The objectives were to study the effects of induced subclinical hypocalcemia [SCH, blood ionized Ca (iCa(2+)) <1.0mM, without recumbency] on physiological responses and function of immune cells in dairy cows. Ten nonpregnant, nonlactating Holstein cows were blocked by lactation and assigned randomly to a normocalcemic (NC; intravenous infusion of 0.9% NaCl i.v. plus 43 g of oral Ca, as Ca sulfate and Ca chloride, at -1 and 11h) or an induced SCH [SCHI, 5% ethylene glycol tetraacetic acid (EGTA), a selective iCa(2+) chelator, intravenous infusion] treatment for 24h, using a crossover design. The sequence of treatments was either NC-SCHI or SCHI-NC, with a 6-d washout period. Ionized Ca was evaluated before, hourly during the infusion period, and at 48 and 72 h, to monitor concentrations and adjust the rate of infusion, maintaining blood iCa(2+) <1.0mM in SCHI throughout the 24-h infusion period. Additional measurements included heart and respiratory rates, rectal temperature, dry matter intake, rumen contractions, whole-blood pH, concentrations of glucose and K in whole blood, concentrations of total Ca, Mg, nonesterified fatty acids, β-hydroxybutyrate, and insulin in plasma, and urinary excretion of Ca. Total and differential leukocyte count in blood was also performed. The concentration of cytosolic iCa(2+) in neutrophils and lymphocytes was quantified and neutrophil function was assayed in vitro. Infusion of a 5% EGTA solution successfully induced SCH in all SCHI cows, resulting in decreased blood iCa(2+) concentrations throughout the 24-h treatment period (0.77 ± 0.01 vs. 1.26 ± 0.01 mM iCa(2+)). Induction of SCH reduced dry matter intake on the day of infusion (5.3 ± 0.8 vs. 9.1 ± 0.8 kg/d) and rumen contractions (1.9 ± 0.2 vs. 2.7 ± 0.2 contractions/2 min) for the last 12h of infusion. Cows in SCHI had decreased plasma insulin concentration (1.44 ± 0.23 vs. 2.32 ± 0.23 ng/mL) evident between 6 and 18 h after the beginning of the infusion, accompanied by increased concentrations of glucose (4.40 ± 0.04 vs. 4.17 ± 0.04 mM). Plasma nonesterified fatty acids concentration was greater for SCHI than NC cows (0.110 ± 0.019 vs. 0.061 ± 0.014 mM). Neutrophils of cows in SCHI had a faster decrease in cytosolic iCa(2+) after stimulation with ionomycin (9.9 ± 1.0 vs. 13.6 ± 1.4 Fluo-4:Fura Red post-end ratio) in vitro. Furthermore, induction of SCH reduced the percentage of neutrophils undergoing phagocytosis (22.1 ± 2.1 vs. 29.3 ± 2.1%) and reduced the oxidative burst response after incubation of pathogenic bacteria (16.1 ± 1.7 vs. 24.2 ± 1.7%). Subclinical hypocalcemia compromised appetite, altered metabolism, and impaired function of immune cells in dairy cows.

No abstract

Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that LiDAR data and forest characteristics are often strongly correlated, it is possible to make use of the former to model, predict, and map forest variables over regions of interest. This entails dealing with the high-dimensional (∼10 2 ) spatially dependent LiDAR outcomes over a large number of locations (∼10 5 −10 6 ). With this in mind, we develop the Spatial Factor Nearest Neighbor Gaussian Process (SF-NNGP) model, and embed it in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of forest variables with associated uncertainty over a large region of boreal forests in interior 1 arXiv:1801.02078v2 [stat.AP] 8 Nov 2018Alaska.

No abstract

This paper investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis-Hastings algorithm for searching the space of models. The tools proposed allow fast and thorough exploration of model spaces that account for hierarchical polynomial structure in the predictors and provide control of the inclusion of false positives in high posterior probability models.