Stochastic Changes Research Articles

A Model of Blood Pressure, Heart Rate, and Vaso-Vagal Responses Produced by Vestibulo-Sympathetic Activation.

Blood Pressure (BP), comprised of recurrent systoles and diastoles, is controlled by central mechanisms to maintain blood flow. Periodic behavior of BP was modeled to study how peak amplitudes and frequencies of the systoles are modulated by vestibular activation. The model was implemented as a relaxation oscillator, driven by a central signal related to Desired BP. Relaxation oscillations were maintained by a second order system comprising two integrators and a threshold element in the feedback loop. The output signal related to BP was generated as a nonlinear function of the derivative of the first state variable, which is a summation of an input related to Desired BP, feedback from the states, and an input from the vestibular system into one of the feedback loops. This nonlinear function was structured to best simulate the shapes of systoles and diastoles, the relationship between BP and Heart Rate (HR) as well as the amplitude modulations of BP and Pulse Pressure. Increases in threshold in one of the feedback loops produced lower frequencies of HR, but generated large pulse pressures to maintain orthostasis, without generating a VasoVagal Response (VVR). Pulse pressures were considerably smaller in the anesthetized rats than during the simulations, but simulated pulse pressures were lowered by including saturation in the feedback loop. Stochastic changes in threshold maintained the compensatory Baroreflex Sensitivity. Sudden decreases in Desired BP elicited non-compensatory VVRs with smaller pulse pressures, consistent with experimental data. The model suggests that the Vestibular Sympathetic Reflex (VSR) modulates BP and HR of an oscillating system by manipulating parameters of the baroreflex feedback and the signals that maintain the oscillations. It also shows that a VVR is generated when the vestibular input triggers a marked reduction in Desired BP.

Multivariate Wishart Stochastic Volatility and Changes in Regime

This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (2006) and Asai and McAleer (2009) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. The model allows for state-dependent (co)variance and correlation levels and state-dependent volatility spillover effects. Parameter estimates are obtained using Bayesian Markov Chain Monte Carlo procedures and filtered estimates of the latent variances and covariances are generated by particle filter techniques. The model is applied to five European stock index return series. The results show that the proposed regime-switching specification substantially improves the in-sample fit and the VaR forecasting performance relative to the basic model.

Multivariate Wishart stochastic volatility and changes in regime

This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (J Bus Econ Stat 24:313–328, 2006) and Asai and McAleer (J Econom 150:182–192, 2009) to encompass regime-switching behavior. The latent state variable is driven by a first-order Markov process. The model allows for state-dependent (co)variance and correlation levels and state-dependent volatility spillover effects. Parameter estimates are obtained using Bayesian Markov Chain Monte Carlo procedures and filtered estimates of the latent variances and covariances are generated by particle filter techniques. The model is applied to five European stock index return series. The results show that the proposed regime-switching specification substantially improves the fit to persistent covariance dynamics relative to the basic model.

Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects.

Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing.

Stability of Ensemble Models Predicts Productivity of Enzymatic Systems.

Stability in a metabolic system may not be obtained if incorrect amounts of enzymes are used. Without stability, some metabolites may accumulate or deplete leading to the irreversible loss of the desired operating point. Even if initial enzyme amounts achieve a stable steady state, changes in enzyme amount due to stochastic variations or environmental changes may move the system to the unstable region and lose the steady-state or quasi-steady-state flux. This situation is distinct from the phenomenon characterized by typical sensitivity analysis, which focuses on the smooth change before loss of stability. Here we show that metabolic networks differ significantly in their intrinsic ability to attain stability due to the network structure and kinetic forms, and that after achieving stability, some enzymes are prone to cause instability upon changes in enzyme amounts. We use Ensemble Modelling for Robustness Analysis (EMRA) to analyze stability in four cell-free enzymatic systems when enzyme amounts are changed. Loss of stability in continuous systems can lead to lower production even when the system is tested experimentally in batch experiments. The predictions of instability by EMRA are supported by the lower productivity in batch experimental tests. The EMRA method incorporates properties of network structure, including stoichiometry and kinetic form, but does not require specific parameter values of the enzymes.

The Qubit as Key to Quantum Physics Part II: Physical Realizations and Applications

Using the simplest possible quantum system—the qubit—the fundamental concepts of quantum physics can be introduced. This highlights the common features of many different physical systems, and provides a unifying framework when teaching quantum physics at the high school or introductory level. In a previous TPT article and in a separate paper posted online, we introduced catchy visualizations of the qubit based on the Bloch sphere or just the unit circle (see also Refs. 3–8 for other approaches highlighting the importance of the qubit). These visualizations open the way to understand basic ideas of quantum physics even without knowledge of the underlying mathematical formalism. In addition, simple mathematics can be introduced to describe the qubit as an abstract object and basic unit of quantum information. This generalizes the digital bit as a basic unit of classical information. The proposed visualizations can be used even at the high school level, while the mathematical explanations are of importance when teaching quantum physics at the undergraduate university level. This approach provides a unified framework to introduce common features of all quantum systems, such as the stochastic behavior and state change of a superposition state under measurement.

Image enhancement via lateral inhibition: An analysis under illumination changes

Image enhancement is a fundamental issue in digital image processing. Image enhancement via lateral inhibition is a relatively new method inspired by the phenomenon that the optic nerves in illuminatingly light regions inhibit those in darkness. However, it is common that the observed images incorporate illumination noises. This potentially limits the utilization of an image enhancement method because amplifying the noises would make the situation even worse. In this study, performance of lateral inhibition to handle illumination changes is investigated through theoretical analyses and simulations on a few transportation cases. Specifically, uniform, linearly varying and stochastic illumination changes are considered. Experimental results confirm that lateral inhibition can enhance the original image without amplifying the illumination noises in the aforementioned three scenarios.

Generalized Polynomial Chaos-Based Fault Detection and Classification for Nonlinear Dynamic Processes

This paper deals with detection and classification of intermittent stochastic faults by combining a generalized polynomial chaos (gPC) representation with either Maximum Likelihood or Bayesian estimators. The gPC is used to propagate stochastic changes in an input variable to measured quantities from which the fault is to be inferred. The fault detection and classification problem is formulated as an inverse problem of identifying the unknown input based on the Maximum Likelihood of fit between predicted and measured output variables, or on a Bayesian inference based estimator which recursively updates the gPC coefficients. Simulation studies compare the proposed methods with a particle filter (PF) to estimate the value of an unknown feed mass fraction of a chemical process. The proposed method is shown to be significantly more efficient in terms of computational effort and less sensitive to user defined tuning parameters than a PF.

Stochastic Focusing and Defocusing in Biological Reaction Networks: Lessons Learned from Accurate Chemical Master Equation (ACME) Solutions

Biological reaction networks are stochastic due to random thermal fluctuations. Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small and often results in unexpected outcomes. For example, it has been shown that a basic enzymatic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the increasing signal noise [1]. Although stochastic simulation algorithm has been widely used to study such systems, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method for the exact solution of the discrete Chemical Master Equation and study the probability landscape of product molecules in the basic enzymatic reaction system used in the original SF study [1]. Examinations of the effects of signal molecules under different stochastic processes show that SF is at play as stochastic changes enhance the system sensitivity. However, we also observed that the noise in signaling under certain stochastic processes in the same reaction network lead to a decrease in the system sensitivities, thus the network experiences stochastic defocusing. We further show that signal molecules following certain stochastic processes in the same reaction network can give rise to noise-induced bistability in the distribution of product molecules. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system. It also points to possible importance of positive and negative feedback loops in such networks for control of the intrinsic noise. [1] Paulsson et. al, 2000, PNAS.

Actomyosin Network Contractility Triggers a Stochastic Transformation into Highly Motile Amoeboid Cells

Cell migration is key for various biological processes and potentially leads to cancer dissemination if reactivated by tumour cells. Here we used zebrafish embryos from blastula to gastrula stages as a model system to monitor cell transformations under defined culture conditions and within the physiological tissue environment. This experimental framework allowed us to uncover a simple polarization mechanism that drives the transformation of embryonic cells into a highly efficient amoeboid migration mode irrespective of the specific progenitor cell type [1]. The observed amoeboid motility switch strongly depends on mechanical and contractile properties of the cellular cytoskeleton and was triggered by stochastic changes in cortex architecture when cells were exposed to biochemical stimuli or compressive forces from the 3D environment. Supported by theoretical modeling we show that cell transformation is induced by cortical instabilities above a critical threshold level of Myosin II mediated contractility, resulting in polarized cells with exceptionally high retrograde cortical flows and a pronounced increase in cortical actomyosin density towards the cell rear. We further show through a combination of experiments and theory that these cortical flows are essential for stabilization of cell polarity and cell shape and, in addition, provide the intracellular force required for cell locomotion in confined environments. Finally, we provide evidence for the existence of a similar amoeboid migration phenotype within the gastrulating embryo in vivo and show that amoeboid cell transformations can be induced in response to embryo wounding.[1] V. Ruprecht, S. Wieser, A. Callan-Jones, M. Smutny, H. Morita, K. Sako, V. Barone, M. Ritsch-Marte, M. Sixt, R. Voituriez, and C.-P. Heisenberg, “Cortical contractility triggers a stochastic switch to fast amoeboid cell motility”, Cell, vol. 160, no. 4, pp. 673-685, Feb. 2015.

Probing Single-Molecule Ion Channel Conformational Dynamics in Living Cells

Stochastic and inhomogeneous conformational changes regulate the function and dynamics of ion channels that are crucial for cell functions, neuronal signaling, and brain functions. We have developed a new combined approaches of using single ion channel patch-clamp electrical recording and single-molecule fluorescence imaging for probing ion channel conformational changes simultaneously with the electrical single channel recording. By combining real-time single-molecule fluorescence imaging measurements with real-time single-channel electric current measurements in artificail lipid bilayers and in living cell membranes, we were able to probe single ion-channel-protein conformational changes simultaneously, and thus providing an understanding the dynamics and mechanism of ion-channel proteins at the molecular level. We will focus our discussion on the new development and results of real-time imaging of the dynamics of gramicidin, colicin, and NMDA receptor ion channels in lipid bilayers and living cells. We have probed NMDA (N-Methyl-D-Aspartate) receptor ion channel in live HEK-293 cell, especially, the single ion channel open-close activity and its associated protein conformational changes simultaneously. Furthermore, we have revealed that the seemingly identical electrically off states are associated with multiple conformational states. Based on our experimental results, we have proposed a multistate clamshell model to interpret the NMDA receptor open-close dynamics. Our results shed light on new perspectives of the intrinsic interplay of lipid membrane dynamics, solvation dynamics, and the ion channel functions.

Acute and chronic safety and efficacy of dose dependent creatine nitrate supplementation and exercise performance.

BackgroundCreatine monohydrate (CrM) and nitrate are popular supplements for improving exercise performance; yet have not been investigated in combination. We performed two studies to determine the safety and exercise performance-characteristics of creatine nitrate (CrN) supplementation.MethodsStudy 1 participants (N = 13) ingested 1.5 g CrN (CrN-Low), 3 g CrN (CrN-High), 5 g CrM or a placebo in a randomized, crossover study (7d washout) to determine supplement safety (hepatorenal and muscle enzymes, heart rate, blood pressure and side effects) measured at time-0 (unsupplemented), 30-min, and then hourly for 5-h post-ingestion. Study 2 participants (N = 48) received the same CrN treatments vs. 3 g CrM in a randomized, double-blind, 28d trial inclusive of a 7-d interim testing period and loading sequence (4 servings/d). Day-7 and d-28 measured Tendo™ bench press performance, Wingate testing and a 6x6-s bicycle ergometer sprint. Data were analyzed using a GLM and results are reported as mean ± SD or mean change ± 95 % CI.ResultsIn both studies we observed several significant, yet stochastic changes in blood markers that were not indicative of potential harm or consistent for any treatment group. Equally, all treatment groups reported a similar number of minimal side effects. In Study 2, there was a significant increase in plasma nitrates for both CrN groups by d-7, subsequently abating by d-28. Muscle creatine increased significantly by d-7 in the CrM and CrN-High groups, but then decreased by d-28 for CrN-High. By d-28, there were significant increases in bench press lifting volume (kg) for all groups (PLA, 126.6, 95 % CI 26.3, 226.8; CrM, 194.1, 95 % CI 89.0, 299.2; CrN-Low, 118.3, 95 % CI 26.1, 210.5; CrN-High, 267.2, 95 % CI 175.0, 359.4, kg). Only the CrN-High group was significantly greater than PLA (p < 0.05). Similar findings were observed for bench press peak power (PLA, 59.0, 95 % CI 4.5, 113.4; CrM, 68.6, 95 % CI 11.4, 125.8; CrN-Low, 40.9, 95 % CI −9.2, 91.0; CrN-High, 60.9, 95 % CI 10.8, 111.1, W) and average power.ConclusionsCreatine nitrate delivered at 3 g was well-tolerated, demonstrated similar performance benefits to 3 g CrM, in addition, within the confines of this study, there were no safety concerns.

Preparation of Papers for IFAC Conferences & Symposia: Migration Planning for Global Production Networks using Markovian Decision Processes

Modern globalization leads companies into a changing environment with a highly uncertain future development of key drivers of change. Especially, global production networks are affected by uncertainty and dynamic changes. Reactiveness becomes of crucial importance, as the adaptation to environmental conditions is the key to maintain competitive advantages. This article presents an approach for flexible migration planning in global production networks. The focus is on the formulation of a Markovian Decision Process (MDP) that enables the identification of optimal reactions to stochastic changes of key drivers of change. The formulation includes the description of a multi-level modelling approach for global production networks. Furthermore the valuation model of the reward function of the MDP is discussed in detail. Finally, the paper provides a brief description of exemplary optimization results solving the MDP by backward induction.

PROBABILISTIC FLOW DISTRIBUTION AS A REACTION TO THE STOCHASTICITY OF THE LOAD IN THE POWER SYSTEM

For the analysis and control of power systems deterministic approaches that are implemented in the form of well-known methods and models of calculation of steady-state and transient modes are mostly use in current practice. With the use of these methods it is possible to obtain solutions only for fixed circuit parameters of the system scheme and assuming that active and reactive powers as well as generation in nodal points of the network remain the same. In reality the stochastic character of power consumption cause the casual fluctuations of voltages at the nodes and power flows in electric power lines of the power system. Such casual fluctuations of operation can be estimated with the use of probabilistic simulation of the power flows. In the article the results of research of the influence of depth of casual fluctuations of the load power of the system on the probability distribution of voltage at nodes as well as on the flows of active and reactive power in the lines are presented. Probabilistic modeling of flow under stochastic load change is performed for different levels of fluctuations and under loading of the mode of the system up to peak load power. Test study to quantify the effect of stochastic variability of loads on the probabilistic distribution parameters of the modes was carried out on behalf of the electrical network of the real power system. The results of the simulation of the probability flow distribution for these fluctuations of the load, represented in the form of discrete sample values of the active power obtained with the use of the analytical Monte-Carlo method, and real data measurements of their values in the network under examination were compared.

Asymptotic Analysis of Forward Performance Processes in Incomplete Markets and Their Ill-Posed HJB Equations

We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. The dynamics of the prices of the traded assets depend on a pair of stochastic factors, namely, a slow factor (e.g., a macroeconomic indicator) and a fast factor (e.g., stochastic volatility). We analyze the associated forward performance SPDE and provide explicit formulae for the leading order and first order correction terms for the forward investment process and the optimal feedback portfolios. They both depend on the investor's initial preferences and the dynamically changing investment opportunities. The leading order terms resemble their time-monotone counterparts, but with the appropriate stochastic time changes resulting from averaging phenomena. The first order terms compile the reaction of the investor to both the changes in the market input and his recent performance. Our analysis is based on an expansion of the underlying ill-posed HJB equation, and it is supplemented b...

Расчёт справедливой цены барьерного опциона в модели (B,S)-рынка с переключением параметров

Расчёт справедливой цены барьерного опциона в модели (B,S)-рынка с переключением параметров

Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution function.

BackgroundGenome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs.ResultsAnalysis of the integrated GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and CO2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %.ConclusionsThe multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0396-0) contains supplementary material, which is available to authorized users.

Environmental changes affect the assembly of soil bacterial community primarily by mediating stochastic processes.

Both 'species fitness difference'-based deterministic processes, such as competitive exclusion and environmental filtering, and 'species fitness difference'-independent stochastic processes, such as birth/death and dispersal/colonization, can influence the assembly of soil microbial communities. However, how both types of processes are mediated by anthropogenic environmental changes has rarely been explored. Here we report a novel and general pattern that almost all anthropogenic environmental changes that took place in a grassland ecosystem affected soil bacterial community assembly primarily through promoting or restraining stochastic processes. We performed four experiments mimicking 16 types of environmental changes and separated the compositional variation of soil bacterial communities caused by each environmental change into deterministic and stochastic components, with a recently developed method. Briefly, because the difference between control and treatment communities is primarily caused by deterministic processes, the deterministic change was quantified as (mean compositional variation between treatment and control) - (mean compositional variation within control). The difference among replicate treatment communities is primarily caused by stochastic processes, so the stochastic change was estimated as (mean compositional variation within treatment) - (mean compositional variation within control). The absolute of the stochastic change was greater than that of the deterministic change across almost all environmental changes, which was robust for both taxonomic and functional-based criterion. Although the deterministic change may become more important as environmental changes last longer, our findings showed that changes usually occurred through mediating stochastic processes over 5years, challenging the traditional determinism-dominated view.

Effect of Combined Stress on Morphological Changes and Expression of NO Synthases in Rat Ventral Hippocampus.

Adult rats were subjected to 7-day combined stress with stochastic changes of stressors of different modalities (noise, vibration, pulsating bright light) along with mobility restriction and elevated temperature in the chamber during stress exposures (daily 30-min sessions). Circulatory disorders, inhibition of endothelial NO-synthase expression in endothelial cells of the microcirculatory bed, perivascular edema, pronounced degenerative changes, and enhanced expression of inducible NO synthase in CA3 pyramidal neurons in the ventral hippocampus of stressed 12-month-old rats were observed. These findings can attest to the involvement NOdependent mechanisms and different contribution of NO synthase isoforms into the formation of hippocampal neuronal damage.

TOWARD IMPACT FUNCTIONS FOR STOCHASTIC CLIMATE CHANGE

Most functions of economic impact assume that climate change is smooth. We here propose impact functions that have stochastic climate change as an input. These functions are identical in shape and have similar parameters as do deterministic impact functions. The mean stochastic impacts are thus similar to deterministic impacts. Welfare effects are larger, and the stochasticity premium is larger than the risk premium. Results suggest that stochasticity is more important for past impacts than for future impacts. This outcome is partly caused by an underestimation of natural variability in the 21st century climate projections.