The mass balance model perfectly fits both Hall et al. underfeeding data and Horton et al. overfeeding data

Background & Aims Recently, the validity of mass balance model (MBM) was questioned based on two feeding studies. Thus, we simulated both of these feeding trials. Methods MBM describes the temporal evolution of body weight and body composition under a wide variety of feeding experiments. This computational study simulated, utilizing MBM, the underfeeding trial by Hall et al. (Cell Metab. 2015;22:427-36) and the overfeeding trial by Horton et al. (Am J Clin Nutr. 1995;62:19-29). Results Our simulation results indicate that data from both of these feeding trials perfectly match MBM-based predictions, i.e., MBM gives a remarkably accurate description of experimental data. Conclusions It is becoming increasingly clear that our model (MBM) is perfectly able to predict body weight and body composition fluctuations under a wide variety of feeding experiments.

Predication of body weight of the domestic rabbit at different stages of growth using linear body measurements

Data on body weight and linear body measurements (LBMs) namely ear length (EL), body width (BW), body length (BL), Head to shoulder (HS), Shoulder to tail (ST) and length of leg (LL) of 363 progeny of locally adapted Dutch , New Zealand White and crossbred rabbits at 3,6,9 and 12 weeks of age were analyzed to obtain phenotypic correlations between the various traits and prediction equations for body weight, using different linear body measurements. There were high and positive correlations between each of the linear body measurements and body weight and between the LBM themselves. The values ranged between 0.57 and 0.85, 0.34 and 0.89, 0.27 and 0.83 and 0.41 and 0.75 in weeks 3, 6, 9 and 12, respectively. Hence, it was possible to predict body weight of live rabbits from their linear body measurements, as an aid to farmers in areas where sensitive scales are not available. Except in purebred New Zealand White NZWxNZW, body: length (BL) was a good predictor of 3- week body weight (IBW) in all breed groups. Body width (BW) and BL only were good predictors of 6-week body weight in all breed groups. For predicting 9- week body weight, shoulder to tail drop (ST) was important in all breed groups in addition to either BW, ear length (EL) or body length (BL). ST and BW were important predictors of 12-week body weight generally in all breed groups.

Prediction of body weight with morphometric traits in some broiler chicken strains

Morphometric traits also called linear body measurements or conformation traits are important parameters in predicting body weight especially in commercial breeders and producers. Thus, the study was carried out to predict body weight of broiler using linear body measurement. In this study, a total of 270 day old broiler chicks comprising of 90 chicks each of Abor Acre, Ross and Marshal Strains were used. Data were collected on body weight using body measurements to include breast length (BRL), thigh width (TW), shank length (SL), keel length (KL), wing length and drumstick length (DL). The regression analysis was simple linear regression. The values of the coefficient of determination (R2) in Abor Acre, Ross and Marshall strains ranged from 89.8 – 99.8; 88.4 – 98.9; and 80.8 – 99.5 respectively with thigh width showing the highest % R2 value of 99.8% in week 2; 66.5 – 97.9; 60.3 – 80.4 and 28.6 – 72.3 respectively with breast length (97.9%) having the highest % R2 value. This showed that breast length was the best predictor of the body weight of the broiler in week 4; 38.5 – 100; 88.0 – 98.6; 17.0 – 94.8 with shank length (100%) showing a 100% R2 value. This showed that breast length was the best predictor of the body weight of the broiler in week 4; 38.5 – 100; 88.0 – 98.6; 17.0 – 94.8 with shank length (100%) showing a 100% R2 value in week 6; 76.9 – 96.3, 72.2 – 88.8 and 58.1 – 97.6 respectively with wing length recording the highest % R2 value in week 6; 76.9 – 96.3, 72.2 – 88.8 and 58.1 – 97.6 respectively with wing length recording the highest value (97.9%) week 8. The different strains had different coefficient of determination (R2) values above 50% with different linear body parameter at different ages of the birds, indicating that any of the linear body parameter could be used to predict body weight of broiler chicken although, accuracy of prediction increased with increasing R2 value. Amongst all the linear body parameters evaluated, the shank length of Abor Acres strain had highest R2 value (100%) in week 6. Thus shank length was the best linear body parameter with 100% accuracy of prediction, and may be useful criterion in estimation of growth and prediction of body weight.

Observations on some morphometric parameters and body weight of Fulani ecotype chicken sampled in Niger State, Nigeria

The study was conducted to evaluate morphometric traits (body length, body girth, wing length, Shank length, thigh length) and their association with body weight in Fulani Ecotype Chicken (FEC). Seventy-eight Fulani ecotype chickens were used for the experiment.They were fed compounded diet of 24% CP and 3213kcal/kg of metabolize energy for the first eight weeks, and 20% CP and 2948 kcal/kg from 9-12 weeks. The birds were kept on deep litter throughout the experiment. Feed and water were offered ad libitum.Measurements were taken on body weight and five morphometric parameters at 4, 8 and 12 weeks of the experiment for analysis. The results showed that body weight had the highest coefficient of variation (34.90%) at the end of the 4 week, thigh length at the end of the 8 week (5.08%), and body girth at the end of the 12 week (33.00%). Correlation between body weight and the morphometric parameters evaluated were observed to be positive and significant (p<0.05) except for correlation between body weight and thigh length at 4 week. The direct effects of the parameters measured at 4, 8 and 12 weeks were all significant (p<0.05) except for thigh length at week 4. Conclusively, the study revealed the existence of a positive relationship between the measured morphometric traits and body weight of Fulani ecotype chicken at 4, 8 and 12 weeks of the experiment and that shank length could be used to predict body weight at 4,8 and 12 weeks based on the high coefficient of determination(R ). Observations on some morphometric parameters and body weight of Fulani ecotype chicken sampled in Niger State, Nigeria

Phenotypic correlations between body weight and morphometric traits in broiler chicken strains

A total of 270 one day-old broiler chicks comprising of 90 chicks each ofAborAcre, Ross and Marshal Strains were used for the study. The study was carried out to determine the correlation between the body weight and other morphometric measurements in the broiler strains from 2 to 8 weeks of age and predict body weight of the broiler using linear body measurement. Data were collected on body weight and body measurements to include breast length (BRL), thigh width (TW), shank length (SL), keel length (KL), wing length and drumstick length (DL). There were strong positive and significant (p>0.01) correlations between body weight (BWT) and all morphometric traits in the three broiler strains studied, except breast length (BRL) that showed weak but significant (p

Prediction of Body Weight of a Person Lying on a Smart Mat in Nonrestraint and Unconsciousness Conditions

We want to predict body weight while lying in bed for an elderly patient who is unable to move by himself/herself. To this end, we have implemented a prototype system that estimates the body weight of a person lying on a smart mat in nonrestraint and unconsciousness conditions. A total of 128 FSR (force sensing resistor) sensors were placed in a 16 × 8-grid structure on the smart mat. We formulated three methods based on the features to be applied: segmentation, average cumulative sum of pressure, and serialization. All the proposed methods were implemented with four different machine-learning models: regression, deep neural network (DNN), convolutional neural network (CNN), and random forest. We compared their performance using MAE and RMSE as evaluation criteria. From the experimental results, we chose the serialization method with the DNN model as the best model. Despite the limitations of the presence of dead space due to the wide spacing between the sensors and the small dataset, the MAE and the RMSE of the body weight prediction of the proposed method was 4.608 and 5.796, respectively. That is, it showed an average error of ±4.6 kg for the average weight of 72.9 kg.