Improving Routing Efficiency through Intermediate Target Based Geographic Routing

The greedy strategy of geographical routing may cause the local minimum problem when there is a hole in the routing area. It depends on other strategies such as perimeter routing to find a detour path, which can be long and result in inefficiency of the routing protocol. In this paper, we propose a new approach called Intermediate Target based Geographic Routing (ITGR) to solve the long detour path problem. The basic idea is to use previous experience to determine the destination areas that are shaded by the holes. The novelty of the approach is that a single forwarding path can be used to determine a shaded area that may cover many destination nodes. We design an efficient method for the source to find out whether a destination node belongs to a shaded area. The source then selects an intermediate node as the tentative target and greedily forwards packets to it, which in turn forwards the packet to the final destination by greedy routing. ITGR can combine multiple shaded areas to improve the efficiency of representation and routing. We perform simulations and demonstrate that ITGR significantly reduces the routing path length, compared with existing geographic routing protocols

Induction of MET Receptor Tyrosine Kinase Down-Regulation through Antibody-Mediated Receptor Clustering

The proto-oncoprotein MET is a receptor tyrosine kinase that plays a key role in cancer cell growth and invasion. We have used fluorescence-tagged antibodies to activate MET in live serum-starved glioblastoma cells and monitor the fate of antibody-bound MET receptor in single cell-based assays. We found that the antibodies induced rapid and transient formation of highly polarized MET clusters on the plasma membrane and promoted the activation of MET, resembling the initial effects of binding to its ligand, HGF. However, the antibody-induced clustering and activation of MET led to the rapid removal of the receptor from cell surface and altered its intracellular processing, resulted in rapid degradation of the receptor. Consequently, while cells pre-treated with HGF remain competent to respond to further HGF stimulation, cells pre-treated with antibodies are refractory to further HGF stimulation due to antibody-mediated MET depletion. Removal of MET by sustained treatment of antibodies blocked cancer cell migration and invasion. Our studies reveal a novel mechanism to alter the recycling process of MET in glioblastoma cancer cells by promoting the receptor degradation through a proteasome-sensitive and lysosome-dependent pathway through the ligand-independent activation of MET using anti-MET antibodies

Scheduling Based on Interruption Analysis and PSO for Strictly Periodic and Preemptive Partitions in Integrated Modular Avionics

Integrated modular avionics introduces the concept of partition and has been widely used in avionics industry. Partitions share the computing resources together. Partition scheduling plays a key role in guaranteeing correct execution of partitions. In this paper, a strictly periodic and preemptive partition scheduling strategy is investigated. First, we propose a partition scheduling model that allows a partition to be interrupted by other partitions, but minimizes the number of interruptions. The model not only retains the execution reliability of the simple partition sets that can be scheduled without interruptions, but also enhances the schedulability of the complex partition sets that can only be scheduled with some interruptions. Based on the model, we propose an optimization framework. First, an interruption analysis method to decide whether a partition set can be scheduled without interruptions is developed. Then, based on the analysis of the scheduling problem, we use the number of interruptions and the sum of execution time for all partitions in a major time frame as the optimization objective functions and use particle swarm optimization (PSO) to solve the optimization problem when the partition sets cannot be scheduled without interruptions. We improve the update strategy for the particles beyond the search space and round all particles before calculating the fitness value in PSO. Finally, the experiments with different partitions are conducted and the results validate the partition scheduling model and illustrate the effectiveness of the optimization framework. In addition, other optimization algorithms, such as genetic algorithm and neural networks, can also be used to solve the partition problem based on our model and solution framework

An Outlier Detection Algorithm Based on Cross-Correlation Analysis for Time Series Dataset

Outlier detection is a very essential problem in a variety of application areas. Many detection methods are deficient for high-dimensional time series data sets containing both isolated and assembled outliers. In this paper, we propose an Outlier Detection method based on Cross-correlation Analysis (ODCA). ODCA consists of three key parts. They are data preprocessing, outlier analysis, and outlier rank. First, we investigate a linear interpolation method to convert assembled outliers into isolated ones. Second, a detection mechanism based on the cross-correlation analysis is proposed for translating the high-dimensional data sets into 1-D cross-correlation function, according to which the isolated outlier is determined. Finally, a multilevel Otsu\u27s method is adopted to help us select the rank thresholds adaptively and output the abnormal samples at different levels. To illustrate the effectiveness of the ODCA algorithm, four experiments are performed using several high-dimensional time series data sets, which include two smallscale sets and two large-scale sets. Furthermore, we compare the proposed algorithm with the detection methods based on wavelet analysis, bilateral filtering, particle swarm optimization, auto-regression, and extreme learning machine. In addition, we discuss the robustness of the ODCA algorithm. The statistical results show that the ODCA algorithm is much better than existing mainstream methods in both effectiveness and time complexity

The Effects of Using Chaotic Map on Improving the Performance of Multiobjective Evolutionary Algorithms

Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects on EAs. This paper focuses on exploring the effects of chaotic maps and giving comprehensive guidance for improving multiobjective evolutionary algorithms (MOEAs) by series of experiments. NSGA-II algorithm, a representative of MOEAs using the nondominated sorting and elitist strategy, is taken as the framework to study the effect of chaotic maps. Ten chaotic maps are applied in MOEAs in three phases, that is, initial population, crossover, and mutation operator. Multiobjective problems (MOPs) adopted are ZDT series problems to show the generality. Since the scale of some sequences generated by chaotic maps is changed to fit for MOPs, the correctness of scaling transformation of chaotic sequences is proved by measuring the largest Lyapunov exponent. The convergence metric γ and diversity metric Δ are chosen to evaluate the performance of new algorithms with chaos. The results of experiments demonstrate that chaotic maps can improve the performance of MOEAs, especially in solving problems with convex and piecewise Pareto front. In addition, cat map has the best performance in solving problems with local optima

Methylated DNMT1 and E2F1 Are Targeted for Proteolysis by L3MBTL3 and CRL4DCAF5 Ubiquitin Ligase

Many non-histone proteins are lysine methylated and a novel function of this modification is to trigger the proteolysis of methylated proteins. Here, we report that the methylated lysine 142 of DNMT1, a major DNA methyltransferase that preserves epigenetic inheritance of DNA methylation patterns during DNA replication, is demethylated by LSD1. A novel methyl-binding protein, L3MBTL3, binds the K142-methylated DNMT1 and recruits a novel CRL4DCAF5 ubiquitin ligase to degrade DNMT1. Both LSD1 and PHF20L1 act primarily in S phase to prevent DNMT1 degradation by L3MBTL3-CRL4DCAF5. Mouse L3MBTL3/MBT-1 deletion causes accumulation of DNMT1 protein, increased genomic DNA methylation, and late embryonic lethality. DNMT1 contains a consensus methylation motif shared by many non-histone proteins including E2F1, a key transcription factor for S phase. We show that the methylation-dependent E2F1 degradation is also controlled by L3MBTL3-CRL4DCAF5. Our studies elucidate for the first time a novel mechanism by which the stability of many methylated non-histone proteins are regulated

Comparative transcriptome analysis and marker development of two closely related Primrose species (Primula poissonii and Primula wilsonii)

BACKGROUND: Primula species are important early spring garden plants with a centre of diversity and speciation in the East Himalaya-Hengduan Mountains in Western China. Studies on population genetics, speciation and phylogeny of Primula have been impeded by a lack of genomic resources. In the present study, we sequenced the transcriptomes of two closely related primrose species, Primula poissonii and Primula wilsonii, using short reads on the Illumina Genome Analyzer platform. RESULTS: We obtained 55,284 and 55,011 contigs with N50 values of 938 and 1,085 for P. poissonii and P. wilsonii, respectively, and 6,654 pairs of putative orthologs were identified between the two species. Estimations of non-synonymous/synonymous substitution rate ratios for these orthologs indicated that 877 of the pairs may be under positive selection (Ka/Ks > 0.5), and functional enrichment analysis revealed that significant proportions of the orthologs were in the categories DNA repair, stress resistance, which may provide some hints as to how the two closely related Primula species adapted differentially to extreme environments, such as habitats characterized by aridity, high altitude and high levels of ionizing radiation. It was possible for the first time to estimate the divergence time between the radiated species pair, P. poissonii and P. wilsonii; this was found to be approximately 0.90 ± 0.57 Mya, which falls between the Donau and Gunz glaciation in the Middle Pleistocene. Primers based on 54 pairs of orthologous SSR-containing sequences between the two Primula species were designed and verified. About half of these pairs successfully amplified for both species. Of the 959 single copy nuclear genes shared by four model plants (known as APVO genes), 111 single copy nuclear genes were verified as being present in both Primula species and exon-anchored and intron-spanned primers were designed for use. CONCLUSION: We characterized the transcriptomes for the two Primula species, and produced an unprecedented amount of genomic resources for these important garden plants. Evolutionary analysis of these two Primula species not only revealed a more precise divergence time, but also provided some novel insights into how differential adaptations occurred in extreme habitats. Furthermore, we developed two sets of genetic markers, single copy nuclear genes and nuclear microsatellites (EST-SSR). Both these sets of markers will facilitate studies on the genetic improvement, population genetics and phylogenetics of this rapidly adapting taxon