Development and assessment of scoring functions for protein identification using PMF data

PMF is one of the major methods for protein identification using the MS technology. It is faster and cheaper than MS/MS. Although PMF does not differentiate trypsin-digested peptides of identical mass, which makes it less informative than MS/MS, current computational methods for PMF have the potential to improve its detection accuracy by better use of the information content in PMF spectra. We developed a number of new probability-based scoring functions for PMF protein identification based on the MOWSE algorithm. We considered a detailed distribution of matching masses in a protein database and peak intensity, as well as the likelihood of peptide matches to be close to each other in a protein sequence. Our computational methods are assessed and compared with other methods using PMF data of 52 gel spots of known protein standards. The comparison shows that our new scoring schemes have higher or comparable accuracies for protein identification in comparison to the existing methods. Our software is freely available upon request. The scoring functions can be easily incorporated into other proteomics software packages.     

Impulsive control of stochastic systems with applications in chaos control, chaos synchronization, and neural networks

Real systems are often subject to both noise perturbations and impulsive effects. In this paper, we study the stability and stabilization of systems with both noise perturbations and impulsive effects. In other words, we generalize the impulsive control theory from the deterministic case to the stochastic case. The method is based on extending the comparison method to the stochastic case. The method presented in this paper is general and easy to apply. Theoretical results on both stability in the pth mean and stability with disturbance attenuation are derived. To show the effectiveness of the basic theory, we apply it to the impulsive control and synchronization of chaotic systems with noise perturbations, and to the stability of impulsive stochastic neural networks. Several numerical examples are also presented to verify the theoretical results.     

含芴共轭高分子的合成、表征及性能研究——推荐一个高分子化学综合实验

推荐了一个高分子化学综合实验——含芴共轭高分子的合成、表征及性能研究。实验内容包括利用Sonogashira偶联反应合成共轭高分子,采用核磁共振和红外光谱等检测手段对其结构进行表征,并利用紫外-可见光谱、荧光和热重分析对其性能进行研究。本实验结合了高分子化学和聚合物仪器分析与表征的知识点,建议纳入高分子专业高年级综合实验课程。     

Chaos and asymptotical stability in discrete-time recurrent neural networks with generalized input-output function

We theoretically investigate the asymptotical stability, local bifurcations and chaos of discrete-time recurrent neural networks with the form of

Molecular communication through stochastic synchronization induced by extracellular fluctuations

We model a synthetic gene regulatory network in a microbial cell, and investigate the effect of noises on cell-cell communication in a well-mixed multicellular system. A biologically plausible model is developed for cellular communication in an indirectly coupled multicellular system. Without extracellular noises, all cells, in spite of interaction among them, behave irregularly due to independent intracellular noises. On the other hand, extracellular noises that are common to all cells can induce collective dynamics and stochastically synchronize the multicellular system by actively enhancing the integrated interchange of signaling molecules.     

Detecting direct associations in a network by information theoretic approaches

Detecting direct associations or inferring networks based on the observed data is an important issue in many fields, including biology, physics, engineering and social studies. In this work, we focus on the information theoretic approaches in the network reconstruction or the direct association detection, in particular,for biological networks. We not only review the traditional approaches or measurements on the associations among the observed variables, such as correlation coefficient, mutual information and conditional mutual information(CMI), but also summarize recently developed theories and methods. The new theoretic works include:information geometry to give a unified framework in detecting causality/association, the partial independence to alleviate the singularity of CMI, and multiscale analysis of CMI to avoid the underestimation issue of CMI.The new methods include part mutual information(PMI) and partial associations(PA), which improve the old measurements in avoiding both overestimation and underestimation. All those theories and methods make important contributions as major advances in the development of network inference.     

Incremental stability analysis of stochastic hybrid systems

In this paper, we study the incremental stability of stochastic hybrid systems, based on the contraction theory, and derive sufficient conditions of global stability for such systems. As a special case, the conditions to ensure the second moment exponential stability which is also called exponential stability in the mean square of stochastic hybrid systems are obtained. The theoretical results in this paper extend previous works from deterministic or stochastic systems to general stochastic hybrid systems, which can be applied to qualitative and quantitative analysis of many physical and biological phenomena. An illustrative example is given to show the effectiveness of our results.     

Oscillatory dynamics in a simple gene regulatory network mediated by small RNAs

More and more experiments show that small RNAs regulate gene expression by repressing translation of messenger RNAs (mRNAs) or degrading mRNAs. In this paper, we incorporate the small RNAs into a simple gene regulatory network and investigate its dynamical behaviors. In addition, we also derive the theoretical results of globally asymptotic stability and provide the sufficient conditions for the oscillation of the simple gene regulatory network, and further demonstrate that the amplitudes against the change of delay in the gene regulatory network are robust.     

Nanodiamonds Interfere with Wnt‐Regulated Cell Migration and Adipocyte Differentiation in Cells and Embryonic Development In Vivo

Biocompatible nanoparticles hold a great promise for biomedical applications, whereas their biosafety has raised extensive concerns. Nanodiamonds (NDs) are generally regarded as “inert” nanocarriers and widely employed in biomedical studies; however, it is yet to explore their biological effects in more general contexts. In this study, the authors observe that intracellular NDs block signal transduction of the Wnt signaling pathway, an effect that is not caused by general cytotoxicity. The authors find that NDs attenuate activities of Wnt signaling in several types of cell lines and in Zebrafish, and interfere with Wnt signaling‐controlled biological processes, including cancer cell migration, adipocyte differentiation, and embryonic development. Significantly, the authors show that intracellular NDs trigger degradation of the disheveled protein, a key component of Wnt signaling cascade, while they do not affect protein stability of other Wnt signal transducers or other signaling molecules. This work thus illustrates a novel crosstalk between nanoparticles and the Wnt signaling pathway, and expands the understanding of biological effects induced by nanoparticles. In addition, given the clinical implications of Wnt signaling in tumorigenesis and cancer metastasis, this study also provides the rationale for potential applications of NDs in cancer therapies.     

Spatiotemporal Transformer Neural Network for Time-Series Forecasting

Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.