Prediction of likelihood of conception in dairy cows using milk mid-infrared spectra collected before the first insemination and machine learning algorithms.

Accurate and ex-ante prediction of cows' likelihood of conception (LC) based on milk composition information could improve reproduction management on dairy farms. Milk composition is already routinely measured by mid-infrared (MIR) spectra, which are known to change with advancing stages of pregnancy. For lactating cows, MIR spectra may also be used for predicting the LC. Our objectives were to classify the LC at first insemination using milk MIR spectra data collected from calving to first insemination and to identify the spectral regions that contribute the most to the prediction of LC at first insemination. After quality control, 4,866 MIR spectra, milk production, and reproduction records from 3,451 Holstein cows were used. The classification accuracy and area under the curve (AUC) of 6 models comprising different predictors and 3 machine learning methods were estimated and compared. The results showed that partial least square discriminant analysis (PLS-DA) and random forest had higher prediction accuracies than logistic regression. The classification accuracy of good and poor LC cows and AUC in herd-by-herd validation of the best model were 76.35 ± 10.60% and 0.77 ± 0.11, respectively. All wavenumbers with values of variable importance in the projection higher than 1.00 in PLS-DA belonged to 3 spectral regions, namely from 1,003 to 1,189, 1,794 to 2,260, and 2,300 to 2,660 cm-1. In conclusion, the model can predict LC in dairy cows from a high productive TMR system before insemination with a relatively good accuracy, allowing farmers to intervene in advance or adjust the insemination schedule for cows with a poor predicted LC.

MicroRNA 29 modulates ß-cell mitochondrial metabolism and insulin secretion via underlying miR-29-OXPHOS complex pathways.

AIM: MicroRNAs (miRNAs) regulate ß-cell function, and ß-cell mitochondria and insulin secretion are perturbed in diabetes. We aimed to identify key miRNAs regulating ß-cell mitochondrial metabolism and novel ß-cell miRNA-mitochondrial pathways. METHODS: TargetScan (http://www.targetscan.org/) was used to predict if 16 miRNAs implicated in ß-cell function target 27 cis-eGenes implicated in mitochondrial activity. The expression of candidate miRNAs and insulin secretion after 24 and 1 h pre-incubation in 2.8, 11.1- and 16.7-mM glucose was measured in clonal INS-1 832/13 ß-cells. MiR-29 silenced INS-1 832/13 cells were assessed for insulin secretion (glucose, pyruvate, and K+), target cis-eGene expression (Ndufv3 and Ndufa10 components of mitochondrial complex I (CI)), OXPHOS (CI-V) protein expression, and mitochondrial OXPHOS respiration/activity. The expression of differentially expressed miR-29 miRNAs was evaluated in Goto-Kakizaki (GK) rat, db/db mouse and type 2 diabetic (T2D) human islets, as well as NMRI mouse islets cultured under glucolipotoxic conditions. RESULTS: MiR-29, miR-15 and miR-124 were predicted to regulate ~20 cis-eGenes, while miR-29 alone was predicted to regulate ≥12 of these in rat and human species. MiR-29 expression and insulin secretion were reduced in INS-1 832/13 cells after 24 h in elevated glucose. MiR-29 knockdown increased all tested insulin secretory responses, Nudfv3, Ndufa10, complex I and II expression, and cellular mitochondrial OXPHOS. MiR-29 expression was reduced in db/db islets but increased in GK rat and T2D human islets. CONCLUSION: We conclude miR-29 is a key miRNA in regulating ß-cell mitochondrial metabolism and insulin secretion via underlying miR-29-OXPHOS complex pathways. Furthermore, we infer reduced miR-29 expression compensatorily enhances insulin secretion under glucotoxicity.

Identification and characterisation of de novo germline structural variants in two commercial pig lines using trio-based whole genome sequencing.

BACKGROUND: De novo mutations arising in the germline are a source of genetic variation and their discovery broadens our understanding of genetic disorders and evolutionary patterns. Although the number of de novo single nucleotide variants (dnSNVs) has been studied in a number of species, relatively little is known about the occurrence of de novo structural variants (dnSVs). In this study, we investigated 37 deeply sequenced pig trios from two commercial lines to identify dnSVs present in the offspring. The identified dnSVs were characterised by identifying their parent of origin, their functional annotations and characterizing sequence homology at the breakpoints. RESULTS: We identified four swine germline dnSVs, all located in intronic regions of protein-coding genes. Our conservative, first estimate of the swine germline dnSV rate is 0.108 (95% CI 0.038-0.255) per generation (one dnSV per nine offspring), detected using short-read sequencing. Two detected dnSVs are clusters of mutations. Mutation cluster 1 contains a de novo duplication, a dnSNV and a de novo deletion. Mutation cluster 2 contains a de novo deletion and three de novo duplications, of which one is inverted. Mutation cluster 2 is 25 kb in size, whereas mutation cluster 1 (197 bp) and the other two individual dnSVs (64 and 573 bp) are smaller. Only mutation cluster 2 could be phased and is located on the paternal haplotype. Mutation cluster 2 originates from both micro-homology as well as non-homology mutation mechanisms, where mutation cluster 1 and the other two dnSVs are caused by mutation mechanisms lacking sequence homology. The 64 bp deletion and mutation cluster 1 were validated through PCR. Lastly, the 64 bp deletion and the 573 bp duplication were validated in sequenced offspring of probands with three generations of sequence data. CONCLUSIONS: Our estimate of 0.108 dnSVs per generation in the swine germline is conservative, due to our small sample size and restricted possibilities of dnSV detection from short-read sequencing. The current study highlights the complexity of dnSVs and shows the potential of breeding programs for pigs and livestock species in general, to provide a suitable population structure for identification and characterisation of dnSVs.

Observational study on associations between resilience indicators based on daily milk yield in first lactation and lifetime profitability.

Resilience is the ability of cows to be minimally affected by disturbances, such as pathogens, heat waves, and changes in feed quality, or to quickly recover. Obvious advantages of resilience are good animal welfare and easy and pleasant management for farmers. Furthermore, economic effects are also expected, but these remain to be determined. The goal of this study was to investigate the association between resilience and lifetime gross margin, using indicators of resilience calculated from fluctuations in daily milk yield using an observational study. Resilience indicators and lifetime gross margin were calculated for 1,325 cows from 21 herds. These cows were not alive anymore and, therefore, had complete lifetime data available for many traits. The resilience indicators were the natural log-transformed variance (LnVar) and the lag-1 autocorrelation (rauto) of daily milk yield deviations from cow-specific lactation curves in parity 1. Good resilience is indicated by low LnVar (small yield response to disturbances) and low rauto (quick yield recovery to baseline). Lifetime gross margin was calculated as the sum of all revenues minus the sum of all costs throughout life. Included revenues were from milk, calf value, and slaughter of the cow. Included costs were from feed, rearing, insemination, management around calving, disease treatments, and destruction in case of death on farm. Feed intake was unknown and, therefore, lifetime feed costs had to be estimated based on milk yield records. The association of each resilience indicator with lifetime gross margin, and also with the underlying revenues and costs, was investigated using analysis of covariance (ANCOVA) models. Mean daily milk yield in first lactation, herd, and year of birth were included as covariates and factors. Natural log-transformed variance had a significantly negative association with lifetime gross margin, which means that cows with stable milk yield (low LnVar, good resilience) in parity 1 generated on average a higher lifetime gross margin than cows that had the same milk yield level but with more fluctuations. The association with lifetime gross margin could be mainly attributed to higher lifetime milk revenues for cows with low LnVar, due to a longer lifespan. Unlike LnVar, rauto was not significantly associated with lifetime gross margin or any of the underlying lifetime costs and revenues. However, it was significantly associated with yearly treatment costs, which is important for ease of management. In conclusion, the importance of resilience for total profit generated by a cow at the end of life was confirmed by the significant association of LnVar with lifetime gross margin, although effects of differences in feed efficiency between resilient and less resilient cows remain to be studied. The economic advantage can be mainly ascribed to benefits of long lifespan.

Characterization of an immune-evading doxycycline-inducible lentiviral vector for gene therapy in the spinal cord.

Gene therapy is a powerful approach to promote spinal cord regeneration. For a clinical application it is important to restrict therapeutic gene expression to the appropriate time window to limit unwanted side effects. The doxycycline (dox)-inducible system is a widely used regulatable gene expression platform, however, this system depends on a bacterial-derived immunogenic transactivator. The foreign origin of this transactivator prevents reliable regulation of therapeutic gene expression and currently limits clinical translation. The glycine-alanine repeat (GAR) of Epstein-Barr virus nuclear antigen-1 protein inhibits its presentation to cytotoxic T cells, allowing virus-infected cells to evade the host immune system. We developed a chimeric transactivator (GARrtTA) and show that GARrtTA has an immune-evading advantage over "classical" rtTA in vivo. Direct comparison of lentiviral vectors expressing rtTA and GARrtTA in the rat spinal cord shows that the GARrtTA system is inducible for 6 doxycycline-cycles over a 47 week period, whereas with the rtTA-based system luciferase reporter expression declines during the 3rd cycle and is no longer re-inducible, indicating that GARrtTA provides an immune-advantage over rtTA. Immunohistochemistry revealed that GARrtTA expressing cells in the spinal cord appear healthier and survive better than rtTA expressing cells. Characterization of the immune response shows that expression of GARrtTA, in contrast to rtTA, does not recruit cytotoxic T-cells to the transduced spinal cord. This study demonstrates that fusion of the GAR domain to rtTA results in a functional doxycycline-inducible transactivator with a clear immune-advantage over the classical rtTA in vivo.

Estimation of macro- and micro-genetic environmental sensitivity in unbalanced datasets.

Genotype-by-environment interaction is caused by variation in genetic environmental sensitivity (GES), which can be subdivided into macro- and micro-GES. Macro-GES is genetic sensitivity to macro-environments (definable environments often shared by groups of animals), while micro-GES is genetic sensitivity to micro-environments (individual environments). A combined reaction norm and double hierarchical generalised linear model (RN-DHGLM) allows for simultaneous estimation of base genetic, macro- and micro-GES effects. The accuracy of variance components estimated using a RN-DHGLM has been explicitly studied for balanced data and recommendation of a data size with a minimum of 100 sires with at least 100 offspring each have been made. In the current study, the data size (numbers of sires and progeny) and structure requirements of the RN-DHGLM were investigated for two types of unbalanced datasets. Both datasets had a variable number of offspring per sire, but one dataset also had a variable number of offspring within macro-environments. The accuracy and bias of the estimated macro- and micro-GES effects and the estimated breeding values (EBVs) obtained using the RN-DHGLM depended on the data size. Reasonably accurate and unbiased estimates were obtained with data containing 500 sires with 20 offspring or 100 sires with 50 offspring, regardless of the data structure. Variable progeny group sizes, alone or in combination with an unequal number of offspring within macro-environments, had little impact on the dispersion of the EBVs or the bias and accuracy of variance component estimation, but resulted in lower accuracies of the EBVs. Compared to genetic correlations of zero, a genetic correlation of 0.5 between base genetic, macro- and micro-GES components resulted in a slight decrease in the percentage of replicates that converged out of 100 replicates, but had no effect on the dispersion and accuracy of variance component estimation or the dispersion of the EBVs. The results show that it is possible to apply the RN-DHGLM to unbalanced datasets to obtain estimates of variance due to macro- and micro-GES. Furthermore, the levels of accuracy and bias of variance estimates when analysing macro- and micro-GES simultaneously are determined by average family size, with limited impact from variability in family size and/or cohort size. This creates opportunities for the use of field data from populations with unbalanced data structures when estimating macro- and micro-GES.

Mitochondrial clearance of calcium facilitated by MICU2 controls insulin secretion.

OBJECTIVE: Transport of Ca2+ into pancreatic ß cell mitochondria facilitates nutrient-mediated insulin secretion. However, the underlying mechanism is unclear. Recent establishment of the molecular identity of the mitochondrial Ca2+ uniporter (MCU) and associated proteins allows modification of mitochondrial Ca2+ transport in intact cells. We examined the consequences of deficiency of the accessory protein MICU2 in rat and human insulin-secreting cells and mouse islets. METHODS: siRNA silencing of Micu2 in the INS-1 832/13 and EndoC-ßH1 cell lines was performed; Micu2-/- mice were also studied. Insulin secretion and mechanistic analyses utilizing live confocal imaging to assess mitochondrial function and intracellular Ca2+ dynamics were performed. RESULTS: Silencing of Micu2 abrogated GSIS in the INS-1 832/13 and EndoC-ßH1 cells. The Micu2-/- mice also displayed attenuated GSIS. Mitochondrial Ca2+ uptake declined in MICU2-deficient INS-1 832/13 and EndoC-ßH1 cells in response to high glucose and high K+. MICU2 silencing in INS-1 832/13 cells, presumably through its effects on mitochondrial Ca2+ uptake, perturbed mitochondrial function illustrated by absent mitochondrial membrane hyperpolarization and lowering of the ATP/ADP ratio in response to elevated glucose. Despite the loss of mitochondrial Ca2+ uptake, cytosolic Ca2+ was lower in siMICU2-treated INS-1 832/13 cells in response to high K+. It was hypothesized that Ca2+ accumulated in the submembrane compartment in MICU2-deficient cells, resulting in desensitization of voltage-dependent Ca2+ channels, lowering total cytosolic Ca2+. Upon high K+ stimulation, MICU2-silenced cells showed higher and prolonged increases in submembrane Ca2+ levels. CONCLUSIONS: MICU2 plays a critical role in ß cell mitochondrial Ca2+ uptake. ß cell mitochondria sequestered Ca2+ from the submembrane compartment, preventing desensitization of voltage-dependent Ca2+ channels and facilitating GSIS.

Tools for optimising pharmacotherapy in psychiatry (therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests): focus on antidepressants.

Objectives: More than 40 drugs are available to treat affective disorders. Individual selection of the optimal drug and dose is required to attain the highest possible efficacy and acceptable tolerability for every patient.Methods: This review, which includes more than 500 articles selected by 30 experts, combines relevant knowledge on studies investigating the pharmacokinetics, pharmacodynamics and pharmacogenetics of 33 antidepressant drugs and of 4 drugs approved for augmentation in cases of insufficient response to antidepressant monotherapy. Such studies typically measure drug concentrations in blood (i.e. therapeutic drug monitoring) and genotype relevant genetic polymorphisms of enzymes, transporters or receptors involved in drug metabolism or mechanism of action. Imaging studies, primarily positron emission tomography that relates drug concentrations in blood and radioligand binding, are considered to quantify target structure occupancy by the antidepressant drugs in vivo. Results: Evidence is given that in vivo imaging, therapeutic drug monitoring and genotyping and/or phenotyping of drug metabolising enzymes should be an integral part in the development of any new antidepressant drug.Conclusions: To guide antidepressant drug therapy in everyday practice, there are multiple indications such as uncertain adherence, polypharmacy, nonresponse and/or adverse reactions under therapeutically recommended doses, where therapeutic drug monitoring and cytochrome P450 genotyping and/or phenotyping should be applied as valid tools of precision medicine.

Competitive gene flow does not necessarily maximize the genetic gain of genomic breeding programs in the presence of genotype-by-environment interaction.

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (Ndams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (rg) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (Gd), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 Gd_rg combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21-1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain.

Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level.

Resilient cows are minimally affected in their functioning by disturbances, and if affected, they quickly recover. Previously, the variance and autocorrelation of daily deviations from a lactation curve were proposed as resilience indicators. These traits were heritable and genetically associated with good health and longevity. However, it was unknown if selection for these indicators would lead to desired changes in the phenotype. The first aim of this study was to investigate if forward prediction of the resilience indicators in another environment was possible. Therefore, the resilience indicator records were split into 2 subsets, each containing half of the daughters of each sire, split within sire into cows that calved in early year-seasons and cows that calved in more recent year-seasons. Genetic correlations between the subsets were then estimated for each resilience indicator. The second aim was to estimate genetic correlations between the resilience indicators and traits describing production responses to actual disturbances. The disturbances were a heat wave in July 2015 and yield disturbances at herd level. The latter were selected by decreases in mean yield of all primiparous cows in a herd, indicating that a disturbance occurred. The data set used for calculation of the resilience indicators and the traits describing yield responses contained 62,932,794 daily milk yield records on 199,104 primiparous cows. Genetic correlations (rg) between recent and earlier daughter groups were 1 for both resilience indicators, which suggests that selection will result in changes in the phenotype in the next generation. Furthermore, low variance was genetically correlated with weak response in milk yield to both the heat wave and herd disturbances (rg 0.47 to 0.97). Low autocorrelation was genetically correlated with reduced perturbation length and quick recovery after the heat wave and herd disturbances (0.28 to 0.97). These results suggest that variance and autocorrelation cover different aspects of resilience, and should be combined in a resilience index. In conclusion, genetic selection for the resilience indicators will likely result in favorable changes in the traits themselves, and in response and recovery to actual disturbances, which confirms that they are useful resilience indicators.

Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages.

Resilience is the ability of cows to cope with disturbances, such as pathogens or heat waves. To breed for improved resilience, it is important to know whether resilience genetically changes throughout life. Therefore, the aim was to perform a genetic analysis on 2 resilience indicators based on data from 3 periods of the first lactation (d 11-110, 111-210, and 211-340) and the first 3 full lactations, and to estimate genetic correlations with health traits. The resilience indicators were the natural log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily deviations in milk yield from an expected lactation curve. Low LnVar and rauto indicate low variability in daily milk yield and quick recovery, and were expected to indicate good resilience. Data of 200,084 first, 155,784 second, and 89,990 third lactations were used. Heritabilities were similar based on different lactation periods (0.12-0.15 for LnVar, 0.05-0.06 for rauto). However, the heritabilities of the resilience indicators based on full first lactation were higher than those based on lactation periods (0.20 for LnVar, 0.08 for rauto), due to lower residual variances. Heritabilities decreased from 0.20 in full lactation 1 to 0.19 in full lactation 3 for LnVar and from 0.08 to 0.06 for rauto. For LnVar, as well as for rauto, the strongest genetic correlation between lactation periods was between period 2 and 3 (0.97 for LnVar, 0.96 for rauto) and the weakest between period 1 and 3 (0.81 for LnVar, 0.65 for rauto). Similarly, for both traits the genetic correlation between full lactations was strongest between lactations 2 and 3 (0.99 for LnVar, 0.95 for rauto) and weakest between lactations 1 and 3 (0.91 for LnVar, 0.71 for rauto). For LnVar, genetic correlations with resilience-related traits, such as udder health, ketosis, and longevity, adjusted for correlations with milk yield, were almost always favorable (-0.59 to 0.02). In most cases these genetic correlations were stronger based on full lactations than on lactation periods. Genetic correlations were similar across full lactations, but the correlation with udder health increased substantially from -0.31 in lactation 1 to -0.51 in lactation 3. For rauto, genetic correlations with resilience-related traits were always favorable in lactation period 1 and in most full lactations, but not in the other lactation periods. However, correlations were weak (-0.27 to 0.15). Therefore, as a resilience indicator for breeding, LnVar is preferred over rauto. A multitrait index based on estimated breeding values for LnVar in lactations 1, 2, and 3 is recommended to improve resilience throughout the lifetime of a cow.

Between-herd variation in resilience and relations to herd performance.

Resilient cows are minimally affected in their functioning by infections and other disturbances, and recover quickly. Herd management is expected to have an effect on disturbances and the resilience of cows, and this effect was investigated in this study. Two resilience indicators were first recorded on individual cows. The effect of herd-year on these resilience indicators was then estimated and corrected for genetic and year-season effects. The 2 resilience indicators were the variance and the lag-1 autocorrelation of daily milk yield deviations from an expected lactation curve. Low variance and autocorrelation indicate that a cow does not fluctuate much around her expected milk yield and is, thus, subject to few disturbances, or little affected by disturbances (resilient). The herd-year estimates of the resilience indicators were estimated for 9,917 herd-year classes based on records of 227,655 primiparous cows from 2,644 herds. The herd-year estimates of the resilience indicators were then related to herd performance variables. Large differences in the herd-year estimates of the 2 resilience indicators (variance and autocorrelation) were observed between herd-years, indicating an effect of management on these traits. Furthermore, herd-year classes with a high variance tended to have a high proportion of cows with a rumen acidosis indication (r = 0.31), high SCS (r = 0.19), low fat content (r = -0.18), long calving interval (r = 0.14), low survival to second lactation (r = -0.13), large herd size (r = 0.12), low lactose content (r = -0.12), and high production (r = 0.10). These correlations support that herds with high variance are not resilient. The correlation between the variance and the proportion of cows with a rumen acidosis indication suggests that feed management may have an important effect on the variance. Herd-year classes with a high autocorrelation tended to have a high proportion of cows with a ketosis indication (r = 0.14) and a high production (r = 0.13), but a low somatic cell score (r = -0.17) and a low proportion of cows with a rumen acidosis indication (r = -0.12). These correlations suggest that high autocorrelation at herd level indicates either good or poor resilience, and is thus a poor resilience indicator. However, the combination of a high variance and a high autocorrelation is expected to indicate many fluctuations with slow recovery. In conclusion, herd management, in particular feed management, seems to affect herd resilience.

Antibody and Local Cytokine Response to Respiratory Syncytial Virus Infection in Community-Dwelling Older Adults.

Respiratory syncytial virus (RSV) is increasingly recognized for causing severe morbidity and mortality in older adults, but there are few studies on the RSV-induced immune response in this population. Information on the immunological processes at play during RSV infection in specific risk groups is essential for the rational and targeted design of novel vaccines and therapeutics. Here, we assessed the antibody and local cytokine response to RSV infection in community-dwelling older adults (≥60 years of age). During three winters, serum and nasopharyngeal swab samples were collected from study participants during acute respiratory infection and recovery. RSV IgG enzyme-linked immunosorbent assays (ELISA) and virus neutralization assays were performed on serum samples from RSV-infected individuals (n = 41) and controls (n = 563 and n = 197, respectively). Nasal RSV IgA and cytokine concentrations were determined using multiplex immunoassays in a subset of participants. An in vitro model of differentiated primary bronchial epithelial cells was used to assess RSV-induced cytokine responses over time. A statistically significant increase in serum neutralization titers and IgG concentrations was observed in RSV-infected participants compared to controls. During acute RSV infection, a statistically significant local upregulation of beta interferon (IFN-ß), IFN-λ1, IFN-γ, interleukin 1ß (IL-1ß), tumor necrosis factor alpha (TNF-α), IL-6, IL-10, CXCL8, and CXCL10 was found. IFN-ß, IFN-λ1, CXCL8, and CXCL10 were also upregulated in the epithelial model upon RSV infection. In conclusion, this study provides novel insights into the basic immune response to RSV infection in an important and understudied risk population, providing leads for future studies that are essential for the prevention and treatment of severe RSV disease in older adults.IMPORTANCE Respiratory syncytial virus (RSV) can cause severe morbidity and mortality in certain risk groups, especially infants and older adults. Currently no (prophylactic) treatment is available, except for a partially effective yet highly expensive monoclonal antibody. RSV therefore remains a major public health concern. To allow targeted development of novel vaccines and therapeutics, it is of great importance to understand the immunological mechanisms that underlie (protection from) severe disease in specific risk populations. Since most RSV-related studies focus on infants, there are only very limited data available concerning the response to RSV in the elderly population. Therefore, in this study, RSV-induced antibody responses and local cytokine secretion were assessed in community-dwelling older adults. These data provide novel insights that will benefit ongoing efforts to design safe and effective prevention and treatment strategies for RSV in an understudied risk group.

Possibilities for a specific breeding program for organic dairy production.

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding.

The ability of a cow to cope with environmental disturbances, such as pathogens or heat waves, is called resilience. To improve resilience through breeding, we need resilience indicators, which could be based on the fluctuation patterns in milk yield resulting from disturbances. The aim of this study was to explore 3 traits that describe fluctuations in milk yield as indicators for breeding resilient cows: the variance, autocorrelation, and skewness of the deviations from individual lactation curves. We used daily milk yield records of 198,754 first-parity cows, recorded by automatic milking systems. First, we estimated a lactation curve for each cow using 4 different methods: moving average, moving median, quantile regression, and Wilmink curve. We then calculated the log-transformed variance (LnVar), lag-1 autocorrelation (rauto), and skewness (Skew) of the daily deviations from these curves as resilience indicators. A genetic analysis of the resilience indicators was performed, and genetic correlations between resilience indicators and health, longevity, fertility, metabolic, and production traits were estimated. The heritabilities differed between LnVar (0.20 to 0.24), rauto (0.08 to 0.10), and Skew (0.01 to 0.02), and the genetic correlations among the indicators were weak to moderate. For rauto and Skew, genetic correlations with health, longevity, fertility, and metabolic traits were weak or the opposite of what we expected. Therefore, rauto and Skew have limited value as resilience indicators. However, lower LnVar was genetically associated with better udder health (genetic correlations from -0.22 to -0.32), better longevity (-0.28 to -0.34), less ketosis (-0.27 to -0.33), better fertility (-0.06 to -0.17), higher BCS (-0.29 to -0.40), and greater dry matter intake (-0.53 to -0.66) at the same level of milk yield. These correlations support LnVar as an indicator of resilience. Of all 4 curve-fitting methods, LnVar based on quantile regression systematically had the strongest genetic correlations with health, longevity, and fertility traits. Thus, quantile regression is considered the best curve-fitting method. In conclusion, LnVar based on deviations from a quantile regression curve is a promising resilience indicator that can be used to breed cows that are better at coping with disturbances.

Genomic selection improves the possibility of applying multiple breeding programs in different environments.

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Pathogenesis of Respiratory Syncytial Virus Infection in BALB/c Mice Differs Between Intratracheal and Intranasal Inoculation.

Human respiratory syncytial virus (RSV) is a major cause of severe lower respiratory tract disease requiring hospitalization in infants. There are no market-approved vaccines or antiviral agents available, but a growing number of vaccines and therapeutics are in (pre)clinical stages of development. Reliable animal models are crucial to evaluate new vaccine concepts, but in vivo RSV research is hampered by the lack of well-characterized animal models that faithfully mimic the pathogenesis of RSV infection in humans. Mice are frequently used in RSV infection and vaccination studies. However, differences in the use of mouse strains, RSV subtypes, and methodology often lead to divergent study outcomes. To our knowledge, a comparison between different RSV inoculation methods in mice has not been described in the literature, even though multiple methods are being used across different studies. In this study, we evaluated various pathological and immunological parameters in BALB/c mice after intratracheal or intranasal inoculation with RSV-A2. Our study reveals that intranasal inoculation induces robust pathology and inflammation, whereas this is not the case for intratracheal inoculation. As immunopathology is an important characteristic of RSV disease in infants, these data suggest that in mice intranasal inoculation is a more appropriate method to study RSV infection than intratracheal inoculation. These findings will contribute to the rational experimental design of future in vivo RSV experiments.

The Effect of the Time Interval Between Radiation and Hyperthermia on Clinical Outcome in 400 Locally Advanced Cervical Carcinoma Patients.

Background: Addition of deep hyperthermia to radiotherapy results in improved local control (LC) and overall survival compared to radiotherapy alone in cervical carcinoma patients. Based on preclinical data, the time interval between radiotherapy, and hyperthermia is expected to influence treatment outcome. Clinical studies addressing the effect of time interval are sparse. The repercussions for clinical applications are substantial, as the time between radiotherapy and hyperthermia should be kept as short as possible. In this study, we therefore investigated the effect of the time interval between radiotherapy and hyperthermia on treatment outcome. Methods: We analyzed all primary cervical carcinoma patients treated between 1996 and 2016 with thermoradiotherapy at our institute. Data on patients, tumors and treatments were collected, including the thermal dose parameters TRISE and CEM43T90. Follow-up data on tumor status and survival as well as late toxicity were collected. Data was analyzed using Cox proportional hazards analysis and Kaplan Meier analysis. Results: 400 patients were included. Kaplan Meier and univariate Cox analysis showed no effect of the time interval (range 30-230 min) on any clinical outcome measure. Besides known prognostic factors, thermal dose parameters TRISE and CEM43T90 had a significant effect on LC. In multivariate analysis, the thermal dose parameter TRISE (HR 0.649; 95% CI 0.501-0.840) and the use of image guided brachytherapy (HR 0.432; 95% CI 0.214-0.972), but not the time interval, were significant predictors of LC and disease specific survival. Conclusions: The time interval between radiotherapy and hyperthermia, up to 4 h, has no effect on clinical outcome. These results are re-ensuring for our current practice of delivering hyperthermia within maximal 4 h after radiotherapy.

Genetic analysis of udder conformation traits derived from automatic milking system recording in dairy cows.

Cartesian teat coordinates measured by automatic milking systems (AMS) provide new opportunities to record udder conformation traits and to study changes in udder conformation genetically and phenotypically within and between parities. The objective of this study was to estimate heritabilities and repeatabilities of AMS-based udder conformation traits within parities, to estimate genetic correlations between parities for AMS-based udder conformation traits, and to estimate genetic correlations between AMS-based udder conformation traits and classifier-based udder conformation traits, longevity, and udder health. Data from 70 herds, including 12,663 first-parity cows, 10,206 second-parity cows, and 7,627 third-parity cows, were analyzed using univariate and bivariate mixed animal models. Heritabilities of the AMS udder conformation traits were large (0.37-0.67) and genetic correlations between the AMS udder conformation traits and classifier-based traits were strong (>0.91). Repeatabilities within parities were large as well (0.89-0.97), indicating that a single record on udder conformation per lactation reflects udder conformation well. Genetic correlations of AMS udder conformation traits between parities were strong (0.88-1.00) and were stronger than the permanent environmental correlations. This shows that udder conformation changes over parities, but this change is mostly due to nongenetic factors. Based on these results, the current herd classification system, where cows are scored on udder conformation once in first parity, is sufficient. The AMS udder conformation traits as defined in this study have limited value as replacement for classifier-based udder conformation traits because they have smaller genetic correlations with functional traits than classifier-based traits. In summary, udder conformation hardly changes genetically between parities and is highly repeatable within parities. Udder conformation traits based on AMS need fine-tuning before they can replace classifier-based traits, and AMS teat coordinates probably contain additional information about udder health that is yet to be explored.