Towards Transformation Rule Composition

Many model transformation problems require different intermediate transformation steps. For example, platform-specific models (PSM) are often generated from platform-independent models (PIM) by chains of model transformations. This requires the presence of several intermediate meta-models between those of the PIM and the PSM. Thus, most of the effort is needed to define a transformation mechanism for each intermediate step. The solution proposed in this paper is to investigate whether it is possible to generate a single transformation from a chain of transformations, solely involving the initial PIM and final PSM meta-models. The presented work focuses on the composition of transformations at the rule level. We apply the automatic procedure for composing rules in the context of the evolution of the Enterprise Java Beans (EJB) language, transforming UML models into EJB 2.0 models and then to EJB 3.0 models

Verification of Model Transformations to Refactoring Mobile Social Networks

Verification of model processing programs, where only the definitions of the program and the languages of the models to be transformed are analyzed, has become a fundamental issue in model-based software engineering. This analysis may become very complex, but it is performed only once and the results are independent from concrete input models. The formal background of verification methods for graph rewriting-based model transformations has become a subject of research recently. In previous work, we have provided fundamental formal and algorithmic background of a (semi-)automated verification approach for graph transformations. This work concludes these components and put them together to introduce the implementation of a verification system fully integrated into a model transformation framework, VMTS. The strong points of our approach is its usability, its implementation in an existing tool, and its extendibility, which are demonstrated on a case study in the application domain of mobile centric social networks. Our results show that the verification of graph rewriting-based model transformations can be largely automated

Automated Verification by Declarative Description of Graph Rewriting-Based Model Transformations

Usually, verification of graph rewriting-based model transformations is performed manually, however, the industrial applications require automated methods. In several cases, transformation developers are interested in the offline analysis, when only the definition of the transformation and the specification of the modeling languages are taken into account. Hence, the analysis must be performed only once, and the results are independent from the concrete input models. For this purpose, transformations should be specified in a formalism that can be automatically analyzed. Based on our previous work that presented the mathematical background, this paper provides a platform-independent, declarative formalism for the specification of graph rewriting-based model transformations, and demonstrates its applicability on a case study of refactoring mobile-based social network models. Our results prove that several functional properties of the model transformations can be automatically verified, moreover, the capabilities of our methods can be extended in the future

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Nevirapine and Efavirenz Elicit Different Changes in Lipid Profiles in Antiretroviral- Therapy-Naive Patients Infected with HIV-1

BACKGROUND: Patients infected with HIV-1 initiating antiretroviral therapy (ART) containing a non-nucleoside reverse transcriptase inhibitor (NNRTI) show presumably fewer atherogenic lipid changes than those initiating most ARTs containing a protease inhibitor. We analysed whether lipid changes differed between the two most commonly used NNRTIs, nevirapine (NVP) and efavirenz (EFV). METHODS AND FINDINGS: Prospective analysis of lipids and lipoproteins was performed in patients enrolled in the NVP and EFV treatment groups of the 2NN study who remained on allocated treatment during 48 wk of follow-up. Patients were allocated to NVP (n = 417), or EFV (n = 289) in combination with stavudine and lamivudine. The primary endpoint was percentage change over 48 wk in high-density lipoprotein cholesterol (HDL-c), total cholesterol (TC), TC:HDL-c ratio, non-HDL-c, low-density lipoprotein cholesterol, and triglycerides. The increase of HDL-c was significantly larger for patients receiving NVP (42.5%) than for patients receiving EFV (33.7%; p = 0.036), while the increase in TC was lower (26.9% and 31.1%, respectively; p = 0.073), resulting in a decrease of the TC:HDL-c ratio for patients receiving NVP (−4.1%) and an increase for patients receiving EFV (+5.9%; p < 0.001). The increase of non-HDL-c was smaller for patients receiving NVP (24.7%) than for patients receiving EFV (33.6%; p = 0.007), as were the increases of triglycerides (20.1% and 49.0%, respectively; p < 0.001) and low-density lipoprotein cholesterol (35.0% and 40.0%, respectively; p = 0.378). These differences remained, or even increased, after adjusting for changes in HIV-1 RNA and CD4+ cell levels, indicating an effect of the drugs on lipids over and above that which may be explained by suppression of HIV-1 infection. The increases in HDL-c were of the same order of magnitude as those seen with the use of the investigational HDL-c-increasing drugs. CONCLUSION: NVP-containing ART shows larger increases in HDL-c and decreases in TC:HDL-c ratio than an EFV-containing regimen. Based on these findings, protease-inhibitor-sparing regimens based on non-nucleoside reverse transcriptase inhibitor, particularly those containing NVP, may be expected to result in a reduced risk of coronary heart disease

Parity-related molecular signatures and breast cancer subtypes by estrogen receptor status

INTRODUCTION: Relationships of parity with breast cancer risk are complex. Parity is associated with decreased risk of postmenopausal hormone receptor–positive breast tumors, but may increase risk for basal-like breast cancers and early-onset tumors. Characterizing parity-related gene expression patterns in normal breast and breast tumor tissues may improve understanding of the biological mechanisms underlying this complex pattern of risk. METHODS: We developed a parity signature by analyzing microRNA microarray data from 130 reduction mammoplasty (RM) patients (54 nulliparous and 76 parous). This parity signature, together with published parity signatures, was evaluated in gene expression data from 150 paired tumors and adjacent benign breast tissues from the Polish Breast Cancer Study, both overall and by tumor estrogen receptor (ER) status. RESULTS: We identified 251 genes significantly upregulated by parity status in RM patients (parous versus nulliparous; false discovery rate = 0.008), including genes in immune, inflammation and wound response pathways. This parity signature was significantly enriched in normal and tumor tissues of parous breast cancer patients, specifically in ER-positive tumors. CONCLUSIONS: Our data corroborate epidemiologic data, suggesting that the etiology and pathogenesis of breast cancers vary by ER status, which may have implications for developing prevention strategies for these tumors