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Additional file 3: Arrive 10 check list.

Additional file 1: Custom Brachyspira #12977 and JH002 diet ingredient and nutrient specs.

Additional file 4: Humane Intervention Point checklist.

Additional file 2: Fecal consistency score.

Additional file 1. Raw absolute quantification gene expression data. Median copy number per 20 ng equivalent cDNA in maternal endometrium (END), fetal placenta (PLC), fetal liver (LVR), and fetal kidney (KID) tissues that were derived from fetuses of Sham inoculated control and PRRSV-2 challenged dams at 21 days post maternal infection. Color indicates median expression values of a given gene within a given tissue, with the highest value being coded green and lowest coded red within gene.

Additional file 1: Table S1. Comparison of the linear and cubic spline reaction norm model using NurCLc. Table S2. Comparison of the linear and cubic spline reaction norm model using FinCLc. Table S3. Comparison of the linear and cubic spline reaction norm model using NurCLg. Table S4. Comparison of the linear and cubic spline reaction norm model using FinCLg. Table S5. Comparison of the linear and cubic spline reaction norm model using CLg

Additional file 2: Figure S1. Estimates of genetic correlations from the linear and the cubic spline reaction norm model for average daily gain (ADG, kg/d) in the challenge nursery using challenge load derived from early finisher growth rate. Figure S2. Estimates of genetic correlations from the linear and the cubic spline reaction norm model for average daily gain (ADG, kg/d) in the finisher using challenge load derived from the clinical disease traits across the challenge nursery and finisher. Figure S3. Estimates of genetic correlations from the linear and the cubic spline reaction norm model for treatment rate in the challenge nursery using challenge load derived from the clinical disease traits across the challenge nursery and finisher. Figure S4. Estimates of genetic correlations from the linear and the cubic spline reaction norm model for treatment rate across the challenge nursery and finisher using challenge load derived from the clinical disease traits across the challenge nursery and finisher. Figure S5. Distribution and relationships of estimated breeding values for slope (including fixed effect estimate) from the cubic spline reaction norm model for average daily gain (ADG, kg/d) and treatment rate (TRT) in or across (combined) the challenge nursery and finisher. Figure S6. Distribution and relationships of estimated breeding values for spline coefficient (including fixed effect estimate) from the cubic spline reaction norm model for average daily gain (ADG, kg/d) and treatment rate (TRT) in or across (combined) the challenge nursery and finisher. Figure S7. Estimates of breeding values for four animals as a function of challenge load from the cubic spline reaction norm model for average daily gain (ADG, kg/d) and treatment rate (TRT) in or across (combined) the challenge nursery and finisher.

Supplemental material, sj-pdf-1-vet-10.1177_03009858221105053 for Effect of porcine reproductive and respiratory syndrome virus 2 on angiogenesis and cell proliferation at the maternal-fetal interface by Javier A. Barrera-Zarate, Susan E. Detmer, J. Alex Pasternak, Glenn Hamonic, Daniel J. MacPhee and John C. S. Harding in Veterinary Pathology

Additional file 3: Table S2. Browsing regions for candidate genes located within 1-Mb on either side of lead SNPs (FDR

Additional file 2. Experiment 1 (21 DPI) PRRSV RNA concentration by fetal group. Viral load expressed in log (base 10) in fetal serum (left) and thymus (right) for each of the four phenotypical fetal groups: non-infected control (CTRL), PRRSV-infected low viral load viable (LVL), PRRSV-infected high viral load viable (HVL-VIA), PRRSV-infected high viral load meconium stained (VHL-MEC).

Additional file 1: Fig. S1. Custom-made individual water intake recording system, consisting of a 3 L bowl, closed on 3 sides to reduce waste, with a nipple that can be activated by the pig, and an in-line water meter, as well as a water level meter for the bowl. The system allows for each visit by an individual pig, identified by radio frequency tags, the recording of time of day, duration, and water disappearance from the nipple and from the bowl. Fig. S2. Raw daily feed intake (FI) and water disappearance (WI) for a randomly selected animal (0132). Both WI and FI had large day-to-day variation and had concurrent drops at around 120 and 160 days. Fig. S3. Raw daily feed intake duration (FIDUR) and water intake duration (WIDUR) for a randomly selected animal (0132). Fig. S4. Raw daily feed intake visits (FInVisits) and water intake visits (WInVisits) for a randomly selected animal (0132). Fig. S5. Raw daily feed intake rate (FIRT) and water disappearance rate (WIRT) for a randomly selected animal (0132). Fig. S6. Raw water disappearance (WI) and predicted water disappearance (WI_RR) patterns defined using quadratic random regression model for for individual pigs in batch 1A. Fig. S7. Histogram for average daily water disappearance (ADWI) and water dispensed (ADWD). Fig. S8. Raw daily water disappearance (WI), number of visits (WInVisits), and duration (WIDUR) for a randomly selected animal (0159).

Additional file 3. Experiment 2 (12 DPI) PRRSV RNA concentration by fetal group. Viral load expressed in log (base 10) in fetal serum (left) and thymus (right) for each of the four phenotypical fetal groups in heart (A), brain (B), and thymus (C): non-infected control (CTRL), uninfected fetuses from inoculated dams (UNINF), PRRSV-infected high viral load viable (HVL-VIA), PRRSV-infected high viral load meconium stained (VHL-MEC) fetuses.

Additional file 1: Fig. S1. Illustration of gene annotation, combining the Ensembl (ENS) pig reference genome sequence assembly, release 92 (25,580 genes) and ISO-seq (ISO) based annotation (24,486 genes). Fig. S2. Distribution of genes based on the proportion of samples with non-zero count for each gene. Genes with non-zero counts in at least 80% of samples were used in further analyses (15,872 genes). Fig. S3. Boxplots of normalized read counts across the 912 samples by count per million (CPM; a) and the trimmed mean of M values (TMM; b) based on the EdgeR package in R (b). The log2 transformation was applied to the normalized counts plus 1 to obtain scaled expression values. Fig. S4. The number and overlap of genes whose expression was significantly (FDR

Additional file 1: Figure S1. Violin plots for descriptive statistics for white blood cell traits in Blood 1, Blood 3, and Blood 4 collected at 2-weeks before, and at 2- and 6-weeks after the challenge, respectively. Figure S2. Violin plots for descriptive statistics for red blood cell traits in Blood 1, Blood 3, and Blood 4 collected at 2-weeks before, and at 2- and 6-weeks after the challenge, respectively. Figure S3. Violin plots for descriptive statistics for platelet traits in Blood 1, Blood 3, and Blood 4 collected at 2-weeks before, and at 2- and 6-weeks after the challenge, respectively. Table S1. Genetic correlations between red blood cell traits of MCH, MCV, and RBC traits in Blood 1, Blood 3, and Blood 4.

Additional file 2: Figure S4. Multidimensional scaling (MDS) plots showing the first three dimensions (C1, C2, and C3) of the population structure for genotyped animals based on pairwise identity-by-state distance. Figure S5. (A) Manhattan plot for EOSB3. (B) Quantile-Quantile plot for EOSB3; Figure S6. (A) Manhattan plot for MONO ∆ 13. (B) Quantile-Quantile plot for MONO ∆ 13; Figure S7. Manhattan plots (A, C, E) and Quantile-Quantile plots (B, D, F) for MCH in Blood 1, Blood 3, and Blood 4, respectively; Figure S8. Manhattan plots (A, C) and Quantile-Quantile plots (B, D) for MCHC in Blood 3 and for the change of MCHC from Blood 1 to Blood 4, respectively; Figure S9. Manhattan plots (A, C, E) and Quantile-Quantile plots (B, D, F) for MCV in Blood 1, Blood 3, and Blood 4, respectively; Figure S10. Manhattan plots (A, C, E) and Quantile-Quantile plots (B, D, F) for RBC in Blood 1, Blood 3, and Blood 4, respectively; Figure S11. Manhattan plots (A, C) and Quantile-Quantile plots (B, D) for MPV in Blood 1 and Blood 4, respectively; Figure S12. Manhattan plots (A, C, E) and Quantile-Quantile plots (B, D, F) for PLT in Blood 1, Blood 3, and Blood 4, respectively.

Additional file 2.

Supplemental material, sj-pdf-1-vdi-10.1177_1040638720985825 for Samples sizes required to accurately quantify viral load and histologic lesion severity at the maternal–fetal interface of PRRSV-inoculated pregnant gilts by Carolina M. Malgarin, Javier B. Zarate, Predrag Novakovic, Susan E. Detmer, Daniel J. MacPhee and John C. S. Harding in Journal of Veterinary Diagnostic Investigation

Additional file 1: Table 1. Summary of calibrator and reaction control results for ELISA assays. Table 2. Summary of calibrator and reaction control results for Immunoturbidimetry assay.

Additional file 1. TUNEL staining in three fetal tissues of uninfected control fetuses. Scant cells with TUNEL positive staining are observed at higher magnification of fetal thymus (B) and liver (D), but not in heart (F).

Additional file 1: Figure S1. Distribution of sample/positive ratios (S/P) for IgG and IgM natural antibodies and for total IgG in blood of young healthy piglets. Figure S2. The LD-decay in a r2 vs distance plot for chromosome 7. Table S1. The list of genes located in or around the significant genomic region windows associated with loge-transformed sample/positive ratios for IgG natural antibodies. Table S2. The list of potential genes around the significant genomic region windows associated with IgM NAb levels.