Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query

Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments

A major open question in microbiome research is whether we can predict how the components of a diet collectively determine the taxonomic composition of microbial communities. Motivated by this challenge, here we ask whether communities assembled in a mixed nutrient environment can be predicted from those assembled in every single nutrient in isolation. To that end, we first formulate a quantitative null model of community assembly in a mixture of nutrients that recruit species independently. We then test the model predictions by assembling replicate communities in synthetic environments that contain either a pair of nutrients, or each nutrient in isolation. We find that the null, naturally additive model generally predicts well the family-level community composition. However, we also identify systematic deviations from the additive predictions that reflect generic patterns of nutrient dominance at the family-level of taxonomy. Pairs of more-similar nutrients (e.g. two sugars) are on average more additive than pairs of more dissimilar nutrients (e.g. one sugar and one organic acid). A simple dominance rule emerges, where we find that sugars generally dominate organic acids. This simple dominance rule extends to most families and most sugar-organic acid pairs in our experiment. Our results suggest that regularities in the ways nutrients interact may help us predict how microbial communities respond to changes in nutrient composition.

Semi-Automatic Sensor Ontology Matching Based on Interactive Multi-Objective Evolutionary Algorithm

Since different sensor ontologies are developed independently and for different requirements, a concept in one sensor ontology could be described with different terminologies or in different context in another sensor ontology, which leads to the ontology heterogeneity problem. To bridge the semantic gap between the sensor ontologies, authors propose a semi-automatic sensor ontology matching technique based on an Interactive MOEA (IMOEA), which can utilize the user's knowledge to direct MOEA's search direction. In particular, authors construct a new multi-objective optimal model for the sensor ontology matching problem, and design an IMOEA with t-dominance rule to solve the sensor ontology matching problem. In experiments, the benchmark track and anatomy track from the Ontology Alignment Evaluation Initiative (OAEI) and two pairs of real sensor ontologies are used to test performance of the authors' proposal. The experimental results show the effectiveness of the approach.

Criteria for the a Priori Shortest Path Generation in Uncertain Time-Varying Transportation Networks

This paper proposes a new definition of uncertain time-varying network to capture the uncertain and dynamic characteristics of the network with discrete uncertain link travel times. To find the a priori non-dominated paths in this type of network, three comparison criteria based on the uncertain measure, namely, deterministic dominance rule, first-order uncertain dominance rule and uncertain expected value dominance rule, are proposed to generate non-dominated paths in a single time interval and a time period, as more than one path may exist between an origin and destination for a given departure time. The proposed comparison methods are then applied to solving a simple uncertain time-varying network. The computational results verify the efficiency of three dominance rules for finding non-dominated paths.

A Branch and Bound Algorithm for Project Scheduling Problem with Spatial Resource Constraints

With respect to the block assembly schedule in a shipbuilding enterprise, a spatial resource constrained project scheduling problem (SRCPSP) is proposed, which aims to minimize the makespan of a project under the constraints of the availability of a two-dimensional spatial resource and the precedence relationship between tasks. In order to solve SRCPSP to the optimum, a branch and bound algorithm (BB) is developed. For the BB-SRCPSP, first, an implicitly enumerative branch scheme is presented. Secondly, a precedence based lower bound, as well as an effective dominance rule, is employed for pruning. Next, a heuristic based algorithm is used to decide the order of a node to be selected for expansion such that the efficiency of the algorithm is further improved. In addition, a maximal space based arrangement is applied to the configuration of the areas required each day in an available area. Finally, the simulation experiment is conducted to illustrate the effectiveness of the BB-SRCPSP.

FUZZY DOMINANCE: A NEW APPROACH FOR RANKING FUZZY VARIABLES VIA CREDIBILITY MEASURE

Comparison of fuzzy variables is considered one of the most important topics in fuzzy theory. A new approach for ranking fuzzy variable via credibility measure — fuzzy dominance is presented in this paper. Some basic properties of fuzzy dominance are investigated. As an illustration, the cases of fuzzy dominance rule for triangular fuzzy variables are examined.