Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression

In order to effectively evaluate the diet nutritional value of dairy cows,it is essential to accurately predict the diet nutrients digestibility(DND).Conventional predicting DND methods are usually based on the least squares linear regression analysis(LS-LRA),which often relies on a large amount of training samples to accomplish reliable predictions.However,in real-world applications,it is often extremely difficult,costly and time-consuming to obtain a large number of measured samples,especially for the DND prediction of dairy cows.This paper applies a Gaussian process regression(GPR)technique to predict the DND indicators of dairy cows in small samples.To evaluate prediction accuracy effectively,we compared the GPR technique with the LS-LRA,radial basis function artificial neural network(RBF-ANN),support vector regression(SVR)and least squares support vector regression(LS-SVR)methods,using the required sample data obtained from actual digestion experiments.The prediction results indicate that the GPR technique is superior to other conventional methods(especially the LS-LRA method)in predicting the main DND indicators of dairy cows such as dry matter digestibility(DMD),organic matter digestibility(OMD),neutral detergent fiber(NDFD),acid detergent fiber(ADFD),and crude protein digestibility(CPD).It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples,which is often the case in the prediction of DND indicators of dairy cows,and then more coincide with actual needs.

A meta-analysis of the effects of energy intake on risk of digestive cancers

AIM:To quantitatively assess the relationship between energy intake and the incidence of digestive cancers in a meta-analysis of cohort studies.METHODS:We searched MEDLINE,EMBASE,Science Citation Index Expanded,and the bibliographies of retrieved articles.Studies were included if they reported relative risks(RRs) and corresponding 95% CIs of digestive cancers with respect to total energy intake.When RRs were not available in the published article,they were computed from the exposure distributions.Data were extracted independently by two investigators and discrepancies were resolved by discussion with a third investigator.We performed fixed-effects meta-analyses and meta-regressions to compute the summary RR for highest versus lowest category of energy intake and for per unit energy intake and digestive cancer incidence by giving each study-specific RR a weight that was proportional to its precision.RESULTS:Nineteen studies consisting of 13 independent cohorts met the inclusion criteria.The studiesincluded 995 577 participants and 5620 incident cases of digestive cancer with an average follow-up of 11.1 years.A significant inverse association was observed between energy intake and the incidence of digestive cancers.The RR of digestive cancers for the highest compared to the lowest caloric intake category was 0.90(95% CI 0.81-0.98,P < 0.05).The RR for an increment of 239 kcal/d energy intake was 0.97(95% CI 0.95-0.99,P < 0.05) in the fixed model.In subgroup analyses,we noted that energy intake was associated with a reduced risk of colorectal cancer(RR 0.90,95% CI 0.81-0.99,P < 0.05) and an increased risk of gastric cancer(RR 1.19,95% CI 1.08-1.31,P < 0.01).There appeared to be no association with esophageal(RR 0.96,95% CI 0.86-1.07,P > 0.05) or pancreatic(RR 0.79,95% CI 0.49-1.09,P > 0.05) cancer.Associations were also similar in studies from North America and Europe.The RR was 1.02(95% CI 0.79-1.25,P > 0.05) when considering the six studies conducted in North America and 0.87(95% CI 0.77-0.98,P < 0.05) for the five studies from Europe.CONCLUSION:Our findings suggest that high energy intake may reduce the total digestive cancer incidence and has a preventive effect on colorectal cancer.