Factors influencing Manx Shearwater grounding on the west coast of Scotland

Grounding of thousands of newly fledged petrels and shearwaters (family Procellariidae) in built‐up areas due to artificial light is a global problem. Due to their anatomy these grounded birds find it difficult to take off from built‐up areas and many fall victim to predation, cars, dehydration or starvation. This research investigated a combination of several factors that may influence the number of Manx Shearwater Puffinus puffinus groundings in a coastal village of Scotland located close to a nesting site for this species. A model was developed that used meteorological variables and moon cycle to predict the daily quantity of birds that were recovered on the ground. The model, explaining 46.32% of the variance of the data, revealed how new moon and strong onshore winds influence grounding. To a lesser extent, visibility conditions can also have an effect on grounding probabilities. The analysis presented in this study can improve rescue campaigns of not only Manx Shearwaters but also other species attracted to the light pollution by predicting conditions leading to an increase in the number of groundings. It could also inform local authorities when artificial light intensity needs to be reduced.     

A Theoretical Foundation for Life-Cycle Assessment

The presence of value judgments in life-cycle impact assessment (LCIA) has been a constant source of controversy. According to a common interpretation, the international standard on LCIA requires that the assessment methods used in published comparisons be "value free." Epistemologists argue that even natural science rests on "constitutive" and "contextual" value judgments. The example of the equivalency potential for climate change, the global warming potential (GWP), demonstrates that any impact assessment method inevitably contains not only constitutive and contextual values, but also preference values. Hence, neither life-cycle assessment (LCA) as a whole nor any of its steps can be "value free." As a result, we suggest a more comprehensive definition of objectivity in LCA that allows arguments about values and their relationship to facts. We distinguish three types of truth claims: factual claims, which are based on natural science; normative claims, which refer to preference values; and relational claims, which address the proper relation between factual knowledge and values. Every assessment method, even the GWP, requires each type of claim. Rational arguments can be made about each type of claim. Factual truth claims can be assessed using the scientific method. Normative claims can be based on ethical arguments. The values of individuals or groups can be elicited using various social science methods. Relational claims must follow the rules of logic. Relational claims are most important for the development of impact assessment methods. Because LCAs are conducted to satisfy the need of decision makers to consider environmental impacts, relational claims about impact assessment methods should refer to this goal. This article introduces conditions that affect environmental decision making and discusses how LCA—values and all—can be defended as a rational response to the challenge of moving uncertain scientific information into the policy arena.     

Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo

We provide a framework for Bayesian coalescent inference from microsatellite data that enables inference of population history parameters averaged over microsatellite mutation models. To achieve this we first implemented a rich family of microsatellite mutation models and related components in the software package BEAST. BEAST is a powerful tool that performs Bayesian MCMC analysis on molecular data to make coalescent and evolutionary inferences. Our implementation permits the application of existing nonparametric methods to microsatellite data. The implemented microsatellite models are based on the replication slippage mechanism and focus on three properties of microsatellite mutation: length dependency of mutation rate, mutational bias toward expansion or contraction, and number of repeat units changed in a single mutation event. We develop a new model that facilitates microsatellite model averaging and Bayesian model selection by transdimensional MCMC. With Bayesian model averaging, the posterior distributions of population history parameters are integrated across a set of microsatellite models and thus account for model uncertainty. Simulated data are used to evaluate our method in terms of accuracy and precision of estimation and also identification of the true mutation model. Finally we apply our method to a red colobus monkey data set as an example.     

Taking the Relational Turn: Biosemiotics and Some New Trends in Biology

A cluster of similar trends emerging in separate fields of science and philosophy points to new opportunities to apply biosemiotic ideas as tools for conceptual integration in theoretical biology. I characterize these developments as the outcome of a “relational turn” in these disciplines. They signal a shift of attention away from objects and things and towards relational structures and processes. Increasingly sophisticated research technologies of molecular biology have generated an enormous quantity of experimental data, sparking a need for relational approaches that could help to find recurrent patterns in the mass of data. Earlier conceptions of relational biology and cybernetics, once deemed too abstract and speculative, are now resurrected and applied by means of new computational and simulation tools. I think this receptivity should be extended to incorporate nets of semiotic relations as heuristic guides for discerning global patterns of interactions in living systems. In this article I review aspects of systems biology and new directions in evolutionary theory, focusing on the role of circular and downward causation in relational structures and dynamical networks. I also indicate promising avenues of integration of some ideas of biosemiotics with those emerging from these new currents in biology. Relational developments in biology bear a telling similarity to a parallel relational turn presently manifest in the philosophy of science, rooted in the philosophy of physics and mathematics and in different varieties of structural and informational realism. The recognition of the relational nature of reality within these disciplines entails a tacit repudiation of nominalistic biases in science that have hindered the reception of semitiotic conceptions in biology. In previous investigations I explored connections between two kinds of relational structures: the networks of self-referential circular loops that appear pervasively in living systems, and the triadic relational structures that Peircean semiotics places at the basis of all semiotic transactions. Current relational views in the sciences seem oblivious to the difference between dyadic and triadic relations. Incorporating this essential distinction from biosemiotics into other fields could be a first step in seizing the opportunities opened by the relational turn for a renewal of biology and of natural philosophy in general, across disciplinary boundaries.     

A method for representing and developing process models

Scientists investigate the dynamics of complex systems with quantitative models, employing them to synthesize knowledge, to explain observations, and to forecast future system behavior. Complete specification of systems is impossible, so models must be simplified abstractions. Thus, the art of modeling involves deciding which system elements to include and determining how they should be represented. We view modeling as search through a space of candidate models that is guided by model objectives, theoretical knowledge, and empirical data. In this contribution, we introduce a method for representing process-based models that facilitates the discovery of structures that explain observed behavior. This representation casts dynamic systems as interacting sets of processes that act on entities. Using this approach, a modeler first encodes relevant ecological knowledge into a library of generic entities and processes, then instantiates these theoretical components, and finally assembles candidate models from these elements. We illustrate this methodology with a model of the Ross Sea ecosystem.     

A component-based software environment for visualizing large macromolecular assemblies

The interactive visualization of large biological assemblies poses a number of challenging problems, including the development of multiresolution representations and new interaction methods for navigating and analyzing these complex systems. An additional challenge is the development of flexible software environments that will facilitate the integration and interoperation of computational models and techniques from a wide variety of scientific disciplines. In this paper, we present a component-based software development strategy centered on the high-level, object-oriented, interpretive programming language: Python. We present several software components, discuss their integration, and describe some of their features that are relevant to the visualization of large molecular assemblies. Several examples are given to illustrate the interoperation of these software components and the integration of structural data from a variety of experimental sources. These examples illustrate how combining visual programming with component-based software development facilitates the rapid prototyping of novel visualization tools.     

Conceptualization of above and below relationships by an insect

Relational rules such as 'same' or 'different' are mastered by humans and non-human primates and are considered as abstract conceptual thinking as they require relational learning beyond perceptual generalization. Here, we investigated whether an insect, the honeybee (Apis mellifera), can form a conceptual representation of an above/below spatial relationship. In experiment 1, bees were trained with differential conditioning to choose a variable target located above or below a black bar that acted as constant referent throughout the experiment. In experiment 2, two visual stimuli were aligned vertically, one being the referent, which was kept constant throughout the experiment, and the other the target, which was variable. In both experiments, the distance between the target and the referent, and their location within the visual field was systematically varied. In both cases, bees succeeded in transferring the learned concept to novel stimuli, preserving the trained spatial relation, thus showing an ability to manipulate this relational concept independently of the physical nature of the stimuli. Absolute location of the referent into the visual field was not a low-level cue used by the bees to solve the task. The honeybee is thus capable of conceptual learning despite having a miniature brain, showing that such elaborated learning form is not a prerogative of vertebrates.     

Relational memory: a daytime nap facilitates the abstraction of general concepts

It is increasingly evident that sleep strengthens memory. However, it is not clear whether sleep promotes relational memory, resultant of the integration of disparate memory traces into memory networks linked by commonalities. The present study investigates the effect of a daytime nap, immediately after learning or after a delay, on a relational memory task that requires abstraction of general concept from separately learned items. Specifically, participants learned English meanings of Chinese characters with overlapping semantic components called radicals. They were later tested on new characters sharing the same radicals and on explicitly stating the general concepts represented by the radicals. Regardless of whether the nap occurred immediately after learning or after a delay, the nap participants performed better on both tasks. The results suggest that sleep--even as brief as a nap--facilitates the reorganization of discrete memory traces into flexible relational memory networks.     

On the Rationality of Decision-Making Studies: Part 1: Decision Models of Treatment Choice

With reference to both critiques and empirical studies, the theoretical and methodological grounding of anthropological research on medical decision making is examined in this article, giving particular attention to the construction and evaluation of cognitively oriented decision models. A decisionmodeling study carried out in the Mexican village of Pichátaro (in conjunction with James C. Young) frames an exploration of some of the tensions and points of contention about the aims and designs of cognitively oriented studies of decision modeling. While a decision model can provide a reasonably good guide to an understanding of treatment actions and the culturally based rationality that underlies them, such models fall short when they are oriented primarily around predicting treatment accounts. They should also attend to the jointly cultural, personal, social, and cognitive constructive processes through which meaning is conferred upon the occurrence of illness, [care seeking, decision models, decision making, Mexico]     

Global Kinetic Explorer: A new computer program for dynamic simulation and fitting of kinetic data

We describe a new dynamic kinetic simulation program that allows multiple data sets to be fit simultaneously to a single model based on numerical integration of the rate equations describing the reaction mechanism. Unlike other programs that allow fitting based on numerical integration of rate equations, in the dynamic simulation rate constants, output factors, and starting concentrations of reactants can be scrolled while observing the change in the shape of the simulated reaction curves. Fast dynamic simulation facilitates the exploration of initial parameters that serve as the starting point for nonlinear regression in fitting data and facilitates exploration of the relationships between individual constants and observable reactions. The exploration of parameter space by dynamic simulation provides a powerful tool for learning kinetics and for evaluating the extent to which parameters are constrained by the data. This feature is critical to avoid overly complex models that are not supported by the data.     

Evolutionary integration and morphological diversification in complex morphological structures: mandible shape divergence in spiny rats (Rodentia, Echimyidae)

The rodent mandible has become a paradigm for studies on the development and evolution of complex morphological structures. We use a combination of geometric and multivariate morphometric methods in order to assess the correspondence between integration patterns and a priori biological models in the context of evolutionary shape divergence in the mandible of rodents of the family Echimyidae. The correlation of shape distances among operational taxonomic units (individuals, species, genera) in separate morphogenetic components allowed the construction of integration matrices among mandible components for data sets corresponding to varying levels of genetic divergence (intergeneric, interspecific, and intrapopulational). The integration matrices were associated with a priori biological (developmental, genetical, modular) models, and the maximum integration axes (singular warps) were compared with realized axes of maximum interspecific variation (relative warps). The integration pattern and intensity were not stable in data sets with different levels of genetic divergence, and the varying functional demands during the ecological radiation in the family were probably responsible for the differences in observed integration patterns. Developmental and genetic models were significantly associated with the interspecific integration patterns observed, suggesting a role for neutral evolution during the evolutionary divergence of mandible shape. However, directional and stabilizing selection were not discarded as processes responsible for the generation of interspecific integration. The choreography of the morphogenetic components in the mandible is highly flexible and the integrated groups of components can be reorganized depending on functional demands during evolutionary shape changes.     

Comparison and Mapping Facilitate Relation Discovery and Predication

Relational concepts play a central role in human perception and cognition, but little is known about how they are acquired. For example, how do we come to understand that physical force is a higher-order multiplicative relation between mass and acceleration, or that two circles are the same-shape in the same way that two squares are? A recent model of relational learning, DORA (Discovery of Relations by Analogy; Doumas, Hummel & Sandhofer, 2008), predicts that comparison and analogical mapping play a central role in the discovery and predication of novel higher-order relations. We report two experiments testing and confirming this prediction.     

MMT--a pathway modeling tool for data from rapid sampling experiments

The identification of metabolic regulation is a major concern in metabolic engineering. Metabolic regulation phenomena depend on intracellular compounds such as enzymes, metabolites and cofactors. A complete understanding of metabolic regulation requires quantitative information about these compounds under in vivo conditions. This quantitative knowledge in combination with the known network of metabolic pathways allows the construction of mathematical models that describe the dynamic changes in metabolite concentrations over time. Rapid sampling combined with pulse experiments is a useful tool for the identification of metabolic regulation owing to the transient data they provide. Enzymatic tests in combination with ESI-LC-MS (Electrospray Ionization Liquid Chromatographic Tandem Mass Spectrometry) and HPLC measurements have been used to identify up to 30 metabolites and nucleotides from rapid sampling experiments. A metabolic modeling tool (MMT) that is built on a relational database was developed specifically for analysis of rapid sampling experiments. The tool allows to construct complex pathway models with information stored in the relational database. Parameter fitting and simulation algorithms for the resulting system of Ordinary Differential Equations (ODEs) are part of MMT. Additionally explicit sensitivity functions are calculated. The integration of all necessary algorithms in one tool allows fast model analysis and comparison. Complex models have been developed to describe the central metabolic pathways of Escherichia coli during a glucose pulse experiment.     

Towards building the silicon cell: a modular approach

Systems Biology aims to understand quantitatively how properties of biological systems can be understood as functions of the characteristics of, and interactions between their macromolecular components. Whereas, traditional biochemistry focused on isolation and characterization of cellular components, the challenge for Systems Biology lies in integration of this knowledge and the knowledge about molecular interactions. Computer models play an important role in this integration. We here discuss an approach with which we aim to link kinetic models on small parts of metabolism together, so as to form detailed kinetic models of larger chunks of metabolism, and ultimately of the entire living cell. Specifically, we will discuss techniques that can be used to model a sub-network in isolation of a larger network of which it is a part, while still maintaining the dynamics of the larger complete network. We will start by outlining the JWS online system, the silicon cell project, and the type of models we propose. JWS online is a model repository, which can be used for the storage, simulation and analysis of kinetic models. We advocate to integrate a top-down approach, where measurements on the complete system are used to derive fluxes in a detailed structural model, with a bottom-up approach, consisting of the integration of molecular mechanism-based detailed kinetic models into the structural model.     

Integration of Retinal and Extraretinal Information across Eye Movements

Visual perception is burdened with a highly discontinuous input stream arising from saccadic eye movements. For successful integration into a coherent representation, the visuomotor system needs to deal with these self-induced perceptual changes and distinguish them from external motion. Forward models are one way to solve this problem where the brain uses internal monitoring signals associated with oculomotor commands to predict the visual consequences of corresponding eye movements during active exploration. Visual scenes typically contain a rich structure of spatial relational information, providing additional cues that may help disambiguate self-induced from external changes of perceptual input. We reasoned that a weighted integration of these two inherently noisy sources of information should lead to better perceptual estimates. Volunteer subjects performed a simple perceptual decision on the apparent displacement of a visual target, jumping unpredictably in sync with a saccadic eye movement. In a critical test condition, the target was presented together with a flanker object, where perceptual decisions could take into account the spatial distance between target and flanker object. Here, precision was better compared to control conditions in which target displacements could only be estimated from either extraretinal or visual relational information alone. Our findings suggest that under natural conditions, integration of visual space across eye movements is based upon close to optimal integration of both retinal and extraretinal pieces of information.     

Current densities induced in swine and rat models by power-frequency electric fields

Measurements have been made of vector current densities induced by vertical, uniform, 60-Hz electric fields in the torsos of homogeneous models of swine and rats. The observed data were a strong function of the five grounding configurations invested: all four feet grounded, only front feet grounded, only rear feet grounded, left front and right rear feet grounded, and right front and left rear feet grounded. In the first configuration and with an exposure field strength of 10 kV/m, average total current densities induced in the torsos of pigs and rats were 34 nA/cm2 and 20 nA/cm2, respectively. The corresponding value for human exposure is about 250 nA/cm2, 7.3 and 12.5 times larger than for swine and rats, respectively. Current densities measured at 60 Hz can be linearly extrapolated to frequencies in a range extending from at least 1 Hz to 1 MHz. Human and animal current-density data can provide an improved rationale for extrapolating biological data across species. In addition, these data can be used to validate the predictions of numerical models.     

A guide to choosing and implementing reference models for social network analysis

Analysing social networks is challenging. Key features of relational data require the use of non-standard statistical methods such as developing system-specific null, or reference, models that randomize one or more components of the observed data. Here we review a variety of randomization procedures that generate reference models for social network analysis. Reference models provide an expectation for hypothesis testing when analysing network data. We outline the key stages in producing an effective reference model and detail four approaches for generating reference distributions: permutation, resampling, sampling from a distribution, and generative models. We highlight when each type of approach would be appropriate and note potential pitfalls for researchers to avoid. Throughout, we illustrate our points with examples from a simulated social system. Our aim is to provide social network researchers with a deeper understanding of analytical approaches to enhance their confidence when tailoring reference models to specific research questions.     

Cognitive representation in transitive inference: A comparison of four corvid species

During operant transitive inference experiments, subjects are trained on adjacent stimulus pairs in an implicit linear hierarchy in which responses to higher ranked stimuli are rewarded. Two contrasting forms of cognitive representation are often used to explain resulting choice behavior. Associative representation is based on memory for the reward history of each stimulus. Relational representation depends on memory for the context in which stimuli have been presented. Natural history characteristics that require accurate configural memory, such as social complexity or reliance on cached food, should tend to promote greater use of relational representation. To test this hypothesis, four corvid species with contrasting natural histories were trained on the transitive inference task: pinyon jays, Gymnorhinus cyanocephalus; Clark's nutcrackers, Nucifraga columbiana; azure-winged magpies, Cyanopica cyanus; and western scrub jays, Aphelocoma californica. A simplified computer model of associative representation displayed a characteristic pattern of accuracy as a function of position in the hierarchy. Analysis of the deviation of each subject's performance from this predicted pattern yielded an index of reliance on relational representation. Regression of index scores against rankings of social complexity and caching reliance indicated that both traits were significantly and independently associated with greater use of relational representation.