Laterality of expression in portraiture: putting your best cheek forward.

Portraits, both photographic and painted, are often produced with more of one side of the face showing than the other. Typically, the left side of the face is overrepresented, with the head turned slightly to the sitter''s right. This leftward bias is weaker for painted male portraits and non-existent for portraits of scientists from the Royal Society. What mechanism might account for this bias? Examination of portraits painted by left- and right-handers and of self-portraits suggests that the bias is not determined by a mechanical preference of the artist or by the viewer''s aesthetics. The leftward bias seems to be determined by the sitters and their desire to display the left side of their face, which is controlled by the emotive, right cerebral hemisphere. When we asked people to portray as much emotion as possible when posing for a family portrait, they tended to present the left side of their face. When asked to pose as scientists and avoid portraying emotion, participants tended to present their right side. The motivation to portray emotion, or conceal it, might explain why portraits of males show a reduced leftward bias, and also why portraits of scientists from the Royal Society show no leftward bias.     

Garrotes, springs, ratchets, and whips: putting dynamin models to the test

The GTPase dynamin is essential for clathrin-mediated endocytosis. Numerous new and exciting discoveries regarding dynamin function in vivo and in vitro have led to various models in which dynamin functions directly in membrane fission and the release of clathrin-coated vesicles from the plasma membrane. This would make dynamin unique among GTPases in its ability to act as a mechanochemical enzyme. Here we review the various models and their supporting data. We then discuss new findings that raise doubts as to whether dynamin breaks the paradigm that governs regulatory GTPases.     

Mixed models: getting the best use of parasitological data

Statistical analysis of parasitological data provides a powerful method for understanding the biological processes underlying parasite infection. However, robust and reliable analysis of parasitological data from natural and experimental infections is often difficult where: (1) the distribution of parasites between hosts is aggregated; (2) multiple measurements are made on the same individual host in longitudinal studies; or (3) data are from 'noisy' natural systems. Mixed models, which allow multiple error terms, provide an excellent opportunity to overcome these problems, and their application to the analysis of various types of parasitological data are reviewed here.     

Computational models in systems biology: standards,dissemination, and best practices

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Predictive models in aerobiology: data transformation

This paper attempts to evaluate the effect of mathematical transformations of pollen and meteorogical data used in aerobiological forecasting models. Stepwise multiple regression equations were developed in order to facilitate short term forecasts during the pre-peak period. The daily mean pollen data (x _i) expressed as number of pollen grains per cubic metre of air were used directly and transformed into different scales: log(x _i + 1), ln((x ₁1000/Σp) + 1) and √x _i, where Σp is the sum of the daily mean values throughout the season. Thirteen meteorological parameters and the variable time were used as forecasting variables. The most reliable forecasts were obtained with data transformed by ‘square root’ and with untransformed data. Based on the results obtained, we recommend that the data be transformed by means of the square root if they do not show a normal distribution and that non-linear statistics be used in this kind of study.     

Inverse and forward dynamics: models of multi-body systems

Connected multi-body systems exhibit notoriously complex behaviour when driven by external and internal forces and torques. The problem of reconstructing the internal forces and/or torques from the movements and known external forces is called the 'inverse dynamics problem', whereas calculating motion from known internal forces and/or torques and resulting reaction forces is called the 'forward dynamics problem'. When stepping forward to cross the street, people use muscle forces that generate angular accelerations of their body segments and, by virtue of reaction forces from the street, a forward acceleration of the centre of mass of their body. Inverse dynamics calculations applied to a set of motion data from such an event can teach us how temporal patterns of joint torques were responsible for the observed motion. In forward dynamics calculations we may attempt to create motion from such temporal patterns, which is extremely difficult, because of the complex mechanical linkage along the chains forming the multi-body system. To understand, predict and sometimes control multi-body systems, we may want to have mathematical expressions for them. The Newton-Euler, Lagrangian and Featherstone approaches have their advantages and disadvantages. The simulation of collisions and the inclusion of muscle forces or other internal forces are discussed. Also, the possibility to perform a mixed inverse and forward dynamics calculation are dealt with. The use and limitations of these approaches form the conclusion.     

Contemporary issues,current best practice and ways forward in soil protist ecology

Soil protists are increasingly studied due to a release from previous methodological constraints and the acknowledgement of their immense diversity and functional importance in ecosystems. However, these studies often lack sufficient depth in knowledge, which is visible in the form of falsely used terms and false- or over-interpreted data with conclusions that cannot be drawn from the data obtained. As we welcome that also non-experts include protists in their still mostly bacterial and/or fungal-focused studies, our aim here is to help avoid some common errors. We provide suggestions for current terms to use when working on soil protists, like protist instead of protozoa, predator instead of grazer, microorganisms rather than microflora and other terms to be used to describe the prey spectrum of protists. We then highlight some dos and don'ts in soil protist ecology including challenges related to interpreting 18S rRNA gene amplicon sequencing data. We caution against the use of standard bioinformatic settings optimized for bacteria and the uncritical reliance on incomplete and partly erroneous reference databases. We also show why causal inferences cannot be drawn from sequence-based correlation analyses or any sampling/monitoring, study in the field without thorough experimental confirmation and sound understanding of the biology of taxa. Together, we envision this work to help non-experts to more easily include protists in their soil ecology analyses and obtain more reliable interpretations from their protist data and other biodiversity data that, in the end, will contribute to a better understanding of soil ecology.     

When the optimal is not the best: parameter estimation in complex biological models

Background

The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values.

Results

We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit.

Conclusions

The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally.     

On our best behavior: optimality models in human behavioral ecology

This paper discusses problems associated with the use of optimality models in human behavioral ecology. Optimality models are used in both human and non-human animal behavioral ecology to test hypotheses about the conditions generating and maintaining behavioral strategies in populations via natural selection. The way optimality models are currently used in behavioral ecology faces significant problems, which are exacerbated by employing the so-called ‘phenotypic gambit’: that is, the bet that the psychological and inheritance mechanisms responsible for behavioral strategies will be straightforward. I argue that each of several different possible ways we might interpret how optimality models are being used for humans face similar and additional problems. I suggest some ways in which human behavioral ecologists might adjust how they employ optimality models; in particular, I urge the abandonment of the phenotypic gambit in the human case.     

Intraspecific variation in plant size,secondary plant compounds,herbivory and parasitoid assemblages during secondary succession

During secondary succession on abandoned agricultural fields the diversity and abundance of insect communities often increases, whereas the performance and nutritional quality of early successional plants often declines. As the diversity and abundance of insects on a single plant are determined by characteristics of the environment as well as of the host plant, it is difficult to predict how insects associated with a single plant species will change during succession. We examined how plant characteristics of the early successional plant species ragwort (Jacobaea vulgaris), and the herbivores and parasitoids associated with these plants change during secondary succession. In ten grasslands that differed in time since abandonment (3–26 years), we measured the size and primary and secondary chemistry of individual ragwort plants. For each plant we also recorded the presence of herbivores in flowers, leaves and stems, and reared parasitoids from these plant parts. Ragwort plants were significantly larger but had lower nitrogen concentrations in recently abandoned sites than in older sites. Pyrrolizidine alkaloid (PA) composition varied among plants within sites but also differed significantly among sites. However, there was no relationship between the age of a site and PA composition. Even though plant size decreased with time since abandonment, the abundance of stem-boring insects and parasitoids emerging from stems significantly increased with site age. The proportion of plants with flower and leaf herbivory and the number of parasitoids emerging from flowers and leaves was not related to site age. Parasitoid diversity significantly increased with site age. The results of our study show that ragwort and insect characteristics both change during secondary succession, but that insect herbivore and parasitoid abundances are not directly related to plant size or nutritional quality.     

Reduction,refinement and replacement: putting the immune system to work

Many chemicals are known to be, or have been implicated as, contact allergens, and allergic contact dermatitis is an important occupational and environmental health issue. It is the responsibility of toxicologists to identify those chemicals that have the potential to induce skin sensitisation, and to assess the conditions under which there will exist a risk to human health. This article describes progress that has been made in the development of new approaches to the toxicological evaluation of skin sensitisation, and the benefits to animal welfare that such developments have already produced, and are likely to produce in the future. In this context, the local lymph node assay is described with regard to hazard identification and risk assessment, and possible strategies for the development of in vitro approaches to safety assessment are discussed.     

Predictive models in ecology: Comparison of performances and assessment of applicability

Ecological systems are governed by complex interactions which are mainly nonlinear. In order to capture the inherent complexity and nonlinearity of ecological, and in general biological systems, empirical models recently gained popularity. However, although these models, particularly connectionist approaches such as multilayered backpropagation networks, are commonly applied as predictive models in ecology to a wide variety of ecosystems and questions, there are no studies to date aiming to assess the performance, both in terms of data fitting and generalizability, and applicability of empirical models in ecology. Our aim is hence to provide an overview for nature of the wide range of the data sets and predictive variables, from both aquatic and terrestrial ecosystems with different scales of time-dependent dynamics, and the applicability and robustness of predictive modeling methods on such data sets by comparing different empirical modeling approaches. The models used in this study range from predicting the occurrence of submerged plants in shallow lakes to predicting nest occurrence of bird species from environmental variables and satellite images. The methods considered include k-nearest neighbor (k-NN), linear and quadratic discriminant analysis (LDA and QDA), generalized linear models (GLM) feedforward multilayer backpropagation networks and pseudo-supervised network ARTMAP.Our results show that the predictive performances of the models on training data could be misleading, and one should consider the predictive performance of a given model on an independent test set for assessing its predictive power. Moreover, our results suggest that for ecosystems involving time-dependent dynamics and periodicities whose frequency are possibly less than the time scale of the data considered, GLM and connectionist neural network models appear to be most suitable and robust, provided that a predictive variable reflecting these time-dependent dynamics included in the model either implicitly or explicitly. For spatial data, which does not include any time-dependence comparable to the time scale covered by the data, on the other hand, neighborhood based methods such as k-NN and ARTMAP proved to be more robust than other methods considered in this study. In addition, for predictive modeling purposes, first a suitable, computationally inexpensive method should be applied to the problem at hand a good predictive performance of which would render the computational cost and efforts associated with complex variants unnecessary.     

Dynamic updating of distributed neural representations using forward models

In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527–533, 2003; Oztop et al. in Neural Netw 19(3):254–271, 2006; Wolpert et al. in Philos Trans R Soc 358:593–602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brain.     

CONTRAfold: RNA secondary structure prediction without physics-based models

MOTIVATION: For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models. RESULTS: In this paper, we present CONTRAfold, a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. In a series of cross-validation experiments, we show that grammar-based secondary structure prediction methods formulated as CLLMs consistently outperform their SCFG analogs. Furthermore, CONTRAfold, a CLLM incorporating most of the features found in typical thermodynamic models, achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction. AVAILABILITY: Source code for CONTRAfold is available at http://contra.stanford.edu/contrafold/.