Comparative effects of enzymatic soybean,fish meal and milk powder in diets on growth performance,immunological parameters,SCFAs production and gut microbiome of weaned piglets

Background:The objective of this study was to evaluate the replacement effects of milk powder(MK)and fish meal(FM)by enzymatic soybean(ESB)in diets on growth performance,immunological parameters,SCFAs production and gut microbiome of weaned piglets.Methods:A total of 128 piglets with initial body weight at 6.95±0.46 kg,were randomly assigned into 4 dietary treatments with 8 replicates per treatment and 4 piglets per replicate for a period of 14 d.Piglets were offered isonitrogenous and iso-energetic diets as follows:CON diet with MK and FM as high quality protein sources,ESB plus FM diet with ESB replacing MK,ESB plus MK diet with ESB replacing FM,and ESB diet with ESB replacing both MK and FM.Results:No significant differences were observed in growth performance among all treatments(P>0.05).However,piglets fed ESB plus FM or ESB diet had increased diarrhea index(P<0.01),and lower digestibility of dry matter(DM),gross energy(GE)or crude protein(CP),relative to piglets fed CON diet(P<0.01).Moreover,the inclusion of ESB in diet markedly decreased the plasma concentration of HPT and fecal concentration of butyric acid(BA)(P<0.01).The High-throughput sequencing of 16S rRNA gene V3−V4 region of gut microbiome revealed that the inclusion of ESB in diet increased the alpha diversity,and the linear discriminant analysis effect size(LEfSe)showed that piglets fed with ESB plus FM or ESB diet contained more gut pathogenic bacteria,such as g_Peptococcus,g_Veillonella and g_Helicobacter.Conclusion:The inclusion of ESB in diet did not markedly affect growth performance of piglets,but the replacement of MK or both MK and FM by ESB increased diarrhea index,which could be associated with lower nutrients digestibility and more gut pathogenic bacteria.However,piglets fed diet using ESB to replace FM did not markedly affect gut health-related parameters,indicating the potential for replacing FM with ESB in weaning diet.

Focusing on the Importance of Features for CTR Prediction

TraditionalCTR recommendation models have concentrated on howto learn low-order and high-order characteristics.The majority of them make many efforts at combining low-order and high-order functions.However,they ignore the importance of the attentionmechanism for learning input features.The ECABiNet model is proposed in this article to enhance the performance of CTR.On the one hand,the ECABiNet model can learn the importance of features dynamically via the LayerNorm and ECANET layers.On the other hand,through the use of a biinteraction layer and a DNN layer,it is capable of effectively learning the feature interactions.According to the experimental results on two public datasets,the ECABiNet model is more effective than the previous CTR model.

User Attribute Prediction Method Based on Stacking Multimodel Fusion

The user’s age and gender play a vital role within the user portrait.In view of the lack of basic attribute information,such as the age and gender of users,this paper constructs an attribute prediction method based on stacking multimodel integration.The user’s browsing and clicking history is analyzed to predict the user’s basic attributes.First,LR,RF,XGBoost,and ExtraTree were selected as the base classifiers for the first layer of the stacking framework,and the training results of the first layer were input as new training data into the second layer LightGBM for training.Experiments show that the proposed model can improve the accuracy of prediction results.

Factorization Machine Based on Bitwise Feature Importance for CTR Prediction

Click-through-rate(CTR)prediction is a crucial task in recommendation systems.The accuracy of CTR prediction is strongly influenced by the precise extraction of essential data and the modeling strategy chosen.The data of the CTR task are often very sparse,and Factorization Machines(FMs)are a class of general predictors working effectively with it.However,the performance of FMs can be limited by the fixed feature representation and the same weight of different features.In this work,we propose an improved Bitwise Feature Importance Factorization Machine(BFIFM)to improve the accuracy.The necessity of learning the degree of effect of the same feature under various situations is learned through the low-order intersection method,and the deep neural network(DNN)in our model is used in parallel to study high-order intersections.According to the final results obtained,the BFIFM model significantly outperforms other state-of-the-art models.