Mechanism of action of Wuzi Yanzong pill in the treatment of oligoasthenozoospermia in rats determined via serum metabolomics

Objective:To investigate the mechanism of action of Wuzi Yanzong pill(WYP)in rats with oligoasthenozoospermia(OAZ)via metabolomics and to provide a possible basis for improving this WYP-based treatment.Methods:A rat model of OAZ was established by treating male SpragueeDawley rats with glucosides from Tripterygium wilfordii Hook.F.Seventy-two rats were randomly divided into six groups:control,L-carnitine(positive control),model,and low-,medium-,and high-dose WYP groups.Rats in the experimental groups were treated with WYP for 4 weeks.At the end of the treatment period,sperm cell quality(density,motility,and viability)was assessed using a semen analysis system,mitochondrial membrane potential(MMP)was assessed using flow cytometry,and testicular injury was assessed using hematoxylin and eosin staining to validate the therapeutic effect of WYP in OAZ.Further,serum metabolomics-based analysis was performed using high-performance liquid chromatography-mass spectrometry to identify differential metabolic pathways and possible mechanisms of action of WYP in OAZ treatment.Results:A rat model of OAZ was considered successfully-established after comparing the quality of spermatozoa in the model group to that in the control group.WYP-M and WYP-H treatments significantly improved sperm cell density,motility,and viability compared with those in the model group(all P<.05).Compared with the model group,both WYP-M and WYP-H treatments increased MMP values(P=.006 and P=.021 respectively),while there was no significant difference in the L-carnitine group.L-carnitine and WYP administration reversed damage to the testes to varying degrees compared with that in the model group.Further,44 differential metabolites and four metabolic pathways,especially autophagy pathway,related to OAZ were identified via metabolomics.Conclusions:WYP improves sperm cell quality and MMP in OAZ primarily via autophagy regulation.These findings can be employed to improve the efficacy of WYP in humans.

A Simple yet Effective Framework for Active Learning to Rank

While China has become the largest online market in the world with approximately 1 billion internet users,Baidu runs the world's largest Chinese search engine serving more than hundreds of millions of daily active users and responding to billions of queries per day.To handle the diverse query requests from users at the web-scale,Baidu has made tremendous efforts in understanding users'queries,retrieving relevant content from a pool of trillions of webpages,and ranking the most relevant webpages on the top of the res-ults.Among the components used in Baidu search,learning to rank(LTR)plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models.To reduce the costs and time con-sumption of query/webpage labelling,we study the problem of active learning to rank(active LTR)that selects unlabeled queries for an-notation and training in this work.Specifically,we first investigate the criterion-Ranking entropy(RE)characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints,using a query-by-com-mittee(QBC)method.Then,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a query.Our empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries more.Finally,we combine these two complementary criteria as the sample selection strategies for active learning.Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models to achieve higher discounted cumulative gain(i.e.,the relative improvement DCG4=1.38%)with the same budgeted labellingefforts.

Polygene-based evolutionary algorithms with frequent pattern mining

In this paper, we introduce polygene-based evolution, a novel framework for evolutionary algorithms （EAs） that features distinctive operations in the evolutionary process. In traditional EAs, the primitive evolution unit is a gene, wherein genes are independent components during evolution. In polygene-based evolutionary algorithms （PGEAs）, the evolution unit is a polygene, i.e., a set of co-regulated genes. Discovering and maintaining quality polygenes can play an effective role in evolving quality individuals. Polygenes generalize genes, and PGEAs generalize EAs. Implementing the PGEA framework involves three phases： （Ⅰ） polygene discovery, （Ⅱ） polygene planting, and （Ⅲ） polygene-compatible evolution. For Phase I, we adopt an associative classificationbased approach to discover quality polygenes. For Phase Ⅱ, we perform probabilistic planting to maintain the diversity of individuals. For Phase Ⅲ, we incorporate polygenecompatible crossover and mutation in producing the next generation of individuals. Extensive experiments on function optimization benchmarks in comparison with the conventional and state-of-the-art EAs demonstrate the potential of the approach in terms of accuracy and efficiency improvement.