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In multiple myeloma (MM), malignant plasma cells reside in the bone marrow, where they accumulate in close contact with stromal cells. The mechanisms responsible for the chemotaxis of malignant plasma cells are still poorly understood. Thus, we investigated the mechanisms involved in the chemotaxis of MDN and XG2 MM cell lines. Both cell lines strongly expressed CCR9, CXCR3 and CXCR4 chemokine receptors but only migrated toward CXCL12. Activation of CXCR4 by CXCL12 resulted in the association of CXCR4 with CD45 and activation of PLCβ3, AKT, RhoA, IκBα and ERK1/2. Using siRNA-silencing techniques, we showed CD45/CXCR4 association is essential for CXCL12-induced migration of MM cells. Thymoquinone (TQ), the major active component of the medicinal herb Nigella sativa Linn, has been described as a chemopreventive and chemotherapeutic compound. TQ treatment strongly inhibited CXCL12-mediated chemotaxis in MM cell lines as well as primary cells isolated from MM patients, but not normal PBMCs. Moreover, TQ significantly down-regulated CXCR4 expression and CXCL12-mediated CXCR4/CD45 association in MM cells. Finally, TQ also induced the relocalization of cytoplasmic Fas/CD95 to the membrane of MM cells and increased CD95-mediated apoptosis by 80%. In conclusion, we demonstrate the potent anti-myeloma activity of TQ, providing a rationale for further clinical evaluation.

Following the emergence and global spread of a novel H1N1 influenza virus in 2009, two A(H1N1)pdm/09 influenza vaccines produced from the A/California/07/09 H1N1 strain were selected and used for the national immunisation programme in the United Kingdom: an adjuvanted split virion vaccine and a non-adjuvanted whole virion vaccine. In this study, we assessed the immune responses generated in inbred large white pigs (Babraham line) following vaccination with these vaccines and after challenge with A(H1N1)pdm/09 virus three months post-vaccination. Both vaccines elicited strong antibody responses, which included high levels of influenza-specific IgG1 and haemagglutination inhibition titres to H1 virus. Immunisation with the adjuvanted split vaccine induced significantly higher interferon gamma production, increased frequency of interferon gamma-producing cells and proliferation of CD4(-)CD8(+) (cytotoxic) and CD4(+)CD8(+) (helper) T cells, after in vitro re-stimulation. Despite significant differences in the magnitude and breadth of immune responses in the two vaccinated and mock treated groups, similar quantities of viral RNA were detected from the nasal cavity in all pigs after live virus challenge. The present study provides support for the use of the pig as a valid experimental model for influenza infections in humans, including the assessment of protective efficacy of therapeutic interventions.

The research leading to these results has received funding from the European Community's Seventh Framework Program (FP7, 2007-2013), the Research Infrastructures Action under grant FP7-228393 (a NADIR project); from the project AGL2010-22200-C02-01 from the Spanish Ministry of Science and Innovation; the UK's BBSRC grant BBS/E/I/00002014.

We study a general optimization problem in which coefficients in the objective are uncertain. We use capacities (lower probabilities) to model such uncertainty. Two popular criteria in imprecise probability, namely maximality and E-admissibility, are employed to compare solutions. We characterize non-dominated solutions with respect to these criteria in terms of well-known notions in multi-objective optimization. These characterizations are novel and make it possible to derive several interesting results. Specially, for convex problems, maximality and E-admissibility are equivalent for any capacities even though the set of associated acts is not convex, and in case of 2-monotone capacities, finding an arbitrary non-dominated solution and checking if a given solution is non-dominated are both tractable. For combinatorial problems, we show a general result: in case of 2-monotone capacities, if the deterministic version of the problem can be solved in polynomial time, checking E-admissibility can also be done in polynomial time. Lastly, for the matroid optimization problem, more refined results are also obtained thanks to these characterizations, namely the connectedness of E-admissible solutions and an outer approximation based on the greedy algorithm for non-dominated solutions with respect to maximality.

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.

The aim of this paper is to detect a stator inter-turn short circuit in a synchronous machine through the analysis of the external magnetic field measured by external flux sensors. The paper exploits a methodology previously developed, based on the analysis of the behavior with load variation of sensitive spectral lines issued from two flux sensors positioned at 180° from each other around the machine. Further developments to improve this method were made, in which more than two flux sensors were used to keep a good sensitivity for stator fault detection. The method is based on the Pearson correlation coefficient calculated from sensitive spectral lines at different load operating conditions. Fusion information with belief function is then applied to the correlation coefficients, which enable the detection of an incipient fault in any phase of the machine. The method has the advantage to be fully non-invasive and does not require knowledge of the healthy state.

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

Following the emergence and global spread of a novel H1N1 influenza virus in 2009, two A(H1N1)pdm/09 influenza vaccines produced from the A/California/07/09 H1N1 strain were selected and used for the national immunisation programme in the United Kingdom: an adjuvanted split virion vaccine and a non-adjuvanted whole virion vaccine. In this study, we assessed the immune responses generated in inbred large white pigs (Babraham line) following vaccination with these vaccines and after challenge with A(H1N1)pdm/09 virus three months post-vaccination. Both vaccines elicited strong antibody responses, which included high levels of influenza-specific IgG1 and haemagglutination inhibition titres to H1 virus. Immunisation with the adjuvanted split vaccine induced significantly higher interferon gamma production, increased frequency of interferon gamma-producing cells and proliferation of CD4.sup.- CD8.sup.+ (cytotoxic) and CD4.sup.+ CD8.sup.+ (helper) T cells, after in vitro re-stimulation. Despite significant differences in the magnitude and breadth of immune responses in the two vaccinated and mock treated groups, similar quantities of viral RNA were detected from the nasal cavity in all pigs after live virus challenge. The present study provides support for the use of the pig as a valid experimental model for influenza infections in humans, including the assessment of protective efficacy of therapeutic interventions.

The quality of the proposed solutions by Case-Based Reasoning (CBR) systems is highly dependent on recorded experiences and their describing attributes. Hence, to keep them offering accurate and efficient responses for a long time frame, the maintenance of Case Bases (CB) and Vocabulary knowledge is required. However, maintenance operations are usually unable to exploit provided domain-experts knowledge although this kind of systems are widely applied in several real-life contexts. This offered prior knowledge is handled, in our work, in form of pairwise constraints: Regarding cases, Must-Link (ML) affirms that two given problems should have the same solution, and Cannot-Link (CL) informs that two problems cannot have the same solution. These constraints may also regard vocabulary knowledge in such a way that ML is generated when prior knowledge affirm that two given features offer correlated values, therefore, similar information, and CL is built when they provide different information. This paper proposes a new constrained & integrated method, named CIMMEP, encoding Constrained & Integrated Maintaining Method based on Evidential Policies, for maintaining both vocabulary and CB through eliminating redundancy and noisiness. Since CBR systems handle real-world experiences, which are full of uncertainty, CIMMEP manages this imperfection using a powerful tool called the belief function theory.

Data fusion, within the evidence theory framework, consists of obtaining a unique belief function by the combination of several belief functions induced from various information sources. Considerable attention has been paid to combination rules dealing with beliefs induced from non-distinct information sources. The most popular fusion rule is the cautious conjunctive rule, proposed by Denœux. This latter has the empty set, called also the conflict, as an absorbing element. In fact, the mass assigned to the conflict tends toward 1 when applying a high number of the cautious conjunctive operator, and consequently, the conflict loses its initial role as an alarm signal announcing that there is a kind of disagreement between sources. This problem has led to the introduction of the normalized cautious rule which totally ignores the mass assigned to the conflict. An intermediate rule between the cautious conjunctive and the normalized cautious rules, named the cautious Combination With Adaptive Conflict (cautious CWAC), has been proposed to preserve the initial alarm role of the conflict. Despite this diversification, no great effort has been devoted until now to find out the most convenient combination rule. Thus, in this paper, we suggest to evaluate and compare the cautious conjunctive, the normalized cautious and the cautious CWAC rules in order to pick out the most appropriate one within the classifier fusion framework.