Proteomics identification of differentially expressed proteins in the muscle of dysferlin myopathy patients

The muscular dystrophies are a large and heterogeneous group of neuromuscular disorders that can be classified according to the mode of inheritance, the clinical phenotype and the molecular defect. To better understand the pathological mechanisms of dysferlin myopathy we compared the protein-expression pattern in the muscle biopsies of six patients with this disease with six patients with limb girdle muscular dystrophy 2A, five with facioscapulohumeral dystrophy and six normal control subjects. To investigate differences in the expression levels of skeletal muscle proteins we used 2-DE and MS. Western blot or immunohistochemistry confirmed relevant results. The study showed specific increase expression of proteins involved in fast-to-slow fiber type conversion (ankyrin repeat protein 2), type I predominance (phosphorylated forms of slow troponin T), sarcomere stabilization (actinin-associated LIM protein), protein ubiquitination (TRIM 72) and skeletal muscle differentiation (Rho-GDP-dissociation inhibitor ly-GDI) in dysferlin myopathy. As anticipated, we also found differential expression of proteins common to all the muscular dystrophies studied. This comparative proteomic analysis suggests that in dysferlin myopathy (i) the type I fiber predominance is an active process of fiber type conversion rather than a selective loss of type II fibers and (ii) the dysregulation of proteins involved in muscle differentiation further confirms the role of dysferlin in this process.     

An improved version of ISICS: A program for calculating K-, L- and M-shell cross sections from PWBA and ECPSSR theory using a personal computer

The formatting of the M-shell atomic parameters imbedded in file XCSC.H in ISICS has been corrected. The problem only affected cross section calculations for Uranium and heavier elements. The corrected version of ISICS has been re-compiled and is now available.

New version program summary

Program title: ISICSCatalogue identifier: ADDS_v3_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADDS_v3_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.: 4645No. of bytes in distributed program, including test data, etc.: 106 731Distribution format: tar.gzProgramming language: C++Computer: 80486 or higher-level PCsOperating system: WINDOWS 98 through WINDOWS XPClassification: 16.7Does the new version supersede the previous version?: YesNature of problem: Ionization and X-ray production cross section calculations for ion-atom collisions.Solution method: Numerical integration of form factor using a logarithmic transform and Gaussian quadrature, plus exact integration limits.Reasons for new version: The formatting of the M-shell atomic parameters involving cross section calculations for Uranium and heavier elements needed to be corrected.Summary of revisions: The affected file XCSC.H in ISICS has been corrected and ISICS has been recompiled.Restrictions: The consumed CPU time increases with the atomic shell (K, L, M), but execution is still very fast.Running time: This depends on which shell and the number of different energies to be used in the calculation. For example, to calculate K-shell cross sections for protons striking carbon for 19 different proton energies it took less than 10 s; to calculate M-shell cross sections for protons on gold for 21 proton energies it took 4.2 min.     

Neural network classifier and multiple hypothesis image segmentation for dietary assessment using calorie calculator

Nowadays, dietary assessment becomes the emerging system for evaluating the person’s food intake. In this paper, the multiple hypothesis image segmentation and feed-forward neural network classifier are proposed for dietary assessment to enhance the performance. Initially, the segmentation is applied to input image which is used to determine the regions where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the significant feature of food items is extracted by the global feature and local feature extraction method. After the features are obtained, the classification is performed for each segmented region using feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food area volume and (ii) calorie and nutrition measure based on mass value. The outcome of the proposed method attains 96% of accuracy value which provides the better classification performance.     

The convergence analysis and specification of the Population-Based Incremental Learning algorithm

In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p⁽⁰⁾ is fixed and the learning rate α is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals.     

Barrier Lyapunov Functions for the control of output-constrained nonlinear systems

In this paper, we present control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint. To prevent constraint violation, we employ a Barrier Lyapunov Function, which grows to infinity when its arguments approach some limits. By ensuring boundedness of the Barrier Lyapunov Function in the closed loop, we ensure that those limits are not transgressed. Besides the nominal case where full knowledge of the plant is available, we also tackle scenarios wherein parametric uncertainties are present. Asymptotic tracking is achieved without violation of the constraint, and all closed loop signals remain bounded, under a mild condition on the initial output. Furthermore, we explore the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions. We also compare our control with one that is based on a Quadratic Lyapunov Function, and we show that our control requires less restrictive initial conditions. A numerical example is provided to illustrate the performance of the proposed control.     

A comprehensive quality model for service-oriented systems

In a service-oriented system, a quality (or Quality of Service) model is used (i) by service requesters to specify the expected quality levels of service delivery; (ii) by service providers to advertise quality levels that their services achieve; and (iii) by service composers when selecting among alternative services those that are to participate in a service composition. Expressive quality models are needed to let requesters specify quality expectations, providers advertise service qualities, and composers finely compare alternative services. Having observed many similarities between various quality models proposed in the literature, we review these and integrate them into a single quality model, called QVDP. We highlight the need for integration of priority and dependency information within any quality model for services and propose precise submodels for doing so. Our intention is for the proposed model to serve as a reference point for further developments in quality models for service-oriented systems. To this aim, we extend the part of the UML metamodel specialized for Quality of Service with QVDP concepts unavailable in UML.

ISICS2011, an updated version of ISICS: A program for calculation K-, L-, and M-shell cross sections from PWBA and ECPSSR theories using a personal computer

In this new version of ISICS, called ISICS2011, a few omissions and incorrect entries in the built-in file of electron binding energies have been corrected; operational situations leading to un-physical behavior have been identified and flagged.

New version program summary

Program title: ISICS2011Catalogue identifier: ADDS_v5_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADDS_v5_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.: 6011No. of bytes in distributed program, including test data, etc.: 130 587Distribution format: tar.gzProgramming language: CComputer: 80486 or higher-level PCsOperating system: WINDOWS XP and all earlier operating systemsClassification: 16.7Catalogue identifier of previous version: ADDS_v4_0Journal reference of previous version: Comput. Phys. Commun. 180 (2009) 1716.Does the new version supersede the previous version?: YesNature of problem: Ionization and X-ray production cross section calculations for ion–atom collisions.Solution method: Numerical integration of form factor using a logarithmic transform and Gaussian quadrature, plus exact integration limits.Reasons for new version: General need for higher precision in output format for projectile energies; some built-in binding energies needed correcting; some anomalous results occur due to faulty read-in data or calculated parameters becoming un-physical; erroneous calculations could result for the L and M shells when restricted K-shell options are inadvertently chosen; to achieve general compatibility with ISICSoo, a companion C++ version that is portable to Linux and MacOS platforms, has been submitted for publication in the CPC Program Library approximately at the same time as this present new standalone version of ISICS [1].Summary of revisions: The format field for projectile energies in the output has been expanded from two to four decimal places in order to distinguish between closely spaced energy values. There were a few entries in the executable binding energy file that needed correcting; K shell of Eu, M shells of Zn, M1 shell of Kr. The corrected values were also entered in the ENERGY.DAT file. In addition, an alternate data file of binding energies is included, called ENERGY_GW.DAT, which is more up-to-date [2]. Likewise, an alternate atomic parameters data file is now included, called FLOURE_JC.DAT, which is more up-to-date [3] fluorescence yields for the K and L shells and Coster–Kronig parameters for the L shell. Both data files can be read in using the -f usage option. To do this, the original energy file should be renamed and saved (e.g., ENERGY_BB.DAT) and the new file (ENERGY_GW.DAT ) should be duplicated as ENERGY.DAT to be read in using the -f option. Similarly for reading in an alternate FLOURE.DAT file. As with previous versions, the user can also simply input different values of any input quantity by invoking the “specify your own parameters” option from the main menu. You can also use this option to simply check the values of the built-in values of the parameters. If it still happens that a zero binding energy for a particular sub-shell is read in, the program will not completely abort, but will calculate results for the other sub-shells while setting the affected sub-shell output to zero. In calculating the Coulomb deflection factor, if the quantity inside the radical sign of the parameter zs becomes zero or negative, to prevent the program from aborting, the PWBA cross sections are still calculated while the ECPSSR cross sections are set to zero. This situation can happen for very low energy collisions, such as were noticed for helium ions on copper at energies of E?11.2 keV. It was observed during the engineering of ISICSoo [1] that erroneous calculations could result for the L- and M-shell cases when restricted K-shell R or HSR scaling options were inappropriately chosen. The program has now been fixed so that these inappropriate options are ignored for the L and M shells. In the previous versions, the usage for inputting a batch data file was incorrectly stated in the Users Manual as -Bxxx; the correct designation is -Fxxx, or alternatively, -Ixxx, as indicated on the usage screen in running the program.A revised Users Manual is also available.Restrictions: The consumed CPU time increases with the atomic shell (K, L, M), but execution is still very fast.Running time: This depends on which shell and the number of different energies to be used in the calculation. The running time is not significantly changed from the previous version.References:

• [1] 

  M. Batic, M.G. Pia, S. Cipolla, Comput. Phys. Commun. (2011), submitted for publication.

• [2] 

  http://www.jlab.org/~gwyn/ebindene.html.

• [3] 

  J. Campbell, At. Data Nucl. Data Tables 85 (2003) 291.

     

The minimum information about failures for solving non-local tasks in message-passing systems

We first define the basic notions of local and non-local tasks for distributed systems. Intuitively, a task is local if, in a system with no failures, each process can compute its output value locally by applying some local function on its own input value (so the output value of each process depends only on the process’ own input value, not on the input values of the other processes); a task is nonlocal otherwise. All the interesting distributed tasks, including all those that have been investigated in the literature (e.g., consensus, set agreement, renaming, atomic commit, etc.) are non-local. In this paper we consider non-local tasks and determine the minimum information about failures that is necessary to solve such tasks in message-passing distributed systems. As part of this work, we also introduces weak set agreement—a natural weakening of set agreement—and show that, in some precise sense, it is the weakest nonlocal task in message-passing systems.     

Failure detection and consensus in the crash-recovery model

Summary. We study the problems of failure detection and consensus in asynchronous systems in which processes may crash and recover, and links may lose messages. We first propose new failure detectors that are particularly suitable to the crash-recovery model. We next determine under what conditions stable storage is necessary to solve consensus in this model. Using the new failure detectors, we give two consensus algorithms that match these conditions: one requires stable storage and the other does not. Both algorithms tolerate link failures and are particularly efficient in the runs that are most likely in practice – those with no failures or failure detector mistakes. In such runs, consensus is achieved within time and with 4 n messages, where is the maximum message delay and n is the number of processes in the system. Received: May 1998 / Accepted: November 1999