dataesr/scientific-paragraphs-categorization huggingface dataset:
| Trichomonas gallinae sequence identity and method validation The seventh sequence was found in four turtle doves from Senegal, representing a new strain with 99. 64% similarity to a T. tenax isolate (A/N KX061780) and is hereafter named Ttl-TD (A/N MT720718). Six Fe-hyd variants were identified, two of which were identical to sequences in GenBank (Figure  2 ).
| Haemosporidia prevalence and method validation We detected nine haemosporidian lineages, representing Haemoproteus sp. (n = 3) and Leucocytozoon sp. (n = 6) (Table  3 ); no Plasmodium infections were detected. All three Haemoproteus sp. lineages matched sequences on GenBank with 100% cover and 100% identity (Table  3 ), including those previously found in nestling turtle doves in the UK  (Dunn, Stockdale, et al. , 2017) ; one Haemoproteus lineage was amplified using the Leucocytozoon primer set, and also matched MalAvi strain STRORI01. The six Leucocytozoon sp. lineages matched four strains on the MalAvi database in addition to sequences on GenBank (Table  3 ). All of these Leucocytozoon lineages -including two lineages novel in this system -had been previously reported from nestling turtle doves in the UK  (Dunn, Stockdale et al.
F I G U R E 1 Maximum clade credibility tree of the ITS ribosomal region of all unique Trichomonas gallinae sequences from GenBank, using Bayesian inferenced in BEAST. Sequences isolated from turtle doves in our study are marked with a *; all other sequences are labelled with an example GenBank accession number and the full list of sequences analysed is provided in Appendix S2; sequences isolated from turtle doves in our and other studies are emboldened. Also labelled are the hosts and location from which each sequence has been isolated. If a sequence was isolated from more than one species within a family, or more than one country within a continent, the sequence is labelled with that family or continent; if a sequence was isolated from only one species within a family, or only one country within a continent, the sequence is labelled with the species and or country.
| Coinfections Coinfection by T. gallinae, Haemoproteus sp. and Leucocytozoon sp. , was examined in 49 adult turtle doves from the UK between F I G U R E 2 Maximum clade credibility tree of the Fe-hyd ribosomal region of all unique Trichomonas gallinae sequences from GenBank, using Bayesian inference in BEAST. Sequences isolated from turtle doves in our study are marked with a *; all other sequences are labelled with an example GenBank accession number and the full list of sequences analysed is provided in Appendix S3; sequences isolated from turtle doves in our and other studies are emboldened. Also labelled are the hosts and location from which each sequence has been isolated. If a sequence was isolated from more than one species within a family, or more than one country within a continent, the sequence is labelled with that family or continent; if a sequence was isolated from only one species within a family, or only one country within a continent, the sequence is labelled with the species and or country.
Branch reliability is provided as a proportion of 1000 bootstrap replicates TA B L E 3 Haemoparasite lineages detected as part of this study, with their closest matches on MalAvi and GenBank databases. All sequences had 100% overlap with their closest match on GenBank. Where MalAvi match and identity = NA, the amplified region does not overlap with the MalAvi barcode region from the UK population only. Both positive and negative associations were found between coinfecting parasite strains (Table  6b ; Appendix S4). Negative associations were found between T. gallinae strains type A, GEO and type C, but positive associations were found between T. gallinae type A and Leucocytozoon LA-TD, and between T. gallinae type C and both Leucocytozoon LB-TD and LE-TD (Figure  5 ).
TA B L E 4 The number of turtle dove samples tested and found positive for Trichomonas gallinae infection in each population and year, along with the number of samples successfully sequenced at ITS and Fe-hyd regions. Birds from Burkina Faso were caught during the winter of 2012-2013 so the data are combined; UK samples from 2011 to 2012 are not included here as they are previously published elsewhere A high prevalence of haemosporidian infection was also found in nestling turtle doves from the populations in which we sampled adults  (Dunn, Stockdale, et al. , 2017). A 92% prevalence of T. gallinae infection has also been reported from turtle doves in Spain, Italy and Germany  (Marx et al. , 2017). For T. gallinae, all HTS sequences matched with Sanger sequences where samples were sequenced using both methods, and HTS repeatability was 100% which suggests that HTS, when combined with the pipeline that we used, is a reliable method to detect and identify T.
to determine the closest sequence match. Poor quality sequences (less than 180 bp for ITS region, double peaks throughout the length of the chromatogram or a sequence failing in one direction) were removed from further analysis. MiSeq sequences were demultiplexed into sample files according to Fi5 and Ri7 indexes by the Illumina miseq control software (v2. 5. 0. 5). trimmomatic v0. 36 (Bolger et al. , 2014) was used to remove Illumina adapter sequences, low quality bases in the leading or trailing ends and low quality sequences that did not meet the minimum Phred quality score of 20 or the minimum length of 100 bp. Paired end reads were aligned using flash 1. 2. 11 (Magoč & Salzberg, 2011);.
Fe-hyd sequences separately, all available unique ITS (n = 19) and Fe-hyd (n = 39) records were downloaded from GenBank (by using the search terms 'Trichomonas gallinae ITS' and "Trichomonas gallinae Fe hydrogenase or Fe-hydrogenase or iron hydrogenase"). All downloaded sequences including duplicate strains, along with host species and locations for each are given in Appendices S2 (ITS) and S3(Fe-hyd). Strain nomenclature was adopted from Chi et al. (2013) , or the reporting authors for more recently discovered types. Strains were aligned and trimmed to 224 bp (ITS) or 591 bp (Fe-hyd). We included T. vaginalis as an outgroup in both trees; Tetratrichomonas gallinarum was also included in the ITS tree, but no Fe-hyd sequence was available.
Development and implementation of the high throughput sequencing protocols were supported by the Natural Environment Research Council (NERC), National Biomolecular Analysis Facility at the University of Sheffield, under NBAF grant to SG (NBAF873). Sample collection from Senegal in 2014 and Burkina Faso in 2012/13 was funded by grants from the British Ornithologists' Union and the African Bird Club to JCD. Samples were imported to the UK under Defra Import Licences PATH/201/2012/1, PATH/201/2012/2 and PATH/15/0482. We would like to acknowledge all the RSPB staff, volunteers and students from the University of Leeds for their contribution towards data collection. Sample collection in the UK was carried out under licence from the Home Office, with bird ringing carried out under licence from the British Trust for Ornithology.
Morris and Simon J. Goodman designed the study, Rebecca C. Thomas, Jenny C. Dunn, Chris Orsman, John Mallord, Cyril Eraud, and Lormée Hervé collected field data, Rebecca C. Thomas, Deborah A. Dawson, Helen Hipperson and Gavin J. Horsburgh contributed to laboratory analysis. Rebecca C. Thomas and Jenny C. Dunn carried out phylogenetic and statistical analyses, and all authors contributed towards writing and editing the manuscript. B EN EFIT-S H A R I N G S TATEM ENTA research collaboration was developed with scientists from France, with all collaborators included as coauthors or in the acknowledgements, depending on their level of input. The results of the research have been shared with the broader scientific community through appropriate public databases and data repositories (see above). The research addresses a primary concern, in this case the conservation of the turtle dove.
DATA AVA I L A B I L I T Y S TAT E M E N T Trichomonas gallinae sequence data are available on GenBank under Accession Numbers MN587086-MN587101, MT418239-MT418250 and MT720718. Data from NGS sequencing runs and metadata are available from the Sequence Read Archive (SRA) under Bioproject accession number PRJNA578480; file accession numbers SRR16955911-967 (Trichomonas gallinae).
Lineage (this study) Parasite genus MalAvi GenBank match % identity match % identity N Citation HB-TD Haemoproteus NA NA AB741490 100 12 Yoshimura et al. (2014) KX832608 100 Dunn, Stockdale et al. , 2017 KX832606 100 Dunn, Stockdale et al. , 2017 KX832604 100 Dunn, Stockdale et al. , 2017 KX832570 100 Dunn, Stockdale et al. , 2017 KX832567 100 Dunn, Stockdale et al. , 2017 HC-TD Haemoproteus NA NA KX832602 100 10 Dunn, Stockdale et al. , 2017 HD-TD Haemoproteus STRORI01 100 KX832569 100 1 Dunn, Stockdale et al. , 2017 KX832568 100 Dunn, Stockdale et al. , 2017 LC428005 100 K. Tanaka, D. Sumiyama, T. Kanazawa, Y. Sato and K. Murata (unpublished data) LA-TD Leucocytozoon AEMO02 100 KX832556 100 2 Dunn, Stockdale et al. , 2017 KX832555 100 Dunn, Stockdale et al. , 2017 KT779209 100 Y. L. Huang, S.
Climatic niche breadth, climatic niche position and geographic range size Estimating species realised climatic niches (i. e. the set of suitable environmental conditions accessible to the species and constrained by biotic interactions  (Jackson and Overpeck, 2000)  requires data for the full geographical range of species together with the corresponding environmental variables  (Guisan et al. , 2017). We used IUCN range maps (https://www. iucnredlist. org/) as an estimate of the geographic range of species. Environmental information was obtained in the form of climatic layers for 19 bioclimatic variables (https://www. worldclim. org/) at a 10 min resolution (approximately 340 km 2 at the equator). To estimate species climatic niches, we first performed a PCA on the 19 bioclimatic variables from which we extracted the two first axes (explaining 55% and 19% of the total variance, respectively) to construct a two-dimensional space representing the climatic conditions available on Earth.
For each species, we then selected the environmental values that were associated with its IUCN polygon within the two-dimensional space and used a kernel density estimator (KDE; see Supplementary Data S1) to delineate an envelope representing an estimate of the climatic niche of species (Fig. 1 ;  Broennimann et al. , 2012). Once the niche was defined, we extracted its area (i. e. an estimate of niche breadth) and computed its centroid (i. e. an estimate of the optimal conditions for the species) along the two PCA axes as the mean of coordinates falling inside the delimited envelope (Fig. 1 ). The area of the geographic range was directly extracted from IUCN polygons. To test the robustness of our results, the analyses presented below were repeated with full and facultative migrants excluded (owing to the difficulty in characterising the climatic niche of migratory species, Supplementary Fig.
Infection prevalence among avian phylogenetic trees To illustrate the effects of host evolutionary history on singleand co-infection probabilities, we followed  Barrow et al. (2019). Specifically, we used 1000 trees from BirdTree. org (backbone tree,  Hackett et al. , 2008)  to generate a consensus tree using the ls. consensus function (package phytools v. 0. 6-00, Revell, 2020, Phylogenetic tools for comparative biology). The prevalence of both singleand co-infections, as well as the infection prevalence of each haemosporidian genus separately, was calculated from the raw data for each species and mapped along the phylogenetic tree using the contMap function (package phytools, Fig. 2 , Supplementary Figs. S6-S7 ). Because the accuracy of prevalence estimates is lower with small sample sizes  (Jovani and Tella, 2006) , we visualised single-and co-infection patterns across the avian phylogeny using species for which there were at least five samples (1219 samples from 71 species belonging to 35 families from 15 orders).
81) and climatic niche breadth (q = 0. 76), respectively. OA1 and the geographic range size were thus removed from the analyses. However, we also tested the effect of these variables in separate models where they replaced the variables they were correlated with (Supplementary Figs. S8-S9 ). We built two types of phylogenetic multilevel models using Bayesian inference from the brms package  (Bürkner, 2017). First, we used a phylogenetic multinomial model with a ''Categorical" error distribution to study the effects of species attributes and phylogeny on the probability of being single-or co-infected. We chose this distribution because the dependent variable, the infection status, contained three modalities: uninfected (coded 0), singleinfected (coded 1) and co-infected (coded 2). The status 'uninfected' was set as the reference category.
We used the default priors of the brms package and ran three chains with 11,000 iterations. The first 1,000 iterations were considered as burn-in and were thus discarded. Chains were thinned every 10 iterations. To account for phylogenetic uncertainty, we randomly sampled 100 trees from the set of trees extracted from BirdTree. org and ran the analyses on each tree. We then combined all models using the combine_models function. Note that this approach critically depends on the assumption that all trees are equally likely. We verified that each parameter in the model converged by checking that its potential scale reduction factor was below 1. 1  (Gelman and Rubin, 1992). We followed the methods of  Barrow et al. (2019)  to com-pute the phylogenetic signal (denoted k) regarding single-and co-infection probabilities estimated from the multinomial model, as well as for infection probability by each genus as estimated from the three generalised linear models.
We emphasise that the nature of the dataset, but also some methodological choices and assumptions, may have impacted our results, although we do not think that they compromise our main conclusions. First, sampling was carried out on salvaged birds for which the cause of death was not always known. A substantial proportion of birds were found dead near human habitations or traffic lanes, suggesting that death could be the results of collisions with cars or windows. Another portion of the samples comes from a rescue center (Vaux-lierre, 1163 Etoy, Switzerland) that collects injured birds (e. g. hunting accident, collision) and brings their corpses to the museums when the individuals cannot be saved. If the animals died as a result of an accident, it is unlikely that infection with haemosporidian parasites was the cause of death.
A possibility to control this bias could be to estimate the prevalence of haemosporidian parasites in selected natural populations for a subset of species, and use a statistical test (e. g. t-test) to check if the number of samples we have considered is sufficient to provide a correct estimation of infection prevalence (a somewhat a posteriori power analysis that is classically used to estimate the minimum sample size required for an experiment, given a desired significance level, effect size, and statistical power). Despite this drawback, museum collections are invaluable because they give access to rare, protected species or those that are difficult to sample in the wild. For instance, our dataset contains 36 species for which there is currently no information on infection status in the MalAvi database (https://mbio-serv2.
Acknowledgements We would like to thank  Jérôme Wassef  for his help in preparing the tissue samples and  Elie Morrin  for his help in using QGIS. This project was funded by the  Swiss National Science Foundation (SNSF) , grants  31003A_159600  and  31003A_179378  to PC and  CRSK-3_190197  to RP. We also thank anonymous reviewers for their insightful comments that improved the quality of our manuscript.
Image data analysis Image analysis was performed at the Cochin Imaging Facility (Institut Cochin, Paris). A 3D image analysis was performed on x, y, and z planes using Imaris 7. 4 (Bitplane AG). First, superficial planes from top of the slice to 15 mm in depth were removed to exclude T cells located near the cut surface. Cellular speed means were calculated using Imaris. Tracks >10% of the total recording time were included in the analysis. To reveal the relationship between CD8 + T cell motility and the tumor structure (tumor islets and stroma), confocal time-lapse images of T cells were superimposed onto the corresponding gp38 and EpCAM images. CD8 + T cells localized in the stroma were distinguished from those infiltrated in tumor cell nests by looking at individual planes along the z axis.
Videos and images were made by compressing the z information into a single image using Imaris. When a drift in the x, y dimension was noticed, it was corrected using the "Correct 3D Drift" plug-in in Image J. For the automated detection of resident CD8 + T cells in different tumor areas (stroma or tumor islets), we used the Image J software. First, fluorescent images threshold was obtained and converted to binary images. Angles between the cell trajectory vectors, which are the connecting lines between starting points and end points of each track, and tumor-stroma boundaries were calculated using Image J software. Only the cells positioned within a maximum distance of 100 mm from the tumor-stroma interfaces were included in further analysis.
Statistical analysis Statistical analyses were performed using GraphPad Prism version 7. 0 (GraphPad Software). Data was analyzed by means of a Student unpaired t test (comparison of 2 experimental groups), One-way analyses of variance (ANOVA) test (for comparing more than 2 experimental groups) or Two-way ANOVA test (composition of more than 2 variables). Results are expressed as means ± SEM and were considered statistically significant when p< 0. 05.
work was supported in part by the Intramural Research Program of the Center for Cancer Research, National Cancer Institute (NCI), Cancer Innovation Laboratory (CIL) USA, under grant No. 1ZIABC009283-36. The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the United States Government.
Acknowledgments The authors thank  Diego Luti ,  Victoria Blanco ,  Cecilia Noriega ,  Dr. Soledad Miro ,  Sergio Oms  and  Dr. Ivanna Novotny , for animal care. Dr. Pilar Crespo  and  Dr. Paula Abadie  for FACS technical support. Dr. Laura Gatica ,  Lic. Gabriela Furlan  and  Dr. Noelia Maldonado  for cell culture support and  Paula Icely  for overall experimental technical assistance. We acknowledge the  NIH Tetramer Core Facility  for provision of the PE and APC-labeled SIINFEKL/ Kb tetramers.
Stimulated PBMCs were incubated with supernatants from Vero cells infected with Me-Vac encoding IgG4, pembrolizumab, or nivolumab and stained with a PE-labeled antibody specific for human IgG-Fc. Data from three independent experiments were analyzed using one-way ANOVA with Tukey's multiplecomparisons test. (E) Functional assay for pembrolizumab and nivolumab. Human PBMCs were stimulated with SEB and incubated with supernatants from Vero cells infected with MeVac encoding pembrolizumab, nivolumab, or IgG4-Fc. After 40 and 70 h, IL-2 concentration in PBMC culture supernatants was determined using ELISA. Results were analyzed using two-way ANOVA with Tukey's multiplecomparisons test. However, murine tumors show limited permissiveness for measles virus, thus limiting the levels of antibody expression (Figures  1B, 1C,  and S2B ).
2 ng/mL; Heidelberg University Hospital Pharmacy, Heidelberg, Germany) as a competitor. Cell lysate from Vero-aHis cells infected with MeVac IgG1-Fc was used as negative control. Mouse splenocytes, MC38-CD46 cells, and Colo 205 cells incubated with the corresponding cell lysates were stained with a PE anti-HA tag antibody (clone GG8-1F3. 3. 1; Miltenyi Biotech) at a 1:50 dilution for 30 min at 4 C in the dark. DAPI (Sigma-Aldrich) was used as a viability dye. The samples and controls were acquired on a BD FACS-Canto II cytometer (BD). Data were analyzed using the BD FACSDiva software and FlowJo software.
(D) Binding of MeVac-encoded pembrolizumab or nivolumab to PD-1 + human immune cells. Peripheral blood mononuclear cells (PBMCs) from healthy donors were stimulated with PMA and ionomycin, which induces PD-1 expression. Stimulated PBMCs were incubated with supernatants from Vero cells infected with Me-Vac encoding IgG4, pembrolizumab, or nivolumab and stained with a PE-labeled antibody specific for human IgG-Fc. Data from three independent experiments were analyzed using one-way ANOVA with Tukey's multiplecomparisons test. (E) Functional assay for pembrolizumab and nivolumab. Human PBMCs were stimulated with SEB and incubated with supernatants from Vero cells infected with MeVac encoding pembrolizumab, nivolumab, or IgG4-Fc. After 40 and 70 h, IL-2 concentration in PBMC culture supernatants was determined using ELISA.
ACKNOWLEDGMENTS We acknowledge  S. Russell ,  R. Cattaneo ,  A. Jassowicz ,  C. Plass ,  S. Eichmüller , and  W. Osen  for providing cell lines. We thank  J. Albert ,  B. Hoyler , and  S. Sawall  for technical assistance. This work was funded by the  German National Science Foundation (Deutsche Forschungsgemeinschaft ,  DFG , grant  EN 1119/2-2  to C. E. E. ), the  Else Kröner-Fresenius-Stiftung  ( 2019_EKMS. 02  to C. E. E. ), and the  Wilhelm Sander-Stiftung  (grant  2018. 058. 1  to C. E. E. ). R. V. was supported by a scholarship from the  Heidelberg School of Oncology (HSO)  and by the  European Regional Development Fund (ERDF)  project no. 1. 1. 1. 2/VIAA/2/18/292. G. P. -M. received a scholarship from the  Helmholtz International Graduate School for Cancer Research.
However, no option is currently available to expand tumor specific T H 1 lymphocytes in most patients. We have used an optimized reverse immunology approach to identify four novel MHC class II-restricted peptides derived from hTERT referred as "Universal Cancer Peptides" (UCP). UCPVax is a therapeutic cancer vaccine composed of two separate peptides called UCP2 and UCP4 derived from telomerase  (12). This UCPVax vaccine is currently being evaluated in a multicenter phase I/II study in NSCLC (NCT2818426). Results from the phase I study indicated that UCPVax was safe, well tolerated, and immunogenic at the doses and schedule tested, and 1 mg of each peptide was recommended for phase II. UCPVax was immunogenic in 60% of NSCLC patients at least after three injections, with preliminary signals of clinical efficacy in this population.
Design VolATIL study is a multicenter phase II trial to evaluate the combination of atezolizumab and UCPVax in advanced HPV + cancer patients, refractory to at least one line of systemic chemotherapy. The study was developed by the "National Institute of Health and Medical Research (INSERM), Unit 1098" and "Clinical Investigational Center (CIC) 1431". The study is sponsored by the CHU of Besancon and supported by INCa (French National Cancer Institute) and ROCHE. The data management is undertaken by the "Methodology and Quality of Life Unit in Oncology"* from the University Hospital of Besancon. The trial is registered on the clinicaltrials. gov database (NCT03946358). The study has received a favorable opinion from the Ethic Committee "Sud-Est IV" and the authorization from the French National Agency for the Safety of Medicines and Health Products and will be conducted in accordance with the Declaration of Helsinki and the Good Clinical Practice.