dataesr/scientific-paragraphs-categorization huggingface dataset:
The publicly available G. ellingtonae genome assembly, GCA_001723225. 1  (Phillips et al. , 2017) , was used as the reference for nuclear genome variant determination. Mitochondrial variants were determined using the reference assembly KU726971 and KU726972, corresponding to the large-and small-circle mtDNA genome  (Phillips et al. , 2016). Quality filtered reads were mapped to the reference genome using BWA  (Li and Durbin, 2009)  prior to variant calling in Freebayes  (Garrison and Marth, 2012). The resulting variant call file was filtered using VCFtools  (Danecek et al. , 2011)  and BCFtools  (Li et al. , 2009)  to remove indels and retain only single and multiple nucleotide polymorphisms with minimum quality > 30 and per-sample read depth between 3 and 100. Variant calls were imported into the statistical software R (R Core Team, 2021) using the "vcfR"  (Knaus and Grünwald, 2017) , "poppr"  (Kamvar et al.
Additionally, individual G. ellingtonae J2 (n = 22) and cyst (n = 4) morphometric data collected in 2008 was obtained from Dr. Z. Handoo (USDA-ARS Beltsville, MD). Mean (minimum and maximum) G. ellingtonae morphological data from Argentina was also considered  (Lax et al. , 2014). A character phylogeny was constructed in Mesquite  (Maddison and Maddison, 2019 ) from the morphometric attributes for which complete data existed in all populations. When only a range of measurements was available for a given population, synthetic individuals were generated by randomly sampling characters within the measurement range.
Results Occurrence and population densities of Globodera sp. in Northern Chile Globodera sp. cysts were recovered from 9 out of 17 of the samples collected in 2017 from the Antofagasta Region. Cyst densities ranged from 2 to 50 cysts/250 g soil. These populations were collected from Socaire, Talabre, and Toconce. In 2020, only 2 samples out of 16 (13%) had cysts in the Arica y Parinacota Region. Population densities of these samples were 10 and 60 cysts/250 g soil; both samples were collected from Putre. The populations collected from the Arica y Parinacota Region were morphologically identified as G. rostochiensis (data not shown). This identification was further confirmed in the HSP90 phylogeny which showed distinct grouping of the Putre population with identified G. rostochiensis populations (Fig.
, 2019). Hence, in the IM, the dependent variable indicates whether (y = 1) or not (y = 0) a previously non-infected individual has become infected in a time interval. The DSM captures genetic variation in both susceptibility and recovery  (Bijma, 2020) , while the IM captures genetic variation in susceptibility only. Thus, a difference between results of both models suggests the presence of genetic variation in recovery. To address this issue, we will use longitudinal data on digital dermatitis (DD), an endemic infectious claw disease in cattle, collected in Dutch herds of dairy cattle. Our focus is on the differences in estimates of genetic parameters and breeding values for both traits, and on the interpretation of those differences. Hence, our aim is not to find a superior statistical modelling strategy, but to understand the biological meaning of the differences between an analysis of disease status versus an analysis of infection events (i.
| Data sampling The data used in this study were collected from November 2014 to April 2015. The cows were just moved indoors before the start of this observation period. Observations on the DD-status of the hind feet of individual cows were recorded during that period from 12 dairy farms from across the Netherlands, which previously had been reported endemic for DD with mean herd-level prevalence of at least 20%. The number of cows per farm ranged from 88 to 189, and DD status of cows was recorded (in principle) at 11 observation moments per farm (Table  1 ). These 11 observation moments correspond to 10 observation intervals, during which cows could become infected or recover. The 11 observation moments were spaced bi-weekly to accurately document the changes in DD-status over time. The disease status of both hind feet was recorded prior to milking in the milking parlour.
| Data restructuring and editing for the analysis of disease status For the analysis of disease status, we used all records on disease status available in our data set, resulting in 12,195 records on 1,401 cows at 12 farms. M-scores per foot were converted to binary format (0/1), where an M0 score was converted into 0, while feet with M1, M2, M3, M4 or M4. 1 were converted into 1. Next, the scores of the two-hind feed at an observation moment were averaged, so that animals were scored as y DSM = 0, 0. 5 or 1, 0 indicating two noninfected feet, 0. 5 indicating one infected foot and 1 indicating two infected feet. Thus, an animal had up to 11 observations, one for each of the 11 observation moments ("time of observation", TO). Parity was expressed in 5 classes: 1 through 4, and greater than or equal to 5.
| Data restructuring and editing for the analysis of infection events For the analysis of infection events, only data on individuals that had at least one non-infected foot at the beginning of an observation interval are informative (only those individuals can become infected). Hence, while we used all informative records from the full original data set, the number of records is smaller for the analysis of infection events than for the analysis of disease status. Thus, only animals with at least one non-infected foot at the previous observation moment were included in the dependent variable for an observation interval. The resulting total number of records for the IM was 6,099, which is only half of that for the DSM. The M-scores were converted to cases per foot. These cases (C) were considered as "successes" in binomial trials, with binomial total F = 1 or 2 non-infected feet at the start of the observation interval.
A random non-genetic animal effect (a so-called permanent effect of the individual) was included to account for the repeated measures on same animal and its biological disposition. A random genetic effect was included with a genomic relationship matrix (G) constructed according to method 1 of (VanRaden, 2008), calculated using the calc_grm software  (Calus and Vandenplas (2013). The final DSM was y DSM is a vector of observations on the disease status/ score of both feet of the animals, y DSM,i = 0, ½ or 1, X is the known design matrix for all fixed effects found significant (Table  3 ) in univariate analysis including farm, parity, months in milk (MIM), TO (time of observation) and breed, β is the unknown vector of fixed effects, Z 1 and Z 2 and Z 3 are known design matrices for the animal, farm*TO and farm*parity, respectively,u 1 , u 2 and u 3 are unknown vectors of independent random effects of the animal (non-genetic), farm*TO and farm*parity, respectively, and a is the unknown vector of random animal genetic effects (genomic breeding values), with 2 a is the additive genetic variance.
is the vector of independent residuals. The total number of observations for the DSM was 12,195. We also fitted the DSM with a pedigree-based (based on 4 generations) relationship matrix (A), using a ∼ N(0, A 2 a ) , to compare the additive genetic variance and heritability between models with genomic versus pedigree-based relationships. The variance components and GEBV were estimated using the ASREML-w software  (Gilmour et al. , 2008). The GEBV from the DSM were interpreted as the change in the probability of an animal (i) to be infected (y DSM,i = 1) due to its genotype. For example, an animal with a GEBV of 0. 1 will have a 10 per cent point higher probability of being infected due to its genotype than the average cow. The fixed effects and random non-genetic effects are interpreted in the same way.
| Brief summary The main purpose of this study was to present and compare estimated genetic parameters and breeding values for the binary disease status and infection events. Disease status was analysed with a linear mixed model (DSM), while infection events were analysed with a generalized linear mixed model. For both traits, we used all informative records from a data set on the endemic claw disease DD in dairy cattle. Both traits showed significant and substantial additive genetic variance and a significant correlation between the GEBV of the two traits and observed phenotypes in the cross-validation. These results demonstrate the presence of heritable variation, both in DD disease status and in susceptibility to become infected with DD. h 2 P = 2 a 2 a + 2 u 1 + 2 u 2 ∕n + 2 u 3 + 2 ∕n = 0.
7-8. 4%), respectively, and the animal-level risk factors for brucellosis include history of abortion and older age  (Asakura et al. , 2018a). In this previous study  (Asakura et al. , 2018a ) which used the same dataset with the present study, the number of seropositive farms was reported to be nine by an error (farm level prevalence was 58. 8%, 95%CI 33. 5-80. 6%, correctly), but the present study confirms that 10 farms housed sero-positive animals. While risk factors for brucellosis in animals are generally known well, reports of factors associated with higher within-herd prevalence are limited. The present study adds knowledge on this. Although brucellosis is endemic in pastoral and agro-pastoral areas in Tanzania, the information on Brucella species is limited. In the area of the present study -Morogoro region, only a study reported isolation of B.
Relationship between sero-positivity and history of abortion Odds ratios (ORs) were calculated for 3-to 14-year-old females at infected farms to determine whether there is an association between history of abortion as a potential exposure and the occurrence of brucellosis. Data for animals younger than 3 years were not used, as cows infected with brucellosis before abortion occurs often do not have antibodies  (Nielsen and Duncan, 1990) ; indeed, no sero-positive cows between 0 and 2 years of age were identified (see Results). Brucellosis can cause abortion in cows at the first pregnancy after infection  (Nielsen and Duncan, 1990). Farmers were asked about any history of abortion for animals of any age. In animals between 3 and 4 years of age, when the parity number is low, examinations of any causal relationship between infection with Brucella and abortion can proceed without caution.
However, in animals ≥5 years of age, abortion might be occurred several years ago, and in such cases, OR will not show recent relationship between brucellosis infection and abortion. Therefore, the data were stratified into two groups, one for animals between 3 and 4 years of age, and the other for animals over 5 years of age, and these data were analyzed using the Mantel-Haenszel (MH) OR. In this analysis, crude and strataspecific ORs were calculated, and a test for homogeneity was performed using Woolf's statistics  (Woolf, 1955)  to assess differences between strata regarding the strength of the association between infection status and abortion. Finally, the crude OR, MH OR, or strata-specific OR was selected based on the results of tests for homogeneity and rate of change in MH OR compared with the crude OR ([crude OR -MH OR]/crude OR).
Lower DIC values are indicative of a better adequacy to the data, and DIC difference of more than 5 are considered substantial. The FOI and DIC were determined using the Rsero package  (Hoze, 2020) , where Markov Chain Monte Carlo (MCMC) simulation is performed based on rstan software  (Stan Development Team, 2016). To select the best model, 5000 iterations with 4 chains for each model were carried out. For the selected model, further iterations are added up to 40,000. The basic reproduction number (R0)-the average number of secondary cases caused by a single infected animal in a totally susceptible population-was calculated as follows. The average life expectancy of a cow (1/ ) was calculated using Equation  4 :  4 ) 1 ⁄ = ∑ , ( Equation where represents the survival rate by age ( ), calculated as the number of animals at each age divided by the number of animals at age 0.
These data might also suggest that there is regular contact between infected animals and animals at non-infected farms. For future intervention planning discussions, methods for the control of brucellosis over a wide area, including non-infected farms, should be considered. As no records of the time of abortion occurrence were available, leaving only data on cumulative occurrence, MH analyses were performed in the present study. The crude OR chosen suggested a significant relationship between brucellosis and history of abortion. Under field conditions, in which access to facilities for diagnosing animal brucellosis is limited, symptom-based monitoring using a history of abortion can play a key role in identifying high-risk herds. The analysis of covariance for the reproduction performance suggested that infection with brucellosis not only causes abortion in the first pregnancy after infected, but also significant reduction of reproduction performance (42.
In the future, development of a diagnostic tool for infected calves and heifers could enhance our understanding of the contribution of vertical transmission to brucellosis infection dynamics and thus aid in planning an eradication strategy. Another concern in the analysis related to sero-positivity was that all the seven cows with 15 years of age or over were sero-negative, though they accounted for only 1. 5% of the animals sampled. In previous studies, the duration of immunity induced by the S19 vaccine in cattle vaccinated as calves was proven to be quite long (fifth pregnancy), spanning almost the entire productive life of the animals farms and did not include cows as old as those in our dataset (>15 years old). After infection with Brucella, humoral components remove microorganisms via opsonization and bactericidal mechanisms, including antigen-specific antibody production and cellular components, including macrophages, dendritic cells, neutrophils, and innate T cells, which kill both invading microorganisms and infected cells via phagocytic or cytotoxic activity.
Slides were then neutralised in 0. 4 M Tris pH 7. 4 and stained with GelRedTM (Biotium). As positive control for alkaline comet assay, 50 µM H2O2 was directly deposited onto control slides (unexposed cells), then incubated for 5 min at room temperature. As positive control for comet-Fpg, A549 cells were exposed for 20 min at 37 °C to 1 µM riboflavin, then to 10 J/cm² of UVA. At least 50 comets per slide were analysed using Comet IV software (Perceptive Instruments, Suffolk, UK); J o u r n a l P r e -p r o o f results are expressed as % tail DNA (n=3). The absence of MWCNT aggregates in comets, which would potentially interfere with DNA migration, was visually verified. In order to investigate chromosomal damage, the cytokinesis-blocked micronucleus assay was performed as described by Fenech et al.
Statistical analyses Statistical calculations were done in the Graph Pad Prism 7. 02 software package (Graph Pad Software Inc. , La Jolla, CA, USA). First, each data set was tested for normality with the Shapiro-Wilk test. The ANOVA test is robust against deviations in normality and was used for inter-group comparisons, except when the p-value of the Shapiro Wilks test was very low (p<0. 001); or when the standard deviations of the groups were determined to be highly different. Differences in standard deviations were assessed using the Brown-Forsythe test for three or more treatment groups (p<0. 001). The latter test was applied because the ANOVA test is somewhat sensitive to differences in data variability. In case of deviations in normality or in inter-group standard deviations, the non-parametric Kruskall-Wallis test was calculated.
Acknowledgments Excellent technical assistance was provided by  Eva Terrida ,  Michael Guldbrandsen ,  Zdenka O. Kyjovska  and  Natascha Synnøve Olsen. This work was supported by the  EU   FP7   NanoMILE  project grant number  NMP4-LA-2013-310451 , J o u r n a l P r e -p r o o f Danish Centre for Nanosafety 2 and FFIKA, Focused Research Effort on Chemicals in the Working Environment, from the  Danish Government. J o u r n a l P r e -p r o o f.
Acknowledgements We thank the patients for participating in this study and  Dr Jean-Michel Picquenot  and  Dr Brigitte Sigal  for tumour diagnosis expertise. We thank  Odette Mariani  and the  CRB (Centre de ressources biologiques) of Institut Curie and Centre Henri Becquerel  for their support in processing patients' samples. High-throughput sequencing was performed at the  Institut Curie ICGex NGS platform , which is supported by the  ANR-10-EQPX-03  ( Equipx ) and  ANR-10-INBS-09-08  ( France Genomique Consortium ) grants from the  Agence Nationale de la Recherche  (" Investissements d' Avenir" program ). We thank the animal platform of the  Institut Curie.
ACKNOWLEDGMENTS We thank the technical support team for medium preparation and lab management and the scientists working at the  Bacterial Zoonoses Unit  for critical and supportive comments. This work was supported by the  European Union Reference Laboratory for Brucellosis  (grant agreement no. SI2. 801943 ) and the  One Health   EJP  project ( JRP-17 -IDEMBRU: Identification of emerging Brucella  species: new threats for human and animals; grant agreement no. 773830 ). We declare no conflict of interest.
Blinding Randomization was performed through Interactive Voice/Web Response System according to a computer-generated randomization sequence. Participants, healthcare providers, medical monitors, other personnel involved in the conduction of the trial, data collectors, and biometricians were unaware of the treatment that participants were receiving. Moreover, double-blind conditions were secured by using a double dummy technique; the study dose consisted of one tablet (containing TRAM/DKP 75 mg/25 mg or placebo) plus two tablets (containing TRAM/paracetamol 37. 5 mg/ 325 mg or placebo).
Efficacy Evaluation Evaluation of efficacy was based on data entered by patients in electronic diaries (eDiary). The following measures of analgesia were performed: (i) pain relief (PAR) measured on a 5-point verbal rating scale (VRS) (0 = ''no relief'', 1 = ''a little (perceptible) relief'', 2 = ''some (meaningful) relief'', 3 = ''lot of relief'', 4 = ''complete relief'') at the predefined postdose time points (15 min, 30 min, 1 h, 1. 5 h, 2 h, 4 h, 6 h, and 8 h); (ii) PI measured on an 11-point NRS at baseline and at the same predefined post-dose time points. The onset of analgesia was documented using the double stopwatch method over a 2-h period post-dose: following treatment, two stopwatches were automatically activated in the eDiary. Patients were instructed to stop the first stopwatch when they felt ''first perceptible'' PAR (FPPAR, i.
All analyses were performed in SAS V. 9. 3 (SAS) software  [16]. The method of ''last observation carried forward'' was applied among patients who missed more than one consecutive data input; otherwise, the missing value was replaced by the mean of the two non-missing data collected before and after the missing one. This procedure was applied to all efficacy outcomes. After RM intake, PI returned to its baseline level and PAR to zero (''no relief'') for all subsequent time points (i. e. , baseline observation carried forward)  [16]. Adverse drug reactions (ADRs) were coded using the MedDRA dictionary. The incidence of each ADR was summarized by system organ class (SOC), preferred term (PT), treatment and baseline PI group. Safety variables were analyzed by descriptive statistics  [16]. The subgroup analysis was performed on randomized patients stratified according to their baseline PI.
ACKNOWLEDGMENTS Funding. The study sponsor (Menarini Group) contributed to the study design, data analysis and manuscript preparation. The present analysis was funded by Menarini Group as well. Magdi Hanna confirms that he had full access to all the data in the study and had final responsibility for the decision to submit for publication. The journal's Rapid Service Fee was funded by Content Ed Net. Medical Writing Assistance. Editorial Assistance for manuscript preparation and submission was provided by Content Ed Net thanks to an unconditioned grant of the Menarini Group. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
The study was conducted in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki. The study was approved by all the concerned competent authorities and ethics committees. Patients provided their written consent to participate in the study. Data Availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
2017; Guzmán-Verri et al. 2019). MLVA-16 dendrogram was constructed using 593 MLVA bank database profiles  (Grissa et al. 2008). Values obtained for each MLVA-16 marker are in Supplementary Data Sheet 1 and upload to microbes genotyping database (http:// micro besge notyp ing. i2bc. paris-saclay. fr/). WGSA data is accessible at the European Nucleotide Archive (ENA) (http:// www. ebi. ac. uk/ ena/), under the accession codes listed in the Supplementary Data Sheet 1. Other WGSA from various Brucella strains and Ochrobactrum spp. , used for comparative purposes, were obtained from the NCBI Genome database. Multiple sequence alignment for phylogenetic reconstruction reads from two Ochrobactrum spp. , and the 35 Brucella isolates from different hosts were aligned by bwa and mapped with SMALT v.
Acknowledgements We thank the  Municipal Police and Coastguard of Costa Rica , the personnel from PIET and SENASA of  Costa Rica , and  Dr. Hellen Porras  and  Lic. Yinnel Soto-Araya  for their assistance. This work was supported and approved as part of the  National Program of Wildlife of SENASA  and by  CONARE, FIDA , and MICIT programs ( PINN-PND-033-2015-1 ),  San José ,  Costa Rica.
Patients' selection and data collection We retrospectively analysed the whole of the parasitological examinations recorded in the register of the two main hospital laboratories between January 1, 2011, and December 31, 2016. According to the hospital-based protocol, the parasitological examinations were conducted on patients because of symptoms evoking parasitic infections such as diarrheas, abdominal pain, severe anaemia, or when there was a notification of hypereosinophilia in asymptomatic patients, mostly of fortuitous finding. All the stool and digestive fluids samples of laboratories based-database were included. The data of each patient such as their gender, their date of birth and their place of living, as well as the date of the analysis and the type of carried out parasitological examination were recorded.
In this record, the clinical data, the treatments provided and the evolution of patients' health were not registered at the time when the parasitological analysis was conducted and we were not able to provide such data due to anonymisation of the data and the retrospective design of the study. The patients with mixed infections were defined by the coexistence of two or more parasites coexisting in the same sample or in several successive samplings in the week following the first sampling. The data from the rainy season concerned samples collected between December and June and those from the dry season were collected between July and November.