scholarly journals Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 708
Author(s):  
Moran Gershoni ◽  
Joel Ira Weller ◽  
Ephraim Ezra
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Variance Components ◽  
Complete Data ◽  
Variance Component Analysis ◽  
Coefficient Of Determination ◽  
Birth Date ◽  
Analysis Model ◽  
Data Set ◽  
Genetic Evaluations

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight − 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was −0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (pnominal < 1 × 10−8). Considering all SNPs simultaneously, 0.69 of variance among the sires’ transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13–14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Milk Yield and Composition of Mixed-Breed Goats on Rangeland during the Dry Season and the Effect on the Growth of Their Progeny

Biology ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 220
Author(s):  
Manuel J. Flores-Najera ◽  
Venancio Cuevas-Reyes ◽  
Juan M. Vázquez-García ◽  
Sergio Beltrán-López ◽  
César A. Meza-Herrera ◽  
...  
Keyword(s):  
Body Weight ◽  
Weight Gain ◽  
Milk Yield ◽  
Milk Fat ◽  
Body Weight Gain ◽  
Postnatal Growth ◽  
Dry Season ◽  
Weaning Weight ◽  
Northern Mexico ◽  
Mixed Breed

We tested whether the milk yield capacity of mixed-breed goats on a Chihuahuan desert rangeland in northern Mexico during the dry season affects milk composition, body weight gain, and weaning weight of their progeny. Milk yield and composition, and progeny postnatal growth performance, were recorded weekly. One week after kidding, mixed-breed goats (a mixture of Criollo × dairy breeds; n = 40) were allotted into medium (MP) or low (LP) milk yielding groups (20 goats per group). Mean 105-d total milk yield for MP and LP goats was 45.2 ± 12.5 and 20.7 ± 5.2 L, respectively. Milk lactose (4.3 vs. 4.2%) and solids-non-fat (SNF; 8.2 vs. 8.0) differed (p < 0.05) between MP and LP goats; milk protein content tended to differ (p = 0.08) between MP and LP goats with no difference for milk fat content (p > 0.05). Maternal body weight was positively associated with milk yield, milk lactose, and SNF content (p < 0.05 to p < 0.001). Goats giving birth to males produce more milk than goats giving birth to females, but milk fat percentage was higher in goats bearing females (p < 0.001). Milk yield and composition throughout lactation did not influence body weight gain (47.8 vs. 48.7 g/day for kids from MP and LP goats) and weaning weight (6.7 vs. 6.7 kg from MP and LP goats) of the offspring (p > 0.05). Birth weight and weaning weight of the progeny were positively related to maternal body weight (p ≤ 0.05). The postnatal growth of the kids was reduced, extending the time to reach market weight. Nevertheless, non-supplemented mixed-breed goats reared on semi-arid rangeland of northern Mexico have the potential for moderate milk production. Therefore, due to the limited nutrients ingested by grazing goats during the dry season, a nutritional supplement is necessary to keep up milk production and adequate growth of kids.

scholarly journals The Impacts of Waterproof Insulated Jackets on Lamb Performance on a UK Lowland Farm

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 217
Author(s):  
Eleanor Brooke Collins ◽  
Nicola Blackie
Keyword(s):  
Weight Gain ◽  
Cold Stress ◽  
Morbidity And Mortality ◽  
Daily Weight Gain ◽  
Birth Date ◽  
Body Temperatures ◽  
Average Daily Weight Gain ◽  
Treatment Groups ◽  
Surface Temperatures ◽  
Weight Gains

The majority of lamb losses occur within the first two weeks of life, with cold stress being a major cause of lamb morbidity and mortality. This study investigated the effect of insulating lamb jackets on newborn lambs. One hundred and four newborn lambs were randomly allocated by birth date to two treatment groups, (a) non-jacketed (n = 52) or (b) jacketed (n = 52), for fourteen days after birth. The live weights of lambs were recorded regularly up to 21 days, and average daily weight gains were calculated from these data. For the first two days after recruitment to the study, surface and body temperatures of lambs were also recorded. The jackets significantly increased the lambs’ surface temperatures, providing a warmer microclimate and reduced cold stress for jacketed lambs. There was no significant effect of the insulating jackets on estimated body temperatures, live weights or average daily weight gain of the lambs in this study. There were no detrimental effects of the jackets, and no rejection of lambs occurred.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals Safety and Tolerance Evaluation of Milk Fat Globule Membrane-Enriched Infant Formulas: A Randomized Controlled Multicenter Non-Inferiority Trial in Healthy Term Infants

2014 ◽  
Vol 8 ◽  
pp. CMPed.S16962 ◽  
Author(s):  
Claude Billeaud ◽  
Giuseppe Puccio ◽  
Elie Saliba ◽  
Bernard Guillois ◽  
Carole Vaysse ◽  
...  
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Bovine Milk ◽  
Control Group ◽  
Infant Formulas ◽  
Term Infants ◽  
Milk Fat Globule Membrane ◽  
Standard Formula ◽  
Milk Fat Globule ◽  
Fat Globule

Objective This multicenter non-inferiority study evaluated the safety of infant formulas enriched with bovine milk fat globule membrane (MFGM) fractions. Methods Healthy, full-term infants ( n = 119) age ≤14 days were randomized to standard infant formula (control), standard formula enriched with a lipid-rich MFGM fraction (MFGM-L), or standard formula enriched with a protein-rich MFGM fraction (MFGM-P). Primary outcome was mean weight gain per day from enrollment to age 4 months (non-inferiority margin: –3.0 g/day). Secondary (length, head circumference, tolerability, morbidity, adverse events) and exploratory (phospholipids, metabolic markers, immune markers) outcomes were also evaluated. Results Weight gain was non-inferior in the MFGM-L and MFGM-P groups compared with the control group. Among secondary and exploratory outcomes, few between-group differences were observed. Formula tolerance rates were high (>94%) in all groups. Adverse event and morbidity rates were similar across groups except for a higher rate of eczema in the MFGM-P group (13.9% vs control [3.5%], MFGM-L [1.4%]). Conclusion Both MFGM-enriched formulas met the primary safety endpoint of non-inferiority in weight gain and were generally well tolerated, although a higher rate of eczema was observed in the MFGM-P group.

scholarly journals Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods

2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik
Keyword(s):  
Dairy Cattle ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Variance Components ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Bayesian Procedure ◽  
Milk Traits ◽  
Residual Variances ◽  
Estimation Of Variance

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.

scholarly journals The effects of age and body weight on powerlifters: An analysis model of powerlifting performance based on machine learning

2019 ◽  
Vol 18 (3) ◽  
pp. 89-99
Author(s):  
Vinh Huy Chau ◽  
Anh Thu Vo ◽  
Ba Tuan Le
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Body Weight ◽  
Root Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Analysis Model ◽  
Factors Affecting ◽  
Artificial Intelligence Algorithm ◽  
The Relationship

Abstract As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

scholarly journals Sentiment analysis of MOOC reviews via ALBERT-BiLSTM model

2021 ◽  
Vol 336 ◽  
pp. 05008
Author(s):  
Cheng Wang ◽  
Sirui Huang ◽  
Ya Zhou
Keyword(s):  
Sentiment Analysis ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Analysis Model ◽  
Accuracy Rate ◽  
Massive Open Online ◽  
Data Set ◽  
Proposed Model ◽  
Contextual Feature

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

scholarly journals Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 489
Author(s):  
Fadi Almohammed ◽  
Parveen Sihag ◽  
Saad Sh. Sammen ◽  
Krzysztof Adam Ostrowski ◽  
Karan Singh ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Performance Evaluation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Curing Time ◽  
Data Set ◽  
Soft Computing Techniques ◽  
Bacterial Concrete

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

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