Cube law, condition factor and weight-length relationships: history, meta-analysis and recommendations

This study presents a historical review, a meta-analysis, and recommendations for users about weight–length relationships, condition factors and relative weight equations. The historical review traces the developments of the respective concepts. The meta-analysis explores 3929 weight–length relationships of the type W = aLb for 1773 species of fishes. It shows that 82% of the variance in a plot of log a over b can be explained by allometric versus isometric growth patterns and by different body shapes of the respective species. Across species median b = 3.03 is significantly larger than 3.0, thus indicating a tendency towards slightly positive-allometric growth (increase in relative body thickness or plumpness) in most fishes. The expected range of 2.5 < b < 3.5 is confirmed. Mean estimates of b outside this range are often based on only one or two weight–length relationships per species. However, true cases of strong allometric growth do exist and three examples are given. Within species, a plot of log a vs b can be used to detect outliers in weight–length relationships. An equation to calculate mean condition factors from weight–length relationships is given as Kmean = 100aLb−3. Relative weight Wrm = 100W/(amLbm) can be used for comparing the condition of individuals across populations, where am is the geometric mean of a and bm is the mean of b across all available weight–length relationships for a given species. Twelve recommendations for proper use and presentation of weight–length relationships, condition factors and relative weight are given

A field study of team working in a new human supervisory control system

This paper presents a case study of an investigation into team behaviour in an energy distribution company. The main aim was to investigate the impact of major changes in the company on system performance, comprising human and technical elements. A socio-technical systems approach was adopted. There were main differences between the teams investigated in the study: the time of year each control room was studied (i.e. summer or winter),the stage of development each team was in (i.e. 10 months), and the team structure (i.e. hierarchical or heterarchical). In all other respects the control rooms were the same: employing the same technology and within the same organization. The main findings were: the teams studied in the winter months were engaged in more `planning’ and `awareness’ type of activities than those studies in the summer months. Newer teams seem to be engaged in more sharing of information than older teams, which maybe indicative of the development process. One of the hierarchical teams was engaged in more `system-driven’ activities than the heterarchical team studied at the same time of year. Finally, in general, the heterarchical team perceived a greater degree of team working culture than its hierarchical counterparts. This applied research project confirms findings from laboratory research and emphasizes the importance of involving ergonomics in the design of team working in human supervisory control

Stochastic Physics, Complex Systems and Biology

In complex systems, the interplay between nonlinear and stochastic dynamics, e.g., J. Monod's necessity and chance, gives rise to an evolutionary process in Darwinian sense, in terms of discrete jumps among attractors, with punctuated equilibrium, spontaneous random "mutations" and "adaptations". On an evlutionary time scale it produces sustainable diversity among individuals in a homogeneous population rather than convergence as usually predicted by a deterministic dynamics. The emergent discrete states in such a system, i.e., attractors, have natural robustness against both internal and external perturbations. Phenotypic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a perspective.Comment: 10 page

Familiäre Kavernome des Zentralnervensystems: Eine klinische und genetische Studie an 15 deutsche Familien

Zusammenfassung: 1928 beschrieb Hugo Friedrich Kufs erstmalig eine Familie mit zerebralen, retinalen und kutanen Kavernomen. Mittlerweile wurden über 300 weitere Familien beschrieben. Ebenfalls wurden drei Genloci 7q21-q22 (mit dem Gen CCM1), 7p15-p13 (Gen CCM2) und 3q25.2-q27 (Gen CCM3) beschrieben, in denen Mutationen zu Kavernomen führen. Das Genprodukt von CCM1 ist das Protein Krit1 (Krev Interaction Trapped 1), das über verschiedene Mechanismen mit der Angiogenese interagiert. Das neu entdeckte CCM2-Gen enkodiert ein Protein, das möglicherweise eine dem Krit1 ähnliche Funktion in der Regulation der Angiogenese hat. Das CCM3-Gen wurde noch nicht beschrieben. In dieser Arbeit werden sowohl die klinischen und genetischen Befunde bei 15 deutschen Familien beschriebe

A holistic multi-methodology for sustainable renovation

A review of the barriers for building renovation has revealed a lack of methodologies, which can promote sustainability objectives and assist various stakeholders during the design stage of building renovation/retrofitting projects. The purpose of this paper is to develop a Holistic Multi-methodology for Sustainable Renovation, which aims to deal with complexity of renovation projects. It provides a framework through which to involve the different stakeholders in the design process to improve group learning and group decision-making, and hence make the building renovation design process more robust and efficient. Therefore, the paper discusses the essence of multifaceted barriers in building renovation regarding cultural changes and technological/physical changes. The outcome is a proposal for a multi-methodology framework, which is developed by introducing, evaluating and mixing methods from Soft Systems Methodologies (SSM) with Multiple Criteria Decision Making (MCDM). The potential of applying the proposed methodology in renovation projects is demonstrated through a case study

Deterministic and stochastic P systems for modelling cellular processes

This paper presents two approaches based on metabolic and stochastic P systems, together with their associated analysis methods, for modelling biological sys- tems and illustrates their use through two case studies.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/

Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.</p> <p>Results</p> <p>In the present paper we analyze cell cycle regulated genes in <it>S. cerevisiae</it>. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.</p> <p>Conclusion</p> <p>Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.</p

Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first

Influence of the Time Scale on the Construction of Financial Networks

BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. METHODOLOGY/PRINCIPAL FINDINGS: For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper. CONCLUSIONS/SIGNIFICANCE: Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis