Immunoglobulin 1 (IgG1) Fc-glycosylation profiling of anti-citrullinated peptide antibodies from human serum

In several autoimmune disorders, including rheumatoid arthritis (RA), autoantibodies are thought to be the driving force of pathogenicity. Glycosylation of the Fc-part of human Igs is known to modulate biological activity. Hitherto, glycosylation of human IgG-Fc has been analyzed predominantly at the level of total serum IgG, revealing reduced galactosylation in RA. Given the pathogenic relevance of autoantibodies in RA, we wished, in the present study, to address the question whether distinct Fc-glycosylation features are observable at the level of antigen-specific IgG subpopulations. For this purpose, we have developed a method for the microscale purification and Fc-glycosylation analysis of anti-citrullinated peptide antibodies (ACPA). ACPA represent a group of autoantibodies that occur with unique specificity in RA patients. Their presence is associated with increased inflammatory disease activity and rapid joint destruction. Results indicate that ACPA of the IgG1 subclass vary considerably from total serum IgG1 with respect to Fc-galactosylation, with galactosylation being higher on ACPA than on serum IgG1 for some patients, while other patients show higher galactosylation on serum IgG1 than on ACPA. Using this method, studies can be performed on the biological and clinical relevance of ACPA glycosylation within RA patient cohorts.     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

Taxonomy,technology and applications of smart objects

Deployment of embedded technologies is increasingly being examined in industrial supply chains as a means for improving efficiency through greater control over purchase orders, inventory and product related information. Central to this development has been the advent of technologies such as bar codes, Radio Frequency Identification (RFID) systems, and wireless sensors which when attached to a product, form part of the product’s embedded systems infrastructure. The increasing integration of these technologies dramatically contributes to the evolving notion of a “smart product”, a product which is capable of incorporating itself into both physical and information environments. The future of this revolution in objects equipped with smart embedded technologies is one in which objects can not only identify themselves, but can also sense and store their condition, communicate with other objects and distributed infrastructures, and take decisions related to managing their life cycle. The object can essentially “plug” itself into a compatible systems infrastructure owned by different partners in a supply chain. However, as in any development process that will involve more than one end user, the establishment of a common foundation and understanding is essential for interoperability, efficient communication among involved parties and for developing novel applications. In this paper, we contribute to creating that common ground by providing a characterization to aid the specification and construction of “smart objects” and their underlying technologies. Furthermore, our work provides an extensive set of examples and potential applications of different categories of smart objects.     

Left-forbidding cooperating distributed grammar systems

A left-forbidding grammar, introduced in this paper, is a context-free grammar, where a set of nonterminal symbols is attached to each context-free production. Such a production can rewrite a nonterminal provided that no symbol from the attached set occurs to the left of the rewritten nonterminal in the current sentential form. The present paper discusses cooperating distributed grammar systems with left-forbidding grammars as components and gives some new characterizations of language families of the Chomsky hierarchy. In addition, it also proves that twelve nonterminals are enough for cooperating distributed grammar systems working in the terminal derivation mode with two left-forbidding components (including erasing productions) to characterize the family of recursively enumerable languages.     

An adaptive, real-time, traffic monitoring system

In this paper we describe a computer vision-based traffic monitoring system able to detect individual vehicles in real-time. Our fully integrated system first obtains the main traffic variables: counting, speed and category; and then computes a complete set of statistical variables. The objective is to investigate some of the difficulties impeding existing traffic systems to achieve balanced accuracy in every condition; i.e. day and night transitions, shadows, heavy vehicles, occlusions, slow traffic and congestions. The system we present is autonomous, works for long periods of time without human intervention and adapts automatically to the changing environmental conditions. Several innovations, designed to deal with the above circumstances, are proposed in the paper: an integrated calibration and image rectification step, differentiated methods for day and night, an adaptive segmentation algorithm, a multistage shadow detection method and special considerations for heavy vehicle identification and treatment of slow traffic. A specific methodology has been developed to benchmark the accuracy of the different methods proposed.     

Adapting plans in progress in distributed supervisory work: aspects of complexity, coupling, and control

Distributed supervisory control systems often rely on complex and centralized plans to cope with a variety of unanticipated situations. Replanning requires practitioners to forgo standard procedures in favor of making simple plans without simplifying, managing task coupling, and anticipating team needs to provide decentralized and elaborate plans. This article proposes a plan classification scheme to study what features of plans facilitate or hinder adaptation and a framework to examine how features of plans influence the cognitive processes of replanning. The plan features have been assigned to the categories of plan complexity, coupling, and control. Plans are task networks sharing similar features of complexity and coupling to technical systems. The proposed framework sets out to explore how plan features influence the processes of recognizing plan disruptions, reviewing challenges and different team stances, repairing plans to resolve new risks, and reacting by coordinating team efforts to execute plans. The framework draws on the Extended Control Model (ECOM) to integrate the four processes of replanning into a set of control loops. The benefits of this framework are illustrated in the context of operator training and decision support.     

A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today’s performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model’s input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.