A simulation study comparing powers of the multivariate analysis (PROC GLM) with using a mixed model (PROC MIXED) using a variety of covariance structures for various treatment, time, and interaction affect sizes in a repeated measures design is conducted. Type 1 errors are estimated. Powers are estimated for a variety of covariance structures when the actual covariance structure is AR (1). It was found that the estimated powers for treatment effect were all very similar with PROC MIXED with the correct covariance structure having the largest estimated powers. When testing for time and interaction effect, it was found when in doubt that it was better to use a simpler covariance structure. The powers were generally higher in this case than with a more complex covariance structure.

Aims: This simulation study was conducted to check the validity of a MIXED model’s statistical inference when violating the underlying assumptions – normality of random errors when there are unbalanced group sizes and inequality of variance of errors [Scheffe, 1959]. Study Design: Monte Carlo Simulation Study. Place and Duration of Study: North Dakota State University 2020-2021. Methodology: Repeated measures designs (or longitudinal studies) are commonly seen in many research fields, especially in pharmaceutical clinical trials, agricultural research, and psychology. PROC MIXED (SAS Inc.) is a well-known standard tool for analyzing repeated measures data nowadays. The MIXED procedure is based on the standard linear MIXED model, which estimates parameters by maximizing the restricted likelihood. The usual assumption for a standard linear MIXED model is normality. However, the character of data in the real world may be non- smoothed, or non-symmetric, or having heavy tails. We estimate the Type I error rates in different combinations of settings and compare them with the stated Type I error. Conclusion: The main results in this study show us that the MIXED model is reasonably robust to modest violations of the normal distribution. However, when a small sample size associated with a treatment was combined with the effects of that treatment having a large variance, a severe inflation problem on Type I error rates could occur when using the MIXED model procedure. When the Type I errors were found to be inflated, the Group= option was found to often help with this problem. A Sub-Sampling procedure was also found to help with this problem.

Vasoactive Intestinal Peptide (VIP) and Pituitary Adenylate-Cyclase-Activating Peptide (PACAP) are anti-inflammatory neuropeptides that play important roles in human and rodent gut microbiota homeostasis and host immunity. Pharmacologically regulating these neuropeptides is expected to have significant health and feed efficiency benefits for agriculturally relevant animals. However, their expression profile in ruminant tissues is not well characterized. To this end, we screened for VIP and PACAP neuropeptides and their endogenous GPCRs using 15 different tissues from wethers and steers by RT-qPCR. Our results revealed relatively similar expression profiles for both VIP and PACAP neuropeptide ligands in the brain and intestinal tissue of both species. In contrast, the tissue expression profiles for VPAC1, VPAC2, and PAC1 were more widespread and disparate, with VPAC1 being the most diversely expressed receptor with mRNA detection in the brain and throughout the gastrointestinal tract. These data are an important first step to allow for future investigations regarding the VIP and PACAP signaling pathways in livestock ruminant species.

Abstract The efficiency measures of the Stochastic Frontier Analysis (SFA) models are dependent on distributional assumptions of the one-sided error or inefficiency term. Given the intent of earlier researchers in the evaluation of a single inefficiency distribution using Monte Carlo (MC) simulation, much attention has not been paid to the comparative analysis of SFA models. Our paper aims to evaluate the effects of the assumption of the inefficiency distribution and thus compares different SFA model assumptions by conducting a MC simulation. In this paper, we derive the population statistical parameters of truncated normal, half-normal, and exponential inefficiency distributions of SFA models with the objective of having comparable sample mean and sample standard deviation during MC simulation. Thus, MC simulation is conducted to evaluate the statistical properties and robustness of the inefficiency distributions of SFA models and across three different misspecification scenarios, sample sizes, production functions, and input distributions. MC simulation results show that the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the true data generating process is a half-normal inefficiency distribution.

Soybean (Glycine max (L.) Merrill) leaf chlorophyll content is indicative of the plant growth and health issues. However, chlorophyll measurement using the standard chemical procedure is laborious, while the sensor-based electronic options, such as soil plant analysis development (SPAD) meter tend to be highly expensive and made only spot measurements. Therefore, a simpler and less expensive infield method of chlorophyll measurement in soybeans using smartphone camera with image processing and machine learning models was developed. Soybean leaf images (720 images) and SPAD readings were collected from different cultivars (4), with replications (3) and sampling dates (2) from experimental plots. Of the several color vegetation indices (CVIs) tested, the dark green color index (DGCI) had the best correlation with SPAD meter readings (r = 0.90), which was further improved by color calibration (r = 0.93). The results of the random coefficients model showed that both cultivars and sampling dates had no significant effect (0.06 <= P <= 0.96), hence data were combined for the analysis. The simpler statistical linear regression (SLR) and polynomial regression (PR), multiple linear regression as well as the advanced machine learning models (support vector machine (SVM), random forest (RF)) tested with color scheme inputs (RGB, DGCI, range pixel count (RPC) of DGCI, and 'Both' (RPC + RGB)) produced the best chlorophyll prediction with DGCI, RPC, and 'Both' inputs (0.87 < R-2 < 0.89; SPAD units). Overall, these models were not significantly different, but the SVM model found to be the best (R-2 = 0.89 and RMSE = 2.90 SPAD units). The simpler SLR and PR models with DGCI input (R-2 >= 0.87 and RMSE <= 3.1 SPAD units) performed as good as the advanced SVM and RF models. The SVM model had the potential of predicting the chlorophyll directly with the raw RGB input (R-2 = 0.86 and RMSE = 3.20 SPAD units) without the need of using the standard calibration board. The developed methodology of image processing with machine learning modeling and conversion relationship of measuring infield soybean leaf chlorophyll is efficient, inexpensive, not requiring the standard calibration board, and can be easily extended to other large-scale aerial imaging platforms and field crops.

Many tests for the analysis of continuous data have the underlying assumption that the data in question follows a normal distribution (ex.ANOVA, regression, etc.).Within certain research topics, it is common to end up with a dataset that has a disproportionately high number of zero-values but is otherwise relatively normal.These datasets are often referred to as 'zeroinflated' and their analysis can be challenging.An example of where these zero-inflated datasets arise is in plant science.We conducted a simulation study to compare the performance of zeroinflated models to a standard ANOVA model on different types of zero-inflated data.Underlying distributions, experimental design scenario, sample sizes, and percentages of zeros were variables of consideration.In this study, we conduct a Type I error assessment followed by a power comparison between the models.

Photoperiod is considered the universal zeitgeber, regulating physiological processes in numerous animals. However, for animals in light-restricted habitats (e.g. burrows or cavities), thermoperiod may be a more important cue. Our study tested this hypothesis in the alfalfa leafcutting bee, Megachile rotundata, which nests in cavities and undergoes development within a brood cell. We assessed the role of environmental cues (thermoperiod and photoperiod) on the process of adult emergence by examining: (1) whether those cues direct circadian rhythms, (2) which cue is more dominant and (3) how sensitive developing bees and emergence-ready adults are to cues. Although we found that 20% of light penetrates the brood cell, and bees respond to photoperiod by synchronizing emergence, thermoperiod is the dominant cue. When presented with a conflicting zeitgeber, bees entrained to the thermophase instead of the photophase. When temperature cues were removed, we observed free-running of emergence, indicating that underlying circadian mechanisms can be synchronized by daily fluctuations in temperature. We also found that emerging bees were highly sensitive to even small increases in temperature, entraining to a ramp speed of 0.33 degrees C h(-1). The response and sensitivity to temperature cues suggest that M. rotundata evolved a temperature-mediated clock to time emergence from light-restricted cavities.

Probabilities of two types of errors are estimated for the False Discovery Rate Procedure (FDR), Dunnett’s test, and conducting individual t-tests when treatments are being compared against a control. Probabilities for two types of powers are also estimated. The number of treatments considered is 50, 100, 150, and 300 with the first treatment being the control. Differing numbers of treatments equal to the control are considered with equal sample sizes of 3 and 5.

Many studies have been performed on exploring the effects of radio-frequency (RF) energy on biological function in vivo. In particular, gene expression results have been inconclusive due, in part, to a lack of a standardized experimental procedure. This research describes a new far field RF exposure system for unrestrained murine models that reduces experimental error. The experimental procedure includes the materials used, the creation of a patch antenna, the uncertainty analysis of the equipment, characterization of the test room, experimental equipment used and setup, power density and specific absorption rate experiment, and discussion. The result of this research is an experimental exposure system to be applied to future biological studies.

The single seed descent (SSD) method of inbreeding minimizes the amount of genetic sampling. The single-pod descent (SPD) and bulk methods (BM) produce redundant inbred lines that are descended from either the same F2 or F3 plant. However, for soybean [Glycine max (L.) Merr.], the SSD method requires more time to process the seed than the SPD or BM. Our experiment is the first to compare the SSD, SPD and BM by sampling the same population in the field and then evaluating the methods using molecular markers. Our objective was to determine the relative efficiency of the SSD, SPD and BM procedures. We defined unique lines as those lines that were not paired with any other line at a coefficient of similarity (Sxy) level ≥ 0.875, which was an alike-in-state criterion. The efficiency was defined as the number of unique lines developed by each procedure. We genotyped 100 F4:5 lines from each of the three genetic sampling methods, using 21 polymorphic simple sequence repeat markers. The number of unique lines was the same for all three sampling methods at the 0.05 level of Type I error. Based on our criterion, the three sampling methods are equally efficient. Our conclusions were the opposite of all other previously published reports. Each breeder will have to determine the best method for generation advancement, based on the amount of resources required to harvest and process the seed.

Similar to most biological studies, beef contamination classification studies using artificial neural networks are restricted to small datasets. This study evaluates multivariate normal (MVN) technique of synthetic sample generation on small datasets associated with Salmonella contamination in beef. Six experiments were conducted to evaluate the performance of integrated sensor system towards identification of Salmonella contaminated beef packages. Pattern recognition involved using wavelet packet transform for feature extraction from sensor array responses and radial basis function network (RBFN) based classification of contaminated beef packages from uncontaminated packages. The MVN generated synthetic olfactory sensor signatures were used to train and test the RBFN classifiers. For the datasets analyzed in this study, genetic algorithm optimized RBF networks conferred average contamination test classification accuracies of 90.33% +/- 7.68% (mean +/- std. dev.) which were higher compared to the bootstrapped quadratic discriminant analysis based average accuracies. RBFN classifier based average overall classification accuracies of six synthetically generated datasets were in the range of 86.66% -98.89% with highest average overall classification accuracies of 98.89% +/- 1.92%. (C) 2011 Published by Elsevier Ltd.

ABSTRACT Neonatal meningitis Escherichia coli (NMEC) is one of the top causes of neonatal meningitis worldwide. Here, 85 NMEC and 204 fecal E. coli isolates from healthy humans (HFEC) were compared for possession of traits related to virulence, antimicrobial resistance, and plasmid content. This comparison was done to identify traits that typify NMEC and distinguish it from commensal strains to refine the definition of the NMEC subpathotype, identify traits that might contribute to NMEC pathogenesis, and facilitate choices of NMEC strains for future study. A large number of E. coli strains from both groups were untypeable, with the most common serogroups occurring among NMEC being O18, followed by O83, O7, O12, and O1. NMEC strains were more likely than HFEC strains to be assigned to the B2 phylogenetic group. Few NMEC or HFEC strains were resistant to antimicrobials. Genes that best discriminated between NMEC and HFEC strains and that were present in more than 50% of NMEC isolates were mainly from extraintestinal pathogenic E. coli genomic and plasmid pathogenicity islands. Several of these defining traits had not previously been associated with NMEC pathogenesis, are of unknown function, and are plasmid located. Several genes that had been previously associated with NMEC virulence did not dominate among the NMEC isolates. These data suggest that there is much about NMEC virulence that is unknown and that there are pitfalls to studying single NMEC isolates to represent the entire subpathotype.

Vacuum packaged beef strip-loins (fresh and aged) were repackaged on polystyrene trays and over-wrapped with food grade cling film for the storage study. Several volatile compounds such as 3-methyl-1-butanol, 2,3-butanedione, 2-butanone, 3-hydroxy-2-butanone, acetic acid and a few hydrocarbons were detected in the headspace of these tray packaged fresh and aged beef strip loins both in the control and Salmonella typhimurium inoculated samples, in varying concentrations. These compounds were identified using manual headspace solid-phase microextraction (HS-SPME) in combination with gas chromatography/mass spectrometry (GC-MS) over a storage period of 4 days and samples were incubated at 20°C. No naturally occurring Salmonella was present in the control samples. Hexanal (r = 0.99), carbon dioxide (r = 0.98), 3-hydroxy-2-butanone (r = 0.93) and 2-methyl propane (r = 0.95) showed positive correlations with Salmonella population for fresh beef samples. In aged beef samples, 3-methyl-1-butanol (r = 0.99), 3-hydroxy-2-butanone (r = 0.98), carbon dioxide (r = 0.98) and acetic acid (r = 0.86) showed similar trends. In fresh beef samples, F values were significant at p < 0.05 for 3-hydroxy-2-butanone and for carbon dioxide with storage time for fresh beef samples; they were significant for 3-hydroxy-2-butanone, acetic acid and carbon dioxide for aged beef samples.

The arboreal species of spiders on saltcedar, Tamarix spp., at Big Spring, Howard County, TX, were surveyed to determine which species could pose a threat to the success of the biological control agent Diorhabda elongate (Brulle) (Coleoptera: Chrysomelidae). This resulted in a list of the spider genera and species found on tamarisk and some initial information on spider ecology in association with this weed tree. Of the 1,495 specimens of spiders collected above ground level during the 3 years of study at the site, 55 spider species in 15 families were found on saltcedar trees. The most abundant species of spiders were mapped to determine their habitat on the trees and in relation to other species. Orb-weavers (Araneidae) and jumping spiders (Salticidae) were the most diverse families, as well as being common, but mesh web-weavers (Dictynidae), while less diverse, were individually numerous. Only orb-weavers were observed to feed on D. elongate in the field.

Sterile beef (fresh strip loins) samples were inoculated with Salmonella typhimurium, and both control and inoculated samples were stored at 20 degrees C in 20 mL headspace vials covered with food-grade cling film. An array of volatile compounds was detected in the headspace of the control and inoculated samples. The study was conducted for four days, and the volatiles in the headspace were analyzed each day using manual headspace solid-phase microextraction (HS-SPME) in combination with gas chromatography-mass spectrometry (GC-MS). Acetic acid, ethanol, carbon dioxide, and 3-hydroxy-2-butanone were the most prominent compounds detected in the study. The F-tests (Fisher's variance ratio) for the main effect of the sample source established acetic acid and ethanol as compounds of interest for monitoring the status of Salmonella in raw fresh beef Good linear correlations were found between the logarithms of the peak area responses of these compounds with Salmonella count.

Extraintestinal pathogenic Escherichia coli (ExPEC) are major players in human urinary tract infections, neonatal bacterial meningitis, and sepsis. Recently, it has been suggested that there might be a zoonotic component to these infections. To determine whether the E. coli contaminating retail poultry are possible extraintestinal pathogens, and to ascertain the source of these contaminants, they were assessed for their genetic similarities to E. coli incriminated in colibacillosis (avian pathogenic E. coli [APEC]), E. coli isolated from multiple locations of apparently healthy birds at slaughter, and human ExPEC. It was anticipated that the retail poultry isolates would most closely resemble avian fecal E. coli since only apparently healthy birds are slaughtered, and fecal contamination of carcasses is the presumed source of meat contamination. Surprisingly, this supposition proved incorrect, as the retail poultry isolates exhibited gene profiles more similar to APEC than to fecal isolates. These isolates contained a number of ExPEC-associated genes, including those associated with ColV virulence plasmids, and many belonged to the B2 phylogenetic group, known to be virulent in human hosts. Additionally, E. coli isolated from the crops and gizzards of apparently healthy birds at slaughter also contained a higher proportion of ExPEC-associated genes than did the avian fecal isolates examined. Such similarities suggest that the widely held beliefs about the sources of poultry contamination may need to be reassessed. Also, the presence of ExPEC-like clones on retail poultry meat means that we cannot yet rule out poultry as a source of ExPEC human disease.

The changes in the headspace from stored beef strip loins inoculated with Salmonella typhimurium and stored at 20 °C were detected using an electronic nose system. Once the data was obtained six area-based features were extracted from the collected sensor data pertaining to the six metal oxide sensors present in the electronic nose. These extracted features were next dimensionally reduced by principal component analysis (PCA) and the independent components (IC) were extracted by FastICA package. The extracted independent components and principal components (PC) were compared by plotting them individually against the Salmonella population counts. A stepwise linear regression prediction model with the IC and PC as inputs was also built. The prediction model with IC as input performed better with an average prediction accuracy of 82.99%, and root mean squared error (RMSE) of 0.803. For the model using the PC as the input, the average prediction accuracy was 69.64% and the RMSE was 1.358. The results obtained suggest that the use of higher-order statistical techniques like ICA could help in extracting more useful information than PCA and could help in improving the performance of the sensor system. Further analysis needs to be carried out on larger datasets, and by using non-parametric data analysis techniques like artificial neural networks to build the prediction models from the ICA extracted components.

This study was aimed at comparing the ability of conventional culture, the iQ-Check real-time PCR kit, and invA PCR to detect Salmonella in naturally contaminated premarket and retail turkey parts. Premarket (n = 120) turkey parts collected from a commercial turkey processing plant, and retail turkey parts (n = 138) were examined. Both PCR methods detected a significantly greater (P < 0.05) number of positive samples when compared with the conventional culture method for the premarket turkey parts. The indices of total agreement between the conventional culture method and the iQ-Check kit for the premarket and retail parts were 79.2% (95% CI: 70.8, 86) and 90.6% (95% CI: 84.4, 94.9), respectively. When the conventional culture method was compared with invA PCR for Salmonella detection in the premarket and retail parts, the indices of total agreement were 75.8% (95% CI: 67.2, 83.2) and 84.1% (95% CI: 76.9, 89.7), respectively. The rates of false positives (premarket: 31.9%, retail: 9.7%) and false negatives (premarket: 5.9%, retail: 9.7%) were determined between the culture method and the iQ-Check kit. When invA PCR was compared with the culture method, the rates of false positives (premarket: 37.7%, retail: 11.1%) and false negatives (premarket: 5.9%, retail: 18.3%) were obtained. The higher total agreement and the lower rates of both false positives and false negatives for the iQ-Check kit compared with invA PCR for both premarket and retail turkey parts corroborates the use of the iQ-Check kit as a screening tool for Salmonella in poultry meat.