Towards Recommender Systems with Community Detection and Quantum Computing

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves

The data hungry home

It's said that the pleasure is in the giving, not the receiving. This belief is validated by how humans interact with their family, friends and society as well as their gardens, homes, and pets. Yet for ubiquitous devices, this dynamic is reversed with devices as the donors and owners as the recipients. This paper explores an alternative paradigm where these devices are elevated, becoming members of Data Hungry Homes, allowing us to build relationships with them using the principles that we apply to family, pets or houseplants. These devices are developed to fit into a new concept of the home, can symbiotically interact with us and possess needs and traits that yield unexpected positive or negative outcomes from interacting with them. Such relationships could enrich our lives through our endeavours to “feed” our Data Hungry Homes, possibly leading us to explore new avenues and interactions outside and inside the home

LRRK2 protein levels are determined by kinase function and are crucial for kidney and lung homeostasis in mice

Mutations in leucine-rich repeat kinase 2 (LRRK2) cause late-onset Parkinson's disease (PD), but the underlying pathophysiological mechanisms and the normal function of this large multidomain protein remain speculative. To address the role of this protein in vivo, we generated three different LRRK2 mutant mouse lines. Mice completely lacking the LRRK2 protein (knock-out, KO) showed an early-onset (age 6 weeks) marked increase in number and size of secondary lysosomes in kidney proximal tubule cells and lamellar bodies in lung type II cells. Mice expressing a LRRK2 kinase-dead (KD) mutant from the endogenous locus displayed similar early-onset pathophysiological changes in kidney but not lung. KD mutants had dramatically reduced full-length LRRK2 protein levels in the kidney and this genetic effect was mimicked pharmacologically in wild-type mice treated with a LRRK2-selective kinase inhibitor. Knock-in (KI) mice expressing the G2019S PD-associated mutation that increases LRRK2 kinase activity showed none of the LRRK2 protein level and histopathological changes observed in KD and KO mice. The autophagy marker LC3 remained unchanged but kidney mTOR and TCS2 protein levels decreased in KD and increased in KO and KI mice. Unexpectedly, KO and KI mice suffered from diastolic hypertension opposed to normal blood pressure in KD mice. Our findings demonstrate a role for LRRK2 in kidney and lung physiology and further show that LRRK2 kinase function affects LRRK2 protein steady-state levels thereby altering putative scaffold/GTPase activity. These novel aspects of peripheral LRRK2 biology critically impact ongoing attempts to develop LRRK2 selective kinase inhibitors as therapeutics for PD

Mottness at finite doping and charge instabilities in cuprates

The intrinsic instability of underdoped copper oxides towards inhomogeneous states is one of the central puzzles of the physics of correlated materials. The influence of the Mott physics on the doping-temperature phase diagram of copper oxides represents a major issue that is subject of intense theoretical and experimental effort. Here, we investigate the ultrafast electron dynamics in prototypical single-layer Bi-based cuprates at the energy scale of the O-2p\u2192Cu-3d charge-transfer (CT) process. We demonstrate a clear evolution of the CT excitations from incoherent and localized, as in a Mott insulator, to coherent and delocalized, as in a conventional metal. This reorganization of the high-energy degrees of freedom occurs at the critical doping pcr 430.16 irrespective of the temperature, and it can be well described by dynamical mean field theory calculations. We argue that the onset of the low-temperature charge instabilities is the low-energy manifestation of the underlying Mottness that characterizes the p<pcr region of the phase diagram. This discovery sets a new framework for theories of charge order and low-temperature phases in underdoped copper oxides. ArXI

The Involvement of IL-17A in the Murine Response to Sub-Lethal Inhalational Infection with Francisella tularensis

Background: Francisella tularensis is an intercellular bacterium often causing fatal disease when inhaled. Previous reports have underlined the role of cell-mediated immunity and IFNc in the host response to Francisella tularensis infection. Methodology/Principal Findings: Here we provide evidence for the involvement of IL-17A in host defense to inhalational tularemia, using a mouse model of intranasal infection with the Live Vaccine Strain (LVS). We demonstrate the kinetics of IL-17A production in lavage fluids of infected lungs and identify the IL-17A-producing lymphocytes as pulmonary cd and Th17 cells. The peak of IL-17A production appears early during sub-lethal infection, it precedes the peak of immune activation and the nadir of the disease, and then subsides subsequently. Exogenous airway administration of IL-17A or of IL-23 had a limited yet consistent effect of delaying the onset of death from a lethal dose of LVS, implying that IL-17A may be involved in restraining the infection. The protective role for IL-17A was directly demonstrated by in vivo neutralization of IL-17A. Administration of anti IL-17A antibodies concomitantly to a sub-lethal airway infection with 0.16LD50 resulted in a fatal disease. Conclusion: In summary, these data characterize the involvement and underline the protective key role of the IL-17A axis in the lungs from inhalational tularemia

Pathogen-Mediated Proteolysis of the Cell Death Regulator RIPK1 and the Host Defense Modulator RIPK2 in Human Aortic Endothelial Cells

Porphyromonas gingivalis is the primary etiologic agent of periodontal disease that is associated with other human chronic inflammatory diseases, including atherosclerosis. The ability of P. gingivalis to invade and persist within human aortic endothelial cells (HAEC) has been postulated to contribute to a low to moderate chronic state of inflammation, although how this is specifically achieved has not been well defined. In this study, we demonstrate that P. gingivalis infection of HAEC resulted in the rapid cleavage of receptor interacting protein 1 (RIPK1), a mediator of tumor necrosis factor (TNF) receptor-1 (TNF-R1)-induced cell activation or death, and RIPK2, a key mediator of both innate immune signaling and adaptive immunity. The cleavage of RIPK1 or RIPK2 was not observed in cells treated with apoptotic stimuli, or cells stimulated with agonists to TNF-R1, nucleotide oligomerization domain receptor 1(NOD1), NOD2, Toll-like receptor 2 (TLR2) or TLR4. P. gingivalis-induced cleavage of RIPK1 and RIPK2 was inhibited in the presence of a lysine-specific gingipain (Kgp) inhibitor. RIPK1 and RIPK2 cleavage was not observed in HAEC treated with an isogenic mutant deficient in the lysine-specific gingipain, confirming a role for Kgp in the cleavage of RIPK1 and RIPK2. Similar proteolysis of poly (ADP-ribose) polymerase (PARP) was observed. We also demonstrated direct proteolysis of RIPK2 by P. gingivalis in a cell-free system which was abrogated in the presence of a Kgp-specific protease inhibitor. Our studies thus reveal an important role for pathogen-mediated modification of cellular kinases as a potential strategy for bacterial persistence within target host cells, which is associated with low-grade chronic inflammation, a hallmark of pathogen-mediated chronic inflammatory disorders

Lower Respiratory Tract Infection Induced by a Genetically Modified Picornavirus in Its Natural Murine Host

Infections with the picornavirus, human rhinovirus (HRV), are a major cause of wheezing illnesses and asthma exacerbations. In developing a murine model of picornaviral airway infection, we noted the absence of murine rhinoviruses and that mice are not natural hosts for HRV. The picornavirus, mengovirus, induces lethal systemic infections in its natural murine hosts, but small genetic differences can profoundly affect picornaviral tropism and virulence. We demonstrate that inhalation of a genetically attenuated mengovirus, vMC0, induces lower respiratory tract infections in mice. After intranasal vMC0 inoculation, lung viral titers increased, peaking at 24 h postinoculation with viral shedding persisting for 5 days, whereas HRV-A01a lung viral titers decreased and were undetectable 24 h after intranasal inoculation. Inhalation of vMC0, but not vehicle or UV-inactivated vMC0, induced an acute respiratory illness, with body weight loss and lower airway inflammation, characterized by increased numbers of airway neutrophils and lymphocytes and elevated pulmonary expression of neutrophil chemoattractant CXCR2 ligands (CXCL1, CXCL2, CXCL5) and interleukin-17A. Mice inoculated with vMC0, compared with those inoculated with vehicle or UV-inactivated vMC0, exhibited increased pulmonary expression of interferon (IFN-α, IFN-β, IFN-λ), viral RNA sensors [toll-like receptor (TLR)3, TLR7, nucleotide-binding oligomerization domain containing 2 (NOD2)], and chemokines associated with HRV infection in humans (CXCL10, CCL2). Inhalation of vMC0, but not vehicle or UV-inactivated vMC0, was accompanied by increased airway fluid myeloperoxidase levels, an indicator of neutrophil activation, increased MUC5B gene expression, and lung edema, a sign of infection-related lung injury. Consistent with experimental HRV inoculations of nonallergic, nonasthmatic human subjects, there were no effects on airway hyperresponsiveness after inhalation of vMC0 by healthy mice. This novel murine model of picornaviral airway infection and inflammation should be useful for defining mechanisms of HRV pathogenesis in humans

Synthetic Nanoparticles for Vaccines and Immunotherapy

The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugs– recombinant proteins (especially antibodies) or patient-derived cell therapies– but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004

VLPs and particle strategies for cancer vaccines

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Feature selection for recommender systems with quantum computing

The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular,quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science