Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Social Networks, Segregation and Poverty in Sao Paulo

The associations between segregation and urban poverty have been intensely scrutinized by the sociology and urban studies literatures. More recently, several studies have emphasized the importance of social networks for living conditions. Yet relatively few studies have tested the precise effects of social networks, and fewer still have focused on the joint effects of residential segregation and social networks on living conditions. This article explores the associations between networks, segregation and some of the most important dimensions of access to goods and services obtained in markets: escaping from social precariousness and obtaining monetary income. It is based on a study of the personal networks of 209 individuals living in situations of poverty in seven locales in the metropolitan area of Sao Paulo. Using network analysis and multivariate techniques, I show that relational settings strongly influence the access individuals have to markets, leading some individuals into worse living conditions and poverty. At the same time, although segregation plays an important role in poverty, its effects tend to be mediated by the networks in which individuals are embedded. Networks in this sense may enhance or mitigate the effects of isolation produced by space.

Comparative Study of Informal Health Care Networks and Elderly Health Status in Argentina and Cuba

The objective of this study is to evaluate the impact of informal care support networks on the health status, life satisfaction, happiness and anxiety of elderly individuals in Argentina and Cuba. Recent economic changes, demographic changes, the structure of families and changes in women?s labor participation have affected the availability of informal care. Additionally, the growing number of elderly as a percentage of total population has significant implications for both formal and informal care in Argentina and Cuba. Methods: The SABE - Survey on Health, Well-Being, and Aging in Latin America and the Caribbean, 2000 was used as the data source. The survey has a sample of 10,656 individuals aged 60 years and older residing in private households occupied by permanent dwellers in 7 cities in the Latin American and Caribbean region. My study will focus on the Buenos Aires and Havana samples in which there were 1043 individuals and 1905 individuals respectively. General sampling design was used to establish comparability between countries. Individuals requiring assistance are surveyed on their source of help and the relative impact of informal versus paid help is measured for this group. Other measures of social support (number of living children, companionship and number of individuals living in the same dwelling) are used to measure networks for the full sample. Multivariate probit regression analyses were run separately for Cuba and for Argentina to evaluate the marginal impacts of the types of social support on health status, life satisfaction, happiness and anxiety. Results: For Argentina, almost all of the family help variables positively impact good health. Getting help from most other members of the family negatively impacts satisfaction with life. Happiness is affected differently by each of the family help variables but community support increases the likelihood of being happy. Although none of the family or community help variables show statistical significance, most negatively affect anxiety levels. In Cuba, all of the social support variables have a positive marginal impact on the health status of the elderly. In this case, some of the family and community help variables have a negative marginal impact on life satisfaction; however, it appears that having those closest to the elderly, children, spouse, or other family, positively impacts life satisfaction. Most of the support variables negatively impact happiness. Receiving help from a child, spouse or parent is associated with a marginal increase in anxiety, whereas receiving help from a grandchild, another family member or a friend actually reduces anxiety. Discussion: The study highlights the necessity for enhancing the coordination of various care networks in order to provide adequate care and reduce the burdens of old age on the individual, family and society and the need for consistent support for the caregivers. More qualitative work should be done to identify how support is given and what comprises the support. The constant change and advancement of the world, and the growth of the Latin American and Caribbean region, suggests that more updates studies need to be done.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Evaluating the Trade-off Between Energy Efficiency and QoE in Wireless Mesh Networks

The increasing usage of wireless networks creates new challenges for wireless access providers. On the one hand, providers want to satisfy the user demands but on the other hand, they try to reduce the operational costs by decreasing the energy consumption. In this paper, we evaluate the trade-off between energy efficiency and quality of experience for a wireless mesh testbed. The results show that by intelligent service control, resources can be better utilized and energy can be saved by reducing the number of active network components. However, care has to be taken because the channel bandwidth varies in wireless networks. In the second part of the paper, we analyze the trade-off between energy efficiency and quality of experience at the end user. The results reveal that a provider's service control measures do not only reduce the operational costs of the network but also bring a second benefit: they help maximize the battery lifetime of the end-user device.