The microdynamics shaping the relationship between democracy and corruption

Physics has a long tradition of laying rigorous quantitative foundations for social phenomena. Here, we up the ante for physics' forays into the territory of social sciences by (i) empirically documenting a tipping point in the relationship between democratic norms and corruption suppression, and then (ii) demonstrating how such a tipping point emerges from a micro-scale mechanistic model of spin dynamics in a complex network. Specifically, the tipping point in the relationship between democratic norms and corruption suppression is such that democratization has little effect on suppressing corruption below a critical threshold, but a large effect above the threshold. The micro-scale model of spin dynamics underpins this phenomenon by reinterpreting spins in terms of unbiased (i.e. altruistic) and biased (i.e. parochial) other-regarding behaviour, as well as the corresponding voting preferences. Under weak democratic norms, dense social connections of parochialists enable coercing enough opportunist voters to vote in favour of perpetuating parochial in-group bias. Society may, however, strengthen democratic norms in a rapid turn of events during which opportunists adopt altruism and vote to subdue bias. The emerging model outcome at the societal scale thus mirrors the data, implying that democracy either perpetuates or suppresses corruption depending on the prevailing democratic norms.

Postoperative transverse sternal nonunion with a chest wall defect managed by a tibial locking plate and a Gore-Tex dual mesh membrane: a case report

Background

Transverse sternal nonunion is a rare but disabling complication of chest trauma or a transverse sternotomy. Fixation methods, mainly used to manage the more common longitudinal sternal nonunion, often fail, leaving the surgical treatment of transverse nonunion to be a challenge.

Case presentation

We present a case of a highly-disabling, postoperative chest wall defect resulting from transverse sternal nonunion after a transverse thoracosternotomy (clamshell incision) and a concomitant rib resection. Following unsuccessful surgical attempts, the sternal nonunion was fixed with a tibial locking plate and bone grafted, while the post-rib resection chest defect was reconstructed with a Gore-Tex dual mesh membrane. Adequate chest stability was achieved, enabling complete healing of the sternal nonunion and the patient’s complete recovery.

Conclusion

We believe it is important to address both in the rare case of combined postoperative transverse sternal nonunion and the chest wall defect after rib resection. A good outcome was achieved in our patient by fixing the nonunion with an appropriately sized and shaped locking plate with bone grafting and covering the chest defect with a dual mesh membrane.

Autopoietic Influence Hierarchies in Pancreatic β Cells

β cells are biologically essential for humans and other vertebrates. Because their functionality arises from cell-cell interactions, they are also a model system for collective organization among cells. There are currently two contradictory pictures of this organization: the hub-cell idea pointing at leaders who coordinate the others, and the electrophysiological theory describing all cells as equal. We use new data and computational modeling to reconcile these pictures. We find via a network representation of interacting β cells that leaders emerge naturally (confirming the hub-cell idea), yet all cells can take the hub role following a perturbation (in line with electrophysiology).

Effects of quarantine disobedience and mobility restrictions on COVID-19 pandemic waves in dynamical networks

For the global COVID-19 pandemic it is still not adequately understood how quarantine disobedience and change in mobility restrictions influence the pandemic spreading and waves. Here, we propose a new metapopulation epidemiological model as a network composed of equal clusters to predict the course of the epidemic based on the contiguous spreading between the neighbours, the probability of quarantine misbehaviour, and the probability of mobility, which control contacts outside the cluster. We exemplify the model by comparing simulation results with real data on COVID-19 pandemic in Croatia. Fitting the data over the first and second pandemic waves, when the probability of mobility is set by the stringency index, the probability of quarantine misbehaviour is found by a Bayesian optimization yielding a fascinating agreement between the daily COVID-19 deaths and model output and efficiently predicting the timing of pandemic bursts. A sudden increase in the probability of quarantine misbehaviour alongside the sudden increase in the probability of mobility generate the model third wave in good agreement with daily COVID-19 deaths.

Freedom of choice adds value to public goods

Public goods, ranging from judiciary to sanitation to parkland, permeate daily life. They have been a subject of intense interdisciplinary study, with a traditional focus being on participation levels in isolated public goods games (PGGs) as opposed to a more recent focus on participation in PGGs embedded into complex social networks. We merged the two perspectives by arranging voluntary participants into one of three network configurations, upon which volunteers played a number of iterated PGGs within their network neighborhood. The purpose was to test whether the topology of social networks or a freedom to express preferences for some local public goods over others affect participation. The results show that changes in social networks are of little consequence, yet volunteers significantly increase participation when they freely express preferences. Surprisingly, the increase in participation happens from the very beginning of the game experiment, before any information about how others play can be gathered. Such information does get used later in the game as volunteers seek to correlate contributions with higher returns, thus adding significant value to public goods overall. These results are ascribable to a small number of behavioral phenotypes, and suggest that societies may be better off with bottom-up schemes for public goods provision.

β Cells Operate Collectively to Help Maintain Glucose Homeostasis

Residing in the islets of Langerhans in the pancreas, β cells contribute to glucose homeostasis by managing the body's insulin supply. Although it has been acknowledged that healthy β cells engage in heavy cell-to-cell communication to perform their homeostatic function, the exact role and effects of such communication remain partly understood. We offer a novel, to our knowledge, perspective on the subject in the form of 1) a dynamical network model that faithfully mimics fast calcium oscillations in response to above-threshold glucose stimulation and 2) empirical data analysis that reveals a qualitative shift in the cross-correlation structure of measured signals below and above the threshold glucose concentration. Combined together, these results point to a glucose-induced transition in β-cell activity thanks to increasing coordination through gap-junctional signaling and paracrine interactions. Our data and the model further suggest how the conservation of entire cell-cell conductance, observed in coupled but not uncoupled β cells, emerges as a collective phenomenon. An overall implication is that improving the ability to monitor β-cell signaling should offer means to better understand the pathogenesis of diabetes mellitus.

Factors influencing message dissemination through social media

Online social networks strongly impact our daily lives. An internet user (a "Netizen") wants messages to be efficiently disseminated. The susceptible-infected-recovered (SIR) dissemination model is the traditional tool for exploring the spreading mechanism of information diffusion. We here test our SIR-based dissemination model on open and real-world data collected from Twitter. We locate and identify phase transitions in the message dissemination process. We find that message content is a stronger factor than the popularity of the sender. We also find that the probability that a message will be forwarded has a threshold that affects its ability to spread, and when the probability is above the threshold the message quickly achieves mass dissemination.

Locating multiple diffusion sources in time varying networks from sparse observations

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

A generalization of random matrix theory and its application to statistical physics

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

The cost of attack in competing networks

Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator-prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor's nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.

To Invest or Not to Invest, That Is the Question: Analysis of Firm Behavior under Anticipated Shocks

When companies are faced with an upcoming and expected economic shock some of them tend to react better than others. They adapt by initiating investments thus successfully weathering the storm, while others, even though they possess the same information set, fail to adopt the same business strategy and eventually succumb to the crisis. We use a unique setting of the recent financial crisis in Croatia as an exogenous shock that hit the country with a time lag, allowing the domestic firms to adapt. We perform a survival analysis on the entire population of 144,000 firms in Croatia during the period from 2003 to 2015, and test whether investment prior to the anticipated shock makes firms more likely to survive the recession. We find that small and micro firms, which decided to invest, had between 60 and 70% higher survival rates than similar firms that chose not to invest. This claim is supported by both non-parametric and parametric tests in the survival analysis. From a normative perspective this finding could be important in mitigating the negative effects on aggregate demand during strong recessionary periods.

Skill complementarity enhances heterophily in collaboration networks

Much empirical evidence shows that individuals usually exhibit significant homophily in social networks. We demonstrate, however, skill complementarity enhances heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We construct a model to quantify the heterophily by assuming that individuals choose collaborators to maximize utility. Using a huge database of online societies, we find evidence of heterophily in collaboration networks. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of online societies. Furthermore, the degree of skill complementarity is positively correlated with their production output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems.

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

Agent-based mapping of credit risk for sustainable microfinance

By drawing analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk---a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylized rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan payment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.

Interaction between fiscal and monetary policy in a dynamic nonlinear model

The objective of this study is to verify the dynamics between fiscal policy, measured by public debt, and monetary policy, measured by a reaction function of a central bank. Changes in monetary policies due to deviations from their targets always generate fiscal impacts. We examine two policy reaction functions: the first related to inflation targets and the second related to economic growth targets. We find that the condition for stable equilibrium is more restrictive in the first case than in the second. We then apply our simulation model to Brazil and United Kingdom and find that the equilibrium is unstable in the Brazilian case but stable in the UK case.

Conjugation of PolyPEG to interferon alpha extends serum half-life while maintaining low viscosity of the conjugate

The covalent attachment of poly(ethylene glycol) (PEG) to therapeutic proteins is a commonly used approach for extending in vivo half-lives. A potential limitation of this PEGylation strategy is the adverse effect of PEG on conjugate viscosity. Interferon-alpha (IFN) was conjugated via its N-terminal amino group by reductive amination to α-aldehyde functional comb-shaped PolyPEG polymers (50 and 70 kDa) and to linear PEG (30 kDa). In vitro potencies of the purified PEGylated IFN conjugates were measured by reporter gene assay using a HEK293P/ISRE-SEAP cell line. IFN levels were measured in rats following intravenous injection. Viscosities of various linear PEG and PolyPEG polymers along with the polymer-IFN conjugates were determined using a rotational rheometer with cone-and-plate geometry. In vitro potencies and half-lives of the PEGylated IFN conjugates were compared with those of the marketed branched PEG-IFN conjugate PEGASYS. Both PolyPEG-IFN conjugates retained a similar potency as that of the marketed comparator, whereas the linear PEG-IFN conjugate potency was greater. All conjugates showed extended half-lives compared to that of naked IFN, with the PolyPEG conjugates exhibiting the longest half-lives and the linear PEG conjugate, the shortest. Viscosity analysis showed that the linear PEG-IFN conjugate was over twice as viscous as both PolyPEG conjugates. Taken together, this work demonstrates the potential of PolyPEG conjugation to therapeutic proteins as a novel tool for optimizing pharmacokinetic profiles in a way that potentially allows administration of high-dose formulations because of lower conjugate viscosity.

Network risk and forecasting power in phase-flipping dynamical networks

To model volatile real-world network behavior, we analyze a phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-flipping dynamics, and we find the probability that no more than q% of nodes and links fail. We derive higher moments of the fractions of active nodes and active links, fn(t) and fℓ(t), and we define two estimators to quantify the level of risk in a network. We find hysteresis in the correlations of fn(t) due to failures at the node level, and we derive conditional probabilities for phase-flipping in networks. We apply our model to economic and traffic networks.

Capital death in the world market

We study the gross domestic product (GDP) per capita together with the market capitalization (MCAP) per capita as two indicators of the effect of globalization. We find that g, the GDP per capita, as a function of m, the MCAP per capita, follows a power law with average exponent close to 1/3. In addition, the Zipf ranking approach confirms that the m for countries with initially lower values of m tends to grow more rapidly than for countries with initially larger values of m. If the trends over the past 20 years continue to hold in the future, then the Zipf ranking approach leads to the prediction that in about 50 years, all countries participating in globalization will have comparable values of their MCAP per capita. We call this economic state "capital death," in analogy to the physics state of "heat death" predicted by thermodynamic arguments.

Antifungal activity of cinnamic acid derivatives involves inhibition of benzoate 4-hydroxylase (CYP53)

AIMS

CYP53A15, from the sorghum pathogen Cochliobolus lunatus, is involved in detoxification of benzoate, a key intermediate in aromatic compound metabolism in fungi. Because this enzyme is unique to fungi, it is a promising drug target in fungal pathogens of other eukaryotes.

METHODS AND RESULTS

In our work, we showed high antifungal activity of seven cinnamic acid derivatives against C. lunatus and two other fungi, Aspergillus niger and Pleurotus ostreatus. To elucidate the mechanism of action of cinnamic acid derivatives with the most potent antifungal properties, we studied the interactions between these compounds and the active site of C. lunatus cytochrome P450, CYP53A15.

CONCLUSION

We demonstrated that cinnamic acid and at least four of the 42 tested derivatives inhibit CYP53A15 enzymatic activity.

SIGNIFICANCE AND IMPACT OF THE STUDY

By identifying selected derivatives of cinnamic acid as possible antifungal drugs, and CYP53 family enzymes as their targets, we revealed a potential inhibitor-target system for antifungal drug development.

Growth versus government management improvement during economic downturn

In estimating how economic growth depends on various inputs, economists commonly use long periods of data encompassing both main extremes to fluctuations in the economy: recession and expansion. Here we focus on recession years because during expansion even countries with bad economic policies may experience large growth. Specifically, we study how growth depends on the proportion of public-sector workforce, p and competitiveness, quantified by the Global Competitiveness Index, GCI. For the 2008-2011 economic downturn and for 57 countries, we find that the growth rate of GDP per capita, g, decreases with p, and increases with ΔGCI. Further, more competitive countries attract more foreign direct investments per capita, I, than less competitive countries, where I is proportional to GCI(α). We propose a production function, divided into the private and public sectors, where GDP depends on market capitalization, the public (private)-sector workforce, and competitiveness level, used to quantify the public sector efficiency.

The competitiveness versus the wealth of a country

Politicians world-wide frequently promise a better life for their citizens. We find that the probability that a country will increase its per capita GDP (gdp) rank within a decade follows an exponential distribution with decay constant λ = 0.12. We use the Corruption Perceptions Index (CPI) and the Global Competitiveness Index (GCI) and find that the distribution of change in CPI (GCI) rank follows exponential functions with approximately the same exponent as λ, suggesting that the dynamics of gdp, CPI, and GCI may share the same origin. Using the GCI, we develop a new measure, which we call relative competitiveness, to evaluate an economy's competitiveness relative to its gdp. For all European and EU countries during the 2008-2011 economic downturn we find that the drop in gdp in more competitve countries relative to gdp was substantially smaller than in relatively less competitive countries, which is valuable information for policymakers.

Scaling of seismic memory with earthquake size

It has been observed that discrete earthquake events possess memory, i.e., that events occurring in a particular location are dependent on the history of that location. We conduct an analysis to see whether continuous real-time data also display a similar memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic wave form database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake," one of the five most powerful earthquakes ever recorded, which resulted in a tsunami and devastating nuclear accidents. We explore the question of seismic memory through use of mean conditional intervals and detrended fluctuation analysis (DFA). We find that the wave form sign series show power-law anticorrelations while the interval series show power-law correlations. We find size dependence in earthquake autocorrelations: as the earthquake size increases, both of these correlation behaviors strengthen. We also find that the DFA scaling exponent α has no dependence on the earthquake hypocenter depth or epicentral distance.

Statistical tests for power-law cross-correlated processes

For stationary time series, the cross-covariance and the cross-correlation as functions of time lag n serve to quantify the similarity of two time series. The latter measure is also used to assess whether the cross-correlations are statistically significant. For nonstationary time series, the analogous measures are detrended cross-correlations analysis (DCCA) and the recently proposed detrended cross-correlation coefficient, ρ(DCCA)(T,n), where T is the total length of the time series and n the window size. For ρ(DCCA)(T,n), we numerically calculated the Cauchy inequality -1 ≤ ρ(DCCA)(T,n) ≤ 1. Here we derive -1 ≤ ρ DCCA)(T,n) ≤ 1 for a standard variance-covariance approach and for a detrending approach. For overlapping windows, we find the range of ρ(DCCA) within which the cross-correlations become statistically significant. For overlapping windows we numerically determine-and for nonoverlapping windows we derive--that the standard deviation of ρ(DCCA)(T,n) tends with increasing T to 1/T. Using ρ(DCCA)(T,n) we show that the Chinese financial market's tendency to follow the U.S. market is extremely weak. We also propose an additional statistical test that can be used to quantify the existence of cross-correlations between two power-law correlated time series.

Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices

We propose a modified time lag random matrix theory in order to study time-lag cross correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross correlations between the returns. The magnitude of the cross correlations constitutes "bad news" for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time-lag cross correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross correlations between returns (or magnitudes) can be modeled with the autocorrelations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find ten indices that are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

Comparison between response dynamics in transition economies and developed economies

In developed economies, the sign of the price increment influences the volatility in an asymmetric fashion--negative increments tend to result in larger volatility (increments with larger magnitudes), while positive increments result in smaller volatility. We explore whether this asymmetry extends from developed economies to European transition economies and, if so, how such asymmetry changes over time as these transition economies develop and mature. We analyze eleven European transition economies and compare the results with those obtained by analyzing U.S. market indices. Specifically, we calculate parameters that quantify both the volatility asymmetry and the strength of its dependence on prior increments. We find that, like their developed economy counterparts, almost all transition economy indices exhibit a significant volatility asymmetry, and the parameter γ characterizing asymmetry fluctuates more over time for transition economies. We also investigate how the association between volatility and volatility asymmetry varies by type of market. We test the hypothesis of a negative correlation between volatility and volatility asymmetry. We find that, for developed economies, γ experiences local minima during (i) "Black Monday" on October 19, 1987, (ii) the dot-com bubble crash in 2002, and (iii) the 2007-2009 global crisis while for transition economies, γ experiences local maxima during times of economic crisis.

Asymmetry in power-law magnitude correlations

Time series of increments can be created in a number of different ways from a variety of physical phenomena. For example, in the phenomenon of volatility clustering-well-known in finance-magnitudes of adjacent increments are correlated. Moreover, in some time series, magnitude correlations display asymmetry with respect to an increment's sign: the magnitude of |x_{i}| depends on the sign of the previous increment x_{i-1} . Here we define a model-independent test to measure the statistical significance of any observed asymmetry. We propose a simple stochastic process characterized by a an asymmetry parameter lambda and a method for estimating lambda . We illustrate both the test and process by analyzing physiological data.

Size-dependent standard deviation for growth rates: empirical results and theoretical modeling

We study annual logarithmic growth rates R of various economic variables such as exports, imports, and foreign debt. For each of these variables we find that the distributions of R can be approximated by double exponential (Laplace) distributions in the central parts and power-law distributions in the tails. For each of these variables we further find a power-law dependence of the standard deviation sigma(R) on the average size of the economic variable with a scaling exponent surprisingly close to that found for the gross domestic product (GDP) [Phys. Rev. Lett. 81, 3275 (1998)]. By analyzing annual logarithmic growth rates R of wages of 161 different occupations, we find a power-law dependence of the standard deviation sigma(R) on the average value of the wages with a scaling exponent beta approximately 0.14 close to those found for the growth of exports, imports, debt, and the growth of the GDP. In contrast to these findings, we observe for payroll data collected from 50 states of the USA that the standard deviation sigma(R) of the annual logarithmic growth rate R increases monotonically with the average value of payroll. However, also in this case we observe a power-law dependence of sigma(R) on the average payroll with a scaling exponent beta approximately -0.08 . Based on these observations we propose a stochastic process for multiple cross-correlated variables where for each variable (i) the distribution of logarithmic growth rates decays exponentially in the central part, (ii) the distribution of the logarithmic growth rate decays algebraically in the far tails, and (iii) the standard deviation of the logarithmic growth rate depends algebraically on the average size of the stochastic variable.

Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series

Here we propose a new method, detrended cross-correlation analysis, which is a generalization of detrended fluctuation analysis and is based on detrended covariance. This method is designed to investigate power-law cross correlations between different simultaneously recorded time series in the presence of nonstationarity. We illustrate the method by selected examples from physics, physiology, and finance.

Fractionally integrated process with power-law correlations in variables and magnitudes

Motivated by the fact that many empirical time series--including changes of heartbeat intervals, physical activity levels, intertrade times in finance, and river flux values--exhibit power-law anticorrelations in the variables and power-law correlations in their magnitudes, we propose a simple stochastic process that can account for both types of correlations. The process depends on only two parameters, where one controls the correlations in the variables and the other controls the correlations in their magnitudes. We apply the process to time series of heartbeat interval changes and air temperature changes and find that the statistical properties of the modeled time series are in agreement with those observed in the data.

Power-law correlated processes with asymmetric distributions

Motivated by the fact that many physical systems display (i) power-law correlations together with (ii) an asymmetry in the probability distribution, we propose a stochastic process that can model both properties. The process depends on only two parameters, where one controls the scaling exponent of the power-law correlations, and the other controls the degree of asymmetry in the distributions leaving the correlations unaffected. We apply the process to air humidity data and find that the statistical properties of the process are in a good agreement with those observed in the data.

Common scaling patterns in intertrade times of U. S. stocks

We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U.S. economy over a period of 4 yrs. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution, (ii) when appropriately rescaled the probability densities of all companies collapse onto a single curve implying a universal functional form, (iii) the intertrade times exhibit power-law correlated behavior within a trading day and a consistently greater degree of correlation over larger time scales, in agreement with the correlation behavior of the absolute price returns for the corresponding company, and (iv) the magnitude series of intertrade time increments is characterized by long-range power-law correlations suggesting the presence of nonlinear features in the trading dynamics, while the sign series is anticorrelated at small scales. Our results suggest that independent of industry sector, market capitalization and average level of trading activity, the series of intertrade times exhibit possibly universal scaling patterns, which may relate to a common mechanism underlying the trading dynamics of diverse companies. Further, our observation of long-range power-law correlations and a parallel with the crossover in the scaling of absolute price returns for each individual stock, support the hypothesis that the dynamics of transaction times may play a role in the process of price formation.

Effect of hydralazine on interstitial fluid pressure in experimental tumours and in normal tissue

Interstitial fluid pressure (IFP) has been recognised as the most important obstacle in macromolecular drug delivery to solid tumours. The aim of our study was to measure the IFP simultaneously in tumour and in muscle or in subcutis and to determine whether injection of hydralazine reduces differentially tumour IFP with respect to IFP in surrounding and normal tissues. In addition, it was of interest whether the decrease in IFP due to hydralazine depends on tumour volume and/or on initial IFP. Measurements of IFP were performed by means of the wick-in-needle technique and they were obtained on tumours of different size. In both tumour models, hydralazine significantly reduced the pretreatment IFP level. On average IFP decreased by 31% and 14% from the initial value in SAF and LPB tumours, respectively. On the contrary, hydralazine did not decrease IFP in normal tissue. Injection of NaCl solution instead of hydralazine had no effect on IFP either in tumours or in subcutis/muscle. The results of our study on the effect of hydralazine on IFP in SAF and LPB tumour model are in accordance to previously reported studies. The initial IFP in tumour is positively-correlated with the tumour size, while the decrease in the tumour IFP is independent of the initial IFP value. In addition, the decrease in tumour IFP is not correlated to tumour volume.

Semi-preparative chromatographic purification of the enantiomers S-(-)-amlodipine and R-(+)-amlodipine

Pharmacokinetic studies of optically pure compounds after single enantiomer administration are becoming increasingly important. The process of racemization in vivo can diminish all expected advantages of single enantiomer treatment. Amlodipine, one of the calcium channel blockers, currently used in therapy as a racemate, is one of such drugs under study. In order to administer single enantiomers of amlodipine to healthy volunteers both were chromatographically purified and characterised. The two optical isomers of amlodipine, active S-(-)- and non-active R-(+)-amlodipine, were purified using chromatographic procedure adopted from the analytical separation. Enantiomers were successfully converted to benzenesulphonic salt without any racemization. All semi-preparative purifications were monitored with complementary analytical methods, HPLC and CE, along with the determination of optical activity so that the final product was sufficiently defined for further in vivo studies. The analytical method developed for the determination of plasma concentrations of each enantiomer of amlodipine in these studies is also briefly described.