Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms.Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella.Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05.Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases

Particle-hole asymmetric superconducting coherence peaks in overdoped cuprates

To elucidate the superconductor to metal transition at the end of superconducting dome, the overdoped regime has stepped onto the center stage of cuprate research recently. Here, we use scanning tunneling microscopy to investigate the atomic-scale electronic structure of overdoped trilayer Bi-2223 and bilayer Bi-2212 cuprates. At low energies the spectroscopic maps are well described by dispersive quasiparticle interference patterns. However, as the bias increases to the superconducting coherence peak energy, a virtually non-dispersive pattern with sqrt(2)*sqrt(2) periodicity emerges. Remarkably, the position of the coherence peaks exhibits evident particle-hole asymmetry which also modulates with the same period. We propose that this is an extreme quasiparticle interference phenomenon, caused by pairing-breaking scattering between flat anti-nodal Bogoliubov bands, which is ultimately responsible for the superconductor to metal transition.Comment: 15 pages, 4 figure

Downregulation of MicroRNA-4463 Attenuates High-Glucose- and Hypoxia-Induced Endothelial Cell Injury by Targeting PNUTS

Background/Aims: Vascular complications are the main reasons for disability and mortality associated with type 2 diabetes mellitus (T2DM) and numerous microRNAs (miRNAs) are involved in this process. Our previous study demonstrated that miR-4463 was increased in the plasma of T2DM patients combined with arteriosclerosis of low extremity artery (ASO). However, the role of miR-4463 remains unclear. Methods: miR-4463 expression in the vascular tissues of patients with ASO and T2DM and in human umbilical vein endothelial cells (HUVECs) was detected by qPCR. Cell survival and apoptosis was analyzed via Cell Counting Kit-8 and flow cytometry assays, respectively. Protein expression was determined by Western blot and protein subcellular localization was detected with immunofluorescence. A dual-luciferase assay was used to elucidate the target gene of miR-4463. Results: miR-4463 was elevated in the vascular tissues of patients with T2DM and ASO. In HUVECs, both 25 mmol/L glucose (high glucose, HG) and hypoxia induced miR-4463 expression. Downregulation of miR-4463 promoted HUVEC survival and reduced cell apoptosis under HG and/or hypoxic conditions by facilitating the expression of protein phosphatase-1 nuclear targeting subunit (PNUTS), X-linked inhibitor of apoptosis protein (XIAP), p-AKT, p-Bad, increased the Bcl-2/Bax ratio, as well as downregulated cleaved caspase 3 expression. Mechanistically, we identified PNUTS as a direct target gene of miR-4463. Both the inhibition of AKT phosphorylation and silencing of PNUTS diminished the effect of miR-4463 on HUVEC apoptosis. Moreover, downregulation of miR-4463 enhanced PNUTS to enable PTEN nuclear localization, which resulted in AKT phosphorylation. Conclusion: Our results suggest that downregulation of miR-4463 attenuates cell apoptosis by directly enhancing PNUTS expression to promote PTEN nuclear localization, subsequently activating AKT signaling pathway in HUVECs under HG and/ or hypoxic conditions