Genetic algorithms and their use in geophysical problems

NASA Astrophysics Data System (ADS)

Parker, Paul Bradley

Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.

The influence of climatic niche preferences on the population genetic structure of a mistletoe species complex.

PubMed

Ramírez-Barahona, Santiago; González, Clementina; González-Rodríguez, Antonio; Ornelas, Juan Francisco

2017-06-01

The prevalent view on genetic structuring in parasitic plants is that host-race formation is caused by varying degrees of host specificity. However, the relative importance of ecological niche divergence and host specificity to population differentiation remains poorly understood. We evaluated the factors associated with population differentiation in mistletoes of the Psittacanthus schiedeanus complex (Loranthaceae) in Mexico. We used genetic data from chloroplast sequences and nuclear microsatellites to study population genetic structure and tested its association with host preferences and climatic niche variables. Pairwise genetic differentiation was associated with environmental and host preferences, independent of geography. However, environmental predictors appeared to be more important than host preferences to explain genetic structure, supporting the hypothesis that the occurrence of the parasite is largely determined by its own climatic niche and, to a lesser degree, by host specificity. Genetic structure is significant within this mistletoe species complex, but the processes associated with this structure appear to be more complex than previously thought. Although host specificity was not supported as the major determinant of population differentiation, we consider this to be part of a more comprehensive ecological model of mistletoe host-race formation that incorporates the effects of climatic niche evolution. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Modeling the geographic distribution of Bacillus anthracis, the causative agent of anthrax disease, for the contiguous United States using predictive ecological [corrected] niche modeling.

PubMed

Blackburn, Jason K; McNyset, Kristina M; Curtis, Andrew; Hugh-Jones, Martin E

2007-12-01

The ecology and distribution of Bacillus anthracis is poorly understood despite continued anthrax outbreaks in wildlife and livestock throughout the United States. Little work is available to define the potential environments that may lead to prolonged spore survival and subsequent outbreaks. This study used the genetic algorithm for rule-set prediction modeling system to model the ecological niche for B. anthracis in the contiguous United States using wildlife and livestock outbreaks and several environmental variables. The modeled niche is defined by a narrow range of normalized difference vegetation index, precipitation, and elevation, with the geographic distribution heavily concentrated in a narrow corridor from southwest Texas northward into the Dakotas and Minnesota. Because disease control programs rely on vaccination and carcass disposal, and vaccination in wildlife remains untenable, understanding the distribution of B. anthracis plays an important role in efforts to prevent/eradicate the disease. Likewise, these results potentially aid in differentiating endemic/natural outbreaks from industrial-contamination related outbreaks or bioterrorist attacks.

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

PubMed

Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

2005-09-01

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Evolutionary computation in zoology and ecology.

PubMed

Boone, Randall B

2017-12-01

Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species' niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.

Evolutionary computation in zoology and ecology

PubMed Central

2017-01-01

Abstract Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species’ niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate. PMID:29492029

Spread of the Tiger: Global Risk of Invasion by the Mosquito Aedes albopictus

PubMed Central

BENEDICT, MARK Q.; LEVINE, REBECCA S.; HAWLEY, WILLIAM A.; LOUNIBOS, L. PHILIP

2008-01-01

Aedes albopictus, commonly known as the Asian tiger mosquito, is currently the most invasive mosquito in the world. It is of medical importance due to its aggressive daytime human-biting behavior and ability to vector many viruses, including dengue, LaCrosse, and West Nile. Invasions into new areas of its potential range are often initiated through the transportation of eggs via the international trade in used tires. We use a genetic algorithm, Genetic Algorithm for Rule Set Production (GARP), to determine the ecological niche of Ae. albopictus and predict a global ecological risk map for the continued spread of the species. We combine this analysis with risk due to importation of tires from infested countries and their proximity to countries that have already been invaded to develop a list of countries most at risk for future introductions and establishments. Methods used here have potential for predicting risks of future invasions of vectors or pathogens. PMID:17417960

Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

NASA Astrophysics Data System (ADS)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common building blocks in organic chemistry---indicate that MOGAs produce High-quality semiempirical methods that (1) are stable to small perturbations, (2) yield accurate configuration energies on untested and critical excited states, and (3) yield ab initio quality excited-state dynamics. The proposed method enables simulations of more complex systems to realistic, multi-picosecond timescales, well beyond previous attempts or expectation of human experts, and 2--3 orders-of-magnitude reduction in computational cost. While the two applications use simple evolutionary operators, in order to tackle more complex systems, their scalability and limitations have to be investigated. The second part of the thesis addresses some of the challenges involved with a successful design of genetic algorithms and genetic programming for multiscale modeling. The first issue addressed is the scalability of genetic programming, where facetwise models are built to assess the population size required by GP to ensure adequate supply of raw building blocks and also to ensure accurate decision-making between competing building blocks. This study also presents a design of competent genetic programming, where traditional fixed recombination operators are replaced by building and sampling probabilistic models of promising candidate programs. The proposed scalable GP, called extended compact GP (eCGP), combines the ideas from extended compact genetic algorithm (eCGA) and probabilistic incremental program evolution (PIPE) and adaptively identifies, propagates and exchanges important subsolutions of a search problem. Results show that eCGP scales cubically with problem size on both GP-easy and GP-hard problems. Finally, facetwise models are developed to explore limitations of scalability of MOGAs, where the scalability of multiobjective algorithms in reliably maintaining Pareto-optimal solutions is addressed. The results show that even when the building blocks are accurately identified, massive multimodality of the search problems can easily overwhelm the nicher (diversity preserving operator) and lead to exponential scale-up. Facetwise models are developed, which incorporate the combined effects of model accuracy, decision making, and sub-structure supply, as well as the effect of niching on the population sizing, to predict a limit on the growth rate of a maximum number of sub-structures that can compete in the two objectives to circumvent the failure of the niching method. The results show that if the number of competing building blocks between multiple objectives is less than the proposed limit, multiobjective GAs scale-up polynomially with the problem size on boundedly-difficult problems.

Ecological niche modelling of bank voles in Western Europe.

PubMed

Amirpour Haredasht, Sara; Barrios, Miguel; Farifteh, Jamshid; Maes, Piet; Clement, Jan; Verstraeten, Willem W; Tersago, Katrien; Van Ranst, Marc; Coppin, Pol; Berckmans, Daniel; Aerts, Jean-Marie

2013-01-28

The bank vole (Myodes glareolus) is the natural host of Puumala virus (PUUV) in vast areas of Europe. PUUV is one of the hantaviruses which are transmitted to humans by infected rodents. PUUV causes a general mild form of hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover influences disease transmission by controlling both the spatial distribution of vectors or hosts, as well as by facilitating the human contact with them. In this study the use of ecological niche modelling (ENM) for predicting the geographical distribution of bank vole population on the basis of spatial climate information is tested. The Genetic Algorithm for Rule-set Prediction (GARP) is used to model the ecological niche of bank voles in Western Europe. The meteorological data, land cover types and geo-referenced points representing the locations of the bank voles (latitude/longitude) in the study area are used as the primary model input value. The predictive accuracy of the bank vole ecologic niche model was significant (training accuracy of 86%). The output of the GARP models based on the 50% subsets of points used for testing the model showed an accuracy of 75%. Compared with random models, the probability of such high predictivity was low (χ(2) tests, p < 10(-6)). As such, the GARP models were predictive and the used ecologic niche model indeed indicates the ecologic requirements of bank voles. This approach successfully identified the areas of infection risk across the study area. The result suggests that the niche modelling approach can be implemented in a next step towards the development of new tools for monitoring the bank vole's population.

Ecological Niche Modelling of Bank Voles in Western Europe

PubMed Central

Amirpour Haredasht, Sara; Barrios, Miguel; Farifteh, Jamshid; Maes, Piet; Clement, Jan; Verstraeten, Willem W.; Tersago, Katrien; Van Ranst, Marc; Coppin, Pol; Berckmans, Daniel; Aerts, Jean-Marie

2013-01-01

The bank vole (Myodes glareolus) is the natural host of Puumala virus (PUUV) in vast areas of Europe. PUUV is one of the hantaviruses which are transmitted to humans by infected rodents. PUUV causes a general mild form of hemorrhagic fever with renal syndrome (HFRS) called nephropathia epidemica (NE). Vector-borne and zoonotic diseases generally display clear spatial patterns due to different space-dependent factors. Land cover influences disease transmission by controlling both the spatial distribution of vectors or hosts, as well as by facilitating the human contact with them. In this study the use of ecological niche modelling (ENM) for predicting the geographical distribution of bank vole population on the basis of spatial climate information is tested. The Genetic Algorithm for Rule-set Prediction (GARP) is used to model the ecological niche of bank voles in Western Europe. The meteorological data, land cover types and geo-referenced points representing the locations of the bank voles (latitude/longitude) in the study area are used as the primary model input value. The predictive accuracy of the bank vole ecologic niche model was significant (training accuracy of 86%). The output of the GARP models based on the 50% subsets of points used for testing the model showed an accuracy of 75%. Compared with random models, the probability of such high predictivity was low (χ2 tests, p < 10−6). As such, the GARP models were predictive and the used ecologic niche model indeed indicates the ecologic requirements of bank voles. This approach successfully identified the areas of infection risk across the study area. The result suggests that the niche modelling approach can be implemented in a next step towards the development of new tools for monitoring the bank vole’s population. PMID:23358234

How does pollen versus seed dispersal affect niche evolution?

PubMed

Aguilée, Robin; Shaw, Frank H; Rousset, François; Shaw, Ruth G; Ronce, Ophélie

2013-03-01

In heterogeneous landscapes, the genetic and demographic consequences of dispersal influence the evolution of niche width. Unless pollen is limiting, pollen dispersal does not contribute directly to population growth. However, by disrupting local adaptation, it indirectly affects population dynamics. We compare the effect of pollen versus seed dispersal on the evolution of niche width in heterogeneous habitats, explicitly considering the feedback between maladaptation and demography. We consider two scenarios: the secondary contact of two subpopulations, in distinct, formerly isolated habitats, and the colonization of an empty habitat with dispersal between the new and ancestral habitat. With an analytical model, we identify critical levels of genetic variance leading to niche contraction (secondary contact scenario), or expansion (new habitat scenario). We confront these predictions with simulations where the genetic variance freely evolves. Niche contraction occurs when habitats are very different. It is faster as total gene flow increases or as pollen predominates in overall gene flow. Niche expansion occurs when habitat heterogeneity is not too high. Seed dispersal accelerates it, whereas pollen dispersal tends to retard it. In both scenarios very high seed dispersal leads to extinction. Overall, our results predict a wider niche for species dispersing seeds more than pollen. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data.

PubMed

Zhu, Jinning; Xu, Xuan; Tao, Qing; Yi, Panpan; Yu, Dan; Xu, Xinwei

2017-07-01

Ecological niche modeling is an effective tool to characterize the spatial distribution of suitable areas for species, and it is especially useful for predicting the potential distribution of invasive species. The widespread submerged plant Hydrilla verticillata (hydrilla) has an obvious phylogeographical pattern: Four genetic lineages occupy distinct regions in native range, and only one lineage invades the Americas. Here, we aimed to evaluate climatic niche conservatism of hydrilla in North America at the intraspecific level and explore its invasion potential in the Americas by comparing climatic niches in a phylogenetic context. Niche shift was found in the invasion process of hydrilla in North America, which is probably mainly attributed to high levels of somatic mutation. Dramatic changes in range expansion in the Americas were predicted in the situation of all four genetic lineages invading the Americas or future climatic changes, especially in South America; this suggests that there is a high invasion potential of hydrilla in the Americas. Our findings provide useful information for the management of hydrilla in the Americas and give an example of exploring intraspecific climatic niche to better understand species invasion.

Cryptic elevational zonation in trapdoor spiders (Araneae, Antrodiaetidae, Aliatypus janus complex) from the California southern Sierra Nevada.

PubMed

Starrett, James; Hayashi, Cheryl Y; Derkarabetian, Shahan; Hedin, Marshal

2018-01-01

The relative roles of ecological niche conservatism versus niche divergence in promoting montane speciation remains an important topic in biogeography. Here, our aim was to test whether lineage diversification in a species complex of trapdoor spiders corresponds with riverine barriers or with an ecological gradient associated with elevational tiering. Aliatypus janus was sampled from throughout its range, with emphasis on populations in the southern Sierra Nevada Mountains of California. We collected multi-locus genetic data to generate a species tree for A. janus and its close relatives. Coalescent based hypothesis tests were conducted to determine if genetic breaks within A. janus conform to riverine barriers. Ecological niche models (ENM) under current and Last Glacial Maximum (LGM) conditions were generated and hypothesis tests of niche conservatism and divergence were performed. Coalescent analyses reveal deeply divergent genetic lineages within A. janus, likely corresponding to cryptic species. Two primary lineages meet along an elevational gradient on the western slopes of the southern Sierra Nevada Mountains. ENMs under both current and LGM conditions indicate that these groups occupy largely non-overlapping niches. ENM hypothesis testing rejected niche identity between the two groups, and supported a sharp ecological gradient occurring where the groups meet. However, the niche similarity test indicated that the two groups may not inhabit different background niches. The Sierra Nevada Mountains provide a natural laboratory for simultaneously testing ecological niche divergence and conservatism and their role in speciation across a diverse range of taxa. Aliatypus janus represents a species complex with cryptic lineages that may have diverged due to parapatric speciation along an ecological gradient, or been maintained by the evolution of ecological niche differences following allopatric speciation. Copyright © 2017 Elsevier Inc. All rights reserved.

Human-climate interaction during the Early Upper Paleolithic: testing the hypothesis of an adaptive shift between the Proto-Aurignacian and the Early Aurignacian.

PubMed

Banks, William E; d'Errico, Francesco; Zilhão, João

2013-01-01

The Aurignacian technocomplex comprises a succession of culturally distinct phases. Between its first two subdivisions, the Proto-Aurignacian and the Early Aurignacian, we see a shift from single to separate reduction sequences for blade and bladelet production, the appearance of split-based antler points, and a number of other changes in stone tool typology and technology as well as in symbolic material culture. Bayesian modeling of available (14)C determinations, conducted within the framework of this study, indicates that these material culture changes are coincident with abrupt and marked climatic changes. The Proto-Aurignacian occurs during an interval (ca. 41.5-39.9 k cal BP) of relative climatic amelioration, Greenland Interstadials (GI) 10 and 9, punctuated by a short cold stadial. The Early Aurignacian (ca. 39.8-37.9 k cal BP) predominantly falls within the climatic phase known as Heinrich Stadial (HS) 4, and its end overlaps with the beginning of GI 8, the former being predominantly characterized by cold and dry conditions across the European continent. We use eco-cultural niche modeling to quantitatively evaluate whether these shifts in material culture are correlated with environmental variability and, if so, whether the ecological niches exploited by human populations shifted accordingly. We employ genetic algorithm (GARP) and maximum entropy (Maxent) techniques to estimate the ecological niches exploited by humans (i.e., eco-cultural niches) during these two phases of the Aurignacian. Partial receiver operating characteristic analyses are used to evaluate niche variability between the two phases. Results indicate that the changes in material culture between the Proto-Aurignacian and the Early Aurignacian are associated with an expansion of the ecological niche. These shifts in both the eco-cultural niche and material culture are interpreted to represent an adaptive response to the relative deterioration of environmental conditions at the onset of HS4. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation of the impacts of climate change on disease vectors through ecological niche modelling.

PubMed

Carvalho, B M; Rangel, E F; Vale, M M

2017-08-01

Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.

Human niche construction in interdisciplinary focus

PubMed Central

Kendal, Jeremy; Tehrani, Jamshid J.; Odling-Smee, John

2011-01-01

Niche construction is an endogenous causal process in evolution, reciprocal to the causal process of natural selection. It works by adding ecological inheritance, comprising the inheritance of natural selection pressures previously modified by niche construction, to genetic inheritance in evolution. Human niche construction modifies selection pressures in environments in ways that affect both human evolution, and the evolution of other species. Human ecological inheritance is exceptionally potent because it includes the social transmission and inheritance of cultural knowledge, and material culture. Human genetic inheritance in combination with human cultural inheritance thus provides a basis for gene–culture coevolution, and multivariate dynamics in cultural evolution. Niche construction theory potentially integrates the biological and social aspects of the human sciences. We elaborate on these processes, and provide brief introductions to each of the papers published in this theme issue. PMID:21320894

Impact of climate change on vector transmission of Trypanosoma cruzi (Chagas, 1909) in North America.

PubMed

Carmona-Castro, O; Moo-Llanes, D A; Ramsey, J M

2018-03-01

Climate change can influence the geographical range of the ecological niche of pathogens by altering biotic interactions with vectors and reservoirs. The distributions of 20 epidemiologically important triatomine species in North America were modelled, comparing the genetic algorithm for rule-set prediction (GARP) and maximum entropy (MaxEnt), with or without topographical variables. Potential shifts in transmission niche for Trypanosoma cruzi (Trypanosomatida: Trypanosomatidae) (Chagas, 1909) were analysed for 2050 and 2070 in Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. There were no significant quantitative range differences between the GARP and MaxEnt models, but GARP models best represented known distributions for most species [partial-receiver operating characteristic (ROC) > 1]; elevation was an important variable contributing to the ecological niche model (ENM). There was little difference between niche breadth projections for RCP 4.5 and RCP 8.5; the majority of species shifted significantly in both periods. Those species with the greatest current distribution range are expected to have the greatest shifts. Positional changes in the centroid, although reduced for most species, were associated with latitude. A significant increase or decrease in mean niche elevation is expected principally for Neotropical 1 species. The impact of climate change will be specific to each species, its biogeographical region and its latitude. North American triatomines with the greatest current distribution ranges (Nearctic 2 and Nearctic/Neotropical) will have the greatest future distribution shifts. Significant shifts (increases or decreases) in mean elevation over time are projected principally for the Neotropical species with the broadest current distributions. Changes in the vector exposure threat to the human population were significant for both future periods, with a 1.48% increase for urban populations and a 1.76% increase for rural populations in 2050. © 2017 The Royal Entomological Society.

Population genetics and ecological niche of invasive Aedes albopictus in Mexico.

PubMed

Pech-May, Angélica; Moo-Llanes, David A; Puerto-Avila, María Belem; Casas, Mauricio; Danis-Lozano, Rogelio; Ponce, Gustavo; Tun-Ku, Ezequiel; Pinto-Castillo, José Francisco; Villegas, Alejandro; Ibáñez-Piñon, Clemente R; González, Cassandra; Ramsey, Janine M

2016-05-01

The Asian tiger mosquito Aedes albopictus (Skuse), is one of the most invasive mosquito species worldwide. In Mexico it is now recorded in 12 states and represents a serious public health problem, given the recent introduction of Chikungunya on the southern border. The aim of this study was to analyze the population genetics of A. albopictus from all major recorded foci, and model its ecological niche. Niche similarity with that from its autochthonous distribution in Asia and other invaded countries were analyzed and its potential future expansion and potential human exposure in climate change scenarios measured. We analyzed 125 sequences of a 317 bp fragment of the cyt b gene from seven A. albopictus populations across Mexico. The samples belong to 25 haplotypes with moderate population structuring (Fst=0.081, p<0.02) and population expansion. The most prevalent haplotype, found in all principal sites, was shared with the USA, Brazil, France, Madagascar, and Reunion Island. The ecological niche model using Mexican occurrence records covers 79.7% of the country, and has an 83% overlap with the Asian niche projected to Mexico. Both Neotropical and Nearctic regions are included in the Mexican niche model. Currently in Mexico, 38.6 million inhabitants are exposed to A. albopictus, which is expected to increase to 45.6 million by 2070. Genetic evidence supports collection information that A. albopictus was introduced to Mexico principally by land from the USA and Central and South America. Prevalent haplotypes from Mexico are shared with most invasive regions across the world, just as there was high niche similarity with both natural and invaded regions. The important overlap with the Asian niche model suggests a high potential for the species to disperse to sylvatic regions in Mexico. Copyright © 2016 Elsevier B.V. All rights reserved.

Runaway cultural niche construction

PubMed Central

Rendell, Luke; Fogarty, Laurel; Laland, Kevin N.

2011-01-01

Cultural niche construction is a uniquely potent source of selection on human populations, and a major cause of recent human evolution. Previous theoretical analyses have not, however, explored the local effects of cultural niche construction. Here, we use spatially explicit coevolutionary models to investigate how cultural processes could drive selection on human genes by modifying local resources. We show that cultural learning, expressed in local niche construction, can trigger a process with dynamics that resemble runaway sexual selection. Under a broad range of conditions, cultural niche-constructing practices generate selection for gene-based traits and hitchhike to fixation through the build up of statistical associations between practice and trait. This process can occur even when the cultural practice is costly, or is subject to counteracting transmission biases, or the genetic trait is selected against. Under some conditions a secondary hitchhiking occurs, through which genetic variants that enhance the capability for cultural learning are also favoured by similar dynamics. We suggest that runaway cultural niche construction could have played an important role in human evolution, helping to explain why humans are simultaneously the species with the largest relative brain size, the most potent capacity for niche construction and the greatest reliance on culture. PMID:21320897

Corresponding Mitochondrial DNA and Niche Divergence for Crested Newt Candidate Species

PubMed Central

Wielstra, Ben; Beukema, Wouter; Arntzen, Jan W.; Skidmore, Andrew K.; Toxopeus, Albertus G.; Raes, Niels

2012-01-01

Genetic divergence of mitochondrial DNA does not necessarily correspond to reproductive isolation. However, if mitochondrial DNA lineages occupy separate segments of environmental space, this supports the notion of their evolutionary independence. We explore niche differentiation among three candidate species of crested newt (characterized by distinct mitochondrial DNA lineages) and interpret the results in the light of differences observed for recognized crested newt species. We quantify niche differences among all crested newt (candidate) species and test hypotheses regarding niche evolution, employing two ordination techniques (PCA-env and ENFA). Niche equivalency is rejected: all (candidate) species are found to occupy significantly different segments of environmental space. Furthermore, niche overlap values for the three candidate species are not significantly higher than those for the recognized species. As the three candidate crested newt species are, not only in terms of mitochondrial DNA genetic divergence, but also ecologically speaking, as diverged as the recognized crested newt species, our findings are in line with the hypothesis that they represent cryptic species. We address potential pitfalls of our methodology. PMID:23029564

Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest

PubMed Central

Xu, Wumei; Liu, Lu; He, Tianhua; Cao, Min; Sha, Liqing; Hu, Yuehua; Li, Qiaoming; Li, Jie

2016-01-01

A negative species-genetic diversity correlation (SGDC) could be predicted by the niche variation hypothesis, whereby an increase in species diversity within community reduces the genetic diversity of the co-occurring species because of the reduction in average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of the species within community. We tested these predictions within a 20 ha tropical forest dynamics plot (FDP) in the Xishuangbanna tropical seasonal rainforest. We established 15 plots within the FDP and investigated the soil properties, tree diversity, and genetic diversity of a common tree species Beilschmiedia roxburghiana within each plot. We observed a significant negative correlation between tree diversity and the genetic diversity of B. roxburghiana within the communities. Using structural equation modeling, we further determined that the inter-plot environmental characteristics (soil pH and phosphorus availability) directly affected tree diversity and that the tree diversity within the community determined the genetic diversity of B. roxburghiana. Increased soil pH and phosphorus availability might promote the coexistence of more tree species within community and reduce genetic diversity of B. roxburghiana for the reduced average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of B. roxburghiana within community. PMID:26860815

Soil properties drive a negative correlation between species diversity and genetic diversity in a tropical seasonal rainforest.

PubMed

Xu, Wumei; Liu, Lu; He, Tianhua; Cao, Min; Sha, Liqing; Hu, Yuehua; Li, Qiaoming; Li, Jie

2016-02-10

A negative species-genetic diversity correlation (SGDC) could be predicted by the niche variation hypothesis, whereby an increase in species diversity within community reduces the genetic diversity of the co-occurring species because of the reduction in average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of the species within community. We tested these predictions within a 20 ha tropical forest dynamics plot (FDP) in the Xishuangbanna tropical seasonal rainforest. We established 15 plots within the FDP and investigated the soil properties, tree diversity, and genetic diversity of a common tree species Beilschmiedia roxburghiana within each plot. We observed a significant negative correlation between tree diversity and the genetic diversity of B. roxburghiana within the communities. Using structural equation modeling, we further determined that the inter-plot environmental characteristics (soil pH and phosphorus availability) directly affected tree diversity and that the tree diversity within the community determined the genetic diversity of B. roxburghiana. Increased soil pH and phosphorus availability might promote the coexistence of more tree species within community and reduce genetic diversity of B. roxburghiana for the reduced average niche breadth; alternatively, competition could reduce effective population size and therefore genetic diversity of B. roxburghiana within community.

Individual welfare maximization in electricity markets including consumer and full transmission system modeling

NASA Astrophysics Data System (ADS)

Weber, James Daniel

1999-11-01

This dissertation presents a new algorithm that allows a market participant to maximize its individual welfare in the electricity spot market. The use of such an algorithm in determining market equilibrium points, called Nash equilibria, is also demonstrated. The start of the algorithm is a spot market model that uses the optimal power flow (OPF), with a full representation of the transmission system. The OPF is also extended to model consumer behavior, and a thorough mathematical justification for the inclusion of the consumer model in the OPF is presented. The algorithm utilizes price and dispatch sensitivities, available from the Hessian matrix of the OPF, to help determine an optimal change in an individual's bid. The algorithm is shown to be successful in determining local welfare maxima, and the prospects for scaling the algorithm up to realistically sized systems are very good. Assuming a market in which all participants maximize their individual welfare, economic equilibrium points, called Nash equilibria, are investigated. This is done by iteratively solving the individual welfare maximization algorithm for each participant until a point is reached where all individuals stop modifying their bids. It is shown that these Nash equilibria can be located in this manner. However, it is also demonstrated that equilibria do not always exist, and are not always unique when they do exist. It is also shown that individual welfare is a highly nonconcave function resulting in many local maxima. As a result, a more global optimization technique, using a genetic algorithm (GA), is investigated. The genetic algorithm is successfully demonstrated on several systems. It is also shown that a GA can be developed using special niche methods, which allow a GA to converge to several local optima at once. Finally, the last chapter of this dissertation covers the development of a new computer visualization routine for power system analysis: contouring. The contouring algorithm is demonstrated to be useful in visualizing bus-based and transmission line-based quantities.

Range and niche shifts in response to past climate change in the desert horned lizard (Phrynosoma platyrhinos)

PubMed Central

Jezkova, Tereza; Jaeger, Jef R.; Oláh-Hemmings, Viktória; Jones, K. Bruce; Lara-Resendiz, Rafael A.; Mulcahy, Daniel G.; Riddle, Brett R.

2015-01-01

During climate change, species are often assumed to shift their geographic distributions (geographic ranges) in order to track environmental conditions – niches – to which they are adapted. Recent work, however, suggests that the niches do not always remain conserved during climate change but shift instead, allowing populations to persist in place or expand into new areas. We assessed the extent of range and niche shifts in response to the warming climate after the Last Glacial Maximum (LGM) in the desert horned lizard (Phrynosoma platyrhinos), a species occupying the western deserts of North America. We used a phylogeographic approach with mitochondrial DNA sequences to approximate the species range during the LGM by identifying populations that exhibit a genetic signal of population stability versus those that exhibit a signal of a recent (likely post-LGM) geographic expansion. We then compared the climatic niche that the species occupies today with the niche it occupied during the LGM using two models of simulated LGM climate. The genetic analyses indicated that P. platyrhinos persisted within the southern Mojave and Sonoran deserts throughout the latest glacial period and expanded from these deserts northwards, into the western and eastern Great Basin, after the LGM. The climatic niche comparisons revealed that P. platyrhinos expanded its climatic niche after the LGM towards novel, warmer and drier climates that allowed it to persist within the southern deserts. Simultaneously, the species shifted its climatic niche towards greater temperature and precipitation fluctuations after the LGM. We concluded that climatic changes at the end of the LGM promoted both range and niche shifts in this lizard. The mechanism that allowed the species to shift its niche remains unknown, but phenotypic plasticity likely contributes to the species ability to adjust to climate change. PMID:27231410

Range and niche shifts in response to past climate change in the desert horned lizard (Phrynosoma platyrhinos).

PubMed

Jezkova, Tereza; Jaeger, Jef R; Oláh-Hemmings, Viktória; Jones, K Bruce; Lara-Resendiz, Rafael A; Mulcahy, Daniel G; Riddle, Brett R

2016-05-01

During climate change, species are often assumed to shift their geographic distributions (geographic ranges) in order to track environmental conditions - niches - to which they are adapted. Recent work, however, suggests that the niches do not always remain conserved during climate change but shift instead, allowing populations to persist in place or expand into new areas. We assessed the extent of range and niche shifts in response to the warming climate after the Last Glacial Maximum (LGM) in the desert horned lizard ( Phrynosoma platyrhinos ), a species occupying the western deserts of North America. We used a phylogeographic approach with mitochondrial DNA sequences to approximate the species range during the LGM by identifying populations that exhibit a genetic signal of population stability versus those that exhibit a signal of a recent (likely post-LGM) geographic expansion. We then compared the climatic niche that the species occupies today with the niche it occupied during the LGM using two models of simulated LGM climate. The genetic analyses indicated that P. platyrhinos persisted within the southern Mojave and Sonoran deserts throughout the latest glacial period and expanded from these deserts northwards, into the western and eastern Great Basin, after the LGM. The climatic niche comparisons revealed that P. platyrhinos expanded its climatic niche after the LGM towards novel, warmer and drier climates that allowed it to persist within the southern deserts. Simultaneously, the species shifted its climatic niche towards greater temperature and precipitation fluctuations after the LGM. We concluded that climatic changes at the end of the LGM promoted both range and niche shifts in this lizard. The mechanism that allowed the species to shift its niche remains unknown, but phenotypic plasticity likely contributes to the species ability to adjust to climate change.

Adaptation and niche construction in human prehistory: a case study from the southern Scandinavian Late Glacial

PubMed Central

Riede, Felix

2011-01-01

The niche construction model postulates that human bio-social evolution is composed of three inheritance domains, genetic, cultural and ecological, linked by feedback selection. This paper argues that many kinds of archaeological data can serve as proxies for human niche construction processes, and presents a method for investigating specific niche construction hypotheses. To illustrate this method, the repeated emergence of specialized reindeer (Rangifer tarandus) hunting/herding economies during the Late Palaeolithic (ca 14.7–11.5 kyr BP) in southern Scandinavia is analysed from a niche construction/triple-inheritance perspective. This economic relationship resulted in the eventual domestication of Rangifer. The hypothesis of whether domestication was achieved as early as the Late Palaeolithic, and whether this required the use of domesticated dogs (Canis familiaris) as hunting, herding or transport aids, is tested via a comparative analysis using material culture-based phylogenies and ecological datasets in relation to demographic/genetic proxies. Only weak evidence for sustained niche construction behaviours by prehistoric hunter–gatherer in southern Scandinavia is found, but this study nonetheless provides interesting insights into the likely processes of dog and reindeer domestication, and into processes of adaptation in Late Glacial foragers. PMID:21320895

Adaptation and niche construction in human prehistory: a case study from the southern Scandinavian Late Glacial.

PubMed

Riede, Felix

2011-03-27

The niche construction model postulates that human bio-social evolution is composed of three inheritance domains, genetic, cultural and ecological, linked by feedback selection. This paper argues that many kinds of archaeological data can serve as proxies for human niche construction processes, and presents a method for investigating specific niche construction hypotheses. To illustrate this method, the repeated emergence of specialized reindeer (Rangifer tarandus) hunting/herding economies during the Late Palaeolithic (ca 14.7-11.5 kyr BP) in southern Scandinavia is analysed from a niche construction/triple-inheritance perspective. This economic relationship resulted in the eventual domestication of Rangifer. The hypothesis of whether domestication was achieved as early as the Late Palaeolithic, and whether this required the use of domesticated dogs (Canis familiaris) as hunting, herding or transport aids, is tested via a comparative analysis using material culture-based phylogenies and ecological datasets in relation to demographic/genetic proxies. Only weak evidence for sustained niche construction behaviours by prehistoric hunter-gatherer in southern Scandinavia is found, but this study nonetheless provides interesting insights into the likely processes of dog and reindeer domestication, and into processes of adaptation in Late Glacial foragers.

Redefining the Australian Anthrax Belt: Modeling the Ecological Niche and Predicting the Geographic Distribution of Bacillus anthracis

PubMed Central

Barro, Alassane S.; Fegan, Mark; Moloney, Barbara; Porter, Kelly; Muller, Janine; Warner, Simone; Blackburn, Jason K.

2016-01-01

The ecology and distribution of B. anthracis in Australia is not well understood, despite the continued occurrence of anthrax outbreaks in the eastern states of the country. Efforts to estimate the spatial extent of the risk of disease have been limited to a qualitative definition of an anthrax belt extending from southeast Queensland through the centre of New South Wales and into northern Victoria. This definition of the anthrax belt does not consider the role of environmental conditions in the distribution of B. anthracis. Here, we used the genetic algorithm for rule-set prediction model system (GARP), historical anthrax outbreaks and environmental data to model the ecological niche of B. anthracis and predict its potential geographic distribution in Australia. Our models reveal the niche of B. anthracis in Australia is characterized by a narrow range of ecological conditions concentrated in two disjunct corridors. The most dominant corridor, used to redefine a new anthrax belt, parallels the Eastern Highlands and runs from north Victoria to central east Queensland through the centre of New South Wales. This study has redefined the anthrax belt in eastern Australia and provides insights about the ecological factors that limit the distribution of B. anthracis at the continental scale for Australia. The geographic distributions identified can help inform anthrax surveillance strategies by public and veterinary health agencies. PMID:27280981

Redefining the Australian Anthrax Belt: Modeling the Ecological Niche and Predicting the Geographic Distribution of Bacillus anthracis.

PubMed

Barro, Alassane S; Fegan, Mark; Moloney, Barbara; Porter, Kelly; Muller, Janine; Warner, Simone; Blackburn, Jason K

2016-06-01

The ecology and distribution of B. anthracis in Australia is not well understood, despite the continued occurrence of anthrax outbreaks in the eastern states of the country. Efforts to estimate the spatial extent of the risk of disease have been limited to a qualitative definition of an anthrax belt extending from southeast Queensland through the centre of New South Wales and into northern Victoria. This definition of the anthrax belt does not consider the role of environmental conditions in the distribution of B. anthracis. Here, we used the genetic algorithm for rule-set prediction model system (GARP), historical anthrax outbreaks and environmental data to model the ecological niche of B. anthracis and predict its potential geographic distribution in Australia. Our models reveal the niche of B. anthracis in Australia is characterized by a narrow range of ecological conditions concentrated in two disjunct corridors. The most dominant corridor, used to redefine a new anthrax belt, parallels the Eastern Highlands and runs from north Victoria to central east Queensland through the centre of New South Wales. This study has redefined the anthrax belt in eastern Australia and provides insights about the ecological factors that limit the distribution of B. anthracis at the continental scale for Australia. The geographic distributions identified can help inform anthrax surveillance strategies by public and veterinary health agencies.

Correlations of Genotype with Climate Parameters Suggest Caenorhabditis elegans Niche Adaptations

PubMed Central

Evans, Kathryn S.; Zhao, Yuehui; Brady, Shannon C.; Long, Lijiang; McGrath, Patrick T.; Andersen, Erik C.

2016-01-01

Species inhabit a variety of environmental niches, and the adaptation to a particular niche is often controlled by genetic factors, including gene-by-environment interactions. The genes that vary in order to regulate the ability to colonize a niche are often difficult to identify, especially in the context of complex ecological systems and in experimentally uncontrolled natural environments. Quantitative genetic approaches provide an opportunity to investigate correlations between genetic factors and environmental parameters that might define a niche. Previously, we have shown how a collection of 208 whole-genome sequenced wild Caenorhabditis elegans can facilitate association mapping approaches. To correlate climate parameters with the variation found in this collection of wild strains, we used geographic data to exhaustively curate daily weather measurements in short-term (3 month), middle-term (one year), and long-term (three year) durations surrounding the date of strain isolation. These climate parameters were used as quantitative traits in association mapping approaches, where we identified 11 quantitative trait loci (QTL) for three climatic variables: elevation, relative humidity, and average temperature. We then narrowed the genomic interval of interest to identify gene candidates with variants potentially underlying phenotypic differences. Additionally, we performed two-strain competition assays at high and low temperatures to validate a QTL that could underlie adaptation to temperature and found suggestive evidence supporting that hypothesis. PMID:27866149

Drosophila Glypicans Regulate Follicle Stem Cell Maintenance and Niche Competition.

PubMed

Su, Tsu-Yi; Nakato, Eriko; Choi, Pui Yee; Nakato, Hiroshi

2018-04-09

Adult stem cells reside in specialized microenvironments, called niches, which provide signals for stem cells to maintain their undifferentiated and self-renewing state. To maintain stem cell quality, several types of stem cells are known to be regularly replaced by progenitor cells through niche competition. However, the cellular and molecular bases for stem cell competition for niche occupancy are largely unknown. Here, we show that two Drosophila members of the glypican family of heparan sulfate proteoglycans (HSPGs), Dally and Dally-like (Dlp), differentially regulate follicle stem cell (FSC) maintenance and FSC competitiveness for niche occupancy. Lineage analyses of glypican mutant FSC clones showed that dally is essential for normal FSC maintenance. In contrast, dlp is a hyper-competitive mutation: dlp mutant FSC progenitors often eventually occupy the entire epithelial sheet. RNAi knockdown experiments showed that Dally and Dlp play both partially redundant and distinct roles in regulating Jak/Stat, Wg and Hh signaling in FSCs. The Drosophila FSC system offers a powerful genetic model to study the mechanisms by which HSPGs exert specific functions in stem cell replacement and competition. Copyright © 2018, Genetics.

Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection.

PubMed

Bemmels, Jordan B; Title, Pascal O; Ortego, Joaquín; Knowles, L Lacey

2016-10-01

Past climate change has caused shifts in species distributions and undoubtedly impacted patterns of genetic variation, but the biological processes mediating responses to climate change, and their genetic signatures, are often poorly understood. We test six species-specific biologically informed hypotheses about such processes in canyon live oak (Quercus chrysolepis) from the California Floristic Province. These hypotheses encompass the potential roles of climatic niche, niche multidimensionality, physiological trade-offs in functional traits, and local-scale factors (microsites and local adaptation within ecoregions) in structuring genetic variation. Specifically, we use ecological niche models (ENMs) to construct temporally dynamic landscapes where the processes invoked by each hypothesis are reflected by differences in local habitat suitabilities. These landscapes are used to simulate expected patterns of genetic variation under each model and evaluate the fit of empirical data from 13 microsatellite loci genotyped in 226 individuals from across the species range. Using approximate Bayesian computation (ABC), we obtain very strong support for two statistically indistinguishable models: a trade-off model in which growth rate and drought tolerance drive habitat suitability and genetic structure, and a model based on the climatic niche estimated from a generic ENM, in which the variables found to make the most important contribution to the ENM have strong conceptual links to drought stress. The two most probable models for explaining the patterns of genetic variation thus share a common component, highlighting the potential importance of seasonal drought in driving historical range shifts in a temperate tree from a Mediterranean climate where summer drought is common. © 2016 John Wiley & Sons Ltd.

Insights from ecological niche modeling on the taxonomic distinction and niche differentiation between the black-spotted and red-spotted tokay geckoes (Gekko gecko).

PubMed

Zhang, Yueyun; Chen, Chongtao; Li, Li; Zhao, Chengjian; Chen, Weicai; Huang, Yong

2014-09-01

The black-spotted tokay and the red-spotted tokay are morphologically distinct and have largely allopatric distributions. The black-spotted tokay is characterized by a small body size and dark skin with sundry spots, while the red-spotted tokay has a relatively large body size and red spots. Based on morphological, karyotypic, genetic, and distribution differences, recent studies suggested their species status; however, their classifications remain controversial, and additional data such as ecological niches are necessary to establish firm hypotheses regarding their taxonomic status. We reconstructed their ecological niches models using climatic and geographic data. We then performed niche similarity tests (niche identity and background tests) and point-based analyses to explore whether ecological differentiation has occurred, and whether such differences are sufficient to explain the maintenance of their separate segments of environmental ranges. We found that both niche models of the black- and the red-spotted tokay had a good fit and a robust performance, as indicated by the high area under the curve (AUC) values ("black" = 0.982, SD = ± 0.002, "red" = 0.966 ± 0.02). Significant ecological differentiation across the entire geographic range was found, indicating that the involvement of ecological differentiation is important for species differentiation. Divergence along the environmental axes is highly associated with climatic conditions, with isothermality being important for the "black" form, while temperature seasonality, precipitation of warmest quarter, and annual temperature range together being important for the "red" form. These factors are likely important factors in niche differentiation between the two forms, which result in morphological replacement. Overall, beside morphological and genetic differentiation information, our results contribute to additional insights into taxonomic distinction and niche differentiation between the black- and the red-spotted tokay.

Environmental niche divergence among three dune shrub sister species with parapatric distributions

PubMed Central

Chefaoui, Rosa M.; Correia, Otília; Bonal, Raúl; Hortal, Joaquín

2017-01-01

Abstract Background and Aims The geographical distributions of species are constrained by their ecological requirements. The aim of this work was to analyse the effects of environmental conditions, historical events and biogeographical constraints on the diversification of the three species of the western Mediterranean shrub genus Stauracanthus, which have a parapatric distribution in the Iberian Peninsula. Methods Ecological niche factor analysis and generalized linear models were used to measure the response of all Stauracanthus species to the environmental gradients and map their potential distributions in the Iberian Peninsula. The bioclimatic niche overlap between the three species was determined by using Schoener's index. The genetic differentiation of the Iberian and northern African populations of Stauracanthus species was characterized with GenalEx. The effects on genetic distances of the most important environmental drivers were assessed through Mantel tests and non-metric multidimensional scaling. Key Results The three Stauracanthus species show remarkably similar responses to climatic conditions. This supports the idea that all members of this recently diversified clade retain common adaptations to climate and consequently high levels of climatic niche overlap. This contrasts with the diverse edaphic requirements of Stauracanthus species. The populations of the S. genistoides–spectabilis clade grow on Miocene and Pliocene fine-textured sedimentary soils, whereas S. boivinii, the more genetically distant species, occurs on older and more coarse-textured sedimentary substrates. These patterns of diversification are largely consistent with a stochastic process of geographical range expansion and fragmentation coupled with niche evolution in the context of spatially complex environmental fluctuations. Conclusions: The combined analysis of the distribution, realized environmental niche and phylogeographical relationships of parapatric species proposed in this work allows integration of the biogeographical, ecological and evolutionary processes driving the evolution of species adaptations and how they determine their current geographical ranges. PMID:28334085

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparative and functional genomics of the Lactococcus lactis taxon; insights into evolution and niche adaptation.

PubMed

Kelleher, Philip; Bottacini, Francesca; Mahony, Jennifer; Kilcawley, Kieran N; van Sinderen, Douwe

2017-03-29

Lactococcus lactis is among the most widely studied lactic acid bacterial species due to its long history of safe use and economic importance to the dairy industry, where it is exploited as a starter culture in cheese production. In the current study, we report on the complete sequencing of 16 L. lactis subsp. lactis and L. lactis subsp. cremoris genomes. The chromosomal features of these 16 L. lactis strains in conjunction with 14 completely sequenced, publicly available lactococcal chromosomes were assessed with particular emphasis on discerning the L. lactis subspecies division, evolution and niche adaptation. The deduced pan-genome of L. lactis was found to be closed, indicating that the representative data sets employed for this analysis are sufficient to fully describe the genetic diversity of the taxon. Niche adaptation appears to play a significant role in governing the genetic content of each L. lactis subspecies, while (differential) genome decay and redundancy in the dairy niche is also highlighted.

Combining Quantitative Genetic Footprinting and Trait Enrichment Analysis to Identify Fitness Determinants of a Bacterial Pathogen

PubMed Central

Wiles, Travis J.; Norton, J. Paul; Russell, Colin W.; Dalley, Brian K.; Fischer, Kael F.; Mulvey, Matthew A.

2013-01-01

Strains of Extraintestinal Pathogenic Escherichia c oli (ExPEC) exhibit an array of virulence strategies and are a major cause of urinary tract infections, sepsis and meningitis. Efforts to understand ExPEC pathogenesis are challenged by the high degree of genetic and phenotypic variation that exists among isolates. Determining which virulence traits are widespread and which are strain-specific will greatly benefit the design of more effective therapies. Towards this goal, we utilized a quantitative genetic footprinting technique known as transposon insertion sequencing (Tn-seq) in conjunction with comparative pathogenomics to functionally dissect the genetic repertoire of a reference ExPEC isolate. Using Tn-seq and high-throughput zebrafish infection models, we tracked changes in the abundance of ExPEC variants within saturated transposon mutant libraries following selection within distinct host niches. Nine hundred and seventy bacterial genes (18% of the genome) were found to promote pathogen fitness in either a niche-dependent or independent manner. To identify genes with the highest therapeutic and diagnostic potential, a novel Trait Enrichment Analysis (TEA) algorithm was developed to ascertain the phylogenetic distribution of candidate genes. TEA revealed that a significant portion of the 970 genes identified by Tn-seq have homologues more often contained within the genomes of ExPEC and other known pathogens, which, as suggested by the first axiom of molecular Koch's postulates, is considered to be a key feature of true virulence determinants. Three of these Tn-seq-derived pathogen-associated genes—a transcriptional repressor, a putative metalloendopeptidase toxin and a hypothetical DNA binding protein—were deleted and shown to independently affect ExPEC fitness in zebrafish and mouse models of infection. Together, the approaches and observations reported herein provide a resource for future pathogenomics-based research and highlight the diversity of factors required by a single ExPEC isolate to survive within varying host environments. PMID:23990803

Analysis of Genetic Algorithm for Rule-Set Production (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersinia pestis, in California.

PubMed

Adjemian, Jennifer C Z; Girvetz, Evan H; Beckett, Laurel; Foley, Janet E

2006-01-01

More than 20 species of fleas in California are implicated as potential vectors of Yersinia pestis. Extremely limited spatial data exist for plague vectors-a key component to understanding where the greatest risks for human, domestic animal, and wildlife health exist. This study increases the spatial data available for 13 potential plague vectors by using the ecological niche modeling system Genetic Algorithm for Rule-Set Production (GARP) to predict their respective distributions. Because the available sample sizes in our data set varied greatly from one species to another, we also performed an analysis of the robustness of GARP by using the data available for flea Oropsylla montana (Baker) to quantify the effects that sample size and the chosen explanatory variables have on the final species distribution map. GARP effectively modeled the distributions of 13 vector species. Furthermore, our analyses show that all of these modeled ranges are robust, with a sample size of six fleas or greater not significantly impacting the percentage of the in-state area where the flea was predicted to be found, or the testing accuracy of the model. The results of this study will help guide the sampling efforts of future studies focusing on plague vectors.

Distance, elevation and environment as drivers of diversity and divergence in bumble bees across latitude and altitude.

PubMed

Jackson, Jason M; Pimsler, Meaghan L; Oyen, Kennan Jeannet; Koch-Uhuad, Jonathan B; Herndon, James D; Strange, James P; Dillon, Michael E; Lozier, Jeffrey D

2018-06-04

Identifying drivers of dispersal limitation and genetic differentiation is a key goal in biogeography. We examine patterns of population connectivity and genetic diversity using restriction site-associated DNA sequencing (RADseq) in two bumble bee species, Bombus vosnesenskii and Bombus bifarius, across latitude and altitude in mountain ranges from California, Oregon and Washington, U.S.A. Bombus vosnesenskii, which occurs across a broader elevational range at most latitudes, exhibits little population structure while B. bifarius, which occupies a relatively narrow higher elevation niche across most latitudes, exhibits much stronger population differentiation, although gene flow in both species is best explained by isolation with environmental niche resistance. A relationship between elevational habitat breadth and genetic diversity is also apparent, with B. vosnesenskii exhibiting relatively consistent levels of genetic diversity across its range, while B. bifarius has reduced genetic diversity at low latitudes, where it is restricted to high-elevation habitat. The results of this study highlight the importance of the intersect between elevational range and habitat suitability in influencing population connectivity and suggest that future climate warming will have a fragmenting effect even on populations that are presently well connected, as they track their thermal niches upward in montane systems. © 2018 John Wiley & Sons Ltd.

Phylogenetic niche conservatism and the evolutionary basis of ecological speciation.

PubMed

Pyron, R Alexander; Costa, Gabriel C; Patten, Michael A; Burbrink, Frank T

2015-11-01

Phylogenetic niche conservatism (PNC) typically refers to the tendency of closely related species to be more similar to each other in terms of niche than they are to more distant relatives. This has been implicated as a potential driving force in speciation and other species-richness patterns, such as latitudinal gradients. However, PNC has not been very well defined in most previous studies. Is it a pattern or a process? What are the underlying endogenous (e.g. genetic) and exogenous (e.g. ecological) factors that cause niches to be conserved? What degree of similarity is necessary to qualify as PNC? Is it possible for the evolutionary processes causing niches to be conserved to also result in niche divergence in different habitats? Here, we revisit these questions, codifying a theoretical and operational definition of PNC as a mechanistic evolutionary process resulting from several factors. We frame this both from a macroevolutionary and population-genetic perspective. We discuss how different axes of physical (e.g. geographic) and environmental (e.g. climatic) heterogeneity interact with the fundamental process of PNC to produce different outcomes of ecological speciation. We also review tests for PNC, and suggest ways that these could be improved or better utilized in future studies. Ultimately, PNC as a process has a well-defined mechanistic basis in organisms, and future studies investigating ecological speciation would be well served to consider this, and frame hypothesis testing in terms of the processes and expected patterns described herein. The process of PNC may lead to patterns where niches are conserved (more similar than expected), constrained (divergent within a limited subset of available niches), or divergent (less similar than expected), based on degree of phylogenetic relatedness between species. © 2014 Cambridge Philosophical Society.

Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm.

PubMed

Levine, Rebecca S; Peterson, A Townsend; Benedict, Mark Q

2004-02-01

The distribution of the Anopheles gambiae complex of malaria vectors in Africa is uncertain due to under-sampling of vast regions. We use ecologic niche modeling to predict the potential distribution of three members of the complex (A. gambiae, A. arabiensis, and A. quadriannulatus) and demonstrate the statistical significance of the models. Predictions correspond well to previous estimates, but provide detail regarding spatial discontinuities in the distribution of A. gambiae s.s. that are consistent with population genetic studies. Our predictions also identify large areas of Africa where the presence of A. arabiensis is predicted, but few specimens have been obtained, suggesting under-sampling of the species. Finally, we project models developed from African distribution data for the late 1900s into the past and to South America to determine retrospectively whether the deadly 1929 introduction of A. gambiae sensu lato into Brazil was more likely that of A. gambiae sensu stricto or A. arabiensis.

Foraging and farming as niche construction: stable and unstable adaptations

PubMed Central

Rowley-Conwy, Peter; Layton, Robert

2011-01-01

All forager (or hunter–gatherer) societies construct niches, many of them actively by the concentration of wild plants into useful stands, small-scale cultivation, burning of natural vegetation to encourage useful species, and various forms of hunting, collectively termed ‘low-level food production’. Many such niches are stable and can continue indefinitely, because forager populations are usually stable. Some are unstable, but these usually transform into other foraging niches, not geographically expansive farming niches. The Epipalaeolithic (final hunter–gatherer) niche in the Near East was complex but stable, with a relatively high population density, until destabilized by an abrupt climatic change. The niche was unintentionally transformed into an agricultural one, due to chance genetic and behavioural attributes of some wild plant and animal species. The agricultural niche could be exported with modifications over much of the Old World. This was driven by massive population increase and had huge impacts on local people, animals and plants wherever the farming niche was carried. Farming niches in some areas may temporarily come close to stability, but the history of the last 11 000 years does not suggest that agriculture is an effective strategy for achieving demographic and political stability in the world's farming populations. PMID:21320899

Modulation of occluding junctions alters the hematopoietic niche to trigger immune activation

PubMed Central

Khadilkar, Rohan J; Vogl, Wayne; Goodwin, Katharine

2017-01-01

Stem cells are regulated by signals from their microenvironment, or niche. During Drosophila hematopoiesis, a niche regulates prohemocytes to control hemocyte production. Immune challenges activate cell-signalling to initiate the cellular and innate immune response. Specifically, certain immune challenges stimulate the niche to produce signals that induce prohemocyte differentiation. However, the mechanisms that promote prohemocyte differentiation subsequent to immune challenges are poorly understood. Here we show that bacterial infection induces the cellular immune response by modulating occluding-junctions at the hematopoietic niche. Occluding-junctions form a permeability barrier that regulates the accessibility of prohemocytes to niche derived signals. The immune response triggered by infection causes barrier breakdown, altering the prohemocyte microenvironment to induce immune cell production. Moreover, genetically induced barrier ablation provides protection against infection by activating the immune response. Our results reveal a novel role for occluding-junctions in regulating niche-hematopoietic progenitor signalling and link this mechanism to immune cell production following infection. PMID:28841136

Emergence and dynamics of self-producing information niches as a step towards pre-evolutionary organization

PubMed Central

Carter, Richard J.; Wiesner, Karoline

2018-01-01

As a step towards understanding pre-evolutionary organization in non-genetic systems, we develop a model to investigate the emergence and dynamics of proto-autopoietic networks in an interacting population of simple information processing entities (automata). Our simulations indicate that dynamically stable strongly connected networks of mutually producing communication channels emerge under specific environmental conditions. We refer to these distinct organizational steady states as information niches. In each case, we measure the information content by the Shannon entropy, and determine the fitness landscape, robustness and transition pathways for information niches subjected to intermittent environmental perturbations under non-evolutionary conditions. By determining the information required to generate each niche, we show that niche transitions are only allowed if accompanied by an equal or increased level of information production that arises internally or via environmental perturbations that serve as an exogenous source of population diversification. Overall, our simulations show how proto-autopoietic networks of basic information processors form and compete, and under what conditions they persist over time or go extinct. These findings may be relevant to understanding how inanimate systems such as chemically communicating protocells can initiate the transition to living matter prior to the onset of contemporary evolutionary and genetic mechanisms. PMID:29343630

Adaptation of S. cerevisiae to Fermented Food Environments Reveals Remarkable Genome Plasticity and the Footprints of Domestication.

PubMed

Legras, Jean-Luc; Galeote, Virginie; Bigey, Frédéric; Camarasa, Carole; Marsit, Souhir; Nidelet, Thibault; Sanchez, Isabelle; Couloux, Arnaud; Guy, Julie; Franco-Duarte, Ricardo; Marcet-Houben, Marina; Gabaldon, Toni; Schuller, Dorit; Sampaio, José Paulo; Dequin, Sylvie

2018-07-01

The budding yeast Saccharomyces cerevisiae can be found in the wild and is also frequently associated with human activities. Despite recent insights into the phylogeny of this species, much is still unknown about how evolutionary processes related to anthropogenic niches have shaped the genomes and phenotypes of S. cerevisiae. To address this question, we performed population-level sequencing of 82 S. cerevisiae strains from wine, flor, rum, dairy products, bakeries, and the natural environment (oak trees). These genomic data enabled us to delineate specific genetic groups corresponding to the different ecological niches and revealed high genome content variation across the groups. Most of these strains, compared with the reference genome, possessed additional genetic elements acquired by introgression or horizontal transfer, several of which were population-specific. In addition, several genomic regions in each population showed evidence of nonneutral evolution, as shown by high differentiation, or of selective sweeps including genes with key functions in these environments (e.g., amino acid transport for wine yeast). Linking genetics to lifestyle differences and metabolite traits has enabled us to elucidate the genetic basis of several niche-specific population traits, such as growth on galactose for cheese strains. These data indicate that yeast has been subjected to various divergent selective pressures depending on its niche, requiring the development of customized genomes for better survival in these environments. These striking genome dynamics associated with local adaptation and domestication reveal the remarkable plasticity of the S. cerevisiae genome, revealing this species to be an amazing complex of specialized populations.

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

PubMed Central

Shinneman, Douglas J.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate. PMID:26985674

Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation.

PubMed

Shinneman, Douglas J; Means, Robert E; Potter, Kevin M; Hipkins, Valerie D

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Environmental niche divergence among three dune shrub sister species with parapatric distributions.

PubMed

Chozas, Sergio; Chefaoui, Rosa M; Correia, Otília; Bonal, Raúl; Hortal, Joaquín

2017-05-01

The geographical distributions of species are constrained by their ecological requirements. The aim of this work was to analyse the effects of environmental conditions, historical events and biogeographical constraints on the diversification of the three species of the western Mediterranean shrub genus Stauracanthus , which have a parapatric distribution in the Iberian Peninsula. Ecological niche factor analysis and generalized linear models were used to measure the response of all Stauracanthus species to the environmental gradients and map their potential distributions in the Iberian Peninsula. The bioclimatic niche overlap between the three species was determined by using Schoener's index. The genetic differentiation of the Iberian and northern African populations of Stauracanthus species was characterized with GenalEx. The effects on genetic distances of the most important environmental drivers were assessed through Mantel tests and non-metric multidimensional scaling. The three Stauracanthus species show remarkably similar responses to climatic conditions. This supports the idea that all members of this recently diversified clade retain common adaptations to climate and consequently high levels of climatic niche overlap. This contrasts with the diverse edaphic requirements of Stauracanthus species. The populations of the S. genistoides-spectabilis clade grow on Miocene and Pliocene fine-textured sedimentary soils, whereas S. boivinii , the more genetically distant species, occurs on older and more coarse-textured sedimentary substrates. These patterns of diversification are largely consistent with a stochastic process of geographical range expansion and fragmentation coupled with niche evolution in the context of spatially complex environmental fluctuations. : The combined analysis of the distribution, realized environmental niche and phylogeographical relationships of parapatric species proposed in this work allows integration of the biogeographical, ecological and evolutionary processes driving the evolution of species adaptations and how they determine their current geographical ranges. © The Author 2017. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation

USGS Publications Warehouse

Shinneman, Douglas; Means, Robert E.; Potter, Kevin M.; Hipkins, Valerie D.

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Why developmental niche construction is not selective niche construction: and why it matters.

PubMed

Stotz, Karola

2017-10-06

In the last decade, niche construction has been heralded as the neglected process in evolution. But niche construction is just one way in which the organism's interaction with and construction of the environment can have potential evolutionary significance. The constructed environment does not just select for , it also produces new variation. Nearly 3 decades ago, and in parallel with Odling-Smee's article 'Niche-constructing phenotypes', West and King introduced the 'ontogenetic niche' to give the phenomena of exo genetic inheritance a formal name. Since then, a range of fields in the life sciences and medicine has amassed evidence that parents influence their offspring by means other than DNA (parental effects), and proposed mechanisms for how heritable variation can be environmentally induced and developmentally regulated. The concept of 'developmental niche construction' (DNC) elucidates how a diverse range of mechanisms contributes to the transgenerational transfer of developmental resources. My most central of claims is that whereas the selective niche of niche construction theory is primarily used to explain the active role of the organism in its selective environment, DNC is meant to indicate the active role of the organism in its developmental environment. The paper highlights the differences between the construction of the selective and the developmental niche, and explores the overall significance of DNC for evolutionary theory.

Niche construction through phenological plasticity: life history dynamics and ecological consequences.

PubMed

Donohue, Kathleen

2005-04-01

The ability of an organism to alter the environment that it experiences has been termed 'niche construction'. Plants have several ways whereby they can determine the environment to which they are exposed at different life stages. This paper discusses three of these: plasticity in dispersal, flowering timing and germination timing. It reviews pathways through which niche construction alters evolutionary and ecological trajectories by altering the selective environment to which organisms are exposed, the phenotypic expression of plastic characters, and the expression of genetic variation. It provides examples whereby niche construction creates positive or negative feedbacks between phenotypes and environments, which in turn cause novel evolutionary constraints and novel life-history expression. Copyright New Phytologist (2005).

Genetic Basis for Developmental Homeostasis of Germline Stem Cell Niche Number: A Network of Tramtrack-Group Nuclear BTB Factors

PubMed Central

Chalvet, Fabienne; Netter, Sophie; Dos Santos, Nicolas; Poisot, Emilie; Paces-Fessy, Mélanie; Cumenal, Delphine; Peronnet, Frédérique; Pret, Anne-Marie; Théodore, Laurent

2012-01-01

The potential to produce new cells during adult life depends on the number of stem cell niches and the capacity of stem cells to divide, and is therefore under the control of programs ensuring developmental homeostasis. However, it remains generally unknown how the number of stem cell niches is controlled. In the insect ovary, each germline stem cell (GSC) niche is embedded in a functional unit called an ovariole. The number of ovarioles, and thus the number of GSC niches, varies widely among species. In Drosophila, morphogenesis of ovarioles starts in larvae with the formation of terminal filaments (TFs), each made of 8–10 cells that pile up and sort in stacks. TFs constitute organizers of individual germline stem cell niches during larval and early pupal development. In the Drosophila melanogaster subgroup, the number of ovarioles varies interspecifically from 8 to 20. Here we show that pipsqueak, Trithorax-like, batman and the bric-à-brac (bab) locus, all encoding nuclear BTB/POZ factors of the Tramtrack Group, are involved in limiting the number of ovarioles in D. melanogaster. At least two different processes are differentially perturbed by reducing the function of these genes. We found that when the bab dose is reduced, sorting of TF cells into TFs was affected such that each TF contains fewer cells and more TFs are formed. In contrast, psq mutants exhibited a greater number of TF cells per ovary, with a normal number of cells per TF, thereby leading to formation of more TFs per ovary than in the wild type. Our results indicate that two parallel genetic pathways under the control of a network of nuclear BTB factors are combined in order to negatively control the number of germline stem cell niches. PMID:23185495

Genetic and environmental effects on seed weight in subspecies of big sagebrush: Applications for restoration

Treesearch

Bryce A. Richardson; Hector G. Ortiz; Stephanie L. Carlson; Deidre M. Jaeger; Nancy L. Shaw

2015-01-01

The sagebrush steppe is a patchwork of species and subspecies occupying distinct environmental niches across the intermountain regions of western North America. These ecosystems face degradation from disturbances and exotic weeds. Using sagebrush seed that is matched to its appropriate niche is a critical component to successful restoration, improving habitat for the...

Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.

PubMed

Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; He, Zongzhen; Liu, Yajun; Liu, Zhaowen

2017-09-14

Genome-wide association study is especially challenging in detecting high-order disease-causing models due to model diversity, possible low or even no marginal effect of the model, and extraordinary search and computations. In this paper, we propose a niche harmony search algorithm where joint entropy is utilized as a heuristic factor to guide the search for low or no marginal effect model, and two computationally lightweight scores are selected to evaluate and adapt to diverse of disease models. In order to obtain all possible suspected pathogenic models, niche technique merges with HS, which serves as a taboo region to avoid HS trapping into local search. From the resultant set of candidate SNP-combinations, we use G-test statistic for testing true positives. Experiments were performed on twenty typical simulation datasets in which 12 models are with marginal effect and eight ones are with no marginal effect. Our results indicate that the proposed algorithm has very high detection power for searching suspected disease models in the first stage and it is superior to some typical existing approaches in both detection power and CPU runtime for all these datasets. Application to age-related macular degeneration (AMD) demonstrates our method is promising in detecting high-order disease-causing models.

Niche construction game cancer cells play

NASA Astrophysics Data System (ADS)

Bergman, Aviv; Gligorijevic, Bojana

2015-10-01

Niche construction concept was originally defined in evolutionary biology as the continuous interplay between natural selection via environmental conditions and the modification of these conditions by the organism itself. Processes unraveling during cancer metastasis include construction of niches, which cancer cells use towards more efficient survival, transport into new environments and preparation of the remote sites for their arrival. Many elegant experiments were done lately illustrating, for example, the premetastatic niche construction, but there is practically no mathematical modeling done which would apply the niche construction framework. To create models useful for understanding niche construction role in cancer progression, we argue that a) genetic, b) phenotypic and c) ecological levels are to be included. While the model proposed here is phenomenological in its current form, it can be converted into a predictive outcome model via experimental measurement of the model parameters. Here we give an overview of an experimentally formulated problem in cancer metastasis and propose how niche construction framework can be utilized and broadened to model it. Other life science disciplines, such as host-parasite coevolution, may also benefit from niche construction framework adaptation, to satisfy growing need for theoretical considerations of data collected by experimental biology.

Niche construction game cancer cells play.

PubMed

Bergman, Aviv; Gligorijevic, Bojana

2015-10-01

Niche construction concept was originally defined in evolutionary biology as the continuous interplay between natural selection via environmental conditions and the modification of these conditions by the organism itself. Processes unraveling during cancer metastasis include construction of niches, which cancer cells use towards more efficient survival, transport into new environments and preparation of the remote sites for their arrival. Many elegant experiments were done lately illustrating, for example, the premetastatic niche construction, but there is practically no mathematical modeling done which would apply the niche construction framework. To create models useful for understanding niche construction role in cancer progression, we argue that a) genetic, b) phenotypic and c) ecological levels are to be included. While the model proposed here is phenomenological in its current form, it can be converted into a predictive outcome model via experimental measurement of the model parameters. Here we give an overview of an experimentally formulated problem in cancer metastasis and propose how niche construction framework can be utilized and broadened to model it. Other life science disciplines, such as host-parasite coevolution, may also benefit from niche construction framework adaptation, to satisfy growing need for theoretical considerations of data collected by experimental biology.

The shaping of genetic variation in edge-of-range populations under past and future climate change

PubMed Central

Razgour, Orly; Juste, Javier; Ibáñez, Carlos; Kiefer, Andreas; Rebelo, Hugo; Puechmaille, Sébastien J; Arlettaz, Raphael; Burke, Terry; Dawson, Deborah A; Beaumont, Mark; Jones, Gareth; Wiens, John

2013-01-01

With rates of climate change exceeding the rate at which many species are able to shift their range or adapt, it is important to understand how future changes are likely to affect biodiversity at all levels of organisation. Understanding past responses and extent of niche conservatism in climatic tolerance can help predict future consequences. We use an integrated approach to determine the genetic consequences of past and future climate changes on a bat species, Plecotus austriacus. Glacial refugia predicted by palaeo-modelling match those identified from analyses of extant genetic diversity and model-based inference of demographic history. Former refugial populations currently contain disproportionately high genetic diversity, but niche conservatism, shifts in suitable areas and barriers to migration mean that these hotspots of genetic diversity are under threat from future climate change. Evidence of population decline despite recent northward migration highlights the need to conserve leading-edge populations for spearheading future range shifts. PMID:23890483

Stem cell dynamics in the hair follicle niche

PubMed Central

Rompolas, Panteleimon; Greco, Valentina

2014-01-01

Hair follicles are skin appendages of the mammalian skin that have the ability to periodically and stereotypically regenerate in order to continuously produce new hair over our lifetime. The ability of the hair follicle to regenerate is due to the presence of stem cells that along with other cell populations and non-cellular components, including molecular signals and extracellular material, make up a niche microenvironment. Mounting evidence suggests that the niche is critical for regulating stem cell behavior and thus the process of regeneration. Here we review the literature concerning past and current studies that have utilized mouse genetic models, combined with other approaches to dissect the molecular and cellular composition of the hair follicle niche. We also discuss our current understanding of how stem cells operate within the niche during the process of tissue regeneration and the factors that regulate their behavior. PMID:24361866

Niche construction drives social dependence in hermit crabs.

PubMed

Laidre, Mark E

2012-10-23

Organisms can receive not only a genetic inheritance from their ancestors but also an ecological inheritance, involving modifications their ancestors made to the environment through niche construction. Ecological inheritances may persist as a legacy, potentially generating selection pressures that favor sociality. Yet, most proposed cases of sociality being impacted by an ecological inheritance come from organisms that live among close kin and were highly social before their niche construction began. Here, I show that in terrestrial hermit crabs (Coenobita compressus)--organisms that do not live with kin and reside alone, each in its own shell--niche-construction drives social dependence, such that individuals can only survive in remodeled shells handed down from conspecifics. These results suggest that niche construction can be an important initiator of evolutionary pressures to socialize, even among unrelated and otherwise asocial organisms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change

PubMed Central

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-01-01

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change. PMID:27621443

Pollen dispersal slows geographical range shift and accelerates ecological niche shift under climate change.

PubMed

Aguilée, Robin; Raoul, Gaël; Rousset, François; Ronce, Ophélie

2016-09-27

Species may survive climate change by migrating to track favorable climates and/or adapting to different climates. Several quantitative genetics models predict that species escaping extinction will change their geographical distribution while keeping the same ecological niche. We introduce pollen dispersal in these models, which affects gene flow but not directly colonization. We show that plant populations may escape extinction because of both spatial range and ecological niche shifts. Exact analytical formulas predict that increasing pollen dispersal distance slows the expected spatial range shift and accelerates the ecological niche shift. There is an optimal distance of pollen dispersal, which maximizes the sustainable rate of climate change. These conclusions hold in simulations relaxing several strong assumptions of our analytical model. Our results imply that, for plants with long distance of pollen dispersal, models assuming niche conservatism may not accurately predict their future distribution under climate change.

Interactions between Genetic and Ecological Effects on the Evolution of Life Cycles.

PubMed

Rescan, Marie; Lenormand, Thomas; Roze, Denis

2016-01-01

Sexual reproduction leads to an alternation between haploid and diploid phases, whose relative length varies widely across taxa. Previous genetical models showed that diploid or haploid life cycles may be favored, depending on dominance interactions and on effective recombination rates. By contrast, niche differentiation between haploids and diploids may favor biphasic life cycles, in which development occurs in both phases. In this article, we explore the interplay between genetical and ecological factors, assuming that deleterious mutations affect the competitivity of individuals within their ecological niche and allowing different effects of mutations in haploids and diploids (including antagonistic selection). We show that selection on a modifier gene affecting the relative length of both phases can be decomposed into a direct selection term favoring the phase with the highest mean fitness (due to either ecological differences or differential effects of mutations) and an indirect selection term favoring the phase in which selection is more efficient. When deleterious alleles occur at many loci and in the presence of ecological differentiation between haploids and diploids, evolutionary branching often occurs and leads to the stable coexistence of alleles coding for haploid and diploid cycles, while temporal variations in niche sizes may stabilize biphasic cycles.

Range bagging: a new method for ecological niche modelling from presence-only data

PubMed Central

Drake, John M.

2015-01-01

The ecological niche is the set of environments in which a population of a species can persist without introduction of individuals from other locations. A good mathematical or computational representation of the niche is a prerequisite to addressing many questions in ecology, biogeography, evolutionary biology and conservation. A particularly challenging question for ecological niche modelling is the problem of presence-only modelling. That is, can an ecological niche be identified from records drawn only from the set of niche environments without records from non-niche environments for comparison? Here, I introduce a new method for ecological niche modelling from presence-only data called range bagging. Range bagging draws on the concept of a species' environmental range, but was inspired by the empirical performance of ensemble learning algorithms in other areas of ecological research. This paper extends the concept of environmental range to multiple dimensions and shows that range bagging is computationally feasible even when the number of environmental dimensions is large. The target of the range bagging base learner is an environmental tolerance of the species in a projection of its niche and is therefore an ecologically interpretable property of a species' biological requirements. The computational complexity of range bagging is linear in the number of examples, which compares favourably with the main alternative, Qhull. In conclusion, range bagging appears to be a reasonable choice for niche modelling in applications in which a presence-only method is desired and may provide a solution to problems in other disciplines where one-class classification is required, such as outlier detection and concept learning. PMID:25948612

Range bagging: a new method for ecological niche modelling from presence-only data.

PubMed

Drake, John M

2015-06-06

The ecological niche is the set of environments in which a population of a species can persist without introduction of individuals from other locations. A good mathematical or computational representation of the niche is a prerequisite to addressing many questions in ecology, biogeography, evolutionary biology and conservation. A particularly challenging question for ecological niche modelling is the problem of presence-only modelling. That is, can an ecological niche be identified from records drawn only from the set of niche environments without records from non-niche environments for comparison? Here, I introduce a new method for ecological niche modelling from presence-only data called range bagging. Range bagging draws on the concept of a species' environmental range, but was inspired by the empirical performance of ensemble learning algorithms in other areas of ecological research. This paper extends the concept of environmental range to multiple dimensions and shows that range bagging is computationally feasible even when the number of environmental dimensions is large. The target of the range bagging base learner is an environmental tolerance of the species in a projection of its niche and is therefore an ecologically interpretable property of a species' biological requirements. The computational complexity of range bagging is linear in the number of examples, which compares favourably with the main alternative, Qhull. In conclusion, range bagging appears to be a reasonable choice for niche modelling in applications in which a presence-only method is desired and may provide a solution to problems in other disciplines where one-class classification is required, such as outlier detection and concept learning.

Thermal niches of two invasive genotypes of the wheat curl mite Aceria tosichella (Acari: Eriophyidae): congruence between physiological and geographical distribution data

USDA-ARS?s Scientific Manuscript database

The wheat curl mite (WCM; Aceria tosichella) is a major pest of cereals worldwide. It is also a complex of well-defined genetic lineages with divergent physiological traits, which has not been accounted for in applied contexts. The aims of the study were to model the thermal niches of the two most p...

Niche divergence builds the case for ecological speciation in skinks of the Plestiodon skiltonianus species complex

USGS Publications Warehouse

Wogan, Guinevere O.U.; Richmond, Jonathan Q.

2015-01-01

Adaptation to different thermal environments has the potential to cause evolutionary changes that are sufficient to drive ecological speciation. Here, we examine whether climate-based niche divergence in lizards of the Plestiodon skiltonianus species complex is consistent with the outcomes of such a process. Previous work on this group shows that a mechanical sexual barrier has evolved between species that differ mainly in body size and that the barrier may be a by-product of selection for increased body size in lineages that have invaded xeric environments; however, baseline information on niche divergence among members of the group is lacking. We quantified the climatic niche using mechanistic physiological and correlative niche models and then estimated niche differences among species using ordination techniques and tests of niche overlap and equivalency. Our results show that the thermal niches of size-divergent, reproductively isolated morphospecies are significantly differentiated and that precipitation may have been as important as temperature in causing increased shifts in body size in xeric habitats. While these findings alone do not demonstrate thermal adaptation or identify the cause of speciation, their integration with earlier genetic and behavioral studies provides a useful test of phenotype–environment associations that further support the case for ecological speciation in these lizards.

Traffic jam functions in a branched pathway from Notch activation to niche cell fate.

PubMed

Wingert, Lindsey; DiNardo, Stephen

2015-07-01

The niche directs key behaviors of its resident stem cells, and is thus crucial for tissue maintenance, repair and longevity. However, little is known about the genetic pathways that guide niche specification and development. The male germline stem cell niche in Drosophila houses two stem cell populations and is specified within the embryonic gonad, thus making it an excellent model for studying niche development. The hub cells that form the niche are specified early by Notch activation. Over the next few hours, these individual cells then cluster together and take up a defined position before expressing markers of hub cell differentiation. This timing suggests that there are other factors for niche development yet to be defined. Here, we have identified a role for the large Maf transcription factor Traffic jam (Tj) in hub cell specification downstream of Notch. Tj downregulation is the first detectable effect of Notch activation in hub cells. Furthermore, Tj depletion is sufficient to generate ectopic hub cells that can recruit stem cells. Surprisingly, ectopic niche cells in tj mutants remain dispersed in the absence of Notch activation. This led us to uncover a branched pathway downstream of Notch in which Bowl functions to direct hub cell assembly in parallel to Tj downregulation. © 2015. Published by The Company of Biologists Ltd.

Eco-Evo-Devo: developmental symbiosis and developmental plasticity as evolutionary agents.

PubMed

Gilbert, Scott F; Bosch, Thomas C G; Ledón-Rettig, Cristina

2015-10-01

The integration of research from developmental biology and ecology into evolutionary theory has given rise to a relatively new field, ecological evolutionary developmental biology (Eco-Evo-Devo). This field integrates and organizes concepts such as developmental symbiosis, developmental plasticity, genetic accommodation, extragenic inheritance and niche construction. This Review highlights the roles that developmental symbiosis and developmental plasticity have in evolution. Developmental symbiosis can generate particular organs, can produce selectable genetic variation for the entire animal, can provide mechanisms for reproductive isolation, and may have facilitated evolutionary transitions. Developmental plasticity is crucial for generating novel phenotypes, facilitating evolutionary transitions and altered ecosystem dynamics, and promoting adaptive variation through genetic accommodation and niche construction. In emphasizing such non-genomic mechanisms of selectable and heritable variation, Eco-Evo-Devo presents a new layer of evolutionary synthesis.

Genomic Evidence for the Emergence and Evolution of Pathogenicity and Niche Preferences in the Genus Campylobacter

PubMed Central

Iraola, Gregorio; Pérez, Ruben; Naya, Hugo; Paolicchi, Fernando; Pastor, Eugenia; Valenzuela, Sebastián; Calleros, Lucía; Velilla, Alejandra; Hernández, Martín; Morsella, Claudia

2014-01-01

The genus Campylobacter includes some of the most relevant pathogens for human and animal health; the continuous effort in their characterization has also revealed new species putatively involved in different kind of infections. Nowadays, the available genomic data for the genus comprise a wide variety of species with different pathogenic potential and niche preferences. In this work, we contribute to enlarge this available information presenting the first genome for the species Campylobacter sputorum bv. sputorum and use this and the already sequenced organisms to analyze the emergence and evolution of pathogenicity and niche preferences among Campylobacter species. We found that campylobacters can be unequivocally distinguished in established and putative pathogens depending on their repertory of virulence genes, which have been horizontally acquired from other bacteria because the nonpathogenic Campylobacter ancestor emerged, and posteriorly interchanged between some members of the genus. Additionally, we demonstrated the role of both horizontal gene transfers and diversifying evolution in niche preferences, being able to distinguish genetic features associated to the tropism for oral, genital, and gastrointestinal tissues. In particular, we highlight the role of nonsynonymous evolution of disulphide bond proteins, the invasion antigen B (CiaB), and other secreted proteins in the determination of niche preferences. Our results arise from assessing the previously unmet goal of considering the whole available Campylobacter diversity for genome comparisons, unveiling notorious genetic features that could explain particular phenotypes and set the basis for future research in Campylobacter biology. PMID:25193310

Intraspecific ecological niche divergence and reproductive shifts foster cytotype displacement and provide ecological opportunity to polyploids.

PubMed

Karunarathne, Piyal; Schedler, Mara; Martínez, Eric J; Honfi, Ana I; Novichkova, Anastasiia; Hojsgaard, Diego

2018-05-11

Niche divergence between polyploids and their lower ploidy progenitors is one of the primary mechanisms fostering polyploid establishment and adaptive divergence. However, within-species chromosomal and reproductive variability have usually been neglected in community ecology and biodiversity analyses even though they have been recognized to play a role in the adaptive diversification of lineages. We used Paspalum intermedium, a grass species with diverging genetic systems (diploidy vs. autopolyploidy, allogamy vs. autogamy and sexuality vs. apomixis), to recognize the causality of biogeographic patterns, adaptation and ecological flexibility of cytotypes. Chromosome counts and flow cytometry were used to characterize within-species genetic systems diversity. Environmental niche modelling was used to evaluate intraspecific ecological attributes associated with environmental and climatic factors and to assess correlations among ploidy, reproductive modes and ecological conditions ruling species' population dynamics, range expansion, adaptation and evolutionary history. Two dominant cytotypes non-randomly distributed along local and regional geographical scales displayed niche differentiation, a directional shift in niche optima and signs of disruptive selection on ploidy-related ecological aptitudes for the exploitation of environmental resources. Ecologically specialized allogamous sexual diploids were found in northern areas associated with higher temperature, humidity and productivity, while generalist autogamous apomictic tetraploids occurred in southern areas, occupying colder and less productive environments. Four localities with a documented shift in ploidy and four mixed populations in a zone of ecological transition revealed an uneven replacement between cytotypes. Polyploidy and contrasting reproductive traits between cytotypes have promoted shifts in niche optima, and increased ecological tolerance and niche divergence. Ecologically specialized diploids maintain cytotype stability in core areas by displacing tetraploids, while broader ecological preferences and a shift from sexuality to apomixis favoured polyploid colonization in peripheral areas where diploids are displaced, and fostered the ecological opportunity for autotetraploids supporting range expansion to open southern habitats.

Has the "Equal Environments" assumption been tested in twin studies?

PubMed

Eaves, Lindon; Foley, Debra; Silberg, Judy

2003-12-01

A recurring criticism of the twin method for quantifying genetic and environmental components of human differences is the necessity of the so-called "equal environments assumption" (EEA) (i.e., that monozygotic and dizygotic twins experience equally correlated environments). It has been proposed to test the EEA by stratifying twin correlations by indices of the amount of shared environment. However, relevant environments may also be influenced by genetic differences. We present a model for the role of genetic factors in niche selection by twins that may account for variation in indices of the shared twin environment (e.g., contact between members of twin pairs). Simulations reveal that stratification of twin correlations by amount of contact can yield spurious evidence of large shared environmental effects in some strata and even give false indications of genotype x environment interaction. The stratification approach to testing the equal environments assumption may be misleading and the results of such tests may actually be consistent with a simpler theory of the role of genetic factors in niche selection.

Species delimitation in the Stenocereus griseus (Cactaceae) species complex reveals a new species, S. huastecorum

PubMed Central

Alvarado-Sizzo, Hernán; Parra, Fabiola; Arreola-Nava, Hilda Julieta; Terrazas, Teresa; Sánchez, Cristian

2018-01-01

The Stenocereus griseus species complex (SGSC) has long been considered taxonomically challenging because the number of taxa belonging to the complex and their geographical boundaries remain poorly understood. Bayesian clustering and genetic distance-based methods were used based on nine microsatellite loci in 377 individuals of three main putative species of the complex. The resulting genetic clusters were assessed for ecological niche divergence and areolar morphology, particularly spination patterns. We based our species boundaries on concordance between genetic, ecological, and morphological data, and were able to resolve four species, three of them corresponding to S. pruinosus from central Mexico, S. laevigatus from southern Mexico, and S. griseus from northern South America. A fourth species, previously considered to be S. griseus and commonly misidentified as S. pruinosus in northern Mexico showed significant genetic, ecological, and morphological differentiation suggesting that it should be considered a new species, S. huastecorum, which we describe here. We show that population genetic analyses, ecological niche modeling, and morphological studies are complementary approaches for delimiting species in taxonomically challenging plant groups such as the SGSC. PMID:29342184

Species delimitation in the Stenocereus griseus (Cactaceae) species complex reveals a new species, S. huastecorum.

PubMed

Alvarado-Sizzo, Hernán; Casas, Alejandro; Parra, Fabiola; Arreola-Nava, Hilda Julieta; Terrazas, Teresa; Sánchez, Cristian

2018-01-01

The Stenocereus griseus species complex (SGSC) has long been considered taxonomically challenging because the number of taxa belonging to the complex and their geographical boundaries remain poorly understood. Bayesian clustering and genetic distance-based methods were used based on nine microsatellite loci in 377 individuals of three main putative species of the complex. The resulting genetic clusters were assessed for ecological niche divergence and areolar morphology, particularly spination patterns. We based our species boundaries on concordance between genetic, ecological, and morphological data, and were able to resolve four species, three of them corresponding to S. pruinosus from central Mexico, S. laevigatus from southern Mexico, and S. griseus from northern South America. A fourth species, previously considered to be S. griseus and commonly misidentified as S. pruinosus in northern Mexico showed significant genetic, ecological, and morphological differentiation suggesting that it should be considered a new species, S. huastecorum, which we describe here. We show that population genetic analyses, ecological niche modeling, and morphological studies are complementary approaches for delimiting species in taxonomically challenging plant groups such as the SGSC.

Tracking climate change in a dispersal-limited species: reduced spatial and genetic connectivity in a montane salamander.

PubMed

Velo-Antón, G; Parra, J L; Parra-Olea, G; Zamudio, K R

2013-06-01

Tropical montane taxa are often locally adapted to very specific climatic conditions, contributing to their lower dispersal potential across complex landscapes. Climate and landscape features in montane regions affect population genetic structure in predictable ways, yet few empirical studies quantify the effects of both factors in shaping genetic structure of montane-adapted taxa. Here, we considered temporal and spatial variability in climate to explain contemporary genetic differentiation between populations of the montane salamander, Pseudoeurycea leprosa. Specifically, we used ecological niche modelling (ENM) and measured spatial connectivity and gene flow (using both mtDNA and microsatellite markers) across extant populations of P. leprosa in the Trans-Mexican Volcanic Belt (TVB). Our results indicate significant spatial and genetic isolation among populations, but we cannot distinguish between isolation by distance over time or current landscape barriers as mechanisms shaping population genetic divergences. Combining ecological niche modelling, spatial connectivity analyses, and historical and contemporary genetic signatures from different classes of genetic markers allows for inference of historical evolutionary processes and predictions of the impacts future climate change will have on the genetic diversity of montane taxa with low dispersal rates. Pseudoeurycea leprosa is one montane species among many endemic to this region and thus is a case study for the continued persistence of spatially and genetically isolated populations in the highly biodiverse TVB of central Mexico. © 2013 John Wiley & Sons Ltd.

c-Kit-Mediated Functional Positioning of Stem Cells to Their Niches Is Essential for Maintenance and Regeneration of Adult Hematopoiesis

PubMed Central

Kimura, Yuki; Ding, Bisen; Imai, Norikazu; Nolan, Daniel J.; Butler, Jason M.; Rafii, Shahin

2011-01-01

The mechanism by which hematopoietic stem and progenitor cells (HSPCs) through interaction with their niches maintain and reconstitute adult hematopoietic cells is unknown. To functionally and genetically track localization of HSPCs with their niches, we employed novel mutant loxPs, lox66 and lox71 and Cre-recombinase technology to conditionally delete c-Kit in adult mice, while simultaneously enabling GFP expression in the c-Kit-deficient cells. Conditional deletion of c-Kit resulted in hematopoietic failure and splenic atrophy both at steady state and after marrow ablation leading to the demise of the treated adult mice. Within the marrow, the c-Kit-expressing GFP+ cells were positioned to Kit ligand (KL)-expressing niche cells. This c-Kit-mediated cellular adhesion was essential for long-term maintenance and expansion of HSPCs. These results lay the foundation for delivering KL within specific niches to maintain and restore hematopoiesis. PMID:22046410

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus.

PubMed

García-Betancur, Juan-Carlos; Goñi-Moreno, Angel; Horger, Thomas; Schott, Melanie; Sharan, Malvika; Eikmeier, Julian; Wohlmuth, Barbara; Zernecke, Alma; Ohlsen, Knut; Kuttler, Christina; Lopez, Daniel

2017-09-12

A central question to biology is how pathogenic bacteria initiate acute or chronic infections. Here we describe a genetic program for cell-fate decision in the opportunistic human pathogen Staphylococcus aureus , which generates the phenotypic bifurcation of the cells into two genetically identical but different cell types during the course of an infection. Whereas one cell type promotes the formation of biofilms that contribute to chronic infections, the second type is planktonic and produces the toxins that contribute to acute bacteremia. We identified a bimodal switch in the agr quorum sensing system that antagonistically regulates the differentiation of these two physiologically distinct cell types. We found that extracellular signals affect the behavior of the agr bimodal switch and modify the size of the specialized subpopulations in specific colonization niches. For instance, magnesium-enriched colonization niches causes magnesium binding to S. aureus teichoic acids and increases bacterial cell wall rigidity. This signal triggers a genetic program that ultimately downregulates the agr bimodal switch. Colonization niches with different magnesium concentrations influence the bimodal system activity, which defines a distinct ratio between these subpopulations; this in turn leads to distinct infection outcomes in vitro and in an in vivo murine infection model. Cell differentiation generates physiological heterogeneity in clonal bacterial infections and helps to determine the distinct infection types.

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus

PubMed Central

García-Betancur, Juan-Carlos; Goñi-Moreno, Angel; Horger, Thomas; Schott, Melanie; Sharan, Malvika; Eikmeier, Julian; Wohlmuth, Barbara; Zernecke, Alma; Ohlsen, Knut; Kuttler, Christina

2017-01-01

A central question to biology is how pathogenic bacteria initiate acute or chronic infections. Here we describe a genetic program for cell-fate decision in the opportunistic human pathogen Staphylococcus aureus, which generates the phenotypic bifurcation of the cells into two genetically identical but different cell types during the course of an infection. Whereas one cell type promotes the formation of biofilms that contribute to chronic infections, the second type is planktonic and produces the toxins that contribute to acute bacteremia. We identified a bimodal switch in the agr quorum sensing system that antagonistically regulates the differentiation of these two physiologically distinct cell types. We found that extracellular signals affect the behavior of the agr bimodal switch and modify the size of the specialized subpopulations in specific colonization niches. For instance, magnesium-enriched colonization niches causes magnesium binding to S. aureusteichoic acids and increases bacterial cell wall rigidity. This signal triggers a genetic program that ultimately downregulates the agr bimodal switch. Colonization niches with different magnesium concentrations influence the bimodal system activity, which defines a distinct ratio between these subpopulations; this in turn leads to distinct infection outcomes in vitro and in an in vivo murine infection model. Cell differentiation generates physiological heterogeneity in clonal bacterial infections and helps to determine the distinct infection types. PMID:28893374

Population genetic structure of gray wolves (Canis lupus) in a marine archipelago suggests island-mainland differentiation consistent with dietary niche.

PubMed

Stronen, Astrid V; Navid, Erin L; Quinn, Michael S; Paquet, Paul C; Bryan, Heather M; Darimont, Christopher T

2014-06-10

Emerging evidence suggests that ecological heterogeneity across space can influence the genetic structure of populations, including that of long-distance dispersers such as large carnivores. On the central coast of British Columbia, Canada, wolf (Canis lupus L., 1758) dietary niche and parasite prevalence data indicate strong ecological divergence between marine-oriented wolves inhabiting islands and individuals on the coastal mainland that interact primarily with terrestrial prey. Local holders of traditional ecological knowledge, who distinguish between mainland and island wolf forms, also informed our hypothesis that genetic differentiation might occur between wolves from these adjacent environments. We used microsatellite genetic markers to examine data obtained from wolf faecal samples. Our results from 116 individuals suggest the presence of a genetic cline between mainland and island wolves. This pattern occurs despite field observations that individuals easily traverse the 30 km wide study area and swim up to 13 km among landmasses in the region. Natal habitat-biased dispersal (i.e., the preference for dispersal into familiar ecological environments) might contribute to genetic differentiation. Accordingly, this working hypothesis presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present.

Integrative analyses of speciation and divergence in Psammodromus hispanicus (Squamata: Lacertidae).

PubMed

Fitze, Patrick S; Gonzalez-Jimena, Virginia; San-Jose, Luis M; San Mauro, Diego; Aragón, Pedro; Suarez, Teresa; Zardoya, Rafael

2011-11-30

Genetic, phenotypic and ecological divergence within a lineage is the result of past and ongoing evolutionary processes, which lead ultimately to diversification and speciation. Integrative analyses allow linking diversification to geological, climatic, and ecological events, and thus disentangling the relative importance of different evolutionary drivers in generating and maintaining current species richness. Here, we use phylogenetic, phenotypic, geographic, and environmental data to investigate diversification in the Spanish sand racer (Psammodromus hispanicus). Phylogenetic, molecular clock dating, and phenotypic analyses show that P. hispanicus consists of three lineages. One lineage from Western Spain diverged 8.3 (2.9-14.7) Mya from the ancestor of Psammodromus hispanicus edwardsianus and P. hispanicus hispanicus Central lineage. The latter diverged 4.8 (1.5-8.7) Mya. Molecular clock dating, together with population genetic analyses, indicate that the three lineages experienced northward range expansions from southern Iberian refugia during Pleistocene glacial periods. Ecological niche modelling shows that suitable habitat of the Western lineage and P. h. edwardsianus overlap over vast areas, but that a barrier may hinder dispersal and genetic mixing of populations of both lineages. P. h. hispanicus Central lineage inhabits an ecological niche that overlaps marginally with the other two lineages. Our results provide evidence for divergence in allopatry and niche conservatism between the Western lineage and the ancestor of P. h. edwardsianus and P. h. hispanicus Central lineage, whereas they suggest that niche divergence is involved in the origin of the latter two lineages. Both processes were temporally separated and may be responsible for the here documented genetic and phenotypic diversity of P. hispanicus. The temporal pattern is in line with those proposed for other animal lineages. It suggests that geographic isolation and vicariance played an important role in the early diversification of the group, and that lineage diversification was further amplified through ecological divergence.

Resource Utilization and Environmental and Spatio-Temporal Overlap of a Hilltopping Lycaenid Butterfly Community in the Colombian Andes

PubMed Central

Prieto, Carlos; Dahners, Hans W.

2009-01-01

Coexistence by a great number of species could reflect niche segregation at several resource axes. Differences in the use of a hilltop as mating site for a Eumaeini (Lycaenidae) community were measured to test whether niche segregation exists within this group. Specimens were collected throughout 21 samplings between July-October of 2004 and July-October of 2005. Two environmental variables and three temporal-spacial variables were analyzed utilizing null models with three randomization algorithms. Significant differences were found among the species with respect to utilization of vertical space, horizontal space, temporary distribution and environmental temperature. The species did not show significant differences with respect to light intensity. For all samplings, the niche overlap observed in the two environmental variables were higher or significantly higher than expected by chance, suggesting that niche segregation does not exist due to competition within these variables. Similar results were observed for temporal distribution. Some evidence of niche segregation was found in vertical space and horizontal space variables where some samples presented lower overlap than expected by chance. The results pointed out that community's assemblage could be mainly shaped in two ways. The first is that species with determined habitat requirements fit into unoccupied niche spaces. The second is by niche segregation in the vertical space distribution variable. PMID:19613456

Environmental heterogeneity explains the genetic structure of Continental and Mediterranean populations of Fraxinus angustifolia Vahl.

PubMed

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations.

Environmental Heterogeneity Explains the Genetic Structure of Continental and Mediterranean Populations of Fraxinus angustifolia Vahl

PubMed Central

Temunović, Martina; Franjić, Jozo; Satovic, Zlatko; Grgurev, Marin; Frascaria-Lacoste, Nathalie; Fernández-Manjarrés, Juan F.

2012-01-01

Tree species with wide distributions often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in a wind-pollinated Mediterranean tree species, Fraxinus angustifolia Vahl, within a recognised glacial refugium in Croatia. We sampled 11 populations from environmentally divergent habitats within the Continental and Mediterranean biogeographical regions. We combined genetic data analyses based on nuclear microsatellite loci, multivariate statistics on environmental data and ecological niche modelling (ENM). We identified a geographic structure with a high genetic diversity and low differentiation in the Continental region, which contrasted with the significantly lower genetic diversity and higher population divergence in the Mediterranean region. The positive and significant correlation between environmental and genetic distances after controlling for geographic distance suggests an important influence of ecological divergence of the sites in shaping genetic variation. The ENM provided support for niche differentiation between the populations from the Continental and Mediterranean regions, suggesting that contemporary populations may represent two divergent ecotypes. Ecotype differentiation was also supported by multivariate environmental and genetic distance analyses. Our results suggest that despite extensive gene flow in continental areas, long-term stability of heterogeneous environments have likely promoted genetic divergence of ashes in this region and can explain the present-day genetic variation patterns of these ancient populations. PMID:22905171

Evolutionary engineering for industrial microbiology.

PubMed

Vanee, Niti; Fisher, Adam B; Fong, Stephen S

2012-01-01

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

A global design of high power Nd 3+-Yb 3+ co-doped fiber lasers

NASA Astrophysics Data System (ADS)

Fan, Zhang; Chuncan, Wang; Tigang, Ning

2008-09-01

A global optimization method - niche hybrid genetic algorithm (NHGA) based on fitness sharing and elite replacement is applied to optimize Nd3+-Yb3+ co-doped fiber lasers (NYDFLs) for obtaining maximum signal output power. With a objective function and different pumping powers, five critical parameters (the fiber length, L; the proportion of pump power for pumping Nd3+, η; Nd3+ and Yb3+ concentrations, NNd and NYb and output mirror reflectivity, Rout) of the given NYDFLs are optimized by solving the rate and power propagation equations. Results show that dividing equally the input pump power among 808 nm (Nd3+) and 940 nm (Yb3+) is not an optimal choice and the pump power of Nd3+ ions should be kept around 10-13.78% of the total pump power. Three optimal schemes are obtained by NHGA and the highest slope efficiency of the laser is able to reach 80.1%.

Genomic evidence for the emergence and evolution of pathogenicity and niche preferences in the genus Campylobacter.

PubMed

Iraola, Gregorio; Pérez, Ruben; Naya, Hugo; Paolicchi, Fernando; Pastor, Eugenia; Valenzuela, Sebastián; Calleros, Lucía; Velilla, Alejandra; Hernández, Martín; Morsella, Claudia

2014-09-04

The genus Campylobacter includes some of the most relevant pathogens for human and animal health; the continuous effort in their characterization has also revealed new species putatively involved in different kind of infections. Nowadays, the available genomic data for the genus comprise a wide variety of species with different pathogenic potential and niche preferences. In this work, we contribute to enlarge this available information presenting the first genome for the species Campylobacter sputorum bv. sputorum and use this and the already sequenced organisms to analyze the emergence and evolution of pathogenicity and niche preferences among Campylobacter species. We found that campylobacters can be unequivocally distinguished in established and putative pathogens depending on their repertory of virulence genes, which have been horizontally acquired from other bacteria because the nonpathogenic Campylobacter ancestor emerged, and posteriorly interchanged between some members of the genus. Additionally, we demonstrated the role of both horizontal gene transfers and diversifying evolution in niche preferences, being able to distinguish genetic features associated to the tropism for oral, genital, and gastrointestinal tissues. In particular, we highlight the role of nonsynonymous evolution of disulphide bond proteins, the invasion antigen B (CiaB), and other secreted proteins in the determination of niche preferences. Our results arise from assessing the previously unmet goal of considering the whole available Campylobacter diversity for genome comparisons, unveiling notorious genetic features that could explain particular phenotypes and set the basis for future research in Campylobacter biology. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Predicting fundamental and realized distributions based on thermal niche: A case study of a freshwater turtle

NASA Astrophysics Data System (ADS)

Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco; Ribeiro, Bruno R.

2018-04-01

Species distribution models (SDM) have been broadly used in ecology to address theoretical and practical problems. Currently, there are two main approaches to generate SDMs: (i) correlative, which is based on species occurrences and environmental predictor layers and (ii) process-based models, which are constructed based on species' functional traits and physiological tolerances. The distributions estimated by each approach are based on different components of species niche. Predictions of correlative models approach species realized niches, while predictions of process-based are more akin to species fundamental niche. Here, we integrated the predictions of fundamental and realized distributions of the freshwater turtle Trachemys dorbigni. Fundamental distribution was estimated using data of T. dorbigni's egg incubation temperature, and realized distribution was estimated using species occurrence records. Both types of distributions were estimated using the same regression approaches (logistic regression and support vector machines), both considering macroclimatic and microclimatic temperatures. The realized distribution of T. dorbigni was generally nested in its fundamental distribution reinforcing theoretical assumptions that the species' realized niche is a subset of its fundamental niche. Both modelling algorithms produced similar results but microtemperature generated better results than macrotemperature for the incubation model. Finally, our results reinforce the conclusion that species realized distributions are constrained by other factors other than just thermal tolerances.

Influence of late Quaternary climate change on present patterns of genetic variation in valley oak, Quercus lobata Née.

PubMed

Gugger, Paul F; Ikegami, Makihiko; Sork, Victoria L

2013-07-01

Phylogeography and ecological niche models (ENMs) suggest that late Quaternary glacial cycles have played a prominent role in shaping present population genetic structure and diversity, but have not applied quantitative methods to dissect the relative contribution of past and present climate vs. other forces. We integrate multilocus phylogeography, climate-based ENMs and multivariate statistical approaches to infer the effects of late Quaternary climate change on contemporary genetic variation of valley oak (Quercus lobata Née). ENMs indicated that valley oak maintained a stable distribution with local migration from the last interglacial period (~120 ka) to the Last Glacial Maximum (~21 ka, LGM) to the present compared with large-scale range shifts for an eastern North American white oak (Quercus alba L.). Coast Range and Sierra Nevada foothill populations diverged in the late Pleistocene before the LGM [104 ka (28-1622)] and have occupied somewhat distinct climate niches, according to ENMs and coalescent analyses of divergence time. In accordance with neutral expectations for stable populations, nuclear microsatellite diversity positively correlated with niche stability from the LGM to present. Most strikingly, nuclear and chloroplast microsatellite variation significantly correlated with LGM climate, even after controlling for associations with geographic location and present climate using partial redundancy analyses. Variance partitioning showed that LGM climate uniquely explains a similar proportion of genetic variance as present climate (16% vs. 11-18%), and together, past and present climate explains more than geography (19%). Climate can influence local expansion-contraction dynamics, flowering phenology and thus gene flow, and/or impose selective pressures. These results highlight the lingering effect of past climate on genetic variation in species with stable distributions. © 2013 John Wiley & Sons Ltd.

Donor Satellite Cell Engraftment is Significantly Augmented When the Host Niche is Preserved and Endogenous Satellite Cells are Incapacitated

PubMed Central

Boldrin, Luisa; Neal, Alice; Zammit, Peter S; Muntoni, Francesco; Morgan, Jennifer E

2012-01-01

Stem cell transplantation is already in clinical practice for certain genetic diseases and is a promising therapy for dystrophic muscle. We used the mdx mouse model of Duchenne muscular dystrophy to investigate the effect of the host satellite cell niche on the contribution of donor muscle stem cells (satellite cells) to muscle regeneration. We found that incapacitation of the host satellite cells and preservation of the muscle niche promote donor satellite cell contribution to muscle regeneration and functional reconstitution of the satellite cell compartment. But, if the host niche is not promptly refilled, or is filled by competent host satellite cells, it becomes nonfunctional and donor engraftment is negligible. Application of this regimen to aged host muscles also promotes efficient regeneration from aged donor satellite cells. In contrast, if the niche is destroyed, yet host satellite cells remain proliferation-competent, donor-derived engraftment is trivial. Thus preservation of the satellite cell niche, concomitant with functional impairment of the majority of satellite cells within dystrophic human muscles, may improve the efficiency of stem cell therapy. Stem Cells2012;30:1971–1984 PMID:22730231

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

Genetic Pool Information Reflects Highly Suitable Areas: The Case of Two Parapatric Endangered Species of Tuco-tucos (Rodentia: Ctenomiydae)

PubMed Central

Galiano, Daniel; Bernardo-Silva, Jorge; de Freitas, Thales R. O.

2014-01-01

Conservation of small mammals requires knowledge of the genetically and ecologically meaningful spatial scales at which species respond to habitat modifications. Conservation strategies can be improved through the use of ecological niche models and genetic data to classify areas of high environmental suitability. In this study, we applied a Maxent model integrated with genetic information (nucleotide diversity, haplotype diversity and Fu's Fs neutrality tests) to evaluate potential genetic pool populations with highly suitable areas for two parapatric endangered species of tuco-tucos (Ctenomys minutus and C. lami). Our results demonstrated that both species were largely influenced by vegetation and soil variables at a landscape scale and inhabit a highly specific niche. Ctenomys minutus was also influenced by the variable altitude; the species was associated with low altitudes (sea level). Our model of genetic data associated with environmental suitability indicate that the genetic pool data were associated with highly suitable areas for C. minutus. This pattern was not evident for C. lami, but this outcome could be a consequence of the restricted range of the species. The preservation of species requires not only detailed knowledge of their natural history and genetic structure but also information on the availability of suitable areas where species can survive, and such knowledge can aid significantly in conservation planning. This finding reinforces the use of these two techniques for planning conservation actions. PMID:24819251

Genetic Algorithms and Local Search

NASA Technical Reports Server (NTRS)

Whitley, Darrell

1996-01-01

The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

Genetic Diversity and Ecological Niche Modelling of Wild Barley: Refugia, Large-Scale Post-LGM Range Expansion and Limited Mid-Future Climate Threats?

PubMed Central

Russell, Joanne; van Zonneveld, Maarten; Dawson, Ian K.; Booth, Allan; Waugh, Robbie; Steffenson, Brian

2014-01-01

Describing genetic diversity in wild barley (Hordeum vulgare ssp. spontaneum) in geographic and environmental space in the context of current, past and potential future climates is important for conservation and for breeding the domesticated crop (Hordeum vulgare ssp. vulgare). Spatial genetic diversity in wild barley was revealed by both nuclear- (2,505 SNP, 24 nSSR) and chloroplast-derived (5 cpSSR) markers in 256 widely-sampled geo-referenced accessions. Results were compared with MaxEnt-modelled geographic distributions under current, past (Last Glacial Maximum, LGM) and mid-term future (anthropogenic scenario A2, the 2080s) climates. Comparisons suggest large-scale post-LGM range expansion in Central Asia and relatively small, but statistically significant, reductions in range-wide genetic diversity under future climate. Our analyses support the utility of ecological niche modelling for locating genetic diversity hotspots and determine priority geographic areas for wild barley conservation under anthropogenic climate change. Similar research on other cereal crop progenitors could play an important role in tailoring conservation and crop improvement strategies to support future human food security. PMID:24505252

Ecological and genetic divergence between two lineages of Middle American túngara frogs Physalaemus (= Engystomops) pustulosus

PubMed Central

2010-01-01

Background Uncovering how populations of a species differ genetically and ecologically is important for understanding evolutionary processes. Here we combine population genetic methods (microsatellites) with phylogenetic information (mtDNA) to define genetic population clusters of the wide-spread Neotropical túngara frog (Physalaemus pustulosus). We measure gene flow and migration within and between population clusters and compare genetic diversity between population clusters. By applying ecological niche modeling we determine whether the two most divergent genetic groups of the túngara frog (1) inhabit different habitats, and (2) are separated geographically by unsuitable habitat across a gap in the distribution. Results Most population structure is captured by dividing all sample localities into two allopatric genetic lineages. The Northern genetic lineage (NW Costa Rica) is genetically homogenous while the Southern lineage (SW Costa Rica and Panama) is sub-divided into three population clusters by both microsatellite and mtDNA analyses. Gene flow is higher within the Northern lineage than within the Southern lineage, perhaps due to increased landscape heterogeneity in the South. Niche modeling reveals differences in suitable habitat between the Northern and Southern lineages: the Northern lineage inhabits dry/pine-oak forests, while the Southern lineage is confined to tropical moist forests. Both lineages seem to have had little movement across the distribution gap, which persisted during the last glacial maximum. The lack of movement was more pronounced for the Southern lineage than for the Northern lineage. Conclusions This study confirms the finding of previous studies that túngara frogs diverged into two allopatric genetic lineages north and south of the gap in the distribution in central Costa Rica several million years ago. The allopatric distribution is attributed to unsuitable habitat and probably other unknown ecological factors present across the distribution gap. Niche conservatism possibly contributes to preventing movements across the gap and gene flow between both groups. Genetic and ecological data indicate that there is the potential for ecological divergence in allopatry between lineages. In this context we discuss whether the Northern and Southern lineages should be recognized as separate species, and we conclude that further studies of pre- and post-zygotic isolation are needed for a final assessment. Identified population clusters should motivate future behavioral and ecological research regarding within-species biodiversity and speciation mechanisms. PMID:20482771

Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling

PubMed Central

Escobar, Luis E.; Craft, Meggan E.

2016-01-01

Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks. PMID:27547199

Advances and Limitations of Disease Biogeography Using Ecological Niche Modeling.

PubMed

Escobar, Luis E; Craft, Meggan E

2016-01-01

Mapping disease transmission risk is crucial in public and animal health for evidence based decision-making. Ecology and epidemiology are highly related disciplines that may contribute to improvements in mapping disease, which can be used to answer health related questions. Ecological niche modeling is increasingly used for understanding the biogeography of diseases in plants, animals, and humans. However, epidemiological applications of niche modeling approaches for disease mapping can fail to generate robust study designs, producing incomplete or incorrect inferences. This manuscript is an overview of the history and conceptual bases behind ecological niche modeling, specifically as applied to epidemiology and public health; it does not pretend to be an exhaustive and detailed description of ecological niche modeling literature and methods. Instead, this review includes selected state-of-the-science approaches and tools, providing a short guide to designing studies incorporating information on the type and quality of the input data (i.e., occurrences and environmental variables), identification and justification of the extent of the study area, and encourages users to explore and test diverse algorithms for more informed conclusions. We provide a friendly introduction to the field of disease biogeography presenting an updated guide for researchers looking to use ecological niche modeling for disease mapping. We anticipate that ecological niche modeling will soon be a critical tool for epidemiologists aiming to map disease transmission risk, forecast disease distribution under climate change scenarios, and identify landscape factors triggering outbreaks.

Space-time dynamics of Stem Cell Niches: a unified approach for Plants.

PubMed

Pérez, Maria Del Carmen; López, Alejandro; Padilla, Pablo

2013-06-01

Many complex systems cannot be analyzed using traditional mathematical tools, due to their irreducible nature. This makes it necessary to develop models that can be implemented computationally to simulate their evolution. Examples of these models are cellular automata, evolutionary algorithms, complex networks, agent-based models, symbolic dynamics and dynamical systems techniques. We review some representative approaches to model the stem cell niche in Arabidopsis thaliana and the basic biological mechanisms that underlie its formation and maintenance. We propose a mathematical model based on cellular automata for describing the space-time dynamics of the stem cell niche in the root. By making minimal assumptions on the cell communication process documented in experiments, we classify the basic developmental features of the stem-cell niche, including the basic structural architecture, and suggest that they could be understood as the result of generic mechanisms given by short and long range signals. This could be a first step in understanding why different stem cell niches share similar topologies, not only in plants. Also the fact that this organization is a robust consequence of the way information is being processed by the cells and to some extent independent of the detailed features of the signaling mechanism.

Space-time dynamics of stem cell niches: a unified approach for plants.

PubMed

Pérez, Maria del Carmen; López, Alejandro; Padilla, Pablo

2013-04-02

Many complex systems cannot be analyzed using traditional mathematical tools, due to their irreducible nature. This makes it necessary to develop models that can be implemented computationally to simulate their evolution. Examples of these models are cellular automata, evolutionary algorithms, complex networks, agent-based models, symbolic dynamics and dynamical systems techniques. We review some representative approaches to model the stem cell niche in Arabidopsis thaliana and the basic biological mechanisms that underlie its formation and maintenance. We propose a mathematical model based on cellular automata for describing the space-time dynamics of the stem cell niche in the root. By making minimal assumptions on the cell communication process documented in experiments, we classify the basic developmental features of the stem-cell niche, including the basic structural architecture, and suggest that they could be understood as the result of generic mechanisms given by short and long range signals. This could be a first step in understanding why different stem cell niches share similar topologies, not only in plants. Also the fact that this organization is a robust consequence of the way information is being processed by the cells and to some extent independent of the detailed features of the signaling mechanism.

Population genetic structure of gray wolves (Canis lupus) in a marine archipelago suggests island-mainland differentiation consistent with dietary niche

PubMed Central

2014-01-01

Background Emerging evidence suggests that ecological heterogeneity across space can influence the genetic structure of populations, including that of long-distance dispersers such as large carnivores. On the central coast of British Columbia, Canada, wolf (Canis lupus L., 1758) dietary niche and parasite prevalence data indicate strong ecological divergence between marine-oriented wolves inhabiting islands and individuals on the coastal mainland that interact primarily with terrestrial prey. Local holders of traditional ecological knowledge, who distinguish between mainland and island wolf forms, also informed our hypothesis that genetic differentiation might occur between wolves from these adjacent environments. Results We used microsatellite genetic markers to examine data obtained from wolf faecal samples. Our results from 116 individuals suggest the presence of a genetic cline between mainland and island wolves. This pattern occurs despite field observations that individuals easily traverse the 30 km wide study area and swim up to 13 km among landmasses in the region. Conclusions Natal habitat-biased dispersal (i.e., the preference for dispersal into familiar ecological environments) might contribute to genetic differentiation. Accordingly, this working hypothesis presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present. PMID:24915756

Lineage analysis of quiescent regenerative stem cells in the adult brain by genetic labelling reveals spatially restricted neurogenic niches in the olfactory bulb.

PubMed

Giachino, Claudio; Taylor, Verdon

2009-07-01

The subventricular zone (SVZ) of the lateral ventricles is the major neurogenic region in the adult mammalian brain, harbouring neural stem cells within defined niches. The identity of these stem cells and the factors regulating their fate are poorly understood. We have genetically mapped a population of Nestin-expressing cells during postnatal development to study their potential and fate in vivo. Taking advantage of the recombination characteristics of a nestin::CreER(T2) allele, we followed a subpopulation of neural stem cells and traced their fate in a largely unrecombined neurogenic niche. Perinatal nestin::CreER(T2)-expressing cells give rise to multiple glial cell types and neurons, as well as to stem cells of the adult SVZ. In the adult SVZ nestin::CreER(T2)-expressing neural stem cells give rise to several neuronal subtypes in the olfactory bulb (OB). We addressed whether the same population of neural stem cells play a role in SVZ regeneration. Following anti-mitotic treatment to eliminate rapidly dividing progenitors, relatively quiescent nestin::CreER(T2)-targeted cells are spared and contribute to SVZ regeneration, generating new proliferating precursors and neuroblasts. Finally, we have identified neurogenic progenitors clustered in ependymal-like niches within the rostral migratory stream (RMS) of the OB. These OB-RMS progenitors generate neuroblasts that, upon transplantation, graft, migrate and differentiate into granule and glomerular neurons. In summary, using conditional lineage tracing we have identified neonatal cells that are the source of neurogenic and regenerative neural stem cells in the adult SVZ and occupy a novel neurogenic niche in the OB.

Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America.

PubMed

McIntyre, Shannon; Rangel, Elizabeth F; Ready, Paul D; Carvalho, Bruno M

2017-03-24

Before 1996 the phlebotomine sand fly Lutzomyia neivai was usually treated as a synonym of the morphologically similar Lutzomyia intermedia, which has long been considered a vector of Leishmania braziliensis, the causative agent of much cutaneous leishmaniasis in South America. This report investigates the likely range changes of both sand fly species in response to a stabilisation climate change scenario (RCP4.5) and a high greenhouse gas emissions one (RCP8.5). Ecological niche modelling was used to identify areas of South America with climates currently suitable for each species, and then the future distributions of these climates were predicted based on climate change scenarios. Compared with the previous ecological niche model of L. intermedia (sensu lato) produced using the GARP algorithm in 2003, the current investigation modelled the two species separately, making use of verified presence records and additional records after 2001. Also, the new ensemble approach employed ecological niche modelling algorithms (including Maximum Entropy, Random Forests and Support Vector Machines) that have been widely adopted since 2003 and perform better than GARP, as well as using a more recent climate change model (HadGEM2) considered to have better performance at higher resolution than the earlier one (HadCM2). Lutzomyia intermedia was shown to be the more tropical of the two species, with its climatic niche defined by higher annual mean temperatures and lower temperature seasonality, in contrast to the more subtropical L. neivai. These different latitudinal ranges explain the two species' predicted responses to climate change by 2050, with L. intermedia mostly contracting its range (except perhaps in northeast Brazil) and L. neivai mostly shifting its range southwards in Brazil and Argentina. This contradicts the findings of the 2003 report, which predicted more range expansion. The different findings can be explained by the improved data sets and modelling methods. Our findings indicate that climate change will not always lead to range expansion of disease vectors such as sand flies. Ecological niche models should be species specific, carefully selected and combined in an ensemble approach.

Impact of Geography and Climate on the Genetic Differentiation of the Subtropical Pine Pinus yunnanensis

PubMed Central

Zhao, Wei; Wang, Xiao-Ru

2013-01-01

Southwest China is a biodiversity hotspot characterized by complex topography, heterogeneous regional climates and rich flora. The processes and driving factors underlying this hotspot remain to be explicitly tested across taxa to gain a general understanding of the evolution of biodiversity and speciation in the region. In this study, we examined the role played by historically neutral processes, geography and environment in producing the current genetic diversity of the subtropical pine Pinus yunnanensis. We used genetic and ecological methods to investigate the patterns of genetic differentiation and ecological niche divergence across the distribution range of this species. We found both continuous genetic differentiation over the majority of its range, and discrete isolated local clusters. The discrete differentiation between two genetic groups in the west and east peripheries is consistent with niche divergence and geographical isolation of these groups. In the central area of the species’ range, population structure was shaped mainly by neutral processes and geography rather than by ecological selection. These results show that geographical and environmental factors together created stronger and more discrete genetic differentiation than isolation by distance alone, and illustrate the importance of ecological factors in forming or maintaining genetic divergence across a complex landscape. Our findings differ from other phylogenetic studies that identified the historical drainage system in the region as the primary factor shaping population structure, and highlight the heterogeneous contributions that geography and environment have made to genetic diversity among taxa in southwest China. PMID:23840668

Impact of Geography and Climate on the Genetic Differentiation of the Subtropical Pine Pinus yunnanensis.

PubMed

Wang, Baosheng; Mao, Jian-Feng; Zhao, Wei; Wang, Xiao-Ru

2013-01-01

Southwest China is a biodiversity hotspot characterized by complex topography, heterogeneous regional climates and rich flora. The processes and driving factors underlying this hotspot remain to be explicitly tested across taxa to gain a general understanding of the evolution of biodiversity and speciation in the region. In this study, we examined the role played by historically neutral processes, geography and environment in producing the current genetic diversity of the subtropical pine Pinus yunnanensis. We used genetic and ecological methods to investigate the patterns of genetic differentiation and ecological niche divergence across the distribution range of this species. We found both continuous genetic differentiation over the majority of its range, and discrete isolated local clusters. The discrete differentiation between two genetic groups in the west and east peripheries is consistent with niche divergence and geographical isolation of these groups. In the central area of the species' range, population structure was shaped mainly by neutral processes and geography rather than by ecological selection. These results show that geographical and environmental factors together created stronger and more discrete genetic differentiation than isolation by distance alone, and illustrate the importance of ecological factors in forming or maintaining genetic divergence across a complex landscape. Our findings differ from other phylogenetic studies that identified the historical drainage system in the region as the primary factor shaping population structure, and highlight the heterogeneous contributions that geography and environment have made to genetic diversity among taxa in southwest China.

Genetics of Gonadal Stem Cell Renewal

PubMed Central

Greenspan, Leah Joy; de Cuevas, Margaret

2015-01-01

Stem cells are necessary for the maintenance of many adult tissues. Signals within the stem cell microenvironment, or niche, regulate the self-renewal and differentiation capability of these cells. Misregulation of these signals through mutation or damage can lead to overgrowth or depletion of different stem cell pools. In this review, we focus on the Drosophila testis and ovary, both of which contain well-defined niches, as well as the mouse testis, which has become a more approachable stem cell system with recent technical advances. We discuss the signals that regulate gonadal stem cells in their niches, how these signals mediate self-renewal and differentiation under homeostatic conditions, and how stress, whether from mutations or damage, can cause changes in cell fate and drive stem cell competition. PMID:26355592

Striking Phenotypic Variation yet Low Genetic Differentiation in Sympatric Lake Trout (Salvelinus namaycush)

PubMed Central

Coon, Andrew; Carson, Robert; Debes, Paul V.

2016-01-01

The study of population differentiation in the context of ecological speciation is commonly assessed using populations with obvious discreteness. Fewer studies have examined diversifying populations with occasional adaptive variation and minor reproductive isolation, so factors impeding or facilitating the progress of early stage differentiation are less understood. We detected non-random genetic structuring in lake trout (Salvelinus namaycush) inhabiting a large, pristine, postglacial lake (Mistassini Lake, Canada), with up to five discernible genetic clusters having distinctions in body shape, size, colouration and head shape. However, genetic differentiation was low (FST = 0.017) and genetic clustering was largely incongruent between several population- and individual-based clustering approaches. Genotype- and phenotype-environment associations with spatial habitat, depth and fish community structure (competitors and prey) were either inconsistent or weak. Striking morphological variation was often more continuous within than among defined genetic clusters. Low genetic differentiation was a consequence of relatively high contemporary gene flow despite large effective population sizes, not migration-drift disequilibrium. Our results suggest a highly plastic propensity for occupying multiple habitat niches in lake trout and a low cost of morphological plasticity, which may constrain the speed and extent of adaptive divergence. We discuss how factors relating to niche conservatism in this species may also influence how plasticity affects adaptive divergence, even where ample ecological opportunity apparently exists. PMID:27680019

[Generation of functional organs from pluripotent stem cells].

PubMed

Miyamoto, Tatsuyuki; Nakauchi, Hiromitsu

2015-10-01

Hematopoietic stem cells (HSCs) have played a major role in stem cell biology, providing many conceptual ideas and models. Among them is the concept of the "niche", a special bone-marrow microenvironment that by exchanging cues regulates stem-cell fate. The HSC niche also plays an important role in HSC transplantation. Successful engraftment of donor HSCs depends on myeloablative pretreatment to empty the niche. The concept of the stem-cell niche has now been extended to the generation of organs. We postulated that an empty "organ niche" exists in a developing animal when development of an organ is genetically disabled. This organ niche should be developmentally compensated by blastocyst complementation using wild-type primary stem cells (PSCs). We proved the principle of organogenesis from xenogeneic PSCs in an embryo unable to form a specific organ, demonstrating the generation of functionally normal rat pancreas by injecting rat PSCs into pancreatogenesis-disabled mouse embryos. This principle has held in pigs. When pancreatogenesis-disabled pig embryos underwent complementation with blastomeres from wild-type pig embryos to produce chimeric pigs, the chimeras had normal pancreata and survived to adulthood. Demonstration of the generation of a functional organ from PSCs in pigs is a very important step toward generation of human cells, tissues, and organs from individual patients' own PSCs in large animals.

[Current and future ecological niche of Leishmaniasis (Kinetoplastida: Trypanosomatidae) in the Neotropical region].

PubMed

Moo-Llanes, David A

2016-09-01

The leishmaniasis is a complex disease system, caused by the protozoan parasite Leishmania and transmitted to humans by the vector Lutzomyia spp. Since it is listed as a neglected disease according to the World Health Organization, the aim of this study was to determine the current and future niche of cutaneous and visceral leishmaniasis in the Neotropical region. We built the ecological niche model (ENM) of cutaneous (N= 2 910 occurrences) and visceral (N= 851 occurrences) leishmaniasis using MaxEnt algorithm. Nine bioclimatic variables (BIO1, BIO4, BIO5, BIO6, BIO7, BIO12, BIO13, BIO14, BIO15 (downloaded from the Worldclim) and disease occurrences data were used for the construction of ENM for three periods (current, 2050 and 2070) and four climate change scenarios (RCP 2.6, 4.5, 6.0 y 8.5). We analyzed the number of pixels occupied, identity niche, modified niche (stable, loss, and gain) and seasonality. Our analyses indicated the expansion for cutaneous leishmaniasis (CL), a comparison for visceral leishmaniasis (VL). We rejected the null hypothesis of niche identity between CL and VL with Hellinger’s index = 0.91 (0.92-0.98) and Schoener’s Index = 0.67 (0.85-1.00) but with an overlap niche of 56.3 %. The differences between the two leishmaniasis types were detected in relation to RCP scenarios and niche shifts (area gained / loss). Seasonality was more important for CL. We provided a current picture of CL and VL distributions and the predicted distributional changes associated to different climate change scenarios for the Neotropical region. We can anticipate that increasing range is likely although it will depend locally on the future trends in weather seasonality.

Pliocene-Pleistocene ecological niche evolution shapes the phylogeography of a Mediterranean plant group.

PubMed

Benítez-Benítez, C; Escudero, M; Rodríguez-Sánchez, F; Martín-Bravo, S; Jiménez-Mejías, P

2018-04-01

Estimating species ability to adapt to environmental changes is crucial to understand their past and future response to climate change. The Mediterranean Basin has experienced remarkable climatic changes since the Miocene, which have greatly influenced the evolution of the Mediterranean flora. Here, we examine the evolutionary history and biogeographic patterns of two sedge sister species (Carex, Cyperaceae) restricted to the western Mediterranean Basin, but with Pliocene fossil record in central Europe. In particular, we estimated the evolution of climatic niches through time and its influence in lineage differentiation. We carried out a dated phylogenetic-phylogeographic study based on seven DNA regions (nDNA and ptDNA) and fingerprinting data (AFLPs), and modelled ecological niches and species distributions for the Pliocene, Pleistocene and present. Phylogenetic and divergence time analyses revealed that both species form a monophyletic lineage originated in the late Pliocene-early Pleistocene. We detected clear genetic differentiation between both species with distinct genetic clusters in disjunct areas, indicating the predominant role of geographic barriers limiting gene flow. We found a remarkable shift in the climatic requirements between Pliocene and extant populations, although the niche seems to have been relatively conserved since the Pleistocene split of both species. This study highlights how an integrative approach combining different data sources and analyses, including fossils, allows solid and robust inferences about the evolutionary history of a plant group since the Pliocene. © 2018 John Wiley & Sons Ltd.

Problem solving with genetic algorithms and Splicer

NASA Technical Reports Server (NTRS)

Bayer, Steven E.; Wang, Lui

1991-01-01

Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.

How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end.

PubMed

Terribile, L C; Diniz-Filho, J A F; De Marco, P

2010-05-01

The use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.

The bone marrow niche, stem cells, and leukemia: impact of drugs, chemicals, and the environment

PubMed Central

Greim, Helmut; Kaden, Debra A.; Larson, Richard A.; Palermo, Christine M.; Rice, Jerry M.; Ross, David; Snyder, Robert

2014-01-01

Hematopoietic stem cells (HSCs) are a unique population of somatic stem cells that can both self-renew for long-term reconstitution of HSCs and differentiate into hematopoietic progenitor cells, which in turn give rise, in a hierarchical manner, to the entire myeloid and lymphoid lineages. The differentiation and maturation of these lineages occurs in the bone marrow niche, a microenvironment that regulates self-renewal, survival, differentiation, and proliferation, with interactions among signaling pathways in the HSCs and the niche required to establish and maintain homeostasis. The accumulation of genetic mutations and cytogenetic abnormalities within cells of the partially differentiated myeloid lineage, particularly as a result of exposure to benzene or cytotoxic anticancer drugs, can give rise to malignancies like acute myeloid leukemia and myelodysplastic syndrome. Better understanding of the mechanisms driving these malignancies and susceptibility factors, both within hematopoietic progenitor cells and cells within the bone marrow niche, may lead to the development of strategies for prevention of occupational and cancer therapy–induced disease. PMID:24495159

Genetically engineered mesenchymal stromal cells produce IL-3 and TPO to further improve human scaffold-based xenograft models.

PubMed

Carretta, Marco; de Boer, Bauke; Jaques, Jenny; Antonelli, Antonella; Horton, Sarah J; Yuan, Huipin; de Bruijn, Joost D; Groen, Richard W J; Vellenga, Edo; Schuringa, Jan Jacob

2017-07-01

Recently, NOD-SCID IL2Rγ -/- (NSG) mice were implanted with human mesenchymal stromal cells (MSCs) in the presence of ceramic scaffolds or Matrigel to mimic the human bone marrow (BM) microenvironment. This approach allowed the engraftment of leukemic samples that failed to engraft in NSG mice without humanized niches and resulted in a better preservation of leukemic stem cell self-renewal properties. To further improve our humanized niche scaffold model, we genetically engineered human MSCs to secrete human interleukin-3 (IL-3) and thrombopoietin (TPO). In vitro, these IL-3- and TPO-producing MSCs were superior in expanding human cord blood (CB) CD34 + hematopoietic stem/progenitor cells. MLL-AF9-transduced CB CD34 + cells could be transformed efficiently along myeloid or lymphoid lineages on IL-3- and TPO-producing MSCs. In vivo, these genetically engineered MSCs maintained their ability to differentiate into bone, adipocytes, and other stromal components. Upon transplantation of MLL-AF9-transduced CB CD34 + cells, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) developed in engineered scaffolds, in which a significantly higher percentage of myeloid clones was observed in the mouse compartments compared with previous models. Engraftment of primary AML, B-cell ALL, and biphenotypic acute leukemia (BAL) patient samples was also evaluated, and all patient samples could engraft efficiently; the myeloid compartment of the BAL samples was better preserved in the human cytokine scaffold model. In conclusion, we show that we can genetically engineer the ectopic human BM microenvironment in a humanized scaffold xenograft model. This approach will be useful for functional study of the importance of niche factors in normal and malignant human hematopoiesis. Copyright © 2017 ISEH - International Society for Experimental Hematology. All rights reserved.

Genetic algorithms using SISAL parallel programming language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tejada, S.

1994-05-06

Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.

A Phylogeographic Assessment of the Malagasy Giant Chameleons (Furcifer verrucosus and Furcifer oustaleti)

PubMed Central

Raxworthy, Christopher J.

2016-01-01

The Malagasy giant chameleons (Furcifer oustaleti and Furcifer verrucosus) are sister species that are both broadly distributed in Madagascar, and also endemic to the island. These species are also morphologically similar and, because of this, have been frequently misidentified in the field. Previous studies have suggested that cryptic species are nested within this chameleon group, and two subspecies have been described in F. verrucosus. In this study, we utilized a phylogeographic approach to assess genetic diversification within these chameleons. This was accomplished by (1) identifying clades within each species supported by both mitochondrial and nuclear DNA, (2) assessing divergence times between clades, and (3) testing for niche divergence or conservatism. We found that both F. oustaleti and F. verrucosus could be readily identified based on genetic data, and within each species, there are two well-supported clades. However, divergence times are not contemporary and spatial patterns are not congruent. Diversification within F. verrucosus occurred during the Plio-Pleistocene, and there is evidence for niche divergence between a southwestern and southeastern clade, in a region of Madagascar that shows no obvious landscape barriers to dispersal. Diversification in F. oustaleti occurred earlier in the Pliocene or Miocene, and niche conservatism is supported with two genetically distinct clades separated at the Sofia River in northwestern Madagascar. Divergence within F. verrucosus is most consistent with patterns expected from ecologically mediated speciation, whereas divergence in F. oustaleti most strongly matches the patterns expected from the riverine barrier hypothesis. PMID:27257819

Evolution is a cooperative process: the biodiversity-related niches differentiation theory (BNDT) can explain why.

PubMed

Gatti, Roberto Cazzolla

2011-01-01

A. McFayden and G.E. Hutchinson defined a niche as a multidimensional space or hypervolume within the environment that allows an individual or a species to survive, we consider niches as a fundamental ecological variable that regulate species' composition and relation in ecosystems. Successively the niche concept has been associated to the genetic term "phenotype" by MacArthurstressing the importance on what a species or a genome can show outside, either in the environmental functions or in body characteristics. Several indexes have been developed to evaluate the grade of overlapping and similarities of species' niches, even utilizing the theory of information. However, which are the factors that determine the number of species that can coexist in a determinate environment and why a generalist species do not compete until the exclusion of the remaining species to maximize its fitness, is still quite unknown. Moreover, there are few studies and theories that clearly explain why the number of niches is so variable through ecosystems and how can several species live in the same basal niche, intended in a comprehensive sense as the range of basic conditions (temperature, humidity, food-guild, etc.). Here I show that the number of niches in an ecosystem depends on the number of species present in a particular moment and that the species themselves allow the enhancement of niches in terms of space and number. I found that using a three-dimensional model as hypervolume and testing the theory on a Mediterranean, temperate and tropical forest ecosystem it is possible to demonstrate that each species plays a fundamental role in facilitating the colonization by other species by simply modifying the environment and exponentially increasing the available niches' space and number. I resumed these hypothesis, after some preliminary empiric tests, in the Biodiversity-related Niches Differentiation Theory (BNDT), stressing with these definition that the process of niches differentiation is strictly addressed by species. This approach has various consequences, first in consideration of relations among species and second in terms of a better understanding of cooperation/competition dynamics.

Comparative Genomics of the Listeria monocytogenes ST204 Subgroup

PubMed Central

Fox, Edward M.; Allnutt, Theodore; Bradbury, Mark I.; Fanning, Séamus; Chandry, P. Scott

2016-01-01

The ST204 subgroup of Listeria monocytogenes is among the most frequently isolated in Australia from a range of environmental niches. In this study we provide a comparative genomics analysis of food and food environment isolates from geographically diverse sources. Analysis of the ST204 genomes showed a highly conserved core genome with the majority of variation seen in mobile genetic elements such as plasmids, transposons and phage insertions. Most strains (13/15) harbored plasmids, which although varying in size contained highly conserved sequences. Interestingly 4 isolates contained a conserved plasmid of 91,396 bp. The strains examined were isolated over a period of 12 years and from different geographic locations suggesting plasmids are an important component of the genetic repertoire of this subgroup and may provide a range of stress tolerance mechanisms. In addition to this 4 phage insertion sites and 2 transposons were identified among isolates, including a novel transposon. These genetic elements were highly conserved across isolates that harbored them, and also contained a range of genetic markers linked to stress tolerance and virulence. The maintenance of conserved mobile genetic elements in the ST204 population suggests these elements may contribute to the diverse range of niches colonized by ST204 isolates. Environmental stress selection may contribute to maintaining these genetic features, which in turn may be co-selecting for virulence markers relevant to clinical infection with ST204 isolates. PMID:28066377

Comparative Genomics of the Listeria monocytogenes ST204 Subgroup.

PubMed

Fox, Edward M; Allnutt, Theodore; Bradbury, Mark I; Fanning, Séamus; Chandry, P Scott

2016-01-01

The ST204 subgroup of Listeria monocytogenes is among the most frequently isolated in Australia from a range of environmental niches. In this study we provide a comparative genomics analysis of food and food environment isolates from geographically diverse sources. Analysis of the ST204 genomes showed a highly conserved core genome with the majority of variation seen in mobile genetic elements such as plasmids, transposons and phage insertions. Most strains (13/15) harbored plasmids, which although varying in size contained highly conserved sequences. Interestingly 4 isolates contained a conserved plasmid of 91,396 bp. The strains examined were isolated over a period of 12 years and from different geographic locations suggesting plasmids are an important component of the genetic repertoire of this subgroup and may provide a range of stress tolerance mechanisms. In addition to this 4 phage insertion sites and 2 transposons were identified among isolates, including a novel transposon. These genetic elements were highly conserved across isolates that harbored them, and also contained a range of genetic markers linked to stress tolerance and virulence. The maintenance of conserved mobile genetic elements in the ST204 population suggests these elements may contribute to the diverse range of niches colonized by ST204 isolates. Environmental stress selection may contribute to maintaining these genetic features, which in turn may be co-selecting for virulence markers relevant to clinical infection with ST204 isolates.

Deficiency in DNA damage response of enterocytes accelerates intestinal stem cell aging in Drosophila.

PubMed

Park, Joung-Sun; Jeon, Ho-Jun; Pyo, Jung-Hoon; Kim, Young-Shin; Yoo, Mi-Ae

2018-03-07

Stem cell dysfunction is closely linked to tissue and organismal aging and age-related diseases, and heavily influenced by the niche cells' environment. The DNA damage response (DDR) is a key pathway for tissue degeneration and organismal aging; however, the precise protective role of DDR in stem cell/niche aging is unclear. The Drosophila midgut is an excellent model to study the biology of stem cell/niche aging because of its easy genetic manipulation and its short lifespan. Here, we showed that deficiency of DDR in Drosophila enterocytes (ECs) accelerates intestinal stem cell (ISC) aging. We generated flies with knockdown of Mre11 , Rad50 , Nbs1 , ATM , ATR , Chk1 , and Chk2 , which decrease the DDR system in ECs. EC-specific DDR depletion induced EC death, accelerated the aging of ISCs, as evidenced by ISC hyperproliferation, DNA damage accumulation, and increased centrosome amplification, and affected the adult fly's survival. Our data indicated a distinct effect of DDR depletion in stem or niche cells on tissue-resident stem cell proliferation. Our findings provide evidence of the essential role of DDR in protecting EC against ISC aging, thus providing a better understanding of the molecular mechanisms of stem cell/niche aging.

Agrobacterium uses a unique ligand-binding mode for trapping opines and acquiring a competitive advantage in the niche construction on plant host.

PubMed

Lang, Julien; Vigouroux, Armelle; Planamente, Sara; El Sahili, Abbas; Blin, Pauline; Aumont-Nicaise, Magali; Dessaux, Yves; Moréra, Solange; Faure, Denis

2014-10-01

By modifying the nuclear genome of its host, the plant pathogen Agrobacterium tumefaciens induces the development of plant tumours in which it proliferates. The transformed plant tissues accumulate uncommon low molecular weight compounds called opines that are growth substrates for A. tumefaciens. In the pathogen-induced niche (the plant tumour), a selective advantage conferred by opine assimilation has been hypothesized, but not experimentally demonstrated. Here, using genetics and structural biology, we deciphered how the pathogen is able to bind opines and use them to efficiently compete in the plant tumour. We report high resolution X-ray structures of the periplasmic binding protein (PBP) NocT unliganded and liganded with the opine nopaline (a condensation product of arginine and α-ketoglurate) and its lactam derivative pyronopaline. NocT exhibited an affinity for pyronopaline (K(D) of 0.6 µM) greater than that for nopaline (KD of 3.7 µM). Although the binding-mode of the arginine part of nopaline/pyronopaline in NocT resembled that of arginine in other PBPs, affinity measurement by two different techniques showed that NocT did not bind arginine. In contrast, NocT presented specific residues such as M117 to stabilize the bound opines. NocT relatives that exhibit the nopaline/pyronopaline-binding mode were only found in genomes of the genus Agrobacterium. Transcriptomics and reverse genetics revealed that A. tumefaciens uses the same pathway for assimilating nopaline and pyronopaline. Fitness measurements showed that NocT is required for a competitive colonization of the plant tumour by A. tumefaciens. Moreover, even though the Ti-plasmid conjugal transfer was not regulated by nopaline, the competitive advantage gained by the nopaline-assimilating Ti-plasmid donors led to a preferential horizontal propagation of this Ti-plasmid amongst the agrobacteria colonizing the plant-tumour niche. This work provided structural and genetic evidences to support the niche construction paradigm in bacterial pathogens.

Colonization in North American Arid Lands: The Journey of Agarito (Berberis trifoliolata) Revealed by Multilocus Molecular Data and Packrat Midden Fossil Remains

PubMed Central

Angulo, Diego F.; Amarilla, Leonardo D.; Anton, Ana M.; Sosa, Victoria

2017-01-01

Here we conduct research to understand the evolutionary history of a shrubby species known as Agarito (Berberis trifoliolata), an endemic species to the Chihuahuan Desert. We identify genetic signatures based on plastid DNA and AFLP markers and perform niche modelling and spatial connectivity analyses as well as niche modelling based on records in packrats to elucidate whether orogenic events such as mountain range uplift in the Miocene or the contraction/expansion dynamics of vegetation in response to climate oscillations in the Pliocene/Pleistocene had an effect on evolutionary processes in Agarito. Our results of current niche modelling and palaeomodelling showed that the area currently occupied by Berberis trifoliolata is substantially larger than it was during the Last Interglacial period and the Last Glacial Maximum. Agarito was probably confined to small areas in the Northeastern and gradually expanded its distribution just after the Last Glacial Maximum when the weather in the Chihuahuan Desert and adjacent regions became progressively warmer and drier. The most contracted range was predicted for the Interglacial period. Populations remained in stable areas during the Last Glacial Maximum and expanded at the beginning of the Holocene. Most genetic variation occured in populations from the Sierra Madre Oriental. Two groups of haplotypes were identified: the Mexican Plateau populations and certain Northeastern populations. Haplogroups were spatially connected during the Last Glacial Maximum and separated during interglacial periods. The most important prediction of packrat middens palaeomodelling lies in the Mexican Plateau, a finding congruent with current and past niche modelling predictions for agarito and genetic results. Our results corroborate that these climate changes in the Pliocene/Pleistocene affected the evolutionary history of agarito. The journey of agarito in the Chihuahuan Desert has been dynamic, expanding and contracting its distribution range and currently occupying the largest area in its history. PMID:28146559

Agrobacterium Uses a Unique Ligand-Binding Mode for Trapping Opines and Acquiring A Competitive Advantage in the Niche Construction on Plant Host

PubMed Central

Planamente, Sara; El Sahili, Abbas; Blin, Pauline; Aumont-Nicaise, Magali; Dessaux, Yves; Moréra, Solange; Faure, Denis

2014-01-01

By modifying the nuclear genome of its host, the plant pathogen Agrobacterium tumefaciens induces the development of plant tumours in which it proliferates. The transformed plant tissues accumulate uncommon low molecular weight compounds called opines that are growth substrates for A. tumefaciens. In the pathogen-induced niche (the plant tumour), a selective advantage conferred by opine assimilation has been hypothesized, but not experimentally demonstrated. Here, using genetics and structural biology, we deciphered how the pathogen is able to bind opines and use them to efficiently compete in the plant tumour. We report high resolution X-ray structures of the periplasmic binding protein (PBP) NocT unliganded and liganded with the opine nopaline (a condensation product of arginine and α-ketoglurate) and its lactam derivative pyronopaline. NocT exhibited an affinity for pyronopaline (KD of 0.6 µM) greater than that for nopaline (KD of 3.7 µM). Although the binding-mode of the arginine part of nopaline/pyronopaline in NocT resembled that of arginine in other PBPs, affinity measurement by two different techniques showed that NocT did not bind arginine. In contrast, NocT presented specific residues such as M117 to stabilize the bound opines. NocT relatives that exhibit the nopaline/pyronopaline-binding mode were only found in genomes of the genus Agrobacterium. Transcriptomics and reverse genetics revealed that A. tumefaciens uses the same pathway for assimilating nopaline and pyronopaline. Fitness measurements showed that NocT is required for a competitive colonization of the plant tumour by A. tumefaciens. Moreover, even though the Ti-plasmid conjugal transfer was not regulated by nopaline, the competitive advantage gained by the nopaline-assimilating Ti-plasmid donors led to a preferential horizontal propagation of this Ti-plasmid amongst the agrobacteria colonizing the plant-tumour niche. This work provided structural and genetic evidences to support the niche construction paradigm in bacterial pathogens. PMID:25299655

Ecological niche differentiation of polyploidization is not supported by environmental differences among species in a cosmopolitan grass genus.

PubMed

Visser, Vernon; Molofsky, Jane

2015-01-01

• Polyploidization frequently results in the creation of new plant species, the establishment of which is thought to often be facilitated by ecological niche differentiation from the diploid species. We tested this hypothesis using the cosmopolitan grass genus Phalaris (Poaceae), consisting of 19 species that range from diploid to tetraploid to hexaploid. Specifically, we tested whether (1) polyploids occupy more extreme environments and/or (2) have broader niche breadths and/or (3) whether the polyploid species' distributions indicate a niche shift from diploid species.• We employed a bootstrapping approach using distribution data for each species and eight environmental variables to investigate differences between species in the means, extremes, and breadths of each environmental variable. We used a kernel smoothing technique to quantify niche overlap between species.• Although we found some support for the three hypotheses for a few diploid-polyploid pairs and for specific environmental variables, none of these hypotheses were generally supported.• Our results suggest that these commonly held hypotheses about the effects of polyploidization on ecological distributions are not universally applicable. Correlative biogeographic studies like ours provide a necessary first step for suggesting specific hypotheses that require experimental verification. A combination of genetic, physiological, and ecological studies will be required to achieve a better understanding of the role of polyploidization in niche evolution. © 2015 Botanical Society of America, Inc.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

Helicobacter pylori oipA, vacA and dupA genetic diversity in individual hosts.

PubMed

Matteo, Mario José; Armitano, Rita Inés; Granados, Gabriela; Wonaga, Andrés Dario; Sánches, Christian; Olmos, Martín; Catalano, Mariana

2010-01-01

Helicobacter pylori putative virulence factors can undergo a continuously evolving mechanism as an approach to bacterial adaptation to the host changing environment during chronic infection. oipA, vacA and dupA genetic diversity among isolates from multiple biopsies (niches) from the antrum and corpus of 40 patients was investigated. A set of 229 isolates was examined. Direct DNA sequence analysis of amplified fragments was used to study oipA 'on/off' expression status as well as the presence of C or T insertion in jhp0917 that originates a continuous (jhp0917-jhp0918) dupA gene. vacA alleles were identified by multiplex PCR. Different inter-niches oipA CT repeat patterns were observed in nine patients; in six of these, 'on' and 'off' mixed patterns were found. In three of these nine patients, different vacA alleles were also observed in a single host. Inter-niche dupA differences involved the absence and presence of jhp0917 and/or jhp0918 or mutations in dupA, including those that may originate a non-functional gene, and they were also present in two patients with mixed oipA CT patterns and in another seven patients. Evidence of mixed infection was observed in two patients only. In conclusion, oipA and dupA genes showed similar inter-niche variability, occurring in approximately 1/4 patients. Conversely, vacA allele microevolution seemed to be a less common event, occurring in approximately 1/10 patients, probably due to the mechanism that this gene evolves 'in vivo'.

Genome-culture coevolution promotes rapid divergence of killer whale ecotypes.

PubMed

Foote, Andrew D; Vijay, Nagarjun; Ávila-Arcos, María C; Baird, Robin W; Durban, John W; Fumagalli, Matteo; Gibbs, Richard A; Hanson, M Bradley; Korneliussen, Thorfinn S; Martin, Michael D; Robertson, Kelly M; Sousa, Vitor C; Vieira, Filipe G; Vinař, Tomáš; Wade, Paul; Worley, Kim C; Excoffier, Laurent; Morin, Phillip A; Gilbert, M Thomas P; Wolf, Jochen B W

2016-05-31

Analysing population genomic data from killer whale ecotypes, which we estimate have globally radiated within less than 250,000 years, we show that genetic structuring including the segregation of potentially functional alleles is associated with socially inherited ecological niche. Reconstruction of ancestral demographic history revealed bottlenecks during founder events, likely promoting ecological divergence and genetic drift resulting in a wide range of genome-wide differentiation between pairs of allopatric and sympatric ecotypes. Functional enrichment analyses provided evidence for regional genomic divergence associated with habitat, dietary preferences and post-zygotic reproductive isolation. Our findings are consistent with expansion of small founder groups into novel niches by an initial plastic behavioural response, perpetuated by social learning imposing an altered natural selection regime. The study constitutes an important step towards an understanding of the complex interaction between demographic history, culture, ecological adaptation and evolution at the genomic level.

Genome-culture coevolution promotes rapid divergence of killer whale ecotypes

PubMed Central

Foote, Andrew D.; Vijay, Nagarjun; Ávila-Arcos, María C.; Baird, Robin W.; Durban, John W.; Fumagalli, Matteo; Gibbs, Richard A.; Hanson, M. Bradley; Korneliussen, Thorfinn S.; Martin, Michael D.; Robertson, Kelly M.; Sousa, Vitor C.; Vieira, Filipe G.; Vinař, Tomáš; Wade, Paul; Worley, Kim C.; Excoffier, Laurent; Morin, Phillip A.; Gilbert, M. Thomas P.; Wolf, Jochen B.W.

2016-01-01

Analysing population genomic data from killer whale ecotypes, which we estimate have globally radiated within less than 250,000 years, we show that genetic structuring including the segregation of potentially functional alleles is associated with socially inherited ecological niche. Reconstruction of ancestral demographic history revealed bottlenecks during founder events, likely promoting ecological divergence and genetic drift resulting in a wide range of genome-wide differentiation between pairs of allopatric and sympatric ecotypes. Functional enrichment analyses provided evidence for regional genomic divergence associated with habitat, dietary preferences and post-zygotic reproductive isolation. Our findings are consistent with expansion of small founder groups into novel niches by an initial plastic behavioural response, perpetuated by social learning imposing an altered natural selection regime. The study constitutes an important step towards an understanding of the complex interaction between demographic history, culture, ecological adaptation and evolution at the genomic level. PMID:27243207

Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change.

PubMed

Maguire, Kaitlin C; Shinneman, Douglas J; Potter, Kevin M; Hipkins, Valerie D

2018-03-14

Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.

Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change

USGS Publications Warehouse

Maguire, Kaitlin C.; Shinneman, Douglas; Potter, Kevin M.; Hipkins, Valerie D.

2018-01-01

Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.

Evolution of lactase persistence: an example of human niche construction

PubMed Central

Gerbault, Pascale; Liebert, Anke; Itan, Yuval; Powell, Adam; Currat, Mathias; Burger, Joachim; Swallow, Dallas M.; Thomas, Mark G.

2011-01-01

Niche construction is the process by which organisms construct important components of their local environment in ways that introduce novel selection pressures. Lactase persistence is one of the clearest examples of niche construction in humans. Lactase is the enzyme responsible for the digestion of the milk sugar lactose and its production decreases after the weaning phase in most mammals, including most humans. Some humans, however, continue to produce lactase throughout adulthood, a trait known as lactase persistence. In European populations, a single mutation (−13910*T) explains the distribution of the phenotype, whereas several mutations are associated with it in Africa and the Middle East. Current estimates for the age of lactase persistence-associated alleles bracket those for the origins of animal domestication and the culturally transmitted practice of dairying. We report new data on the distribution of −13910*T and summarize genetic studies on the diversity of lactase persistence worldwide. We review relevant archaeological data and describe three simulation studies that have shed light on the evolution of this trait in Europe. These studies illustrate how genetic and archaeological information can be integrated to bring new insights to the origins and spread of lactase persistence. Finally, we discuss possible improvements to these models. PMID:21320900

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Latitudinal environmental niches and riverine barriers shaped the phylogeography of the Central Chilean endemic Dioscorea humilis (Dioscoreaceae).

PubMed

Viruel, Juan; Catalán, Pilar; Segarra-Moragues, José Gabriel

2014-01-01

The effects of Pleistocene glaciations and geographical barriers on the phylogeographic patterns of lowland plant species in Mediterranean-climate areas of Central Chile are poorly understood. We used Dioscorea humilis (Dioscoreaceae), a dioecious geophyte extending 530 km from the Valparaíso to the Bío-Bío Regions, as a case study to disentangle the spatio-temporal evolution of populations in conjunction with latitudinal environmental changes since the Last Inter-Glacial (LIG) to the present. We used nuclear microsatellite loci, chloroplast (cpDNA) sequences and environmental niche modelling (ENM) to construct current and past scenarios from bioclimatic and geographical variables and to infer the evolutionary history of the taxa. We found strong genetic differentiation at nuclear microsatellite loci between the two subspecies of D. humilis, probably predating the LIG. Bayesian analyses of population structure revealed strong genetic differentiation of the widespread D. humilis subsp. humilis into northern and southern population groups, separated by the Maipo river. ENM revealed that the ecological niche differentiation of both groups have been maintained up to present times although their respective geographical distributions apparently fluctuated in concert with the climatic oscillations of the Last Glacial Maximum (LGM) and the Holocene. Genetic data revealed signatures of eastern and western postglacial expansion of the northern populations from the central Chilean depression, whereas the southern ones experienced a rapid southward expansion after the LGM. This study describes the complex evolutionary histories of lowland Mediterranean Chilean plants mediated by the summed effects of spatial isolation caused by riverine geographical barriers and the climatic changes of the Quaternary.

Latitudinal Environmental Niches and Riverine Barriers Shaped the Phylogeography of the Central Chilean Endemic Dioscorea humilis (Dioscoreaceae)

PubMed Central

Viruel, Juan; Catalán, Pilar; Segarra-Moragues, José Gabriel

2014-01-01

The effects of Pleistocene glaciations and geographical barriers on the phylogeographic patterns of lowland plant species in Mediterranean-climate areas of Central Chile are poorly understood. We used Dioscorea humilis (Dioscoreaceae), a dioecious geophyte extending 530 km from the Valparaíso to the Bío-Bío Regions, as a case study to disentangle the spatio-temporal evolution of populations in conjunction with latitudinal environmental changes since the Last Inter-Glacial (LIG) to the present. We used nuclear microsatellite loci, chloroplast (cpDNA) sequences and environmental niche modelling (ENM) to construct current and past scenarios from bioclimatic and geographical variables and to infer the evolutionary history of the taxa. We found strong genetic differentiation at nuclear microsatellite loci between the two subspecies of D. humilis, probably predating the LIG. Bayesian analyses of population structure revealed strong genetic differentiation of the widespread D. humilis subsp. humilis into northern and southern population groups, separated by the Maipo river. ENM revealed that the ecological niche differentiation of both groups have been maintained up to present times although their respective geographical distributions apparently fluctuated in concert with the climatic oscillations of the Last Glacial Maximum (LGM) and the Holocene. Genetic data revealed signatures of eastern and western postglacial expansion of the northern populations from the central Chilean depression, whereas the southern ones experienced a rapid southward expansion after the LGM. This study describes the complex evolutionary histories of lowland Mediterranean Chilean plants mediated by the summed effects of spatial isolation caused by riverine geographical barriers and the climatic changes of the Quaternary. PMID:25295517

Genomic evidence of demographic fluctuations and lack of genetic structure across flyways in a long distance migrant, the European turtle dove.

PubMed

Calderón, Luciano; Campagna, Leonardo; Wilke, Thomas; Lormee, Hervé; Eraud, Cyril; Dunn, Jenny C; Rocha, Gregorio; Zehtindjiev, Pavel; Bakaloudis, Dimitrios E; Metzger, Benjamin; Cecere, Jacopo G; Marx, Melanie; Quillfeldt, Petra

2016-11-07

Understanding how past climatic oscillations have affected organismic evolution will help predict the impact that current climate change has on living organisms. The European turtle dove, Streptopelia turtur, is a warm-temperature adapted species and a long distance migrant that uses multiple flyways to move between Europe and Africa. Despite being abundant, it is categorized as vulnerable because of a long-term demographic decline. We studied the demographic history and population genetic structure of the European turtle dove using genomic data and mitochondrial DNA sequences from individuals sampled across Europe, and performing paleoclimatic niche modelling simulations. Overall our data suggest that this species is panmictic across Europe, and is not genetically structured across flyways. We found the genetic signatures of demographic fluctuations, inferring an effective population size (Ne) expansion that occurred between the late Pleistocene and early Holocene, followed by a decrease in the Ne that started between the mid Holocene and the present. Our niche modelling analyses suggest that the variations in the Ne are coincident with recent changes in the availability of suitable habitat. We argue that the European turtle dove is prone to undergo demographic fluctuations, a trait that makes it sensitive to anthropogenic impacts, especially when its numbers are decreasing. Also, considering the lack of genetic structure, we suggest all populations across Europe are equally relevant for conservation.

Comparison of genetic algorithms with conjugate gradient methods

NASA Technical Reports Server (NTRS)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Current Technologies Based on the Knowledge of the Stem Cells Microenvironments.

PubMed

Mawad, Damia; Figtree, Gemma; Gentile, Carmine

2017-01-01

The stem cell microenvironment or niche plays a critical role in the regulation of survival, differentiation and behavior of stem cells and their progenies. Recapitulating each aspect of the stem cell niche is therefore essential for their optimal use in in vitro studies and in vivo as future therapeutics in humans. Engineering of optimal conditions for three-dimensional stem cell culture includes multiple transient and dynamic physiological stimuli, such as blood flow and tissue stiffness. Bioprinting and microfluidics technologies, including organs-on-a-chip, are among the most recent approaches utilized to replicate the three-dimensional stem cell niche for human tissue fabrication that allow the integration of multiple levels of tissue complexity, including blood flow. This chapter focuses on the physico-chemical and genetic cues utilized to engineer the stem cell niche and provides an overview on how both bioprinting and microfluidics technologies are improving our knowledge in this field for both disease modeling and tissue regeneration, including drug discovery and toxicity high-throughput assays and stem cell-based therapies in humans.

Evolution under changing climates: climatic niche stasis despite rapid evolution in a non-native plant.

PubMed

Alexander, Jake M

2013-09-22

A topic of great current interest is the capacity of populations to adapt genetically to rapidly changing climates, for example by evolving the timing of life-history events, but this is challenging to address experimentally. I use a plant invasion as a model system to tackle this question by combining molecular markers, a common garden experiment and climatic niche modelling. This approach reveals that non-native Lactuca serriola originates primarily from Europe, a climatic subset of its native range, with low rates of admixture from Asia. It has rapidly refilled its climatic niche in the new range, associated with the evolution of flowering phenology to produce clines along climate gradients that mirror those across the native range. Consequently, some non-native plants have evolved development times and grow under climates more extreme than those found in Europe, but not among populations from the native range as a whole. This suggests that many plant populations can adapt rapidly to changed climatic conditions that are already within the climatic niche space occupied by the species elsewhere in its range, but that evolution to conditions outside of this range is more difficult. These findings can also help to explain the prevalence of niche conservatism among non-native species.

Software For Genetic Algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steve E.

1992-01-01

SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Fine-Scale Ecological and Genetic Population Structure of Two Whitefish (Coregoninae) Species in the Vicinity of Industrial Thermal Emissions.

PubMed

Graham, Carly F; Eberts, Rebecca L; Morgan, Thomas D; Boreham, Douglas R; Lance, Stacey L; Manzon, Richard G; Martino, Jessica A; Rogers, Sean M; Wilson, Joanna Y; Somers, Christopher M

2016-01-01

Thermal pollution from industrial processes can have negative impacts on the spawning and development of cold-water fish. Point sources of thermal effluent may need to be managed to avoid affecting discrete populations. Correspondingly, we examined fine-scale ecological and genetic population structure of two whitefish species (Coregonus clupeaformis and Prosopium cylindraceum) on Lake Huron, Canada, in the immediate vicinity of thermal effluent from nuclear power generation. Niche metrics using δ13C and δ15N stable isotopes showed high levels of overlap (48.6 to 94.5%) in resource use by adult fish captured in areas affected by thermal effluent compared to nearby reference locations. Isotopic niche size, a metric of resource use diversity, was 1.3- to 2.8-fold higher than reference values in some thermally affected areas, indicative of fish mixing. Microsatellite analyses of genetic population structure (Fst, STRUCTURE and DAPC) indicated that fish captured at all locations in the vicinity of the power plant were part of a larger population extending beyond the study area. In concert, ecological and genetic markers do not support the presence of an evolutionarily significant unit in the vicinity of the power plant. Thus, future research should focus on the potential impacts of thermal emissions on development and recruitment.

Fine-Scale Ecological and Genetic Population Structure of Two Whitefish (Coregoninae) Species in the Vicinity of Industrial Thermal Emissions

PubMed Central

Graham, Carly F.; Eberts, Rebecca L.; Morgan, Thomas D.; Boreham, Douglas R.; Lance, Stacey L.; Manzon, Richard G.; Martino, Jessica A.; Rogers, Sean M.; Wilson, Joanna Y.; Somers, Christopher M.

2016-01-01

Thermal pollution from industrial processes can have negative impacts on the spawning and development of cold-water fish. Point sources of thermal effluent may need to be managed to avoid affecting discrete populations. Correspondingly, we examined fine-scale ecological and genetic population structure of two whitefish species (Coregonus clupeaformis and Prosopium cylindraceum) on Lake Huron, Canada, in the immediate vicinity of thermal effluent from nuclear power generation. Niche metrics using δ13C and δ15N stable isotopes showed high levels of overlap (48.6 to 94.5%) in resource use by adult fish captured in areas affected by thermal effluent compared to nearby reference locations. Isotopic niche size, a metric of resource use diversity, was 1.3- to 2.8-fold higher than reference values in some thermally affected areas, indicative of fish mixing. Microsatellite analyses of genetic population structure (Fst, STRUCTURE and DAPC) indicated that fish captured at all locations in the vicinity of the power plant were part of a larger population extending beyond the study area. In concert, ecological and genetic markers do not support the presence of an evolutionarily significant unit in the vicinity of the power plant. Thus, future research should focus on the potential impacts of thermal emissions on development and recruitment. PMID:26807722

Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

NASA Astrophysics Data System (ADS)

Balakrishnan, D.; Quan, C.; Tay, C. J.

2013-06-01

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

An Efficient Rank Based Approach for Closest String and Closest Substring

PubMed Central

2012-01-01

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. PMID:22675483

A hybrid genetic algorithm for resolving closely spaced objects

NASA Technical Reports Server (NTRS)

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Tracing early stages of species differentiation: Ecological, morphological and genetic divergence of Galápagos sea lion populations

PubMed Central

2008-01-01

Background Oceans are high gene flow environments that are traditionally believed to hamper the build-up of genetic divergence. Despite this, divergence appears to occur occasionally at surprisingly small scales. The Galápagos archipelago provides an ideal opportunity to examine the evolutionary processes of local divergence in an isolated marine environment. Galápagos sea lions (Zalophus wollebaeki) are top predators in this unique setting and have an essentially unlimited dispersal capacity across the entire species range. In theory, this should oppose any genetic differentiation. Results We find significant ecological, morphological and genetic divergence between the western colonies and colonies from the central region of the archipelago that are exposed to different ecological conditions. Stable isotope analyses indicate that western animals use different food sources than those from the central area. This is likely due to niche partitioning with the second Galápagos eared seal species, the Galápagos fur seal (Arctocephalus galapagoensis) that exclusively dwells in the west. Stable isotope patterns correlate with significant differences in foraging-related skull morphology. Analyses of mitochondrial sequences as well as microsatellites reveal signs of initial genetic differentiation. Conclusion Our results suggest a key role of intra- as well as inter-specific niche segregation in the evolution of genetic structure among populations of a highly mobile species under conditions of free movement. Given the monophyletic arrival of the sea lions on the archipelago, our study challenges the view that geographical barriers are strictly needed for the build-up of genetic divergence. The study further raises the interesting prospect that in social, colonially breeding mammals additional forces, such as social structure or feeding traditions, might bear on the genetic partitioning of populations. PMID:18485220

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

ERIC Educational Resources Information Center

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Niches, models, and climate change: Assessing the assumptions and uncertainties

PubMed Central

Wiens, John A.; Stralberg, Diana; Jongsomjit, Dennis; Howell, Christine A.; Snyder, Mark A.

2009-01-01

As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some “hotspots” of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option. PMID:19822750

Boiler-turbine control system design using a genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.; Lee, K.Y.

1995-12-01

This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.

Understanding invasion history and predicting invasive niches using genetic sequencing technology in Australia: case studies from Cucurbitaceae and Boraginaceae.

PubMed

Shaik, Razia S; Zhu, Xiaocheng; Clements, David R; Weston, Leslie A

2016-01-01

Part of the challenge in dealing with invasive plant species is that they seldom represent a uniform, static entity. Often, an accurate understanding of the history of plant introduction and knowledge of the real levels of genetic diversity present in species and populations of importance is lacking. Currently, the role of genetic diversity in promoting the successful establishment of invasive plants is not well defined. Genetic profiling of invasive plants should enhance our understanding of the dynamics of colonization in the invaded range. Recent advances in DNA sequencing technology have greatly facilitated the rapid and complete assessment of plant population genetics. Here, we apply our current understanding of the genetics and ecophysiology of plant invasions to recent work on Australian plant invaders from the Cucurbitaceae and Boraginaceae. The Cucurbitaceae study showed that both prickly paddy melon ( Cucumis myriocarpus ) and camel melon ( Citrullus lanatus ) were represented by only a single genotype in Australia, implying that each was probably introduced as a single introduction event. In contrast, a third invasive melon, Citrullus colocynthis , possessed a moderate level of genetic diversity in Australia and was potentially introduced to the continent at least twice. The Boraginaceae study demonstrated the value of comparing two similar congeneric species; one, Echium plantagineum , is highly invasive and genetically diverse, whereas the other, Echium vulgare , exhibits less genetic diversity and occupies a more limited ecological niche. Sequence analysis provided precise identification of invasive plant species, as well as information on genetic diversity and phylogeographic history. Improved sequencing technologies will continue to allow greater resolution of genetic relationships among invasive plant populations, thereby potentially improving our ability to predict the impact of these relationships upon future spread and better manage invaders possessing potentially diverse biotypes and exhibiting diverse breeding systems, life histories and invasion histories.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

Genetic monitoring detects an overlooked cryptic species and reveals the diversity and distribution of three invasive Rattus congeners in south Africa

PubMed Central

2011-01-01

Background South Africa's long and extensive trade activity has ensured ample opportunities for exotic species introduction. Whereas the rich biodiversity of endemic southern African fauna has been the focus of many studies, invasive vertebrates are generally overlooked despite potential impacts on biodiversity, health and agriculture. Genetic monitoring of commensal rodents in South Africa which uncovered the presence of Rattus tanezumi, a South-East Asian endemic not previously known to occur in Africa, provided the impetus for expanded studies on all invasive Rattus species present. Results To this end, intensified sampling at 28 South African localities and at one site in Swaziland, identified 149 Rattus specimens. Cytochrome b gene sequencing revealed the presence of two R. tanezumi, seven Rattus rattus and five Rattus norvegicus haplotypes in south Africa. Phylogenetic results were consistent with a single, recent R. tanezumi introduction and indicated that R. norvegicus and R. rattus probably became established following at least two and three independent introductions, respectively. Intra- and inter-specific diversity was highest in informal human settlements, with all three species occurring at a single metropolitan township site. Rattus norvegicus and R. rattus each occurred sympatrically with Rattus tanezumi at one and five sites, respectively. Karyotyping of selected R. rattus and R. tanezumi individuals identified diploid numbers consistent with those reported previously for these cryptic species. Ordination of bioclimatic variables and MaxEnt ecological niche modelling confirmed that the bioclimatic niche occupied by R. tanezumi in south Africa was distinct from that occupied in its naturalised range in south-east Asia suggesting that factors other than climate may influence the distribution of this species. Conclusions This study has highlighted the value of genetic typing for detecting cryptic invasive species, providing historical insights into introductions and for directing future sampling. The apparent ease with which a cryptic species can become established signals the need for broader implementation of genetic monitoring programmes. In addition to providing baseline data and potentially identifying high-risk introduction routes, the predictive power of ecological niche modelling is enhanced when species records are genetically verified. PMID:21324204

Language and other artifacts: socio-cultural dynamics of niche construction

PubMed Central

Sinha, Chris

2015-01-01

Niche construction theory is a relatively new approach in evolutionary biology that seeks to integrate an ecological dimension into the Darwinian theory of evolution by natural selection. It is regarded by many evolutionary biologists as providing a significant revision of the Neo-Darwinian modern synthesis that unified Darwin’s theory of natural and sexual selection with 20th century population genetics. Niche construction theory has been invoked as a processual mediator of social cognitive evolution and of the emergence and evolution of language. I argue that language itself can be considered as a biocultural niche and evolutionary artifact. I provide both a general analysis of the cognitive and semiotic status of artifacts, and a formal analysis of language as a social and semiotic institution, based upon a distinction between the fundamental semiotic relations of “counting as” and “standing for.” I explore the consequences for theories of language and language learning of viewing language as a biocultural niche. I suggest that not only do niches mediate organism-organism interactions, but also that organisms mediate niche-niche interactions in ways that affect evolutionary processes, with the evolution of human infancy and childhood as a key example. I argue that language as a social and semiotic system is not only grounded in embodied engagements with the material and social-interactional world, but also grounds a sub-class of artifacts of particular significance in the cultural history of human cognition. Symbolic cognitive artifacts materially and semiotically mediate human cognition, and are not merely informational repositories, but co-agentively constitutive of culturally and historically emergent cognitive domains. I provide examples of the constitutive cognitive role of symbolic cognitive artifacts drawn from my research with my colleagues on cultural and linguistic conceptualizations of time, and their cultural variability. I conclude by reflecting on the philosophical and social implications of understanding artifacts co-agentively. PMID:26539144

Genetics-based control of a mimo boiler-turbine plant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Contrasting responses to a climate regime change by sympatric, ice-dependent predators.

PubMed

Younger, Jane L; van den Hoff, John; Wienecke, Barbara; Hindell, Mark; Miller, Karen J

2016-03-15

Models that predict changes in the abundance and distribution of fauna under future climate change scenarios often assume that ecological niche and habitat availability are the major determinants of species' responses to climate change. However, individual species may have very different capacities to adapt to environmental change, as determined by intrinsic factors such as their dispersal ability, genetic diversity, generation time and rate of evolution. These intrinsic factors are usually excluded from forecasts of species' abundance and distribution changes. We aimed to determine the importance of these factors by comparing the impact of the most recent climate regime change, the late Pleistocene glacial-interglacial transition, on two sympatric, ice-dependent meso-predators, the emperor penguin (Aptenodytes forsteri) and Weddell seal (Leptonychotes weddellii). We reconstructed the population trend of emperor penguins and Weddell seals in East Antarctica over the past 75,000 years using mitochondrial DNA sequences and an extended Bayesian skyline plot method. We also assessed patterns of contemporary population structure and genetic diversity. Despite their overlapping distributions and shared dependence on sea ice, our genetic data revealed very different responses to climate warming between these species. The emperor penguin population grew rapidly following the glacial-interglacial transition, but the size of the Weddell seal population did not change. The expansion of emperor penguin numbers during the warm Holocene may have been facilitated by their higher dispersal ability and gene flow among colonies, and fine-scale differences in preferred foraging locations. The vastly different climate change responses of two sympatric ice-dependent predators suggests that differing adaptive capacities and/or fine-scale niche differences can play a major role in species' climate change responses, and that adaptive capacity should be considered alongside niche and distribution in future species forecasts.

Vulnerability of dynamic genetic conservation units of forest trees in Europe to climate change.

PubMed

Schueler, Silvio; Falk, Wolfgang; Koskela, Jarkko; Lefèvre, François; Bozzano, Michele; Hubert, Jason; Kraigher, Hojka; Longauer, Roman; Olrik, Ditte C

2014-05-01

A transnational network of genetic conservation units for forest trees was recently documented in Europe aiming at the conservation of evolutionary processes and the adaptive potential of natural or man-made tree populations. In this study, we quantified the vulnerability of individual conservation units and the whole network to climate change using climate favourability models and the estimated velocity of climate change. Compared to the overall climate niche of the analysed target species populations at the warm and dry end of the species niche are underrepresented in the network. However, by 2100, target species in 33-65 % of conservation units, mostly located in southern Europe, will be at the limit or outside the species' current climatic niche as demonstrated by favourabilities below required model sensitivities of 95%. The highest average decrease in favourabilities throughout the network can be expected for coniferous trees although they are mainly occurring within units in mountainous landscapes for which we estimated lower velocities of change. Generally, the species-specific estimates of favourabilities showed only low correlations to the velocity of climate change in individual units, indicating that both vulnerability measures should be considered for climate risk analysis. The variation in favourabilities among target species within the same conservation units is expected to increase with climate change and will likely require a prioritization among co-occurring species. The present results suggest that there is a strong need to intensify monitoring efforts and to develop additional conservation measures for populations in the most vulnerable units. Also, our results call for continued transnational actions for genetic conservation of European forest trees, including the establishment of dynamic conservation populations outside the current species distribution ranges within European assisted migration schemes. © 2013 John Wiley & Sons Ltd.

Adaptive Genetic Divergence along Narrow Environmental Gradients in Four Stream Insects

PubMed Central

Watanabe, Kozo; Kazama, So; Omura, Tatsuo; Monaghan, Michael T.

2014-01-01

A central question linking ecology with evolutionary biology is how environmental heterogeneity can drive adaptive genetic divergence among populations. We examined adaptive divergence of four stream insects from six adjacent catchments in Japan by combining field measures of habitat and resource components with genome scans of non-neutral Amplified Fragment Length Polymorphism (AFLP) loci. Neutral genetic variation was used to measure gene flow and non-neutral genetic variation was used to test for adaptive divergence. We identified the environmental characteristics contributing to divergence by comparing genetic distances at non-neutral loci between sites with Euclidean distances for each of 15 environmental variables. Comparisons were made using partial Mantel tests to control for geographic distance. In all four species, we found strong evidence for non-neutral divergence along environmental gradients at between 6 and 21 loci per species. The relative contribution of these environmental variables to each species' ecological niche was quantified as the specialization index, S, based on ecological data. In each species, the variable most significantly correlated with genetic distance at non-neutral loci was the same variable along which each species was most narrowly distributed (i.e., highest S). These were gradients of elevation (two species), chlorophyll-a, and ammonia-nitrogen. This adaptive divergence occurred in the face of ongoing gene flow (F st = 0.01–0.04), indicating that selection was strong enough to overcome homogenization at the landscape scale. Our results suggest that adaptive divergence is pronounced, occurs along different environmental gradients for different species, and may consistently occur along the narrowest components of species' niche. PMID:24681871

Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

NASA Astrophysics Data System (ADS)

Moon, Byung-Young

2005-12-01

The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.

Phylogeographic analysis and environmental niche modeling of the plain-bellied watersnake (Nerodia erythrogaster) reveals low levels of genetic and ecological differentiation

PubMed Central

Makowsky, Robert; Marshall, John C.; McVay, John; Chippindale, Paul T.; Rissler, Leslie J.

2012-01-01

Species that exhibit geographically defined phenotypic variation traditionally have been divided into subspecies. Subspecies based on phenotypic features may not comprise monophyletic groups due to selection, gene flow, and/or convergent evolution. In many taxonomic groups the number of species once designated as widespread is dwindling rapidly, and many workers reject the concept of subspecies altogether. We tested whether currently recognized subspecies in the plain-bellied watersnake Nerodia erythrogaster are concordant with relationships based on mitochondrial markers, and whether it represents a single widespread species. The range of this taxon spans multiple potential biogeographic barriers (especially the Mississippi and Apalachicola Rivers) that correspond with lineage breaks in many species, including other snakes. We sequenced three mitochondrialgenes (NADH-II, Cyt-b, Cox-I) from 156 geo-referenced specimens and developed ecological niche models using Maxent and spatially-explicit climate data to examine historical and ecological factors affecting variation in N. erythrogaster across its range. Overall, we found little support for the recognized subspecies as either independent evolutionary lineages or geographically circumscribed units and conclude that although some genetic and niche differentiation has occurred, most populations assigned to N. erythrogaster appear to represent a single, widespread species. However, additional sampling and application of nuclear markers are necessary to clarify the status of the easternmost populations. PMID:20302955

Interpreting the Process behind Endemism in China by Integrating the Phylogeography and Ecological Niche Models of the Stachyridopsis ruficeps

PubMed Central

Liu, Huatao; Wang, Wenjuan; Song, Gang; Qu, Yanhua; Li, Shou-Hsien; Fjeldså, Jon; Lei, Fumin

2012-01-01

An area of endemism (AOE) is a complex expression of the ecological and evolutionary history of a species. Here we aim to address the principal drivers of avian diversification in shaping patterns of endemism in China by integrating genetic, ecological, and distributional data on the Red-headed Tree Babbler (Stachyridopsis ruficeps), which is distributed across the eastern Himalayas and south China. We sequenced two mtDNA markers from 182 individuals representing all three of the primary AOEs in China. Phylogenetic inferences were used to reconstruct intraspecific phylogenetic relationships. Divergence time and population demography were estimated to gain insight into the evolutionary history of the species. We used Ecological niche modeling to predict species’ distributions during the Last Glacial Maximum (LGM) and in the present. Finally, we also used two quantitative tests, an identity test and background test to assess the similarity of ecological niche preferences between adjacent lineages. We found five primary reciprocally monophyletic clades, typically separated approximately 0.2–2.27 MYA, of which three were deeply isolated endemic lineages located in the three AOEs. All phylogroups were detected to have undergone population expansion during the past 0.3 MY. Niche models showed discontinuous habitats, and there were three barriers of less suitable habitat during the LGM and in modern times. Ecoclimatic niches may diverge significantly even over recent timescales, as each phylogroup had a unique distribution, and unique niche characteristics. Vicariant events associated with geographical and ecological barriers, glacial refuges and ecological differentiation may be the main drivers forming the pattern of endemism in China. PMID:23056441

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Training product unit neural networks with genetic algorithms

NASA Technical Reports Server (NTRS)

Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

1991-01-01

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

New Results in Astrodynamics Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.

1998-01-01

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

Unveiling Current Guanaco Distribution in Chile Based upon Niche Structure of Phylogeographic Lineages: Andean Puna to Subpolar Forests

PubMed Central

González, Benito A.; Samaniego, Horacio; Marín, Juan Carlos; Estades, Cristián F.

2013-01-01

Niche description and differentiation at broad geographic scales have been recent major topics in ecology and evolution. Describing the environmental niche structure of sister taxa with known evolutionary trajectories stands out as a useful exercise in understanding niche requirements. Here we model the environmental niche structure and distribution of the recently resolved phylogeography of guanaco (Lama guanicoe) lineages on the western slope of the southern Andes. Using a maximum entropy framework, field data, and information on climate, topography, human density, and vegetation cover, we identify differences between the two subspecies (L.g.cacsilensis, L.g.guanicoe) and their intermediate-hybrid lineage, that most likely determine the distribution of this species. While aridity seems to be a major factor influencing the distribution at the species-level (annual precipitation <900 mm), we also document important differences in niche specificity for each subspecies, where distribution of Northern lineage is explained mainly by elevation (mean = 3,413 m) and precipitation seasonality (mean = 161 mm), hybrid lineage by annual precipitation (mean = 139 mm), and Southern subspecies by annual precipitation (mean = 553 mm), precipitation seasonality (mean = 21 mm) and grass cover (mean = 8.2%). Among lineages, we detected low levels of niche overlap: I (Similarity Index) = 0.06 and D (Schoener’s Similarity Index) = 0.01; and higher levels when comparing Northern and Southern subspecies with hybrids lineage (I = 0.32-0.10 and D = 0.12-0.03, respectively). This suggests that important ecological and/or evolutionary processes are shaping the niche of guanacos in Chile, producing discrepancies when comparing range distribution at the species-level (81,756 km2) with lineages-level (65,321 km2). The subspecies-specific description of niche structure is provided here based upon detailed spatial distribution of the lineages of guanacos in Chile. Such description provides a scientific tool to further develop large scale plans for habitat conservation and preservation of intraspecific genetic variability for this far ranging South American camelid, which inhabits a diversity of ecoregion types from Andean puna to subpolar forests. PMID:24265726

Unveiling current Guanaco distribution in chile based upon niche structure of phylogeographic lineages: Andean puna to subpolar forests.

PubMed

González, Benito A; Samaniego, Horacio; Marín, Juan Carlos; Estades, Cristián F

2013-01-01

Niche description and differentiation at broad geographic scales have been recent major topics in ecology and evolution. Describing the environmental niche structure of sister taxa with known evolutionary trajectories stands out as a useful exercise in understanding niche requirements. Here we model the environmental niche structure and distribution of the recently resolved phylogeography of guanaco (Lama guanicoe) lineages on the western slope of the southern Andes. Using a maximum entropy framework, field data, and information on climate, topography, human density, and vegetation cover, we identify differences between the two subspecies (L.g.cacsilensis, L.g.guanicoe) and their intermediate-hybrid lineage, that most likely determine the distribution of this species. While aridity seems to be a major factor influencing the distribution at the species-level (annual precipitation <900 mm), we also document important differences in niche specificity for each subspecies, where distribution of Northern lineage is explained mainly by elevation (mean = 3,413 m) and precipitation seasonality (mean = 161 mm), hybrid lineage by annual precipitation (mean = 139 mm), and Southern subspecies by annual precipitation (mean = 553 mm), precipitation seasonality (mean = 21 mm) and grass cover (mean = 8.2%). Among lineages, we detected low levels of niche overlap: I (Similarity Index) = 0.06 and D (Schoener's Similarity Index) = 0.01; and higher levels when comparing Northern and Southern subspecies with hybrids lineage ( I = 0.32-0.10 and D = 0.12-0.03, respectively). This suggests that important ecological and/or evolutionary processes are shaping the niche of guanacos in Chile, producing discrepancies when comparing range distribution at the species-level (81,756 km(2)) with lineages-level (65,321 km(2)). The subspecies-specific description of niche structure is provided here based upon detailed spatial distribution of the lineages of guanacos in Chile. Such description provides a scientific tool to further develop large scale plans for habitat conservation and preservation of intraspecific genetic variability for this far ranging South American camelid, which inhabits a diversity of ecoregion types from Andean puna to subpolar forests.

Revealing hidden species diversity in closely related species using nuclear SNPs, SSRs and DNA sequences - a case study in the tree genus Milicia.

PubMed

Daïnou, Kasso; Blanc-Jolivet, Céline; Degen, Bernd; Kimani, Priscilla; Ndiade-Bourobou, Dyana; Donkpegan, Armel S L; Tosso, Félicien; Kaymak, Esra; Bourland, Nils; Doucet, Jean-Louis; Hardy, Olivier J

2016-12-01

Species delimitation in closely related plant taxa can be challenging because (i) reproductive barriers are not always congruent with morphological differentiation, (ii) use of plastid sequences might lead to misinterpretation, (iii) rare species might not be sampled. We revisited molecular-based species delimitation in the African genus Milicia, currently divided into M. regia (West Africa) and M. excelsa (from West to East Africa). We used 435 samples collected in West, Central and East Africa. We genotyped SNP and SSR loci to identify genetic clusters, and sequenced two plastid regions (psbA-trnH, trnC-ycf6) and a nuclear gene (At103) to confirm species' divergence and compare species delimitation methods. We also examined whether ecological niche differentiation was congruent with sampled genetic structure. West African M. regia, West African and East African M. excelsa samples constituted three well distinct genetic clusters according to SNPs and SSRs. In Central Africa, two genetic clusters were consistently inferred by both types of markers, while a few scattered samples, sympatric with the preceding clusters but exhibiting leaf traits of M. regia, were grouped with the West African M. regia cluster based on SNPs or formed a distinct cluster based on SSRs. SSR results were confirmed by sequence data from the nuclear region At103 which revealed three distinct 'Fields For Recombination' corresponding to (i) West African M. regia, (ii) Central African samples with leaf traits of M. regia, and (iii) all M. excelsa samples. None of the plastid sequences provide indication of distinct clades of the three species-like units. Niche modelling techniques yielded a significant correlation between niche overlap and genetic distance. Our genetic data suggest that three species of Milicia could be recognized. It is surprising that the occurrence of two species in Central Africa was not reported for this well-known timber tree. Globally, our work highlights the importance of collecting samples in a systematic way and the need for combining different nuclear markers when dealing with species complexes. Recognizing cryptic species is particularly crucial for economically exploited species because some hidden taxa might actually be endangered as they are merged with more abundant species.

The touch dome defines an epidermal niche specialized for mechanosensory signaling

PubMed Central

Doucet, Yanne S.; Woo, Seung-Hyun; Ruiz, Marlon E.; Owens, David M.

2013-01-01

Summary In mammalian skin, Merkel cells are mechanoreceptor cells that are required for the perception of gentle touch. Recent evidence indicates that mature Merkel cells descend from the proliferative layer of skin epidermis; however, the stem cell niche for Merkel cell homeostasis has not been reported. Here, we provide the first genetic evidence for maintenance of mature Merkel cells during homeostasis by Krt17+ stem cells located in epidermal touch domes of hairy skin and in the tips of the rete ridges of glabrous skin. Lineage tracing analysis indicated that the entire pool of mature Merkel cells is turned over every 7–8 weeks in adult epidermis and that Krt17+ stem cells also maintain squamous differentiation in the touch dome and in glabrous skin. Finally, selective genetic ablation of Krt17+ touch dome keratinocytes indicates that these cells, and not mature Merkel cells, are primarily responsible for maintaining innervation of the Merkel cell-neurite complex. PMID:23727240

Predicting invasion risk using measures of introduction effort and environmental niche models.

PubMed

Herborg, Leif-Matthias; Jerde, Christopher L; Lodge, David M; Ruiz, Gregory M; MacIsaac, Hugh J

2007-04-01

The Chinese mitten crab (Eriocheir sinensis) is native to east Asia, is established throughout Europe, and is introduced but geographically restricted in North America. We developed and compared two separate environmental niche models using genetic algorithm for rule set prediction (GARP) and mitten crab occurrences in Asia and Europe to predict the species' potential distribution in North America. Since mitten crabs must reproduce in water with >15% per hundred salinity, we limited the potential North American range to freshwater habitats within the highest documented dispersal distance (1260 km) and a more restricted dispersal limit (354 km) from the sea. Applying the higher dispersal distance, both models predicted the lower Great Lakes, most of the eastern seaboard, the Gulf of Mexico and southern extent of the Mississippi River watershed, and the Pacific northwest as suitable environment for mitten crabs, but environmental match for southern states (below 35 degrees N) was much lower for the European model. Use of the lower range with both models reduced the expected range, especially in the Great Lakes, Mississippi drainage, and inland areas of the Pacific Northwest. To estimate the risk of introduction of mitten crabs, the amount of reported ballast water discharge into major United States ports from regions in Asia and Europe with established mitten crab populations was used as an index of introduction effort. Relative risk of invasion was estimated based on a combination of environmental match and volume of unexchanged ballast water received (July 1999-December 2003) for major ports. The ports of Norfolk and Baltimore were most vulnerable to invasion and establishment, making Chesapeake Bay the most likely location to be invaded by mitten crabs in the United States. The next highest risk was predicted for Portland, Oregon. Interestingly, the port of Los Angeles/Long Beach, which has a large shipping volume, had a low risk of invasion. Ports such as Jacksonville, Florida, had a medium risk owing to small shipping volume but high environmental match. This study illustrates that the combination of environmental niche- and vector-based models can provide managers with more precise estimates of invasion risk than can either of these approaches alone.

Niche construction on Bali: the gods of the countryside

PubMed Central

Lansing, J. Stephen; Fox, Karyn M.

2011-01-01

Human niche construction encompasses both purely biological phenomena, such as the evolution of lactose tolerance, and dual inheritance theory, which investigates the transmission of cultural information. But does niche construction help to explain phenomena in which conscious intention also plays a role? The creation of the engineered landscape of Balinese rice terraces offers a test case. Population genetic analysis and archaeological evidence are used to investigate whether this phenomenon emerged historically from trial and error by generations of farmers, or alternatively was designed by Bali's rulers. In light of strong support for the former hypothesis, two models are developed to explore the emergence of functional structure at both local and global scales. As time goes forward and selected patterns of irrigation schedules are implemented, local variation in rice harvests influences future decisions by the farmers, creating a coupled human–natural system governed by feedback from the environment. This mathematical analysis received a measure of empirical support when government agricultural policies severed the local feedback channels, resulting in the almost instantaneous collapse of rice harvests. The historical process of niche construction may also have included an evolution of religious consciousness, reflected in the beliefs and practices of the water temple cult. PMID:21320905

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Adaptive genomic divergence under high gene flow between freshwater and brackish-water ecotypes of prickly sculpin (Cottus asper) revealed by Pool-Seq.

PubMed

Dennenmoser, Stefan; Vamosi, Steven M; Nolte, Arne W; Rogers, Sean M

2017-01-01

Understanding the genomic basis of adaptive divergence in the presence of gene flow remains a major challenge in evolutionary biology. In prickly sculpin (Cottus asper), an abundant euryhaline fish in northwestern North America, high genetic connectivity among brackish-water (estuarine) and freshwater (tributary) habitats of coastal rivers does not preclude the build-up of neutral genetic differentiation and emergence of different life history strategies. Because these two habitats present different osmotic niches, we predicted high genetic differentiation at known teleost candidate genes underlying salinity tolerance and osmoregulation. We applied whole-genome sequencing of pooled DNA samples (Pool-Seq) to explore adaptive divergence between two estuarine and two tributary habitats. Paired-end sequence reads were mapped against genomic contigs of European Cottus, and the gene content of candidate regions was explored based on comparisons with the threespine stickleback genome. Genes showing signals of repeated differentiation among brackish-water and freshwater habitats included functions such as ion transport and structural permeability in freshwater gills, which suggests that local adaptation to different osmotic niches might contribute to genomic divergence among habitats. Overall, the presence of both repeated and unique signatures of differentiation across many loci scattered throughout the genome is consistent with polygenic adaptation from standing genetic variation and locally variable selection pressures in the early stages of life history divergence. © 2016 John Wiley & Sons Ltd.

Hybridization at an ecotone: ecological and genetic barriers between three Iberian vipers.

PubMed

Tarroso, Pedro; Pereira, Ricardo J; Martínez-Freiría, Fernando; Godinho, Raquel; Brito, José C

2014-03-01

The formation of stable genetic boundaries between emerging species is often diagnosed by reduced hybrid fitness relative to parental taxa. This reduced fitness can arise from endogenous and/or exogenous barriers to gene flow. Although detecting exogenous barriers in nature is difficult, we can estimate the role of ecological divergence in driving species boundaries by integrating molecular and ecological niche modelling tools. Here, we focus on a three-way secondary contact zone between three viper species (Vipera aspis, V. latastei and V. seoanei) to test for the contribution of ecological divergence to the development of reproductive barriers at several species traits (morphology, nuclear DNA and mitochondrial DNA). Both the nuclear and mitochondrial data show that all taxa are genetically distinct and that the sister species V. aspis and V. latastei hybridize frequently and backcross over several generations. We find that the three taxa have diverged ecologically and meet at a hybrid zone coincident with a steep ecotone between the Atlantic and Mediterranean biogeographical provinces. Integrating landscape and genetic approaches, we show that hybridization is spatially restricted to habitats that are suboptimal for parental taxa. Together, these results suggest that niche separation and adaptation to an ecological gradient confer an important barrier to gene flow among taxa that have not achieved complete reproductive isolation. © 2014 John Wiley & Sons Ltd.

Fine-scale ecological and genetic population structure of two whitefish (Coregoninae) species in the vicinity of industrial thermal emissions

DOE PAGES

Graham, Carly F.; Eberts, Rebecca L.; Morgan, Thomas D.; ...

2016-01-25

Thermal pollution from industrial processes can have negative impacts on the spawning and development of cold-water fish. Point sources of thermal effluent may need to be managed to avoid affecting discrete populations. Correspondingly, we examined fine-scale ecological and genetic population structure of two whitefish species ( Coregonus clupeaformis and Prosopium cylindraceum) on Lake Huron, Canada, in the immediate vicinity of thermal effluent from nuclear power generation. Niche metrics using δ 13C and δ 15N stable isotopes showed high levels of overlap (48.6 to 94.5%) in resource use by adult fish captured in areas affected by thermal effluent compared to nearbymore » reference locations. Isotopic niche size, a metric of resource use diversity, was 1.3- to 2.8-fold higher than reference values in some thermally affected areas, indicative of fish mixing. Microsatellite analyses of genetic population structure (F st, STRUCTURE and DAPC) indicated that fish captured at all locations in the vicinity of the power plant were part of a larger population extending beyond the study area. In concert, ecological and genetic markers do not support the presence of an evolutionarily significant unit in the vicinity of the power plant. Furthermore, future research should focus on the potential impacts of thermal emissions on development and recruitment.« less

Fine-scale ecological and genetic population structure of two whitefish (Coregoninae) species in the vicinity of industrial thermal emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graham, Carly F.; Eberts, Rebecca L.; Morgan, Thomas D.

Thermal pollution from industrial processes can have negative impacts on the spawning and development of cold-water fish. Point sources of thermal effluent may need to be managed to avoid affecting discrete populations. Correspondingly, we examined fine-scale ecological and genetic population structure of two whitefish species ( Coregonus clupeaformis and Prosopium cylindraceum) on Lake Huron, Canada, in the immediate vicinity of thermal effluent from nuclear power generation. Niche metrics using δ 13C and δ 15N stable isotopes showed high levels of overlap (48.6 to 94.5%) in resource use by adult fish captured in areas affected by thermal effluent compared to nearbymore » reference locations. Isotopic niche size, a metric of resource use diversity, was 1.3- to 2.8-fold higher than reference values in some thermally affected areas, indicative of fish mixing. Microsatellite analyses of genetic population structure (F st, STRUCTURE and DAPC) indicated that fish captured at all locations in the vicinity of the power plant were part of a larger population extending beyond the study area. In concert, ecological and genetic markers do not support the presence of an evolutionarily significant unit in the vicinity of the power plant. Furthermore, future research should focus on the potential impacts of thermal emissions on development and recruitment.« less

Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

DTIC Science & Technology

2016-12-01

Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street... Genetic Algorithm 5a. CONTRACT NUMBER W199SR-15-2-001 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Justin L Paul 5d. PROJECT

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, X. R.; Wang, X.

2016-03-01

When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang

2017-01-01

Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.

Regional Crustal Velocity Models for Northern Arabian Platform and Turkish-Iranian Plateau

NASA Astrophysics Data System (ADS)

Aleqabi, G.; Wysession, M.; Ghalib, H.

2008-12-01

The geological structure of the Northern Arabian platform and surrounding mountains is dominated by the collision and suturing of the Arabian plate with the Eurasian plate and the formation of the Turkish-Iranian plateau. The structure of the Northern Arabian platform and surrounding region is poorly constrained. A recent deployment of 10 broadband seismometers in northern and central Iraq provides an opportunity to refine velocity models of the region. We have applied the Niching Genetic Algorithm waveform inversion technique to Rayleigh and Love waves traversing the Northern Arabian platform, the Zagros fold belt, the southern Turkish Plateau, the Iranian Plateau. Results show variations in crustal thickness and shear wave velocity between the Northern Arabian platform and the Turkish-Iranian plateau. In general the shear wave velocities are higher in the Northern Arabian platform than in the Plateaus. Variation of shear velocities within each of the provinces reflects the diversity in tectonic environment across the Zagros fold belt and the complex tectonic history of the region. Crustal thickness results show little crustal thickening has occurred due to collision.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

NASA Astrophysics Data System (ADS)

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

PubMed

Mehdinejadiani, Behrouz

2017-08-01

This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.

A Test of Genetic Algorithms in Relevance Feedback.

ERIC Educational Resources Information Center

Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

2002-01-01

Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

Transonic Wing Shape Optimization Using a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

2002-01-01

A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

An improved genetic algorithm and its application in the TSP problem

NASA Astrophysics Data System (ADS)

Li, Zheng; Qin, Jinlei

2011-12-01

Concept and research actuality of genetic algorithm are introduced in detail in the paper. Under this condition, the simple genetic algorithm and an improved algorithm are described and applied in an example of TSP problem, where the advantage of genetic algorithm is adequately shown in solving the NP-hard problem. In addition, based on partial matching crossover operator, the crossover operator method is improved into extended crossover operator in order to advance the efficiency when solving the TSP. In the extended crossover method, crossover operator can be performed between random positions of two random individuals, which will not be restricted by the position of chromosome. Finally, the nine-city TSP is solved using the improved genetic algorithm with extended crossover method, the efficiency of whose solution process is much higher, besides, the solving speed of the optimal solution is much faster.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

ERIC Educational Resources Information Center

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

The potential of genetic algorithms for conceptual design of rotor systems

NASA Technical Reports Server (NTRS)

Crossley, William A.; Wells, Valana L.; Laananen, David H.

1993-01-01

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)

NASA Astrophysics Data System (ADS)

Li, Xin-ran; Wang, Xin

2017-04-01

When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

NASA Technical Reports Server (NTRS)

Wang, Lui; Valenzuela-Rendon, Manuel

1993-01-01

The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

Satellite Cells and the Muscle Stem Cell Niche

PubMed Central

Yin, Hang; Price, Feodor

2013-01-01

Adult skeletal muscle in mammals is a stable tissue under normal circumstances but has remarkable ability to repair after injury. Skeletal muscle regeneration is a highly orchestrated process involving the activation of various cellular and molecular responses. As skeletal muscle stem cells, satellite cells play an indispensible role in this process. The self-renewing proliferation of satellite cells not only maintains the stem cell population but also provides numerous myogenic cells, which proliferate, differentiate, fuse, and lead to new myofiber formation and reconstitution of a functional contractile apparatus. The complex behavior of satellite cells during skeletal muscle regeneration is tightly regulated through the dynamic interplay between intrinsic factors within satellite cells and extrinsic factors constituting the muscle stem cell niche/microenvironment. For the last half century, the advance of molecular biology, cell biology, and genetics has greatly improved our understanding of skeletal muscle biology. Here, we review some recent advances, with focuses on functions of satellite cells and their niche during the process of skeletal muscle regeneration. PMID:23303905

Clonality of bacterial consortia in root canals and subjacent gingival crevices.

PubMed

Parahitiyawa, Nipuna B; Chu, Frederick C S; Leung, Wai K; Yam, Wing C; Jin, Li Jian; Samaranayake, Lakshman P

2015-02-01

No oral niche can be considered to be segregated from the subjacent milieu because of the complex community behavior and nature of the oral biofilms. The aim of this study was to address the paucity of information on how these species are clonally related to the subjacent gingival crevice bacteria. We utilized a metagenomic approach of amplifying 16S rDNA from genomic DNA, cloning, sequencing and analysis using LIBSHUFF software to assess the genetic homogeneity of the bacterial species from two infected root canals and subjacent gingival crevices. The four niches studied yielded 186 clones representing 54 phylotypes. Clone library comparisons using LIBSHUFF software indicated that each niche was inhabited by a unique flora. Further, 42% of the clones were of hitherto unknown phylotypes indicating the extent of bacterial diversity, especially in infected root canals and subjacent gingival crevices. We believe data generated through this novel analytical tool shed new light on understanding oral microbial ecosystems. © 2014 Wiley Publishing Asia Pty Ltd.

A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming.

PubMed

Cotto, Olivier; Wessely, Johannes; Georges, Damien; Klonner, Günther; Schmid, Max; Dullinger, Stefan; Thuiller, Wilfried; Guillaume, Frédéric

2017-05-05

Withstanding extinction while facing rapid climate change depends on a species' ability to track its ecological niche or to evolve a new one. Current methods that predict climate-driven species' range shifts use ecological modelling without eco-evolutionary dynamics. Here we present an eco-evolutionary forecasting framework that combines niche modelling with individual-based demographic and genetic simulations. Applying our approach to four endemic perennial plant species of the Austrian Alps, we show that accounting for eco-evolutionary dynamics when predicting species' responses to climate change is crucial. Perennial species persist in unsuitable habitats longer than predicted by niche modelling, causing delayed range losses; however, their evolutionary responses are constrained because long-lived adults produce increasingly maladapted offspring. Decreasing population size due to maladaptation occurs faster than the contraction of the species range, especially for the most abundant species. Monitoring of species' local abundance rather than their range may likely better inform on species' extinction risks under climate change.

In vivo imaging: shining a light on stem cells in the living animal.

PubMed

Nguyen, Phong Dang; Currie, Peter David

2018-03-28

Stem cells are undifferentiated cells that play crucial roles during development, growth and regeneration. Traditionally, these cells have been primarily characterised by histology, cell sorting, cell culture and ex vivo methods. However, as stem cells interact in a complex environment within specific tissue niches, there has been increasing interest in examining their in vivo behaviours, particularly in response to injury. Advances in imaging technologies and genetic tools have converged to enable unprecedented access to the endogenous stem cell niche. In this Spotlight article, we highlight how in vivo imaging can probe a range of biological processes that relate to stem cell activity, behaviour and control. © 2018. Published by The Company of Biologists Ltd.

An Improved Heuristic Method for Subgraph Isomorphism Problem

NASA Astrophysics Data System (ADS)

Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin

2017-09-01

This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.

Control of plant stem cell function by conserved interacting transcriptional regulators

PubMed Central

Zhou, Yun; Liu, Xing; Engstrom, Eric M.; Nimchuk, Zachary L.; Pruneda-Paz, Jose L.; Tarr, Paul T.; Yan, An; Kay, Steve A.; Meyerowitz, Elliot M.

2014-01-01

SUMMARY Plant stem cells in the shoot apical meristem (SAM) and root apical meristem (RAM) provide for postembryonic development of above-ground tissues and roots, respectively, while secondary vascular stem cells sustain vascular development1–4. WUSCHEL (WUS), a homeodomain transcription factor expressed in the rib meristem of the SAM, is a key regulatory factor controlling stem cell populations in the Arabidopsis SAM5–6 and is thought to establish the shoot stem cell niche via a feedback circuit with the CLAVATA3 (CLV3) peptide signaling pathway7. WUSCHEL-RELATED HOMEOBOX5 (WOX5), specifically expressed in root quiescent center (QC), defines QC identity and functions interchangeably with WUS in control of shoot and root stem cell niches8. WOX4, expressed in Arabidopsis procambial cells, defines the vascular stem cell niche9–11. WUS/WOX family proteins are evolutionarily and functionally conserved throughout the plant kingdom12 and emerge as key actors in the specification and maintenance of stem cells within all meristems13. However, the nature of the genetic regime in stem cell niches that centers on WOX gene function has been elusive, and molecular links underlying conserved WUS/WOX function in stem cell niches remain unknown. Here we demonstrate that the Arabidopsis HAIRY MERISTEM (HAM)family transcription regulators act as conserved interacting co-factors with WUS/WOX proteins. HAM and WUS share common targets in vivo and their physical interaction is important in driving downstream transcriptional programs and in promoting shoot stem cell proliferation. Differences in the overlapping expression patterns of WOX and HAM family members underlie the formation of diverse stem cell niche locations, and the HAM family is essential for all of these stem cell niches. These findings establish a new framework for the control of stem cell production during plant development. PMID:25363783

Phylogeography and Ecological Niche Modeling of the Desert Iguana (Dipsosaurus dorsalis, Baird & Girard 1852) in the Baja California Peninsula.

PubMed

Valdivia-Carrillo, Tania; García-De León, Francisco J; Blázquez, Ma Carmen; Gutiérrez-Flores, Carina; González Zamorano, Patricia

2017-09-01

Understanding the factors that explain the patterns of genetic structure or phylogeographic breaks at an intraspecific level is key to inferring the mechanisms of population differentiation in its early stages. These topics have been well studied in the Baja California region, with vicariance and the dispersal ability of individuals being the prevailing hypothesis for phylogeographic breaks. In this study, we evaluated the phylogeographic patterns in the desert iguana (Dipsosaurus dorsalis), a species with a recent history in the region and spatial variation in life history traits. We analyzed a total of 307 individuals collected throughout 19 localities across the Baja California Peninsula with 15 microsatellite DNA markers. Our data reveal the existence of 3 geographically discrete genetic populations with moderate gene flow and an isolation-by-distance pattern presumably produced by the occurrence of a refugium in the Cape region during the Pleistocene Last Glacial Maximum. Bayesian methods and ecological niche modeling were used to assess the relationship between population genetic structure and present and past climatic preferences of the desert iguana. We found that the present climatic heterogeneity of the Baja California Peninsula has a marked influence on the population genetic structure of the species, suggesting that there are alternative explanations besides vicariance. The information obtained in this study provides data allowing a better understanding of how historical population processes in the Baja California Peninsula can be understood from an ecological perspective. © The American Genetic Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Genetic islands in pome fruit pathogenic and non-pathogenic Erwinia species and related plasmids

PubMed Central

Llop, Pablo

2015-01-01

New pathogenic bacteria belonging to the genus Erwinia associated with pome fruit trees (Erwinia, E. piriflorinigrans, E. uzenensis) have been increasingly described in the last years, and comparative analyses have found that all these species share several genetic characteristics. Studies at different level (whole genome comparison, virulence genes, plasmid content, etc.) show a high intraspecies homogeneity (i.e., among E. amylovora strains) and also abundant similarities appear between the different Erwinia species: presence of plasmids of similar size in the pathogenic species; high similarity in several genes associated with exopolysaccharide production and hence, with virulence, as well as in some other genes, in the chromosomes. Many genetic similarities have been observed also among some of the plasmids (and genomes) from the pathogenic species and E. tasmaniensis or E. billingiae, two epiphytic species on the same hosts. The amount of genetic material shared in this genus varies from individual genes to clusters, genomic islands and genetic material that even may constitute a whole plasmid. Recent research on evolution of erwinias point out the horizontal transfer acquisition of some genomic islands that were subsequently lost in some species and several pathogenic traits that are still present. How this common material has been obtained and is efficiently maintained in different species belonging to the same genus sharing a common ecological niche provides an idea of the origin and evolution of the pathogenic Erwinia and the interaction with non-pathogenic species present in the same niche, and the role of the genes that are conserved in all of them. PMID:26379649

Genetic algorithms for adaptive real-time control in space systems

NASA Technical Reports Server (NTRS)

Vanderzijp, J.; Choudry, A.

1988-01-01

Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.

Genetic diversity of the human pathogen Vibrio vulnificus: a new phylogroup.

PubMed

Broza, Yoav Y; Raz, Nili; Lerner, Larisa; Danin-Poleg, Yael; Kashi, Yechezkel

2012-02-15

The biotype 3 group of the human pathogen Vibrio vulnificus emerged in Israel probably as a result of genome hybridization of two bacterial populations. We performed a genomic and phylogenetic study of V. vulnificus strains isolated from the environmental niche from which this group emerged - fish aquaculture in Israel. The genetic relationships and evolutionary aspects of 188 environmental and clinical isolates of the bacterium were studied by genomic typing. Genetic relations were determined based on variation at 12 variable number tandem repeat (VNTR, also termed SSR) loci. Analysis revealed a new cluster, in addition to the main groups of biotype 1& 2 and biotype 3. Similar grouping results were obtained with three different statistical approaches. Isolates forming this new cluster presented unclear biochemical profile nevertheless were not identified as biotype 1 or biotype 3. Further examination of representative strains by multilocus sequence typing (MLST) of 10 housekeeping genes and 5 conserved hypothetical genes supported the identification of this as yet undiscovered phylogroup (phenotypically diverse), termed clade A herein. This new clonal subgroup includes environmental as well as clinical isolates. The results highlight the fish aquaculture environment, and possibly man-made ecological niches as a whole, as a source for the emergence of new pathogenic strains. Copyright © 2011 Elsevier B.V. All rights reserved.

The evolution of environmental tolerance and range size: a comparison of geographically restricted and widespread Mimulus.

PubMed

Sheth, Seema N; Angert, Amy L

2014-10-01

The geographic ranges of closely related species can vary dramatically, yet we do not fully grasp the mechanisms underlying such variation. The niche breadth hypothesis posits that species that have evolved broad environmental tolerances can achieve larger geographic ranges than species with narrow environmental tolerances. In turn, plasticity and genetic variation in ecologically important traits and adaptation to environmentally variable areas can facilitate the evolution of broad environmental tolerance. We used five pairs of western North American monkeyflowers to experimentally test these ideas by quantifying performance across eight temperature regimes. In four species pairs, species with broader thermal tolerances had larger geographic ranges, supporting the niche breadth hypothesis. As predicted, species with broader thermal tolerances also had more within-population genetic variation in thermal reaction norms and experienced greater thermal variation across their geographic ranges than species with narrow thermal tolerances. Species with narrow thermal tolerance may be particularly vulnerable to changing climatic conditions due to lack of plasticity and insufficient genetic variation to respond to novel selection pressures. Conversely, species experiencing high variation in temperature across their ranges may be buffered against extinction due to climatic changes because they have evolved tolerance to a broad range of temperatures. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Climate-mediated adaptation after mainland colonization of an ancestrally subtropical island lizard, Anolis carolinensis.

PubMed

Campbell-Staton, S C; Edwards, S V; Losos, J B

2016-11-01

Climate-mediated evolution plays an integral role in species migration and range expansion. Gaining a clearer understanding of how climate affects demographic history and adaptation provides fundamental insight into the generation of intra- and interspecific diversity. In this study, we used the natural colonization of the green anole (Anolis carolinensis) from the island of Cuba to mainland North America to investigate the role of evolution at the niche, phenotypic and genetic levels after long-term establishment in a novel environment. The North American green anole occupies a broader range of thermal habitats than its Cuban sister species. We documented niche expansion in the mainland green anole, mediated primarily through adaptation to winter temperatures. Common garden experiments strongly suggest a genetic component to differences in thermal performance found between populations in different temperature regimes. Analysis of geographic variation in population structure based on 53 486 single nucleotide variants from RAD loci revealed increased genetic isolation between populations in different vs. similar thermal environments. Selection scans for environment-allele correlations reveal 19 genomic loci of known function that may have played a role in the physiological adaptation of A. carolinensis to temperate environments on the mainland. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Diversity hotspots of the laurel forest on Tenerife, Canary Islands: a phylogeographic study of Laurus and Ixanthus.

PubMed

Betzin, Anja; Thiv, Mike; Koch, Marcus A

2016-09-01

Macaronesian laurel forest is among the worldwide hotspots of threatened biodiversity. With increasing evidence that woodland composition on the Canary Islands changed dramatically during the last few thousand years, the aim of this study was to find evidence for substantial recent population dynamics of two representative species from laurel forest. Amplified fragment length polymorphism (AFLP) was used to evaluate fine-scaled genetic variation of the paradigmatic tree Laurus novocanariensis (Lauraceae) and a long-lived herbaceous gentian from core laurel forest, Ixanthus viscosus (Gentianaceae), on Tenerife. Bioclimatic variables were analysed to study the respective climate niches. A chloroplast DNA screening was performed to evaluate additional genetic variation. Genetic diversity of the laurel tree showed severe geographic partitioning. On Tenerife, fine-scaled Bayesian clustering of genetic variation revealed a western and an eastern gene pool, separated by a zone of high admixture and with a third major gene pool. Compared with genetic clusters found on the other Canary Islands, the East-West differentiation on Tenerife seems to be more recent than differentiation between islands. This is substantiated by the finding of extremly low levels of chloroplast DNA-based polymorphisms. Ixanthus showed no geographic structuring of genetic variation. Genetic data from Tenerife indicate contemporary gene flow and dispersal on a micro/local scale rather than reflecting an old and relic woodland history. In particular for Laurus, it is shown that this species occupies a broad bioclimatic niche. This is not correlated with its respective distribution of genetic variation, therefore indicating its large potential for contemporary rapid and effective colonization. Ixanthus is more specialized to humid conditions and is mostly found in the natural Monteverde húmedo vegetation types, but even for this species indications for long-term persistence in the respective bioclimatically differentiated regions was not find. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Dissection and staining of Drosophila larval ovaries.

PubMed

Maimon, Iris; Gilboa, Lilach

2011-05-13

Many organs depend on stem cells for their development during embryogenesis and for maintenance or repair during adult life. Understanding how stem cells form, and how they interact with their environment is therefore crucial for understanding development, homeostasis and disease. The ovary of the fruit fly Drosophila melanogaster has served as an influential model for the interaction of germ line stem cells (GSCs) with their somatic support cells (niche) (1, 2). The known location of the niche and the GSCs, coupled to the ability to genetically manipulate them, has allowed researchers to elucidate a variety of interactions between stem cells and their niches (3-12). Despite the wealth of information about mechanisms controlling GSC maintenance and differentiation, relatively little is known about how GSCs and their somatic niches form during development. About 18 somatic niches, whose cellular components include terminal filament and cap cells (Figure 1), form during the third larval instar (13-17). GSCs originate from primordial germ cells (PGCs). PGCs proliferate at early larval stages, but following the formation of the niche a subgroup of PGCs becomes GSCs (7, 16, 18, 19). Together, the somatic niche cells and the GSCs make a functional unit that produces eggs throughout the lifetime of the organism. Many questions regarding the formation of the GSC unit remain unanswered. Processes such as coordination between precursor cells for niches and stem cell precursors, or the generation of asymmetry within PGCs as they become GSCs, can best be studied in the larva. However, a methodical study of larval ovary development is physically challenging. First, larval ovaries are small. Even at late larval stages they are only 100μm across. In addition, the ovaries are transparent and are embedded in a white fat body. Here we describe a step-by-step protocol for isolating ovaries from late third instar (LL3) Drosophila larvae, followed by staining with fluorescent antibodies. We offer some technical solutions to problems such as locating the ovaries, staining and washing tissues that do not sink, and making sure that antibodies penetrate into the tissue. This protocol can be applied to earlier larval stages and to larval testes as well.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

NASA Astrophysics Data System (ADS)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

NASA Technical Reports Server (NTRS)

Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria

2009-01-01

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

KLF4-dependent perivascular cell plasticity mediates pre-metastatic niche formation and metastasis

PubMed Central

Murgai, Meera; Ju, Wei; Eason, Matthew; Kline, Jessica; Beury, Daniel; Kaczanowska, Sabina; Miettinen, Markku M; Kruhlak, Michael; Lei, Haiyan; Shern, Jack F; Cherepanova, Olga A.; Owens, Gary K; Kaplan, Rosandra N

2017-01-01

A deeper understanding of the metastatic process is required for the development of new therapies that improve patient survival. Metastatic tumor cell growth and survival in distant organs is facilitated by the formation of a pre-metastatic niche composed of hematopoietic cells, stromal cells, and extracellular matrix (ECM). Perivascular cells, including vascular smooth muscle cells (vSMCs) and pericytes, are involved in new vessel formation and in promoting stem cell maintenance and proliferation. Given the well-described plasticity of perivascular cells, we hypothesize that perivascular cells similarly regulate tumor cell fate at metastatic sites. Using perivascular cell-specific and pericyte-specific lineage-tracing models, we trace the fate of perivascular cells in the pre-metastatic and metastatic microenvironments. We show that perivascular cells lose the expression of traditional vSMC/pericyte markers in response to tumor-secreted factors and exhibit increased proliferation, migration, and ECM synthesis. Increased expression of the pluripotency gene Klf4 in these phenotypically-switched perivascular cells promotes a less differentiated state characterized by enhanced ECM production that establishes a pro-metastatic fibronectin-rich environment. Genetic inactivation of Klf4 in perivascular cells decreases pre-metastatic niche formation and metastasis. Our data reveal a previously unidentified role for perivascular cells in pre-metastatic niche formation and uncover novel strategies for limiting metastasis. PMID:28920957

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Correlation of Lactobacillus rhamnosus Genotypes and Carbohydrate Utilization Signatures Determined by Phenotype Profiling

PubMed Central

Lambert, Jolanda; van Limpt, Kees; Wels, Michiel; Smokvina, Tamara; Knol, Jan; Kleerebezem, Michiel

2015-01-01

Lactobacillus rhamnosus is a bacterial species commonly colonizing the gastrointestinal (GI) tract of humans and also frequently used in food products. While some strains have been studied extensively, physiological variability among isolates of the species found in healthy humans or their diet is largely unexplored. The aim of this study was to characterize the diversity of carbohydrate utilization capabilities of human isolates and food-derived strains of L. rhamnosus in relation to their niche of isolation and genotype. We investigated the genotypic and phenotypic diversity of 25 out of 65 L. rhamnosus strains from various niches, mainly human feces and fermented dairy products. Genetic fingerprinting of the strains by amplified fragment length polymorphism (AFLP) identified 11 distinct subgroups at 70% similarity and suggested niche enrichment within particular genetic clades. High-resolution carbohydrate utilization profiling (OmniLog) identified 14 carbon sources that could be used by all of the strains tested for growth, while the utilization of 58 carbon sources differed significantly between strains, enabling the stratification of L. rhamnosus strains into three metabolic clusters that partially correlate with the genotypic clades but appear uncorrelated with the strain's origin of isolation. Draft genome sequences of 8 strains were generated and employed in a gene-trait matching (GTM) analysis together with the publicly available genomes of L. rhamnosus GG (ATCC 53103) and HN001 for several carbohydrates that were distinct for the different metabolic clusters: l-rhamnose, cellobiose, l-sorbose, and α-methyl-d-glucoside. From the analysis, candidate genes were identified that correlate with l-sorbose and α-methyl-d-glucoside utilization, and the proposed function of these genes could be confirmed by heterologous expression in a strain lacking the genes. This study expands our insight into the phenotypic and genotypic diversity of the species L. rhamnosus and explores the relationships between specific carbohydrate utilization capacities and genotype and/or niche adaptation of this species. PMID:26048937

An investigation of messy genetic algorithms

NASA Technical Reports Server (NTRS)

Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

1990-01-01

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

Bacillus anthracis Diversity and Geographic Potential across Nigeria, Cameroon and Chad: Further Support of a Novel West African Lineage

PubMed Central

Blackburn, Jason K.; Odugbo, Moses Ode; Van Ert, Matthew; O’Shea, Bob; Mullins, Jocelyn; Perrenten, Vincent; Maho, Angaya; Hugh-Jones, Martin; Hadfield, Ted

2015-01-01

Zoonoses, diseases affecting both humans and animals, can exert tremendous pressures on human and veterinary health systems, particularly in resource limited countries. Anthrax is one such zoonosis of concern and is a disease requiring greater public health attention in Nigeria. Here we describe the genetic diversity of Bacillus anthracis in Nigeria and compare it to Chad, Cameroon and a broader global dataset based on the multiple locus variable number tandem repeat (MLVA-25) genetic typing system. Nigerian B. anthracis isolates had identical MLVA genotypes and could only be resolved by measuring highly mutable single nucleotide repeats (SNRs). The Nigerian MLVA genotype was identical or highly genetically similar to those in the neighboring countries, confirming the strains belong to this unique West African lineage. Interestingly, sequence data from a Nigerian isolate shares the anthrose deficient genotypes previously described for strains in this region, which may be associated with vaccine evasion. Strains in this study were isolated over six decades, indicating a high level of temporal strain stability regionally. Ecological niche models were used to predict the geographic distribution of the pathogen for all three countries. We describe a west-east habitat corridor through northern Nigeria extending into Chad and Cameroon. Ecological niche models and genetic results show B. anthracis to be ecologically established in Nigeria. These findings expand our understanding of the global B. anthracis population structure and can guide regional anthrax surveillance and control planning. PMID:26291625

The microgeographical patterns of morphological and molecular variation of a mixed ploidy population in the species complex Actinidia chinensis.

PubMed

Liu, Yifei; Li, Dawei; Yan, Ling; Huang, Hongwen

2015-01-01

Polyploidy and hybridization are thought to have significant impacts on both the evolution and diversification of the genus Actinidia, but the structure and patterns of morphology and molecular diversity relating to ploidy variation of wild Actinidia plants remain much less understood. Here, we examine the distribution of morphological variation and ploidy levels along geographic and environmental variables of a large mixed-ploidy population of the A. chinensis species complex. We then characterize the extent of both genetic and epigenetic diversity and differentiation exhibited between individuals of different ploidy levels. Our results showed that while there are three ploidy levels in this population, hexaploids were constituted the majority (70.3%). Individuals with different ploidy levels were microgeographically structured in relation to elevation and extent of niche disturbance. The morphological characters examined revealed clear difference between diploids and hexaploids, however tetraploids exhibited intermediate forms. Both genetic and epigenetic diversity were high but the differentiation among cytotypes was weak, suggesting extensive gene flow and/or shared ancestral variation occurred in this population even across ploidy levels. Epigenetic variation was clearly correlated with changes in altitudes, a trend of continuous genetic variation and gradual increase of epigenomic heterogeneities of individuals was also observed. Our results show that complex interactions between the locally microgeographical environment, ploidy and gene flow impact A. chinensis genetic and epigenetic variation. We posit that an increase in ploidy does not broaden the species habitat range, but rather permits A. chinensis adaptation to specific niches.

The restoration of the endangered Sambucus palmensis after 30 years of conservation actions in the Garajonay National Park: genetic assessment and niche modeling.

PubMed

Rodríguez-Rodríguez, Priscila; Fernández de Castro, Alejandro G; Sosa, Pedro A

2018-01-01

The translocation of individuals or the reinforcement of populations are measures in the genetic rescue of endangered species. Although it can be controversial to decide which and how many individuals must be reintroduced, populations can benefit from reinforcements. Sambucus palmensis is a critically endangered endemic to the Canary Islands. During the past 30 years, the Garajonay National Park (La Gomera) has carried out an intensive program of translocations using cuttings, due to the low germination rates of seeds. To assess the effect of the restorations on the population genetics of S. palmensis in La Gomera, we collected 402 samples from all the restored sites and all known natural individuals, which were genotyped with seven microsatellite markers. In addition, we conducted a species distribution modeling approach to assess how restorations fit the ecological niche of the species. Results show that there is a high proportion of clone specimens due to the propagation method, and the natural clonal reproduction of the species. Nonetheless, the observed heterozygosity has increased with the restorations and there still are private alleles and unique genotypes in the natural populations that have not been considered in the restorations. The population of Liria constitutes a very important genetic reservoir for the species. To optimize future reintroductions, we have proposed a list of specimens that are suitable for the extraction of seeds or cuttings in a greenhouse, as well as new suitable areas obtained by the species distribution models.

Heritable Bovine Rumen Bacteria Are Phylogenetically Related and Correlated with the Cow’s Capacity To Harvest Energy from Its Feed

PubMed Central

Sasson, Goor; Kruger Ben-Shabat, Sheerli; Seroussi, Eyal; Doron-Faigenboim, Adi; Shterzer, Naama; Yaacoby, Shamay; Berg Miller, Margret E.; White, Bryan A.; Halperin, Eran

2017-01-01

ABSTRACT Ruminants sustain a long-lasting obligatory relationship with their rumen microbiome dating back 50 million years. In this unique host-microbiome relationship, the host’s ability to digest its feed is completely dependent on its coevolved microbiome. This extraordinary alliance raises questions regarding the dependent relationship between ruminants’ genetics and physiology and the rumen microbiome structure, composition, and metabolism. To elucidate this relationship, we examined the association of host genetics with the phylogenetic and functional composition of the rumen microbiome. We accomplished this by studying a population of 78 Holstein-Friesian dairy cows, using a combination of rumen microbiota data and other phenotypes from each animal with genotypic data from a subset of 47 animals. We identified 22 operational taxonomic units (OTUs) whose abundances were associated with rumen metabolic traits and host physiological traits and which showed measurable heritability. The abundance patterns of these microbes can explain high proportions of variance in rumen metabolism and many of the host physiological attributes such as its energy-harvesting efficiency. Interestingly, these OTUs shared higher phylogenetic similarity between themselves than expected by chance, suggesting occupation of a specific ecological niche within the rumen ecosystem. The findings presented here suggest that ruminant genetics and physiology are correlated with microbiome structure and that host genetics may shape the microbiome landscape by enriching for phylogenetically related taxa that may occupy a unique niche. PMID:28811339

Demographic stability and high historical connectivity explain the diversity of a savanna tree species in the Quaternary

PubMed Central

Telles, Mariana P. C.; Chaves, Lázaro J.; Lima-Ribeiro, Matheus S.; Collevatti, Rosane G.

2017-01-01

Abstract Background and Aims Cyclic glaciations were frequent throughout the Quaternary and this affected species distribution and population differentiation worldwide. The present study reconstructed the demographic history and dispersal routes of Eugenia dysenterica lineages and investigated the effects of Quaternary climate change on its spatial pattern of genetic diversity. Methods A total of 333 individuals were sampled from 23 populations and analysed by sequencing four regions of the chloroplast DNA and the internal transcribed spacer of the nuclear DNA. The analyses were performed using a multi-model inference approach based on ecological niche modelling and statistical phylogeography. Key Results Coalescent simulation showed that population stability through time is the most likely scenario. The palaeodistribution dynamics predicted by the ecological niche models revealed that the species was potentially distributed across a large area, extending over Central-Western Brazil through the last glaciation. The lineages of E. dysenterica dispersed from Central Brazil towards populations at the northern, western and south-eastern regions. A historical refugium through time may have favoured lineage dispersal and the maintenance of genetic diversity. Conclusions The results suggest that the central region of the Cerrado biome is probably the centre of distribution of E. dysenterica and that the spatial pattern of its genetic diversity may be the outcome of population stability throughout the Quaternary. The lower genetic diversity in populations in the south-eastern Cerrado biome is probably due to local climatic instability during the Quaternary. PMID:28115317

Global Optimization of a Periodic System using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Stucke, David; Crespi, Vincent

2001-03-01

We use a novel application of a genetic algorithm global optimizatin technique to find the lowest energy structures for periodic systems. We apply this technique to colloidal crystals for several different stoichiometries of binary and trinary colloidal crystals. This application of a genetic algorithm is decribed and results of likely candidate structures are presented.

Research and application of multi-agent genetic algorithm in tower defense game

NASA Astrophysics Data System (ADS)

Jin, Shaohua

2018-04-01

In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.

Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2001-01-01

A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic algorithms as global random search methods

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.

1995-01-01

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

Louis, S.J.; Raines, G.L.

2003-01-01

We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

NASA Astrophysics Data System (ADS)

Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

2017-08-01

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

CONJUGAL GENE TRANSFER IN THE RHIZOSPHERE OF WATER GRASS (ECHINOCHLORA CRUSGALLI): INFLUENCE OF ROOT EXUDATE AND BACTERIAL ACTIVITY

EPA Science Inventory

The premise that genetic exchange is primarily localized in niches characterized by dense bacterial populations and high availability of growth substrates was tested by relating conjugal gene transfer of an RP4 derivative to availability of root exudates and bacterial metabolic a...

Insight into genome variability in the Fusarium Incarnatum-equiseti species complex through comparative analysis of secondary metabolic biosynthetic gene clusters

USDA-ARS?s Scientific Manuscript database

The genus Fusarium comprises 22 species complexes that together include approximately 300 phylogenetically distinct species. A major focus in Fusarium literature is to understand the genetic basis of niche specialization, secondary metabolites (SM) production, and host interactions in closely relate...

A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems

NASA Astrophysics Data System (ADS)

Thammano, Arit; Teekeng, Wannaporn

2015-05-01

The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.

The role of pollinators in the evolution of corolla shape variation, disparity and integration in a highly diversified plant family with a conserved floral bauplan

PubMed Central

Gómez, José M.; Torices, Ruben; Lorite, Juan; Klingenberg, Christian Peter; Perfectti, Francisco

2016-01-01

Background and Aims Brassicaceae is one of the most diversified families in the angiosperms. However, most species from this family exhibit a very similar floral bauplan. In this study, we explore the Brassicaceae floral morphospace, examining how corolla shape variation (an estimation of developmental robustness), integration and disparity vary among phylogenetically related species. Our aim is to check whether these floral attributes have evolved in this family despite its apparent morphological conservation, and to test the role of pollinators in driving this evolution. Methods Using geometric morphometric tools, we calculated the phenotypic variation, disparity and integration of the corolla shape of 111 Brassicaceae taxa. We subsequently inferred the phylogenetic relationships of these taxa and explored the evolutionary lability of corolla shape. Finally, we sampled the pollinator assemblages of every taxon included in this study, and determined their pollination niches using a modularity algorithm. We explore the relationship between pollination niche and the attributes of corolla shape. Key Results Phylogenetic signal was weak for all corolla shape attributes. All taxa had generalized pollination systems. Nevertheless, they belong to different pollination niches. There were significant differences in corolla shape among pollination niches even after controlling for the phylogenetic relationship of the plant taxa. Corolla shape variation and disparity was significantly higher in those taxa visited mostly by nocturnal moths, indicating that this pollination niche is associated with a lack of developmental robustness. Corolla integration was higher in those taxa visited mostly by hovering long-tongued flies and long-tongued large bees. Conclusions Corolla variation, integration and disparity were evolutionarily labile and evolved very recently in the evolutionary history of the Brassicaceae. These floral attributes were strongly related to the pollination niche. Even in a plant clade having a very generalized pollination system and exhibiting a conserved floral bauplan, pollinators can drive the evolution of important developmental attributes of corolla shape. PMID:26884512

A New Challenge for Compression Algorithms: Genetic Sequences.

ERIC Educational Resources Information Center

Grumbach, Stephane; Tahi, Fariza

1994-01-01

Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Refined genetic algorithm -- Economic dispatch example

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheble, G.B.; Brittig, K.

1995-02-01

A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.

Immune allied genetic algorithm for Bayesian network structure learning

NASA Astrophysics Data System (ADS)

Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

2012-06-01

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

Flexible Space-Filling Designs for Complex System Simulations

DTIC Science & Technology

2013-06-01

interior of the experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with...Computer Experiments, Design of Experiments, Genetic Algorithm , Latin Hypercube, Response Surface Methodology, Nearly Orthogonal 15. NUMBER OF PAGES 147...experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with minimal correlations

Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft

NASA Technical Reports Server (NTRS)

Wells, Valana L.

1996-01-01

This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.

Self-calibration of a noisy multiple-sensor system with genetic algorithms

NASA Astrophysics Data System (ADS)

Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

1996-01-01

This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2004-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.

2005-01-01

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

Exploring gene-culture interactions: insights from handedness, sexual selection and niche-construction case studies.

PubMed

Laland, Kevin N

2008-11-12

Genes and culture represent two streams of inheritance that for millions of years have flowed down the generations and interacted. Genetic propensities, expressed throughout development, influence what cultural organisms learn. Culturally transmitted information, expressed in behaviour and artefacts, spreads through populations, modifying selection acting back on populations. Drawing on three case studies, I will illustrate how this gene-culture coevolution has played a critical role in human evolution. These studies explore (i) the evolution of handedness, (ii) sexual selection with a culturally transmitted mating preference, and (iii) cultural niche construction and human evolution. These analyses shed light on how genes and culture shape each other, and on the significance of feedback mechanisms between biological and cultural processes.

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

The role of historical and contemporary processes on phylogeographic structure and genetic diversity in the Northern Cardinal, Cardinalis cardinalis

PubMed Central

2011-01-01

Background Earth history events such as climate change are believed to have played a major role in shaping patterns of genetic structure and diversity in species. However, there is a lag between the time of historical events and the collection of present-day samples that are used to infer contemporary population structure. During this lag phase contemporary processes such as dispersal or non-random mating can erase or reinforce population differences generated by historical events. In this study we evaluate the role of both historical and contemporary processes on the phylogeography of a widespread North American songbird, the Northern Cardinal, Cardinalis cardinalis. Results Phylogenetic analysis revealed deep mtDNA structure with six lineages across the species' range. Ecological niche models supported the same geographic breaks revealed by the mtDNA. A paleoecological niche model for the Last Glacial Maximum indicated that cardinals underwent a dramatic range reduction in eastern North America, whereas their ranges were more stable in México. In eastern North America cardinals expanded out of glacial refugia, but we found no signature of decreased genetic diversity in areas colonized after the Last Glacial Maximum. Present-day demographic data suggested that population growth across the expansion cline is positively correlated with latitude. We propose that there was no loss of genetic diversity in areas colonized after the Last Glacial Maximum because recent high-levels of gene flow across the region have homogenized genetic diversity in eastern North America. Conclusion We show that both deep historical events as well as demographic processes that occurred following these events are critical in shaping genetic pattern and diversity in C. cardinalis. The general implication of our results is that patterns of genetic diversity are best understood when information on species history, ecology, and demography are considered simultaneously. PMID:21599972

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

Rao, Yunqing; Qi, Dezhong; Li, Jinling

2013-01-01

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

Pose estimation for augmented reality applications using genetic algorithm.

PubMed

Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

2005-12-01

This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm

NASA Technical Reports Server (NTRS)

Le Riche, Rodolphe; Haftka, Raphael T.

1992-01-01

The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Novel Concordance Between Geographic, Environmental, and Genetic Structure in the Ecological Generalist Prickly Sculpin (Cottus asper) in California.

PubMed

Baumsteiger, Jason; Kinziger, Andrew P; Aguilar, Andres

2016-11-01

Ecological generalists may contain a wealth of information concerning diversity, ecology, and geographic connectivity throughout their range. We explored these ideas in prickly sculpin (Cottus asper), a small generalist freshwater fish species where coastal forms have potentially undergone radiations into inland lacustrine and riverine environments. Using a 962bp cytochrome b mtDNA marker and 11 microsatellites, we estimated diversity, divergence times, gene flow, and structure among populations at 43 locations throughout California. We then incorporated genetic and GIS data into ecological niche models to assess ecological conditions within identified groups. Though not reciprocally monophyletic, unique mtDNA haplotypes, microsatellite clustering, and measures of isolation by distance (Coastal: r = 0.960, P < 0.001; Inland: r = 0.277, P = 0.148) suggest 2 novel taxonomic groups, Coastal and Inland (constrained to Great Central Valley). Divergence estimates of 41-191 kya combined with the regional biogeographic history suggest geographic barriers are absent between groups since divergence, but ecological niche modeling revealed significant environmental differences (t = 10.84, P < 0.001). Introgressed individuals were also discovered between groups in an ecologically and geographically intermediate region. Population structure was limited, predominately found in tributaries of the San Joaquin basin in the Inland group. Overall, C. asper exhibited substantial genetic diversity, despite its ecological generality, reflecting California's historically unique and complex hydrology. More broadly, this study illustrates variable environments within the range of a generalist species may mask genetic divergences and should not be overlooked in biodiversity assessments. © The American Genetic Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Molecular Genetic Analysis of Chlamydia Species.

PubMed

Sixt, Barbara S; Valdivia, Raphael H

2016-09-08

Species of Chlamydia are the etiologic agent of endemic blinding trachoma, the leading cause of bacterial sexually transmitted diseases, significant respiratory pathogens, and a zoonotic threat. Their dependence on an intracellular growth niche and their peculiar developmental cycle are major challenges to elucidating their biology and virulence traits. The last decade has seen tremendous advances in our ability to perform a molecular genetic analysis of Chlamydia species. Major achievements include the generation of large collections of mutant strains, now available for forward- and reverse-genetic applications, and the introduction of a system for plasmid-based transformation enabling complementation of mutations; expression of foreign, modified, or reporter genes; and even targeted gene disruptions. This review summarizes the current status of the molecular genetic toolbox for Chlamydia species and highlights new insights into their biology and new challenges in the nascent field of Chlamydia genetics.

Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater

DTIC Science & Technology

2010-12-01

German Collection of Microorganisms and Cell Cultures) GA Genetic Algorithms GA-ANN Genetic Algorithm Artificial Neural Network GMO genetically...for in situ treatment of perchlorate in groundwater. This is accomplished without the addition of genetically engineered microorganisms ( GMOs ) to the...perchlorate, even in the presence of oxygen and without the addition of genetically engineered microorganisms ( GMOs ) to the environment. This approach

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V

2014-01-01

Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

Distributed genetic algorithms for the floorplan design problem

NASA Technical Reports Server (NTRS)

Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

1991-01-01

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Large scale genomic analysis shows no evidence for pathogen adaptation between the blood and cerebrospinal fluid niches during bacterial meningitis

PubMed Central

Lees, John A.; Kremer, Philip H. C.; Manso, Ana S.; Croucher, Nicholas J.; Ferwerda, Bart; Serón, Mercedes Valls; Oggioni, Marco R.; Parkhill, Julian; Brouwer, Matthijs C.; van der Ende, Arie; van de Beek, Diederik

2017-01-01

Recent studies have provided evidence for rapid pathogen genome diversification, some of which could potentially affect the course of disease. We have previously described such variation seen between isolates infecting the blood and cerebrospinal fluid (CSF) of a single patient during a case of bacterial meningitis. Here, we performed whole-genome sequencing of paired isolates from the blood and CSF of 869 meningitis patients to determine whether such variation frequently occurs between these two niches in cases of bacterial meningitis. Using a combination of reference-free variant calling approaches, we show that no genetic adaptation occurs in either invaded niche during bacterial meningitis for two major pathogen species, Streptococcus pneumoniae and Neisseria meningitidis. This study therefore shows that the bacteria capable of causing meningitis are already able to do this upon entering the blood, and no further sequence change is necessary to cross the blood–brain barrier. Our findings place the focus back on bacterial evolution between nasopharyngeal carriage and invasion, or diversity of the host, as likely mechanisms for determining invasiveness. PMID:28348877

Fermentation assays reveal differences in sugar and (off-) flavor metabolism across different Brettanomyces bruxellensis strains.

PubMed

Crauwels, Sam; Van Opstaele, Filip; Jaskula-Goiris, Barbara; Steensels, Jan; Verreth, Christel; Bosmans, Lien; Paulussen, Caroline; Herrera-Malaver, Beatriz; de Jonge, Ronnie; De Clippeleer, Jessika; Marchal, Kathleen; De Samblanx, Gorik; Willems, Kris A; Verstrepen, Kevin J; Aerts, Guido; Lievens, Bart

2017-01-01

Brettanomyces (Dekkera) bruxellensis is an ascomycetous yeast of major importance in the food, beverage and biofuel industry. It has been isolated from various man-made ecological niches that are typically characterized by harsh environmental conditions such as wine, beer, soft drink, etc. Recent comparative genomics studies revealed an immense intraspecific diversity, but it is still unclear whether this genetic diversity also leads to systematic differences in fermentation performance and (off-)flavor production, and to what extent strains have evolved to match their ecological niche. Here, we present an evaluation of the fermentation properties of eight genetically diverse B. bruxellensis strains originating from beer, wine and soft drinks. We show that sugar consumption and aroma production during fermentation are determined by both the yeast strain and composition of the medium. Furthermore, our results indicate a strong niche adaptation of B. bruxellensis, most clearly for wine strains. For example, only strains originally isolated from wine were able to thrive well and produce the typical Brettanomyces-related phenolic off-flavors 4-ethylguaiacol and 4-ethylphenol when inoculated in red wine. Sulfite tolerance was found as a key factor explaining the observed differences in fermentation performance and off-flavor production. Sequence analysis of genes related to phenolic off-flavor production, however, revealed only marginal differences between the isolates tested, especially at the amino acid level. Altogether, our study provides novel insights in the Brettanomyces metabolism of flavor production, and is highly relevant for both the wine and beer industry. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Genetic Competence Drives Genome Diversity in Bacillus subtilis

PubMed Central

Chevreux, Bastien; Serra, Cláudia R; Schyns, Ghislain; Henriques, Adriano O

2018-01-01

Abstract Prokaryote genomes are the result of a dynamic flux of genes, with increases achieved via horizontal gene transfer and reductions occurring through gene loss. The ecological and selective forces that drive this genomic flexibility vary across species. Bacillus subtilis is a naturally competent bacterium that occupies various environments, including plant-associated, soil, and marine niches, and the gut of both invertebrates and vertebrates. Here, we quantify the genomic diversity of B. subtilis and infer the genome dynamics that explain the high genetic and phenotypic diversity observed. Phylogenomic and comparative genomic analyses of 42 B. subtilis genomes uncover a remarkable genome diversity that translates into a core genome of 1,659 genes and an asymptotic pangenome growth rate of 57 new genes per new genome added. This diversity is due to a large proportion of low-frequency genes that are acquired from closely related species. We find no gene-loss bias among wild isolates, which explains why the cloud genome, 43% of the species pangenome, represents only a small proportion of each genome. We show that B. subtilis can acquire xenologous copies of core genes that propagate laterally among strains within a niche. While not excluding the contributions of other mechanisms, our results strongly suggest a process of gene acquisition that is largely driven by competence, where the long-term maintenance of acquired genes depends on local and global fitness effects. This competence-driven genomic diversity provides B. subtilis with its generalist character, enabling it to occupy a wide range of ecological niches and cycle through them. PMID:29272410

Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

NASA Astrophysics Data System (ADS)

Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

2017-01-01

Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

INFLUENCE OF ROOT EXUDATES AND BACTERIAL METABOLIC ACTIVITY ON APPARENT CONJUGAL GENE TRANSFER FREQUENCIES IN THE RHIZOSPHERE OF WATER GRASS (ECHINOCLORA CRUSGALLI)

EPA Science Inventory

The premise that genetic exchange is primarily localized in niches characterized by dense bacterial populations and high availability of growth substrates was tested by relating conjugal gene transfer of an RP4 derivative to availability of root exudates and bacterial metabolic a...

An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

NASA Technical Reports Server (NTRS)

Weir, John M.; Wells, B. Earl

2003-01-01

Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.

1999-04-12

The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less

Live imaging of the Drosophila spermatogonial stem cell niche reveals novel mechanisms regulating germline stem cell output

PubMed Central

Sheng, X. Rebecca; Matunis, Erika

2011-01-01

Adult stem cells modulate their output by varying between symmetric and asymmetric divisions, but have rarely been observed in living intact tissues. Germline stem cells (GSCs) in the Drosophila testis are anchored to somatic hub cells and were thought to exclusively undergo oriented asymmetric divisions, producing one stem cell that remains hub-anchored and one daughter cell displaced out of the stem cell-maintaining micro-environment (niche). We developed extended live imaging of the Drosophila testis niche, allowing us to track individual germline cells. Surprisingly, new wild-type GSCs are generated in the niche during steady-state tissue maintenance by a previously undetected event we term `symmetric renewal', where interconnected GSC-daughter cell pairs swivel such that both cells contact the hub. We also captured GSCs undergoing direct differentiation by detaching from the hub. Following starvation-induced GSC loss, GSC numbers are restored by symmetric renewals. Furthermore, upon more severe (genetically induced) GSC loss, both symmetric renewal and de-differentiation (where interconnected spermatogonia fragment into pairs while moving towards then establishing contact with the hub) occur simultaneously to replenish the GSC pool. Thus, stereotypically oriented stem cell divisions are not always correlated with an asymmetric outcome in cell fate, and changes in stem cell output are governed by altered signals in response to tissue requirements. PMID:21752931

Predicting ecological roles in the rhizosphere using metabolome and transportome modeling

DOE PAGES

Larsen, Peter E.; Collart, Frank R.; Dai, Yang; ...

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. New algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad’s ecological role in the rhizosphere: a biofilm, biocontrol agent, promoter ofmore » plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism’s transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, Peter E.; Collart, Frank R.; Dai, Yang

2015-09-02

The ability to obtain complete genome sequences from bacteria in environmental samples, such as soil samples from the rhizosphere, has highlighted the microbial diversity and complexity of environmental communities. However, new algorithms to analyze genome sequence information in the context of community structure are needed to enhance our understanding of the specific ecological roles of these organisms in soil environments. We present a machine learning approach using sequenced Pseudomonad genomes coupled with outputs of metabolic and transportomic computational models for identifying the most predictive molecular mechanisms indicative of a Pseudomonad's ecological role in the rhizosphere: a biofilm, biocontrol agent, promotermore » of plant growth, or plant pathogen. Computational predictions of ecological niche were highly accurate overall with models trained on transportomic model output being the most accurate (Leave One Out Validation F-scores between 0.82 and 0.89). The strongest predictive molecular mechanism features for rhizosphere ecological niche overlap with many previously reported analyses of Pseudomonad interactions in the rhizosphere, suggesting that this approach successfully informs a system-scale level understanding of how Pseudomonads sense and interact with their environments. The observation that an organism's transportome is highly predictive of its ecological niche is a novel discovery and may have implications in our understanding microbial ecology. The framework developed here can be generalized to the analysis of any bacteria across a wide range of environments and ecological niches making this approach a powerful tool for providing insights into functional predictions from bacterial genomic data.« less

Deep Genetic Divergence between Disjunct Refugia in the Arctic-Alpine King’s Crown, Rhodiola integrifolia (Crassulaceae)

PubMed Central

DeChaine, Eric G.; Forester, Brenna R.; Schaefer, Hanno; Davis, Charles C.

2013-01-01

Despite the strength of climatic variability at high latitudes and upper elevations, we still do not fully understand how plants in North America that are distributed between Arctic and alpine areas responded to the environmental changes of the Quaternary. To address this question, we set out to resolve the evolutionary history of the King’s Crown, Rhodiola integrifolia using multi-locus population genetic and phylogenetic analyses in combination with ecological niche modeling. Our population genetic analyses of multiple anonymous nuclear loci revealed two major clades within R. integrifolia that diverged from each other ~ 700 kya: one occurring in Beringia to the north (including members of subspecies leedyi and part of subspecies integrifolia), and the other restricted to the Southern Rocky Mountain refugium in the south (including individuals of subspecies neomexicana and part of subspecies integrifolia). Ecological niche models corroborate our hypothesized locations of refugial areas inferred from our phylogeographic analyses and revealed some environmental differences between the regions inhabited by its two subclades. Our study underscores the role of geographic isolation in promoting genetic divergence and the evolution of endemic subspecies in R. integrifolia. Furthermore, our phylogenetic analyses of the plastid spacer region trnL-F demonstrate that among the native North American species, R. integrifolia and R. rhodantha are more closely related to one another than either is to R. rosea. An understanding of these historic processes lies at the heart of making informed management decisions regarding this and other Arctic-alpine species of concern in this increasingly threatened biome. PMID:24282505

Deep genetic divergence between disjunct Refugia in the Arctic-Alpine King's Crown, Rhodiola integrifolia (Crassulaceae).

PubMed

DeChaine, Eric G; Forester, Brenna R; Schaefer, Hanno; Davis, Charles C

2013-01-01

Despite the strength of climatic variability at high latitudes and upper elevations, we still do not fully understand how plants in North America that are distributed between Arctic and alpine areas responded to the environmental changes of the Quaternary. To address this question, we set out to resolve the evolutionary history of the King's Crown, Rhodiola integrifolia using multi-locus population genetic and phylogenetic analyses in combination with ecological niche modeling. Our population genetic analyses of multiple anonymous nuclear loci revealed two major clades within R. integrifolia that diverged from each other ~ 700 kya: one occurring in Beringia to the north (including members of subspecies leedyi and part of subspecies integrifolia), and the other restricted to the Southern Rocky Mountain refugium in the south (including individuals of subspecies neomexicana and part of subspecies integrifolia). Ecological niche models corroborate our hypothesized locations of refugial areas inferred from our phylogeographic analyses and revealed some environmental differences between the regions inhabited by its two subclades. Our study underscores the role of geographic isolation in promoting genetic divergence and the evolution of endemic subspecies in R. integrifolia. Furthermore, our phylogenetic analyses of the plastid spacer region trnL-F demonstrate that among the native North American species, R. integrifolia and R. rhodantha are more closely related to one another than either is to R. rosea. An understanding of these historic processes lies at the heart of making informed management decisions regarding this and other Arctic-alpine species of concern in this increasingly threatened biome.

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

NASA Astrophysics Data System (ADS)

Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna

2017-12-01

The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

2012-08-01

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-05

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.

New optimization model for routing and spectrum assignment with nodes insecurity

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-04-01

By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.

The Applications of Genetic Algorithms in Medicine.

PubMed

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-11-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

2015-01-01

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2015-01-01

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2012-01-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

A genetic algorithm for replica server placement

NASA Astrophysics Data System (ADS)

Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl

2011-12-01

Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Benford, Andrew; Tinker, Michael L.

2004-01-01

The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.

Superscattering of light optimized by a genetic algorithm

NASA Astrophysics Data System (ADS)

Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.

2014-07-01

We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

PubMed Central

Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

2015-01-01

A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Stem Cell Pathology.

PubMed

Fu, Dah-Jiun; Miller, Andrew D; Southard, Teresa L; Flesken-Nikitin, Andrea; Ellenson, Lora H; Nikitin, Alexander Yu

2018-01-24

Rapid advances in stem cell biology and regenerative medicine have opened new opportunities for better understanding disease pathogenesis and the development of new diagnostic, prognostic, and treatment approaches. Many stem cell niches are well defined anatomically, thereby allowing their routine pathological evaluation during disease initiation and progression. Evaluation of the consequences of genetic manipulations in stem cells and investigation of the roles of stem cells in regenerative medicine and pathogenesis of various diseases such as cancer require significant expertise in pathology for accurate interpretation of novel findings. Therefore, there is an urgent need for developing stem cell pathology as a discipline to facilitate stem cell research and regenerative medicine. This review provides examples of anatomically defined niches suitable for evaluation by diagnostic pathologists, describes neoplastic lesions associated with them, and discusses further directions of stem cell pathology.

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

Testing the genetic predictions of a biogeographical model in a dominant endemic Eastern Pacific coral (Porites panamensis) using a genetic seascape approach

PubMed Central

Saavedra-Sotelo, Nancy C; Calderon-Aguilera, Luis E; Reyes-Bonilla, Héctor; Paz-García, David A; López-Pérez, Ramón A; Cupul-Magaña, Amilcar; Cruz-Barraza, José A; Rocha-Olivares, Axayácatl

2013-01-01

The coral fauna of the Eastern Tropical Pacific (ETP) is depauperate and peripheral; hence, it has drawn attention to the factors allowing its survival. Here, we use a genetic seascape approach and ecological niche modeling to unravel the environmental factors correlating with the genetic variation of Porites panamensis, a hermatypic coral endemic to the ETP. Specifically, we test if levels of diversity and connectivity are higher among abundant than among depauperate populations, as expected by a geographically relaxed version of the Abundant Center Hypothesis (rel-ACH). Unlike the original ACH, referring to a geographical center of distribution of maximal abundance, the rel-ACH refers only to a center of maximum abundance, irrespective of its geographic position. The patterns of relative abundance of P. panamensis in the Mexican Pacific revealed that northern populations from Baja California represent its center of abundance; and southern depauperate populations along the continental margin are peripheral relative to it. Genetic patterns of diversity and structure of nuclear DNA sequences (ribosomal DNA and a single copy open reading frame) and five alloenzymatic loci partially agreed with rel-ACH predictions. We found higher diversity levels in peninsular populations and significant differentiation between peninsular and continental colonies. In addition, continental populations showed higher levels of differentiation and lower connectivity than peninsular populations in the absence of isolation by distance in each region. Some discrepancies with model expectations may relate to the influence of significant habitat discontinuities in the face of limited dispersal potential. Environmental data analyses and niche modeling allowed us to identify temperature, water clarity, and substrate availability as the main factors correlating with patterns of abundance, genetic diversity, and structure, which may hold the key to the survival of P. panamensis in the face of widespread environmental degradation. PMID:24324860

Pleistocene glacial refugia across the Appalachian Mountains and coastal plain in the millipede genus Narceus: Evidence from population genetic, phylogeographic, and paleoclimatic data

PubMed Central

Walker, Matt J; Stockman, Amy K; Marek, Paul E; Bond, Jason E

2009-01-01

Background Species that are widespread throughout historically glaciated and currently non-glaciated areas provide excellent opportunities to investigate the role of Pleistocene climatic change on the distribution of North American biodiversity. Many studies indicate that northern animal populations exhibit low levels of genetic diversity over geographically widespread areas whereas southern populations exhibit relatively high levels. Recently, paleoclimatic data have been combined with niche-based distribution modeling to locate possible refugia during the Last Glacial Maximum. Using phylogeographic, population, and paleoclimatic data, we show that the distribution and mitochondrial data for the millipede genus Narceus are consistent with classical examples of Pleistocene refugia and subsequent post-glacial population expansion seen in other organismal groups. Results The phylogeographic structure of Narceus reveals a complex evolutionary history with signatures of multiple refugia in southeastern North America followed by two major northern expansions. Evidence for refugial populations were found in the southern Appalachian Mountains and in the coastal plain. The northern expansions appear to have radiated from two separate refugia, one from the Gulf Coastal Plain area and the other from the mid-Atlantic coastal region. Distributional models of Narceus during the Last Glacial Maximum show a dramatic reduction from the current distribution, with suitable ecological zones concentrated along the Gulf and Atlantic coastal plain. We found a strong correlation between these zones of ecological suitability inferred from our paleo-model with levels of genetic diversity derived from phylogenetic and population estimates of genetic structuring. Conclusion The signature of climatic change, during and after the Pleistocene, on the distribution of the millipede genus Narceus is evident in the genetic data presented. Niche-based historical distribution modeling strengthens the conclusions drawn from the genetic data and proves useful in identifying probable refugia. Such interdisciplinary biogeographic studies provide a comprehensive approach to understanding these processes that generate and maintain biodiversity as well as the framework necessary to explore questions regarding evolutionary diversification of taxa. PMID:19183468

A genetic algorithm for solving supply chain network design model

NASA Astrophysics Data System (ADS)

Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

2013-09-01

Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

NASA Astrophysics Data System (ADS)

Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.

2017-09-01

The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

DTIC Science & Technology

2010-03-01

17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of...problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of

Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz

NASA Astrophysics Data System (ADS)

Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao

2018-05-01

In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Comparative genetic responses to climate in the varieties of Pinus ponderosa and Pseudotsuga menziesii: reforestation

Treesearch

Gerald E. Rehfeldt; Barry C. Jaquish; Cuauhtemoc Saenz-Romero; Dennis G. Joyce; Laura P. Leites; J. Bradley St Clair; Javier Lopez-Upton

2014-01-01

Impacts of climate change on the climatic niche of the sub-specific varieties of Pinus ponderosa and Pseudotsuga menziesii and on the adaptedness of their populations are considered from the viewpoint of reforestation. In using climate projections from an ensemble of 17 general circulation models targeting the decade surrounding 2060, our analyses suggest that a...

Phylogeography of two Australian species of funnel web spider (Araneae: Mygalomorphae: Hexathelidae) in Tallaganda State Forest, New South Wales

Treesearch

Amber S. Beavis; David M. Rowell

2006-01-01

Decomposing logs are habitat for invertebrate species occupying a range of ecological niches. A collaborative research project is examining patterns of genetic endemism among saproxylic (dependent on decaying wood) invertebrates across the Tallaganda region of New South Wales, Australia. An earlier study of an unnamed species of 'giant' Collembolon revealed...

Defining Differential Genetic Signatures in CXCR4- and the CCR5-Utilizing HIV-1 Co-Linear Sequences

PubMed Central

Aiamkitsumrit, Benjamas; Dampier, Will; Martin-Garcia, Julio; Nonnemacher, Michael R.; Pirrone, Vanessa; Ivanova, Tatyana; Zhong, Wen; Kilareski, Evelyn; Aldigun, Hazeez; Frantz, Brian; Rimbey, Matthew; Wojno, Adam; Passic, Shendra; Williams, Jean W.; Shah, Sonia; Blakey, Brandon; Parikh, Nirzari; Jacobson, Jeffrey M.; Moldover, Brian; Wigdahl, Brian

2014-01-01

The adaptation of human immunodeficiency virus type-1 (HIV-1) to an array of physiologic niches is advantaged by the plasticity of the viral genome, encoded proteins, and promoter. CXCR4-utilizing (X4) viruses preferentially, but not universally, infect CD4+ T cells, generating high levels of virus within activated HIV-1-infected T cells that can be detected in regional lymph nodes and peripheral blood. By comparison, the CCR5-utilizing (R5) viruses have a greater preference for cells of the monocyte-macrophage lineage; however, while R5 viruses also display a propensity to enter and replicate in T cells, they infect a smaller percentage of CD4+ T cells in comparison to X4 viruses. Additionally, R5 viruses have been associated with viral transmission and CNS disease and are also more prevalent during HIV-1 disease. Specific adaptive changes associated with X4 and R5 viruses were identified in co-linear viral sequences beyond the Env-V3. The in silico position-specific scoring matrix (PSSM) algorithm was used to define distinct groups of X4 and R5 sequences based solely on sequences in Env-V3. Bioinformatic tools were used to identify genetic signatures involving specific protein domains or long terminal repeat (LTR) transcription factor sites within co-linear viral protein R (Vpr), trans-activator of transcription (Tat), or LTR sequences that were preferentially associated with X4 or R5 Env-V3 sequences. A number of differential amino acid and nucleotide changes were identified across the co-linear Vpr, Tat, and LTR sequences, suggesting the presence of specific genetic signatures that preferentially associate with X4 or R5 viruses. Investigation of the genetic relatedness between X4 and R5 viruses utilizing phylogenetic analyses of complete sequences could not be used to definitively and uniquely identify groups of R5 or X4 sequences; in contrast, differences in the genetic diversities between X4 and R5 were readily identified within these co-linear sequences in HIV-1-infected patients. PMID:25265194

Endless forms: human behavioural diversity and evolved universals.

PubMed

Smith, Eric Alden

2011-02-12

Human populations have extraordinary capabilities for generating behavioural diversity without corresponding genetic diversity or change. These capabilities and their consequences can be grouped into three categories: strategic (or cognitive), ecological and cultural-evolutionary. Strategic aspects include: (i) a propensity to employ complex conditional strategies, some certainly genetically evolved but others owing to directed invention or to cultural evolution; (ii) situations in which fitness payoffs (or utilities) are frequency-dependent, so that there is no one best strategy; and (iii) the prevalence of multiple equilibria, with history or minor variations in starting conditions (path dependence) playing a crucial role. Ecological aspects refer to the fact that social behaviour and cultural institutions evolve in diverse niches, producing various adaptive radiations and local adaptations. Although environmental change can drive behavioural change, in humans, it is common for behavioural change (especially technological innovation) to drive environmental change (i.e. niche construction). Evolutionary aspects refer to the fact that human capacities for innovation and cultural transmission lead to diversification and cumulative cultural evolution; critical here is institutional design, in which relatively small shifts in incentive structure can produce very different aggregate outcomes. In effect, institutional design can reshape strategic games, bringing us full circle.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

Genetic structure and bio-climatic modeling support allopatric over parapatric speciation along a latitudinal gradient.

PubMed

Rossetto, Maurizio; Allen, Chris B; Thurlby, Katie A G; Weston, Peter H; Milner, Melita L

2012-08-20

Four of the five species of Telopea (Proteaceae) are distributed in a latitudinal replacement pattern on the south-eastern Australian mainland. In similar circumstances, a simple allopatric speciation model that identifies the origins of genetic isolation within temporal geographic separation is considered as the default model. However, secondary contact between differentiated lineages can result in similar distributional patterns to those arising from a process of parapatric speciation (where gene flow between lineages remains uninterrupted during differentiation). Our aim was to use the characteristic distributional patterns in Telopea to test whether it reflected the evolutionary models of allopatric or parapatric speciation. Using a combination of genetic evidence and environmental niche modelling, we focused on three main questions: do currently described geographic borders coincide with genetic and environmental boundaries; are there hybrid zones in areas of secondary contact between closely related species; did species distributions contract during the last glacial maximum resulting in distributional gaps even where overlap and hybridisation currently occur? Total genomic DNA was extracted from 619 individuals sampled from 36 populations representing the four species. Seven nuclear microsatellites (nSSR) and six chloroplast microsatellites (cpSSR) were amplified across all populations. Genetic structure and the signature of admixture in overlap zones was described using the Bayesian clustering methods implemented in STUCTURE and NewHybrids respectively. Relationships between chlorotypes were reconstructed as a median-joining network. Environmental niche models were produced for all species using environmental parameters from both the present day and the last glacial maximum (LGM).The nSSR loci amplified a total of 154 alleles, while data for the cpSSR loci produced a network of six chlorotypes. STRUCTURE revealed an optimum number of five clusters corresponding to the four recognised species with the additional division of T. speciosissima into populations north and south of the Shoalhaven River valley. Unexpectedly, the northern disjunct population of T. oreades grouped with T. mongaensis and was identified as a hybrid swarm by the Bayesian assignment test implemented in NewHybrids. Present day and LGM environmental niche models differed dramatically, suggesting that distributions of all species had repeatedly expanded and contracted in response to Pleistocene climatic oscillations and confirming strongly marked historical distributional gaps among taxes. Genetic structure and bio-climatic modeling results are more consistent with a history of allopatric speciation followed by repeated episodes of secondary contact and localised hybridisation, rather than with parapatric speciation. This study on Telopea shows that the evidence for temporal exclusion of gene flow can be found even outside obvious geographical contexts, and that it is possible to make significant progress towards excluding parapatric speciation as a contributing evolutionary process.

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio

2013-09-01

Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Genetic Algorithm Approaches for Actuator Placement

NASA Technical Reports Server (NTRS)

Crossley, William A.

2000-01-01

This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2013-05-01

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

The role of pollinators in the evolution of corolla shape variation, disparity and integration in a highly diversified plant family with a conserved floral bauplan.

PubMed

Gómez, José M; Torices, Ruben; Lorite, Juan; Klingenberg, Christian Peter; Perfectti, Francisco

2016-04-01

Brassicaceae is one of the most diversified families in the angiosperms. However, most species from this family exhibit a very similar floral bauplan. In this study, we explore the Brassicaceae floral morphospace, examining how corolla shape variation (an estimation of developmental robustness), integration and disparity vary among phylogenetically related species. Our aim is to check whether these floral attributes have evolved in this family despite its apparent morphological conservation, and to test the role of pollinators in driving this evolution. Using geometric morphometric tools, we calculated the phenotypic variation, disparity and integration of the corolla shape of 111 Brassicaceae taxa. We subsequently inferred the phylogenetic relationships of these taxa and explored the evolutionary lability of corolla shape. Finally, we sampled the pollinator assemblages of every taxon included in this study, and determined their pollination niches using a modularity algorithm. We explore the relationship between pollination niche and the attributes of corolla shape. Phylogenetic signal was weak for all corolla shape attributes. All taxa had generalized pollination systems. Nevertheless, they belong to different pollination niches. There were significant differences in corolla shape among pollination niches even after controlling for the phylogenetic relationship of the plant taxa. Corolla shape variation and disparity was significantly higher in those taxa visited mostly by nocturnal moths, indicating that this pollination niche is associated with a lack of developmental robustness. Corolla integration was higher in those taxa visited mostly by hovering long-tongued flies and long-tongued large bees. Corolla variation, integration and disparity were evolutionarily labile and evolved very recently in the evolutionary history of the Brassicaceae. These floral attributes were strongly related to the pollination niche. Even in a plant clade having a very generalized pollination system and exhibiting a conserved floral bauplan, pollinators can drive the evolution of important developmental attributes of corolla shape. © The Author 2016. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Temperature is better than precipitation as a predictor of plant community assembly across a dryland region

USGS Publications Warehouse

Butterfield, Bradley J.; Munson, Seth M.

2016-01-01

QuestionHow closely do plant communities track climate? Research suggests that plant species converge toward similar environmental tolerances relative to the environments that they experience. Whether these patterns apply to severe environments or scale up to plant community-level patterns of relative climatic tolerances is poorly understood. Using estimates of species' climatic tolerances acquired from occurrence records, we determined the contributions of individual species' climatic niche breadths and environmental filtering to the relationships between community-average climatic tolerances and the local climates experienced by those communities.LocationSouthwestern United States drylands.MethodsInterspecific variation in niche breadth was assessed as a function of species' climatic optima (median climatic niche value). The relationships between climatic optima and tolerances were used as null expectations for the relationship between abundance-weighted mean climatic tolerances of communities and the local climate of that community. Deviations from this null expectation indicate that species with greater or lesser climatic tolerances are favoured relative to co-occurring species. The intensity of environmental filtering was estimated by comparing the range of climatic tolerances within each community to a null distribution generated from a random assembly algorithm.ResultsThe temperature niches of species were consistently symmetrical and of similar breadths, regardless of their temperature optima. In contrast, precipitation niches were skewed toward wetter conditions, and niche breadth increased with increasing precipitation optima. At the community level, relationships with climate were much stronger for temperature than for precipitation. Furthermore, cold and heat were stronger assembly filters than drought or precipitation, with the intensity of environmental filtering increasing at both ends of climatic gradients. Community-average climatic tolerances did deviate significantly from null expectations, indicating that species with higher or lower relative climatic tolerances were favoured under certain conditions.ConclusionsDespite strong water limitation of plant performance in dryland ecosystems, communities tracked variation in temperature much more closely, intimating strong responses to anticipated temperature increases. Furthermore, abundance distributions were biased toward species with higher or lower relative climatic tolerances under different climatic conditions, but predictably so, indicating the need for assembly models that include processes other than simple environmental filtering.

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

Employability of genetic counselors with a PhD in genetic counseling.

PubMed

Wallace, Jody P; Myers, Melanie F; Huether, Carl A; Bedard, Angela C; Warren, Nancy Steinberg

2008-06-01

The development of a PhD in genetic counseling has been discussed for more than 20 years, yet the perspectives of employers have not been assessed. The goal of this qualitative study was to gain an understanding of the employability of genetic counselors with a PhD in genetic counseling by conducting interviews with United States employers of genetic counselors. Study participants were categorized according to one of the following practice areas: academic, clinical, government, industry, laboratory, or research. All participants were responsible for hiring genetic counselors in their institutions. Of the 30 employers interviewed, 23 envisioned opportunities for individuals with a PhD degree in genetic counseling, particularly in academic and research settings. Performing research and having the ability to be a principal investigator on a grant was the primary role envisioned for these individuals by 22/30 participants. Employers expect individuals with a PhD in genetic counseling to perform different roles than MS genetic counselors with a master's degree. This study suggests there is an employment niche for individuals who have a PhD in genetic counseling that complements, and does not compete with, master's prepared genetic counselors.

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

2005-01-01

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Species delimitation in plants using the Qinghai–Tibet Plateau endemic Orinus (Poaceae: Tridentinae) as an example

PubMed Central

Su, Xu; Wu, Guili; Li, Lili; Liu, Jianquan

2015-01-01

Background and Aims Accurate identification of species is essential for the majority of biological studies. However, defining species objectively and consistently remains a challenge, especially for plants distributed in remote regions where there is often a lack of sufficient previous specimens. In this study, multiple approaches and lines of evidence were used to determine species boundaries for plants occurring in the Qinghai–Tibet Plateau, using the genus Orinus (Poaceae) as a model system for an integrative approach to delimiting species. Methods A total of 786 individuals from 102 populations of six previously recognized species were collected for niche, morphological and genetic analyses. Three plastid DNA regions (matK, rbcL and trnH-psbA) and one nuclear DNA region [internal transcribed space (ITS)] were sequenced. Key Results Whereas six species had been previously recognized, statistical analyses based on character variation, molecular data and niche differentiation identified only two well-delimited clusters, together with a third possibly originating from relatively recent hybridization between, or historical introgression from, the other two. Conclusions Based on a principle of integrative species delimitation to reconcile different sources of data, the results provide compelling evidence that the six previously recognized species of the genus Orinus that were examined should be reduced to two, with new circumscriptions, and a third, identified in this study, should be described as a new species. This empirical study highlights the value of applying genetic differentiation, morphometric statistics and ecological niche modelling in an integrative approach to re-circumscribing species boundaries. The results produce relatively objective, operational and unbiased taxonomic classifications of plants occurring in remote regions. PMID:25987712

Species delimitation in plants using the Qinghai-Tibet Plateau endemic Orinus (Poaceae: Tridentinae) as an example.

PubMed

Su, Xu; Wu, Guili; Li, Lili; Liu, Jianquan

2015-07-01

Accurate identification of species is essential for the majority of biological studies. However, defining species objectively and consistently remains a challenge, especially for plants distributed in remote regions where there is often a lack of sufficient previous specimens. In this study, multiple approaches and lines of evidence were used to determine species boundaries for plants occurring in the Qinghai-Tibet Plateau, using the genus Orinus (Poaceae) as a model system for an integrative approach to delimiting species. A total of 786 individuals from 102 populations of six previously recognized species were collected for niche, morphological and genetic analyses. Three plastid DNA regions (matK, rbcL and trnH-psbA) and one nuclear DNA region [internal transcribed space (ITS)] were sequenced. Whereas six species had been previously recognized, statistical analyses based on character variation, molecular data and niche differentiation identified only two well-delimited clusters, together with a third possibly originating from relatively recent hybridization between, or historical introgression from, the other two. Based on a principle of integrative species delimitation to reconcile different sources of data, the results provide compelling evidence that the six previously recognized species of the genus Orinus that were examined should be reduced to two, with new circumscriptions, and a third, identified in this study, should be described as a new species. This empirical study highlights the value of applying genetic differentiation, morphometric statistics and ecological niche modelling in an integrative approach to re-circumscribing species boundaries. The results produce relatively objective, operational and unbiased taxonomic classifications of plants occurring in remote regions. © The Author 2015. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

2008-01-01

other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP 󈨦), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

NASA Astrophysics Data System (ADS)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automatic page layout using genetic algorithms for electronic albuming

NASA Astrophysics Data System (ADS)

Geigel, Joe; Loui, Alexander C. P.

2000-12-01

In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.

An application of traveling salesman problem using the improved genetic algorithm on android google maps

NASA Astrophysics Data System (ADS)

Narwadi, Teguh; Subiyanto

2017-03-01

The Travelling Salesman Problem (TSP) is one of the best known NP-hard problems, which means that no exact algorithm to solve it in polynomial time. This paper present a new variant application genetic algorithm approach with a local search technique has been developed to solve the TSP. For the local search technique, an iterative hill climbing method has been used. The system is implemented on the Android OS because android is now widely used around the world and it is mobile system. It is also integrated with Google API that can to get the geographical location and the distance of the cities, and displays the route. Therefore, we do some experimentation to test the behavior of the application. To test the effectiveness of the application of hybrid genetic algorithm (HGA) is compare with the application of simple GA in 5 sample from the cities in Central Java, Indonesia with different numbers of cities. According to the experiment results obtained that in the average solution HGA shows in 5 tests out of 5 (100%) is better than simple GA. The results have shown that the hybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher complexity.

Antagonistic interactions peak at intermediate genetic distance in clinical and laboratory strains of Pseudomonas aeruginosa.

PubMed

Schoustra, Sijmen E; Dench, Jonathan; Dali, Rola; Aaron, Shawn D; Kassen, Rees

2012-03-22

Bacteria excrete costly toxins to defend their ecological niche. The evolution of such antagonistic interactions between individuals is expected to depend on both the social environment and the strength of resource competition. Antagonism is expected to be weak among highly similar genotypes because most individuals are immune to antagonistic agents and among dissimilar genotypes because these are unlikely to be competing for the same resources and antagonism should not yield much benefit. The strength of antagonism is therefore expected to peak at intermediate genetic distance. We studied the ability of laboratory strains of Pseudomonas aeruginosa to prevent growth of 55 different clinical P. aeruginosa isolates derived from cystic fibrosis patients. Genetic distance was determined using genetic fingerprints. We found that the strength of antagonism was maximal among genotypes of intermediate genetic distance and we show that genetic distance and resource use are linked. Our results suggest that the importance of social interactions like antagonism may be modulated by the strength of resource competition.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

NASA Astrophysics Data System (ADS)

Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen

2018-01-01

We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

PubMed

Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D

2017-08-01

Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.

Ecological niche partitioning between Anopheles gambiae molecular forms in Cameroon: the ecological side of speciation

PubMed Central

Simard, Frédéric; Ayala, Diego; Kamdem, Guy Colince; Pombi, Marco; Etouna, Joachim; Ose, Kenji; Fotsing, Jean-Marie; Fontenille, Didier; Besansky, Nora J; Costantini, Carlo

2009-01-01

Background Speciation among members of the Anopheles gambiae complex is thought to be promoted by disruptive selection and ecological divergence acting on sets of adaptation genes protected from recombination by polymorphic paracentric chromosomal inversions. However, shared chromosomal polymorphisms between the M and S molecular forms of An. gambiae and insufficient information about their relationship with ecological divergence challenge this view. We used Geographic Information Systems, Ecological Niche Factor Analysis, and Bayesian multilocus genetic clustering to explore the nature and extent of ecological and chromosomal differentiation of M and S across all the biogeographic domains of Cameroon in Central Africa, in order to understand the role of chromosomal arrangements in ecological specialisation within and among molecular forms. Results Species distribution modelling with presence-only data revealed differences in the ecological niche of both molecular forms and the sibling species, An. arabiensis. The fundamental environmental envelope of the two molecular forms, however, overlapped to a large extent in the rainforest, where they occurred in sympatry. The S form had the greatest niche breadth of all three taxa, whereas An. arabiensis and the M form had the smallest niche overlap. Correspondence analysis of M and S karyotypes confirmed that molecular forms shared similar combinations of chromosomal inversion arrangements in response to the eco-climatic gradient defining the main biogeographic domains occurring across Cameroon. Savanna karyotypes of M and S, however, segregated along the smaller-scale environmental gradient defined by the second ordination axis. Population structure analysis identified three chromosomal clusters, each containing a mixture of M and S specimens. In both M and S, alternative karyotypes were segregating in contrasted environments, in agreement with a strong ecological adaptive value of chromosomal inversions. Conclusion Our data suggest that inversions on the second chromosome of An. gambiae are not causal to the evolution of reproductive isolation between the M and S forms. Rather, they are involved in ecological specialization to a similar extent in both genetic backgrounds, and most probably predated lineage splitting between molecular forms. However, because chromosome-2 inversions promote ecological divergence, resulting in spatial and/or temporal isolation between ecotypes, they might favour mutations in other ecologically significant genes to accumulate in unlinked chromosomal regions. When such mutations occur in portions of the genome where recombination is suppressed, such as the pericentromeric regions known as speciation islands in An. gambiae, they would contribute further to the development of reproductive isolation. PMID:19460146

Optimization of genomic selection training populations with a genetic algorithm

USDA-ARS?s Scientific Manuscript database

In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

The impact of seasonality on niche breadth, distribution range and species richness: a theoretical exploration of Janzen's hypothesis.

PubMed

Hua, Xia

2016-07-27

Being invoked as one of the candidate mechanisms for the latitudinal patterns in biodiversity, Janzen's hypothesis states that the limited seasonal temperature variation in the tropics generates greater temperature stratification across elevations, which makes tropical species adapted to narrower ranges of temperatures and have lower effective dispersal across elevations than species in temperate regions. Numerous empirical studies have documented latitudinal patterns in species elevational ranges and thermal niche breadths that are consistent with the hypothesis, but the theoretical underpinnings remain unclear. This study presents the first mathematical model to examine the evolutionary processes that could back up Janzen's hypothesis and assess the effectiveness of limited seasonal temperature variation to promote speciation along elevation in the tropics. Results suggest that trade-offs in thermal tolerances provide a mechanism for Janzen's hypothesis. Limited seasonal temperature variation promotes gradient speciation not due to the reduction in gene flow that is associated with narrow thermal niche, but due to the pleiotropic effects of more stable divergent selection of thermal tolerance on the evolution of reproductive incompatibility. The proposed modelling approach also provides a potential way to test a speciation model against genetic data. © 2016 The Author(s).

Modelling the multidimensional niche by linking functional traits to competitive performance

PubMed Central

Maynard, Daniel S.; Leonard, Kenneth E.; Drake, John M.; Hall, David W.; Crowther, Thomas W.; Bradford, Mark A.

2015-01-01

Linking competitive outcomes to environmental conditions is necessary for understanding species' distributions and responses to environmental change. Despite this importance, generalizable approaches for predicting competitive outcomes across abiotic gradients are lacking, driven largely by the highly complex and context-dependent nature of biotic interactions. Here, we present and empirically test a novel niche model that uses functional traits to model the niche space of organisms and predict competitive outcomes of co-occurring populations across multiple resource gradients. The model makes no assumptions about the underlying mode of competition and instead applies to those settings where relative competitive ability across environments correlates with a quantifiable performance metric. To test the model, a series of controlled microcosm experiments were conducted using genetically related strains of a widespread microbe. The model identified trait microevolution and performance differences among strains, with the predicted competitive ability of each organism mapped across a two-dimensional carbon and nitrogen resource space. Areas of coexistence and competitive dominance between strains were identified, and the predicted competitive outcomes were validated in approximately 95% of the pairings. By linking trait variation to competitive ability, our work demonstrates a generalizable approach for predicting and modelling competitive outcomes across changing environmental contexts. PMID:26136444

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

NASA Astrophysics Data System (ADS)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Fireworks algorithm for mean-VaR/CVaR models

NASA Astrophysics Data System (ADS)

Zhang, Tingting; Liu, Zhifeng

2017-10-01

Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic.

PubMed

Assis, Jorge; Araújo, Miguel B; Serrão, Ester A

2018-01-01

Intraspecific genetic variability is critical for species adaptation and evolution and yet it is generally overlooked in projections of the biological consequences of climate change. We ask whether ongoing climate changes can cause the loss of important gene pools from North Atlantic relict kelp forests that persisted over glacial-interglacial cycles. We use ecological niche modelling to predict genetic diversity hotspots for eight species of large brown algae with different thermal tolerances (Arctic to warm temperate), estimated as regions of persistence throughout the Last Glacial Maximum (20,000 YBP), the warmer Mid-Holocene (6,000 YBP), and the present. Changes in the genetic diversity within ancient refugia were projected for the future (year 2100) under two contrasting climate change scenarios (RCP2.6 and RCP8.5). Models predicted distributions that matched empirical distributions in cross-validation, and identified distinct refugia at the low latitude ranges, which largely coincide among species with similar ecological niches. Transferred models into the future projected polewards expansions and substantial range losses in lower latitudes, where richer gene pools are expected (in Nova Scotia and Iberia for cold affinity species and Gibraltar, Alboran, and Morocco for warm-temperate species). These effects were projected for both scenarios but were intensified under the extreme RCP8.5 scenario, with the complete borealization (circum-Arctic colonization) of kelp forests, the redistribution of the biogeographical transitional zones of the North Atlantic, and the erosion of global gene pools across all species. As the geographic distribution of genetic variability is unknown for most marine species, our results represent a baseline for identification of locations potentially rich in unique phylogeographic lineages that are also climatic relics in threat of disappearing. © 2017 John Wiley & Sons Ltd.

Circum-Mediterranean phylogeography of a bat coupled with past environmental niche modeling: A new paradigm for the recolonization of Europe?

PubMed

Bilgin, Raşit; Gürün, Kanat; Rebelo, Hugo; Puechmaille, Sebastien J; Maracı, Öncü; Presetnik, Primoz; Benda, Petr; Hulva, Pavel; Ibáñez, Carlos; Hamidovic, Daniela; Fressel, Norma; Horáček, Ivan; Karataş, Ayşegül; Karataş, Ahmet; Allegrini, Benjamin; Georgiakakis, Panagiotis; Gazaryan, Suren; Nagy, Zoltan L; Abi-Said, Mounir; Lučan, Radek K; Bartonička, Tomáš; Nicolaou, Haris; Scaravelli, Dino; Karapandža, Branko; Uhrin, Marcel; Paunović, Milan; Juste, Javier

2016-06-01

The isolation of populations in the Iberian, Italian and Balkan peninsulas during the ice ages define four main paradigms that explain much of the known distribution of intraspecific genetic diversity in Europe. In this study we investigated the phylogeography of a wide-spread bat species, the bent-winged bat, Miniopterus schreibersii around the Mediterranean basin and in the Caucasus. Environmental Niche Modeling (ENM) analysis was applied to predict both the current distribution of the species and its distribution during the last glacial maximum (LGM). The combination of genetics and ENM results suggest that the populations of M. schreibersii in Europe, the Caucasus and Anatolia went extinct during the LGM, and the refugium for the species was a relatively small area to the east of the Levantine Sea, corresponding to the Mediterranean coasts of present-day Syria, Lebanon, Israel, and northeastern and northwestern Egypt. Subsequently the species first repopulated Anatolia, diversified there, and afterwards expanded into the Caucasus, continental Europe and North Africa after the end of the LGM. The fossil record in Iberia and the ENM results indicate continuous presence of Miniopterus in this peninsula that most probably was related to the Maghrebian lineage during the LGM, which did not persist afterwards. Using our results combined with similar findings in previous studies, we propose a new paradigm explaining the general distribution of genetic diversity in Europe involving the recolonization of the continent, with the main contribution from refugial populations in Anatolia and the Middle East. The study shows how genetics and ENM approaches can complement each other in providing a more detailed picture of intraspecific evolution. Copyright © 2016 Elsevier Inc. All rights reserved.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

NASA Astrophysics Data System (ADS)

Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

2018-03-01

Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

Research on laser marking speed optimization by using genetic algorithm.

PubMed

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.

Tag SNP selection via a genetic algorithm.

PubMed

Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

2010-10-01

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit

NASA Astrophysics Data System (ADS)

Rong, R. W.; Ming, T. F.

2017-12-01

In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.

Mapping black panthers: Macroecological modeling of melanism in leopards (Panthera pardus).

PubMed

da Silva, Lucas G; Kawanishi, Kae; Henschel, Philipp; Kittle, Andrew; Sanei, Arezoo; Reebin, Alexander; Miquelle, Dale; Stein, Andrew B; Watson, Anjali; Kekule, Laurence Bruce; Machado, Ricardo B; Eizirik, Eduardo

2017-01-01

The geographic distribution and habitat association of most mammalian polymorphic phenotypes are still poorly known, hampering assessments of their adaptive significance. Even in the case of the black panther, an iconic melanistic variant of the leopard (Panthera pardus), no map exists describing its distribution. We constructed a large database of verified records sampled across the species' range, and used it to map the geographic occurrence of melanism. We then estimated the potential distribution of melanistic and non-melanistic leopards using niche-modeling algorithms. The overall frequency of melanism was ca. 11%, with a significantly non-random spatial distribution. Distinct habitat types presented significantly different frequencies of melanism, which increased in Asian moist forests and approached zero across most open/dry biomes. Niche modeling indicated that the potential distributions of the two phenotypes were distinct, with significant differences in habitat suitability and rejection of niche equivalency between them. We conclude that melanism in leopards is strongly affected by natural selection, likely driven by efficacy of camouflage and/or thermoregulation in different habitats, along with an effect of moisture that goes beyond its influence on vegetation type. Our results support classical hypotheses of adaptive coloration in animals (e.g. Gloger's rule), and open up new avenues for in-depth evolutionary analyses of melanism in mammals.

Mapping black panthers: Macroecological modeling of melanism in leopards (Panthera pardus)

PubMed Central

da Silva, Lucas G.; Kawanishi, Kae; Henschel, Philipp; Kittle, Andrew; Sanei, Arezoo; Reebin, Alexander; Miquelle, Dale; Stein, Andrew B.; Watson, Anjali; Kekule, Laurence Bruce; Machado, Ricardo B.

2017-01-01

The geographic distribution and habitat association of most mammalian polymorphic phenotypes are still poorly known, hampering assessments of their adaptive significance. Even in the case of the black panther, an iconic melanistic variant of the leopard (Panthera pardus), no map exists describing its distribution. We constructed a large database of verified records sampled across the species’ range, and used it to map the geographic occurrence of melanism. We then estimated the potential distribution of melanistic and non-melanistic leopards using niche-modeling algorithms. The overall frequency of melanism was ca. 11%, with a significantly non-random spatial distribution. Distinct habitat types presented significantly different frequencies of melanism, which increased in Asian moist forests and approached zero across most open/dry biomes. Niche modeling indicated that the potential distributions of the two phenotypes were distinct, with significant differences in habitat suitability and rejection of niche equivalency between them. We conclude that melanism in leopards is strongly affected by natural selection, likely driven by efficacy of camouflage and/or thermoregulation in different habitats, along with an effect of moisture that goes beyond its influence on vegetation type. Our results support classical hypotheses of adaptive coloration in animals (e.g. Gloger’s rule), and open up new avenues for in-depth evolutionary analyses of melanism in mammals. PMID:28379961

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Development of variable microsatellite loci and range-wide characterization of nuclear genetic diversity in the important dryland shrub antelope bitterbrush (Purshia tridentata)

Treesearch

M.E. Horning; R.C. Cronn

2009-01-01

Antelope bitterbrush (Purshia tridentata Pursh DC; Rosaceae) is an arid-land shrub that occupies an important ecological niche in various fire-dominated communities across much of the Western United States. Because of its importance as a browse for large mammals and a food source for granivores, P. tridentata is frequently planted...

Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm

ERIC Educational Resources Information Center

Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.

2009-01-01

Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

ERIC Educational Resources Information Center

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.

2008-01-01

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Fit-for-purpose: species distribution model performance depends on evaluation criteria - Dutch Hoverflies as a case study.

PubMed

Aguirre-Gutiérrez, Jesús; Carvalheiro, Luísa G; Polce, Chiara; van Loon, E Emiel; Raes, Niels; Reemer, Menno; Biesmeijer, Jacobus C

2013-01-01

Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species' data characteristics (i.e. sample size, spatial distribution).

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

PubMed

Liu, Chun; Kroll, Andreas

2016-01-01

Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Genetic algorithm for nuclear data evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arthur, Jennifer Ann

These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

2013-01-01

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

Fuel management optimization using genetic algorithms and expert knowledge

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

2008-06-01

postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Comparing the Healthy Nose and Nasopharynx Microbiota Reveals Continuity As Well As Niche-Specificity

PubMed Central

De Boeck, Ilke; Wittouck, Stijn; Wuyts, Sander; Oerlemans, Eline F. M.; van den Broek, Marianne F. L.; Vandenheuvel, Dieter; Vanderveken, Olivier; Lebeer, Sarah

2017-01-01

To improve our understanding of upper respiratory tract (URT) diseases and the underlying microbial pathogenesis, a better characterization of the healthy URT microbiome is crucial. In this first large-scale study, we obtained more insight in the URT microbiome of healthy adults. Hereto, we collected paired nasal and nasopharyngeal swabs from 100 healthy participants in a citizen-science project. High-throughput 16S rRNA gene V4 amplicon sequencing was performed and samples were processed using the Divisive Amplicon Denoising Algorithm 2 (DADA2) algorithm. This allowed us to identify the bacterial richness and diversity of the samples in terms of amplicon sequence variants (ASVs), with special attention to intragenus variation. We found both niches to have a low overall species richness and uneven distribution. Moreover, based on hierarchical clustering, nasopharyngeal samples could be grouped into some bacterial community types at genus level, of which four were supported to some extent by prediction strength evaluation: one intermixed type with a higher bacterial diversity where Staphylococcus, Corynebacterium, and Dolosigranulum appeared main bacterial members in different relative abundances, and three types dominated by either Moraxella, Streptococcus, or Fusobacterium. Some of these bacterial community types such as Streptococcus and Fusobacterium were nasopharynx-specific and never occurred in the nose. No clear association between the nasopharyngeal bacterial profiles at genus level and the variables age, gender, blood type, season of sampling, or common respiratory allergies was found in this study population, except for smoking showing a positive association with Corynebacterium and Staphylococcus. Based on the fine-scale resolution of the ASVs, both known commensal and potential pathogenic bacteria were found within several genera – particularly in Streptococcus and Moraxella – in our healthy study population. Of interest, the nasopharynx hosted more potential pathogenic species than the nose. To our knowledge, this is the first large-scale study using the DADA2 algorithm to investigate the microbiota in the “healthy” adult nose and nasopharynx. These results contribute to a better understanding of the composition and diversity of the healthy microbiome in the URT and the differences between these important URT niches. Trial Registration: Ethical Committee of Antwerp University Hospital, B300201524257, registered 23 March 2015, ClinicalTrials.gov Identifier: NCT02 933983. PMID:29238339

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

NASA Astrophysics Data System (ADS)

Wu, Q. H.; Ma, J. T.

1993-09-01

A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.

Algorithme intelligent d'optimisation d'un design structurel de grande envergure

NASA Astrophysics Data System (ADS)

Dominique, Stephane

The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.

Using worldwide edaphic data to model plant species niches: An assessment at a continental extent

PubMed Central

Galvão, Franklin; Villalobos, Fabricio; De Marco Júnior, Paulo

2017-01-01

Ecological niche modeling (ENM) is a broadly used tool in different fields of plant ecology. Despite the importance of edaphic conditions in determining the niche of terrestrial plant species, edaphic data have rarely been included in ENMs of plant species perhaps because such data are not available for many regions. Recently, edaphic data has been made available at a global scale allowing its potential inclusion and evaluation on ENM performance for plant species. Here, we take advantage of such data and address the following main questions: What is the influence of distinct predictor variables (e.g. climatic vs edaphic) on different ENM algorithms? and what is the relationship between the performance of different predictors and geographic characteristics of species? We used 125 plant species distributed over the Neotropical region to explore the effect on ENMs of using edaphic data available from the SoilGrids database and its combination with climatic data from the CHELSA database. In addition, we related these different predictor variables to geographic characteristics of the target species and different ENM algorithms. The use of different predictors (climatic, edaphic, and both) significantly affected model performance and spatial complexity of the predictions. We showed that the use of global edaphic plus climatic variables generates ENMs with similar or better accuracy compared to those constructed only with climate variables. Moreover, the performance of models considering these different predictors, separately or jointly, was related to geographic properties of species records, such as number and distribution range. The large geographic extent, the variability of environments and the different species’ geographical characteristics considered here allowed us to demonstrate that global edaphic data adds useful information for plant ENMs. This is particularly valuable for studies of species that are distributed in regions where more detailed information on soil properties is poor or does not even exist. PMID:29049298

Research on Laser Marking Speed Optimization by Using Genetic Algorithm

PubMed Central

Wang, Dongyun; Yu, Qiwei; Zhang, Yu

2015-01-01

Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

NASA Astrophysics Data System (ADS)

An, M.; Assumpcao, M.

2003-12-01

The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.

Distinct population structure for co-occurring Anopheles goeldii and Anopheles triannulatus in Amazonian Brazil

PubMed Central

McKeon, Sascha Naomi; Moreno, Marta; Sallum, Maria Anise; Povoa, Marinete Marins; Conn, Jan Evelyn

2013-01-01

To evaluate whether environmental heterogeneity contributes to the genetic heterogeneity in Anopheles triannulatus, larval habitat characteristics across the Brazilian states of Roraima and Pará and genetic sequences were examined. A comparison with Anopheles goeldii was utilised to determine whether high genetic diversity was unique to An. triannulatus. Student t test and analysis of variance found no differences in habitat characteristics between the species. Analysis of population structure of An. triannulatus and An. goeldii revealed distinct demographic histories in a largely overlapping geographic range. Cytochrome oxidase I sequence parsimony networks found geographic clustering for both species; however nuclear marker networks depicted An. triannulatus with a more complex history of fragmentation, secondary contact and recent divergence. Evidence of Pleistocene expansions suggests both species are more likely to be genetically structured by geographic and ecological barriers than demography. We hypothesise that niche partitioning is a driving force for diversity, particularly in An. triannulatus. PMID:23903977

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project

DOE PAGES

Weston, David J.; Turetsky, Merritt R.; Johnson, Matthew G.; ...

2017-10-27

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even ‘extend’ to influence community structure and ecosystem level processes. The progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Therefore, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. We introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration,more » biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses.« less

Next generation sequencing--implications for clinical practice.

PubMed

Raffan, Eleanor; Semple, Robert K

2011-01-01

Genetic testing in inherited disease has traditionally relied upon recognition of the presenting clinical syndrome and targeted analysis of genes known to be linked to that syndrome. Consequently, many patients with genetic syndromes remain without a specific diagnosis. New 'next-generation' sequencing (NGS) techniques permit simultaneous sequencing of enormous amounts of DNA. A slew of research publications have recently demonstrated the tremendous power of these technologies in increasing understanding of human genetic disease. These approaches are likely to be increasingly employed in routine diagnostic practice, but the scale of the genetic information yielded about individuals means that caution must be exercised to avoid net harm in this setting. Use of NGS in a research setting will increasingly have a major but indirect beneficial impact on clinical practice. However, important technical, ethical and social challenges need to be addressed through informed professional and public dialogue before it finds its mature niche as a direct tool in the clinical diagnostic armoury.

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weston, David J.; Turetsky, Merritt R.; Johnson, Matthew G.

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even ‘extend’ to influence community structure and ecosystem level processes. The progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Therefore, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. We introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration,more » biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses.« less

The Sphagnome Project: enabling ecological and evolutionary insights through a genus-level sequencing project.

PubMed

Weston, David J; Turetsky, Merritt R; Johnson, Matthew G; Granath, Gustaf; Lindo, Zoë; Belyea, Lisa R; Rice, Steven K; Hanson, David T; Engelhardt, Katharina A M; Schmutz, Jeremy; Dorrepaal, Ellen; Euskirchen, Eugénie S; Stenøien, Hans K; Szövényi, Péter; Jackson, Michelle; Piatkowski, Bryan T; Muchero, Wellington; Norby, Richard J; Kostka, Joel E; Glass, Jennifer B; Rydin, Håkan; Limpens, Juul; Tuittila, Eeva-Stiina; Ullrich, Kristian K; Carrell, Alyssa; Benscoter, Brian W; Chen, Jin-Gui; Oke, Tobi A; Nilsson, Mats B; Ranjan, Priya; Jacobson, Daniel; Lilleskov, Erik A; Clymo, R S; Shaw, A Jonathan

2018-01-01

Considerable progress has been made in ecological and evolutionary genetics with studies demonstrating how genes underlying plant and microbial traits can influence adaptation and even 'extend' to influence community structure and ecosystem level processes. Progress in this area is limited to model systems with deep genetic and genomic resources that often have negligible ecological impact or interest. Thus, important linkages between genetic adaptations and their consequences at organismal and ecological scales are often lacking. Here we introduce the Sphagnome Project, which incorporates genomics into a long-running history of Sphagnum research that has documented unparalleled contributions to peatland ecology, carbon sequestration, biogeochemistry, microbiome research, niche construction, and ecosystem engineering. The Sphagnome Project encompasses a genus-level sequencing effort that represents a new type of model system driven not only by genetic tractability, but by ecologically relevant questions and hypotheses. © 2017 UT-Battelle New Phytologist © 2017 New Phytologist Trust.

Impacts of biogeographic history and marginal population genetics on species range limits: a case study of Liriodendron chinense.

PubMed

Yang, Aihong; Dick, Christopher W; Yao, Xiaohong; Huang, Hongwen

2016-05-10

Species ranges are influenced by past climate oscillations, geographical constraints, and adaptive potential to colonize novel habitats at range limits. This study used Liriodendron chinense, an important temperate Asian tree species, as a model system to evaluate the roles of biogeographic history and marginal population genetics in determining range limits. We examined the demographic history and genetic diversity of 29 L. chinense populations using both chloroplast and nuclear microsatellite loci. Significant phylogeographic structure was recovered with haplotype clusters coinciding with major mountain regions. Long-term demographical stability was suggested by mismatch distribution analyses, neutrality tests, and ecological niche models (ENM) and suggested the existence of LGM refuges within mountain regions. Differences in genetic diversity between central and marginal populations were not significant for either genomic region. However, asymmetrical gene flow was inferred from central populations to marginal populations, which could potentially limit range adaptation and expansion of L. chinense.

A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications

NASA Astrophysics Data System (ADS)

Entezari-Maleki, Reza; Movaghar, Ali

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.

Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria

NASA Astrophysics Data System (ADS)

Jollyta, Deny; Johan; Hajjah, Alyauma

2017-12-01

The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.

A theoretical comparison of evolutionary algorithms and simulated annealing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.

1995-08-28

This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

Design of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

2010-01-01

decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging

Combinatorial optimization problem solution based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Zhang, Peng

2017-08-01

Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.

Evolution of eukaryotic microbial pathogens via covert sexual reproduction

PubMed Central

Heitman, Joseph

2010-01-01

Sexual reproduction enables eukaryotic organisms to re-assort genetic diversity and purge deleterious mutations, producing better-fit progeny. Sex arose early and pervades eukaryotes. Fungal and parasite pathogens once thought asexual have maintained cryptic sexual cycles, including unisexual or parasexual reproduction. As pathogens become niche and host-adapted, sex appears to specialize to promote inbreeding and clonality yet maintain out-crossing potential. During self-fertile sexual modes, sex itself may generate genetic diversity de novo. Mating-type loci govern fungal sexual identity; how parasites establish sexual identity is unknown. Comparing and contrasting fungal and parasite sex promises to reveal how microbial pathogens evolved and are evolving. PMID:20638645

Potential distribution of Mexican primates: modeling the ecological niche with the maximum entropy algorithm.

PubMed

Vidal-García, Francisca; Serio-Silva, Juan Carlos

2011-07-01

We developed a potential distribution model for the tropical rain forest species of primates of southern Mexico: the black howler monkey (Alouatta pigra), the mantled howler monkey (Alouatta palliata), and the spider monkey (Ateles geoffroyi). To do so, we applied the maximum entropy algorithm from the ecological niche modeling program MaxEnt. For each species, we used occurrence records from scientific collections, and published and unpublished sources, and we also used the 19 environmental coverage variables related to precipitation and temperature from WorldClim to develop the models. The predicted distribution of A. pigra was strongly associated with the mean temperature of the warmest quarter (23.6%), whereas the potential distributions of A. palliata and A. geoffroyi were strongly associated with precipitation during the coldest quarter (52.2 and 34.3% respectively). The potential distribution of A. geoffroyi is broader than that of the Alouatta spp. The areas with the greatest probability of presence of A. pigra and A. palliata are strongly associated with riparian vegetation, whereas the presence of A. geoffroyi is more strongly associated with the presence of rain forest. Our most significant contribution is the identification of areas with a high probability of the presence of these primate species, which is information that can be applied to planning future studies and then establishing criteria for the creation of areas to primate conservation in Mexico.

Archived DNA reveals fisheries and climate induced collapse of a major fishery.

PubMed

Bonanomi, Sara; Pellissier, Loïc; Therkildsen, Nina Overgaard; Hedeholm, Rasmus Berg; Retzel, Anja; Meldrup, Dorte; Olsen, Steffen Malskær; Nielsen, Anders; Pampoulie, Christophe; Hemmer-Hansen, Jakob; Wisz, Mary Susanne; Grønkjær, Peter; Nielsen, Einar Eg

2015-10-22

Fishing and climate change impact the demography of marine fishes, but it is generally ignored that many species are made up of genetically distinct locally adapted populations that may show idiosyncratic responses to environmental and anthropogenic pressures. Here, we track 80 years of Atlantic cod (Gadus morhua) population dynamics in West Greenland using DNA from archived otoliths in combination with fish population and niche based modeling. We document how the interacting effects of climate change and high fishing pressure lead to dramatic spatiotemporal changes in the proportions and abundance of different genetic populations, and eventually drove the cod fishery to a collapse in the early 1970s. Our results highlight the relevance of fisheries management at the level of genetic populations under future scenarios of climate change.

Archived DNA reveals fisheries and climate induced collapse of a major fishery

PubMed Central

Bonanomi, Sara; Pellissier, Loïc; Therkildsen, Nina Overgaard; Hedeholm, Rasmus Berg; Retzel, Anja; Meldrup, Dorte; Olsen, Steffen Malskær; Nielsen, Anders; Pampoulie, Christophe; Hemmer-Hansen, Jakob; Wisz, Mary Susanne; Grønkjær, Peter; Nielsen, Einar Eg

2015-01-01

Fishing and climate change impact the demography of marine fishes, but it is generally ignored that many species are made up of genetically distinct locally adapted populations that may show idiosyncratic responses to environmental and anthropogenic pressures. Here, we track 80 years of Atlantic cod (Gadus morhua) population dynamics in West Greenland using DNA from archived otoliths in combination with fish population and niche based modeling. We document how the interacting effects of climate change and high fishing pressure lead to dramatic spatiotemporal changes in the proportions and abundance of different genetic populations, and eventually drove the cod fishery to a collapse in the early 1970s. Our results highlight the relevance of fisheries management at the level of genetic populations under future scenarios of climate change. PMID:26489934

Archived DNA reveals fisheries and climate induced collapse of a major fishery

NASA Astrophysics Data System (ADS)

Bonanomi, Sara; Pellissier, Loïc; Therkildsen, Nina Overgaard; Hedeholm, Rasmus Berg; Retzel, Anja; Meldrup, Dorte; Olsen, Steffen Malskær; Nielsen, Anders; Pampoulie, Christophe; Hemmer-Hansen, Jakob; Wisz, Mary Susanne; Grønkjær, Peter; Nielsen, Einar Eg

2015-10-01

Fishing and climate change impact the demography of marine fishes, but it is generally ignored that many species are made up of genetically distinct locally adapted populations that may show idiosyncratic responses to environmental and anthropogenic pressures. Here, we track 80 years of Atlantic cod (Gadus morhua) population dynamics in West Greenland using DNA from archived otoliths in combination with fish population and niche based modeling. We document how the interacting effects of climate change and high fishing pressure lead to dramatic spatiotemporal changes in the proportions and abundance of different genetic populations, and eventually drove the cod fishery to a collapse in the early 1970s. Our results highlight the relevance of fisheries management at the level of genetic populations under future scenarios of climate change.

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

NASA Astrophysics Data System (ADS)

Bay, Annick; Mayer, Alexandre

2014-09-01

The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

EPA Science Inventory

In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

Horizontal gene transfer between bacteria.

PubMed

Heuer, Holger; Smalla, Kornelia

2007-01-01

Horizontal gene transfer (HGT) refers to the acquisition of foreign genes by organisms. The occurrence of HGT among bacteria in the environment is assumed to have implications in the risk assessment of genetically modified bacteria which are released into the environment. First, introduced genetic sequences from a genetically modified bacterium could be transferred to indigenous micro-organisms and alter their genome and subsequently their ecological niche. Second, the genetically modified bacterium released into the environment might capture mobile genetic elements (MGE) from indigenous micro-organisms which could extend its ecological potential. Thus, for a risk assessment it is important to understand the extent of HGT and genome plasticity of bacteria in the environment. This review summarizes the present state of knowledge on HGT between bacteria as a crucial mechanism contributing to bacterial adaptability and diversity. In view of the use of GM crops and microbes in agricultural settings, in this mini-review we focus particularly on the presence and role of MGE in soil and plant-associated bacteria and the factors affecting gene transfer.

Mutations in Soviet public health science: post-Lysenko medical genetics, 1969-1991.

PubMed

Bauer, Susanne

2014-09-01

This paper traces the integration of human genetics with Soviet public health science after the Lysenko era. For nearly three decades, USSR biology pursued its own version of anti-bourgeois, Soviet 'creative Darwinism', departing from western, post-WWII scientific developments. After Lysenko was suspended, research niches of immunology, biophysics and mutation research formed the basis of new departments at the Institute of Medical Genetics, which was founded in 1969 as part of the Soviet Academy of Medical Sciences. Focussing on early research activities and collaborations at the institute, I show how the concept of mutagenesis, a pivotal issue during the Cold War, became mobilized from Drosophila genetics to human heredity and to society as a whole. This mode of scaling up and down through population studies shaped not only Soviet human biology and genetics; it also brought about changes in clinical practice and public health as well as in the monitoring and regulation of mutagenic agents in the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Strain gage selection in loads equations using a genetic algorithm

NASA Technical Reports Server (NTRS)

1994-01-01

Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.

A hybrid genetic algorithm for solving bi-objective traveling salesman problems

NASA Astrophysics Data System (ADS)

Ma, Mei; Li, Hecheng

2017-08-01

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.

Assembly of the Caenorhabditis elegans gut microbiota from diverse soil microbial environments

PubMed Central

Berg, Maureen; Stenuit, Ben; Ho, Joshua; Wang, Andrew; Parke, Caitlin; Knight, Matthew; Alvarez-Cohen, Lisa; Shapira, Michael

2016-01-01

It is now well accepted that the gut microbiota contributes to our health. However, what determines the microbiota composition is still unclear. Whereas it might be expected that the intestinal niche would be dominant in shaping the microbiota, studies in vertebrates have repeatedly demonstrated dominant effects of external factors such as host diet and environmental microbial diversity. Hypothesizing that genetic variation may interfere with discerning contributions of host factors, we turned to Caenorhabditis elegans as a new model, offering the ability to work with genetically homogenous populations. Deep sequencing of 16S rDNA was used to characterize the (previously unknown) worm gut microbiota as assembled from diverse produce-enriched soil environments under laboratory conditions. Comparisons of worm microbiotas with those in their soil environment revealed that worm microbiotas resembled each other even when assembled from different microbial environments, and enabled defining a shared core gut microbiota. Community analyses indicated that species assortment in the worm gut was non-random and that assembly rules differed from those in their soil habitat, pointing at the importance of competitive interactions between gut-residing taxa. The data presented fills a gap in C. elegans biology. Furthermore, our results demonstrate a dominant contribution of the host niche in shaping the gut microbiota. PMID:26800234

Comparative genomic and plasmid analysis of beer-spoiling and non-beer-spoiling Lactobacillus brevis isolates.

PubMed

Bergsveinson, Jordyn; Ziola, Barry

2017-12-01

Beer-spoilage-related lactic acid bacteria (BSR LAB) belong to multiple genera and species; however, beer-spoilage capacity is isolate-specific and partially acquired via horizontal gene transfer within the brewing environment. Thus, the extent to which genus-, species-, or environment- (i.e., brewery-) level genetic variability influences beer-spoilage phenotype is unknown. Publicly available Lactobacillus brevis genomes were analyzed via BlAst Diagnostic Gene findEr (BADGE) for BSR genes and assessed for pangenomic relationships. Also analyzed were functional coding capacities of plasmids of LAB inhabiting extreme niche environments. Considerable genetic variation was observed in L. brevis isolated from clinical samples, whereas 16 candidate genes distinguish BSR and non-BSR L. brevis genomes. These genes are related to nutrient scavenging of gluconate or pentoses, mannose, and metabolism of pectin. BSR L. brevis isolates also have higher average nucleotide identity and stronger pangenome association with one another, though isolation source (i.e., specific brewery) also appears to influence the plasmid coding capacity of BSR LAB. Finally, it is shown that niche-specific adaptation and phenotype are plasmid-encoded for both BSR and non-BSR LAB. The ultimate combination of plasmid-encoded genes dictates the ability of L. brevis to survive in the most extreme beer environment, namely, gassed (i.e., pressurized) beer.

Theoretical Perspectives on the Statics and Dynamics of Species’ Borders in Patchy Environments

PubMed Central

Holt, Robert D.; Barfield, Michael

2016-01-01

Understanding range limits is a fundamental problem in ecology and evolutionary biology. In 1963, Mayr argued that “contaminating” gene flow from central populations constrained adaptation in marginal populations, preventing range expansion, while in 1984, Bradshaw suggested that absence of genetic variation prevented species from occurring everywhere. Understanding stability of range boundaries requires unraveling the interplay of demography, gene flow, and evolution of populations in concrete landscape settings. We walk through a set of interrelated spatial scenarios that illustrate interesting complexities of this interplay. To motivate our individual-based model results, we consider a hypothetical zooplankter in a landscape of discrete water bodies coupled by dispersal. We examine how patterns of dispersal influence adaptation in sink habitats where conditions are outside the species’ niche. The likelihood of observing niche evolution (and thus range expansion) over any given timescale depends on (1) the degree of initial maladaptation; (2) pattern (pulsed vs. continuous, uni- vs. bidirectional), timing (juvenile vs. adult), and rate of dispersal (and hence population size); (3) mutation rate; (4) sexuality; and (5) the degree of heterogeneity in the occupied range. We also show how the genetic architecture of polygenic adaptation is influenced by the interplay of selection and dispersal in heterogeneous landscapes. PMID:21956092

Sexual selection and conflict as engines of ecological diversification.

PubMed

Bonduriansky, Russell

2011-12-01

Ecological diversification presents an enduring puzzle: how do novel ecological strategies evolve in organisms that are already adapted to their ecological niche? Most attempts to answer this question posit a primary role for genetic drift, which could carry populations through or around fitness "valleys" representing maladaptive intermediate phenotypes between alternative niches. Sexual selection and conflict are thought to play an ancillary role by initiating reproductive isolation and thereby facilitating divergence in ecological traits through genetic drift or local adaptation. Here, I synthesize theory and evidence suggesting that sexual selection and conflict could play a more central role in the evolution and diversification of ecological strategies through the co-optation of sexual traits for viability-related functions. This hypothesis rests on three main premises, all of which are supported by theory and consistent with the available evidence. First, sexual selection and conflict often act at cross-purposes to viability selection, thereby displacing populations from the local viability optimum. Second, sexual traits can serve as preadaptations for novel viability-related functions. Third, ancestrally sex-limited sexual traits can be transferred between sexes. Consequently, by allowing populations to explore a broad phenotypic space around the current viability optimum, sexual selection and conflict could act as powerful drivers of ecological adaptation and diversification.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

Rabow, A. A.; Scheraga, H. A.

1996-01-01

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904

The genetic algorithm: A robust method for stress inversion

NASA Astrophysics Data System (ADS)

Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.

2017-01-01

The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Optimization of multicast optical networks with genetic algorithm

NASA Astrophysics Data System (ADS)

Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

2007-11-01

In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Air data system optimization using a genetic algorithm

NASA Technical Reports Server (NTRS)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...

2014-10-15

In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

2016-01-01

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Inferences from the Historical Distribution of Wild and Domesticated Maize Provide Ecological and Evolutionary Insight

PubMed Central

Hufford, Matthew B.; Martínez-Meyer, Enrique; Gaut, Brandon S.; Eguiarte, Luis E.; Tenaillon, Maud I.

2012-01-01

Background The species Zea mays includes both domesticated maize (ssp. mays) and its closest wild relatives known as the teosintes. While genetic and archaeological studies have provided a well-established history of Z. mays evolution, there is currently minimal description of its current and past distribution. Here, we implemented species distribution modeling using paleoclimatic models of the last interglacial (LI; ∼135,000 BP) and the last glacial maximum (LGM; ∼21,000 BP) to hindcast the distribution of Zea mays subspecies over time and to revisit current knowledge of its phylogeography and evolutionary history. Methodology/Principal Findings Using a large occurrence data set and the distribution modeling MaxEnt algorithm, we obtained robust present and past species distributions of the two widely distributed teosinte subspecies (ssps. parviglumis and mexicana) revealing almost perfect complementarity, stable through time, of their occupied distributions. We also investigated the present distributions of primitive maize landraces, which overlapped but were broader than those of the teosintes. Our data reinforced the idea that little historical gene flow has occurred between teosinte subspecies, but maize has served as a genetic bridge between them. We observed an expansion of teosinte habitat from the LI, consistent with population genetic data. Finally, we identified locations potentially serving as refugia for the teosintes throughout epochs of climate change and sites that should be targeted in future collections. Conclusion/Significance The restricted and highly contrasting ecological niches of the wild teosintes differ substantially from domesticated maize. Variables determining the distributions of these taxa can inform future considerations of local adaptation and the impacts of climate change. Our assessment of the changing distributions of Zea mays taxa over time offers a unique glimpse into the history of maize, highlighting a strategy for the study of domestication that may prove useful for other species. PMID:23155371

Modeling potential habitats for alien species Dreissena polymorpha in continental USA

USGS Publications Warehouse

Mingyang, Li; Yunwei, Ju; Kumar, Sunil; Stohlgren, Thomas J.

2008-01-01

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.

Inversion of ocean-bottom seismometer (OBS) waveforms for oceanic crust structure: a synthetic study

NASA Astrophysics Data System (ADS)

Li, Xueyan; Wang, Yanbin; Chen, Yongshun John

2016-08-01

The waveform inversion method is applied—using synthetic ocean-bottom seismometer (OBS) data—to study oceanic crust structure. A niching genetic algorithm (NGA) is used to implement the inversion for the thickness and P-wave velocity of each layer, and to update the model by minimizing the objective function, which consists of the misfit and cross-correlation of observed and synthetic waveforms. The influence of specific NGA method parameters is discussed, and suitable values are presented. The NGA method works well for various observation systems, such as those with irregular and sparse distribution of receivers as well as single receiver systems. A strategy is proposed to accelerate the convergence rate by a factor of five with no increase in computational complexity; this is achieved using a first inversion with several generations to impose a restriction on the preset range of each parameter and then conducting a second inversion with the new range. Despite the successes of this method, its usage is limited. A shallow water layer is not favored because the direct wave in water will suppress the useful reflection signals from the crust. A more precise calculation of the air-gun source signal should be considered in order to better simulate waveforms generated in realistic situations; further studies are required to investigate this issue.

Diazepam Binding Inhibitor Promotes Stem Cell Expansion Controlling Environment-Dependent Neurogenesis.

PubMed

Dumitru, Ionut; Neitz, Angela; Alfonso, Julieta; Monyer, Hannah

2017-04-05

Plasticity of adult neurogenesis supports adaptation to environmental changes. The identification of molecular mediators that signal these changes to neural progenitors in the niche has remained elusive. Here we report that diazepam binding inhibitor (DBI) is crucial in supporting an adaptive mechanism in response to changes in the environment. We provide evidence that DBI is expressed in stem cells in all neurogenic niches of the postnatal brain. Focusing on the hippocampal subgranular zone (SGZ) and employing multiple genetic manipulations in vivo, we demonstrate that DBI regulates the balance between preserving the stem cell pool and neurogenesis. Specifically, DBI dampens GABA activity in stem cells, thereby sustaining the proproliferative effect of physical exercise and enriched environment. Our data lend credence to the notion that the modulatory effect of DBI constitutes a general mechanism that regulates postnatal neurogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Potential distribution of mosquito vector species in a primary malaria endemic region of Colombia

PubMed Central

Altamiranda-Saavedra, Mariano; Arboleda, Sair; Parra, Juan L.; Peterson, A. Townsend

2017-01-01

Rapid transformation of natural ecosystems changes ecological conditions for important human disease vector species; therefore, an essential task is to identify and understand the variables that shape distributions of these species to optimize efforts toward control and mitigation. Ecological niche modeling was used to estimate the potential distribution and to assess hypotheses of niche similarity among the three main malaria vector species in northern Colombia: Anopheles nuneztovari, An. albimanus, and An. darlingi. Georeferenced point collection data and remotely sensed, fine-resolution satellite imagery were integrated across the Urabá –Bajo Cauca–Alto Sinú malaria endemic area using a maximum entropy algorithm. Results showed that An. nuneztovari has the widest geographic distribution, occupying almost the entire study region; this niche breadth is probably related to the ability of this species to colonize both, natural and disturbed environments. The model for An. darlingi showed that most suitable localities for this species in Bajo Cauca were along the Cauca and Nechí river. The riparian ecosystems in this region and the potential for rapid adaptation by this species to novel environments, may favor the establishment of populations of this species. Apparently, the three main Colombian Anopheles vector species in this endemic area do not occupy environments either with high seasonality, or with low seasonality and high NDVI values. Estimated overlap in geographic space between An. nuneztovari and An. albimanus indicated broad spatial and environmental similarity between these species. An. nuneztovari has a broader niche and potential distribution. Dispersal ability of these species and their ability to occupy diverse environmental situations may facilitate sympatry across many environmental and geographic contexts. These model results may be useful for the design and implementation of malaria species-specific vector control interventions optimized for this important malaria region. PMID:28594942

Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; Verdery, Ashton M.; McDaniel, Phillip M.; Rindfuss, Ronald R.

2012-01-01

Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops. PMID:24187378

Global thermal niche models of two European grasses show high invasion risks in Antarctica.

PubMed

Pertierra, Luis R; Aragón, Pedro; Shaw, Justine D; Bergstrom, Dana M; Terauds, Aleks; Olalla-Tárraga, Miguel Ángel

2017-07-01

The two non-native grasses that have established long-term populations in Antarctica (Poa pratensis and Poa annua) were studied from a global multidimensional thermal niche perspective to address the biological invasion risk to Antarctica. These two species exhibit contrasting introduction histories and reproductive strategies and represent two referential case studies of biological invasion processes. We used a multistep process with a range of species distribution modelling techniques (ecological niche factor analysis, multidimensional envelopes, distance/entropy algorithms) together with a suite of thermoclimatic variables, to characterize the potential ranges of these species. Their native bioclimatic thermal envelopes in Eurasia, together with the different naturalized populations across continents, were compared next. The potential niche of P. pratensis was wider at the cold extremes; however, P. annua life history attributes enable it to be a more successful colonizer. We observe that particularly cold summers are a key aspect of the unique Antarctic environment. In consequence, ruderals such as P. annua can quickly expand under such harsh conditions, whereas the more stress-tolerant P. pratensis endures and persist through steady growth. Compiled data on human pressure at the Antarctic Peninsula allowed us to provide site-specific biosecurity risk indicators. We conclude that several areas across the region are vulnerable to invasions from these and other similar species. This can only be visualized in species distribution models (SDMs) when accounting for founder populations that reveal nonanalogous conditions. Results reinforce the need for strict management practices to minimize introductions. Furthermore, our novel set of temperature-based bioclimatic GIS layers for ice-free terrestrial Antarctica provide a mechanism for regional and global species distribution models to be built for other potentially invasive species. © 2017 John Wiley & Sons Ltd.

Analyzing the responses of species assemblages to climate change across the Great Basin, USA.

NASA Astrophysics Data System (ADS)

Henareh Khalyani, A.; Falkowski, M. J.; Crookston, N.; Yousef, F.

2016-12-01

The potential impacts of climate change on the future distribution of tree species in not well understood. Climate driven changes in tree species distribution could cause significant changes in realized species niches, potentially resulting in the loss of ecotonal species as well as the formation on novel assemblages of overlapping tree species. In an effort to gain a better understating of how the geographic distribution of tree species may respond to climate change, we model the potential future distribution of 50 different tree species across 70 million ha in the Great Basin, USA. This is achieved by leveraging a species realized niche model based on non-parametric analysis of species occurrences across climatic, topographic, and edaphic variables. Spatially explicit, high spatial resolution (30 m) climate variables (e.g., precipitation, and minimum, maximum, and mean temperature) and associated climate indices were generated on an annual basis between 1981-2010 by integrating climate station data with digital elevation data (Shuttle Radar Topographic Mission (SRTM) data) in a thin plate spline interpolation algorithm (ANUSPLIN). Bioclimate models of species niches in in the cotemporary period and three following 30 year periods were then generated by integrating the climate variables, soil data, and CMIP 5 general circulation model projections. Our results suggest that local scale contemporary variations in species realized niches across space are influenced by edaphic and topographic variables as well as climatic variables. The local variability in soil properties and topographic variability across space also affect the species responses to climate change through time and potential formation of species assemblages in future. The results presented here in will aid in the development of adaptive forest management techniques aimed at mitigating negative impacts of climate change on forest composition, structure, and function.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

PNPLA3 variant and portal/periportal histological pattern in patients with biopsy-proven non-alcoholic fatty liver disease: a possible role for oxidative stress.

PubMed

Carpino, Guido; Pastori, Daniele; Baratta, Francesco; Overi, Diletta; Labbadia, Giancarlo; Polimeni, Licia; Di Costanzo, Alessia; Pannitteri, Gaetano; Carnevale, Roberto; Del Ben, Maria; Arca, Marcello; Violi, Francesco; Angelico, Francesco; Gaudio, Eugenio

2017-11-17

Pathogenesis of non-alcoholic fatty liver disease (NAFLD) is influenced by predisposing genetic variations, dysmetabolism, systemic oxidative stress, and local cellular and molecular cross-talks. Patatin-like phospholipase domain containing 3 (PNPLA3) gene I148M variant is a known determinant of NAFLD. Aims were to evaluate whether PNPLA3 I148M variant was associated with a specific histological pattern, hepatic stem/progenitor cell (HpSC) niche activation and serum oxidative stress markers. Liver biopsies were obtained from 54 NAFLD patients. The activation of HpSC compartment was evaluated by the extension of ductular reaction (DR); hepatic stellate cells, myofibroblasts (MFs), and macrophages were evaluated by immunohistochemistry. Systemic oxidative stress was assessed measuring serum levels of soluble NOX2-derived peptide (sNOX2-dp) and 8-isoprostaglandin F 2α (8-iso-PGF 2α ). PNPLA3 carriers showed higher steatosis, portal inflammation and HpSC niche activation compared to wild-type patients. DR was correlated with NAFLD activity score (NAS) and fibrosis score. Serum 8-iso-PGF 2α were significantly higher in I148M carriers compared to non-carriers and were correlated with DR and portal inflammation. sNox2-dp was correlated with NAS and with HpSC niche activation. In conclusion, NAFLD patients carrying PNPLA3 I148M are characterized by a prominent activation of HpSC niche which is associated with a more aggressive histological pattern (portal fibrogenesis) and increased oxidative stress.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place-based approach.

PubMed

Schwalm, Donelle; Epps, Clinton W; Rodhouse, Thomas J; Monahan, William B; Castillo, Jessica A; Ray, Chris; Jeffress, Mackenzie R

2016-04-01

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species' niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species' niches, resulting in predictions that are generally limited to climate-occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place-based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence-absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981-2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local-scale differences in the realized niche of the American pika. This variation resulted in diverse and - in some cases - highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place-based approach to species distribution modeling that includes fine-scale factors when assessing current and future climate impacts on species' distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas. © 2015 John Wiley & Sons Ltd.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Ortho Image and DTM Generation with Intelligent Methods

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadeghian, S.

2013-10-01

Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.

Ecological and Genetic Divergences with Gene Flow of Two Sister Species (Leucomeris decora and Nouelia insignis) Driving by Climatic Transition in Southwest China.

PubMed

Zhao, Yujuan; Yin, Genshen; Pan, Yuezhi; Gong, Xun

2018-01-01

Understanding of the processes of divergence and speciation is a major task for biodiversity researches and may offer clearer insight into mechanisms generating biological diversity. Here, we employ an integrative approach to explore genetic and ecological differentiation of Leucomeris decora and Nouelia insignis distributed allopatrically along the two sides of the biogeographic boundary 'Tanaka Line' in Southwest China. We addressed these questions using ten low-copy nuclear genes and nine plastid DNA regions sequenced among individuals sampled from 28 populations across their geographic ranges in China. Phylogenetic, coalescent-based population genetic analyses, approximate Bayesian computation (ABC) framework and ecological niche models (ENMs) were conducted. We identified a closer phylogenetic relationship in maternal lineage of L. decora with N. insignis than that between L . decora and congeneric Leucomeris spectabilis . A deep divergence between the two species was observed and occurred at the boundary between later Pliocene and early Pleistocene. However, the evidence of significant chloroplast DNA gene flow was also detected between the marginal populations of L. decora and N. insignis . Niche models and statistical analyses showed significant ecological differentiation, and two nuclear loci among the ten nuclear genes may be under divergent selection. These integrative results imply that the role of climatic shift from Pliocene to Pleistocene may be the prominent factor for the divergence of L . decora and N . insignis , and population expansion after divergence may have given rise to chloroplast DNA introgression. The divergence was maintained by differential selection despite in the face of gene flow.

Genome Evolution and Plasticity of Serratia marcescens, an Important Multidrug-Resistant Nosocomial Pathogen

PubMed Central

Iguchi, Atsushi; Nagaya, Yutaka; Pradel, Elizabeth; Ooka, Tadasuke; Ogura, Yoshitoshi; Katsura, Keisuke; Kurokawa, Ken; Oshima, Kenshiro; Hattori, Masahira; Parkhill, Julian; Sebaihia, Mohamed; Coulthurst, Sarah J.; Gotoh, Naomasa; Thomson, Nicholas R.; Ewbank, Jonathan J.; Hayashi, Tetsuya

2014-01-01

Serratia marcescens is an important nosocomial pathogen that can cause an array of infections, most notably of the urinary tract and bloodstream. Naturally, it is found in many environmental niches, and is capable of infecting plants and animals. The emergence and spread of multidrug-resistant strains producing extended-spectrum or metallo beta-lactamases now pose a threat to public health worldwide. Here we report the complete genome sequences of two carefully selected S. marcescens strains, a multidrug-resistant clinical isolate (strain SM39) and an insect isolate (strain Db11). Our comparative analyses reveal the core genome of S. marcescens and define the potential metabolic capacity, virulence, and multidrug resistance of this species. We show a remarkable intraspecies genetic diversity, both at the sequence level and with regards genome flexibility, which may reflect the diversity of niches inhabited by members of this species. A broader analysis with other Serratia species identifies a set of approximately 3,000 genes that characterize the genus. Within this apparent genetic diversity, we identified many genes implicated in the high virulence potential and antibiotic resistance of SM39, including the metallo beta-lactamase and multiple other drug resistance determinants carried on plasmid pSMC1. We further show that pSMC1 is most closely related to plasmids circulating in Pseudomonas species. Our data will provide a valuable basis for future studies on S. marcescens and new insights into the genetic mechanisms that underlie the emergence of pathogens highly resistant to multiple antimicrobial agents. PMID:25070509

Genetic tool development underpins recent advances in thermophilic whole‐cell biocatalysts

PubMed Central

Taylor, M. P.; van Zyl, L.; Tuffin, I. M.; Leak, D. J.; Cowan, D. A.

2011-01-01

Summary The environmental value of sustainably producing bioproducts from biomass is now widely appreciated, with a primary target being the economic production of fuels such as bioethanol from lignocellulose. The application of thermophilic prokaryotes is a rapidly developing niche in this field, driven by their known catabolic versatility with lignocellulose‐derived carbohydrates. Fundamental to the success of this work has been the development of reliable genetic and molecular systems. These technical tools are now available to assist in the development of other (hyper)thermophilic strains with diverse phenotypes such as hemicellulolytic and cellulolytic properties, branched chain alcohol production and other ‘valuable bioproduct’ synthetic capabilities. Here we present an insight into the historical limitations, recent developments and current status of a number of genetic systems for thermophiles. We also highlight the value of reliable genetic methods for increasing our knowledge of thermophile physiology. We argue that the development of robust genetic systems is paramount in the evolution of future thermophilic based bioprocesses and make suggestions for future approaches and genetic targets that will facilitate this process. PMID:21310009

Speed challenge: a case for hardware implementation in soft-computing

NASA Technical Reports Server (NTRS)

Daud, T.; Stoica, A.; Duong, T.; Keymeulen, D.; Zebulum, R.; Thomas, T.; Thakoor, A.

2000-01-01

For over a decade, JPL has been actively involved in soft computing research on theory, architecture, applications, and electronics hardware. The driving force in all our research activities, in addition to the potential enabling technology promise, has been creation of a niche that imparts orders of magnitude speed advantage by implementation in parallel processing hardware with algorithms made especially suitable for hardware implementation. We review our work on neural networks, fuzzy logic, and evolvable hardware with selected application examples requiring real time response capabilities.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve

2000-01-01

A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.

Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

PubMed

Nguyen, Hai Van; Finkelstein, Eric Andrew; Mital, Shweta; Gardner, Daphne Su-Lyn

2017-11-01

Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m 2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic Algorithms for Multiple-Choice Problems

NASA Astrophysics Data System (ADS)

Aickelin, Uwe

2010-04-01

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

A synthetic genetic edge detection program.

PubMed

Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

2009-06-26

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

A Synthetic Genetic Edge Detection Program

PubMed Central

Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

2009-01-01

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

Constrained minimization of smooth functions using a genetic algorithm

NASA Technical Reports Server (NTRS)

Moerder, Daniel D.; Pamadi, Bandu N.

1994-01-01

The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.

Reciprocal interactions between endothelial cells and macrophages in angiogenic vascular niches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baer, Caroline; Squadrito, Mario Leonardo; Iruela-Arispe, M. Luisa, E-mail: arispe@mcdb.ucla.edu

The ability of macrophages to promote vascular growth has been associated with the secretion and local delivery of classic proangiogenic factors (e.g., VEGF-A and proteases). More recently, a series of studies have also revealed that physical contact of macrophages with growing blood vessels coordinates vascular fusion of emerging sprouts. Interestingly, the interactions between macrophages and vascular endothelial cells (ECs) appear to be bidirectional, such that activated ECs also support the expansion and differentiation of proangiogenic macrophages from myeloid progenitors. Here, we discuss recent findings suggesting that dynamic angiogenic vascular niches might also exist in vivo, e.g. in tumors, where sproutingmore » blood vessels and immature myeloid cells like monocytes engage in heterotypic interactions that are required for angiogenesis. Finally, we provide an account of emerging mechanisms of cell-to-cell communication that rely on secreted microvesicles, such as exosomes, which can offer a vehicle for the rapid exchange of molecules and genetic information between macrophages and ECs engaged in angiogenesis. -- Highlights: • Macrophages promote angiogenesis by secreting proangiogenic factors. • Macrophages modulate angiogenesis via cell-to-cell contacts with endothelial cells. • Endothelial cells promote the differentiation of proangiogenic macrophages. • Macrophages and endothelial cells may cooperate to form angiogenic vascular niches.« less

Stroma provides an intestinal stem cell niche in the absence of epithelial Wnts.

PubMed

Kabiri, Zahra; Greicius, Gediminas; Madan, Babita; Biechele, Steffen; Zhong, Zhendong; Zaribafzadeh, Hamed; Edison; Aliyev, Jamal; Wu, Yonghui; Bunte, Ralph; Williams, Bart O; Rossant, Janet; Virshup, David M

2014-06-01

Wnt/β-catenin signaling supports intestinal homeostasis by regulating proliferation in the crypt. Multiple Wnts are expressed in Paneth cells as well as other intestinal epithelial and stromal cells. Ex vivo, Wnts secreted by Paneth cells can support intestinal stem cells when Wnt signaling is enhanced with supplemental R-Spondin 1 (RSPO1). However, in vivo, the source of Wnts in the stem cell niche is less clear. Genetic ablation of Porcn, an endoplasmic reticulum resident O-acyltransferase that is essential for the secretion and activity of all vertebrate Wnts, confirmed the role of intestinal epithelial Wnts in ex vivo culture. Unexpectedly, mice lacking epithelial Wnt activity (Porcn(Del)/Villin-Cre mice) had normal intestinal proliferation and differentiation, as well as successful regeneration after radiation injury, indicating that epithelial Wnts are dispensable for these processes. Consistent with a key role for stroma in the crypt niche, intestinal stromal cells endogenously expressing Wnts and Rspo3 support the growth of Porcn(Del) organoids ex vivo without RSPO1 supplementation. Conversely, increasing pharmacologic PORCN inhibition, affecting both stroma and epithelium, reduced Lgr5 intestinal stem cells, inhibited recovery from radiation injury, and at the highest dose fully blocked intestinal proliferation. We conclude that epithelial Wnts are dispensable and that stromal production of Wnts can fully support normal murine intestinal homeostasis.

Nesting of colon and ovarian cancer cells in the endothelial niche is associated with alterations in glycan and lipid metabolism.

PubMed

Halama, Anna; Guerrouahen, Bella S; Pasquier, Jennifer; Satheesh, Noothan J; Suhre, Karsten; Rafii, Arash

2017-01-04

The metabolic phenotype of a cancer cell is determined by its genetic makeup and microenvironment, which dynamically modulates the tumor landscape. The endothelial cells provide both a promoting and protective microenvironment - a niche for cancer cells. Although metabolic alterations associated with cancer and its progression have been fairly defined, there is a significant gap in our understanding of cancer metabolism in context of its microenvironment. We deployed an in vitro co-culture system based on direct contact of cancer cells with endothelial cells (E4 + EC), mimicking the tumor microenvironment. Metabolism of colon (HTC15 and HTC116) and ovarian (OVCAR3 and SKOV3) cancer cell lines was profiled with non-targeted metabolic approaches at different time points in the first 48 hours after co-culture was established. We found significant, coherent and non-cell line specific changes in fatty acids, glycerophospholipids and carbohydrates over time, induced by endothelial cell contact. The metabolic patterns pinpoint alterations in hexosamine biosynthetic pathway, glycosylation and lipid metabolism as crucial for cancer - endothelial cells interaction. We demonstrated that "Warburg effect" is not modulated in the initial stage of nesting of cancer cell in the endothelial niche. Our study provides novel insight into cancer cell metabolism in the context of the endothelial microenvironment.

Platelets prime hematopoietic and vascular niche to drive angiocrine-mediated liver regeneration.

PubMed

Shido, Koji; Chavez, Deebly; Cao, Zhongwei; Ko, Jane; Rafii, Shahin; Ding, Bi-Sen

2017-01-01

In mammals, the livers regenerate after chemical injury or resection of hepatic lobe by hepatectomy. How liver regeneration is initiated after mass loss remains to be defined. Here, we report that following liver injury, activated platelets deploy SDF-1 and VEGF-A to stimulate CXCR7 + liver sinusoidal endothelial cell (LSEC) and VEGFR1 + myeloid cell, orchestrating hepatic regeneration. After carbon tetrachloride (CCl 4 ) injection or hepatectomy, platelets and CD11b + VEGFR1 + myeloid cells were recruited LSEC, and liver regeneration in both models was impaired in thrombopoietin-deficient ( Thpo -/- ) mice lacking circulating platelets. This impeded regeneration phenotype was recapitulated in mice with either conditional ablation of Cxcr7 in LSEC ( Cxcr7 iΔ/iΔ ) or Vegfr1 in myeloid cell ( Vegfr1 lysM/lysM ). Both Vegfr1 lysM/lysM and Cxcr7 iΔ/iΔ mice exhibited suppressed expression of hepatocyte growth factor and Wnt2, two crucial trophogenic angiocrine factors instigating hepatocyte propagation. Of note, administration of recombinant thrombopoietin restored the prohibited liver regeneration in the tested genetic models. As such, our data suggest that platelets and myeloid cells jointly activate the vascular niche to produce pro-regenerative endothelial paracrine/angiocrine factors. Modulating this "hematopoietic-vascular niche" might help to develop regenerative therapy strategy for hepatic disorders.

Fine-Scale Habitat Segregation between Two Ecologically Similar Top Predators.

PubMed

Palomares, Francisco; Fernández, Néstor; Roques, Severine; Chávez, Cuauhtemoc; Silveira, Leandro; Keller, Claudia; Adrados, Begoña

2016-01-01

Similar, coexisting species often segregate along the spatial ecological axis. Here, we examine if two top predators (jaguars and pumas) present different fine-scale habitat use in areas of coexistence, and discuss if the observed pattern can be explained by the risk of interference competition between them. Interference competition theory predicts that pumas should avoid habitats or areas used by jaguars (the dominant species), and as a consequence should present more variability of niche parameters across study areas. We used non-invasive genetic sampling of faeces in 12 different areas and sensor satellite fine-scale habitat indices to answer these questions. Meta-analysis confirmed differences in fine-scale habitat use between jaguars and pumas. Furthermore, average marginality of the realized niches of pumas was more variable than those of jaguars, and tolerance (a measure of niche breadth) was on average 2.2 times higher in pumas than in jaguars, as expected under the interference competition risk hypothesis. The use of sensor satellite fine-scale habitat indices allowed the detection of subtle differences in the environmental characteristics of the habitats used by these two similar top predators, which, as a rule, until now were recorded using the same general habitat types. The detection of fine spatial segregation between these two top predators was scale-dependent.

Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

NASA Technical Reports Server (NTRS)

Baskaran, Subbiah; Noever, D.

1999-01-01

Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

Study of genetic direct search algorithms for function optimization

NASA Technical Reports Server (NTRS)

Zeigler, B. P.

1974-01-01

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

An Adaptive Immune Genetic Algorithm for Edge Detection

NASA Astrophysics Data System (ADS)

Li, Ying; Bai, Bendu; Zhang, Yanning

An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

Convergence properties of simple genetic algorithms

NASA Technical Reports Server (NTRS)

Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.

1974-01-01

The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.

A genetic algorithm approach in interface and surface structure optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

Estimation of radiative and conductive properties of a semitransparent medium using genetic algorithms

NASA Astrophysics Data System (ADS)

Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.

2016-06-01

The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.

An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail

NASA Astrophysics Data System (ADS)

Kaplan, Selçuk

2017-04-01

The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.

Application of artificial intelligence to search ground-state geometry of clusters

NASA Astrophysics Data System (ADS)

Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.

2002-08-01

We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.

Application of genetic algorithms to focal mechanism determination

NASA Astrophysics Data System (ADS)

Kobayashi, Reiji; Nakanishi, Ichiro

1994-04-01

Genetic algorithms are a new class of methods for global optimization. They resemble Monte Carlo techniques, but search for solutions more efficiently than uniform Monte Carlo sampling. In the field of geophysics, genetic algorithms have recently been used to solve some non-linear inverse problems (e.g., earthquake location, waveform inversion, migration velocity estimation). We present an application of genetic algorithms to focal mechanism determination from first-motion polarities of P-waves and apply our method to two recent large events, the Kushiro-oki earthquake of January 15, 1993 and the SW Hokkaido (Japan Sea) earthquake of July 12, 1993. Initial solution and curvature information of the objective function that gradient methods need are not required in our approach. Moreover globally optimal solutions can be efficiently obtained. Calculation of polarities based on double-couple models is the most time-consuming part of the source mechanism determination. The amount of calculations required by the method designed in this study is much less than that of previous grid search methods.

Optimized design on condensing tubes high-speed TIG welding technology magnetic control based on genetic algorithm

NASA Astrophysics Data System (ADS)

Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming

2013-05-01

An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.

Testing the link between population genetic differentiation and clade diversification in Costa Rican orchids.

PubMed

Kisel, Yael; Moreno-Letelier, Alejandra C; Bogarín, Diego; Powell, Martyn P; Chase, Mark W; Barraclough, Timothy G

2012-10-01

Species population genetics could be an important factor explaining variation in clade species richness. Here, we use newly generated amplified fragment length polymorphism (AFLP) data to test whether five pairs of sister clades of Costa Rican orchids that differ greatly in species richness also differ in average neutral genetic differentiation within species, expecting that if the strength of processes promoting differentiation within species is phylogenetically heritable, then clades with greater genetic differentiation should diversify more. Contrary to expectation, neutral genetic differentiation does not correlate directly with total diversification in the clades studied. Neutral genetic differentiation varies greatly among species and shows no heritability within clades. Half of the variation in neutral genetic differentiation among populations can be explained by ecological variables, and species-level traits explain the most variation. Unexpectedly, we find no isolation by distance in any species, but genetic differentiation is greater between populations occupying different niches. This pattern corresponds with those observed for microscopic eukaryotes and could reflect effective widespread dispersal of tiny and numerous orchid seeds. Although not providing a definitive answer to whether population genetics processes affect clade diversification, this work highlights the potential for addressing new macroevolutionary questions using a comparative population genetic approach. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.

Optimal sensor placement for spatial lattice structure based on genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian

2008-10-01

Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Bacteriocins from Lactobacillus plantarum – production, genetic organization and mode of action

PubMed Central

Todorov, Svetoslav D.

2009-01-01

Bacteriocins are biologically active proteins or protein complexes that display a bactericidal mode of action towards usually closely related species. Numerous strains of bacteriocin producing Lactobacillus plantarum have been isolated in the last two decades from different ecological niches including meat, fish, fruits, vegetables, and milk and cereal products. Several of these plantaricins have been characterized and the aminoacid sequence determined. Different aspects of the mode of action, fermentation optimization and genetic organization of the bacteriocin operon have been studied. However, numerous of bacteriocins produced by different Lactobacillus plantarum strains have not been fully characterized. In this article, a brief overview of the classification, genetics, characterization, including mode of action and production optimization for bacteriocins from Lactic Acid Bacteria in general, and where appropriate, with focus on bacteriocins produced by Lactobacillus plantarum, is presented. PMID:24031346

What underlies the diversity of brain tumors?

PubMed Central

Swartling, Fredrik J.; Hede, Sanna-Maria; Weiss, William A.

2012-01-01

Glioma and medulloblastoma represent the most commonly occurring malignant brain tumors in adults and in children respectively. Recent genomic and transcriptional approaches present a complex group of diseases, and delineate a number of molecular subgroups within tumors that share a common histopathology. Differences in cells of origin, regional niches, developmental timing and genetic events all contribute to this heterogeneity. In an attempt to recapitulate the diversity of brain tumors, an increasing array of genetically engineered mouse models (GEMMs) has been developed. These models often utilize promoters and genetic drivers from normal brain development, and can provide insight into specific cells from which these tumors originate. GEMMs show promise in both developmental biology and developmental therapeutics. This review describes numerous murine brain tumor models in the context of normal brain development, and the potential for these animals to impact brain tumor research. PMID:23085857

Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: implications for evolutionary history and conservation

Treesearch

Douglas J. Shinneman; Robert E. Means; Kevin M. Potter; Valerie D. Hipkins; Tzen-Yuh Chiang

2016-01-01

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique...

Prioritizing the Components of Vulnerability: A Genetic Algorithm Minimization of Flood Risk

NASA Astrophysics Data System (ADS)

Bongolan, Vena Pearl; Ballesteros, Florencio; Baritua, Karessa Alexandra; Junne Santos, Marie

2013-04-01

We define a flood resistant city as an optimal arrangement of communities according to their traits, with the goal of minimizing the flooding vulnerability via a genetic algorithm. We prioritize the different components of flooding vulnerability, giving each component a weight, thus expressing vulnerability as a weighted sum. This serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/ tv penetration. The designs produced reflect the relative importance of the components, and we observed a synchronicity between the interacting components, giving us a more consistent design.

Algorithmic Trading with Developmental and Linear Genetic Programming

NASA Astrophysics Data System (ADS)

Wilson, Garnett; Banzhaf, Wolfgang

A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

Supervised chaos genetic algorithm based state of charge determination for LiFePO4 batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Shen, Yanqing

2018-04-01

LiFePO4 battery is developed rapidly in electric vehicle, whose safety and functional capabilities are influenced greatly by the evaluation of available cell capacity. Added with adaptive switch mechanism, this paper advances a supervised chaos genetic algorithm based state of charge determination method, where a combined state space model is employed to simulate battery dynamics. The method is validated by the experiment data collected from battery test system. Results indicate that the supervised chaos genetic algorithm based state of charge determination method shows great performance with less computation complexity and is little influenced by the unknown initial cell state.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hudec, Ján; Gramatová, Elena

2015-07-01

The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Fast optimization of glide vehicle reentry trajectory based on genetic algorithm

NASA Astrophysics Data System (ADS)

Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei

2018-02-01

An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.

On Directly Solving SCHRÖDINGER Equation for H+2 Ion by Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saha, Rajendra; Bhattacharyya, S. P.

Schrödinger equation (SE) is sought to be solved directly for the ground state of H+2 ion by invoking genetic algorithm (GA). In one approach the internuclear distance (R) is kept fixed, the corresponding electronic SE for H+2 is solved by GA at each R and the full potential energy curve (PEC) is constructed. The minimum of the PEC is then located giving Ve and Re. Alternatively, Ve and Re are located in a single run by allowing R to vary simultaneously while solving the electronic SE by genetic algorithm. The performance patterns of the two strategies are compared.

Applying a Genetic Algorithm to Reconfigurable Hardware

NASA Technical Reports Server (NTRS)

Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

2004-01-01

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...

Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman

2012-01-01

In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.

Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

PubMed

Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

2018-06-25

The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

Optimization of beam orientation in radiotherapy using planar geometry

NASA Astrophysics Data System (ADS)

Haas, O. C. L.; Burnham, K. J.; Mills, J. A.

1998-08-01

This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali

2013-04-01

The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping

NASA Astrophysics Data System (ADS)

Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius

2018-02-01

Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.