dataesr/scientific-paragraphs-categorization huggingface dataset:
In sum we used empirical data to complete the parametrization of Eqs. 1 and 2, and then compared predicted scaling exponents with the empirical exponents obtained by phylogenetic generalized least squares regressions and quantile regressions for the abundance of feather mites across bird species, following  Hechinger (2013). We show, using a large dataset on feather mite abundance, how a biologically-informed parametrization of the metabolic theory of ecology proposed by Hechinger (  2013 ) is a powerful approach to understanding why symbiont abundance differs between host species.
Manecorro Ringing Station  ( ICTS-RBD-CSIC ) received economic support from the  Junta de Andalucía. We thank  Doñana National Park  for the authorisations and support during the preparation of the ringing programmes. We thank all the ringers that help us with the measurements of the primaries at  Manecorro Ringing Station. JD was supported by the  European Commission  ( H2020-MSCA-IF-2019 ,  INTROSYM : 886532 ). SFG was supported by the  Spanish Ministry of Economy and Competitiveness  ( CGL2010-15734/BOS ). LZG was supported by a found from the  National Research, Development, and Innovation Office -NKFIH  ( K-135841 ). OG was supported by the  Spanish Ministry of Economy  (Ref. CGL2014-56041-JIN ). ML was supported by  "la Caixa" Foundation  (grant  ID100010434 ). HP was supported by a  Natural Sciences and Engineering Research Council of Canada (NSERC)   Discovery Grant.
of records reported by Doña et al. (2016)  for each mite species in each bird species, using only the most reliable bird-mite associations (i. e. , those with quality score = 2; see Doña et al. , 2016  for more details). Feather mite abundance data Data were obtained from FeatherMites, the largest dataset available on feather mite abundances (see Díaz-Real et al. , 2014 for details), where, for each bird individual, the total number of vane-dwelling feather mites was counted (i. e. , without differentiating between mite species) on the 19 flight feathers (10 primaries, six secondaries, and three (Supporting Information). To do so, we gathered data from adult female morphology because they are typically the largest (e. g. Atyeo & Braasch 1966; Santana 1976 ) and more abundant life stage (e.
Since we could not find data on the morphology of certain feather mite species in our dataset, some bird species were not included in the analyses, leading to a final dataset of 26,604 individual birds from 106 passerine species. Given the non-normal frequency distribution of feather mite abundance (Díaz- Real et al. , 2014) , we used quantiles of mite counts at regular intervals from the 5 th (Q5).
Moreover, culls can comprise a single component of multifaceted management plans. Co-occurring interventions, such as vaccination of humans and domestic animals or food provisioning of wildlife reservoirs, can obscure how culling affects crossspecies transmission risk or transmission dynamics in the reservoir  (10). Last, data collected alongside culls are generally restricted to incidence of infection. The growing capacity for pathogen genomic sequencing could provide direct insights into the spatiotemporal responses of hosts and pathogens to culls, but sequencing has rarely, if ever, been carried out alongside culls. Joint ecological and evolutionary approaches that could derive mechanistic explanations and actionable insights from what are effectively uncontrolled natural experiments would remove a bottleneck to evidence-based management.
Specifically, after confirming that culling reduced bat population size using questionnaire data describing bat attacks on livestock, we tested whether culling increased or decreased spillover to livestock using Bayesian state-space models (SSMs) that incorporated culling effort alongside other covariates describing the spatiotemporal dynamics of rabies and livestock vaccination effort. SSMs are particularly suited for this challenge because they jointly model biological and observation processes, enabling probabilistic inference of intervention efficacy, underlying natural variation, and potential confounders  (18, 19). We next quantified how culling affected the spatial spread of rabies within vampire bat populations using whole genome sequencing of rabies viruses and Bayesian phylogeographic analysis.
Beyond bat culling, the program included investments in livestock vaccination and education of farmers and local authorities on rabies risk and management. We first evaluated how culls affected bat population size, using the intensity of bat bites on livestock as a proxy  (23). Specifically, we used data from questionnaires on bat bites carried out in the 33 culled districts of Apurimac (mean, 662 farms surveyed per month; range, 355 to 860). A binomial generalized linear model (GLM) and a binomial generalized linear mixed model (GLMM) showed that both the percentage of animals bitten per farm (GLMM: slope = -0. 053; SD = 0. 0009; P < 2 × 10 -16 ) and the percentage of farms reporting bat bites (GLM: slope = -0. 027; SD = 0. 002; P < 2 × 10 -16 ) decreased during the culling period (Fig. 2 ).
Livestock rabies data The monthly number of rabies outbreaks in livestock and the monthly number of livestock vaccinated against rabies in the study area from 2003 to October 2019 (200 months) were provided by the National Service of Agrarian Health of Peru (SENASA). Briefly, SENASA responds to reports of animal morbidity and mortality from livestock owners or local veterinarians. When clinical signs are consistent with rabies (e. g. , ataxia, hind limb paralysis, and seizure), a brain sample is collected and tested for rabies using the fluorescent antibody test at the national headquarters in Lima, Peru. Over the course of our study, SENASA implemented no major changes in rabies surveillance, and an earlier analysis of nonrabies pathogens in the same livestock populations found no systematic increase in animal health surveillance effort since an initial rise, coinciding with the implementation of the current surveillance system (2003 to 2008)  (27).
Our dataset described the date and GPS locations of 1029 outbreaks in the AAC area. Three outbreaks from northern Cusco (see Fig. 1A ) were excluded because of the prior knowledge that these were caused by a distinct lineage of vampire bat rabies that was believed not to circulate elsewhere in AAC  (20). Brains from laboratory confirmed rabies positive livestock were provided to this study for sequencing (data S1). SENASA also recorded the characteristics of farms that had suspected or confirmed rabies outbreaks. Most AAC properties with livestock were family farms (76%), with mixed production of meat and dairy and a small number of animals (median, 11 animals; SD = 33. 2). Farms with exclusive production of meat or dairy were rare, 6% and 0. 6%, respectively, and showed no clear changes in frequency by year (binomial GLM, P > 0.
Bat bite questionnaires To approximate changes in bat abundance throughout the culling period, 14 technicians from the culling program recorded the number of domestic livestock with visible evidence of vampire bat bites. Bites are easily recognized by their size, shape, and prolonged bleeding from anticoagulants in bat saliva. Each technician was trained at the beginning of the project to identify bat bites, to use GPS to record property locations, and to record animal bite data. Technicians were assigned a fixed number of districts and a target number of properties to survey monthly. Paper records of data were reviewed and validated by SENASA and were digitized for this study. During visits, technicians actively counted the number of bitten animals with assistance from property owners. This effort varied across the 33 evaluated districts [mean, 22 (range, 1 to 91) properties evaluated per district per month; mean, 310 (range, 6 to 1491) individual animals evaluated per district per month].
Culling data All culling activities were carried out by the Regional Government of Apurimac under the authority of regional resolution number 368-2018 GR. APURIMAC. GR. For each cull, bats were captured by setting mist nets (6 or 12 m by 2. 6 m) outside of known vampire bat roosts (25. 9% of capture events) or at properties where bats were reported to have recently attacked livestock (74. 1% of capture events, fig. S6 ). For captures at foraging locations, nets were installed at ground level at 6:00 p. m. and opened from the time of darkness until dawn. The number and size of nets used varied according to the orientation and accessibility of livestock corrals. Captures avoided nights around the full moon and nights with rain or heavy winds. Captures at roosts were carried out diurnally with bats encouraged to leave roosts by entering roosts with butterfly nets or using smoke.
Priors and model fit Unless specified above, the remaining coefficients were drawn from Gaussian priors with mean of 0 and variance of 100. The exception was the two neighborhood parameters in the zero-inflated process that were given an exponential prior distribution with mean of 0. 5 to preserve their positivity. The models were fit in JAGS 4. 3 [using R package "rjags";  (45) ] for 50,000 iterations with the first 25,000 interactions discarded as burn-in and retaining every 10th iteration. Convergence across three independent chains was assessed through visual inspection of the posterior chains and a Gelman-Rubin diagnostic test [in R package "coda";  (46) ].
Multiplex whole genome amplification, library preparation, and sequencing of rabies viruses RNA was extracted from brain samples collected between 2013 and 2016 using TRIzol, according to the manufacturer's instructions and was shipped on dry ice to the MRC-University of Glasgow Centre for Virus Research in the United Kingdom. Whole genome sequencing was also carried out using banked RNA extracted from a subset of older samples (1997 to 2012) for which N and/or G-L intergenic regions were previously described  (20). Multiplex primers were constructed with primalscheme (44) using complete VBR genomes from South America available on GenBank (accession numbers AB519642, KM594041, and EU293113) and "hybrid" genomes generated by swapping out the nucleoprotein gene from public genomes for representative nucleoprotein gene sequences from each circulating rabies lineage in Peru.
Phylogenetic analyses Nucleoprotein gene analysis To classify viral lineages circulating in AAC, a phylogenetic analysis was carried out using 319 sequences generated here, along with representative VBR nucleoprotein gene sequences available from GenBank (n = 316). Sequences were aligned in Multiple Alignment using Fast Fourier Transform (MAFFT) and analyzed in BEAST v. 1. 10. 4 using the Bayesian skyline demographic model, the relaxed lognormal molecular clock, and separate General Time Reversible (GTR) + gamma substitution models for codon positions 1 + 2 versus codon position 3. Tip dates were converted to decimal form where information on the month and day of collection was available. When only collection year was available (primarily for reference sequences), we assigned 1 year of uncertainty in the collection date.
Whole genome sequences Phylogeographic analyses were carried out on whole genome sequences of L3 viruses detected in AAC between January 1997 and January 2016. This analysis excluded sequences with missing spatial or temporal metadata (n = 6), sequences that comprised a clade of long branches basal to the remaining monophyletic clade of L3 sequences (n = 5), and five sequences with possible sequencing and/or metadata errors (residuals of relationship between temporal and evolutionary divergence from the estimated root > 0. 01), creating a final alignment of 290 rabies virus genomes (data S1). TempEst v. 1. 5. 3 analysis of a maximum likelihood tree built from this alignment in iqTree found strong evidence of clock-like evolution (slope = 3. 16 × 10 -4 , R 2 [coefficient of determination] = 0.
Evolutionary analysis of drivers of viral dispersal We used the posterior set of spatiotemporally annotated phylogenies from BEAST to analyze how culling, along with other environmental and spatiotemporal factors, affected the speed of viral spatial spread. We first used the R package Seraphim to extract the inferred locations and times of each branch in each of 1000 randomly sampled, post-burn-in trees  (53). We then used the distance traveled and time-elapsed to infer branch specific velocities. Existing software to evaluate the environmental effects on phylogenetic branch velocities is now unable to accommodate explanatory variables that vary throughout the history of the tree (e. g. , variable culling effort through time) or to separate effects at the origin versus destination of dispersal events.
We measured culling at the district level as a spatiotemporally explicit binary variable. Additional explanatory variables hypothesized to affect viral dispersal velocity included livestock density (sum of cows, sheep, goats, and pigs from Food and Agriculture Organization's Gridded Livestock of the World) and landscape ruggedness ("tri" in the terrain function of the raster package in R), each calculated within a 10-km radius of each inferred branch origin or destination. To investigate systematic changes in viral velocity during the epizootic, we also included the temporal and geographic distance between each branch origin and the inferred MRCA of its associated tree. Because greater local sampling intensity might facilitate detection of rare long distance viral dispersals, we included the average number of suspected or confirmed rabies outbreaks per year within 10 km of each branch origin or destination during the years when rabies was locally present as a proxy for sampling intensity.
Acknowledgments: We are grateful to the  National Service of Agrarian Health (SENASA) of Peru  for providing datasets on rabies incidence and vaccination in livestock and for allowing access to rabies positive samples for whole genome sequencing. We thank  E. Marquez  for assistance with digitalization of field records. We thank the  Streicker Lab  and three anonymous reviewers for helpful feedback on earlier versions of this manuscript. Funding: This work was supported by  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Programme , grant  852957  (M. V. );  Wellcome Trust   Senior Research Fellowship , grant  217221/Z/19/Z  (A. B. , J. B. , and D. G. S. );  Sir Henry Dale Fellowship , jointly funded by the  Wellcome Trust and Royal Society , grant  102507/Z/13/Z  (J.
Funding The authors are supported by an " Investissement d'Avenir " grant from the  Agence Nationale de la Recherche  ( CEBA ANR-10-LABX-2501 ). JFG, CC, and MEB were supported by the joint  National Science Foundation -National Institutes of Health -National Institute of Food and Agriculture (NSF-NIH-NIFA)   Ecology and Evolution of Infectious Disease program  ( DEB 1911457 ), BdT by  European Regional Development Fund (RESERVOIRS)  funding, and PLT benefited from funding from  Agence nationale de  recherches sur le sida et les hépatites virales -maladies infectieuses émergentes (ANRS-MIE). Funding sources had no involvement in the preparation and writing of the article, data interpretation, and developed ideas nor in the decision to submit the article for publication.
scRNA-seq analysis Raw sequence reads were analyzed using cellranger 6. 1. 2 (10× Genomics) with the human GRCh38 reference genome and the corresponding GTF file. The output matrices from cellranger were analyzed using Seurat 4. 0 (Details in Supporting information). Briefly, integrated Seurat objects were normalized and clustered. Differentially expressed genes (DEGs) were identified using Seurat:: FindMarkers. Enriched biological processes were extracted using clusterProfiler::enrichGO. For gene set enrichment analysis (GSEA), genes used for the GO enrichment analysis were selected and biological processes were extracted using clusterProfiler::gseGO; the top two signatures were visualized using clusterProfiler::gseaplot. Module scores were calculated with the gene list from the GSEA molecular signature datasets, or selected genes presented in Table  S4.
Statistics Statistical analyses were performed using R/Bioconductor (R 4. 2. 1/Bioconductor 3. 16; Table  S6 ), GraphPad Prism (8. 3. 0; GraphPad Software, San Diego, CA, USA) or SPICE). Groups were compared using the nonparametric Mann-Whitney U test. P < 0. 05 was considered to indicate significance. For correlation analysis, correlation coefficients and P-values were calculated using Spearman's correlation test. For the polyfunctionality analysis, Pvalues were calculated using SPICE software.
B A FIGURE 4 Quantitative differences in HBV-specific CD8 + T cells depending on the targeted antigen. (A) Relative frequency of HBV-specific CD8 + T cell subpopulations showing each function. P-values were calculated using the Wilcoxon matched-pair signed rank test and analyzed using SPICE software. Three gating strategies: "MFI high", "MFI low", and "total" used to calculate the frequencies are labeled in the header as "high", "low", and "total" respectively. (B) Frequencies of polyfunctional HBV-specific cells positive for each marker against the indicated antigens. P-values were calculated using the permutation test and analyzed using SPICE software. Statistical significance is indicated by asterisks based on P-values (*P<0. 05, **P<0. 01), ns, not significant. Studies on HBs-transgenic mice have shown that HBV replication is inhibited by HBs-specific CD8 + T cells, even though HBs-specific CD8 + T cells have no harmful activity on infected cells  (26).
Funding The authors declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the  Japan Society for the Promotion of Science   Grant-in-Aid for Scientific Research (B)  (grant number  20H03728 ),  Scientific Research (C)  (grant number  JP20K06405 ), and the  Japan Agency for Medical Research and Development  (grant numbers  21fk0210057 ,  21fk0410040 , and  22fk0310516 ).
A Phase 1 study in humans with a primary safety outcome and a secondary pharmacokinetics outcome (https://clinicaltrials. gov, ID: NCT02022306) showed no severe adverse events either with single or multiple doses. Pharmacokinetic modelling showed that these concentrations can be reached in the plasma of subjects receiving short courses of TD-6450. This physiologically relevant screen identified multiple mechanisms of action, and safe drugs with strong potential as malaria transmission-blocking agents which could be rapidly tested in clinical trials. Between 2000 and 2015, malaria incidence and mortality decreased by 27% and 50%, respectively. The decline in incidence stopped between 2015 and 2019, and in 2020 there was even a worrying re-increase of both incidence and mortality; with 241 million of malaria-related cases and 627,000 deaths 1.
Pharmacokinetics of TD-6450 TD-6450 was discovered and developed for the treatment of Hepatitis C virus infection by Theravance Biopharma Inc, but was stopped after phase II for strategic reasons. The first phase I clinical trial was completed in 2014 (clinical trials ID: NCT02022306). The primary objective of this study was to evaluate the safety and tolerability of single ascending dose (SAD) and multiple ascending dose (MAD) in healthy subjects (Table  2 ). The secondary objective was to determine the pharmacokinetics (PK) of TD-6450 in healthy subjects for both SAD and MAD. The PK data obtained from this phase I study were used to make a concentration-time modeling. Goodness-of-fit was high (Fig. 6A ) with data from the single ascending dose (SAD) and multiple ascending dose (MAD) studies performed in healthy volunteers (Fig.
Hits were either confirmed by DRA (Blue X), or not (blue empty triangles). Source data are provided as a Source Data file. decrease in relative bioavailability from doses of 240 mg to 500 mg (i. e. 24. 7% decrease per 100 mg increase in dose, above 240 mg); dose on elimination clearance implemented as a saturation function reaching full saturation at the higher dose; duration of treatment on relative bioavailability implemented as a saturation function reaching full saturation at steady-state dosing; food intake on relative bioavailability, implemented as a categorical covariate effect (i. e. , 68. 5% higher bioavailability when administered with food); and food intake on absorption rate implemented as a categorical covariate effect (i. e. 93. 3% longer absorption time when administered with food) (Table  3 ).
Statistical analysis and Z' quantification From the analysis of imaging plates, the percentage of gametocyte infection was calculated based on the number of gametocytes divided by the number of total cells. This was done for upstream (UP) and downstream (DS) samples. Killing was calculated using the following formula: Killing rate = 100 À ðð%UP * 100Þ=Average % UP negative controlsÞÞ ð1Þ Retention rate was calculated as previously described  25  with the following formula: Retention rate = 100 * ðð%UP À %DSÞ=%UPÞ ð 2Þ Killing (x axis) and retention (y axis) rates were then graphed. A regression line with a 95% prediction band was calculated using GraphPad Prism 5. 1 software. Outliers were excluded from linear regression calculation. Only those compounds that fell outside the upper prediction band in both readouts were finally selected as hits.
Dose-response analysis and IC 50 determination Dose-response analyses (DRA) were done for killing effect and stiffening activity for each selected hit as defined above. Results from OperaPhenix were graphed with the x axis representing the compound concentration and the y axis the killing or retention rate. Using GraphPad 5. 1 Prism software (GraphPad Software, San Diego, CA, USA), the results were transformed into logarithmic form and a log(agonist) vs response curve was tracked (variable slope). The concentration at which 50% of killing or retention is inhibited (IC 50 ) was calculated by the software from the equation of the curve: y = B + ðT À BÞ=ð1 + 10 ðLogIC50ÀxÞ * γÞ ð4Þ where B is bottom value, T is top value and ɣ is the hillslope.
Up and downstream samples were incubated for 30 min with Hoechst 33342 nuclear staining (1/1000 diluted in PBS, Thermo Fischer cat. no. H3570), washed, resuspended in 200 µL of PBS and then loaded into an empty 96-well plate before FACSCanto (BD Biosciences, Franklin Lakes, New Jersey, USA) quantification. Data from cytofluorimetry were analyzed with FlowJo V12. As per previously described experiences with this technique, to avoid the risk of microbead layers saturation, a microsphiltration experiment was repeated when the upstream gametocytemia exceeded 10%  25, 69.
Blinded safety, tolerability, and available PK data after each dose cohort were reviewed by the Safety Data Review Committee (SDRC) before escalation to the next dosing cohort for both part A and B. Subjects of the clinical study were randomly assigned to each cohort. Randomizations codes were assigned to the subjects. All study subjects, study investigators, and the sponsor's staff involved in the conduct of the study were blinded to treatment assignment. The only personnel who had access to the randomization code before database lock were: The pharmacist or pharmacy staff member who prepared drug or placebo capsules for administration to the subject according to the randomization code and the bioanalytical contract laboratory that analyzed the PK samples (plasma and urine). Study drug for each subject was appropriately labeled to facilitate the correct administration to the assigned subject.