John is the only one of the four evangelists who recounts Jesus’ (possibly apocryphal) statement to Pilate that he was in fact a king whose role was “to bear witness to the truth; and all who are on the side of truth listen to my voice.” Pilate is said to have replied to this, “What is truth?”
This is a question that Jerry Coyne never really engages in his excellent new book , which purports to explain why evolution is “true.” This raises the question of who his intended audience is. But we’ll get there in a minute. First, make no mistake: this is a wonderful book, as far as the explanation of many of the interesting lines of evidence and case histories for evolution go. Coyne is a professor at the University of Chicago who specializes in the genetics of speciation (his previous book on the subject, with H. Allen Orr, is widely recognized ). He explains the evidence for evolution not just in terms of speciation, however. He revisits many of Darwin’s arguments, such as the progression of fossils, the importance of vestigial organs, how evolution explains biogeographic patterns, and sexual selection. But he is also able to go far beyond the evidence available to Darwin, with topics such as genetic and molecular support for species divergence, and the record of human evolution.
The first-degree power-law polynomial function is frequently used to describe activity metabolism for steady swimming animals. This function has been used in hydrodynamics-based metabolic studies to evaluate important parameters of energetic costs, such as the standard metabolic rate and the drag power indices. In theory, however, the power-law polynomial function of any degree greater than one can be used to describe activity metabolism for steady swimming animals. In fact, activity metabolism has been described by the conventional exponential function and the cubic polynomial function, although only the power-law polynomial function models drag power since it conforms to hydrodynamic laws. Consequently, the first-degree power-law polynomial function yields incorrect parameter values of energetic costs if activity metabolism is governed by the power-law polynomial function of any degree greater than one. This issue is important in bioenergetics because correct comparisons of energetic costs among different steady swimming animals cannot be made unless the degree of the power-law polynomial function derives from activity metabolism. In other words, a hydrodynamics-based functional form of activity metabolism is a power-law polynomial function of any degree greater than or equal to one. Therefore, the degree of the power-law polynomial function should be treated as a parameter, not as a constant. This new treatment not only conforms to hydrodynamic laws, but also ensures correct comparisons of energetic costs among different steady swimming animals. Furthermore, the exponential power-law function, which is a new hydrodynamics-based functional form of activity metabolism, is a special case of the power-law polynomial function. Hence, the link between the hydrodynamics of steady swimming and the exponential-based metabolic model is defined.
Understanding the mechanisms that underlie pattern formation is one of the major challenges of developmental biology. The complexity and beauty of the patterns on butterfly wings, fish scales, or bird feathers are not only remarkable products of developmental processes but puzzles that tease our intellects. If we are to understand these beautiful products of cellular activity, we need to first investigate simpler patterns, which are more tractable experimentally. A good example is the subdivision of an embryo along its main axis, which can be represented as a polarized subdivision of a cellular field. Over 40 years ago, Lewis Wolpert offered a conceptual solution to this problem in the form of the French Flag model . The central element of the proposal is that spatial gradients of substances called morphogens are the cause of such subdivision (Figure 1, left panel). The idea is simple: specific concentration thresholds in the gradient are detected by cells in the target tissue and lead to the expression of distinct sets of target genes. The crucial ingredient of the argument was a precise and direct correlation between the input (the gradient) and the output (the response of the tissue)–each threshold corresponds precisely to a border of an expression territory.
Knowledge of specific domain-domain interactions (DDIs) is essential to understand the functional significance of protein interaction networks. Despite the availability of an enormous amount of data on protein-protein interactions (PPIs), very little is known about specific DDIs occurring in them. Here, we present a top-down approach to accurately infer functionally relevant DDIs from PPI data. We created a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs by combining datasets from five major interaction databases. We introduced an integrated scoring system that uses a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. This method outperforms similar existing methods both in the accuracy of prediction and in the coverage of domain interaction space. We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis. Our results show that only 23% of these DDIs are conserved in at least two species and only 3.8% in at least 4 species, indicating a rather low conservation across species. Pair-wise analysis of DDI conservation revealed a ‘sliding conservation’ pattern between the evolutionarily neighboring species. Our methodology and the high-confidence DDI predictions generated in this study can help to better understand the functional significance of PPIs at the modular level, thus can significantly impact further experimental investigations in systems biology research.
Building an accurate neural network diagram of the vertebrate nervous system is a major challenge in neuroscience. Diverse groups of neurons that function together form complex patterns of connections often spanning large regions of brain tissue, with uncertain borders. Although serial-section transmission electron microscopy remains the optimal tool for fine anatomical analyses, the time and cost of the undertaking has been prohibitive. We have assembled a complete framework for ultrastructural mapping using conventional transmission electron microscopy that tremendously accelerates image analysis. This framework combines small-molecule profiling to classify cells, automated image acquisition, automated mosaic formation, automated slice-to-slice image registration, and large-scale image browsing for volume annotation. Terabyte-scale image volumes requiring decades or more to assemble manually can now be automatically built in a few months. This makes serial-section transmission electron microscopy practical for high-resolution exploration of all complex tissue systems (neural or nonneural) as well as for ultrastructural screening of genetic models.
The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this ‘illusion’ to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies. Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI. The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond.