Metabolomics analysis for skin ulceration syndrome of Apostichopus japonicus based on UPLC/Q-TOF MS

Skin ulceration syndrome(SUS)is the main diseases aff ected the development of sea cucumber(Apostichopus japonicus)culture industries.To better observe the changes in the sea cucumber A.japonicus with SUS and understand the pathogenesis of the disease,activities of superoxide dismutase(SOD),catalase(CAT),and level of malondialdehyde(MDA)in coelomic fluid were detected using the Assay Kit and metabolites in the body wall were assessed using ultra-performance liquid chromatography and quadrupole-time of flight mass spectrometry.The results indicated that level of MDA was increased during SUS compared with healthy individuals(P<0.01),but activities of SOD and CAT were reduced(P<0.05).In metabolomics analysis,metabolites,such as adenosine,choline,betaine aldehyde,palmitic acid,and taurine,were found to be upregulated and 2-oxoadipic acid,anthranilic acid(vitamin L1),thioetheramide-PC,cholesterol-3-sulfate,and pentadecanoic acid were downregulated(VIP>1 and P<0.1).Pathway enrichment analysis indicated most enrichment of KEGG pathways were mainly related to energy metabolism,immunity,and osmoregulation such as ABC transporters,glycine,serine and threonine metabolism,tryptophan metabolism and neuroactive ligand-receptor interaction.Our study reflected the difference in enzyme activity and metabolites between A.japonicus with SUS and those without,which will provide reference data for investigating SUS.

A COMPARATIVE STUDY ON SHILLING DETECTION METHODS FOR TRUSTWORTHY RECOMMENDATIONS

Uncovering shilling attackers hidden in recommender systems is very crucial to enhance the robustness and trustworthiness of product recommendation. Many shilling attack detection algorithms have been proposed so far, and they exhibit complementary advantage and disadvantage towards various types of attackers. In this paper, we provide a thorough experimental comparison of several well-known detectors, including supervised C4.5 and NB, unsupervised PCA and MDS, semi-supervised HySAD methods, as well as statistical analysis methods. MovieLens 100K is the most widely-used dataset in the realm of shilling attack detection, and thus it is selected as the benchmark dataset. Meanwhile, seven types of shilling attacks generated by average-filling and random-filling model are compared in our experiments. As a result of our analysis, we show clearly causes and essential characteristics insider attackers that might determine the success or failure of different kinds of detectors.