Effects of lactic acid bacteria-fermented formula milk supplementation on ileal microbiota,transcriptomic profile,and mucosal immunity in weaned piglets

Background:Lactic acid bacteria(LAB)participating in milk fermentation naturally release and enrich the fermented dairy product with a broad range of bioactive metabolites,which has numerous roles in the intestinal health-promot-ing of the consumer.However,information is lacking regarding the application prospect of LAB fermented milk in the animal industry.This study investigated the effects of lactic acid bacteria-fermented formula milk(LFM)on the growth performance,intestinal immunity,microbiota composition,and transcriptomic responses in weaned piglets.A total of 24 male weaned piglets were randomly divided into the control(CON)and LFM groups.Each group consisted of 6 replicates(cages)with 2 piglets per cage.Each piglet in the LFM group were supplemented with 80 mL LFM three times a day,while the CON group was treated with the same amount of drinking water.Results:LFM significantly increased the average daily gain of piglets over the entire 14 d(P<0.01)and the average daily feed intake from 7 to 14 d(P<0.05).Compared to the CON group,ileal goblet cell count,villus-crypt ratio,sIgA,and lactate concentrations in the LFM group were significantly increased(P<0.05).Transcriptomic analysis of ileal mucosa identified 487 differentially expressed genes(DEGs)between two groups.Especially,DEGs involved in the intestinal immune network for IgA production pathways,such as polymeric immunoglobulin receptor(PIGR),were significantly up-regulated(P<0.01)by LFM supplementation.Moreover,trefoil factor 2(TFF2)in the LFM group,one of the DEGs involved in the secretory function of goblet cells,was also significantly up-regulated(P<0.01).Sequenc-ing of the 16S rRNA gene of microbiota demonstrated that LFM led to selective enrichment of lactate-producing and short-chain fatty acid(SCFA)-producing bacteria in the ileum,such as an increase in the relative abundance of Entero-coccus(P=0.09)and Acetitomaculum(P<0.05).Conclusions:LFM can improve intestinal health and immune tolerance,thus enhancing the growth performance of weaned piglets.The changes in microbiota and metabolites induced by LFM might mediate the regulation of the secretory function of goblet cells.

Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions

This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.

Zeroth-Order Methods for Online Distributed Optimization with Strongly Pseudoconvex Cost Functions

This paper studies an online distributed optimization problem over multi-agent systems.In this problem,the goal of agents is to cooperatively minimize the sum of locally dynamic cost functions.Different from most existing works on distributed optimization,here we consider the case where the cost function is strongly pseudoconvex and real gradients of objective functions are not available.To handle this problem,an online zeroth-order stochastic optimization algorithm involving the single-point gradient estimator is proposed.Under the algorithm,each agent only has access to the information associated with its own cost function and the estimate of the gradient,and exchange local state information with its immediate neighbors via a time-varying digraph.The performance of the algorithm is measured by the expectation of dynamic regret.Under mild assumptions on graphs,we prove that if the cumulative deviation of minimizer sequence grows within a certain rate,then the expectation of dynamic regret grows sublinearly.Finally,a simulation example is given to illustrate the validity of our results.