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Evolution of the eyes of vipers with and without infrared-sensing pit organs
We examined lens and brille transmittance, photoreceptors, visual pigments, and visual opsin gene sequences of viperid snakes with and without infrared-sensing pit organs. Ocular media transmittance is high in both groups. Contrary to previous reports, small as well as large single cones occur in pit vipers. Non-pit vipers differ from pit vipers in having a twotiered retina, but few taxa have been examined for this poorly understood feature. All vipers sampled express rh1, sws1 and lws visual opsin genes. Opsin spectral tuning varies but not in accordance with the presence/absence of pit organs, and not always as predicted from gene sequences. The visual opsin genes were generally under purifying selection, with positive selection at spectral tuning amino acids in RH1 and SWS1 opsins, and at retinal pocket stabilization sites in RH1 or LWS (and without substantial differences between pit and nonpit vipers). Lack of evidence for sensory trade-off between viperid eyes (in the aspects examined) and pit organs might be explained by the high degree of neural integration of vision and infrared detection; the latter representing an elaboration of an existing sense with addition of a novel sense organ, rather than involving the evolution of a wholly novel sensory system
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial
Life (ECAL) 2013, Taormina, Ital
Differential Functional Constraints Cause Strain-Level Endemism in Polynucleobacter Populations.
The adaptation of bacterial lineages to local environmental conditions creates the potential for broader genotypic diversity within a species, which can enable a species to dominate across ecological gradients because of niche flexibility. The genus Polynucleobacter maintains both free-living and symbiotic ecotypes and maintains an apparently ubiquitous distribution in freshwater ecosystems. Subspecies-level resolution supplemented with metagenome-derived genotype analysis revealed that differential functional constraints, not geographic distance, produce and maintain strain-level genetic conservation in Polynucleobacter populations across three geographically proximal riverine environments. Genes associated with cofactor biosynthesis and one-carbon metabolism showed habitat specificity, and protein-coding genes of unknown function and membrane transport proteins were under positive selection across each habitat. Characterized by different median ratios of nonsynonymous to synonymous evolutionary changes (dN/dS ratios) and a limited but statistically significant negative correlation between the dN/dS ratio and codon usage bias between habitats, the free-living and core genotypes were observed to be evolving under strong purifying selection pressure. Highlighting the potential role of genetic adaptation to the local environment, the two-component system protein-coding genes were highly stable (dN/dS ratio, < 0.03). These results suggest that despite the impact of the habitat on genetic diversity, and hence niche partition, strong environmental selection pressure maintains a conserved core genome for Polynucleobacter populations. IMPORTANCE Understanding the biological factors influencing habitat-wide genetic endemism is important for explaining observed biogeographic patterns. Polynucleobacter is a genus of bacteria that seems to have found a way to colonize myriad freshwater ecosystems and by doing so has become one of the most abundant bacteria in these environments. We sequenced metagenomes from locations across the Chicago River system and assembled Polynucleobacter genomes from different sites and compared how the nucleotide composition, gene codon usage, and the ratio of synonymous (codes for the same amino acid) to nonsynonymous (codes for a different amino acid) mutations varied across these population genomes at each site. The environmental pressures at each site drove purifying selection for functional traits that maintained a streamlined core genome across the Chicago River Polynucleobacter population while allowing for site-specific genomic adaptation. These adaptations enable Polynucleobacter to become dominant across different riverine environmental gradients
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Starting from a high-level problem description in terms of partial
differential equations using abstract tensor notation, the Chemora framework
discretizes, optimizes, and generates complete high performance codes for a
wide range of compute architectures. Chemora extends the capabilities of
Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient
manner for complex applications, without low-level code tuning. Chemora
achieves parallelism through MPI and multi-threading, combining OpenMP and
CUDA. Optimizations include high-level code transformations, efficient loop
traversal strategies, dynamically selected data and instruction cache usage
strategies, and JIT compilation of GPU code tailored to the problem
characteristics. The discretization is based on higher-order finite differences
on multi-block domains. Chemora's capabilities are demonstrated by simulations
of black hole collisions. This problem provides an acid test of the framework,
as the Einstein equations contain hundreds of variables and thousands of terms.Comment: 18 pages, 4 figures, accepted for publication in Scientific
Programmin
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and
BA has been found to be very efficient. As a result, the literature has
expanded significantly in the last 3 years. This paper provides a timely review
of the bat algorithm and its new variants. A wide range of diverse applications
and case studies are also reviewed and summarized briefly here. Further
research topics are also discussed.Comment: 10 page
Freeze-drying modeling and monitoring using a new neuro-evolutive technique
This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
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