Uncovering interactions in the frequency domain

Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs recorded from distributed sites in the right hemisphere of a macaque monkey

ZX-Calculus: Cyclotomic Supplementarity and Incompleteness for Clifford+T quantum mechanics

The ZX-Calculus is a powerful graphical language for quantum mechanics and quantum information processing. The completeness of the language -- i.e. the ability to derive any true equation -- is a crucial question. In the quest of a complete ZX-calculus, supplementarity has been recently proved to be necessary for quantum diagram reasoning (MFCS 2016). Roughly speaking, supplementarity consists in merging two subdiagrams when they are parameterized by antipodal angles. We introduce a generalised supplementarity -- called cyclotomic supplementarity -- which consists in merging n subdiagrams at once, when the n angles divide the circle into equal parts. We show that when n is an odd prime number, the cyclotomic supplementarity cannot be derived, leading to a countable family of new axioms for diagrammatic quantum reasoning.We exhibit another new simple axiom that cannot be derived from the existing rules of the ZX-Calculus, implying in particular the incompleteness of the language for the so-called Clifford+T quantum mechanics. We end up with a new axiomatisation of an extended ZX-Calculus, including an axiom schema for the cyclotomic supplementarity.Comment: Mathematical Foundations of Computer Science, Aug 2017, Aalborg, Denmar

MicroRNAs miR-27a and miR-143 Regulate Porcine Adipocyte Lipid Metabolism

MicroRNAs (miRNAs) are non-coding small RNAs that play roles in regulating gene expression. Some miRNAs have been classed as epigenetic regulators of metabolism and energy homeostasis. Previous reports indicated that the miRNAs miR-27a and miR-143 were involved in lipid metabolism in human and rodents. To determine the roles of porcine miR-27a and miR-143 in adipocyte lipid metabolism, porcine adipocytes were cultured and allowed to induce differentiation for 10 days. The lipid-filled adipocytes were then transfected with miRNA mimics and inhibitors. We measured how the indicators of adipogenesis and adipolysis in porcine adipocytes were affected by the over-expression and by the inhibition of both miR-27a and miR-143. The results indicated that the over-expression of miR-27a could accelerate adipolysis releasing significantly more glycerol and free fatty acids than the negative control (P < 0.001), while the mimic of miR-143 expression, promoted adipogenesis by accumulating more triglycerides (P < 0.001) in the adipocytes. In addition, we demonstrated that there was good correlation (r > 0.98, P < 0.001) between the indicators of adipolysis in cell lysates and in the culture medium

Up-Converting Nanoparticle-Based Immunochromatographic Strip for Multi-Residue Detection of Three Organophosphorus Pesticides in Food

Organophosphorus (OP) pesticides are widely used to control pests because of their high activity. This study described a rapid and sensitive lateral flow immunochromatographic (LFIC) assay based on up-converting nanoparticles (UCNPs) for multi-residue detection of three OP pesticides. The developed assay integrated novel fluorescent material UCNPs labeled with a broad-specific monoclonal antibody. Based on the competitive platform by immobilized antigen in the test zone, the optimized UCNPs-LFIC assay enabled sensitive detection for parathion, parathion-methyl, and fenitrothion with IC50 of 3.44, 3.98, and 12.49 ng/mL (R2 ≥ 0.9776) within 40 min. The detectable ability ranged from 0.98 to 250 ng/mL. There was no cross-reactivity with fenthion, phoxim, isocarbophos, chlorpyrifos, or triazophos, even at a high concentration of 500 ng/mL. Matrix interference from various agricultural products was also studied in food sample detection. In the spiked test, recoveries of the three OP pesticides ranged from 67 to 120% and relative standard deviations were below 19.54%. These results indicated that the proposed strip assay can be an alternative screening tool for rapid detection of the three OP pesticides in food samples

DACT2 is frequently methylated in human gastric cancer and methylation of DACT2 activated Wnt signaling

Dapper, Dishevelled-associated antagonist of beta-catenin (DACT), is a key regulator of Wnt signaling pathway. The purpose of this study is to explore the epigenetic changes and the function of DACT2 in human gastric cancer (GC). Eight human gastric cancer cell lines, 167 cases of primary gastric cancer and 8 cases of normal gastric mucosa were involved in this study. In addition, methylation Specific PCR (MSP), semi-quantitative RT-PCR, colony formation assay, flow cytometry assay, siRNA, immunofluorescence techniques and xenograft mice models were employed. The results indicate that DACT2 is frequently methylated in human primary gastric cancer (55.7%), and that methylation of DACT2 is associated with lost or reduction in its expression (X-2 test, P&lt;0.01). We found that DACT2 expression was regulated by promoter region hypermethylation. Methylation of DACT2 is associated with tumor differentiation, invasion and intravascular cancerous emboli (X-2 test, P&lt;0.05, P&lt;0.05 and P&lt;0.05). In gastric cancer patients treated with 5-FU and cisplatin, the five-year survival rates are higher in DACT2 methylated cases. DACT2 inhibits cell proliferation, migration and invasion in gastric cancer cells and suppresses gastric cancer xenografts in mice. Restoration of DACT2 expression inhibits both canonical and noncanonical WNT signaling in SGC7901 cells. Restoration of DACT2 expression sensitized gastric cancer cells to paclitaxel and 5-FU. In conclusion, DACT2 is frequently methylated in human gastric cancer and DACT2 expression is silenced by promoter region hypermethylation. DACT2 suppressed gastric cancer proliferation, invasion and metastasis by inhibiting Wnt signaling both in vitro and in vivo.OncologySCI(E)[email protected]; [email protected]

Decomposing Neural Synchrony: Toward an Explanation for Near-Zero Phase-Lag in Cortical Oscillatory Networks

Background: Synchronized oscillation in cortical networks has been suggested as a mechanism for diverse functions ranging from perceptual binding to memory formation to sensorimotor integration. Concomitant with synchronization is the occurrence of near-zero phase-lag often observed between network components. Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles. Methodology/Principal Findings: Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1) positively correlated common input with no significant relative time delay and (2) bidirectional interaction. Thus far, no empirical test of these hypotheses has been possible for lack of means to tease apart the specific causes underlying the observed synchrony. In this work simulation examples were first used to illustrate the ideas. A quantitative method that decomposes the statistical interdependence between two cortical areas into a feed-forward, a feed-back and a common-input component was then introduced and applied to test the hypotheses on multichannel local field potential recordings from two behaving monkeys. Conclusion/Significance: The near-zero phase-lag phenomenon is important in the study of large-scale oscillatory networks. A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon. Given the critical role that oscillatory activity is likely to play in the regulation of biological processes at al

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time