Comparative omics of CCM signaling complex(CSC)

Background:Cerebral cavernous malformations(CCMs),a major neurosurgical condition,characterized by abnormally dilated intracranial capillaries,result in increased susceptibility to stroke.KRIT1(CCM1),MGC4607(CCM2),and PDCD10(CCM3)have been identified as causes of CCMs in which at least one of them is disrupted in most familial cases.Our goal is to identify potential biomarkers and genetic modifiers of CCMs,using a global comparative omics approach across several in vitro studies and multiple in vivo animal models.We hypothesize that through analysis of the CSC utilizing various omics,we can identify potential biomarkers and genetic modifiers,by systemically evaluating effectors and binding partners of the CSC as well as second layer interactors.Methods:We utilize a comparative omics approach analyzing multiple CCMs deficient animal models across nine independent studies at the genomic,transcriptomic,and proteomic levels to dissect alterations in various signaling cascades.Results:Our analysis revealed a large set of genes that were validated across multiple independent studies,suggesting an important role for these identified genes in CCM pathogenesis.Conclusion:This is currently one of the largest comparative omics analysis of CCM deficiencies across multiple models,allowing us to investigate global alterations among multiple signaling cascades involved in both angiogenic and non-angiogenic events and to also identify potential biomarker candidates of CCMs,which can be used for new therapeutic strategies.

Gamma-aminobutyric acid A receptors in Alzheimer＇s disease： highly localized remodeling of a complex and diverse signaling pathway

Alzheimer＇s disease （AD）, the predominant form of dementia, is a chronic, incurable neurodegenerative disorder presenting with symptoms includ- ing progressive memory loss and disturbed emotional state. It has been estimated that dementia affects over 47 million people worldwide （Prince et al., 2015）, and with 60-80% of cases attributable to AD.

Arbitrary-Dimensional Frequency Estimation of Complex Sinusoid Signals

The problem of increasing computation in pace with the growth of dimension is discussed for arbitrary dimensional frequency estimation of complex sinusoid signals. The conception of matrix core, the form of which doesnt change with dimension, is put forward. The deduced estimation formula shows that a N dimensional frequency estimation could be obtained by N one dimensional calculations. Obviously, while dimension increases, this method could reduce much computation.

Estimation of the complex frequency of a harmonic signal based on a linear least squares method

In this study, we propose a simple linear least squares estimation method(LLS) based on a Fourier transform to estimate the complex frequency of a harmonic signal. We first use a synthetically-generated noisy time series to validate the accuracy and effectiveness of LLS by comparing it with the commonly used linear autoregressive method(AR). For an input frequency of 0.5 m Hz, the calculated deviations from the theoretical value were 0.004‰and 0.008‰ for the LLS and AR methods respectively; and for an input 5 10 6attenuation,the calculated deviations for the LLS and AR methods were 2.4% and 1.6%. Though the theory of the AR method is more complex than that of LLS, the results show LLS is a useful alternative method. Finally, we use LLS to estimate the complex frequencies of the five singlets of the0S2 mode of the Earth’s free oscillation. Not only are the results consistent with previous studies, the method has high estimation precisions, which may prove helpful in determining constraints on the Earth’s interior structures.

Mathematical theory of signal analysis vs. complex analysis method of harmonic analysis

We present recent work of harmonic and signal analysis based on the complex Hardy space approach.

High-elevation Adaptation of Motion Visual Display Modifications in the Toad-Headed Agamid Lizards(Phrynocephalus)

Understanding the process of adaptation is a key mission in modern evolutionary biology.Animals living at high elevations face challenges in energy meta bolism due to several environmental constraints(e.g., oxygen supply, food availa bility,and movement time). Animal behavioral processes are intimately related to energy meta bolism, and therefore, behavioral modifica tions are expected to be an important mechanism for high-elevation adaptation. We tested this behavioral adaptation hypothesis using va ria tions of motion visual displays in toad-headed agamid lizards of the genus Phr ynocephalus. We predicted tha t complexity of visual motion displays would decrease with the increase of elevation, because motion visual displays are energetically costly. Displays of 12 Phr ynocephalus species were collected with elevations ranging from sea level to 4600 m. We quantified display complexity using the number of display components, display duration, pathways of display components, as well as display speed for each species. Association between display complexity and elevation was analyzed using the phylogenetic generalized least squares(PGLS)model. We found that both the number of display components and the average value of tail coil speed were negatively correlated with elevation, suggesting that toad-headed lizards living at high-elevation areas reduced their display complexity to cope with the environmental constraints. Our research provides direct evidence for high-elevation adaptation from a behavioral aspect and illustrates the potential impacts of environment heterogeneity on motion visual display diversification.

Blind source separation algorithm for communication complex signals in communication reconnaissance

Most blind source separation algorithms are only applicable to real signals,while in communication reconnaissance processed signals are complex.To solve this problem,a blind source separation algorithm for communication complex signals is deduced,which is obtained by adopting the Kullback-Leibler divergence to measure the signals’independence.On the other hand,the performance of natural gradient is better than that of stochastic gradient,thus the natural gradient of the cost function is used to optimize the algorithm.According to the conclusion that the signal’s mixing matrix after whitening is orthogonal,we deduce the iterative algorithm by constraining the separating matrix to an orthogonal matrix.Simulation results show that this algorithm can efficiently separate the source signals even in noise circumstances.

Fixed-point ICA algorithm for blind separation of complex mixtures containing both circular and noncircular sources

Fixed-point algorithms are widely used for independent component analysis（ICA） owing to its good convergence. However, most existing complex fixed-point ICA algorithms are limited to the case of circular sources and result in phase ambiguity, that restrict the practical applications of ICA. To solve these problems, this paper proposes a two-stage fixed-point ICA（TS-FPICA） algorithm which considers complex signal model. In this algorithm, the complex signal model is converted into a new real signal model by utilizing the circular coefficients contained in the pseudo-covariance matrix. The algorithm is thus valid to noncircular sources. Moreover, the ICA problem under the new model is formulated as a constrained optimization problem, and the real fixed-point iteration is employed to solve it. In this way, the phase ambiguity resulted by the complex ICA is avoided. The computational complexity and convergence property of TS-FPICA are both analyzed theoretically. Simulation results show that the proposed algorithm has better separation performance and without phase ambiguity in separated signals compared with other algorithms. TS-FPICA convergences nearly fast as the other fixed-point algorithms, but far faster than the joint diagonalization method, e.g. joint approximate diagonalization of eigenmatrices（JADE）.

Auditory detection of sound signals with complex time-frequency characteristics

The mechanism of the human auditory system in detecting sound signals with complex time frequency charcteristics in a white noise background was reviewed and discussed.The efficiency of such auditory detection was assessed by comparing it with that of parallel visual detection of the output of an analogous model displayed on the oscilloscope screen. The results suggest that the detection model of the human auditory system is quite similar to a tone correlator when the time frequency characteristics of the signal are known and to an energy detector when the signal is unknown. The relationship between the threshold signal to noise ratio and the signal duration is derived for different time frequency characteristics.

A Complex Signal

The removal of Taiwan from the common strategic objectives of the U.S.-Japan alliance could mean much more than a change of wording While U.S.and Japanese top diplomats and defense officials celebrated closer cooperation forged by their annual meeting