Towards the knowledge-based design of universal influenza epitope ensemble vaccines

Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly-conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highly-conserved and experimentally-verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96% and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97% and 88% coverage of observed subtypes.

Quantity-setting games with a dominant firm

We consider a possible game-theoretic foundation of Forchheimer's model of dominant-firm price leadership based on quantity-setting games with one large firm and many small firms. If the large firm is the exogenously given first mover, we obtain Forchheimer's model. We also investigate whether the large firm can emerge as a first mover of a timing game.

On Forchheimer's Model of Dominant Firm Price Leadership

We show that in a simple price-setting game with one large firm and many small firms the large firm does not accept the role of the price leader.

Domináns vállalat versenyképességének hatása az ellátási láncban (Effects of competitiveness of the dominant company in the supply chain)

Beszállító, gyártó és vevő vállalatok alkotnak egy ellátási láncot. Optimális esetben a vállalatok integrált rendszerben működnek, az együttműködés bizal mi alapon nyugszik, így a közös stratégiai döntések révén versenyképes lesz a lánc. Az ellátási láncban lehet egy kulcsfontosságú vállalat, amelyik domináns hatalmi pozícióval rendelkezik. A kutatás célja, hogy feltárja a hatalommal rendelkező vállalat szerepét a partnercégek és az ellátási lánc versenyképességének alakulásában. A szakirodalom alapján a hipotézis feltételezi, hogy van kapcsolat a versenyképesség és a domináns vállalat szerepvállalása között. A szerzők elemzése primer kutatáson alapul, a kérdőív válaszait SPSS statisztikai kiértékeléssel végezték. Az eredmények azt mutatták, hogy a domináns vállalat versenyképességétől valóban függ a partnercégek versenyképessége. Szignifikáns kapcsolat megléte bizonyítja a hipotézist. A vizsgálatok kiemelik, hogy a verseny már egy szinttel feljebb, az ellátási lánc szintjén értelmezhető: globális ellátási láncok versenyeznek egymással. _____ Supplier, producer and buyer companies make up a supply chain. In an optimal case the companies are integrated, partnership rests on trust which results in common strategic decisions leading to competitiveness. Each supply chain has a key company with dominant power position. The objective of the research is to analyse how the company with power affects competitiveness of partner firms and the supply chain as a whole. Based on theories hypothesis assumes a link between competitiveness and power position and its influence. Methodology of the analysis is based on primary research; the authors used SPSS statistical analysis to evaluate the answers of questionnaire. Findings include that partner firms’ competitiveness rely on competitiveness of the company with dominant power position. Significant connections prove that the hypothesis is true. Results show that competitiveness is being moved up to supply chain level. Global supply chains compete with each other.

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

I want out of the labels : how Chuck Palahniuk's characters challenge the dominant discourse

The purpose of this study was to explore postmodern identity in the work of Chuck Palahniuk. The characters within Palahniuk's text Invisible Monsters realize the manner in which identity is a construct, and their response is to oppose and redefine it. In my research, I looked at how postmodern identity is defined by some of the leading critical thinkers in the field, and then I applied their thought to Palahniuk's characters. I showed how these characters come to understand the process by which society defines them, and with that realization, they oppose its totalizing definitions. The characters deconstruct the natural attitudes society has towards identity, and they reveal that it is in some way possible to create a unique identity that is not easily definable by the ruling discourse. I concluded that his attention to identity highlights Palahniuk's concern for the place, identity, and influence of his generation.

Molecular diversity, cultivation, and improved detection by fluorescent in situ hybridization of a dominant group of human gut bacteria related to Roseburia spp. or Eubacterium rectale

Peer reviewed

Explaining Interest Group Interactions with Party Group Members in the European Parliament : Dominant Party Groups and Coalition Formation

Peer reviewed

Molecular diversity, cultivation, and improved detection by fluorescent in situ hybridization of a dominant group of human gut bacteria related to Roseburia spp. or Eubacterium rectale

Peer reviewed

Explaining Interest Group Interactions with Party Group Members in the European Parliament : Dominant Party Groups and Coalition Formation

Peer reviewed