ORDINARY LEAST SQUARES REGRESSION OF ORDERED CATEGORICAL DATA: INFERENTIAL IMPLICATIONS FOR PRACTICE

Ordered categorical responses (OCRs) are frequently encountered in many disciplines. Examples of interest in agriculture include quality assessments, such as for soil or food products, and evaluation of lesion severity, such as teat ends status in dairy cattle. OCRs are characterized by multiple categories recorded on a ranked scale that, while apprising relative order, is not informative of absolute magnitude of or proportionality between the categories. A number of statistically sound models for OCRs are available in the statistical literature, such as logistic regression and probit models, but these are commonly underutilized in practice. Instead, the ordinary least squares linear regression (OLSLR) model is often employed despite violation of basic model assumptions. In this study, the inferential implications of OLSLR-based inference on OCRs were investigated using a simulation study that evaluated realized Type I error rate and empirical statistical power. The design of the simulation study was motivated by applications reported in the subject-matter literature. A variety of plausible scenarios were considered for simulation, including various shapes of the frequency distribution and number of categories of the OCR. Using survey data on frequency of antimicrobial use in cattle feedlots, we illustrated the inferential performance of OLSLR on OCRs relative to a probit model

HIERARCHICAL BAYESIAN METHODS TO MODEL HETEROGENEITY IN COW- AND HERD-LEVEL RELATIONSHIPS BETWEEN MILK PRODUCTION AND REPRODUCTION IN DAIRY COWS

Two of the most important broad classifications of phenotypes for successful dairy production are milk yield and fertility. The nature of the relationship between milk production and reproductive performance of dairy cows is uncertain due to conflicting results reported in many studies. A common deficiency in many such studies is an underappreciation of the dual dimension of the production-reproduction relationship, as defined by herd (random or u) level and cow (residual or e) level sources of (co)variation. Our overall hypothesis is that the e- and u- level relationships between milk production and reproduction in dairy cows are heterogeneous and depend upon various herd-related and management factors. Our objective is to develop hierarchical Bayesian extensions that capture heterogeneity in the relationships between traits by mixed effects modeling of u level and e level covariances between traits of interest. We specify a bivariate Bayesian model to jointly model two continuous traits and we apply a square-root free Cholesky decomposition to the variance-covariance matrices of the residuals (cow-level) and random effects (herd-level). As a result, the e- and u-level covariances among the traits are reparameterized into unconstrained and easily interpretable e- and u- regression parameters, respectively. These regression parameters specify the cow- and herd-level relationships, respectively, between the traits and can be easily modeled as functions of relevant fixed and random effects, thereby providing a mixed model extension of Pourahmadi’s method. We validate our method using a simulation study and apply it to data on 305-day milk yield and calving interval of Michigan dairy cows

The role of packaging size on contamination rates during simulated presentation to a sterile field

Objective: The objective of this study was to assess the impact of package size on the contact between medical devices and non-sterile surfaces (i.e. the hands of the practitioner and the outside of the package) during aseptic presentation to a simulated sterile field. Rationale for this objective stems from the decades-long problem of hospital-acquired infections. This work approaches the problem from a unique perspective, namely packaging size. Design: Randomized complete block design with subsampling. Setting: Research study conducted at professional conferences for surgical technologists and nursing professionals. Participants: Ninety-seven healthcare providers, primarily surgical technologists and nurses. Methods: Participants were gloved and asked to present the contents of six pouches of three different sizes to a simulated sterile field. The exterior of pouches and gloves of participants were coated with a simulated contaminant prior to each opening trial. After presentation to the simulated sterile field, the presence of the contaminant on package contents was recorded as indicative of contact with non-sterile surfaces and analyzed in a binary fashion using a generalized linear mixed model. Results: Recruited subjects were 26–64 years of age (81 females, 16 males), with 2.5–44 years of professional experience. Results indicated a significant main effect of pouch size on contact rate of package contents (P = 0.0108), whereby larger pouches induced greater rates of contact than smaller pouches (estimates±SEM: 14.7±2.9% vs. 6.0±1.7%, respectively). Discussion and Conclusion: This study utilized novel methodologies which simulate contamination in aseptic presentation. Results of this work indicate that increased contamination rates are associated with larger pouches when compared to smaller pouches. The results add to a growing body of research which investigate packaging's role in serving as a pathway for product contamination during aseptic presentation. Future work should investigate other packaging design factors (e.g. material, rigidity, and closure systems) and their role in contamination

To See or Not to See: Do Front of Pack Nutrition Labels Affect Attention to Overall Nutrition Information?

Citation: Bix, L., Sundar, R. P., Bello, N. M., Peltier, C., Weatherspoon, L. J., & Becker, M. W. (2015). To See or Not to See: Do Front of Pack Nutrition Labels Affect Attention to Overall Nutrition Information? Plos One, 10(10), 20. doi:10.1371/journal.pone.0139732Background Front of pack (FOP) nutrition labels are concise labels located on the front of food packages that provide truncated nutrition information. These labels are rapidly gaining prominence worldwide, presumably because they attract attention and their simplified formats enable rapid comparisons of nutritional value. Methods Eye tracking was conducted as US consumers interacted with actual packages with and without FOP labels to (1) assess if the presence of an FOP label increases attention to nutrition information when viewers are not specifically tasked with nutrition-related goals; and (2) study the effect of FOP presence on consumer use of more comprehensive, traditional nutrition information presented in the Nutritional Facts Panel (NFP), a mandatory label for most packaged foods in the US. Results Our results indicate that colored FOP labels enhanced the probability that any nutrition information was attended, and resulted in faster detection and longer viewing of nutrition information. However, for cereal packages, these benefits were at the expense of attention to the more comprehensive NFP. Our results are consistent with a potential short cut effect of FOP labels, such that if an FOP was present, participants spent less time attending the more comprehensive NFP. For crackers, FOP labels increased time spent attending to nutrition information, but we found no evidence that their presence reduced the time spent on the nutrition information in the NFP. Conclusions The finding that FOP labels increased attention to overall nutrition information by people who did not have an explicit nutritional goal suggests that these labels may have an advantage in conveying nutrition information to a wide segment of the population. However, for some food types this benefit may come with a short-cut effect; that is, decreased attention to more comprehensive nutrition information. These results have implications for policy and warrant further research into the mechanisms by which FOP labels impact use of nutrition information by consumers for different foods

Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii

Citation: Raithel, S., Johnson, L., Galliart, M., Brown, S., Shelton, J., Herndon, N., & Bello, N. M. (2016). Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii. Bmc Genomics, 17, 16. doi:10.1186/s12864-016-2442-7Background: Differential expression (DE) analysis of RNA-seq data still poses inferential challenges, such as handling of transcripts characterized by low expression levels. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on transcripts characterized by a small number of read counts, so-called low-count transcripts, as motivated by an ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem exhibits qualitative phenotypic divergence consistent with enhanced drought tolerance, plausibly associated with transcripts of low expression levels. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60 % of which are classified as low-count) collected from leaf tissue of individual plants of big bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the inferential performance of recently developed statistical methods for DE analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Results: Both DE methods seemed to properly control family-wise type 1 error on low-count transcripts, whereas edgeR robust showed greater power and DESeq2 showed greater precision and accuracy. However, specification of the degree of freedom parameter under edgeR robust had a non-trivial impact on inference and should be handled carefully. When properly specified, both DE methods showed overall promising inferential performance on low-count transcripts, suggesting that ad-hoc data filtering steps at arbitrary expression thresholds may be unnecessary. A note of caution is in order regarding the approximate nature of DE tests under both methods. Conclusions: Practical recommendations for DE inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications focused on transcripts of low expression levels

Behçet's disease: New insight into the relationship between procoagulant state, endothelial activation/damage and disease activity

Background: Behçet disease (BD) is associated with a prothrombotic state of unknown origin that may lead to life-threatening events. Calibrated Automated Thrombogram (CAT) and Rotational Thromboelastometry (ROTEM) are two global haemostasis assays that may reveal new insights into the physiopathological mechanisms of the disease and its procoagulant condition. Methods. 23 BD patients who had no signs or symptoms of current thrombosis and 33 age- and sex-matched controls were included in the study. We performed ROTEM and CAT tests and assessed erythrocyte count, platelet count, platelet contribution to clot formation and plasma levels of tissue-type plasminogen activator, plasminogen activator inhibitor type 1 (PAI-1), fibrinogen, C-reactive protein (CRP), thrombin-antithrombin III complex (TAT), D-dimer and E-selectin (ES). Results: Both ROTEM and CAT tests showed a hypercoagulable state in the BD patients. Plasma levels of PAI-1, fibrinogen, TAT, CRP and ES were significantly increased in this group compared to controls. The disease activity (DA) was significantly correlated with levels of ES and the maximum clot firmness, and this last one, in turn, correlated with rising levels of ES, PAI-1, CRP and fibrinogen. CAT parameters did not correlate with DA or ES. Conclusions: Both ROTEM and CAT tests reveal that patients with BD have a procoagulant state even in the absence of thrombosis. ROTEM test indicates that increased levels of fibrinogen and PAI-1 may be involved in the prothrombotic state of this pathology, while platelets do not significantly contribute. Moreover, CAT assay demonstrate that plasma from BD patients is able to generate more thrombin than controls in response to the same stimulus and that this effect is independent of the DA and the endothelial impairment suggesting the involvement of another factor in the hypercoagulable state observed in BD patients. This study also shows that endothelium activation/damage may be a contributing factor in both the procoagulant and clinical conditions of BD, as shown by the direct correlation between ES levels, ROTEM parameters and DAThis work was supported by grants from FIS PS09/00531 and FIS PI12/0183

Genomic Prediction Accounting for Residual Heteroskedasticity

Citation: Ou, Z. N., Tempelman, R. J., Steibel, J. P., Ernst, C. W., Bates, R. O., & Bello, N. M. (2016). Genomic Prediction Accounting for Residual Heteroskedasticity. G3-Genes Genomes Genetics, 6(1), 1-13. doi:10.1534/g3.115.022897Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroske-dasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit

An update on modeling dose-response relationships: Accounting for correlated data structure and heterogeneous error variance in linear and nonlinear mixed models

Citation: Gonalves, M. A. D., Bello, N. M., Dritz, S. S., Tokach, M. D., DeRouchey, J. M., Woodworth, J. C., & Goodband, R. D. (2016). An update on modeling dose-response relationships: Accounting for correlated data structure and heterogeneous error variance in linear and nonlinear mixed models. Journal of Animal Science, 94(5), 1940-1950. doi:10.2527/jas2015-0106Advanced methods for dose-response assessments are used to estimate the minimum concentrations of a nutrient that maximizes a given outcome of interest, thereby determining nutritional requirements for optimal performance. Contrary to standard modeling assumptions, experimental data often present a design structure that includes correlations between observations (i.e., blocking, nesting, etc.) as well as heterogeneity of error variances; either can mislead inference if disregarded. Our objective is to demonstrate practical implementation of linear and nonlinear mixed models for dose-response relationships accounting for correlated data structure and heterogeneous error variances. To illustrate, we modeled data from a randomized complete block design study to evaluate the standardized ileal digestible (SID) Trp: Lys ratio dose-response on G:F of nursery pigs. A base linear mixed model was fitted to explore the functional form of G: F relative to Trp: Lys ratios and assess model assumptions. Next, we fitted 3 competing dose-response mixed models to G: F, namely a quadratic polynomial (QP) model, a broken-line linear (BLL) ascending model, and a broken-line quadratic (BLQ) ascending model, all of which included heteroskedastic specifications, as dictated by the base model. The GLIMMIX procedure of SAS (version 9.4) was used to fit the base and QP models and the NLMIXED procedure was used to fit the BLL and BLQ models. We further illustrated the use of a grid search of initial parameter values to facilitate convergence and parameter estimation in nonlinear mixed models. Fit between competing dose-response models was compared using a maximum likelihood-based Bayesian information criterion (BIC). The QP, BLL, and BLQ models fitted on G: F of nursery pigs yielded BIC values of 353.7, 343.4, and 345.2, respectively, thus indicating a better fit of the BLL model. The BLL breakpoint estimate of the SID Trp: Lys ratio was 16.5% (95% confidence interval [16.1, 17.0]). Problems with the estimation process rendered results from the BLQ model questionable. Importantly, accounting for heterogeneous variance enhanced inferential precision as the breadth of the confidence interval for the mean breakpoint decreased by approximately 44%. In summary, the article illustrates the use of linear and nonlinear mixed models for dose-response relationships accounting for heterogeneous residual variances, discusses important diagnostics and their implications for inference, and provides practical recommendations for computational troubleshooting

Effects of amino acids and energy intake during late gestation of high-performing gilts and sows on litter and reproductive performance under commercial conditions

Citation: Goncalves, M. A. D., Gourley, K. M., Dritz, S. S., Tokach, M. D., Bello, N. M., DeRouchey, J. M., . . . Goodband, R. D. (2016). Effects of amino acids and energy intake during late gestation of high-performing gilts and sows on litter and reproductive performance under commercial conditions. Journal of Animal Science, 94(5), 1993-2003. doi:10.2527/jas.2015-0087The objective of this study was to determine the effects of AA and energy intake during late gestation on piglet birth weight and reproductive performance of high-performing (14.5 total born) gilts and sows housed under commercial conditions. At d 90 of gestation, a total of 1,102 females (PIC 1050) were housed in pens by parity group (gilts or sows) with approximately 63 gilts and 80 sows in each pen, blocked by BW within each pen, and each female was randomly assigned to dietary treatments within BW block. Dietary treatments consisted of combinations of 2 standardized ileal digestible (SID) AA intakes (10.7 or 20.0 g/d SID Lys and other AA met or exceeded the NRC [2012] recommendations) and 2 energy intakes (4.50 or 6.75 Mcal/d intake of NE) in a 2 x 2 factorial arrangement. Data were analyzed using generalized linear mixed models specified to recognize pen as the experimental unit for parity and the individual female as the experimental unit for dietary treatments. Results indicate an overall positive effect of high energy intake on BW gain during late gestation, although this effect was more manifest under conditions of high, as opposed to low, AA intake (interaction, P < 0.001). Furthermore, the magnitude of BW gain response to increased energy intake was greater (P < 0.001) for sows compared with gilts. Sows fed high energy intake had a reduced probability of piglets born alive (P < 0.004) compared with those fed low energy, but no evidence for differences was found in gilts. This can be explained by an increased probability (P = 0.002) of stillborns in sows fed high energy intake vs. sows fed low energy intake. There were no evidences for differences among dietary treatments in litter birth weight and individual piglet birth weight of total piglets born. However, individual born alive birth weight was approximately 30 +/- 8.2 g heavier (P = 0.011) for females fed high, as opposed to low, energy intake. Furthermore, piglets born alive were approximately 97 +/- 9.5 g heavier (P < 0.001) for sows than for gilts. Preweaning mortality was decreased (P = 0.034) for females fed high AA intake compared with females fed low AA intake regardless of energy level. In conclusion, 1) BW gain of gilts and sows depended not only on energy but also on AA intake, 2) sows fed increased amount of energy had an increased stillborn rate, and 3) increased energy intake during late gestation had a positive effect on individual piglet birth weight with no evidence for such an effect for AA intake

Effects of standardized ileal digestible valine-to-lysine ratio on growth performance of twenty-five- to forty-five-kilogram pigs under commercial conditions

Citation: Goncalves, M. A. D., Tokach, M. D., Dritz, S. S., Bello, N. M., Touchette, K. J., Goodband, R. D., . . . Woodworth, J. C. (2016). Effects of standardized ileal digestible valine-to-lysine ratio on growth performance of twenty-five- to forty-five-kilogram pigs under commercial conditions. Journal of Animal Science, 94, 19-20. doi:10.2527/msasas2016-043Two experiments were conducted to estimate the standardized ileal digestible (SID) Val:Lys requirement for growth performance in 25- to 45-kg pigs. In Exp. 1, 1134 gilts (PIC 337), initially 31.2 kg (SD 2.0) BW, were used in a 19-d trial with 27 pigs/pen and 7 pens/treatment. In Exp. 2, 2100 gilts (PIC 327), initially 25.4 ± 1.9 kg BW, were used in a 22-d trial with 25 pigs/pen and 12 pens/treatment. In both experiments, treatments were blocked by initial BW in a randomized complete block design. In Exp. 1, there were 6 treatments with SID Val:Lys at 59.0, 62.5, 65.9, 69.6, 73.0, and 75.5%. For Exp. 2, there were 7 treatments with SID Val:Lys at 57.0, 60.6, 63.9, 67.5, 71.1, 74.4, and 78.0%. Diets were formulated to ensure that Lys was the second limiting AA throughout the experiments. Responses were analyzed separately for each experiment using general linear and nonlinear heteroskedastic mixed models, including initial BW as an explanatory covariate and BW block as a random effect. In Exp. 1, ADG linearly increased with increasing SID Val:Lys (P = 0.009; 680, 717, 717, 712, 744, and 726 ± 17.1 g, respectively), whereas no significant treatment differences were observed for G:F (0.467, 0.467, 0.472, 0.474, 0.481, and 0.472 ± 0.0084, respectively). In Exp. 2, ADG (quadratic, P = 0.002; 621, 662, 717, 708, 708, 726, and 717 ± 16.1 g, respectively) and G:F increased (linear, P  78.0) SID Val:Lys. The best-fitting model for G:F was also a QP (prediction equation: ?0.04 + 1.36 × SID Val:Lys ? 0.94 × SID Val:Lys2) with optimum G:F estimated at 72.3% (95% CI 64.0 to > 78.0) SID Val:Lys. In conclusion, 67% SID Val:Lys was able to capture 99% of maximum ADG and G:F in 25- to 45-kg pigs