SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

Investigating sphingolipid behavior and function using metabolic labeling

The past few decades of research have accumulated a body of evidence that membrane lipids are far more than merely the structural components of biological membranes. Instead, membrane lipids play important roles in cellular functions in multiple ways. Sphingolipids are a group of lipids that are involved in various cellular processes that are crucial for cell survival and proliferation. However, our understanding of sphingolipid function is limited due to the complexity of their behaviors and the lack of proper tools to address and decipher this complexity. Chapters 2 and 3 present metabolic labeling with fluorophores and stable isotope tags, respectively, as tools to investigate sphingolipid behaviors. Metabolic labeling enables one to detect and directly observe sphingolipids, and not the activities or levels of the enzymes that metabolize them. Metabolic labeling of cells with fluorescent sphingosines enabled visualization of the sphingosine metabolites in live cells and also showed potential for studies of metabolism and in vitro assays. Use of stable isotope tagged sphingolipid precursors, in conjunction with LC-MS/MS analysis, provided a more comprehensive and complete dataset than traditional radiolabeling, including information about unlabeled as well as labeled species. These tools offer great opportunities to explore sphingolipid behaviors. In Chapter 4, based on the observations that sphingolipids have significant roles in membrane organization and that virus infection requires intense membrane reorganization, the involvement of acid sphingomyelinase or sphingomyelin phosphodiesterase 1 (SMPD1), a sphingolipid metabolizing enzyme, in influenza virus infection and particularly its entry was evaluated using RNAi and a pharmacological inhibitor. Western blotting performed prior to infection showed that a significantly higher level of SMPD1 was present in the medium than in the cells. Lowering SMPD1 levels by RNAi or a functional pharmacologic inhibitor, desipramine, did not cause a statistically meaningful change in influenza virus entry. However, influenza virus infection itself was correlated with upregulated SMPD1 levels at the early phase of infection, opening the possibility that sphingolipids may still play an important role in influenza virus infection. Further investigation of the role of SMPD1 in influenza virus infection is necessary. Lastly, in Chapter 5, the cellular uptake of protein-coated nanoparticles was investigated in an effort to understand how plasma proteins interact with the nanoparticle surface, and to enhance the efficiency of targeted nanoparticle delivery with an in vitro system that mimics the in vivo environment. Formation of the protein corona, the protein layer that adsorbs on the surface of the nanoparticle when it is exposed to a biological fluid, is reported to prevent the desired interactions between the nanoparticles and the target cells. Exploiting the well-established mechanism of opsonin-mediated endocytosis in immune cells, we tested whether the protein corona itself can be used as a targeting moiety. Pre-coating the nanoparticles with γ-globulins provided a simple route to enrich the protein corona with opsonins. However, the increased opsonin levels in the protein corona did not enhance cellular uptake, but instead significantly decreased it. Immunodot blot assay and confocal fluorescence microscopy showed that these nanoparticles were internalized through opsonin-receptor interactions, but the opsonins on the nanoparticle were not accessible. This indicates that other components in the protein corona shielded the opsonins, preventing them from interacting with their target receptor. This study demonstrates that the spatial organization of the targeting moieties is critical, and it must be optimized for more efficient targeted nanoparticle delivery

Interacting models for twisted bilayer graphene: a quantum chemistry approach

The nature of correlated states in twisted bilayer graphene (TBG) at the magic angle has received intense attention in recent years. We present a numerical study of an interacting Bistritzer-MacDonald (IBM) model of TBG using a suite of methods in quantum chemistry, including Hartree-Fock, coupled cluster singles, doubles (CCSD), and perturbative triples (CCSD(T)), as well as a quantum chemistry formulation of the density matrix renormalization group method (DMRG). Our treatment of TBG is agnostic to gauge choices, and hence we present a new gauge-invariant formulation to detect the spontaneous symmetry breaking in interacting models. To benchmark our approach, we focus on a simplified spinless, valleyless IBM model. At integer filling ($\nu=0$), all numerical methods agree in terms of energy and $C_{2z} \mathcal{T}$ symmetry breaking. Additionally, as part of our benchmarking, we explore the impact of different schemes for removing ``double-counting'' in the IBM model. Our results at integer filling suggest that cross-validation of different IBM models may be needed for future studies of the TBG system. After benchmarking our approach at integer filling, we perform the first systematic study of the IBM model near integer filling (for $|\nu|< 0.2$). In this regime, we find that the ground state can be in a metallic and $C_{2z} \mathcal{T}$ symmetry breaking phase. The ground state appears to have low entropy, and therefore can be relatively well approximated by a single Slater determinant. Furthermore, we observe many low entropy states with energies very close to the ground state energy in the near integer filling regime

Ab initio quantum many-body description of superconducting trends in the cuprates

Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order of several doped cuprate materials and structures. We find that we can correctly capture two well-known trends: the pressure effect, where pairing order increases with intra-layer pressure, and the layer effect, where the pairing order varies with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors of the maximal pairing order. Our microscopic analysis further identifies short-range copper spin fluctuations, together with multi-orbital charge fluctuations, as central to the pairing trends. Our work illustrates the possibility of a quantitative computational understanding of high-temperature superconducting materials.Comment: 10 pages, 5 figures, with supplementary material

Formation of Human Colonic Crypt Array by Application of Chemical Gradients Across a Shaped Epithelial Monolayer

Background & Aims The successful culture of intestinal organoids has greatly enhanced our understanding of intestinal stem cell physiology and enabled the generation of novel intestinal disease models. Although of tremendous value, intestinal organoid culture systems have not yet fully recapitulated the anatomy or physiology of the in vivo intestinal epithelium. The aim of this work was to re-create an intestinal epithelium with a high density of polarized crypts that respond in a physiologic manner to addition of growth factors, metabolites, or cytokines to the basal or luminal tissue surface as occurs in vivo. Methods A self-renewing monolayer of human intestinal epithelium was cultured on a collagen scaffold microfabricated with an array of crypt-like invaginations. Placement of chemical factors in either the fluid reservoir below or above the cell-covered scaffolding created a gradient of that chemical across the growing epithelial tissue possessing the in vitro crypt structures. Crypt polarization (size of the stem/proliferative and differentiated cell zones) was assessed in response to gradients of growth factors, cytokines, and bacterial metabolites. Results Chemical gradients applied to the shaped human epithelium re-created the stem/proliferative and differentiated cell zones of the in vivo intestine. Short-chain fatty acids applied as a gradient from the luminal side confirmed long-standing hypotheses that butyrate diminished stem/progenitor cell proliferation and promoted differentiation into absorptive colonocytes. A gradient of interferon-γ and tumor necrosis factor-α significantly suppressed the stem/progenitor cell proliferation, altering crypt formation. Conclusions The in vitro human colon crypt array accurately mimicked the architecture, luminal accessibility, tissue polarity, cell migration, and cellular responses of in vivo intestinal crypts