Interconnecting bilayer networks

A typical complex system should be described by a supernetwork or a network of networks, in which the networks are coupled to some other networks. As the first step to understanding the complex systems on such more systematic level, scientists studied interdependent multilayer networks. In this letter, we introduce a new kind of interdependent multilayer networks, i.e., interconnecting networks, for which the component networks are coupled each other by sharing some common nodes. Based on the empirical investigations, we revealed a common feature of such interconnecting networks, namely, the networks with smaller averaged topological differences of the interconnecting nodes tend to share more nodes. A very simple node sharing mechanism is proposed to analytically explain the observed feature of the interconnecting networks.Comment: 9 page

A Polynomial Model with Line-of-Sight Constraints for Lagrangian Particle Tracking Under Interface Refraction

This paper introduces an improvement of the "Shake-The-Box (STB)" (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) technique using the polynomial calibration model and the line-of-sight constraints (LOSC) to overcome the refractive interface issues in Lagrangian particle tracking (LPT) measurement. The method (named LOSC-LPT) draws inspiration from the two-plane polynomial camera calibration in tomographic particle image velocimetry (Tomo-PIV) (Worth and Nickels, Thesis, 2010) and the STB-based open-source Lagrangian particle tracking (OpenLPT) framework (Tan, Salibindla, Masuk, and Ni, Exp. Fluids 61.2, 2019). The LOSC-LPT introduces polynomial mapping functions into STB calibration in conditions involving gas-solid-liquid interfaces at container walls exhibiting large refractive index variations, which facilitates the realization of particle stereo matching, three-dimensional (3D) triangulation, iterative particle reconstruction, and further refinement of 3D particle position by shaking the LOS. Performance evaluation based on synthetic noise-free images with a particle image density of 0.05 particle per pixel (ppp) in the presence of refractive interfaces demonstrates that LOSC-LPT can detect a higher number of particles and exhibits lower position uncertainty in the reconstructed particles, resulting in higher accuracy and robustness than that achieved with OpenLPT. In the application to an elliptical jet flow in an octagonal tank with refractive interfaces, the use of polynomial mapping results in smaller errors (mean calibration error 1.0 px). Moreover, 3D flow-field reconstructions demonstrate that the LOSC-LPT framework can recover a more accurate 3D Eulerian flow field and capture more complete coherent structures in the flow, and thus holds great potential for widespread application in 3D experimental fluid measurements

A Systematic Analysis of miRNA Transcriptome in Marek’s Disease Virus-Induced Lymphoma Reveals Novel and Differentially Expressed miRNAs

Marek’s disease is a lymphoproliferative neoplastic disease of the chicken, which poses a serious threat to poultry health. Marek’s disease virus (MDV)-induced T-cell lymphoma is also an excellent biomedical model for neoplasia research. Recently, miRNAs have been demonstrated to play crucial roles in mediating neoplastic transformation. To investigate host miRNA expression profiles in the tumor transformation phase of MDV infection, we performed deep sequencing in two MDVinfected samples (tumorous spleen and MD lymphoma from liver), and two non-infected controls (non-infected spleen and lymphocytes). In total, 187 and 16 known miRNAs were identified in chicken and MDV, respectively, and 17 novel chicken miRNAs were further confirmed by qPCR. We identified 28 down-regulated miRNAs and 11 up-regulated miRNAs in MDVinfected samples by bioinformatic analysis. Of nine further tested by qPCR, seven were verified. The gga-miR-181a, gga-miR- 26a, gga-miR-221, gga-miR-222, gga-miR-199*, and gga-miR-140* were down-regulated, and gga-miR-146c was upregulated in MDV-infected tumorous spleens and MD lymphomas. In addition, 189 putative target genes for seven differentially expressed miRNAs were predicted. The luciferase reporter gene assay showed interactions of gga-miR-181a with MYBL1, gga-miR-181a with IGF2BP3, and gga-miR-26a with EIF3A. Differential expression of miRNAs and the predicted targets strongly suggest that they contribute to MDV-induced lymphomagenesis

Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language

With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR). However, despite the commonalities of input format and task goal, there are huge gaps between the behavioral and textual information, which obstruct thoroughly modeling SR as language modeling via PLM. To bridge the gap, we propose a novel Unified pre-trained language model enhanced sequential recommendation (UPSR), aiming to build a unified pre-trained recommendation model for multi-domain recommendation tasks. We formally design five key indicators, namely naturalness, domain consistency, informativeness, noise & ambiguity, and text length, to guide the text-item adaptation and behavior sequence-text sequence adaptation differently for pre-training and fine-tuning stages, which are essential but under-explored by previous works. In experiments, we conduct extensive evaluations on seven datasets with both tuning and zero-shot settings and achieve the overall best performance. Comprehensive model analyses also provide valuable insights for behavior modeling via PLM, shedding light on large pre-trained recommendation models. The source codes will be released in the future

A novel AMPK activator, WS070117, improves lipid metabolism discords in hamsters and HepG2 cells

<p>Abstract</p> <p>Background</p> <p>WS070117 is a novel small molecule compound that significantly improves lipid metabolism disorders in high-fat-diet (HFD) induced hyperlipidemia in hamsters.</p> <p>Methods and Results</p> <p>We evaluated liver/body weight ratio, liver histology, serum and hepatic lipid content in HFD-fed hamsters treated with WS070117 for 8 weeks. Comparing with HFD fed hamsters, WS070117 (2 mg/kg per day and above) reduced serum triglyceride (TAG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C) and hepatic cholesterol and triglyceride contents. Oil Red O staining of liver tissue also showed that WS070117 improved lipid accumulation. We then carried out an experiment in the oleic acid (OLA)-induced steatosis model in HepG2 cell to investigate the lipid-lowering effect of WS070117. Oleic acid (0.25 mM) markedly induced lipid accumulation in HepG2 cells, but WS070117 (10 μM) inhibited cellular lipid accumulation. In OLA-treated HepG2 cells, WS070117 (above 1 μM) treatment reduced lipid contents which synthesized from [1-¹⁴C] labeled acetic acid. Because WS070117 is an analog of adenosine, we evaluated the effect of WS070117 on AMP-activated protein kinase (AMPK) signaling. The results showed that the activation of AMPK in OLA-induced steatosis in HepG2 cells was up-regulated by treatment with 0.1, 1 and 10 μM WS070117. The hepatic cellular AMPK phosphorylation is also up regulated by WS070117 (6 and 18 mg/kg) treatment in HFD fed hamsters.</p> <p>Conclusion</p> <p>These new findings identify WS070117 as a novel molecule that regulates lipid metabolism in the hyperlipidemia hamster model. In vitro and in vivo studies suggested that WS070117 may regulate lipid metabolism through stimulating the activation of AMPK and its downstream pathways.</p

Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction

Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior sub-sequence. This two-stage paradigm, though effective, leads to information loss. Solely using users' lifelong click behaviors doesn't provide a complete picture of their interests, leading to suboptimal performance. In our research, we introduce the Deep Group Interest Network (DGIN), an end-to-end method to model the user's entire behavior history. This includes all post-registration actions, such as clicks, cart additions, purchases, and more, providing a nuanced user understanding. We start by grouping the full range of behaviors using a relevant key (like item_id) to enhance efficiency. This process reduces the behavior length significantly, from O(10^4) to O(10^2). To mitigate the potential loss of information due to grouping, we incorporate two categories of group attributes. Within each group, we calculate statistical information on various heterogeneous behaviors (like behavior counts) and employ self-attention mechanisms to highlight unique behavior characteristics (like behavior type). Based on this reorganized behavior data, the user's interests are derived using the Transformer technique. Additionally, we identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence. The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy. Our comprehensive evaluation, both on industrial and public datasets, validates DGIN's efficacy and efficiency

PRL-3 promotes the motility, invasion, and metastasis of LoVo colon cancer cells through PRL-3-integrin β1-ERK1/2 and-MMP2 signaling

<p>Abstract</p> <p>Background</p> <p>Phosphatase of regenerating liver-3 (PRL-3) plays a causative role in tumor metastasis, but the underlying mechanisms are not well understood. In our previous study, we observed that PRL-3 could decrease tyrosine phosphorylation of integrin β1 and enhance activation of ERK1/2 in HEK293 cells. Herein we aim to explore the association of PRL-3 with integrin β1 signaling and its functional implications in motility, invasion, and metastasis of colon cancer cell LoVo.</p> <p>Methods</p> <p>Transwell chamber assay and nude mouse model were used to study motility and invasion, and metastsis of LoVo colon cancer cells, respectively. Knockdown of integrin β1 by siRNA or lentivirus were detected with Western blot and RT-PCR. The effect of PRL-3 on integrin β1, ERK1/2, and MMPs that mediate motility, invasion, and metastasis were measured by Western blot, immunofluorencence, co-immunoprecipitation and zymographic assays.</p> <p>Results</p> <p>We demonstrated that PRL-3 associated with integrin β1 and its expression was positively correlated with ERK1/2 phosphorylation in colon cancer tissues. Depletion of integrin β1 with siRNA, not only abrogated the activation of ERK1/2 stimulated by PRL-3, but also abolished PRL-3-induced motility and invasion of LoVo cells in vitro. Similarly, inhibition of ERK1/2 phosphorylation with U0126 or MMP activity with GM6001 also impaired PRL-3-induced invasion. In addition, PRL-3 promoted gelatinolytic activity of MMP2, and this stimulation correlated with decreased TIMP2 expression. Moreover, PRL-3-stimulated lung metastasis of LoVo cells in a nude mouse model was inhibited when integrin β1 expression was interfered with shRNA.</p> <p>Conclusion</p> <p>Our results suggest that PRL-3's roles in motility, invasion, and metastasis in colon cancer are critically controlled by the integrin β1-ERK1/2-MMP2 signaling.</p