Nasal immunization of mice with Lactobacillus casei expressing the Pneumococcal Surface Protein A: induction of antibodies, complement deposition and partial protection against Streptococcus pneumoniae challenge

Strategies for the development of new vaccines against Streptococcus pneumoniae infections try to overcome problems such as serotype coverage and high costs, present in currently available vaccines. Formulations based on protein candidates that can induce protection in animal models have been pointed as good alternatives. Among them, the Pneumococcal Surface Protein A (PspA) plays an important role during systemic infection at least in part through the inhibition of complement deposition on the pneumococcal surface, a mechanism of evasion from the immune system. Antigen delivery systems based on live recombinant lactic acid bacteria (LAB) represents a promising strategy for mucosal vaccination, since they are generally regarded as safe bacteria able to elicit both systemic and mucosal immune responses. In this work, the N-terminal region of clade I PspA was constitutively expressed in Lactobacillus casei and the recombinant bacteria was tested as a mucosal vaccine in mice. Nasal immunization with L. casei-PspA 1 induced anti-PspA antibodies that were able to bind to pneumococcal strains carrying both clade 1 and clade 2 PspAs and to induce complement deposition on the surface of the bacteria. In addition, an increase in survival of immunized mice after a systemic challenge with a virulent pneumococcal strain was observed. (C) 2008 Elsevier Masson SAS. All rights reserved.

Does speed matter? : the impact of the EU membership incentive on rule adoption in minority language rights protection

Denna avhandling tar sin utgångspunkt i ett ifrågasättande av effektiviteten i EU:s konditionalitetspolitik avseende minoritetsrättigheter. Baserat på den rationalistiska teoretiska modellen, External Incentives Model of Governance, syftar denna hypotesprövande avhandling till att förklara om tidsavståndet på det potentiella EU medlemskapet påverkar lagstiftningsnivån avseende minoritetsspråksrättigheter. Mätningen av nivån på lagstiftningen avseende minoritetsspråksrättigheter begränsas till att omfatta icke-diskriminering, användning av minoritetsspråk i officiella sammanhang samt minoriteters språkliga rättigheter i utbildningen. Metodologiskt används ett jämförande angreppssätt både avseende tidsramen för studien, som sträcker sig mellan 2003 och 2010, men även avseende urvalet av stater. På basis av det \"mest lika systemet\" kategoriseras staterna i tre grupper efter deras olika tidsavstånd från det potentiella EU medlemskapet. Hypotesen som prövas är följande: ju kortare tidsavstånd till det potentiella EU medlemskapet desto större sannolikhet att staternas lagstiftningsnivå inom de tre områden som studeras har utvecklats till en hög nivå. Studien visar att hypotesen endast bekräftas delvis. Resultaten avseende icke-diskriminering visar att sambandet mellan tidsavståndet och nivån på lagstiftningen har ökat markant under den undersökta tidsperioden. Detta samband har endast stärkts mellan kategorin av stater som ligger tidsmässigt längst bort ett potentiellt EU medlemskap och de två kategorier som ligger närmare respektive närmast ett potentiellt EU medlemskap. Resultaten avseende användning av minoritetsspråk i officiella sammanhang och minoriteters språkliga rättigheter i utbildningen visar inget respektive nästan inget samband mellan tidsavståndet och utvecklingen på lagstiftningen mellan 2003 och 2010.

A stopping rule while searching for optimal solution of facility-location

Solutions to combinatorial optimization, such as p-median problems of locating facilities, frequently rely on heuristics to minimize the objective function. The minimum is sought iteratively and a criterion is needed to decide when the procedure (almost) attains it. However, pre-setting the number of iterations dominates in OR applications, which implies that the quality of the solution cannot be ascertained. A small branch of the literature suggests using statistical principles to estimate the minimum and use the estimate for either stopping or evaluating the quality of the solution. In this paper we use test-problems taken from Baesley's OR-library and apply Simulated Annealing on these p-median problems. We do this for the purpose of comparing suggested methods of minimum estimation and, eventually, provide a recommendation for practioners. An illustration ends the paper being a problem of locating some 70 distribution centers of the Swedish Post in a region.

A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

Rule versus interaction function : evaluating regional aggregations of commuting flows in Sweden

The aim of this paper is to evaluate the performance of two divergent methods for delineating commuting regions, also called labour market areas, in a situation that the base spatial units differ largely in size as a result of an irregular population distribution. Commuting patterns in Sweden have been analyzed with geographical information system technology by delineating commuting regions using two regionalization methods. One, a rule-based method, uses one-way commuting flows to delineate local labour market areas in a top-down procedure based on the selection of predefined employment centres. The other method, the interaction-based Intramax analysis, uses two-way flows in a bottom-up procedure based on numerical taxonomy principles. A comparison of these methods will expose a number of strengths and weaknesses. For both methods, the same data source has been used. The performance of both methods has been evaluated for the country as a whole using resident employed population, self-containment levels and job ratios for criteria. A more detailed evaluation has been done in the Goteborg metropolitan area by comparing regional patterns with the commuting fields of a number of urban centres in this area. It is concluded that both methods could benefit from the inclusion of additional control measures to identify improper allocations of municipalities.

Towards Presidential Rule in Ukraine : Hybrid Regime Dynamics under Semi-Presidentialism

This article sets out to analyse recent regime developments in Ukraine in relation to semi-presidentialism. The article asks: to what extent and in what ways theoretical arguments against semi-presidentialism (premier-presidential and president-parliamentary systems) are relevant for understanding the changing directions of the Ukrainian regime since the 1990s? The article also reviews the by now overwhelming evidence suggesting that President Yanukovych is turning Ukraine into a more authoritarian hybrid regime and raises the question to what extent the president-parliamentary system might serve this end. The article argues that both kinds of semi-presidentialism have, in different ways, exacerbated rather than mitigated institutional conflict and political stalemate. The return to the president-parliamentary system in 2010 – the constitutional arrangement with the most dismal record of democratisation – was a step in the wrong direction. The premier-presidential regime was by no means ideal, but it had at least two advantages. It weakened the presidential dominance and it explicitly anchored the survival of the government in parliament. The return to the 1996 constitution ties in well with the notion that President Viktor Yanukovych has embarked on an outright authoritarian path.

Yet another induction algorithm

Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate
that the rules produced by our induction algorithm have high consistency and simplicity.

Analyzing security protocols using association rule mining

Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.

Depth first rule heneration for text categorization

Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

An examination on the performance of MML causal induction

This paper presents an examination report on the performance of the improved MML based causal model discovery algorithm. In this paper, We firstly describe our improvement to the causal discovery algorithm which introduces a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. It is followed by a detailed examination report on the performance of our improved discovery algorithm. The experimental results of the current version of the discovery system show that: (l) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal networks with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.

Positivism: one ring to rule them all? A tale of information systems research methodology

The tale of research methodology in information systems is told through the fantasy of Tolkien’s Lord of the Rings. The tale is intended to be at once a piece of light hearted fun in its placement of the struggles of research methodology as an epic story but, in the tradition of the court jester, attempts to provide a new perspective on Information Systems (IS) research methodology and our struggles with positivism in particular. Our tale is one of developing a greater maturity and confidence in IS methodology and introduces postmodern methodologies to Information Systems. Our tale, our pastiche, is itself postmodern.