We investigate the traffic flows of the Korean highway system, which contains both public and private transportation information. We find that the traffic flow T ij between city i and j forms a gravity model, the metaphor of physical gravity as described in Newton's law of gravity, P i P j /r 2 ij , where P i represents the population of city i and r ij the distance between cities i and j. It is also shown that the highway network has a heavy tail even though the road network is a rather uniform and homogeneous one. Compared to the highway network, air and public ground transportation establish inhomogeneous systems and have power-law behaviors.

We study the return interval tau between price volatilities that are above a certain threshold q for 31 intraday data sets, including the Standard and Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index. For different threshold q, the probability density function Pq(tau)scales with the mean interval tau as [Formula: see text], similar to that found in daily volatilities. Since the intraday records have significantly more data points compared to the daily records, we could probe for much higher thresholds and still obtain good statistics. We find that the scaling function f(x)is consistent for all 31 intraday data sets in various time resolutions, and the function is well-approximated by the stretched exponential, f(x) similar to e(-ax)(gamma), with gamma=0.38+/-0.05 and a=3.9+/-0.5, which indicates the existence of correlations. We analyze the conditional probability distribution Pq(tau/tau0) for tau following a certain interval tau0, and find Pq(tau/tau0) depends on tau0, which demonstrates memory in intraday return intervals. Also, we find that the mean conditional interval (tau/tau0) increases with tau0, consistent with the memory found for Pq(tau/tau0). Moreover, we find that return interval records, in addition to having short-term correlations as demonstrated by Pq(tau/tau0), have long-term correlations with correlation exponents similar to that of volatility records.

Publication statistics are ubiquitous in the ratings of scientific achievement, with citation counts and paper tallies factoring into an individual's consideration for postdoctoral positions, junior faculty, and tenure. Citation statistics are designed to quantify individual career achievement, both at the level of a single publication, and over an individual's entire career. While some academic careers are defined by a few significant papers ͑possibly out of many͒, other academic careers are defined by the cumulative contribution made by the author's publications to the body of science. Several metrics have been formulated to quantify an individual's publication career, yet none of these metrics account for the collaboration group size, and the time dependence of citation counts. In this paper we normalize publication metrics in order to achieve a universal framework for analyzing and comparing scientific achievement across both time and discipline. We study the publication careers of individual authors over the 50-year period 1958-2008 within six high-impact journals: CELL, the New England Journal of Medicine (NEJM), Nature, the Proceedings of the National Academy of Science (PNAS), Physical Review Letters (PRL), and Science. Using the normalized metrics ͑i͒ "citation shares" to quantify scientific success, and ͑ii͒ "paper shares" to quantify scientific productivity, we compare the career achievement of individual authors within each journal, where each journal represents a local arena for competition. We uncover quantifiable statistical regularity in the probability density function of scientific achievement in all journals analyzed, which suggests that a fundamental driving force underlying scientific achievement is the competitive nature of scientific advancement.

The distribution of the return intervals τ between price volatilities above a threshold height q for financial records has been approximated by a scaling behavior. To explore how accurate is the scaling and therefore understand the underlined non-linear mechanism, we investigate intraday datasets of 500 stocks which consist of the Standard & Poor's 500 index. We show that the cumulative distribution of return intervals has systematic deviations from scaling. We support this finding by studying the m-th moment µ m ≡ (τ / τ ) m 1/m , which show a certain trend with the mean interval τ . We generate surrogate records using the Schreiber method, and find that their cumulative distributions almost collapse to a single curve and moments are almost constant for most range of τ . Those substantial differences suggest that non-linear correlations in the original volatility sequence account for the deviations from a single scaling law. We also find that the original and surrogate records exhibit slight tendencies for short and long τ , due to the discreteness and finite size effects of the records respectively. To avoid as possible those effects for testing the multiscaling behavior, we investigate the moments in the range 10 < τ ≤ 100, and find the exponent α from the power law fitting µ m ∼ τ α has a narrow distribution around α = 0 which depend on m for the 500 stocks. The distribution of α for the surrogate records are very narrow and centered around α = 0. This suggests that the return interval distribution exhibit multiscaling behavior due to the non-linear correlations in the original volatility.

Genistein (GE) was reported to exert a wide spectrum of biological activities, including antioxidant, anti-inflammatory, anti-mutagenic, anticancer, and cardio-protective effects. In addition, both clinical and preclinical studies have recently suggested GE a potential neuroprotective and memory-enhancing drug against neurodegenerative diseases. The animal model of scopolamine (Scop)-induced amnesia is widely used to study underlying mechanisms and treatment of cognitive impairment in neurodegenerative diseases. However, there is no report about the effects of GE on Scop-induced amnesia in mice. Therefore, the present study was carried out to investigate the beneficial effects and potential mechanism of GE against Scop-induced deficits in mice. The mice were orally pretreated with either GE (10, 20, and 40 mg/kg) or donepezil (1.60 mg/kg) for 14 days. After the pretreatment, the open field test was conducted to assess the effect of GE on the locomotor activity of mice. Thereafter, mice were daily injected with Scop (0.75 mg/kg) intraperitoneally to induce memory deficits and subjected to the cognitive behavioral tests including the Object Location Recognition (OLR) experiment and Morris Water Maze (MWM) task. After the behavioral tests, biochemical parameter assay and western blot analysis were used to examine the underlying mechanisms of its action. The results showed that GE administration significantly improved the cognitive performance of Scop-treated mice in OLR and Morris water maze tests, exerting the memory-enhancing effects. Additionally, GE remarkably promoted the cholinergic neurotransmission and protected against the oxidative stress damage in the hippocampus of Scop-treated mice, as indicated by decreasing AChE activity, elevating ChAT activity and Ach level, increasing SOD activity, lowering the level of MDA and increasing GSH content. Furthermore, GE was found to significantly upregulate the expression levels of p-ERK, p-CREB and BDNF proteins in the hippocampus of Scop-treated mice. Taken together, these results for the first time found that GE exerts cognitive-improving effects in Scop-induced amnesia and suggested it may be a potential candidate compound for the treatment of some neurodegenerative diseases such as Alzheimer’s Disease (AD).

No abstract

AMP-activated protein kinase (AMPK), known as a sensor and a master of cellular energy balance, integrates various regulatory signals including anabolic and catabolic metabolic processes. Accompanying the application of genetic methods and a plethora of AMPK agonists, rapid progress has identified AMPK as an attractive therapeutic target for several human diseases, such as cancer, type 2 diabetes, atherosclerosis, myocardial ischemia/reperfusion injury and neurodegenerative disease. The role of AMPK in metabolic and energetic modulation both at the intracellular and whole body levels has been reviewed elsewhere. In the present review, we summarize and update the paradoxical role of AMPK implicated in the diseases mentioned above and put forward the challenge encountered. Thus it will be expected to provide important clues for exploring rational methods of intervention in human diseases.

We analyze the memory in volatility by studying volatility return intervals, defined as the time between two consecutive fluctuations larger than a given threshold, in time periods following stock market crashes. Such an aftercrash period is characterized by the Omori law, which describes the decay in the rate of aftershocks of a given size with time t by a power law with exponent close to 1. A shock followed by such a power law decay in the rate is here called Omori process. Studying several aftercrash time series, we show that the Omori law holds not only after significant market crashes, but also after "intermediate shocks". Moreover, we find self-similar features in the volatility. Specifically, within the aftercrash period there are smaller shocks that themselves constitute Omori processes on smaller scales, similar to the Omori process after the large crash. We call these smaller shocks subcrashes, which are followed by their own aftershocks. We also find similar Omori processes after intermediate crashes in time regimes without a large market crash. By appropriate detrending we remove the influence of the crashes and subcrashes from the data, and find that this procedure significantly reduces the memory in the records. Our results are consistent with the hypothesis that the memory in volatility is related to Omori processes present on different time scales.The correlations of stock returns are important for risk estimation, and can be used for forecasting financial time series. The absolute value of the return, which is a measure for volatility, seems to have a memory [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], so that a return is more likely to be followed by a return with similar absolute value, which leads to periods of large volatility and other periods of small volatility (called volatility clustering in economics). While the absolute value exhibits long-term correlations decaying like a power law [18], the correlations of the return itself decay exponentially with a characteristic time scale of 4 minutes [13,16].Recent studies [19,20,22,23] reveal more information about the temporal structure of the volatility time series by analyzing volatility return intervals, the time between two consecutive events with volatilities larger than a given threshold. These return intervals display memory and volatility clustering, and also scaling properties for different thresholds, which seem to be universal for different time scales and markets [19,20,22,23]. This behavior is similar to what is found in earthquakes [24] and climate [25,26]. Rare extreme events like market crashes constitute a substantial risk for investors, but these rare events do not provide enough data for reliable statistical analysis. Due to the scaling properties, it is possible to analyze the statistics of return intervals for different thresholds by studying only the behavior of small fluctuations occurring very frequently, which have good statistics.Lillo and Mantegna found that after a major stock market crash the rate of volatilities l...