Temporal dynamics of oocyte development, plasma sex steroids and somatic energy reserves during seasonal ovarian maturation in captive Murray cod Maccullochella peelii peellii

The temporal dynamics of oocyte growth, plasma sex steroids and somatic energy stores were examined during a 12 month ovarian maturation cycle in captive Murray cod Maccullochella peelii peelii under simulated natural photothermal conditions. Ovarian function was found to be relatively uninhibited in captivity, with the exception that post-vitellogenic follicles failed to undergo final maturation, resulting in widespread pre-ovulatory atresia. Seasonal patterns of oocyte growth were characterised by cortical alveoli accumulation in March, deposition of lipids in April, and vitellogenesis between May and September. Two distinct batches of vitellogenic oocytes were found in Murray cod ovaries, indicating a capacity for multiple spawns. Plasma profiles of 17β-oestradiol and testosterone were both highly variable during the maturation period suggesting that multiple roles exist for these steroids during different stages of oocyte growth. Condition factor, liver size and visceral fat stores were all found to increase prior to, or during the peak phase of vitellogenic growth. Murray cod appear to strategically utilise episodes of high feeding activity to accrue energy reserves early in the reproductive cycle prior to its deployment during periods of rapid ovarian growth.

Bayesian hierarchical modeling of the temporal dynamics of subjective well-being: A 10 year longitudinal analysis

This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (. n=. 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects

Purpose: Increased risk of arrhythmic events occurs at certain times during the circadian cycle with the highest risk being in the second and fourth quarter of the day. Exercise improves treatment outcome in individuals with cardiovascular disease. How different exercise protocols affect the circadian rhythm and the associated decrease in adverse cardiovascular risk over the circadian cycle has not been shown. Methods: Fifty sedentary male participants were randomized into an 8-week high volume and moderate volume training and a control group. Heart rate was recorded using Polar Electronics and investigated with Cosinor analysis and by Poincaré plot derived features of SD1, SD2 and the complex correlation measure (CCM) at 1-h intervals over the 24-h period. Results: Moderate exercise significantly increased vagal modulation and the temporal dynamics of the heart rate in the second quarter of the circadian cycle (p = 0.004 and p = 0.007 respectively). High volume exercise had a similar effect on vagal output (p = 0.003) and temporal dynamics (p = 0.003). Cosinor analysis confirms that the circadian heart rate displays a shift in the acrophage following moderate and high volume exercise from before waking (1st quarter) to after waking (2nd quarter of day). Conclusions: Our results suggest that exercise shifts vagal influence and increases temporal dynamics of the heart rate to the 2nd quarter of the day and suggest that this may be the underlying physiological change leading to a decrease in adverse arrhythmic events during this otherwise high-risk period.

Social network analysis resolves temporal dynamics of male dominance relationships

Social organization is often studied through point estimates of individual association or interaction patterns, which does not account for temporal changes in the course of familiarization processes and the establishment of social dominance. Here, we present new insights on short-term temporal dynamics in social organization of mixed-sex groups that have the potential to affect sexual selection patterns. Using the live-bearing Atlantic molly (Poecilia mexicana), a species with pronounced male size polymorphism, we investigated social network dynamics of mixed sex experimental groups consisting of eight females and three different-sized males over a period of 5 days. Analyzing association-based social networks as well as direct measures of spatial proximity, we found that large males tended to monopolize most females, while excluding small- and medium-bodied males from access to females. This effect, however, emerged only gradually over time, and different-sized males had equal access to females on day 1 as well as day 2, though to a lesser extent. In this highly aggressive species with strong social dominance stratifications, the observed temporal dynamics in male-female association patterns may balance the presumed reproductive skew among differentially competitive male phenotypes when social structures are unstable (i.e., when individual turnover rates are moderate to high). Ultimately, our results point toward context-dependent sexual selection arising from temporal shifts in social organization.

Modeling propagation dynamics of social network worms

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious- recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.

Reading house prices in Australian capital cities

House prices in the Australian capital cities have been increasing over the last two decades. An over 10% average annual increase arises in the capital cities. In Melbourne, Brisbane and Perth, the house prices increased by more than 15% annually, while the house prices in Darwin increased by even higher at about 21%. It is surprising that, after a decrease in 2008, the house prices in the Australian capital cities show a strong recovery in their last financial year’s increase. How to read the house prices in cities across a country has been an issue of public interest since the late 1980s. Various models were developed to investigate the behaviours of house prices over time or space. A spatio-temporal model, introduced in recent literature, appears advantages in accounting for the spatial effects on house prices. However, the decay of temporal effects and temporal dynamics of the spatial effects cannot be addressed by the spatio-temporal model. This research will suggest a three-part decomposition framework in reading urban house price behaviours. Based on the spatio-temporal model, a time weighted spatio-temporal model is developed. This new model assumes that an urban house price movement should be decomposed by urban characterised factors, time correlated factors and space correlated factors. A time weighted is constructed to capture the temporal decay of the time correlated effects, while a spatio-temporal weight is constructed to account for the timevaried space correlated effects. The house prices of the Australian capital cities are investigated by using the time weighted spatio-temporal model. The empirical findings suggest that the housing markets should be clustered by their geographic locations. The rest parts of this paper are organised as follows. The following section will present a principle for reading urban house prices. The next section will outline the methodologies modelling the time weighted spatio-temporal model. The subsequent section will report the relative data and empirical results, while the final section will generate the conclusions.

Learning user preference patterns for Top-N recommendations

In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE.

A sword with two edges: propagation studies on both positive and negative information in online social networks

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people's behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Social networks in changing environments

Social network analysis (SNA) has become a widespread tool for the study of animal social organisation. However despite this broad applicability, SNA is currently limited by both an overly strong focus on pattern analysis as well as a lack of dynamic interaction models. Here, we use a dynamic modelling approach that can capture the responses of social networks to changing environments. Using the guppy, Poecilia reticulata, we identified the general properties of the social dynamics underlying fish social networks and found that they are highly robust to differences in population density and habitat changes. Movement simulations showed that this robustness could buffer changes in transmission processes over a surprisingly large density range. These simulation results suggest that the ability of social systems to self-stabilise could have important implications for the spread of infectious diseases and information. In contrast to habitat manipulations, social manipulations (e.g. change of sex ratios) produced strong, but short-lived, changes in network dynamics. Lastly, we discuss how the evolution of the observed social dynamics might be linked to predator attack strategies. We argue that guppy social networks are an emergent property of social dynamics resulting from predator–prey co-evolution. Our study highlights the need to develop dynamic models of social networks in connection with an evolutionary framework.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes

Diabetes mellitus is associated with multi-organ system dysfunction including the cardiovascular and autonomic nervous system. Although it is well documented that post-infarct patients are at higher risk of sudden cardiac death, diabetes adds an additional risk associated with autonomic neuropathy. However it is not known how the presence of diabetes in post-infarct patients affects cardiac rhythm. The majority of HRV algorithms for determining cardiac inter-beat interval changes describe only beat-to-beat variation determined over the whole heart rate recording and therefore do not consider the ability of a heart beat to influence a train of succeeding beats nor whether or how the temporal dynamics of the inter-beat intervals changes. This study used Poincaré Plot derived features and incorporated increased lag intervals to compare post-infarct patients with no history of prior infarct with or without diabetes and found that for the nondiabetic post-infarct patients only increased lag of short term correlation (SD1) predicted mortality, whereas in the diabetic post-infarct group only long-term correlations (SD2) significantly predicted mortality at a follow-up period of eight years. Temporal dynamics measured as a complex correlation measure (CCM) was also a significant predictor of mortality only in the diabetic post-infarct cohort. This study highlights the different pathophysiological progression and risk profile associated with presence of diabetes in a post-infarct patient population at eight year follow-up.

Landscape properties mediate the homogenization of bird assemblages during climatic extremes

Extreme weather events, such as drought, have marked impacts on biotic communities. In many regions, a predicted increase in occurrence of such events will be imposed on landscapes already heavily modified by human land use. There is an urgency, therefore, to understand the way in which the effects of such events may be exacerbated, or moderated, by different patterns of landscape change. We used empirical data on woodlanddependent birds in southeast Australia, collected during and after a severe drought, to document temporal change in the composition of bird assemblages in 24 landscapes (each 100 km2) representing a gradient in the cover of native wooded vegetation (from 60% to <2%). We examined (a) whether drought caused region-wide homogenization of the composition of landscape bird assemblages, and (b) whether landscape properties influenced the way assemblages changed in response to drought. To quantify change, we used pairwise indices of assemblage dissimilarity, partitioned into components that represented change in the richness of assemblages and change in the identity of constituent species (turnover). There was widespread loss of woodland birds in response to drought, with only partial recovery following drought-breaking rains. Region-wide, the composition of landscape assemblages became more different over time, primarily caused by turnover-related differentiation. The response of bird assemblages to drought varied between landscapes and was strongly associated with landscape properties. The extent of wooded vegetation had the greatest influence on assemblage change: landscapes with more native vegetation had more stable bird assemblages over time. However, for the component processes of richness- and turnoverrelated compositional change, measures of landscape productivity had a stronger effect. For example, landscapes with more riparian vegetation maintained more stable assemblages in terms of richness. These results emphasize the importance of the total extent of native vegetation, both overall cover and that occurring in productive parts of the landscape, for maintaining bird communities whose composition is resistant to severe drought. While extreme climatic events cannot be prevented, their effects can be ameliorated by managing the pattern of native vegetation in anthropogenic landscapes, with associated benefits for maintaining ecological processes and human well-being.

Understanding spatial and temporal solute transport : case study : nutrient dynamics in cultivated pastures

Chapter six is a case study of modeling nutrient dynamics in cultivated soils.

Temporal causality and the dynamics of judicial appellate caseload, real income and socio-economic complexity in Australia

This study applies Granger causality tests within a multivariate error correction framework to examine the relationship between judicial caseload, real income and urbanization for Australia using annual data from 1904 to 2001. Decomposition of variance and impulse response functions are also considered. The Granger causality results as well as the decomposition of variance and impulse response functions suggest that urbanization is the most exogenous of the three variables in both the long run and short run while judicial caseload and real income are relatively exogenous in the short run.

Temporal causality and the dynamics of exports, human capital and real income in China

This article employs cointegration and error-correction modelling to test the causal relationship between real income, exports and human capital stock using data for China over the period 1960 to 1999. We find that real exports, human capital and real income are cointegrated when real exports is the dependent variable, but are not cointegrated when human capital or real income are the dependent variable. In the short-run we find evidence of bi-directional Granger causality between human capital and real exports, unidirectional Granger causality running from real income to human capital and neutrality between real exports and real income.