dataesr/scientific-paragraphs-categorization huggingface dataset:
Potential use of mitochondrial 12S and 16S rRNA genes for nematode DNA barcoding A DNA barcode is defined as a short sequence of DNA that can accurately identify organisms  [40] [41] [42]. The concept of DNA barcoding was first proposed by Herbert et al. [42]  when they suggested using DNA sequences as taxon 'barcodes' for the global identification of all organisms. Species are usually identified by comparing unknown sequences against known reference sequences. Currently, the universal genetic marker used for DNA barcoding is the 5ʹ-end of the mitochondrial cox1 gene. The cox1 gene has specific properties that make it useful as a genetic marker for DNA barcoding, including multiple copies, robust PCR amplification, nearly identical sequences in individuals of the same species yet sufficient variation between species, and the presence of a reference database for sequence comparison  [40] [41] [42].
However, in order for mitochondrial 12S and 16S rRNA genes to be utilized in DNA barcoding, it is essential to establish sufficient sequences in the reference databases. The advantage of the cox1 gene over other genetic markers, such as mitochondrial 12S and 16S rRNA genes, is the extensive availability of database sequences, allowing for thorough comparisons of unknown sequences. However, the potential of mitochondrial 12S and 16S rRNA genes for DNA barcoding of nematodes cannot be disregarded, as we have shown the capability of these two genetic markers to discriminate between species. Therefore, mitochondrial ribosomal genes have considerable value, not only as markers for molecular systematics studies but also their potential use in DNA barcoding studies. Future studies are needed to evaluate further the potential and utility of mitochondrial rRNA genes for DNA barcoding and to expand the reference databases for these genetic markers.
Mitochondrial 12S Target gene Primer name Sequence (5ʹ-3ʹ) Annealing temperature (°C) Amplicon size (bp) 12S rRNA 12S. C1. F GTG CCA GCT AYC GCG GTT A 55 460 12S. C1. R GRT GAC GGG CRA TAT GTG 12S. C345. F GTW CCA GAA TAA TCGGMTA 46 12S. C345. R ATT GAY GGA TGR TTT GTR C 16S rRNA 16S. C1. F ACG AGA AGA CCC TRG RAA YT 50 240 16S. C1. R GRT YTA AAC TCA AAT CAC G 16S. C345. F AAG ATA AGT CTT YGG AAR YT 45 16S. C345. R GAA YTA AAC TAA TATCAM G and 16S rRNA gene sequences generated in this study were deposited in the NCBI database (NCBI: MT135051-135093 for 12S rRNA; MT151872-151910 for 16S rRNA).
Data Collection Data were obtained from broiler hatcheries where the Ceva Hatchery Immunization Control Keys (CHICK) program was implemented (Ceva Santé Animale, Libourne, France). This program involves teams of more than 175 specialists frequently auditing hatcheries in more than 40 countries worldwide to verify the vaccination process, secure the vaccination technology maintenance, train the hatchery personnel, and improve their performance. During the audits, the different parameters are scored using a tablet-based application, and the results are stored in a dedicated database to be reported to hatchery management  [12]. At the hatchery, data collection was performed as follows: The percentage of rotten eggs was recorded (1) automatically at transfer when using live candling by Laserlife ® live candling equipment (Ceva Ecat-iD Campus, Plouédern, France) or (2) manually at hatch by opening the non-hatched eggs.
Statistical Analysis The dataset was manually curated to identify and remove wrongly imputed data and biologically implausible outliers. All the records with at least one missing value in the variables of interest were removed from the analysis. The effect of the rotten egg percentage on the KPI-i. e. , A, B, C, and D was evaluated independently using a linear mixed effect (LME) model, which allows the inclusion of both fixed and random effects. This approach was considered necessary, since the data were collected at different countries and hatcheries with a myriad of production practices. This intrinsic hierarchical structure leads to non-independence in the data set, preventing the use of standard regression models. Additionally, the hatchery management and handling practices at the country level may have an impact on the response variable; therefore, a hierarchical model was fitted, including the hatchery or the hatchery nested within the country as a random effect.
Since the breeder flock age has a detrimental effect on the hatchability and is also correlated with rotten eggs, it was included in the models to account for its potential confounding effect. All the analyses were performed in R using the nlme package  [13]. The normality and homoscedasticity of the residuals were graphically inspected for all the considered models. Several models were fitted, and the significance of each model improvement over the simpler one was assessed using the likelihood ratio test (LRT). Only the results of the best fitted-model were reported. The statistical significance threshold was set to p < 0. 05 for all the performed tests.
Animals and study design Eight clinically sound French Standardbred horses owned by the Center of Imaging and Research on the Equine Locomotor Affections (CIRALE) were included in the study. There were 4 geldings and 4 mares, median age was 3 years (ranging from 3 to 4) and median weight was 440. 5 kg (ranging from 402 to 483 kg). All horses had been trained for racing before being retired due to a failure to trot at racing speeds. All were conditioned on treadmills before the beginning of the study. The horses included in this study were part of a larger program evaluating the therapeutic effects of stem cell-based therapy on experimentally induced OA. Our study is based on data from the untreated or physiological saline placebo-treated joints, thus excluding data from the joints treated with stem cells (Fig  1 ).
Synovial fluid effusion was also graded in ultrasound images of the palmaro/plantaro-proximal recess of the joint using a similar 5-point scale to that used for the clinical evaluation (Table  2 ). MRI was performed with horses standing under sedation. Low field strength 0. 27 Tesla Magnet and a fetlock receive coil (Hallmarq Veterinary Imaging, Guildford, Surrey, UK) were used. Images were acquired in transverse, dorsal and sagittal planes using T1-weighted gradient echo, T2-weighted fast spin echo (FSE), and Short Tau Inversion Recovery (STIR) FSE sequences with motion correction software (MI) for FSE sequences (S1 Table ). The images obtained were then graded using a previously described semi-quantitative scoring system  [24]  adapted for use in standing equine fetlock joint MRI. Both proximal phalanx and metacarpal/ tarsal condyle were assessed for the presence of oedema-like bone marrow lesions (0-3), subchondral bone sclerosis (0-3) and osteophyte formation (0-3).
The primary antibody was replaced by 3% PBS-diaminobenzidine as a negative control. Slides were observed with an optical microscope (Leica DM2000) connected to a digital camera (Leica DFC420C) driven by image acquisition software (LAS V4. 2). The images obtained were retained and selected for the production of photo plates using image processing software (Adobe, Photoshop CC2015. 5). Lesions were blindly scored according to the OARSI histopathology evaluation system  [28]  by two independent investigators (DJH and MM). This evaluation system took into account the severity of cartilage and bone lesions (grade 0-6), the proportion of affected area in relation to the total area (grade 0-4), and the lesion depth (grade 0-4), allowing a more precise differentiation of the lesions. The total histological score of the lesions was the multiplication of these 3 parameters, with a maximum possible score of 96.
Statistical analysis Statistical analysis was performed after the data had been visually assessed for normality. The variables were first presented descriptively: the mean and standard deviation of the normally distributed continuous outcome (fetlock joint circumference) and the median and quartiles of the other continuous outcomes were calculated at each time point for each fetlock group, while the number of observations contained within each grade and the median were calculated for categorical outcomes, Fetlocks injected on W3 with the stem cell-based therapy were excluded from further statistical analyses, leaving a total of 16 fetlocks for statistical analysis. The comparison of interindividual variability and operated limb variability (left vs right and front vs hind) was carried out by analyzing the values resulting from the differences between W12 and W-1 scores, excluding the post-mortem scores.
Variability in lesion evolution Horses were homogeneous in terms of age, breed, weight and activity. Interindividual statistical analyses did not reveal any significant difference between horses when considering the differences between W12 and W-1 scores of clinical and imaging parameters and the differences between W12 and W-1 values of synovial fluid parameters. Similarly, no significant interindividual differences were observed in the distribution of macroscopic and microscopic scores by the end of the study (S2 Dataset and S2 Table ). In addition, statistical analyses did not reveal any significant differences between left and right fetlocks, with the exception of a radiographic score with higher values for left fetlocks than for right fetlocks (Fig  2   ). Although fore and hind fetlocks were not significantly different for most of the parameters, hind fetlocks produced significantly more synovial effusion after surgery than front fetlocks (Fig  2   ).
Clinical follow-up None of the 8 horses showed sensitivity to digital flexion tests throughout the study period (S2 Dataset). Ninety-four percent (30/32) of the fetlocks showed an increased joint effusion score on W3 compared to baseline (S2 Dataset and  Fig 2). The joint effusion score and the fetlock joint circumference were significantly higher in W12 than in W-1 when comparing the paired values of each fetlock (Figs  2  and 3 ).
Follow-up with imaging techniques At the end of the study (W12), imaging signs of OA were mild to moderate. Indeed, whatever the imaging parameter, the scores reached on W12 were below 50% of the maximal attainable grading score for 11/16 fetlocks (69%). None of the fetlocks had high MRI severity scores The effusion score measured by ultrasound reflected the results of the clinical follow-up, with a significantly higher score recorded in W12 than in W-1 (Figs  2  and 4 ). In addition, there was a progressive increase in osteophyte formation scores (for both radiographic and ultrasonographic evaluation) throughout the study period (Figs 2, 5 and 6, S3  Table  and S2  Dataset), with a significant increase in scores on W12 compared to those of W-1 (Fig  2 ). The total MRI score was also significantly higher on W12 compared to W-1 (Fig  2 ).
Evolution of synovial fluid parameters A significant increase for total protein concentration, total nucleated cell counts and CTX-II concentration was observed between W-1 and W12 when comparing the paired values of each fetlock at that time points (Fig  8 ). Total protein concentration and total nucleated cell counts seemed to be at their highest on W3 and W8 (Fig 8  , S3  Table and S2 Dataset) but no statistical comparisons were made at these stages. PGE 2 variation was not significant.
Post-mortem evaluation All fetlocks showed macroscopic lesion scores below 55% of maximum values (� 5/9), with a majority (12/16 fetlocks) of low to moderate severity scores (� 3/9) (S2 Dataset). Most of   4 ). On histological evaluation, all samples had values below 50% of the maximum lesion score (� 48/96), with a majority showing a low severity score of between 8 and 18 (11/16 samples) (S2 Dataset). No sample had a score < 8 and only one sample showed more severe lesions (a score of 48). The last 4 samples had an intermediate score of between 24 and 32 (Table  4  and Fig 2). Most of the samples were characterized by varying degrees of erosion of the superficial zone of cartilage and by the presence of a peripheral fibrocellular tissue (Fig  10 ) which had replaced the superficial and intermediate zones, or even the deep zone in some samples.
Population and data structure The data were collected during a selection experiment that was conducted at INRAE (UE GenESI, Surgères, France, https ://doi. org/10. 15454 /1. 55724 15481 18584 7E12) on French Large White pigs. Two lines were established by nine generations of divergent selection for RFI from 2000 to 2015  [22]. The G0 generation resulted from the mating of 30 boars and 30 gilts from generation F0 using artificial insemination. Among the G0 animals, 116 boar candidates for selection from all 30 litters were tested for RFI to select six extreme founder boars for each line (LRFI: low RFI, and HRFI: high RFI). The two lines were initiated by mating the selected boars to about 35 random G0 gilts per line. Inbreeding was minimized at each generation. The development of each line continued with the selection of six boars out of 96 tested candidates in each generation from G1 to G9.
Single nucleotide polymorphism (SNP) genotyping data and imputation SNP genotyping data were available for all selected boars and their mates from G0 to G9, additional pigs from response batches of G6 to G8, and all selection candidates in G9. In total, 1647 animals had SNP genotypes, of which 286 animals were genotyped with the Porcine SNP60v2 BeadChip (Illumina) (64,232 SNPs) and 1361 animals with the GGP Porcine HD Array (Illumina) (68,516 SNPs). Genotype quality control excluded SNPs with a call rate lower than 95%, individuals with a call rate lower than 90%, SNPs that were not in Hardy-Weinberg equilibrium (p < 10 -10 ), SNPs with a minor allele frequency lower than 0. 01, and individuals with parentoffspring incompatibility (e. g. , opposite homozygotes) with at least one parent. The PLINK software was used for SNP and individual genotype quality control  [24].
SNPs on the sex chromosomes were removed. After quality control of each SNP chip dataset, the SNPs present in each panel were imputed to the alternative panel using the FImpute software  [25]  in a single step. The two SNP chips shared 42,800 SNPs. The number of genotyped animals retained after imputation was 1643, and the final genotype dataset contained 64,233 informative SNPs. Thus, all animals had equal genotypic information. Genotypes were coded as 0, 1, or 2 for later calculation of the genomic relationship matrix. The number of animals with genotype data per generation and line is in Table  1.
Model and analyses Predictions obtained with BLUP are based on the assumption of no genetic differences between subpopulations  [26, 27]. Therefore, to account for selection in our dataset, all genetic and genomic analyses were carried out with bivariate approaches, i. e. the five other traits were individually paired with the selection index in two-trait model analyses. By including the selection criterion, the analyses of other traits are conditioned based on all the information that was used for selection  [28] [29] [30]. Preliminary analyses were carried out using a general linear model in R (glm procedure) to evaluate the significance (p < 0. 05) of fixed environmental sources of variation. The significant fixed factors included pen size (5 levels: 8, 9, 10, 11, 12 pigs per pen), herd of birth (2 levels), sex (3 levels), and contemporary groups (CG, 99 levels).
BW at slaughter was fitted in the model as a covariate only for BFT. CG were defined as animals born in the same week and raised in the same enclosure. Litter was fitted as a random environmental source of variation and its significance at the 5% level was determined using a likelihood ratio test. y Rij = y sij σ si σ Ri The genetic analyses were performed using the AIREMLF90 and BLUPF90 software  [31]  for the BLUP and ssGBLUP methods, respectively. Prior to ssGBLUP evaluations, the variance components of the traits were obtained using the restricted maximum likelihood algorithm implemented in AIREMLF90. These analyses were performed using all available data and only the full pedigree relationship matrix ( A ). Variance components were estimated with the bivariate animal mixed model as follows: where y is the vector of observations for the index and one of the five studied traits, b is the vector of fixed effects (described above), a is the vector of additive genetic effects, l is the vector of litter effects, and e is the vector of random residuals.
X , Z 1 and Z 2 are the inci- dence matrices for b , a , and l , respectively. Distributions assumed for the random terms are a ∼ N (0, G 0 ⊗ A) , l ∼ N (0, R l ⊗ I) , e ∼ N (0, R e ⊗ I) , where G 0 is a 2 × 2 symmetric (co)variance matrix of direct additive genetic effects, and R l and R e are 2 × 2 symmetric (co)variances y = Xb + Z 1 a + Z 2 l + e matrices of litter and residual effects, respectively. I denotes the identity matrix. Genomic breeding values were estimated using ssG-BLUP with the same models in the BLUPF90 software, with the previously estimated (co)variances and using the H matrix, which is a combined relationship matrix of the A matrix and marker-based relationship matrix ( G ) of genotyped animals  [10, 12]. The G matrix was con- structed and scaled by 2 {p i (1p i )} , where p i is the frequency of the second allele at locus i , following Van- Raden  [32].
Scenarios Two symmetric series of six scenarios, one for each line, were defined for genomic prediction. An overview of the scenarios is shown in Fig. 1. In all scenarios, genotyped animals of the last three generations (G7 to G9, 433 pigs for the LRFI and 399 pigs for the HRFI line) were considered for validation in a given line (target line), and their information was removed from the training dataset. The training sets were structured based on which generations and line were used. Scenario 1 comprised only animals from the target line and was the control scenario since it represented a routine genomic prediction design where all data would be available from the same line. All other scenarios were compared to this control scenario to evaluate which combination of training populations from the two lines achieved a prediction accuracy similar to the control scenario.
When y * is an adjusted phenotype of the animal's own measurement, it suffers from the inability to adjust for the random residual effects. In the optimum situation, the expected value of the correlation would then be the square root of heritability  [35]. Alternatively, using an EBV obtained from a complete dataset as the best predictor of TBV would cause autocorrelation between the reference and evaluated EBV when the training and validation sets are closely related through the pedigree, leading to higher correlations  [35]. Legarra and Reverter  [34]  proposed to complement the cross-validation approach with a semi-parametric approach that can be used in a large number of cases, with the advantage of not requiring knowledge of the TBV or adjustment of phenotypes. The underlying assumptions of this approach are (1) the variance components are similar in the training and validation datasets, and (2) the validation set is sufficiently diverse and large (i.
e. composed of various families). In brief, with their approach, the correlation between EBV using part of the dataset (partial) and EBV obtained using the whole dataset results in an estimator of the ratio of the accuracies of the EBV from these two datasets. We followed this approach to evaluate the potential for genomic prediction when including data from a related line compared to genomic prediction using all data from the target line, which will be referred to as GEBV w (GEBV obtained using the whole dataset), i. e. , to obtain GEBV w for the validation set of each line, two separate ssGBLUP analyses were performed (one per line). GEBV p (GEBV obtained using partial dataset) were the GEBV obtained from the six scenarios for the validation sets in each target line. The criterion for prediction accuracy for each trait and each scenario was then the correlation between GEBV p and GEBV w , r GEBV p , GEBV w.
Computation of prediction accuracies and biases Variance components of the evaluated traits were estimated using the A matrix on the full dataset with both lines combined. All estimated heritabilities were in the range of values reported in the literature for these traits  [8, [39] [40] [41] [42]. Using these variance components, the accuracy of GEBV was computed for the six scenarios to predict validation animals from each line using ssGBLUP. Prediction accuracies were computed using a crossvalidation method combined with a semi-parametric approach  [30]. Indeed, in our case, accuracies of the adjusted phenotypes or of deregressed EBV were too low to be used in a criterion such as r GEBV p , y * / h 2 , since only two-thirds of the individuals had their own phenotype. This would result in larger standard errors of the correlations and, thus, less power to test differences between scenarios, as shown in Additional file 1: Figures  S1  and S2.
The underlying assumptions of the semi-parametric approach are that (1) the validation set is sufficiently diverse and large (i. e. composed of various families), and (2) variance components are similar in the training and validation datasets. The first assumption was well covered in our study, since all breeding individuals, plus some progeny of each family, were phenotyped and genotyped. The second assumption was potentially less covered, which could explain some of the biases in prediction observed. Indeed, when estimating variance components separately in the two lines, different residual variances were estimated for some traits, resulting in lower heritability estimates for DFI (24%), FCR (43%), and RFI (22%) in the LRFI line than in the HRFI line. Legarra and Reverter  [30]  indicated that inflation of predictions in one or the other dataset due to changes in variances can cause biased GEBV.
Overall, it seems that including animals from later generations of both lines (more diverse animals) in the training set contributed to higher accuracies of GEBV in the validation set for FCR and RFI. This might be because the SNP effects segregating in the validation set were better estimated with such a training set. Overall, the comparison of accuracies between scenarios 4 to 6 and scenario 1 did not show an obvious effect of the removal of data of earlier generations from the training dataset. In a study using six levels of truncated data of past generations, accuracies of GEBV of young genotyped pigs were very similar for various reproductive traits  [51].
Inappropriate merging of these matrices can originate from ignoring inbreeding in the structure of A and from changes in allele frequencies at QTL for the traits under selection. In our scenarios, the effect of selection in the last three generations of the validation sets was not explicitly accounted for. However, changes in marker allele frequencies in those generations were accounted for through the G matrix. Furthermore, the (co)variances used for genomic predictions were obtained from bivariate analyses including the selection criterion using the whole dataset (including validation generations). Therefore, there should be no effect of selection on the estimations of the variance components, and the prediction bias of the GEBVs should not be due to biased variance components. Computing separate accuracies and biases for sires (heavily selected) versus dams (not directly selected), could enable quantification of the effect of selection on the prediction biases.