Contemporary genetic, historical genetic and morphological data sets for the Yellow-tufted Honeyeater Lichenostomus melanops

Understanding the evolutionary history of threatened populations can improve their conservation management. Re-establishment of past but recent gene flow could re-invigorate threatened populations and replenish genetic diversity, necessary for population persistence. One of the four nominal subspecies of the common yellow-tufted honeyeater, Lichenostomus melanops cassidix, is critically endangered despite substantial conservation efforts over 55 years. Using a combination of morphometric, genetic and modelling approaches we tested for its evolutionary distinctiveness and conservation merit. We confirmed that cassidix has at least one morphometric distinction. It also differs genetically from the other subspecies in allele frequencies but not phylogenetically, implying that its evolution was recent. Modelling historical distribution supported the lack of vicariance and suggested a possibility of gene flow among subspecies at least since the late Pleistocene. Multi-locus coalescent analyses indicated that cassidix diverged from its common ancestor with neighbouring subspecies gippslandicus sometime from the mid-Pleistocene to the Holocene, and that it has the smallest historical effective population size of all subspecies. It appears that cassidix diverged from its ancestor with gippslandicus through a combination of drift and local selection. From patterns of genetic subdivision on two spatial scales and morphological variation we concluded that cassidix, gippslandicus and (melanops + meltoni) are diagnosable as subspecies. Low genetic diversity and effective population size of cassidix may translate to low genetic fitness and evolutionary potential, thus managed gene flow from gippslandicus is recommended for its recovery.

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Genetic and Biochemical Characterization of the Yeast Spo12 Protein

We have performed a genetic and biochemical analysis of the SPO12 gene, which regulates meiotic nuclear divisions in budding yeast. When sporulated, spo12 mutants undergo a single meiotic nuclear division most closely resembling meiosis II. We observed that Spo12 protein is localized to the nucleus during both meiotic divisions and that Clb1-Cdc28, Clb3-Cdc28, Clb4-Cdc28, and Clb5-Cdc28 kinase activities during meiosis were not affected by a spo12 mutation. Using two-hybrid analysis, we identified several genes, three of which are meiotically induced, that may code for proteins that interact with Spo12p. We also observed that two genes, BNS1 (Bypasses Need for Spo12p), which has homology to SPO12, and SPO13, whose mutant phenotype is like that of spo12, can partially suppress the meiotic defect of spo12 mutants when overexpressed. We found that Spo12p is also localized to the nucleus in vegetative cells and that its level peaks during G2/M. We observed that a spo12 mutation is synthetically lethal in vegetative cells with a mutation in HCT1, a gene necessary for cells to exit mitosis, suggesting that Spo12p may have a role in exit from mitosis.

Customization of the design of antenna using genetic algorithm and finite-difference time-domain technique

Antenna design is an iterative process in which structures are analyzed and changed to comply with certain performance parameters required. The classic approach starts with analyzing a "known" structure, obtaining the value of its performance parameter and changing this structure until the "target" value is achieved. This process relies on having an initial structure, which follows some known or "intuitive" patterns already familiar to the designer. The purpose of this research was to develop a method of designing UWB antennas. What is new in this proposal is that the design process is reversed: the designer will start with the target performance parameter and obtain a structure as the result of the design process. This method provided a new way to replicate and optimize existing performance parameters. The base of the method was the use of a Genetic Algorithm (GA) adapted to the format of the chromosome that will be evaluated by the Electromagnetic (EM) solver. For the electromagnetic study we used XFDTD™ program, based in the Finite-Difference Time-Domain technique. The programming portion of the method was created under the MatLab environment, which serves as the interface for converting chromosomes, file formats and transferring of data between the XFDTD™ and GA. A high level of customization had to be written into the code to work with the specific files generated by the XFDTD™ program. Two types of cost functions were evaluated; the first one seeking broadband performance within the UWB band, and the second one searching for curve replication of a reference geometry. The performance of the method was evaluated considering the speed provided by the computer resources used. Balance between accuracy, data file size and speed of execution was achieved by defining parameters in the GA code as well as changing the internal parameters of the XFDTD™ projects. The results showed that the GA produced geometries that were analyzed by the XFDTD™ program and changed following the search criteria until reaching the target value of the cost function. Results also showed how the parameters can change the search criteria and influence the running of the code to provide a variety of geometries.