Family Size Evolution in Drosophila Chemosensory Gene Families: A Comparative Analysis with a Critical Appraisal of Methods

Gene turnover rates and the evolution of gene family sizes are important aspects of genome evolution. Here, we use curated sequence data of the major chemosensory gene families from Drosophila-the gustatory receptor, odorant receptor, ionotropic receptor, and odorant-binding protein families-to conduct a comparative analysis among families, exploring different methods to estimate gene birth and death rates, including an ad hoc simulation study. Remarkably, we found that the state-of-the-art methods may produce very different rate estimates, which may lead to disparate conclusions regarding the evolution of chemosensory gene family sizes in Drosophila. Among biological factors, we found that a peculiarity of D. sechellia's gene turnover rates was a major source of bias in global estimates, whereas gene conversion had negligible effects for the families analyzed herein. Turnover rates vary considerably among families, subfamilies, and ortholog groups although all analyzed families were quite dynamic in terms of gene turnover. Computer simulations showed that the methods that use ortholog group information appear to be the most accurate for the Drosophila chemosensory families. Most importantly, these results reveal the potential of rate heterogeneity among lineages to severely bias some turnover rate estimation methods and the need of further evaluating the performance of these methods in a more diverse sampling of gene families and phylogenetic contexts. Using branch-specific codon substitution models, we find further evidence of positive selection in recently duplicated genes, which attests to a nonneutral aspect of the gene birth-and-death process.

Clinical evolution after a non-reactive hypothermic EEG following cardiac arrest.

BACKGROUND: Lack of electroencephalography (EEG) background reactivity during therapeutic hypothermia (TH) has been associated with poor outcome in post-anoxic comatose patients. However, decision on intensive care withdrawal is based on normothermic (NT) evaluations. This study aims at exploring whether patients showing recovery of EEG reactivity in NT after a non-reactive EEG in TH differ from those remaining non-reactive. METHODS: Patients with non-reactive EEG during TH were identified from our prospective registry of consecutive comatose adults admitted after successful resuscitation from CA between April 2009 and June 2014. Variables including neurological examination, serum neuron-specific enolase (NSE), procalcitonin, and EEG features were compared regarding impact on functional outcome at 3 months. RESULTS: Seventy-two of 197 patients (37 %) had a non-reactive EEG background during TH with thirteen (18 %) evolving towards reactivity in NT. Compared to those remaining non-reactive (n = 59), they showed significantly better recovery of brainstem reflexes (p < 0.001), better motor responses (p < 0.001), transitory consciousness improvement (p = 0.008), and a tendency toward lower NSE (p = 0.067). One patient recovering EEG reactivity survived with good functional outcome at 3 months. CONCLUSIONS: Recovery of EEG reactivity from TH to NT seems to distinguish two patients' subgroups regarding early neurological assessment and transitory consciousness improvement, corroborating the role of EEG in providing information about cerebral functions. Understanding these dynamic changes encourages maintenance of intensive support in selected patients even after a non-reactive EEG background in TH, as a small subgroup may indeed recover with good functional outcome.

A model for the evolution of reinforcement learning in fluctuating games

Many species are able to learn to associate behaviours with rewards as this gives fitness advantages in changing environments. Social interactions between population members may, however, require more cognitive abilities than simple trial-and-error learning, in particular the capacity to make accurate hypotheses about the material payoff consequences of alternative action combinations. It is unclear in this context whether natural selection necessarily favours individuals to use information about payoffs associated with nontried actions (hypothetical payoffs), as opposed to simple reinforcement of realized payoff. Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily stable learning rule under pairwise symmetric two-action stochastic repeated games played over the individual's lifetime. We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement learning on the evolutionary timescale. This occurs in particular under repeated cooperative interactions with the same partner. By contrast, we find that hypothetical reinforcement learners tend to be favoured under random interactions, but stable polymorphisms can also obtain where trial-and-error learners are maintained at a low frequency. We conclude that specific game structures can select for trial-and-error learning even in the absence of costs of cognition, which illustrates that cost-free increased cognition can be counterselected under social interactions.

Tissue-Specific Evolution of Protein Coding Genes in Human and Mouse.

Protein-coding genes evolve at different rates, and the influence of different parameters, from gene size to expression level, has been extensively studied. While in yeast gene expression level is the major causal factor of gene evolutionary rate, the situation is more complex in animals. Here we investigate these relations further, especially taking in account gene expression in different organs as well as indirect correlations between parameters. We used RNA-seq data from two large datasets, covering 22 mouse tissues and 27 human tissues. Over all tissues, evolutionary rate only correlates weakly with levels and breadth of expression. The strongest explanatory factors of purifying selection are GC content, expression in many developmental stages, and expression in brain tissues. While the main component of evolutionary rate is purifying selection, we also find tissue-specific patterns for sites under neutral evolution and for positive selection. We observe fast evolution of genes expressed in testis, but also in other tissues, notably liver, which are explained by weak purifying selection rather than by positive selection.

Visualizing Business Model Evolution with the Business Model Canvas: Concept and Tool

The Business Model Canvas (BMC) assists in the design of companies' business models. As strategies evolve so too does the business model. Unfortunately, each BMC is a standalone representation. Thus, there is a need to be able to describe transformation from one version of a business model to the next as well as to visualize these operations. To address this issue, and to contribute to computer-assisted business model design, we propose a set of design principles for business model evolution. We also demonstrate a tool that can assist in the creation and navigation of business model versions in a visual and user-friendly way

3-D density structure and geological evolution of Stromboli volcano (Aeolian Islands, Italy) inferred from land-based and sea-surface gravity data

We present the first density model of Stromboli volcano (Aeolian Islands, Italy) obtained by simultaneously inverting land-based (543) and sea-surface (327) relative gravity data. Modern positioning technology, a 1 x 1 m digital elevation model, and a 15 x 15 m bathymetric model made it possible to obtain a detailed 3-D density model through an iteratively reweighted smoothness-constrained least-squares inversion that explained the land-based gravity data to 0.09 mGal and the sea-surface data to 5 mGal. Our inverse formulation avoids introducing any assumptions about density magnitudes. At 125 m depth from the land surface, the inferred mean density of the island is 2380 kg m(-3), with corresponding 2.5 and 97.5 percentiles of 2200 and 2530 kg m-3. This density range covers the rock densities of new and previously published samples of Paleostromboli I, Vancori, Neostromboli and San Bartolo lava flows. High-density anomalies in the central and southern part of the island can be related to two main degassing faults crossing the island (N41 and NM) that are interpreted as preferential regions of dyke intrusions. In addition, two low-density anomalies are found in the northeastern part and in the summit area of the island. These anomalies seem to be geographically related with past paroxysmal explosive phreato-magmatic events that have played important roles in the evolution of Stromboli Island by forming the Scari caldera and the Neostromboli crater, respectively. (C) 2014 Elsevier B.V. All rights reserved.