Characterization and functional roles of paternal RNAs in 2–4 cell bovine embryos

Embryos utilize oocyte-donated RNAs until they become capable of producing RNAs through embryonic genome activation (EGA). The sperm\u2019s influence over pre-EGA RNA content of embryos remains unknown. Recent studies have revealed that sperm donate non-genomic components upon fertilization. Thus, sperm may also contribute to RNA presence in pre-EGA embryos. The first objective of this study was to investigate whether male fertility status is associated with the RNAs present in the bovine embryo prior to EGA. A total of 65 RNAs were found to be differentially expressed between 2\u20134 cell bovine embryos derived from high and low fertility sires. Expression patterns were confirmed for protein phosphatase 1 regulatory subunit 36 (PPP1R36) and ataxin 2 like (ATXN2L) in three new biological replicates. The knockdown of ATXN2L led to a 22.9% increase in blastocyst development. The second objective of this study was to characterize the parental origin of RNAs present in pre-EGA embryos. Results revealed 472 sperm-derived RNAs, 2575 oocyte-derived RNAs, 2675 RNAs derived from both sperm and oocytes, and 663 embryo-exclusive RNAs. This study uncovers an association of male fertility with developmentally impactful RNAs in 2\u20134 cell embryos. This study also provides an initial characterization of paternally-contributed RNAs to pre-EGA embryos. Furthermore, a subset of 2\u20134 cell embryo-specific RNAs was identified

Intensity modulated radiotherapy (IMRT) in the treatment of children and Adolescents - a single institution's experience and a review of the literature

<p>Abstract</p> <p>Background</p> <p>While IMRT is widely used in treating complex oncological cases in adults, it is not commonly used in pediatric radiation oncology for a variety of reasons. This report evaluates our 9 year experience using stereotactic-guided, inverse planned intensity-modulated radiotherapy (IMRT) in children and adolescents in the context of the current literature.</p> <p>Methods</p> <p>Between 1999 and 2008 thirty-one children and adolescents with a mean age of 14.2 years (1.5 - 20.5) were treated with IMRT in our department. This heterogeneous group of patients consisted of 20 different tumor entities, with Ewing's sarcoma being the largest (5 patients), followed by juvenile nasopharyngeal fibroma, esthesioneuroblastoma and rhabdomyosarcoma (3 patients each). In addition a review of the available literature reporting on technology, quality, toxicity, outcome and concerns of IMRT was performed.</p> <p>Results</p> <p>With IMRT individualized dose distributions and excellent sparing of organs at risk were obtained in the most challenging cases. This was achieved at the cost of an increased volume of normal tissue receiving low radiation doses. Local control was achieved in 21 patients. 5 patients died due to progressive distant metastases. No severe acute or chronic toxicity was observed.</p> <p>Conclusion</p> <p>IMRT in the treatment of children and adolescents is feasible and was applied safely within the last 9 years at our institution. Several reports in literature show the excellent possibilities of IMRT in selective sparing of organs at risk and achieving local control. In selected cases the quality of IMRT plans increases the therapeutic ratio and outweighs the risk of potentially increased rates of secondary malignancies by the augmented low dose exposure.</p

Introduction: ADSA and Interbull Joint Breeding and Genetics Symposia.

The Joint ADSA/Interbull Breeding and Genetics Symposia titled “Ten Years of Genomic Selection” and “Data Pipelines for Implementation of Genomic Evaluation of Novel Traits” were held at the 2019 ADSA Annual Meeting, from June 23 to 26, in Cincinnati, Ohio. The objective of the first symposium was to provide a broad overview of 10 years of genomic selection in dairy cattle. In 2009, genomic evaluations were first implemented in the United States and Canada, followed over time by evaluations in many other countries. The rapid uptake of genomic selection has had a dramatic effect on the dairy industry. This symposium highlighted the development and impact of this implementation in all dairy breeds and outlined new developments and future scenarios. The objective of the second symposium was to provide an international view of recent worldwide advances on the development of new data pipelines, with the overall objective of implementing genetic evaluations for novel traits in the era of genomics. The advent of genomics has created an opportunity to focus on and select for expensive traits, which was not feasible with traditional selection. This symposium provided various examples of national and international initiatives to pool data across countries or organizations to exploit the potential of accurate genomic evaluation for novel and expensive trait

Comparison of Poisson, probit and linear models for genetic analysis of number of inseminations to conception and success at first insemination in Iranian Holstein cows

The goals of this study were to estimate genetic parameters and to assess alternative models for genetic evaluation of number of inseminations to conception (INS) and success at first insemination (SF) in Iranian Holstein cows. Two models were considered for each trait: linear and probit models for SF, and linear and Poisson models for INS. Data consisted of 72,124 records of parities 1 to 6 from 27,113 cows having lactation between 1981 and 2007 and distributed over 15 large Holstein herds. Genetic parameters and goodness of fit statistics were estimated using the whole data set and predictive ability of models was assessed via a 4-fold cross-validation based on mean squared error of prediction (MSEP) and correlation between observed and fitted values. Estimates of heritability ranged from 0.039 to 0.062 for SF and 0.040 to 0.165 for INS. The performance of linear and probit models was very similar for SF. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For INS, the linear model performed better than the Poisson model according to goodness of fit statistics, but these two models showed the same predictive ability. Overall, nonlinear models did not outperform linear models for genetic evaluations of SF and INS in Iranian Holstein cows

Comparison of single\u2010breed and multi\u2010breed training populations for infrared predictions of novel phenotypes in holstein cows

In general, Fourier\u2010transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross\u2010validation scenarios aimed at increasing prediction for difficult\u2010to\u2010measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual\u2010purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum \u3b2\u2010hydroxybutyrate (BHB), and kappa casein (k\u2010CN) in the major cattle breed, i.e., Holstein\u2010Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual\u2010purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross\u2010validation (CV) strategies: a within\u2010breed scenario using 10\u2010fold cross\u2010validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10\u2010fold_HO); an across\u2010breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi\u2010breed scenario (BS+HO_10\u2010fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi\u2010breed scenario (Multi\u2010breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual\u2010purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi\u2010breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi\u2010breed. Within\u2010Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k\u2010CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for \u3ba\u2010CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k\u2010CN. Using multiple specialized and dual\u2010purpose animals in the training set outperforms the 10\u2010fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k\u2010CN. When the Multi\u2010breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual\u2010purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased

Integrating genomic and infrared spectral data improves the prediction of milk protein composition in dairy cattle

Background: Over the past decade, Fourier transform infrared (FTIR) spectroscopy has been used to predict novel milk protein phenotypes. Genomic data might help predict these phenotypes when integrated with milk FTIR spectra. The objective of this study was to investigate prediction accuracy for milk protein phenotypes when heterogeneous on-farm, genomic, and pedigree data were integrated with the spectra. To this end, we used the records of 966 Italian Brown Swiss cows with milk FTIR spectra, on-farm information, medium-density genetic markers, and pedigree data. True and total whey protein, and five casein, and two whey protein traits were analyzed. Multiple kernel learning constructed from spectral and genomic (pedigree) relationship matrices and multilayer BayesB assigning separate priors for FTIR and markers were benchmarked against a baseline partial least squares (PLS) regression. Seven combinations of covariates were considered, and their predictive abilities were evaluated by repeated random sub-sampling and herd cross-validations (CV). Results: Addition of the on-farm effects such as herd, days in milk, and parity to spectral data improved predictions as compared to those obtained using the spectra alone. Integrating genomics and/or the top three markers with a large effect further enhanced the predictions. Pedigree data also improved prediction, but to a lesser extent than genomic data. Multiple kernel learning and multilayer BayesB increased predictive performance, whereas PLS did not. Overall, multilayer BayesB provided better predictions than multiple kernel learning, and lower prediction performance was observed in herd CV compared to repeated random sub-sampling CV. Conclusions: Integration of genomic information with milk FTIR spectral can enhance milk protein trait predictions by 25% and 7% on average for repeated random sub-sampling and herd CV, respectively. Multiple kernel learning and multilayer BayesB outperformed PLS when used to integrate heterogeneous data for phenotypic predictions

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood \u3b2-hydroxybutyrate (BHB, mmol/L), and (3) \u3ba-casein expressed as a percentage of nitrogen (\u3ba-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and \u3ba-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for \u3ba-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle