Low frequency pressure oscillation study, phase 1 Interim study

Characteristics of low frequency pressure oscillations in Apollo spacecraft engine

Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics.

Complex cognitive processes require neuronal activity to be coordinated across multiple scales, ranging from local microcircuits to cortex-wide networks. However, multiscale cortical dynamics are not well understood because few experimental approaches have provided sufficient support for hypotheses involving multiscale interactions. To address these limitations, we used, in experiments involving mice, genetically encoded voltage indicator imaging, which measures cortex-wide electrical activity at high spatiotemporal resolution. Here we show that, as mice recovered from anesthesia, scale-invariant spatiotemporal patterns of neuronal activity gradually emerge. We show for the first time that this scale-invariant activity spans four orders of magnitude in awake mice. In contrast, we found that the cortical dynamics of anesthetized mice were not scale invariant. Our results bridge empirical evidence from disparate scales and support theoretical predictions that the awake cortex operates in a dynamical regime known as criticality. The criticality hypothesis predicts that small-scale cortical dynamics are governed by the same principles as those governing larger-scale dynamics. Importantly, these scale-invariant principles also optimize certain aspects of information processing. Our results suggest that during the emergence from anesthesia, criticality arises as information processing demands increase. We expect that, as measurement tools advance toward larger scales and greater resolution, the multiscale framework offered by criticality will continue to provide quantitative predictions and insight on how neurons, microcircuits, and large-scale networks are dynamically coordinated in the brain

Predicting criticality and dynamic range in complex networks: effects of topology

The collective dynamics of a network of coupled excitable systems in response to an external stimulus depends on the topology of the connections in the network. Here we develop a general theoretical approach to study the effects of network topology on dynamic range, which quantifies the range of stimulus intensities resulting in distinguishable network responses. We find that the largest eigenvalue of the weighted network adjacency matrix governs the network dynamic range. Specifically, a largest eigenvalue equal to one corresponds to a critical regime with maximum dynamic range. We gain deeper insight on the effects of network topology using a nonlinear analysis in terms of additional spectral properties of the adjacency matrix. We find that homogeneous networks can reach a higher dynamic range than those with heterogeneous topology. Our analysis, confirmed by numerical simulations, generalizes previous studies in terms of the largest eigenvalue of the adjacency matrix.Comment: 4 pages, 3 figure

Correlation between serum and salivary house dust mite specific immunoglobulins in Allergic Rhinitis (AR) patients and non-allergic rhinitis matched controls

Background and objectives: Allergic rhinitis (AR) is characterised by inflammation of the nasal mucosa associated with IgE mediated immune response to specific allergens. Studies have shown that the most commonly implicated aeroallergens in AR are house dust mites. Dermatophagoides pteronyssinus (Dp), Dermatophagoides farinae (Df) and Blomia tropicalis (Bt) are the three most common mite species in Malaysia. Previous studies have reported the presence of high IgE and non-IgE antibodies in both serum and saliva samples of AR patients, but the correlation between them is not clear. The objective of this study was to determine the levels and correlation of mite specific IgE and IgA against D. farinae and B. tropicalis in serum and salivary samples of AR patients and nonallergic rhinitis matched controls. Methods: A total of 205 adults were studied, they included 103 with AR and 102 healthy controls matched for age and gender. Among the 103 AR patients, 20 had concomitant asthma. Indirect ELISA was used to measure the levels of serum and salivary IgE and IgA respectively. Results: Correlation between the serum and salivary antibody levels was analysed using Spearman correlation test, while the differences between groups were analysed using Mann-Whitney test and Kruskal Wallis test. Serum IgE and IgA levels against D. farinae and B. tropicalis were higher in AR patients. Salivary IgA against D. farinae and salivary IgE against B. tropicalis were significantly higher in AR patients. Serum and salivary IgE levels were positively correlated with serum and salivary IgA levels in AR subjects against both mite species. There was no significant difference in antibodies levels between asthmatic AR and non-asthmatic AR to both mite species in serum and saliva samples. Antibodies levels were not correlated with severity of signs and symptoms in AR patients. Conclusion: The presence of high IgA level in saliva might be a potential investigation tool to test for allergic rhinitis when blood sampling is not an option

Characterizing flows with an instrumented particle measuring Lagrangian accelerations

We present in this article a novel Lagrangian measurement technique: an instrumented particle which continuously transmits the force/acceleration acting on it as it is advected in a flow. We develop signal processing methods to extract information on the flow from the acceleration signal transmitted by the particle. Notably, we are able to characterize the force acting on the particle and to identify the presence of a permanent large-scale vortex structure. Our technique provides a fast, robust and efficient tool to characterize flows, and it is particularly suited to obtain Lagrangian statistics along long trajectories or in cases where optical measurement techniques are not or hardly applicable.Comment: submitted to New Journal of Physic

Avalanches in self-organized critical neural networks: A minimal model for the neural SOC universality class

The brain keeps its overall dynamics in a corridor of intermediate activity and it has been a long standing question what possible mechanism could achieve this task. Mechanisms from the field of statistical physics have long been suggesting that this homeostasis of brain activity could occur even without a central regulator, via self-organization on the level of neurons and their interactions, alone. Such physical mechanisms from the class of self-organized criticality exhibit characteristic dynamical signatures, similar to seismic activity related to earthquakes. Measurements of cortex rest activity showed first signs of dynamical signatures potentially pointing to self-organized critical dynamics in the brain. Indeed, recent more accurate measurements allowed for a detailed comparison with scaling theory of non-equilibrium critical phenomena, proving the existence of criticality in cortex dynamics. We here compare this new evaluation of cortex activity data to the predictions of the earliest physics spin model of self-organized critical neural networks. We find that the model matches with the recent experimental data and its interpretation in terms of dynamical signatures for criticality in the brain. The combination of signatures for criticality, power law distributions of avalanche sizes and durations, as well as a specific scaling relationship between anomalous exponents, defines a universality class characteristic of the particular critical phenomenon observed in the neural experiments. The spin model is a candidate for a minimal model of a self-organized critical adaptive network for the universality class of neural criticality. As a prototype model, it provides the background for models that include more biological details, yet share the same universality class characteristic of the homeostasis of activity in the brain.Comment: 17 pages, 5 figure

Monte Carlo study of the Widom-Rowlinson fluid using cluster methods

The Widom-Rowlinson model of a fluid mixture is studied using a new cluster algorithm that is a generalization of the invaded cluster algorithm previously applied to Potts models. Our estimate of the critical exponents for the two-component fluid are consistent with the Ising universality class in two and three dimensions. We also present results for the three-component fluid.Comment: 13 pages RevTex and 2 Postscript figure

Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches

The size distribution of neuronal avalanches in cortical networks has been reported to follow a power law distribution with exponent close to −1.5, which is a reflection of long-range spatial correlations in spontaneous neuronal activity. However, identifying power law scaling in empirical data can be difficult and sometimes controversial. In the present study, we tested the power law hypothesis for neuronal avalanches by using more stringent statistical analyses. In particular, we performed the following steps: (i) analysis of finite-size scaling to identify scale-free dynamics in neuronal avalanches, (ii) model parameter estimation to determine the specific exponent of the power law, and (iii) comparison of the power law to alternative model distributions. Consistent with critical state dynamics, avalanche size distributions exhibited robust scaling behavior in which the maximum avalanche size was limited only by the spatial extent of sampling (“finite size” effect). This scale-free dynamics suggests the power law as a model for the distribution of avalanche sizes. Using both the Kolmogorov-Smirnov statistic and a maximum likelihood approach, we found the slope to be close to −1.5, which is in line with previous reports. Finally, the power law model for neuronal avalanches was compared to the exponential and to various heavy-tail distributions based on the Kolmogorov-Smirnov distance and by using a log-likelihood ratio test. Both the power law distribution without and with exponential cut-off provided significantly better fits to the cluster size distributions in neuronal avalanches than the exponential, the lognormal and the gamma distribution. In summary, our findings strongly support the power law scaling in neuronal avalanches, providing further evidence for critical state dynamics in superficial layers of cortex

Non-targeting siRNA induces NPGPx expression to cooperate with exoribonuclease XRN2 for releasing the stress

Short interfering RNAs (siRNAs) target specific mRNAs for their degradation mediated by RNA-induced silencing complex (RISC). Persistent activation of siRNA-RISC frequently leads to non-targeting toxicity. However, how cells mediate this stress remains elusive. In this communication, we found that the presence of non-targeting siRNA selectively induced the expression of an endoplasmic reticulum (ER)-resident protein, non-selenocysteine containing phospholipid hydroperoxide glutathione peroxidase (NPGPx), but not other ER-stress proteins including GRP78, Calnexin and XBP1. Cells suffering from constant non-targeting siRNA stress grew slower and prolonged G1 phase, while NPGPx-depleted cells accumulated mature non-targeting siRNA and underwent apoptosis. Upon the stress, NPGPx covalently bound to exoribonuclease XRN2, facilitating XRN2 to remove accumulated non-targeting siRNA. These results suggest that NPGPx serves as a novel responder to non-targeting siRNA-induced stress in facilitating XRN2 to release the non-targeting siRNA accumulation

Multiple Lineages of Human Breast Cancer Stem/Progenitor Cells Identified by Profiling with Stem Cell Markers

Heterogeneity of cancer stem/progenitor cells that give rise to different forms of cancer has been well demonstrated for leukemia. However, this fundamental concept has yet to be established for solid tumors including breast cancer. In this communication, we analyzed solid tumor cancer stem cell markers in human breast cancer cell lines and primary specimens using flow cytometry. The stem/progenitor cell properties of different marker expressing-cell populations were further assessed by in vitro soft agar colony formation assay and the ability to form tumors in NOD/SCID mice. We found that the expression of stem cell markers varied greatly among breast cancer cell lines. In MDA-MB-231 cells, PROCR and ESA, instead of the widely used breast cancer stem cell markers CD44+/CD24-/low and ALDH, could be used to highly enrich cancer stem/progenitor cell populations which exhibited the ability to self renew and divide asymmetrically. Furthermore, the PROCR+/ESA+ cells expressed epithelial-mesenchymal transition markers. PROCR could also be used to enrich cells with colony forming ability from MB-361 cells. Moreover, consistent with the marker profiling using cell lines, the expression of stem cell markers differed greatly among primary tumors. There was an association between metastasis status and a high prevalence of certain markers including CD44+/CD24−/low, ESA+, CD133+, CXCR4+ and PROCR+ in primary tumor cells. Taken together, these results suggest that similar to leukemia, several stem/progenitor cell-like subpopulations can exist in breast cancer