FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interests comprehensively. In such situations, users may experience fatigue because of seeing too many similar recommendations. In this paper, we propose Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user fatigue from non-clicks. Specifically, we first apply Fourier Transformation to the time series generated from non-clicks, obtaining its frequency spectrum which contains comprehensive information about user fatigue. Then the frequency spectrum is modulated by category information of the target item to model the bias that both the upper bound of fatigue and users' patience is different for different categories. Moreover, a gating network is adopted to model the confidence of user fatigue and an auxiliary task is designed to guide the learning of user fatigue, so we can obtain a well-learned fatigue representation and combine it with user interests for the final CTR prediction. Experimental results on real-world datasets validate the superiority of FAN and online A/B tests also show FAN outperforms representative CTR models significantly

Simulations of HIV capsid protein dimerization reveal the effect of chemistry and topography on the mechanism of hydrophobic protein association

Recent work has shown that the hydrophobic protein surfaces in aqueous solution sit near a drying transition. The tendency for these surfaces to expel water from their vicinity leads to self assembly of macromolecular complexes. In this article we show with a realistic model for a biologically pertinent system how this phenomenon appears at the molecular level. We focus on the association of the C-terminal domain (CA-C) of the human immunodeficiency virus (HIV) capsid protein. By combining all-atom simulations with specialized sampling techniques we measure the water density distribution during the approach of two CA-C proteins as a function of separation and amino acid sequence in the interfacial region. The simulations demonstrate that CA-C protein-protein interactions sit at the edge of a dewetting transition and that this mesoscopic manifestation of the underlying liquid-vapor phase transition can be readily manipulated by biology or protein engineering to significantly affect association behavior. While the wild type protein remains wet until contact, we identify a set of in silico mutations, in which three hydrophilic amino acids are replaced with nonpolar residues, that leads to dewetting prior to association. The existence of dewetting depends on the size and relative locations of substituted residues separated by nm length scales, indicating long range cooperativity and a sensitivity to surface topography. These observations identify important details which are missing from descriptions of protein association based on buried hydrophobic surface area

Distant Fathers: Disjointed World of George Eliot

The article discusses distant fathers in the novels of George Eliot within the context of the nineteenth century. In the nineteenth-century Britain, the father’s role is best defined by Nelson, “authority, guidance and financial support”. (Natalie 2011, p.155) The article is devoted to explore the distant or absent fathers, which means no guidance, protection, and financial support to the children. The absence might be the consequences of many aspects relating to fathers. The father could be absent either physically or emotionally. The article argues that Eliot seeks and yearns for a perfect fatherhood by showing some shortcomings of the father and its effects on the lives of their children.

Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories

Magnetic Nanocomposite Hydrogel for Potential Cartilage Tissue Engineering: Synthesis, Characterization, and Cytocompatibility with Bone Marrow Derived Mesenchymal Stem Cells

Hydrogels possess high water content and closely mimic the microenvironment of extracellular matrix. In this study, we created a hybrid hydrogel containing type II collagen, hyaluronic acid (HA), and polyethylene glycol (PEG) and incorporated magnetic nanoparticles into the hybrid hydrogels of type II collagen-HA-PEG to produce a magnetic nanocomposite hydrogel (MagGel) for cartilage tissue engineering. The results showed that both the MagGel and hybrid gel (Gel) were successfully cross-linked and the MagGel responded to an external magnet while maintaining structural integrity. That is, the MagGel could travel to the tissue defect sites in physiological fluids under remote magnetic guidance. The adhesion density of bone marrow derived mesenchymal stem cells (BMSCs) on the MagGel group <i>in vitro</i> was similar to the control group and greater than the Gel group. The morphology of BMSCs was normal and consistent in all groups. We also found that BMSCs engulfed magnetic nanoparticles in culture and the presence of magnetic nanoparticles did not affect BMSC adhesion and morphology. We hypothesized that the ingested nanoparticles may be eventually broken down by lysosome and excreted through exocytosis; further studies are necessary to confirm this. This study reports a promising magnetic responsive nanocomposite hydrogel for potential cartilage tissue engineering applications, which should be further studied for its effects on cell functions when combined with electromagnetic stimulation

Complement Receptor 3 Has Negative Impact on Tumor Surveillance through Suppression of Natural Killer Cell Function

Complement receptor 3 (CR3) is expressed abundantly on natural killer (NK) cells; however, whether it plays roles in NK cell-dependent tumor surveillance is largely unknown. Here, we show that CR3 is an important negative regulator of NK cell function, which has negative impact on tumor surveillance. Mice deficient in CR3 (CD11b−/− mice) exhibited a more activated NK phenotype and had enhanced NK-dependent tumor killing. In a B16-luc melanoma-induced lung tumor growth and metastasis model, mice deficient in CR3 had reduced tumor growth and metastases, compared with WT mice. In addition, adaptive transfer of NK cells lacking CR3 (into NK-deficient mice) mediated more efficient suppression of tumor growth and metastases, compared with the transfer of CR3 sufficient NK cells, suggesting that CR3 can impair tumor surveillance through suppression of NK cell function. In vitro analyses showed that engagement of CR3 with iC3b (classical CR3 ligand) on NK cells negatively regulated NK cell activity and effector functions (i.e. direct tumor cell killing, antibody-dependent NK-mediated tumor killing). Cell signaling analyses showed that iC3b stimulation caused activation of Src homology 2 domain-containing inositol-5-phosphatase-1 (SHIP-1) and JNK, and suppression of ERK in NK cells, supporting that iC3b mediates negative regulation of NK cell function through its effects on SHIP-1, JNK, and ERK signal transduction pathways. Thus, our findings demonstrate a previously unknown role for CR3 in dysregulation of NK-dependent tumor surveillance and suggest that the iC3b/CR3 signaling is a critical negative regulator of NK cell function and may represent a new target for preserving NK cell function in cancer patients and improving NK cell-based therapy