Large-scale sequencing of human cancer genomes and mouse transposon-induced tumors has identified a vast number of genes mutated in different cancers. One of the outstanding challenges in this field is to determine which genes, when mutated, contribute to cellular transformation and tumor progression. To identify new and conserved genes that drive tumorigenesis we have developed a novel cancer model in a distantly related vertebrate species, the zebrafish, Danio rerio. The Sleeping Beauty (SB) T2/Onc transposon system was adapted for somatic mutagenesis in zebrafish. The carp ß-actin promoter was cloned into T2/Onc to create T2/OncZ. Two transgenic zebrafish lines that contain large concatemers of T2/OncZ were isolated by injection of linear DNA into the zebrafish embryo. The T2/OncZ transposons were mobilized throughout the zebrafish genome from the transgene array by injecting SB11 transposase RNA at the 1-cell stage. Alternatively, the T2/OncZ zebrafish were crossed to a transgenic line that constitutively expresses SB11 transposase. T2/OncZ transposon integration sites were cloned by ligation-mediated PCR and sequenced on a Genome Analyzer II. Between 700–6800 unique integration events in individual fish were mapped to the zebrafish genome. The data show that introduction of transposase by transgene expression or RNA injection results in an even distribution of transposon re-integration events across the zebrafish genome. SB11 mRNA injection resulted in neoplasms in 10% of adult fish at ∼10 months of age. T2/OncZ-induced zebrafish tumors contain many mutated genes in common with human and mouse cancer genes. These analyses validate our mutagenesis approach and provide additional support for the involvement of these genes in human cancers. The zebrafish T2/OncZ cancer model will be useful for identifying novel and conserved genetic drivers of human cancers.

This paper proposes a real-time traffic signal intelligent control method with transit-priority. The objective of this control method is to reduce the delays of passengers and special vehicles. Transit-priority is divided into the special transit-priority which is an absolute priority and the normal transit-priority which is a relative priority. When the detectors in the red phase detect special vehicles arrival, the phase will become a special phase, the current green phase must be interrupted, and the special phase will be run. After the special vehicles pass through, the next running phase selection will be done using the phase selection method with normal-transit-priority, by this time, the phase with more urgency will be selected. It embodies transit-priority idea. The green increase time of current phase is inferred by a fuzzy controller of which the inputs are the vehicles number of current phase and next phase. Multi-layer neural network is used to realize this fuzzy controller. Compared with fixed-time control method and the fuzzy control method, simulation research shows that this method obtains a good performance in decreasing the delays of passengers and special vehicles.

Abstract-Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

Analysis and forecasting for short-term traffic flow have become a critical problem in intelligent transportation system (ITS). This paper introduces the basic theory and features of General Regression Neural Network (GRNN) and its advantages. A forecasting model based on GRNN is built for short-term traffic flow time series at urban road section in 10minutes interval. In order to get ideal forecasting results, the search method is used to obtain the number of input neurons and the value of smooth factor. When the number of input neuron and training samples are defined, the model can forecast the next 10-minutes traffic flow using the method of dynamic learning and single-step forecasting. Compared with the forecasting results of the traditional BP neural network (BPNN) which adopts error back-propagation learning method, this model is more accurate, and more suitable for short-term traffic flow forecasting.