Possible ecological risks of transgenic organism release when transgenes affect mating success: Sexual selection and the Trojan gene hypothesis

Widespread interest in producing transgenic organisms is balanced by concern over ecological hazards, such as species extinction if such organisms were to be released into nature. An ecological risk associated with the introduction of a transgenic organism is that the transgene, though rare, can spread in a natural population. An increase in transgene frequency is often assumed to be unlikely because transgenic organisms typically have some viability disadvantage. Reduced viability is assumed to be common because transgenic individuals are best viewed as macromutants that lack any history of selection that could reduce negative fitness effects. However, these arguments ignore the potential advantageous effects of transgenes on some aspect of fitness such as mating success. Here, we examine the risk to a natural population after release of a few transgenic individuals when the transgene trait simultaneously increases transgenic male mating success and lowers the viability of transgenic offspring. We obtained relevant life history data by using the small cyprinodont fish, Japanese medaka (Oryzias latipes) as a model. Our deterministic equations predict that a transgene introduced into a natural population by a small number of transgenic fish will spread as a result of enhanced mating advantage, but the reduced viability of offspring will cause eventual local extinction of both populations. Such risks should be evaluated with each new transgenic animal before release.

Murine hematopoietic reconstitution after tagging and selection of retrovirally transduced bone marrow cells

A major problem facing the effective treatment of patients with cancer is how to get the specific antitumor agent into every tumor cell. In this report we describe the use of a strategy that, by using retroviral vectors encoding a truncated human CD5 cDNA, allows the selection of only the infected cells, and we show the ability to obtain, before bone marrow transplantation, a population of 5-fluouraci-treated murine bone marrow cells that are 100% marked. This marked population of bone marrow cells is able to reconstitute the hematopoietic system in lethally irradiated mice, indicating that the surface marker lacks deleterious effects on the functionality of bone marrow cells. No gross abnormalities in hematopoiesis were detected in mice repopulated with CD5-expressing cells. Nevertheless, a significant proportion of the hematopoietic cells no longer expresses the surface marker CD5 in the 9-month-old recipient mice. This transcriptional inactivity of the proviral long terminal repeat (LTR) was accompanied by de novo methylation of the proviral sequences. Our results show that the use of the CD5 as a retrovirally encoded marker enables the rapid, efficient, and nontoxic selection in vitro of infected primary cells, which can entirely reconstitute the hematopoietic system in mice. These results should now greatly enhance the power of studies aimed at addressing questions such as generation of cancer-negative hematopoiesis.

Spermatogonial stem cell enrichment by multiparameter selection of mouse testis cells

The spermatogonial stem cell initiates and maintains spermatogenesis in the testis. To perform this role, the stem cell must self replicate as well as produce daughter cells that can expand and differentiate to form spermatozoa. Despite the central importance of the spermatogonial stem cell to male reproduction, little is known about its morphological or biochemical characteristics. This results, in part, from the fact that spermatogonial stem cells are an extremely rare cell population in the testis, and techniques for their enrichment are just beginning to be established. In this investigation, we used a multiparameter selection strategy, combining the in vivo cryptorchid testis model with in vitro fluorescence-activated cell sorting analysis. Cryptorchid testis cells were fractionated by fluorescence-activated cell sorting analysis based on light-scattering properties and expression of the cell surface molecules α6-integrin, αv-integrin, and the c-kit receptor. Two important observations emerged from these analyses. First, spermatogonial stem cells from the adult cryptorchid testis express little or no c-kit. Second, the most effective enrichment strategy, in this study, selected cells with low side scatter light-scattering properties, positive staining for α6-integrin, and negative or low αv-integrin expression, and resulted in a 166-fold enrichment of spermatogonial stem cells. Identification of these characteristics will allow further purification of these valuable cells and facilitate the investigation of molecular mechanisms governing spermatogonial stem cell self renewal and hierarchical differentiation.

The contagion indicator hypothesis for parasite-mediated sexual selection.

Hamilton and Zuk [Hamilton, W. D. & Zuk, M. (1982) Science 218, 384-387] proposed that females choosing mates based on the degree of expression of male characters obtain heritable parasite resistance for their offspring. Alternatively, the "contagion indicator" hypothesis posits that females choose mates based on the degree of expression of male characters because the latter indicate a male's degree of infestation of parasites and thus the risk that choosing females and their offspring will acquire these parasites. I examined whether parasite transmittability affects the probability that parasite intensity and male mating success are negatively correlated in intraspecific studies of parasite-mediated sexual selection. When females risk infection of themselves or their future offspring as a result of mating with a parasitized male, negative relationships between parasite intensity and male mating success are significantly more likely to occur than when females do not risk such infection. The direct benefit to females of avoiding parasitic infection is proposed to lead to the linkage between variable secondary sexual characters and the intensity of transmittable parasites. The direct benefits of avoiding associatively transmittable parasites should be considered in future studies of parasite-mediated sexual selection.

Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm.

We present a modelling method to estimate the 3-D geometry and location of homogeneously magnetized sources from magnetic anomaly data. As input information, the procedure needs the parameters defining the magnetization vector (intensity, inclination and declination) and the Earth's magnetic field direction. When these two vectors are expected to be different in direction, we propose to estimate the magnetization direction from the magnetic map. Then, using this information, we apply an inversion approach based on a genetic algorithm which finds the geometry of the sources by seeking the optimum solution from an initial population of models in successive iterations through an evolutionary process. The evolution consists of three genetic operators (selection, crossover and mutation), which act on each generation, and a smoothing operator, which looks for the best fit to the observed data and a solution consisting of plausible compact sources. The method allows the use of non-gridded, non-planar and inaccurate anomaly data and non-regular subsurface partitions. In addition, neither constraints for the depth to the top of the sources nor an initial model are necessary, although previous models can be incorporated into the process. We show the results of a test using two complex synthetic anomalies to demonstrate the efficiency of our inversion method. The application to real data is illustrated with aeromagnetic data of the volcanic island of Gran Canaria (Canary Islands).

A novel information theory method for filter feature selection

In this paper, we propose a novel filter for feature selection. Such filter relies on the estimation of the mutual information between features and classes. We bypass the estimation of the probability density function with the aid of the entropic-graphs approximation of Rényi entropy, and the subsequent approximation of the Shannon one. The complexity of such bypassing process does not depend on the number of dimensions but on the number of patterns/samples, and thus the curse of dimensionality is circumvented. We show that it is then possible to outperform a greedy algorithm based on the maximal relevance and minimal redundancy criterion. We successfully test our method both in the contexts of image classification and microarray data classification.

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation etc., are all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

German Foreign Ministry Archives 1867-1920 a selection [microform].

For detailed list of contents see typewritten list accompanying the microfilms.

Marine mammal species detection and classification

Thesis (Ph.D.)--University of Washington, 2016-04

Choosing organics: A path analysis of factors underlying the selection of organic food among Australian consumers

Path analysis of attitudinal, motivational, demographic and behavioural factors influencing food choice among Australian consumers who had consumed at least some organic food in the preceding 12 months showed that concern with the naturalness of food and the sensory and emotional experience of eating were the major determinants of increasing levels of organic consumption. Increasing consumption was also related to other 'green consumption' behaviours such as recycling and to lower levels of concern with convenience in the purchase and preparation of food. Most of these factors were, in turn, strongly affected by gender and the level of responsibility taken by respondents for food provisioning within their households, a responsibility dominated by women. Education had a slightly negative effect on the levels of concern for sensory and emotional appeal due to lower levels of education among women. Income, age, political and ecological values and willingness to pay a premium for safe and environmentally friendly foods all had extremely minor effects. (C) 2004 Elsevier Ltd. All rights reserved.

A genetic estimation algorithm for parameters of stochastic ordinary differential equations

A generic method for the estimation of parameters for Stochastic Ordinary Differential Equations (SODEs) is introduced and developed. This algorithm, called the GePERs method, utilises a genetic optimisation algorithm to minimise a stochastic objective function based on the Kolmogorov-Smirnov statistic. Numerical simulations are utilised to form the KS statistic. Further, the examination of some of the factors that improve the precision of the estimates is conducted. This method is used to estimate parameters of diffusion equations and jump-diffusion equations. It is also applied to the problem of model selection for the Queensland electricity market. (C) 2003 Elsevier B.V. All rights reserved.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Genetic parameters and predicted response to selection for lean growth in an Australian commercial nucleus pig herd

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.