Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

Quantification of Hair Cortisol Concentration in Common Marmosets (\u3cem\u3eCallithrix jacchus\u3c/em\u3e) and Tufted Capuchins (\u3cem\u3eCebus apella\u3c/em\u3e)

Quantifying cortisol concentration in hair is a non-invasive biomarker of long-term hypothalamic-pituitary-adrenal (HPA) activation, and thus can provide important information on laboratory animal health. Marmosets (Callithrix jacchus) and capuchins (Cebus apella) are New World primates increasingly used in biomedical and neuroscience research, yet published hair cortisol concentrations for these species are limited. Review of the existing published hair cortisol values from marmosets reveals highly discrepant values and the use of variable techniques for hair collection, processing, and cortisol extraction. In this investigation we utilized a well-established, standardized protocol to extract and quantify cortisol from marmoset (n = 12) and capuchin (n = 4) hair. Shaved hair samples were collected from the upper thigh during scheduled exams and analyzed via methanol extraction and enzyme immunoassay. In marmosets, hair cortisol concentration ranged from 2710 – 6267 pg/mg and averaged 4070 ± 304 pg/mg. In capuchins, hair cortisol concentration ranged from 621 – 2089 pg/mg and averaged 1092 ± 338 pg/mg. Hair cortisol concentration was significantly different between marmosets and capuchins, with marmosets having higher concentrations than capuchins. The incorporation of hair cortisol analysis into research protocols provides a non-invasive measure of HPA axis activity over time, which offers insight into animal health. Utilization of standard protocols across laboratories is essential to obtaining valid measurements and allowing for valuable future cross-species comparisons

Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

Identification of novel Cyclooxygenase-2-dependent genes in Helicobacter pylori infection in vivo

<p>Abstract</p> <p>Background</p> <p><it>Helicobacter pylori </it>is a crucial determining factor in the pathogenesis of benign and neoplastic gastric diseases. Cyclooxygenase-2 (Cox-2) is the inducible key enzyme of arachidonic acid metabolism and is a central mediator in inflammation and cancer. Expression of the <it>Cox-2 </it>gene is up-regulated in the gastric mucosa during <it>H. pylori </it>infection but the pathobiological consequences of this enhanced Cox-2 expression are not yet characterized. The aim of this study was to identify novel genes down-stream of Cox-2 in an <it>in vivo </it>model, thereby identifying potential targets for the study of the role of Cox- 2 in <it>H. pylori </it>pathogenesis and the initiation of pre- cancerous changes.</p> <p>Results</p> <p>Gene expression profiles in the gastric mucosa of mice treated with a specific Cox-2 inhibitor (NS398) or vehicle were analysed at different time points (6, 13 and 19 wk) after <it>H. pylori </it>infection. <it>H. pylori </it>infection affected the expression of 385 genes over the experimental period, including regulators of gastric physiology, proliferation, apoptosis and mucosal defence. Under conditions of Cox-2 inhibition, 160 target genes were regulated as a result of <it>H. pylori </it>infection. The Cox-2 dependent subset included those influencing gastric physiology (<it>Gastrin, Galr1</it>), epithelial barrier function (<it>Tjp1, connexin45, Aqp5</it>), inflammation (<it>Icam1</it>), apoptosis (<it>Clu</it>) and proliferation (<it>Gdf3, Igf2</it>). Treatment with NS398 alone caused differential expression of 140 genes, 97 of which were unique, indicating that these genes are regulated under conditions of basal Cox-2 expression.</p> <p>Conclusion</p> <p>This study has identified a panel of novel Cox-2 dependent genes influenced under both normal and the inflammatory conditions induced by <it>H. pylori </it>infection. These data provide important new links between Cox-2 and inflammatory processes, epithelial repair and integrity.</p

Altered glucocorticoid metabolism represents a feature of macroph-aging

The aging process is characterized by a chronic, low-grade inflammatory state, termed "inflammaging." It has been suggested that macrophage activation plays a key role in the induction and maintenance of this state. In the present study, we aimed to elucidate the mechanisms responsible for aging-associated changes in the myeloid compartment of mice. The aging phenotype, characterized by elevated cytokine production, was associated with a dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis and diminished serum corticosteroid levels. In particular, the concentration of corticosterone, the major active glucocorticoid in rodents, was decreased. This could be explained by an impaired expression and activity of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1), an enzyme that determines the extent of cellular glucocorticoid responses by reducing the corticosteroids cortisone/11-dehydrocorticosterone to their active forms cortisol/corticosterone, in aged macrophages and peripheral leukocytes. These changes were accompanied by a downregulation of the glucocorticoid receptor target gene glucocorticoid-induced leucine zipper (GILZ) in vitro and in vivo. Since GILZ plays a central role in macrophage activation, we hypothesized that the loss of GILZ contributed to the process of macroph-aging. The phenotype of macrophages from aged mice was indeed mimicked in young GILZ knockout mice. In summary, the current study provides insight into the role of glucocorticoid metabolism and GILZ regulation during aging

Machine-learned climate model corrections from a global storm-resolving model

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predictions. One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections at each simulation timestep, such that the climate model evolves more like a high-resolution global storm-resolving model (GSRM). We train neural networks to learn the state-dependent temperature, humidity, and radiative flux corrections needed to nudge a 200 km coarse-grid climate model to the evolution of a 3~km fine-grid GSRM. When these corrective ML models are coupled to a year-long coarse-grid climate simulation, the time-mean spatial pattern errors are reduced by 6-25% for land surface temperature and 9-25% for land surface precipitation with respect to a no-ML baseline simulation. The ML-corrected simulations develop other biases in climate and circulation that differ from, but have comparable amplitude to, the baseline simulation

Emulating Fast Processes in Climate Models

Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radiation. Clouds are ubiquitous, diverse, and can change rapidly. In this work, we build the first emulator of an entire cloud microphysical parameterization, including fast phase changes. The emulator performs well in offline and online (i.e. when coupled to the rest of the atmospheric model) tests, but shows some developing biases in Antarctica. Sensitivity tests demonstrate that these successes require careful modeling of the mixed discrete-continuous output as well as the input-output structure of the underlying code and physical process.Comment: Accepted at the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS) December 3, 202

Repeatability of Central and Peripheral Pulse Wave Velocity Measures: The Atherosclerosis Risk in Communities (ARIC) Study

Arterial stiffness measures are emerging tools for risk assessment and stratification for hypertension and cardiovascular disease (CVD). Carotid-femoral pulse wave velocity (cfPWV) is an established measure of central arterial stiffness. Other measures of PWV include femoral-ankle (faPWV), a measure of peripheral stiffness, and brachial-ankle PWV (baPWV), a composite measure of central and peripheral stiffness. Repeatability of central, peripheral, and composite PWV measures has not been adequately examined or compared