dataesr/scientific-paragraphs-categorization huggingface dataset:
Total acute pain avoidance behaviors RST + FSK + RL + JMP + LBW + EQ All pain avoidance behaviors pooled. Data were analyzed using R statistical software  [15]. Initially, a repeated measures mixed model was used, fitting lamb ID as a random effect and treatment, time period, and gender as fixed effects, including first-order interactions where significant. Gender by time period interactions were significant, so male and female data were analyzed separately. Data could not be transformed to satisfy a normal distribution so were analyzed within time points and as the entire 60-min period using Welch's t-test. Total pain avoidance behaviors; restlessness and foot stamping/kicking were analyzed separately, whereas counts of rolling; jumping; licking at the wound site and easing quarters were too low to permit separate analysis.
Methods: We conducted a prospective monocentric study (NCT0281823) including 16 woodworkers with ITAC, 16 healthy exposed woodworkers and 13 healthy, non-exposed, controls. We compared tumor samples with healthy non-exposed samples, both in transcriptome and in methylome analyses. We also investigated wood dust-induced transcriptome modifications of exposed (without tumor) male woodworkers' samples and of contralateral sides of woodworkers with tumors. We conducted in parallel transcriptome and methylome analysis, and then, the transcriptome analysis was focused on the genes highlighted in methylome analysis. We replicated our results on dataset GSE17433. Results: Several clusters of genes enabled the distinction between healthy and ITAC samples. Transcriptomic and IHC analysis confirmed a constant overexpression of CDX2 in ITAC samples, without any specific DNA methylation profile regarding the CDX2 locus.
Global microarray data analysis A global transcriptome analysis was performed to find out if it was possible to distinguish the different samples profiles. All results are presented in Fig. 2  as a k-means. We identified 4 differential clusters between T and N samples. No differential cluster could be identified between N and E samples. Finally, two clusters could identify healthy subjects samples (E and N) and samples of woodworkers with a tumor (T) even in contralateral sides samples (C). These differences persisted even when adjusting for potential confounders (age or wood dust exposure duration and/or latency). We then search for functional annotation enrichment of these clusters (Fig. 2 ). Cluster 1, overexpressed in C and T, is enriched in genes involved in the extracellular matrix such as collagens, matrix metalloproteinases (MMP8, MMP9, MMP11, MMP19 and MMP20), leukocytes differentiation (Runx1, GATA1, GATA3, NFKBID, RELB and interleukins) and muscle contraction such as myosin chains.
Replication of our results with dataset GSE17433 We searched for another dataset of sinonasal adenocarcinomas  [25]  in the Gene Expression Omnibus database (https:// www. ncbi. nlm. nih. gov/ geo/). After normalization of each dataset, common genes of both studies were selected and sorted according to our clustering. Genes overexpressed in tumors in our study (cluster 4) are also overexpressed in GSE17433 (Additional file 7: Figure  4 ). Genes underexpressed in tumors in our study (cluster 5) are also underexpressed in GSE17433. We investigated the expression of the Top-100 discriminant genes of our study in GSE17433, and the Top-100 discriminant genes of Tripodi's study (GSE17433) in our results. Most of these results were concordant (112/143 genes had the same variations, 52/55 when analyzing only genes with significant variations in both studies) (Additional file 3: Table  III ).
General design and population The general design of the study is depicted in Fig. 1. The first set of samples were taken on woodworkers (group 1) with unilateral nasal intestinal adenocarcinomas which were operated on during a three-year period (2014-2016) within the scope of the REFCOR (French Network of Rare Head and Neck Tumors). Samples were collected in olfactory clefts by a noninvasive swabbing technique (for transcriptome analysis), and by biopsies (for DNA methylome and western blot analysis). Then, we included wood dust-exposed volunteers (group 2) and healthy non-exposed volunteers (group 3) as controls from June 2016 to August 2016 and collected samples from the most accessible olfactory cleft (Fig. 1 ). For ethical reasons, we did not perform biopsies on healthy subjects: A previously published methylation dataset of olfactory clefts of asthmatic patients (without wood dust exposure) was used as controls for methylome analysis  [25].
Microarray data analyses Analyses were performed as previously described  [17, 26]. Scanned signals were quantified from all microarrays by GenePix Pro software version 5. 1 (Axon Instruments, Union City, CA). Signals were normalized against a median profile using the LOWESS method  [27]. Genes with similar profiles were grouped using k-means (k = 10, 100 runs) algorithm  [28]  on log-transformed and median center genes with Pearson coefficient as distance and average linkage clustering method. The initialization step consisted of randomly distribute the genes into one of 10 clusters and calculate their centroids. Then, the algorithm proceeded by alternating between two steps: an assignment step aiming to redistribute the observations into the clusters, associating each observation with the nearest centroid (Euclidean method), then an update steps aiming to recalculate the new centroids for each cluster.
DNA methylome analyses We carried out bisulfite conversion of 600 ng of tumor tissue DNA using EZ DNA Methylation kit (Zymo Research, Proteigene, Saint-Marcel, France). The genome-wide profiling of DNA methylome was determined using the Infinium Methylation EPIC BeadChip array (Illumina, Paris, France), according to the manufacturer's instructions. The Infinium Methylation EPIC BeadChip provides coverage of 850,000 CpG probes in enhancer regions, gene bodies, promoters and CpG islands. The arrays were scanned on an Illumina iScan ® system, and raw methylation data were extracted using Illumina's Genome Studio methylation module. For each CpG probe, the methylation level was described as a β value, ranging between 0 (fully unmethylated CpG probe) and 1 (fully methylated CpG probe). Background correction and normalization was implemented using the SWAN method (R Package Minfi)  [32].
We visually inspected the whole-genome distribution of the CpG probes according to their β value. Samples that did not show a methylation profile consistent with a beta distribution were excluded from the epigenome-wide association study (EWAS) and were used in the secondary locus-specific analysis. In the EWAS, we compared the whole DNA methylome profile of nasal cavity tumor samples with that of 72 non-tumor nasal cavity methylomes profiles retrieved from the GEO database (GSE65163)  [15]. For each CpG probe, we compared the mean β values between tumor and non-tumor samples using t-test with Bonferroni correction to account for the multiple testing issue. Due to the low sample size, and considering the exploratory approach of our analysis, we used the smoothed P-value transformation by converting nominal P-values obtained from the t-test to smoothed P-values using a window radius of 3, as previously reported  [33].
Immunochemistry To confirm the observed transcriptomic and methylome modifications, tumor sections were reacted with specific antibodies (CDX2 (clone DAK-CDX2, 1:100 dilution, DAKO, Carpinteria, CA, USA), CACNA1C (HPA039796, 1:50 dilution, Sigma-Aldrich, St Louis, MO, USA), SLC26A10 (primary antibody: HPA044719, 1:200 dilution, Sigma-Aldrich, St Louis, MO, USA), then were stained immunohistochemically by the avidinbiotin complex method. tumor tissue stainings were compared to non-tumor adjacent mucosa stainings as controls.
, 1978; Gilbert et al. , 1988) , although a more recent study found heritability of 0. 2 for IgG in calf serum  (Maltecca et al. , 2009). Nevertheless, the literature remains scarce regarding the genetic determinism of immunoglobulin concentrations in colostrum and serum. During our study, we were able to obtain colostrum samples from 366 Charolais cows on experimental farms, and sera from their calves between 24 and 48 h of age. The IgG 1 levels were quantified in each sample using radial immunodiffusion (RID), and IgG, IgA, and IgM were quantified by ELISA. Using this extremely rare data set, our aim was to generate new knowledge on immunoglobulin levels in beef cattle colostrum, their genetic parameters, the success of passive immune transfer, and its relationship with subsequent performance.
Measurements of IgG 1 , IgG, IgM, and IgA in Calf Serum and Colostrum Bovine IgG 1 were assayed in colostrum and serum samples using RID by Labocéa (Fougères, France). The samples were diluted using the Microlab 625 diluter (Hamilton) at 1:222 or 1:1,000 for serum or colostrum, respectively. The RID bovine IgG 1 test was performed using IDRing Viewer and IDRing Meter software (all from IDBiotech, Issoire, France), according to the manufacturer's instructions. The IgA, IgG, and IgM levels were evaluated in colostrum and serum samples using the Bovine IgA, IgG, and IgM ELISA Quantitation Sets and the ELISA Starter Accessory Kit (Bethyl Laboratories, Montgomery, TX), following the manufacturer's instructions. Seven points of 2 by 2 serial dilutions were performed for standard curves, starting at 500 ng/mL for IgA and 250 ng/mL for IgG and IgM.
Statistical Analyses Eight traits related to immunoglobulins were analyzed: RID-IgG 1 , ELISA-IgG, ELISA-IgA, and ELISA-IgM from the dam colostrum, and the same 4 traits from the calf serum. Descriptive statistics were calculated for each trait using Proc Means under SAS STAT software (SAS Institute Inc. Cary, NC), and tests of significant effects were calculated for each trait using Proc GLM under SAS STAT software. The following effects were tested for both colostrum traits and the calf serum traits: farm-year contemporary group effect, laboratory conditions, dam weight at calving, dam BCS at calving, age of the dam at calving, age of the dam at the onset of cyclicity, maternal instincts, calving difficulty, sex of the calf, and calf birthweight. In addition, the age of the calf (in hours) at the time of serum collection was tested for the calf serum traits only.
ACKNOWLEDGMENTS The authors thank the technical staff at the  Institute for Agriculture, Food and Environment (IN-RAE) experimental farms at Bourges-La Sapinière  (https: / / doi. org/ 10. 15454/ 1. 5483259352597417E12 )and  Le Pin-au-Haras  (https: / / doi. org/ 10. 15454/ 1. 5483257052131956E12 )for their continuous and careful collection of colostrum samples from primiparous Charolais dams, sometimes under difficult conditions, over a 4-yr period. The authors also thank  Labocéa (Fougères, France)  for performing the RID analyses. Finally, the authors thank the  Animal Genetics Division  at  INRAE  for its financial support. The authors have not stated any conflicts of interest.
It is recommended by the World Organisation for Animal Health (OIE) that in circumstances where there is a lack of information on the incidence of disease, data generated by existing control programmes or health schemes, laboratory records or data on the epidemiology of disease can be used by veterinary or medical authorities, to protect human and animal health  [15, 16]. Although epidemiologic analysis of routinely collected laboratory data generated during national bovine brucellosis control programmes has been criticised mainly due to this dataset being a non-representative sample of cattle sera  [17] , and inadequate or poorquality data routinely collected through this system  [18] , countries that cannot afford to undertake national cross-sectional surveys, have gained useful insight into the distribution and occurrence of animal brucellosis.
This study aimed to gain insight into the progress toward bovine brucellosis elimination in the districts and State Vet Areas of Gauteng province, whilst setting a baseline for future monitoring and research. We present a method to mine routinely collected data for valuable epidemiological insight into the distribution and control of bovine brucellosis. Furthermore, we report on important limitations to the laboratory dataset of cattle herds routinely tested for bovine brucellosis and suggest ways to overcome these limitations. By focussing attention on the control of brucellosis in a single province, we were able to frame the problem and suggest solutions for the analysis of routinely collected data for bovine brucellosis surveillance that may be encountered in similar socio-economic areas.
Discussion No significant overall change in herd prevalence or within-herd seroprevalence of bovine brucellosis amongst herds participating in the bovine brucellosis control programme was found over the study period in Gauteng province, except for an artefactual decrease in 2016 which is addressed below. This study found significant variation in the number of bovine brucellosis reactor herds between State Vet Areas. Furthermore, an association was detected between increasing herd size and the occurrence of seropositive herds. However, as herd size increased, the within-herd seroprevalence in these reactor herds was found to decrease. The association between large herd size and the seropositive status of herds is well documented  [27, 28]. To our knowledge, this is the first study to find an inverse relationship between herd size and within-herd seroprevalence of bovine brucellosis from a laboratory dataset.
This finding is in contradiction to a finding of no relationship between herd size and within-herd seroprevalence in a multistage sample cross-sectional study conducted by  Makita et al. (2011)  in Kampala, Uganda. However, in that study, the sample size of seropositive herds was only 11, and it is possible that the effect was missed  [28]. It is also possible that this effect is unique amongst the cohort of herds participating in the control program and represented in the laboratory dataset for this period. In this study, Randfontein and Germiston State Vet Areas had greater odds of having reactor herds and having higher within-herd seroprevalence counts than the Pretoria State Vet Area when controlling for herd size and the year of testing. The finding of variability in herd and cattle prevalence between districts is similar to findings of a cross sectional survey for bovine brucellosis conducted in KwaZulu-Natal across 33 different magisterial districts  [29].
94%  [31] , which is similar to our finding of 1. 4% in Gauteng. Yet, despite this similarity, neither study conducted in Namibia reports the within-herd seroprevalence for the area, making it difficult to compare the burden of cattle brucellosis to farmers in Gauteng to farmers in Namibia. The mean annual bovine brucellosis crude herd prevalence for the six-year period was 22. 1%; this is higher than the cattle herd prevalence reported for Namibia (9. 26%) in the study conducted by Madizingira et al. (2020) but lower than the 30. 1% herd prevalence reported from Zimbabwe  [20]. Both studies used laboratory datasets for analysis. Many factors such as differences in farm production and management systems, cattle movement  (Mangen et al. , 2002)  and effectiveness of bovine brucellosis control programs  (Nicoletti, 2010)  may contribute to the variation between reactor herd prevalence across different areas  [30, 32].
Despite the reasonable estimates of herd and cattle prevalence from this study, it is uncertain how reflective this is of the true prevalence of cattle and herd reactors in Gauteng. Results should therefore be interpreted cautiously and not be used to make inference to the overall herd and within-herd prevalence of bovine brucellosis in the province, as they are applicable only to herds participating in the bovine brucellosis control programme. In addition, results within this group should be considered bearing in mind the following limitations identified during this study. We were unable to identify the reason for testing or unique herds from the available dataset due to the absence of unique herd identifier per record. It was therefore not possible to link the test result to the herd record or the paper record filed at the relevant State Veterinary office, making the immediate tracking or tracing of disease progression or duration of infection within a herd unachievable.
In addition, owner details and farm details were not unique per herd, due to the veterinary official capturing the details of the person handling the cattle on the farm into the sample submission form, instead of the herd owners' name and contact details. This meant that from the submission form, which was also the template for the laboratory report, it was not possible to identify herds by owners. In the dataset, one herd could be associated to the owner's name or any of the workers on the farm who were there handling the cattle on the day of testing. Illegible handwriting and data-capturer mistakes lent further uncertainty to some farm names and owner details. Furthermore, there was no variable within the dataset to indicate if the test was conducted for accreditation, maintenance or diagnostic purposes, which is a barrier to determining the reactor rate within these categories.
Despite the limitations of this study, as discussed above, the analysis of this dataset has provided valuable insight into the within-herd seroprevalence of bovine brucellosis in herds participating in the bovine brucellosis programme in Gauteng. This information can be used to estimate the economic impact of the disease and control strategies, to smalland large-scale farmers in this area. Results also give an indication of the zoonotic risk of brucellosis to cattle handlers across the province, which should be further investigated and reported.
g. , diagnosis by a private vet), automatically entered the scheme. Consequently, the laboratory report dataset included all herds in which a cattle reactor was detected but not the total cattle herds at risk in the province. Individual cow blood samples were collected in dry red-topped serum collection tubes and marked by the animal health technician (AHT). The identity of each cow was captured on the sample collection form by the AHT. The batch of samples was submitted to the Onderstepoort Veterinary Research laboratory (OVR), where it was allocated a unique laboratory number. Each sample was then screened for B. abortus antibodies with the Rose Bengal test (RBT) serological test. Serum that reacted on the RBT was retested with the complement fixation test (CFT) to confirm seropositivity to B.
abortus. Tests were conducted according to OIE standards. Test results for each cow were captured on the original sample collection form prescribed by the national Veterinary services  [43] , and a copy of the completed form was made for data capture at the provincial epidemiology branch of the Veterinary Services, whilst the originals were filed at the respective State Vet Offices. Copies of the herd test results were routinely batched in preparation for collection by an AHT at the end of the month. The AHT then delivered the batch of copies to the administrative clerk for the Gauteng Veterinary Services' epidemiology branch, who captured it into a Microsoft Access ® database. Each entry in the dataset was allocated the unique laboratory report number, and captured information from a single herd test.
Information was captured to primarily monitor the number of herds tested, herd status-defined as positive if one or more cattle reacted to CFT ≥ 60 IU/mL, and the total number of cattle testing CFT ≥ 60 IU/mL positive within a herd. CFT ≥ 60 IU/mL is considered the threshold to rule out possible false positives due to S19 vaccine reactors  [43]. The initiative to capture herd information electronically was taken by managers of the Epidemiology branch in Gauteng, even though it was not a prescribed indicator for monitoring and evaluation purposed. Other variables captured in the dataset included the herd owners' name; farm name; total number of cattle tested, which is used as the proxy for herd size; the date of blood collection and laboratory report; name of the veterinary official collecting the samples; State Vet Area; district area.
Data Analysis Six variables from the raw dataset were selected for analysis: (1) State Vet Area, (2) district, (3) herd size categorised into quartiles, (4) serum sample sender, (5) year of herd test, and (6) herd status, where one or more reactors (CFT ≥ 60 IU/mL) is regarded as a positive herd. Only herds with greater than one animal tested were included in this study, as observations of only one cow tested (n = 268), were assumed to be an individual animal diagnostic test instead of a herd test. Herd sizes greater than 2500 were regarded as outliers (n = 1). Outliers and observations with missing values in the "district" variable (n = 31) were removed from the dataset. The laboratory dataset analysis was conducted using R Version 3. Proportions of reactor cattle and proportion of reactor herds are presented by province by districts within State Vet Areas for the five-year period.
Conclusions Despite the recent report of an increasing trend of bovine brucellosis in the country [27], analysis of routine laboratory test results for Gauteng did not show a significant change in cattle or herd reactor prevalence between 2013 and 2018 in the province. This may indicate that there has not been real progress toward bovine brucellosis elimination during the study period. However, further investigation is needed, given the limitations of this dataset to make inferences on the true prevalence of bovine brucellosis in the province. Subject to uncertainties regarding data quality, routine laboratory test results can be used to provide an indication of bovine brucellosis reactor herds and within-herd seroprevalence in demarcated district areas in the absence of data derived from crosssectional surveys.
Moreover, analysis of this dataset may be used to identify areas at district and state vet area level with high cattle and herd reactor rates and within-herd prevalence rates for further investigation or support in the planning and implementation of bovine brucellosis surveillance and regulatory activities. However, indications of the absence of disease or low prevalence, calculated using laboratory test results data, might only be related to the insufficient sampling of some areas or farms. Based on this study's findings, we firstly recommend planned regular cross-sectional surveys across districts to compile a sample frame and the allocation of unique cattle and herd identifiers to determine the real prevalence and the representativeness of the laboratory data sample, which is a necessary first step to predict if regulatory actions are having any effect on disease prevalence.
Acknowledgments We thank our thirteen anonymous reviewers for their feedback and suggestions, which helped us improve significantly this manuscript. We are grateful to all the staff and summer students working at the  Robert G. White Large Animal Research Station of the University of Alaska Fairbanks  who helped in sample collection and animal handling, particularly to  Sarah Barcalow ,  Thalia Souza ,  Hanna Sfraga ,  Carla Wiletto ,  Christine Terzi ,  Megan Roberts ,  Jean Rein ,  Claire Kepner , and  Charles Ashlock. We thank  Christine Gilman  and  Patricia Medd  from the  Endocrinology Laboratory of the Toronto Zoo  for their hard work in method development and fecal sample analyses. We finally thank  James Wang  for his ongoing support in the lab,  Katrine Maurer  for her help with travel logistics,  Katherine Wynne-Edwards  for her input during the conceptualization of this study, and  Andre Ganswindt  for the stimulating discussions on our results.