A Rule-Based Approach to Analyzing Database Schema Objects with Datalog

Database schema elements such as tables, views, triggers and functions are typically defined with many interrelationships. In order to support database users in understanding a given schema, a rule-based approach for analyzing the respective dependencies is proposed using Datalog expressions. We show that many interesting properties of schema elements can be systematically determined this way. The expressiveness of the proposed analysis is exemplarily shown with the problem of computing induced functional dependencies for derived relations. The propagation of functional dependencies plays an important role in data integration and query optimization but represents an undecidable problem in general. And yet, our rule-based analysis covers all relational operators as well as linear recursive expressions in a systematic way showing the depth of analysis possible by our proposal. The analysis of functional dependencies is well-integrated in a uniform approach to analyzing dependencies between schema elements in general.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

A major T cell antigen of Mycobacterium leprae is a 10-kD heat-shock cognate protein.

Several mycobacterial antigens, identified by monoclonal antibodies and patient sera, have been found to be homologous to stress or heat-shock proteins (hsp) defined in Escherichia coli and yeast. A major antigen recognized by most Mycobacterium leprae-reactive human T cell lines and cell wall-reactive T cell clones is a 10-kD protein that has now been cloned and sequenced. The predicted amino acid sequence of this protein is 44% homologous to the hsp 10 (GroES) of E. coli. The purified native and recombinant 10-kD protein was found to be a stronger stimulator of peripheral blood T cell proliferation than other native and recombinant M. leprae proteins tested. The degree of reactivity paralleled the response to intact M. leprae throughout the spectrum of leprosy. Limiting-dilution analysis of peripheral blood lymphocytes from a patient contact and a tuberculoid patient indicated that approximately one third of M. leprae-reactive T cell precursors responded to the 10-kD antigen. T cell lines derived from lepromin skin tests were strongly responsive to the 10-kD protein. T cell clones reactive to both the purified native and recombinant 10-kD antigens recognized M. leprae-specific epitopes as well as epitopes crossreactive with the cognate antigen of M. tuberculosis. Further, the purified hsp 10 elicited strong delayed-type hypersensitivity reactions in guinea pigs sensitized to M. leprae. The strong T cell responses against the M. leprae 10-kD protein suggest a role for this heat-shock cognate protein in the protective/resistant responses to infection

Numerical analysis of fracture in interpenetrating phase composites based on crack phase field model

A numerical model based on crack phase field analysis is introduced to study the quasi-static fracture process in interpenetrating phase composites (IPCs). Materials were considered elastic solids, and the interface was assumed to be perfectly bonded. Tougher and stiffer tougheners lead to more fracture in the brittle phase, but less fracture in the toughening phase. Thus, the overall fracture performance results from competition between increasing breakage in the brittle phase and declining breakage in the toughening phase. The toughening mechanisms are discussed from both stress-strain and crack topology viewpoints. The toughening phase transfers the load from the crack tip to the whole domain until the maximum stress is reached, and impeded crack growth occurs afterwards. The load transferring and impediment effects made the brittle phase engage in fracture, and several crack propagation patterns were identified for the sacrificial fracture behaviour, namely, crack deflection, crack bridging, crack branching, microcracking and crack blocking. Moreover, fracture in three different microstructures (co-continuous, particle-reinforced, laminar) was compared, and the most effective toughening morphology depends on the tougheners and the loading states. This methodology enables optimum microstructures to be identified to achieve high toughness in aerospace and energy generation applications, increasing safety and reducing weight

XMM-Newton observation of the ultraluminous quasar SDSS J010013.02+280225.8 at redshift 6.326

A brief Chandra observation of the ultraluminous quasar SDSS J010013.02+280225.8 at redshift 6.326 showed it to be a relatively bright, soft X-ray source with a count rate of about 1 count ks−1. In this article, we present results for the quasar from a 65-ks XMM–Newton observation, which constrains its spectral shape well. The quasar is clearly detected with a total of ~460 net counts in the 0.2–10 keV band. The spectrum is characterized by a simple power-law model with a photon index of = 2.30+0.10 −0.10 and the intrinsic 2–10 keV luminosity is 3.14 × 1045 erg s−1. The 1σ upper limit to any intrinsic absorption column density is NH = 6.07 × 1022 cm−2. No significant iron emission lines were detected. We derive an X-rayto-optical flux ratio αox of −1.74 ± 0.01, consistent with the values found in other quasars of comparable ultraviolet luminosity. We did not detect significant flux variations either in the XMM–Newton exposure or between XMM–Newton and Chandra observations, which are separated by ∼8 months. The X-ray observation enables the bolometric luminosity to be calculated after modelling the spectral energy distribution: the accretion rate is found to be sub-Eddington

Prognostic ability of a panel of immunohistochemistry markers – retailoring of an 'old solution'

An urgent requirement exists for new prognostic and predictive assays in breast cancer. Despite the development of high-throughput technologies such as DNA microarrays, it would now appear that immunohistochemistry (IHC) may play an increasingly important role in the clinical management of breast cancer. In this editorial, the authors discuss the potential prognostic ability of a panel of IHC markers, and question whether this well-established assay technology may in fact allow for improved prognostic and predictive tests in breast cancer

Learning Gradient Fields for Shape Generation

In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. Point cloud generation thus amounts to moving randomly sampled points to high-density areas. We generate point clouds by performing stochastic gradient ascent on an unnormalized probability density, thereby moving sampled points toward the high-likelihood regions. Our model directly predicts the gradient of the log density field and can be trained with a simple objective adapted from score-based generative models. We show that our method can reach state-of-the-art performance for point cloud auto-encoding and generation, while also allowing for extraction of a high-quality implicit surface. Code is available at https://github.com/RuojinCai/ShapeGF.Comment: Published in ECCV 2020 (Spotlight); Project page: https://www.cs.cornell.edu/~ruojin/ShapeGF