Proteomic expression profiling of thyroid neoplasms

Thyroid cancer is the most common endocrine neoplasm with multiple histologic subtypes, each associated with different treatments and outcomes. Differentiating benign neoplasms such as follicular adenomas from malignant entities such as follicular carcinomas and papillary carcinoma can be challenging. To define the proteomic profile of different thyroid tumors, we screened an antibody array of 330 features against five thyroid neoplasms: follicular adenoma, follicular carcinoma, papillary carcinoma, anaplastic carcinoma, and medullary carcinoma as well as normal thyroid epithelium. Eight candidate biomarkers; c-erbB-2, Stat5a, Annexin IV, IL-11, RARα, FGF7, Caspase 9, and phospho-c-myc were identified as differentially expressed on the antibody array, and validated with immunohistochemistry on tissue microarrays, with a total of 144 samples of the same variety of thyroid neoplasms. Analysis revealed c-erbB-2, Annexin IV, and Stat5a have potential clinical utility to differentiate follicular adenoma, follicular carcinoma, and papillary carcinoma from each other. By using an antibody array as a discovery platform and a tissue microarray as a first step in validation on a large number of specimens, we have identified new markers that have potential utility in the diagnosis of thyroid neoplasms.     

Access control in feature-oriented programming

In feature-oriented programming (FOP) a programmer decomposes a program in terms of features. Ideally, features are implemented modularly so that they can be developed in isolation. Access control mechanisms in the form of access or visibility modifiers are an important ingredient to attain feature modularity as they allow programmers to hide and expose internal details of a module’s implementation. But developers of contemporary feature-oriented languages have not considered access control mechanisms so far. The absence of a well-defined access control model for FOP breaks encapsulation of feature code and leads to unexpected program behaviors and inadvertent type errors. We raise awareness of this problem, propose three feature-oriented access modifiers, and present a corresponding access modifier model. We offer an implementation of the model on the basis of a fully-fledged feature-oriented compiler. Finally, by analyzing ten feature-oriented programs, we explore the potential of feature-oriented modifiers in FOP.     

Strongdeco: Expansion of analytical,strongly correlated quantum states into a many-body basis

We provide a Mathematica code for decomposing strongly correlated quantum states described by a first-quantized, analytical wave function into many-body Fock states. Within them, the single-particle occupations refer to the subset of Fock–Darwin functions with no nodes. Such states, commonly appearing in two-dimensional systems subjected to gauge fields, were first discussed in the context of quantum Hall physics and are nowadays very relevant in the field of ultracold quantum gases. As important examples, we explicitly apply our decomposition scheme to the prominent Laughlin and Pfaffian states. This allows for easily calculating the overlap between arbitrary states with these highly correlated test states, and thus provides a useful tool to classify correlated quantum systems. Furthermore, we can directly read off the angular momentum distribution of a state from its decomposition. Finally we make use of our code to calculate the normalization factors for Laughlin?s famous quasi-particle/quasi-hole excitations, from which we gain insight into the intriguing fractional behavior of these excitations.Program summaryProgram title: StrongdecoCatalogue identifier: AELA_v1_0Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AELA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 5475No. of bytes in distributed program, including test data, etc.: 31 071Distribution format: tar.gzProgramming language: MathematicaComputer: Any computer on which Mathematica can be installedOperating system: Linux, Windows, MacClassification: 2.9Nature of problem: Analysis of strongly correlated quantum states.Solution method: The program makes use of the tools developed in Mathematica to deal with multivariate polynomials to decompose analytical strongly correlated states of bosons and fermions into a standard many-body basis. Operations with polynomials, determinants and permanents are the basic tools.Running time: The distributed notebook takes a couple of minutes to run.     

Intercomparison of Thermophysical Property Measurements on an Austenitic Stainless Steel

The results of an inter laboratory comparison of thermal conductivity, thermal diffusivity, specific heat capacity, and thermal expansion measurements on austenitic stainless steel in the temperature range between 20 and 1000°C are presented here. Mean values are presented for the physical properties studied. Reliable relative expanded uncertainties can be stated for the properties determined, which were achieved by applying good measurement practice, i.e., 3% for thermal expansion, 5% for specific heat capacity and thermal diffusivity, and 6% for thermal conductivity. The mean values derived from this intercomparison agree well with the results of a previous intercomparison in 1990. An erratum to this article is availabale at .     

Moderne Elektronenstrahltechnologien zum Fügen und zur Randschichtbehandlung. Modern Electron Beam Technologies for Soldering and Surface Treatment

A new generation of beam deflection technique is the basis for the development of electron beam (EB) technologies, which are not realisable with conventional methods. A high frequency 3D‐beam deflection makes possible multi‐field, multi‐pool and multi‐process technologies for EB welding und EB surface treatment, which result in a clear higher productivity and new combinations of properties.     

Linear Bidirectional On-Line Construction of Affix Trees

Affix trees are a generalization of suffix trees that are based on the inherent duality of suffix trees induced by the suffix links. An algorithm is presented that constructs affix trees on-line by expanding the underlying string in both directions and that is the first known algorithm to do this with linear time complexity.     

Stress Versus Coarticulation: Toward an Integrated Approach to Explicit Speech Segmentation.

Although word stress has been hailed as a powerful speech-segmentation cue, the results of 5 cross-modal fragment priming experiments revealed limitations to stress-based segmentation. Specifically, the stress pattern of auditory primes failed to have any effect on the lexical decision latencies to related visual targets. A determining factor was whether the onset of the prime was coarticulated with the preceding speech fragment. Uncoarticulated (i.e., concatenated) primes facilitated priming. Coarticulated ones did not. However, when the primes were presented in a background of noise, the pattern of results reversed, and a strong stress effect emerged: Stress-initial primes caused more pruning than non-initial-stress primes, regardless of the coarticulatory cues. The results underscore the role of coarticulation in the segmentation of clear speech and that of stress in impoverished listening conditions. More generally, they call for an integrated and signal-contingent approach to speech segmentation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.