Statistical Computations on Biological Rhythms I: Dissecting Variable Cycles and Computing Signature Phases in Activity-Event Time Series

We propose a computational methodology to compute and extract circadian rhythmic patterns from an individual animal’s activity-event time series. This lengthy dataset, composed of a sequential event history, contains an unknown number of latent rhythmic cycles of varying duration and missing waveform information. Our computations aim at identifying the onset signature phase which individually indicates a sharp event intensity surge, where a subject-night ends and a brand new cycle’s subject-day begins, and collectively induces a linearity manifesting the individual circadian rhythmicity and information about the average period. Based on the induced linearity, the least squares criterion is employed to choose an optimal sequence of computed onset signature phases among a finite collection derived from the hierarchical factor segmentation (HFS) algorithm. The multiple levels of coding schemes in the HFS algorithm are designed to extract contrasting patterns of aggregation against sparsity of activity events along the entire temporal axis. This optimal sequence dissects the whole time series into a sequence of rhythmic cycles without model assumptions or ad hoc behavioral definitions regarding the missing waveform information. The performance of our methodology is favorably compared with two popular approaches based on the periodogram in a simulation study and in real data analyses. The computer code and data used in this article are available on the JCGS webpage.     

Transcriptional targeting of gene expression in breast cancer by the promoters of protein regulator of cytokinesis 1 and ribonuclease reductase 2

For cancer gene therapy, cancer-specific over- expression of a therapeutic gene is required to reduce side effects derived from expression of the gene in normal cells. To develop such an expression vector, we searched for genes over-expressed and/or specifically expressed in cancer cells using bioinformatics and have selected genes coding for protein regulator of cytokinesis 1 (PRC1) and ribonuclease reductase 2 (RRM2) as candidates. Their cancer-specific expressions were confirmed in both breast cancer cell lines and patient tissues. We compared each promoter's cancer-specific activity in the breast normal and cancer cell lines using the luciferase gene as a reporter and confirmed cancer-specific expression of both PRC1 and RRM2 promoters. To test activities of these promoters in viral vectors, the promoters were also cloned into an adeno-associated viral (AAV) vector containing green fluorescence protein (GFP) as the reporter. The GFP expression levels by these promoters were various depending on cell lines tested and, in MDA-MB-231 cells, GFP activities derived from the PRC1 and RRM2 promoters were as strong as that from the cytomegalovirus (CMV) promoter. Our result showed that a vector containing the PRC1 or RRM2 promoter could be used for breast cancer specific overexpression in gene therapy.     

GOMSS: A Simple Group Oriented (t,m, n) Multi-secret Sharing Scheme

In most (t,n)-Multi-secret sharing ((t,n)-MSS) schemes, an illegal participant, even without any valid share, may recover secrets when there are over t participants in secret reconstructions. To address this problem, the paper presents the notion of Group ori-ented (t,m,n)-multi-secret sharing (or (t,m,n)-GOMSS), in which recovering each secret requires all m (n ≥ m ≥ t) participants to have valid shares and actually participate in secret reconstruction. As an example, the paper then pro-poses a simple (t,m,n)-GOMSS scheme. In the scheme, every shareholder has only one share; to recover a secret, m shareholders construct a Polynomial-based randomized component (PRC) each with the share to form a tightly coupled group, which forces the secret to be recovered only with all m valid PRCs. As a result, the scheme can thwart the above illegal participant attack. The scheme is simple as well as flexible and does not depend on conventional hard problems or one way functions.     

Pseudo real closed fields,pseudo p-adically closed fields and NTP2

The main result of this paper is a positive answer to the Conjecture 5.1 of [14] by A. Chernikov, I. Kaplan and P. Simon: If M is a PRC field, then $Th(M)$ is NTP₂ if and only if M is bounded. In the case of PpC fields, we prove that if M is a bounded PpC field, then $Th(M)$ is NTP₂. We also generalize this result to obtain that, if M is a bounded PRC or PpC field with exactly n orders or p-adic valuations respectively, then $Th(M)$ is strong of burden n. This also allows us to explicitly compute the burden of types, and to describe forking.     

Traceless Synthesis of Asymmetrically Modified Bivalent Nucleosomes

Nucleosomes carry extensive post‐translational modifications (PTMs), which results in complex modification patterns that are involved in epigenetic signaling. Although two copies of each histone coexist in a nucleosome, they may not carry the same PTMs and are often differently modified (asymmetric). In bivalent domains, a chromatin signature prevalent in embryonic stem cells (ESCs), namely H3 methylated at lysine 4 (H3K4me3), coexists with H3K27me3 in asymmetric nucleosomes. We report a general, modular, and traceless method for producing asymmetrically modified nucleosomes. We further show that in bivalent nucleosomes, H3K4me3 inhibits the activity of the H3K27‐specific lysine methyltransferase (KMT) polycomb repressive complex 2 (PRC2) solely on the same histone tail, whereas H3K27me3 stimulates PRC2 activity across tails, thereby partially overriding the H3K4me3‐mediated repressive effect. To maintain bivalent domains in ESCs, PRC2 activity must thus be locally restricted or reversed.     

基于相位变化率的单站EKF无源定位算法研究

传统的无源定位方法主要是利用到达方位角（DOA）信息对雷达等辐射源进行测向交叉定位，但由于该方法对测角精度的敏感性，仅利用DOA信息会导致定位精度低，算法收敛时间长等不良后果。本文探讨了一种融入相位变化率（PRC）信息的推广卡尔曼滤波（EKF）算法，通过仿真，分析了DOA与PRC单独测量与联合测量对定位精度的影响，同时，将融入PRC信息的EKF定位算法与只测角定位算法进行了比较。