Amalia -- A Unified Platform for Parsing and Generation

Contemporary linguistic theories (in particular, HPSG) are declarative in nature: they specify constraints on permissible structures, not how such structures are to be computed. Grammars designed under such theories are, therefore, suitable for both parsing and generation. However, practical implementations of such theories don't usually support bidirectional processing of grammars. We present a grammar development system that includes a compiler of grammars (for parsing and generation) to abstract machine instructions, and an interpreter for the abstract machine language. The generation compiler inverts input grammars (designed for parsing) to a form more suitable for generation. The compiled grammars are then executed by the interpreter using one control strategy, regardless of whether the grammar is the original or the inverted version. We thus obtain a unified, efficient platform for developing reversible grammars.Comment: 8 pages postscrip

From Data Fusion to Knowledge Fusion

The task of {\em data fusion} is to identify the true values of data items (eg, the true date of birth for {\em Tom Cruise}) among multiple observed values drawn from different sources (eg, Web sites) of varying (and unknown) reliability. A recent survey\cite{LDL+12} has provided a detailed comparison of various fusion methods on Deep Web data. In this paper, we study the applicability and limitations of different fusion techniques on a more challenging problem: {\em knowledge fusion}. Knowledge fusion identifies true subject-predicate-object triples extracted by multiple information extractors from multiple information sources. These extractors perform the tasks of entity linkage and schema alignment, thus introducing an additional source of noise that is quite different from that traditionally considered in the data fusion literature, which only focuses on factual errors in the original sources. We adapt state-of-the-art data fusion techniques and apply them to a knowledge base with 1.6B unique knowledge triples extracted by 12 extractors from over 1B Web pages, which is three orders of magnitude larger than the data sets used in previous data fusion papers. We show great promise of the data fusion approaches in solving the knowledge fusion problem, and suggest interesting research directions through a detailed error analysis of the methods.Comment: VLDB'201

A Review of Relational Machine Learning for Knowledge Graphs

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. In particular, we discuss Google’s Knowledge Vault project.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216

Does the Immunocompetent Status of Cancer Patients Have an Impact on Therapeutic DC Vaccination Strategies?

Although different types of therapeutic vaccines against established cancerous lesions in various indications have been developed since the 1990s, their clinical benefit is still very limited. This observed lack of effectiveness in cancer eradication may be partially due to the often deficient immunocompetent status of cancer patients, which may facilitate tumor development by different mechanisms, including immune evasion. The most frequently used cellular vehicle in clinical trials are dendritic cells (DCs), thanks to their crucial role in initiating and directing immune responses. Viable vaccination options using DCs are available, with a positive toxicity profile. For these reasons, despite their limited therapeutic outcomes, DC vaccination is currently considered an additional immunotherapeutic option that still needs to be further explored. In this review, we propose potential actions aimed at improving DC vaccine efficacy by counteracting the detrimental mechanisms recognized to date and implicated in establishing a poor immunocompetent status in cancer patients

Dickkopf-related protein 1 (Dkk1) regulates the accumulation and function of myeloid derived suppressor cells in cancer

Tumor–stroma interactions contribute to tumorigenesis. Tumor cells can educate the stroma at primary and distant sites to facilitate the recruitment of heterogeneous populations of immature myeloid cells, known as myeloid-derived suppressor cells (MDSCs). MDSCs suppress T cell responses and promote tumor proliferation. One outstanding question is how the local and distant stroma modulate MDSCs during tumor progression. Down-regulation of β-catenin is critical for MDSC accumulation and immune suppressive functions in mice and humans. Here, we demonstrate that stroma-derived Dickkopf-1 (Dkk1) targets β-catenin in MDSCs, thus exerting immune suppressive effects during tumor progression. Mice bearing extraskeletal tumors show significantly elevated levels of Dkk1 in bone microenvironment relative to tumor site. Strikingly, Dkk1 neutralization decreases tumor growth and MDSC numbers by rescuing β-catenin in these cells and restores T cell recruitment at the tumor site. Recombinant Dkk1 suppresses β-catenin target genes in MDSCs from mice and humans and anti-Dkk1 loses its antitumor effects in mice lacking β-catenin in myeloid cells or after depletion of MDSCs, demonstrating that Dkk1 directly targets MDSCs. Furthermore, we find a correlation between CD15(+) myeloid cells and Dkk1 in pancreatic cancer patients. We establish a novel immunomodulatory role for Dkk1 in regulating tumor-induced immune suppression via targeting β-catenin in MDSCs

Syntaphilin Ubiquitination Regulates Mitochondrial Dynamics and Tumor Cell Movements.

Syntaphilin (SNPH) inhibits the movement of mitochondria in tumor cells, preventing their accumulation at the cortical cytoskeleton and limiting the bioenergetics of cell motility and invasion. Although this may suppress metastasis, the regulation of the SNPH pathway is not well understood. Using a global proteomics screen, we show that SNPH associates with multiple regulators of ubiquitin-dependent responses and is ubiquitinated by the E3 ligase CHIP (or STUB1) on Lys111 and Lys153 in the microtubule-binding domain. SNPH ubiquitination did not result in protein degradation, but instead anchored SNPH on tubulin to inhibit mitochondrial motility and cycles of organelle fusion and fission, that is dynamics. Expression of ubiquitination-defective SNPH mutant Lys111!Arg or Lys153!Arg increased the speed and distance traveled by mitochondria, repositioned mitochondria to the cortical cytoskeleton, and supported heightened tumor chemotaxis, invasion, and metastasis in vivo. Interference with SNPH ubiquitination activated mitochondrial dynamics, resulting in increased recruitment of the fission regulator dynamin-related protein-1 (Drp1) to mitochondria and Drp1-dependent tumor cell motility. These data uncover nondegradative ubiquitination of SNPH as a key regulator of mitochondrial trafficking and tumor cell motility and invasion. In this way, SNPH may function as a unique, ubiquitination-regulated suppressor of metastasis