Distant Supervision for Entity Linking

Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages). In this way, a large scale of weakly labeled data can be generated without manual annotation and fed to a classifier for linking more newly discovered entities. Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia. Specifically, the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities. Experiments conducted on a dataset of 140,000 items and 60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze the feature performance and improve the F1-measure to 0.545

Large Margin Nearest Neighbor Embedding for Knowledge Representation

Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815

Clinical Data Mining Reveals Analgesic Effects of Lapatinib in Cancer Patients

Microsomal prostaglandin E2 synthase 1 (mPGES-1) is recognized as a promising target for a next generation of anti-inflammatory drugs that are not expected to have the side effects of currently available anti-inflammatory drugs. Lapatinib, an FDA-approved drug for cancer treatment, has recently been identified as an mPGES-1 inhibitor. But the efficacy of lapatinib as an analgesic remains to be evaluated. In the present clinical data mining (CDM) study, we have collected and analyzed all lapatinib-related clinical data retrieved from clinicaltrials.gov. Our CDM utilized a meta-analysis protocol, but the clinical data analyzed were not limited to the primary and secondary outcomes of clinical trials, unlike conventional meta-analyses. All the pain-related data were used to determine the numbers and odd ratios (ORs) of various forms of pain in cancer patients with lapatinib treatment. The ORs, 95% confidence intervals, and P values for the differences in pain were calculated and the heterogeneous data across the trials were evaluated. For all forms of pain analyzed, the patients received lapatinib treatment have a reduced occurrence (OR 0.79; CI 0.70–0.89; P = 0.0002 for the overall effect). According to our CDM results, available clinical data for 12,765 patients enrolled in 20 randomized clinical trials indicate that lapatinib therapy is associated with a significant reduction in various forms of pain, including musculoskeletal pain, bone pain, headache, arthralgia, and pain in extremity, in cancer patients. Our CDM results have demonstrated the significant analgesic effects of lapatinib, suggesting that lapatinib may be repurposed as a novel type of analgesic

Immune modulatory effects of IL-22 on allergen-induced pulmonary inflammation

IL-22 is a Th17/Th22 cytokine that is increased in asthma. However, recent animal studies showed controversial findings in the effects of IL-22 in allergic asthma. To determine the role of IL-22 in ovalbumin-induced allergic inflammation we generated inducible lung-specific IL-22 transgenic mice. Transgenic IL-22 expression and signaling activity in the lung were determined. Ovalbumin (OVA)-induced pulmonary inflammation, immune responses, and airway hyperresponsiveness (AHR) were examined and compared between IL-22 transgenic mice and wild type controls. Following doxycycline (Dox) induction, IL-22 protein was readily detected in the large (CC10 promoter) and small (SPC promoter) airway epithelial cells. IL-22 signaling was evidenced by phosphorylated STAT3. After OVA sensitization and challenge, compared to wild type littermates, IL-22 transgenic mice showed decreased eosinophils in the bronchoalveolar lavage (BAL), and in lung tissue, decreased mucus metaplasia in the airways, and reduced AHR. Among the cytokines and chemokines examined, IL-13 levels were reduced in the BAL fluid as well as in lymphocytes from local draining lymph nodes of IL-22 transgenic mice. No effect was seen on the levels of serum total or OVA-specific IgE or IgG. These findings indicate that IL-22 has immune modulatory effects on pulmonary inflammatory responses in allergen-induced asthma

The global Goursat problem and scattering for nonlinear wave equations

AbstractThe Goursat problem for nonlinear scalar equations on the Einstein Universe M̃, with finite-energy datum, has a unique global solution in the positive-energy, Sobolev-controllable case. Such equations include those of the form □ϑ + H′(ϑ) = 0, where H denotes a hamiltonian that is a fourth-order polynomial, bounded below, in components of the multicomponent scalar section ϑ. In particular, the conformally invariant equation (□ + 1)ϑ + λϑ3 = 0 (λ ⩾ 0) is included. In the higher-dimensional analog R × Sn to the Einstein Universe the same result holds under the stronger conditions on H required for Sobolev controllability. Irrespective of energy positivity, there is a unique local-in-time solution for arbitrary finite-energy Goursat datum, for all n ⩾ 3, establishing evolution from the given lightcone to any sufficiently close lightcone. These results show the existence of wave operators in the sense of scattering theory, and their continuity in the (Einstein) energy metric, for positive-energy equations of the indicated type. They also permit the comprehensive reduction of scattering theory for conformally invariant wave equations in Minkowski space M0 to the Goursat problem in M̃. In particular, any solution of the equation arising from a nonnegative conformally invariant biquadratic interaction Lagrangian on multicomponent scalar sections, having finite Einstein energy at any one time, is asymptotic to solutions of the corresponding multicomponent free wave equation as the Minkowski time x0 → ± ∞. Thus given a finite-Einstein-energy solution of the equation □ƒ + λƒ3 = 0 on M0 (λ ⩾ 0) there exist unique solutions ƒ± of the free wave equation which approach ƒ in the Minkowski energy norm as x0 → ± ∞, and every finite-Einstein-energy solution of the free wave equation is of the form ƒ+ (or ƒ−) for a unique solution ƒ of the nonlinear equation. This generalizes, in part in maximality sharp form, earlier results of Strauss for this equation

A Quantitative LC-MS/MS Method for Simultaneous Determination of Cocaine and Its Metabolites in Whole Blood

As new metabolic pathways of cocaine were recently identified, a high performance liquid chromatography tandem mass spectrometry (LC–MS/MS) method was developed to simultaneously determine cocaine and nine cocaine-related metabolites in whole blood samples. One-step solid phase extraction was used to extract all of the ten compounds and corresponding internal standards from blood samples. All compounds and internal standards extracted were separated on an Atlantis T3 (100 Å, 3 μm, 2.1 mm × 150 mm I.D) column and detected in positive ion and high sensitivity mode with multiple reaction monitoring. This method was validated for its sensitivity, linearity, specificity, accuracy, precision, recovery, and stability. All of the ten compounds were quantifiable ranging from the lower limit of quantification (LLOQs) of ∼10 nM (1.9–3.2 ng/ml) to ∼1000 nM (190–320 ng/ml) without any interfering substance. Accuracy and precision were determined, and both of them were within the acceptance criteria of the United States (US) Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines. The recovery was above 66.7% for all compounds. Stability tests demonstrated the stability of compounds under different storage conditions in whole blood samples. The method was successfully applied to a pharmacokinetic study with co-administration of cocaine and alcohol in rats