scholarly journals HERD OF ORIGIN EFFECTS ON THE PERFORMANCE OF STATION-TESTED BEEF BULLS

1987 ◽  
Vol 67 (2) ◽  
pp. 349-358 ◽  
Author(s):  
S. AMAL ◽  
G. H. CROW
Keyword(s):  
Weight Gain ◽  
Beef Cattle ◽  
Key Words ◽  
Random Effects ◽  
Mixed Model ◽  
Data Sets ◽  
Minor Importance ◽  
Data Set ◽  
Components Of Variance ◽  
Origin Effects

Nine years (1976–1984) of performance data from the Manitoba Bull Testing Station, Douglas were used to investigate herd of origin effects on the 140-d growth performance of bulls. Two data sets were analyzed: the Angus and Hereford (AH) data set contained records on 1649 bulls; the Charolais and Simmental (CS) data set contained records on 1786 bulls. The mixed model for analysis of weight, gain and backfat data included year-breed as a categorical fixed effect, age of bull as a fixed covariate, and herd and sire as random effects in a hierarchial classification. Herd, sire and error components of variance were estimated for weights and gains of bulls during the test as well as for backfat which was measured at the end of the test. At the start of the test 39 and 33% of the variation among bull weights was due to herd of origin effects for the AH and CS data sets, respectively. This proportion dropped gradually to 30 and 22% at the 112-d weighing for the AH and CS data sets, respectively, and remained at these levels for the 140-d weights. For the cumulative 140-d gain on test only 15 and 16% of the total variation were attributed to herds for the AH and CS data sets, respectively. For periodic gains (gains made between two adjacent weighings, usually 28-d intervals), herd of origin was a major source of variation for the 28-d pretest adjustment period (45 and 34% for the AH and CS data sets, respectively), but was of minor importance in subsequent periods. For backfat, herd of origin was a significant though small source of variation. The nature of the herd of origin variation (genetic or environmental) could not be discerned from this analysis though it was evident that a portion of the herd of origin variation was temporary environmental in nature since its magnitude decreased as the test progressed. Assuming that a portion of the herd of origin variation was permanent environmental in nature for both weight and gain and given that herd of origin variation was lower for gain than for weight it is recommended to emphasize test gain as opposed to weight in the comparison of bull performance. Key words: Beef cattle, test station, herd of origin, heritability, growth, backfat

Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination

2021 ◽  
pp. 1-29
Author(s):  
Nicole Sanford ◽  
Todd S. Woodward
Keyword(s):  
Working Memory ◽  
Mixed Model ◽  
Data Sets ◽  
Generation Task ◽  
Data Set ◽  
Set Switching ◽  
Dorsolateral Prefrontal ◽  
Task Conditions ◽  
Experiment Analysis ◽  
Internal Attention

Abstract Background: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. Methods: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. Results: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory–motor attention, (3) motor responses, and (4) task energizing. The “energizing” network—engaged during WM encoding and diminished in patients—exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated “internal attention” network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. Conclusions: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding “energizing” network, WM deficits in schizophrenia may arise from disruption in the “energization” process described by Donald Stuss' model of pFC functions.

scholarly journals CoMM: A Collaborative Mixed Model That Integrates GWAS and eQTL Data Sets to Investigate the Genetic Architecture of Complex Traits

2019 ◽  
Vol 13 ◽  
pp. 117793221988143 ◽  
Author(s):  
Kar-Fu Yeung ◽  
Yi Yang ◽  
Can Yang ◽  
Jin Liu
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mixed Model ◽  
Genome Wide Association Study ◽  
Data Sets ◽  
Transcriptome Data ◽  
Data Set ◽  
Expression Levels

Genome-wide association study (GWAS) analyses have identified thousands of associations between genetic variants and complex traits. However, it is still a challenge to uncover the mechanisms underlying the association. With the growing availability of transcriptome data sets, it has become possible to perform statistical analyses targeted at identifying influential genes whose expression levels correlate with the phenotype. Methods such as PrediXcan and transcriptome-wide association study (TWAS) use the transcriptome data set to fit a predictive model for gene expression, with genetic variants as covariates. The gene expression levels for the GWAS data set are then ‘imputed’ using the prediction model, and the imputed expression levels are tested for their association with the phenotype. These methods fail to account for the uncertainty in the GWAS imputation step, and we propose a collaborative mixed model (CoMM) that addresses this limitation by jointly modelling the multiple analysis steps. We illustrate CoMM’s ability to identify relevant genes in the Northern Finland Birth Cohort 1966 data set and extend the model to handle the more widely available GWAS summary statistics.

scholarly journals Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Guri Feten ◽  
Trygve Almøy ◽  
Are H. Aastveit
Keyword(s):  
Gene Expression ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Correlation Structure ◽  
Data Sets ◽  
Data Set ◽  
Nearest Neighbours ◽  
Complete Matrix ◽  
The Ideal

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.

Methodology for eliminating imbalance of image data sets

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Tatyana Biloborodova ◽  
Inna Skarga-Bandurova ◽  
Mark Koverga
Keyword(s):  
Feature Extraction ◽  
Reinforcement Learning ◽  
Key Words ◽  
Class Imbalance ◽  
Image Data ◽  
Unbalanced Data ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Image Fragment

The methodology of solving the problem of eliminating class imbalance in image data sets is presented. The proposed methodology includes the stages of image fragment extraction, fragment augmentation, feature extraction, duplication of minority objects, and is based on reinforcement learning technology. The degree of imbalance indicator was used as a measure to determine the imbalance of the data set. An experiment was performed using a set of images of the faces of patients with skin rashes, annotated according to the severity of acne. The main steps of the methodology implementation are considered. The results of the classification showed the feasibility of applying the proposed methodology. The accuracy of classification on test data was 85%, which is 5% higher than the result obtained without the use of the proposed methodology. Key words: class imbalance, unbalanced data set, image fragment extraction, augmentation.

Modelling and localizing a stem taper function for Pinus radiata in Spain

2015 ◽  
Vol 45 (6) ◽  
pp. 647-658 ◽  
Author(s):  
Manuel Arias-Rodil ◽  
Ulises Diéguez-Aranda ◽  
Francisco Rodríguez Puerta ◽  
Carlos Antonio López-Sánchez ◽  
Elena Canga Líbano ◽  
...  
Keyword(s):  
Random Effects ◽  
Pinus Radiata ◽  
Mixed Model ◽  
Mixed Effects ◽  
Stem Diameter ◽  
Mixed Effects Models ◽  
Stem Volume ◽  
Diameter Measurement ◽  
Data Set ◽  
Taper Function

The parsimonious taper function proposed by Riemer et al. (1995. Allg. Forst.- Jagdztg. 166(7): 144–147) was fitted for radiata pine (Pinus radiata D. Don) stems in Spain by using a nonlinear mixed modelling approach. Eight candidate models (all possible expansion combinations of the three fixed parameters with random effects) were assessed, and the mixed model with three random effects performed the best according to the goodness-of-fit statistics. An evaluation data set was used to assess the performance of these models in predicting stem diameter along the bole, as well as total stem volume. Four prediction approaches were compared: one subject (tree) specific (SS) and three population specific (ordinary least squares (OLS), mean (M), and population averaged (PA)). The SS responses for a tree were estimated from a prior stem diameter measurement available for that tree, whereas OLS, M, and PA were obtained from the fixed-effects model, from the fixed parameters of mixed-effects models, and by computing mean predictions from the mixed-effects models over the distribution of random effects, respectively. Prediction errors were greater for the M and PA responses than for the OLS response, and therefore, from the prediction point of view, the use of the mixed-effects models is not recommended when an additional stem diameter measurement is not available. The mixed model with three random effects was also selected as the best model for SS estimations. Measurement of an additional stem diameter at a relative tree height of approximately 0.5 provided the best calibrations for stem diameters along the bole and total stem volume predictions. The SS approach increased the flexibility and efficiency of the selected mixed-effects model for localized predictions and thus improved the overall predictive capacity of the base model.

Fibrolytic enzymes increase fiber digestibility and growth rate of steers fed dry forages

1995 ◽  
Vol 75 (4) ◽  
pp. 641-644 ◽  
Author(s):  
K. A. Beauchemin ◽  
L. M. Rode ◽  
V. J. H. Sewalt
Keyword(s):  
Growth Rate ◽  
Weight Gain ◽  
Beef Cattle ◽  
Key Words ◽  
Fibrolytic Enzymes ◽  
Fiber Digestibility ◽  
Alfalfa Hay ◽  
Ad Libitum ◽  
Barley Silage

Seventy-two steers (289 kg) were offered ad libitum cubed alfalfa hay, cubed timothy hay, or barley silage supplemented with incremental levels of xylanase (IU) and cellulase (FPU), combined in a ratio of 1 IU:0.04 FPU. For alfalfa hay, low and moderate levels (900 to 4733 IU kg−1 DM) increased weight gain by up to 30% (P < 0.10), whereas, for timothy hay, the highest level (12 000 IU kg−1 DM) improved gain (P < 0.10) by 36%. No response to enzymes was observed for barley silage. Fibrolytic enzymes improve weight gain of cattle but optimal enzyme levels depend upon the type of forage. Key words: Beef cattle, forages, enzymes, cellulase, xylanase, carbohydrases

GENETIC PARAMETERS OF THE MATERNAL GRANDSIRE CONTRIBUTION TO BEEF CATTLE WEANING WEIGHTS

1982 ◽  
Vol 62 (4) ◽  
pp. 1057-1062
Author(s):  
G. H. CROW ◽  
W. E. HOWELL
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Variance Components ◽  
Genetic Parameters ◽  
Mixed Model ◽  
Genetic Effects ◽  
Performance Data ◽  
Maternal Influence ◽  
Weaning Weight ◽  
Maternal Grandsire

Genetic aspects of maternal influence on weaning weights in beef cattle were examined using analyses within breed and parity of dam (first, second, third and fourth and greater parities) of Angus, Charolais and Hereford Record of Performance data. A mixed model which included herd-year and maternal grandsire (MGS) was used. The data were adjusted for calf sex within breed and parity of dam prior to analysis. The heritability of dam influence on her offspring weaning weight averaged 0.23 for first parity data of the three breeds. Heritability for second and third parities of the three breeds were lower than this but averaged 0.16 in parity four and greater. MGSs contributed significantly to variation in weaning weights. Their contribution, however, is a composite of both direct and maternal genetic effects. Key words: Beef cattle, weaning weight, maternal, variance components, heritability

VALIDATION OF TECHNIQUES USED IN PREDICTING CONSTANT FINISH ENDPOINT TRAITS FROM BEEF PERFORMANCE TEST INFORMATION

1986 ◽  
Vol 66 (4) ◽  
pp. 1125-1129
Author(s):  
M. R. McMORRIS ◽  
J. W. WILTON
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Feed Intake ◽  
Performance Test ◽  
Accurate Prediction ◽  
Regression Coefficients ◽  
Data Sets ◽  
Independent Data ◽  
Prediction Equations ◽  
Test Information

Independent data sets were used to develop and validate prediction equations for weight at market finish (WM), days to market finish (DM) and feed intake to market finish (FM). Use of information currently collected at most bull test stations allowed accurate prediction of WM, DM and, to a lesser extent, FM. Regression coefficients were similar across the data sets used. Correlations of 0.89, 0.76 and 0.81, respectively, were found between predicted and actual values of WM, DM and FM. Key words: Beef cattle, constant finish, performance test information

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

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