Examining the relationship between pubertal stage, adolescent health behaviours and stress

Background. This paper examines the associations between puberty and three important health behavlours (smoking, food intake and exercise) and explores whether these associations are mediated by puberty's relationship to stress and psychological difficulties.Method. Data were taken from the first year of the ongoing, 5-year, Health and Behaviours in Teenagers Study (HABITS). This is a school-based study set in 36 schools in London. In the first year of the study, 4320 students (2578 boys, 1742 girls) in their first year of secondary education took part.Results. Among girls, being more pubertally advanced was associated with a greater likelihood of having tried smoking. Among boys, being more pubertally advanced was associated with a greater likelihood of having tried smoking, a higher intake of high-fat food and higher levels of exercise. More pubertally advanced girls experienced more stress but not more psychological difficulties. There were no associations between puberty and either stress or psychological difficulties in boys. Stress and psychological difficulties were associated with health behaviours in girls and boys, but neither of these factors mediated the relationship between pubertal stage and health behaviours found in girls.Conclusions. These results suggest that the onset of puberty has a marked effect on the development of health behaviours. Puberty was related to an acceleration of the development of unhealthy behaviours, except for exercise behaviour in boys, where advanced puberty was associated with more exercise. These changes were unrelated to adolescent issues of stress and a causal explanation for these associations must be sought elsewhere

Measuring the shielding properties of flexible or rigid enclosures for portable electronics

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Heaviside, in volume 1 of Electromagnetic theory, considered shielding of conducting materials in the form of attenuation. This treatment is still significant in the understanding of shielding effectiveness. He also considered propagation of electromagnetic waves in free-space. What Heaviside (1850–1925) could never have imagined is that 125 years later, there would be devices we know as mobile phones (or cell phones, handies, etc.) with capabilities beyond the dreams of the great science fiction writers of the day like H. G. Wells (1866–1949) or Jules Verne (1828–1905). More than this, that there would be a need for law enforcement agencies, among others, to use electromagnetically shielded enclosures to protect electronic equipment from communicating with the ‘outside world’. Nevertheless, Heaviside’s work is still fundamental to the developments discussed here. This paper provides a review of Heaviside’s view of shielding and propagation provided in volume 1 of Electromagnetic theory and develops that to the design of new experiments to test the shielding of these portable enclosures in a mode-stirred reverberation chamber, a test environment that relies entirely on reflections from conducting surfaces for its operation

An Extended Gene Protein/Products Boolean Network Model Including Post-Transcriptional Regulation

Background: Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs. Results: In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit. Conclusions: The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanism

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing

The authors would like to thank the support on this research by the CRISP Project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Performance Analysis and Functional Verification of the Stop-and-Wait Protocol in HOL

Real-time systems usually involve a subtle interaction of a number of distributed components and have a high degree of parallelism, which makes their performance analysis quite complex. Thus, traditional techniques, such as simulation, or the state-based formal methods usually fail to produce reasonable results. In this paper, we propose to use higher-order-logic (HOL) theorem proving for the performance analysis of real-time systems. The idea is to formalize the real-time system as a logical conjunction of HOL predicates, whereas each one of these predicates define an autonomous component or process of the given real-time system. The random or unpredictable behavior found in these components is modeled as random variables. This formal specification can then be used in a HOL theorem prover to reason about both functional and performance related properties of the given real-time system. In order to illustrate the practical effectiveness of our approach, we present the analysis of the Stop-and-Wait protocol, which is a classical example of real-time systems. The functional correctness of the protocol is verified by proving that the protocol ensures reliable data transfers. Whereas, the average message delay relation is verified in HOL for the sake of performance analysis. The paper includes the protocol’s formalization details along with the HOL proof sketches for the major theorems

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model

Data-driven competitive facilitative tree interactions and their implications on nature-based solutions

Spatio-temporal data are more ubiquitous and richer than even before and the availability of such data poses great challenges in data analytics. Ecological facilitation, the positive effect of density of individuals on the individual's survival across a stress gradient, is a complex phenomenon. A large number of tree individuals coupled with soil moisture, temperature, and water stress data across a long temporal period were followed. Data-driven analysis in the absence of hypothesis was performed. Information theoretic analysis of multiple statistical models was employed in order to quantify the best data-driven index of vegetation density and spatial scale of interactions. Sequentially, tree survival was quantified as a function of the size of the individual, vegetation density, and time at the optimal spatial interaction scale. Land surface temperature and soil moisture were also statistically explained by tree size, density, and time. Results indicated that in space both facilitation and competition co-exist in the same ecosystem and the sign and magnitude of this depend on the spatial scale. Overall, within the optimal data-driven spatial scale, tree survival was best explained by the interaction between density and year, sifting overall from facilitation to competition through time. However, small sized trees were always facilitated by increased densities, while large sized trees had either negative or no density effects. Tree size was more important predictor than density in survival and this has implications for nature-based solutions: maintaining large tree individuals or planting species that can become large-sized can safeguard against tree-less areas by promoting survival at long time periods through harsh environmental conditions. Large trees had also a significant effect in moderating land surface temperature and this effect was higher than the one of vegetation density on temperature