Roadway traffic crash prediction using a state-space model based support vector regression approach.

Conventional traffic crash analyzing methods focus on identifying the relationship between traffic crash outcomes and impact risk factors and explaining the effects of risk factors, which ignore the changes of roadway systems and can lead to inaccurate results in traffic crash predictions. To address this issue, an innovative two-step method is proposed and a support vector regression (SVR) model is formulated into state-space model (SSM) framework for traffic crash prediction. The SSM was developed in the first step to identify the dynamic evolution process of the roadway systems that are caused by the changes of traffic flow and predict the changes of impact factors in roadway systems. Using the predicted impact factors, the SVR model was incorporated in the second step to perform the traffic crash prediction. A five-year dataset that obtained from 1152 roadway segments in Tennessee was employed to validate the model effectiveness. The proposed models result in an average prediction MAPE of 7.59%, a MAE of 0.11, and a RMSD of 0.32. For the performance comparison, a SVR model and a multivariate negative binomial (MVNB) model were developed to do the same task. The results show that the proposed model has superior performances in terms of prediction accuracy compared to the SVR and MVNB models. Compared to the SVR and MVNB models, the benefit of incorporating a state-space model to identify the changes of roadway systems is significant evident in the proposed models for all crash types, and the prediction accuracy that measured by MAPE can be improved by 4.360% and 6.445% on average, respectively. Apart from accuracy improvement, the proposed models are more robust and the predictions can retain a smoother pattern. Furthermore, the results show that the proposed model has a more precise and synchronized response behavior to the high variations of the observed data, especially for the phenomenon of extra zeros.

Robust Consensus of Nonlinear Multiagent Systems With Switching Topology and Bounded Noises.

Consensus of multiagent systems (MASs) is an intriguing topic in recent years due to its widely used application in robotics, biology, computer, and social science. In the real world, the evolution of MAS is inevitably involved in dynamical environments and the recent development of MAS calls for novel tools for the analysis of MAS with dynamic topology. In addition, the interactions between agents are generally nonlinear and environmental noises are ubiquitous in the communication channels between agents. However, the existing investigation on MAS places little attention on nonlinear models and the inner relationship between external disturbance and consensus is still unclear. Facing these problems, this paper considers an MAS in which the interactions between agents are nonlinear and the communication between agents are infected by environmental noises. By using a novel method of nonsmooth Lyapunov candidate, it has been demonstrated that such an MAS can realize robust consensus under the conditions of jointly (sequentially) connected topology and bounded noises. Finally, simulation results validate the effectiveness of these criteria.

piRBase: a web resource assisting piRNA functional study.

piRNAs are a class of small RNAs that is most abundantly expressed in the animal germ line. Presently, substantial research is going on to reveal the functions of piRNAs in the epigenetic and post-transcriptional regulation of transposons and genes. A piRNA database for collection, annotation and structuring of these data will be a valuable contribution to the field, and we have therefore developed the piRBase platform which integrates various piRNA-related high-throughput data. piRBase has the largest collection of piRNAs among existing databases, and contains at present 77 million piRNA sequences from nine organisms. Repeat-derived and gene-derived piRNAs, which possibly participate in the regulation of the corresponding elements, have been given particular attention. Furthermore, epigenetic data and reported piRNA targets were also collected. To our knowledge, this is the first piRNA database that systematically integrates epigenetic and post-transcriptional regulation data to support piRNA functional analysis. We believe that piRBase will contribute to a better understanding of the piRNA functions. Database URL: http://www.regulatoryrna.org/database/piRNA/

The influences of PRG-1 on the expression of small RNAs and mRNAs.

BACKGROUND: In metazoans, Piwi-related Argonaute proteins play important roles in maintaining germline integrity and fertility and have been linked to a class of germline-enriched small RNAs termed piRNAs. Caenorhabditis elegans encodes two Piwi family proteins called PRG-1 and PRG-2, and PRG-1 interacts with the C. elegans piRNAs (21U-RNAs). Previous studies found that mutation of prg-1 causes a marked reduction in the expression of 21U-RNAs, temperature-sensitive defects in fertility and other phenotypic defects. RESULTS: In this study, we wanted to systematically demonstrate the function of PRG-1 in the regulation of small RNAs and their targets. By analyzing small RNAs and mRNAs with and without a mutation in prg-1 during C. elegans development, we demonstrated that (1) mutation of prg-1 leads to a decrease in the expression of 21U-RNAs, and causes 35 ~ 40% of miRNAs to be down-regulated; (2) in C. elegans, approximately 3% (6% in L4) of protein-coding genes are differentially expressed after mutating prg-1, and 60 ~ 70% of these substantially altered protein-coding genes are up-regulated; (3) the target genes of the down-regulated miRNAs and the candidate target genes of the down-regulated 21U-RNAs are enriched in the up-regulated protein-coding genes; and (4) PRG-1 regulates protein-coding genes by down-regulating small RNAs (miRNAs and 21U-RNAs) that target genes that participate in the development of C. elegans. CONCLUSIONS: In prg-1-mutated C. elegans, the expression of miRNAs and 21U-RNAs was reduced, and the protein-coding targets, which were associated with the development of C. elegans, were up-regulated. This may be the mechanism underlying PRG-1 function.