant nucleotide database  (Altschul et al.
, 1997). Phylogenetic analysis was conducted using the 53 most closely related myxobolid myxosporeans sequences with BLAST similarity > 80%, included 29 sequences of Myxobolus species, 23 sequences of Henneguya species and 1 of Thelohanellus species. Parvicapsula bicornis (EF429097) sequence was used as outgroup. The sequences were edited and aligned with ClustalW within BioEdit version 7. 1. 3. 0 (Hall, 1999)  and phylogenetic relationships were performed using maximum likelihood (ML) methods with a Kimura 2-parameter (K2P) evolution sequence model in the MEGA 6. 0 program  (Tamura et al. , 2013). Bootstrap analysis (1000 replicates) was employed to assess the relative robustness of the tree branches. Other alignments, including species that clustered together with the O. cocama isolate in the phylogenetic tree, were used to produce a pairwise similarity using MEGA 6.
Molecular characterization and phylogenetic analysis The sequencing of the SSU rDNA from the myxospores of M. iquitoensis n. sp. , resulted in sequence of 1926 bp with a GC content of 48. 3%. BLASTn search revealed that the sequence obtained did not match any known myxozoan species sequences available in the GenBank database. The highest percentage of identity belonged to Myxobolus figueirae Naldoni, Maia, Correa, Da Silva and Adriano, 2018 (query coverage 93%, maximum identities 88%), a parasite of the Amazon pimelodid fish Phractocephalus hemioliopterus. Phylogenetic analysis based on the most closely related myxozoan sequences placed the sequence within a subclade containing myxobolid parasite species of native South American fishes. In this same subclade, M. iquitoensis n. sp. appears as a close species of M.
figueirae (GenBank accession MG181226). Genetic distance analysis considering only of SSU rDNA sequences of the myxobolids that cluster together in the same clade (Fig. 3 ) with the new parasite herein described showed the largest genetic divergence among these species, with a difference of 11% to M. figueirae, 14. 9% to Myxobolus porofilus  Adriano, Arana, Ceccarelli and Cordeiro, 2002 , 15. 7% to Myxobolus curimatae Zatti, Naldoni, Silva, Adriano, 2015 and 17. 1% to Myxobolus prochilodus Zatti, Arana, Maia and Adriano, 2016.
the 35 specimens of O. cocama examined in the present study, the gill filaments of five (14. 3%) harbored plasmodia of an unknown parasite belonging to the genus Myxobolus. Plasmodia were not found in any other organ and no clinical signs were observed in the parasitized organ. The description of the novel species is Myxobolus iquitoensis n. sp. Type host: Otocinclus cocama (Siluriformes: Loriicaridae) Site of infection: Gill filaments Type locality: Yanayacu brook, Municipality of Jenaro Herrera (4°58′58″S, 74°19′38″W), Department of Loreto, Peru. Prevalence: From 35 examined fish, five were infected (14. 3%). Type of material: Syntypes-Air-dried slide stained with Giemsa (MZUSP 8507) deposited in the collection of Museum of Zoology of the University of São Paulo -USP, São Paulo, Brazil. The SSU rDNA sequence was deposited in GenBank under accession number MN995338.
Acknowledgements The authors thank  São Paulo Research Foundation ,  FAPESP , for Post-Doc fellowship to  P. D. Mathews  (grant No. 2018/20482-3 ). O. Mertins thanks  FAPESP  for research financial support ( 2015/23948-5 ;  2016/13368-4 ). A. C. Morandini was funded by  CNPq  ( 304961/2016-7 ), and  FAPESP  ( 2015/21007-9 ). T. Milanin was supported by a  Post-Doc fellowship  from  FAPESP  (grant. No. 2015/18807-3 ). L. L. Espinoza thanks  Vicerectorado de Investigación y Postgrado ,  Universidad Nacional Mayor de San Marcos  for research financial support (grant. No. A17080131 ). The authors thank  Dr. T. S. Backer  for revising the grammar of the text.
9%) compared to the previous studies  22. In a recent inter-laboratory comparison of this RIDT performed in 22 different laboratories using a panel of 10 samples, overall concordance was 99. 5%  23. Only one recent multicentric study showed unsatisfactory overall RIDT performance  24. Samples from three different datasets were tested and provided variable sensitivity and specificity values compared to DFAT. For example, sensitivity and specificity obtained with the first panel (n=51) and the second panel (n=31) of samples from experimental infected animals, all tested in laboratory A, gave a sensitivity of 16% and 43%, respectively, whereas the specificity was 100% for both. Conversely, the results of the third panel (n=30) of field clinical samples analyzed by laboratory B provided a complete concordance with the results of DFAT, which was further nearly completely confirmed by laboratory A (85% sensitivity and 100% specificity).
Batch-to-batch variation was suggested as a possible explanation for the fluctuating relatively low sensitivity with RIDT  24. At the same time, another study performed a similar validation process of the above described RIDT, with a modification of the manufacturer recommended protocol 14. The pre-dilution step (1:10) in PBS was omitted during preparation of the brain material. Based on this simpler modified protocol, the authors obtained sensitivity and specificity of 95. 3% and 93. 3%, respectively, compared to DFAT by testing, under laboratory conditions, a dataset of 73 animal brain samples, naturally or experimentally infected with various RABV strains. The study presented the first evaluation of this RIDT in a field setting (Chad, Africa). In 48 clinical brain samples, sensitivity and specificity were 94.
2% and 95. 8%, respectively (Table  7 ). However, these preliminary but promising results were obtained on a limited sample dataset and need to be further confirmed on a large number of positive and negative samples, especially for those tested in enzootic areas, to avoid any potential underestimating or bias due to the current heterogenous datasets. The RIDT test is suitable to detect lyssavirus in brain biopsies from infected animals, where the level of lyssavirus antigens is important. However, the test limit of detection remains high when testing titrated virus suspension (Table  8 ; Figure  7 ).
Acknowledgments This work was supported through the  Global Alliance for Vaccines and Immunisation (GAVI) , the  Wolfermann Nägeli Foundation , the  Swiss African Research Cooperation (SARECO) , the  SWF Stiftung für wissenschaftliche Forschung , the  Freiwillige Akademische Gesellschaft (FAG) Basel , the  Bilateral Science and Technology Cooperation Programme of Switzerland with Asia  and the  Novartis Foundation  for biomedical research. We thank especially the dog owners, the veterinary personnel and the laboratory staff for their great commitment. We also want to acknowledge  Lisa Crump  for the language editing.
The most enriched GO cluster identified from genes under-expressed in Ctcf -/-macrophages included 'cellular response to interferon beta' (p < 5x10 -5 ; S1C Fig) , and was not enriched in over-expressed genes. A second GO term cluster including 'response to virus' was also specifically enriched in under-expressed genes (p < 5x10 -5 ) (S1C Fig). These analyses demonstrate that in myeloid cells, CTCF is required for a full-blown interferon response, and is partially co-localized with TRIM33. Using chromosome conformation capture experiments followed by deep sequencing (3Cseq,  [38] ), we have shown that the DNA loop between ICE and the Ifnb1 gene was not modified in Trim33 -/-macrophages  [24]. To identify distant regions that could participate in the transcriptional regulation of Ifnb1 in macrophages, we analyzed these data on a larger scale (all accession numbers of publicly available datasets used in this study are provided in S1 Table ).
Second, rs12553564 is located in a predicted regulatory element as defined by the Ensembl v80 database. Third, rs12553564 is overlapped by experimentally defined transcription factor binding sites established by the Encode consortium. Fourth, the nucleotide affected in rs12553564 is located at a position conserved across mammals as determined with the GERP++ tool (GerpRS score > 2). And fifth, rs12553564 overlaps several transcription factor binding sites that are conserved across mammals. Although these results do not exclude that other linked variants contribute to the observed phenotype, they prompted us to prioritize rs12553564 for further analysis. Variant rs12553564 is an A to G substitution located on chromosome 9 at position 21,017,241 (genome version GRCh38), in the third intron of PTPLAD2.
Identification of conserved TFBS motifs All available position frequency matrices for human transcription factors (TF) were retrieved from the JASPAR CORE 2018 database  [52] , and only the matrix with the highest information content was kept for each TF. Matrices were then converted to position weight matrices using the log probability ratio method with a pseudocount of 0. 8 (default value), and assuming an equal prior probability for each possible nucleotide. Multiz 46 way alignments where downloaded from UCSC (http://hgdownload. cse. ucsc. edu/goldenPath/hg19/multiz46way) and used to establish the regions that are orthologous to the IFNB1 locus (hg19 -chr9:20-22Mb) in mice (mm9) and 44 other verterbrate species. To detect TFBS that are conserved across species around each SNP, we used the searchSeq function from the TFBSTools package to identify TFs with a predicted binding score of >85% of their maximum PWM matrix value across all 46 orthologous sequences in a 21bp window around the SNP, and specifically retained the TFBS that overlapped the human variant.
Data from [40]. (D) Signal from ChIP-seq experiments targeting different transcription factors and histone marks over the Ptplad2 gene. The results of the 3C-seq experiment from the Ifnb1 promoter viewpoint is shown in red. Identified enhancers are indicated with yellow circles, and numbered. Data from [41]  (C/EBPα), [42]  (NF-κB p65), and [43]  (H3K4me1 and H3K27ac). (E) Plasmids encoding firefly luciferase under the control of the Ifnb1 promoter (P) flanked or not with the enhancers identified above, each labelled with its number, were transfected into NIH3T3 cells together with a plasmid coding for Renilla luciferase under the CMV promoter. After 30 hrs, luciferase levels were measured. Results are expressed as the ratio of firefly to Renilla luciferase, normalized to the mean value of P.
Acknowledgments We thank members of the  Romeo laboratory  for helpful comments and discussion, and  C. Carles  for critical reading of the manuscript. We thank  F. Letourneur  for the sequencing of ChIP-seq experiments. Sequencing data were analyzed with the expert help of O. Alibert. We thank  M. -L. Arcangeli  and  M. Laviron  for kindly providing the EL4 cell line.
Therefore, a better characterization of the process could help identify some gene or protein candidates to target pharmacologically and suggest combinations of drugs that would favor/increase antitumor immune response. To this end, we have built a mathematical model of the major cell types that intervene in ICD, namely cancer cells, DCs, CD8 + and CD4 + T cells. Our model not only integrates intracellular mechanisms within each individual cell entity, but also incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell types. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type.
In order to preserve the feasibility of the simulations, we properly restricted the number of interactions and processes. Tumor cells, dendritic cells, CD4 + and CD8 + T lymphocytes have been selected as the main players. Our model focuses on intercellular communications between the different cell types, considers current knowledge on the timing of events, and takes into account death and proliferation of tumor cells in diverse contexts. The model is based on a discrete Boolean formalism and is simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level (an extended presentation of UPMaBoSS is provided in  Stoll et al. , 2020). The Boolean description of the model entities is a strong approximation.
In the present work, it fits the current biological knowledge, which is mainly qualitative. However, the grammar of MaBoSS allows to represent discrete levels of each model entity, corresponding to different concentration levels or different status of a protein (e. g. , phosphorylated, or in complex) entailing different levels of activity. An example of multi-level dynamics of the protein p53 in the context of p53/Mdm2 interaction is provided at the following address: https://github. com/sysbio-curie/MaBoSS-env-2. 0/tree/ master/tutorial/MaBoSS-2. 0. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different types of cell populations. Furthermore, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted by proper simulations.
MATERIALS AND METHODS For modeling ICD, we use the stochastic Boolean simulation framework UPMaBoSS  (Stoll et al. , 2020). It is based on the previously defined MaBoSS grammar  (Stoll et al. , 2012) , which was extended to simulate the dynamics of the cell populations. Details about the software are provided below. More information about software accessibility, use cases, and jupyter notebooks including the code used for all the analyses presented here are available on a GitHub repository: https:// github. com/sysbio-curie/ICD.
Signaling Pathways Described as MaBoSS Models MaBoSS  (Stoll et al. , 2012 (Stoll et al. , , 2017 ) is a software dedicated to the modeling of signaling pathways based on a Boolean probabilistic description. Given a Boolean model, MaBoSS produces time dependent probabilities of network states. In MaBoSS, two transition rates (activation/inhibition) are associated with each node of the network. These rates are written in a specific grammar, which combines Boolean states of other nodes with logical (AND, OR, NOT) and arithmetic operators. In addition to this set of transition rates, a MaBoSS simulation requires the definition of an initial condition, i. e. , a probability distribution of network states at time zero. In practice, a MaBoSS model is encoded into two files: a bnd file containing the logical rules and the definition of the transition rates, and a cfg file containing the parameters of the model (initial conditions and values for the transition rates) together with the simulation parameters.
Receptors are updated according to the value of their regulators. An UPMaBoSS model is encoded into three files: bnd and cfg files written in MaBoSS grammar, completed by a specific upp file. The upp file contains the declaration of the Death and Division nodes, together with the updating rules for the parameters controlling the transition rates of the receptors. All simulations of the ICD models reported here are encoded in the jupyter notebooks provided as Supplementary Material. These notebooks can be executed on any computer with a recent docker or a conda environment installed, as indicated in the dedicated GitHub repository (https://github. com/sysbio-curie/ ICD). The full parameter sensitivity analysis is not included in the jupyter notebooks, as it is time consuming (taking over 24 h) and has been performed on a computer cluster.
On the other hand, a cytokine receptor node is conditioned by the presence of the cells presenting it. The two types of nodes, ligand and receptor, are dynamically linked, as the probability attributed to the ligand node impacts the rate of transition toward the activation of the receptor node. At each step of the simulation, after its first activation, the transition rate for the receptor is controlled by the ligands. All the logical rules are written to account for the biological knowledge whenever it is reported in the literature. For instance, the rule for the CD8 + T cell receptor node TCR-I is: CD28 & CD8, which reads as: when both CD28 (the receptor of CD80) and a naive CD8 + T cell are present, TCR_I will be activated at a rate $u_TCR_I$, which is defined in the cfg file. The model files can be found at: https://github.
On the other hand, studies in macrophages carrying the IBD-associated T108M polymorphism in GPR35 result in enhanced metabolic activity compared to the wild-type GPR35, which is the opposite effect compared to GPR35 loss of function  (Schneditz et al. , 2019; Tsukahara et al. , 2017). These data suggest the (H) Disease activity scores for DSS colitis parameters assessed daily for 7 days of vehicle-or LPA-injected Gpr35 wt or Gpr35 DCx3cr1 mice. Data are indicated as mean ± SD for four mice per group. (I) Colon lengths measured from Figure  S5M. (J and K) Representative endoscopy images (J) and H&E staining (K) of colons corresponding to day 7 of DSS colitis from Gpr35 wt or Gpr35 DCx3cr1 mice that received daily vehicle or LPA injections. (L) Histological DSS colitis scores calculated from (K).
Hematoxylin-eosin (H&E) staining and histological scoring 5 mm paraffin sections from mouse colon were stained with H&E. Histological scores for colonic inflammation were assessed semiquantitatively using the following criteria  (Souza et al. , 2017; Steinert et al. , 2017) : mucosal architecture (0:normal, 1-3: mild-extensive damage); cellular infiltration (0:normal, 1-3: mild-transmural); goblet cell depletion (0:no, 1:yes); crypt abscesses (0:no, 1:yes); extend of muscle thickening (0: normal, 1-3: mild-extensive). Tissues were scored by at least two blinded investigators and data is presented by the mean.
Cells were washed in PBS containing 2% Fecal Bovine Serum (FBS), 0. 1% sodium azide, and 10 mM EDTA (FACS buffer) and stained for surface antigens for 20 min at 4 C. For intracellular staining, cells were further fixed and permeabilized in Cytofix/Cytoperm solution according to the manufacturer's instructions (BD Biosciences) followed by incubation with antibodies against intracellular antigens for 20 min at 4 C. Cells were then resuspended in FACS buffer and flow cytometric analysis was performed on a Fortessa flow cytometer (BD Biosciences). Data were analyzed using FlowJo software version 10. 0. 7r2 (TreeStar). In all experiments, doublet discrimination was done on forward scatter (FSC-H) versus FSC-A plot. Mononuclear phagocyte staining was done using antibodies eVolve655-conjugated anti-CD45 clone 30-F11 (eBioscience), biotin-labeled anti-CD3 clone 145-2C11, anti-CD19 clone 6D5 and anti-NK.
ACKNOWLEDGMENTS This work is part of the Ph. D. thesis of  B. K. Philippe Demougin  ( Biozentrum, Basel ) and  Christian Beisel  ( Genomics Facility Basel, ETH Z€ urich ) helped with RNA-seq. Calculations were performed at the sciCORE (https://scicore. unibas. ch/)scientific computing center at the  University of Basel. J. H. N. was supported by  SNSF  grants  310030_175548  and  316030_170809. The  SNSF  grant  31AC-0_198951  to J. H. N. financed the publication of our manuscript without restrictions (gold open access). T. K. was supported by  SNSF   M. D. Ph. D. fellowship  323530_183981. The Swiss IBD Cohort Investigators were supported by  SNSF  grant  33CS30-148422. E. J. V. was supported by grants from the  Swedish Research Council VR  grant  K2015-68X-22765-01-6 ,  Formas  grant  FR-2016/0005 ,  Cancerfonden  ( 19 0395  Pj), and the  Wallenberg Academy Fellow program.
DNA sequencing and phylogenetic analysis DNA sequencing was carried out in the PT. Genetica Science (Jakarta, Indonesia). The gene sequences were analyzed using Basic Local Alignment Search Tool (BLAST)  (Altschul et al. , 1997). To identify different species, phylogenetic trees were constructed using MEGA 7 with the 1,000× bootstrap test. The results of BLAST Homology were deposited to the DNA Data Bank of Japan (DDBJ, www. ddbj. nig. ac. jp) in order to obtain the accession number.
Antiviral activity was observed using Olympus model CX23LEDRFS1 Optical Microcope and cells were tested using the neutral red dye method. The protection of the extract from virus-infected cells was expressed by 50% effective antiviral extract concentration (EC 50 ). OD was measured at 540 nm. The OD was related directly to the percentage of viable cells, which was inversely related to the cytopathic effect. EC 50 values were determined as the percentage of cell protection (%P): ((OD t virus -OD c virus)/(OD c MOCK -OD c virus)) × 100. OD c and OD t were the OD values of the virus control and test sample, OD c MOCK was the OD of mock-infected control  (Langlois et al. , 1986). Interpretation of the data was presented using a system of linear regression equations.
ACKNOWLEDGEMENTS The current review covers a very broad topic, and the authors apologize to the colleagues whose work was not mentioned or cited in this paper because of space constraints and the limited number of references that are allowed. The work from the authors' laboratories described in this review article was supported by the intramural program of the  NIAAA (Bin Gao ),  U01 AA022614 , and  R01 DK099205  ( Tatiana Kisseleva ) and  AA011576  and  AA017729  ( Gyongyi Szabo ).