Effects of Providing Novel Feedstuffs to Livestock on Production and Skeletal Muscle Growth

As the population increases and available land for food production decreases, it is necessary for livestock producers to continually work towards increasing livestock production efficiency. In livestock operations, feed accounts for the majority of input costs associated with raising livestock. As such, it is necessary to improve growth and production of livestock animals, while also optimizing feed utilization. Different feedstuffs can be included in the diet of livestock animals to maximize growth and production. However, the effects of some of these novel feedstuffs on growth and production of livestock animals has not been elucidated. As such, we investigated the effects of including two novel alfalfa products, ProLEAF MAX™ (a pellet composed of alfalfa leaves) and ProFiber Plus™ (alfalfa stems), in the diets of beef steers, dairy heifers, and lactating dairy cows. We hypothesized that inclusion of ProLEAF MAX™ and ProFiber Plus™ in the diet would result in improved growth and performance of beef steers, growth and development of dairy heifers, and milk yield and milk components of lactating dairy cows. We found that inclusion of ProFiber Plus™ in the diet of beef steers and dairy heifers decreases feed costs without affecting overall growth in steers, but decreases growth in dairy heifers and inclusion of the two novel alfalfa products in the diet of lactating dairy cows results in improved milk yield and milk components. Additionally, we examined the effects of supplementing murine myoblasts with polyamines and polyamine precursors to further investigate novel products that may be able to be utilized in the diets of livestock animals to increase growth. We hypothesized that supplementation of polyamines and their precursors would result in improved growth of skeletal muscle cells (myoblasts). Treatment of myoblasts with polyamines and their precursors improves proliferation rates and alters mRNA expression of genes involved in polyamine biosynthesis, cell proliferation, and protein synthesis. Collectively, our observations suggest that various novel feedstuffs, whether it be alfalfa processed differently or amino acid derivatives (polyamines), have the potential to improve various growth and/or production measures. However, additional research is required to fully understand the potential of including these products in the diet

The effect of alternative permutation testing strategies on the performance of multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>Multifactor Dimensionality Reduction (MDR) is a novel method developed to detect gene-gene interactions in case-control association analysis by exhaustively searching multi-locus combinations. While the end-goal of analysis is hypothesis generation, significance testing is employed to indicate statistical interest in a resulting model. Because the underlying distribution for the null hypothesis of no association is unknown, non-parametric permutation testing is used. Lately, there has been more emphasis on selecting all statistically significant models at the end of MDR analysis in order to avoid missing a true signal. This approach opens up questions about the permutation testing procedure. Traditionally omnibus permutation testing is used, where one permutation distribution is generated for all models. An alternative is <it>n</it>-locus permutation testing, where a separate distribution is created for each <it>n</it>-level of interaction tested.</p> <p>Findings</p> <p>In this study, we show that the false positive rate for the MDR method is at or below a selected alpha level, and demonstrate the conservative nature of omnibus testing. We compare the power and false positive rates of both permutation approaches and find omnibus permutation testing optimal for preserving power while protecting against false positives.</p> <p>Conclusion</p> <p>Omnibus permutation testing should be used with the MDR method.</p

Importance of a Dietary Cation-Anion Difference in Peripartum Dairy Cows

At calving, nutrient requirements of dairy cows increase to support milk synthesis. Energy and protein requirements are increased at the initiation of lactation (Moore et al., 2000). Additionally, calcium requirements increase tremendously to meet the demands of lactation (Moore et al., 2000). Calving and subsequent milk synthesis can cause calcium concentrations in the blood to drop. When the demand for calcium exceeds the cow’s ability to mobilize calcium, hypocalcemia (low blood calcium) occurs, which can negatively impact production. This fact sheet reviews hypocalcemia in dairy cows and how to implement hypocalcemia prevention strategies

Turbofan aft duct suppressor study

Suppressions due to acoustic treatment in the annular exhaust duct of a model fan were theoretically predicted and compared with measured suppressions. The predictions are based on the modal analysis of sound propagation in a straight annular flow duct with segmented treatment. Modal distributions of the fan noise source (fan-stator interaction only) were measured using in-duct modal probes. The flow profiles were also measured in the vicinity of the modal probes. The acoustic impedance of the single degree of freedom treatment was measured in the presence of grazing flow. The measured values of mode distribution of the fan noise source, the flow velocity profile and the acoustic impedance of the treatment in the duct were used as input to the prediction program. The predicted suppressions, under the assumption of uniform flow in the duct, compared well with the suppressions measured in the duct for all test conditions. The interaction modes generated by the rotor-stator interaction spanned a cut-off ratio range from nearly 1 to 7

Turbofan aft duct suppressor study. Contractor's data report of mode probe signal data

Acoustic modal distributions were measured in a fan test model having an annular exhaust duct for comparison with theoretically predicted acoustic suppression values. This report contains the amplitude and phase data of the acoustic signals sensed by the transducers of the two mode probes employed in the measurement. Each mode probe consisted of an array of 12 transducers sensing the acoustic field at three axial positions and four radial positions

Multifactor Dimensionality Reduction as a Filter-Based Approach for Genome Wide Association Studies

Advances in genotyping technology and the multitude of genetic data available now provide a vast amount of data that is proving to be useful in the quest for a better understanding of human genetic diseases through the study of genetic variation. This has led to the development of approaches such as genome wide association studies (GWAS) designed specifically for interrogating variants across the genome for association with disease, typically by testing single locus, univariate associations. More recently it has been accepted that epistatic (interaction) effects may also be great contributors to these genetic effects, and GWAS methods are now being applied to find epistatic effects. The challenge for these methods still remain in prioritization and interpretation of results, as it has also become standard for initial findings to be independently investigated in replication cohorts or functional studies. This is motivating the development and implementation of filter-based approaches to prioritize variants found to be significant in a discovery stage for follow-up for replication. Such filters must be able to detect both univariate and interactive effects. In the current study we present and evaluate the use of multifactor dimensionality reduction (MDR) as such a filter, with simulated data and a wide range of effect sizes. Additionally, we compare the performance of the MDR filter to a similar filter approach using logistic regression (LR), the more traditional approach used in GWAS analysis, as well as evaporative cooling (EC)-another prominent machine learning filtering method. The results of our simulation study show that MDR is an effective method for such prioritization, and that it can detect main effects, and interactions with or without marginal effects. Importantly, it performed as well as EC and LR for main effect models. It also significantly outperforms LR for various two-locus epistatic models, while it has equivalent results as EC for the epistatic models. The results of this study demonstrate the potential of MDR as a filter to detect gene–gene interactions in GWAS studies

An R package implementation of multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users.</p> <p>Results</p> <p>We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at <url>http://www.r-project.org/</url>. We also provide data examples to illustrate the use and functionality of the package.</p> <p>Conclusions</p> <p>MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users.</p

Carboplatin/taxane-induced gastrointestinal toxicity: a pharmacogenomics study on the SCOTROC1 trial

Carboplatin/taxane combination is first-line therapy for ovarian cancer. However, patients can encounter treatment delays, impaired quality of life, even death because of chemotherapy-induced gastrointestinal (GI) toxicity. A candidate gene study was conducted to assess potential association of genetic variants with GI toxicity in 808 patients who received carboplatin/taxane in the Scottish Randomized Trial in Ovarian Cancer 1 (SCOTROC1). Patients were randomized into discovery and validation cohorts consisting of 404 patients each. Clinical covariates and genetic variants associated with grade III/IV GI toxicity in discovery cohort were evaluated in replication cohort. Chemotherapy-induced GI toxicity was significantly associated with seven single-nucleotide polymorphisms in the ATP7B, GSR, VEGFA and SCN10A genes. Patients with risk genotypes were at 1.53 to 18.01 higher odds to develop carboplatin/taxane-induced GI toxicity (P&#60;0.01). Variants in the VEGF gene were marginally associated with survival time. Our data provide potential targets for modulation/inhibition of GI toxicity in ovarian cancer patients

A comparison of internal validation techniques for multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data.</p> <p>Results</p> <p>MDR with 3WS is computationally approximately five times faster than 5-fold cross-validation. The power to find the exact true disease loci without detecting false positive loci is higher with 5-fold cross-validation than with 3WS before pruning. However, the power to find the true disease causing loci in addition to false positive loci is equivalent to the 3WS. With the incorporation of a pruning procedure after the 3WS, the power of the 3WS approach to detect only the exact disease loci is equivalent to that of MDR with cross-validation. In the real data application, the cross-validation and 3WS analyses indicate the same two-locus model.</p> <p>Conclusions</p> <p>Our results reveal that the performance of the two internal validation methods is equivalent with the use of pruning procedures. The specific pruning procedure should be chosen understanding the trade-off between identifying all relevant genetic effects but including false positives and missing important genetic factors. This implies 3WS may be a powerful and computationally efficient approach to screen for epistatic effects, and could be used to identify candidate interactions in large-scale genetic studies.</p