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Sample records for adaptive landscape classification

  1. Adaptive Classification of Landscape Process and Function: An Integration of Geoinformatics and Self-Organizing Maps

    SciTech Connect

    Coleman, Andre M.

    2009-07-17

    The advanced geospatial information extraction and analysis capabilities of a Geographic Information System (GISs) and Artificial Neural Networks (ANNs), particularly Self-Organizing Maps (SOMs), provide a topology-preserving means for reducing and understanding complex data relationships in the landscape. The Adaptive Landscape Classification Procedure (ALCP) is presented as an adaptive and evolutionary capability where varying types of data can be assimilated to address different management needs such as hydrologic response, erosion potential, habitat structure, instrumentation placement, and various forecast or what-if scenarios. This paper defines how the evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Establishing relationships among high-dimensional datasets through neurocomputing based pattern recognition methods can help 1) resolve large volumes of data into a structured and meaningful form; 2) provide an approach for inferring landscape processes in areas that have limited data available but exhibit similar landscape characteristics; and 3) discover the value of individual variables or groups of variables that contribute to specific processes in the landscape. Classification of hydrologic patterns in the landscape is demonstrated.

  2. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    SciTech Connect

    Coleman, Andre Michael

    2008-06-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  3. [Landscape classification: research progress and development trend].

    PubMed

    Liang, Fa-Chao; Liu, Li-Ming

    2011-06-01

    Landscape classification is the basis of the researches on landscape structure, process, and function, and also, the prerequisite for landscape evaluation, planning, protection, and management, directly affecting the precision and practicability of landscape research. This paper reviewed the research progress on the landscape classification system, theory, and methodology, and summarized the key problems and deficiencies of current researches. Some major landscape classification systems, e. g. , LANMAP and MUFIC, were introduced and discussed. It was suggested that a qualitative and quantitative comprehensive classification based on the ideology of functional structure shape and on the integral consideration of landscape classification utility, landscape function, landscape structure, physiogeographical factors, and human disturbance intensity should be the major research directions in the future. The integration of mapping, 3S technology, quantitative mathematics modeling, computer artificial intelligence, and professional knowledge to enhance the precision of landscape classification would be the key issues and the development trend in the researches of landscape classification.

  4. From landscape to domain: Soils role in landscape classifications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil landscape classifications are designed to divide landscapes into units with significance for the provisioning and regulating of ecosystem services and the development of conservation plans for natural resources. More specifically, such classifications serve as the basis for stratifying manageme...

  5. [Wetland landscape ecological classification: research progress].

    PubMed

    Cao, Yu; Mo, Li-jiang; Li, Yan; Zhang, Wen-mei

    2009-12-01

    Wetland landscape ecological classification, as a basis for the studies of wetland landscape ecology, directly affects the precision and effectiveness of wetland-related research. Based on the history, current status, and latest progress in the studies on the theories, indicators, and methods of wetland landscape classification, some scientific wetland classification systems, e.g., NWI, Ramsar, and HGM, were introduced and discussed in this paper. It was suggested that a comprehensive classification method based on HGM and on the integral consideration of wetlands spatial structure, ecological function, ecological process, topography, soil, vegetation, hydrology, and human disturbance intensity should be the major future direction in this research field. Furthermore, the integration of 3S technologies, quantitative mathematics, landscape modeling, knowledge engineering, and artificial intelligence to enhance the automatization and precision of wetland landscape ecological classification would be the key issues and difficult topics in the studies of wetland landscape ecological classification.

  6. Oregon Hydrologic Landscapes: A Classification Framework

    EPA Science Inventory

    There is a growing need for hydrologic classification systems that can provide a basis for broad-scale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors such as climate change. We developed a hydrologic landscape (HL) classifica...

  7. Hydrologic Landscape Classification to Estimate Bristol Bay Watershed Hydrology

    EPA Science Inventory

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on cl...

  8. Global view of bionetwork dynamics: adaptive landscape.

    PubMed

    Ao, Ping

    2009-02-01

    Based on recent work, I will give a nontechnical brief review of a powerful quantitative concept in biology, adaptive landscape, initially proposed by S. Wright over 70 years ago, reintroduced by one of the founders of molecular biology and by others in different biological contexts, but apparently forgotten by modern biologists for many years. Nevertheless, this concept finds an increasingly important role in the development of systems biology and bionetwork dynamics modeling, from phage lambda genetic switch to endogenous network for cancer genesis and progression. It is an ideal quantification to describe the robustness and stability of bionetworks. Here, I will first introduce five landmark proposals in biology on this concept, to demonstrate an important common thread in theoretical biology. Then I will discuss a few recent results, focusing on the studies showing theoretical consistency of adaptive landscape. From the perspective of a working scientist and of what is needed logically for a dynamical theory when confronting empirical data, the adaptive landscape is useful both metaphorically and quantitatively, and has captured an essential aspect of biological dynamical processes. Though at the theoretical level the adaptive landscape must exist and it can be used across hierarchical boundaries in biology, many associated issues are indeed vague in their initial formulations and their quantitative realizations are not easy, and are good research topics for quantitative biologists. I will discuss three types of open problems associated with the adaptive landscape in a broader perspective.

  9. Defining the landscape of adaptive genetic diversity.

    PubMed

    Eckert, Andrew J; Dyer, Rodney J

    2012-06-01

    Whether they are used to describe fitness, genome architecture or the spatial distribution of environmental variables, the concept of a landscape has figured prominently in our collective reasoning. The tradition of landscapes in evolutionary biology is one of fitness mapped onto axes defined by phenotypes or molecular sequence states. The characteristics of these landscapes depend on natural selection, which is structured across both genomic and environmental landscapes, and thus, the bridge among differing uses of the landscape concept (i.e. metaphorically or literally) is that of an adaptive phenotype and its distribution across geographical landscapes in relation to selective pressures. One of the ultimate goals of evolutionary biology should thus be to construct fitness landscapes in geographical space. Natural plant populations are ideal systems with which to explore the feasibility of attaining this goal, because much is known about the quantitative genetic architecture of complex traits for many different plant species. What is less known are the molecular components of this architecture. In this issue of Molecular Ecology, Parchman et al. (2012) pioneer one of the first truly genome-wide association studies in a tree that moves us closer to this form of mechanistic understanding for an adaptive phenotype in natural populations of lodgepole pine (Pinus contorta Dougl. ex Loud.). PMID:22676074

  10. ASSESSMENT OF LANDSCAPE CHARACTERISTICS ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of misclassifying pixels during thematic image classification. However, there has been a lack of empirical evidence, to support these hypotheses. This...

  11. Hydrologic classification of Bristol Bay, Alaska using hydrologic landscapes

    NASA Astrophysics Data System (ADS)

    Todd, J.; Wigington, P. J.; Sproles, E.

    2013-12-01

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on climate, terrain and underlying geology. Such characterization of landscapes into areas of common hydrologic patterning is particularly instructive in regions where site specific hydrologic data is sparse or spatially incomplete. By using broad scale landscape metrics to organize the landscape into discrete, characterized units, natural resources managers can gain valuable understanding of landscape patterning and how locations may be differentially affected by a variety of environmental stressors ranging from land use change to management of salmon resources to climate change. Further, the heterogeneity of aquatic habitats and undisturbed hydrologic regimes within this area are a known principal driver for its region-wide fisheries stability. The use of hydrologic landscapes offers an opportunity to better characterize the hydrologic and landscape influences on structuring biotic populations at a regional scale. We have undertaken a hydrologic landscape approach for the Bristol Bay region of Alaska to gain a better understanding of the overall hydrologic environment found in this region since its hydrologic patterning plays a principal role in structuring its world-renowned salmon fishery. Heretofore, a characterization of the entire Bristol Bay region into discrete hydrologic units has not been undertaken. Our classification structure includes indices of annual climate and seasonality, terrain, and geology. Following categorization of landscape units, we compared hydrologic landscape units to locations of available long term streamflow for characterization of expected hydrologic behavior. This demonstration of hydrologic landscapes in Bristol Bay, Alaska shows the utility of using large

  12. Hydrologic Classification of Bristol Bay, Alaska Using Hydrologic Landscapes

    NASA Astrophysics Data System (ADS)

    Todd, J.; Wigington, P. J., Jr.; Sproles, E. A.

    2014-12-01

    The use of hydrologic landscapes has proven to be a useful tool for broad scale assessment and classification of landscapes across the United States. These classification systems help organize larger geographical areas into areas of similar hydrologic characteristics based on climate, terrain and underlying geology. Such characterization of landscapes into areas of common hydrologic patterning is particularly instructive where site specific hydrologic data is sparse or spatially incomplete. By using broad scale landscape metrics to organize the landscape into discrete, characterized units, natural resources managers can gain valuable understanding of landscape patterning and how locations may be differentially affected by a variety of environmental stressors ranging from land use change to climate change. The heterogeneity of aquatic habitats and undisturbed hydrologic regimes within Bristol Bay are a known principal driver for its overall fisheries stability and the use of hydrologic landscapes offers the ability to better characterize the hydrologic and landscape influences on structuring biotic populations at a regional scale. Here we classify the entire Bristol Bay region into discrete hydrologic landscape units based on indices of annual climate and seasonality, terrain, and geology. We then compared hydrologic landscape units to locations of available long term streamflow for characterization of expected hydrologic behavior where streamflow data was lacking. This demonstration of hydrologic landscapes in Bristol Bay, Alaska shows the utility of using large-scale datasets on climate, terrain and geology to infer broad scale hydrologic patterning within a data poor area. Disclaimer: The authors' views expressed here do not necessarily reflect views or policies of USEPA.

  13. The sensory ecology of adaptive landscapes

    PubMed Central

    Jordan, Lyndon A.; Ryan, Michael J.

    2015-01-01

    In complex environments, behavioural plasticity depends on the ability of an animal to integrate numerous sensory stimuli. The multidimensionality of factors interacting to shape plastic behaviour means it is difficult for both organisms and researchers to predict what constitutes an adaptive response to a given set of conditions. Although researchers may be able to map the fitness pay-offs of different behavioural strategies in changing environments, there is no guarantee that the study species will be able to perceive these pay-offs. We thus risk a disconnect between our own predictions about adaptive behaviour and what is behaviourally achievable given the umwelt of the animal being studied. This may lead to erroneous conclusions about maladaptive behaviour in circumstances when the behaviour exhibited is the most adaptive possible given sensory limitations. With advances in the computational resources available to behavioural ecologists, we can now measure vast numbers of interactions among behaviours and environments to create adaptive behavioural surfaces. These surfaces have massive heuristic, predictive and analytical potential in understanding adaptive animal behaviour, but researchers using them are destined to fail if they ignore the sensory ecology of the species they study. Here, we advocate the continued use of these approaches while directly linking them to perceptual space to ensure that the topology of the generated adaptive landscape matches the perceptual reality of the animal it intends to study. Doing so will allow predictive models of animal behaviour to reflect the reality faced by the agents on adaptive surfaces, vastly improving our ability to determine what constitutes an adaptive response for the animal in question. PMID:26018831

  14. Seri Landscape Classification and Spatial Reference

    ERIC Educational Resources Information Center

    O'Meara, Carolyn

    2010-01-01

    This thesis contributes to the growing field of ethnophysiography, a new subfield of cognitive anthropology that aims to determine the universals and variation in the categorization of landscape objects across cultures. More specifically, this work looks at the case of the Seri people of Sonora, Mexico to investigate the way they categorize…

  15. Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests

    PubMed Central

    Brown, Stuart C.; Lester, Rebecca E.; Versace, Vincent L.; Fawcett, Jonathon; Laurenson, Laurie

    2014-01-01

    Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of

  16. Hydrologic landscape regionalisation using deductive classification and random forests.

    PubMed

    Brown, Stuart C; Lester, Rebecca E; Versace, Vincent L; Fawcett, Jonathon; Laurenson, Laurie

    2014-01-01

    Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of

  17. Adaptive multiclass classification for brain computer interfaces.

    PubMed

    Llera, A; Gómez, V; Kappen, H J

    2014-06-01

    We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, & Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.

  18. Adaptive, template moderated, spatially varying statistical classification.

    PubMed

    Warfield, S K; Kaus, M; Jolesz, F A; Kikinis, R

    2000-03-01

    A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy from medical images. The new algorithm is a form of spatially varying statistical classification, in which an explicit anatomical template is used to moderate the segmentation obtained by statistical classification. The algorithm consists of an iterated sequence of spatially varying classification and nonlinear registration, which forms an adaptive, template moderated (ATM), spatially varying statistical classification (SVC). Classification methods and nonlinear registration methods are often complementary, both in the tasks where they succeed and in the tasks where they fail. By integrating these approaches the new algorithm avoids many of the disadvantages of each approach alone while exploiting the combination. The ATM SVC algorithm was applied to several segmentation problems, involving different image contrast mechanisms and different locations in the body. Segmentation and validation experiments were carried out for problems involving the quantification of normal anatomy (MRI of brains of neonates) and pathology of various types (MRI of patients with multiple sclerosis, MRI of patients with brain tumors, MRI of patients with damaged knee cartilage). In each case, the ATM SVC algorithm provided a better segmentation than statistical classification or elastic matching alone. PMID:10972320

  19. Adaptation and extinction in experimentally fragmented landscapes.

    PubMed

    Fakheran, Sima; Paul-Victor, Cloé; Heichinger, Christian; Schmid, Bernhard; Grossniklaus, Ueli; Turnbull, Lindsay A

    2010-11-01

    Competition and disturbance are potent ecological forces that shape evolutionary trajectories. These forces typically work in opposition: when disturbance is infrequent, densities are high and competition is intense. In contrast, frequent disturbance creates a low-density environment in which competition is weak and good dispersal essential. We exploited recent advances in genomic research to quantify the response to selection by these powerful ecological forces at the phenotypic and molecular genetic level in experimental landscapes. We grew the annual plant Arabidopsis thaliana in discrete patches embedded in a hostile matrix and varied the number and size of patches and the intensity of disturbance, by creating both static and dynamic landscapes. In static landscapes all patches were undisturbed, whereas in dynamic landscapes all patches were destroyed in each generation, forcing seeds to disperse to new locations. We measured the resulting changes in phenotypic, genetic, and genotypic diversity after five generations of selection. Simulations revealed that the observed loss of genetic diversity dwarfed that expected under drift, with dramatic diversity loss, particularly from dynamic landscapes. In line with ecological theory, static landscapes favored good competitors; however, competitive ability was linked to growth rate and not, as expected, to seed mass. In dynamic landscapes, there was strong selection for increased dispersal ability in the form of increased inflorescence height and reduced seed mass. The most competitive genotypes were almost eliminated from highly disturbed landscapes, raising concern over the impact of increased levels of human-induced disturbance in natural landscapes.

  20. Adaptive landscapes: Top-down and bottom-up perspectives

    NASA Astrophysics Data System (ADS)

    Kerr, Benjamin

    Sewall Wright introduced the metaphor of the adaptive landscape, a map from genotype to fitness, more than 80 years ago to help describe his view of adaptive evolution. This metaphor has been immensely popular and has been used in a variety of incarnations. However, a systematic study of the genotype-fitness map presents significant problems. The space of possible genotypes is vast, and the mapping is likely dependent on both environment and the composition of genotypes in a population. In this talk, I will discuss some of these problems and present experimental strategies for uncovering features of adaptive landscapes. In particular, I will discuss how population structure can be used as an experimental variable to elucidate landscape topography and how a combination of experimental evolution and genetic engineering can reveal important landscape features in changing environments. I will also present some potential applications of this work to the problem of antibiotic resistance and potential implications for evolutionary rescue in the face of global climate change. For some of these topics, the classic notion of the adaptive landscape must itself be adapted; however, I propose that there are fruitful ways to continue to apply this metaphor.

  1. Dynamic LiDAR-NDVI classification of fluvial landscape units

    NASA Astrophysics Data System (ADS)

    Ramírez-Núñez, Carolina; Parrot, Jean-François

    2015-04-01

    The lower basin of the Coatzacoalcos River is a wide floodplain in which, during the wet season, local and major flooding are distinguished. Both types of floods, intermittent and regional, are important in terms of resources; the regional flood sediments enrich the soils of the plains and intermittent floods allow obtaining aquatic resources for subsistence during the heatwave. In the floodplain different abandoned meanders and intermittent streams are quickly colonized by aquatic vegetation. However, from the 1990s, the Coatzacoalcos River floodplain has important topographic changes due to mining, road and bridges construction; erosion and sedimentation requires continuous parcel boundaries along with the increasing demand of channel reparation, embankments, levees and bridges associated to tributaries. NDVI data, LiDAR point cloud and various types of flood simulations taking into account the DTM are used to classify the dynamic landscape units. These units are associated to floods in relation with water resources, agriculture and livestock. In the study area, the first returns of the point cloud allow extracting vegetation strata. The last returns correspond to the bare earth surface, especially in this area with few human settlements. The surface that is not covered by trees or by aquatic vegetation, correspond to crops, pastures and bare soils. The classification is obtained by using the NDVI index coupled with vegetation strata and water bodies. The result shows that 47.96% of the area does not present active vegetation and it includes 31.53% of bare soils. Concerning the active vegetation, pastures, bushes and trees represent respectively 25.59%, 11.14% and 13.25%. The remaining 1.25% is distributed between water bodies with aquatic vegetation, trees and shrubs. Dynamic landscape units' classification represents a tool for monitoring water resources in a fluvial plain. This approach can be also applied to forest management, environmental services and

  2. Towards a general theory of adaptive walks on rugged landscapes.

    PubMed

    Kauffman, S; Levin, S

    1987-09-01

    Adaptive evolution, to a large extent, is a complex combinatorial optimization process. In this article we take beginning steps towards developing a general theory of adaptive "walks" via fitter variants in such optimization processes. We introduce the basic idea of a space of entities, each a 1-mutant neighbor of many other entities in the space, and the idea of a fitness ascribed to each entity. Adaptive walks proceed from an initial entity, via fitter neighbors, to locally or globally optimal entities that are fitter than their neighbors. We develop a general theory for the number of local optima, lengths of adaptive walks, and the number of alternative local optima accessible from any given initial entity, for the baseline case of an uncorrelated fitness landscape. Most fitness landscapes are correlated, however. Therefore we develop parts of a universal theory of adaptation on correlated landscapes by adaptive processes that have sufficient numbers of mutations per individual to "jump beyond" the correlation lengths in the underlying landscape. In addition, we explore the statistical character of adaptive walks in two independent complex combinatorial optimization problems, that of evolving a specific cell type in model genetic networks, and that of finding good solutions to the traveling salesman problem. Surprisingly, both show similar statistical features, encouraging the hope that a general theory for adaptive walks on correlated and uncorrelated landscapes can be found. In the final section we explore two limits to the efficacy of selection. The first is new, and surprising: for a wide class of systems, as the complexity of the entities under selection increases, the local optima that are attainable fall progressively closer to the mean properties of the underlying space of entities. This may imply that complex biological systems, such as genetic regulatory systems, are "close" to the mean properties of the ensemble of genomic regulatory systems explored

  3. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes.

    PubMed

    Forester, Brenna R; Jones, Matthew R; Joost, Stéphane; Landguth, Erin L; Lasky, Jesse R

    2016-01-01

    The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

  4. Classification of shallow groundwater types in a Dutch coversand landscape

    NASA Astrophysics Data System (ADS)

    Pedroli, Bas

    1990-07-01

    The numerical classification of shallow groundwater samples is described on the basis of their chemical composition. Seven times in the course of one year, water was sampled from 50 piezometric tubes installed in a sandy lowland area of 20 km 2, in use as a nature reserve and farming land. Water-table depth varies between 0 and 5 m below soil surface in the study area. A total of 416 samples was analysed. Cluster analysis and subsequent discriminant analysis were applied to the log-transformed concentrations. Two subsets were considered, one with samples of pH < 5, the other with samples of pH > 5. In both cases the 8-cluster solution of a maximum-likelihood clustering method appeared to be stable. The characteristics of the resulting 16 water types were readily interpretable with reference to landscape setting and temporal variation. The spatial distribution of the water types is presented on a map. The chemical composition of groundwater proves to be relatively constant over a large part of the area, owing to high inputs of mineralization products or fertilizers in the recharge areas. Variation is largest around the relatively small seepage area, in zones where infiltrating water interferes with discharging deep groundwater, comparable to Van Wirdum's poikilotrophic zones. Key factors for statistical classification of groundwater types differ depending on the nature of the water types considered. In this study, Mg appeared to discriminate better than Ca between the water types in both subsets. pH discriminates only in the near-neutral subset and the anions in the acid subset.

  5. IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy.
    Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

  6. Greedy adaptive walks on a correlated fitness landscape.

    PubMed

    Park, Su-Chan; Neidhart, Johannes; Krug, Joachim

    2016-05-21

    We study adaptation of a haploid asexual population on a fitness landscape defined over binary genotype sequences of length L. We consider greedy adaptive walks in which the population moves to the fittest among all single mutant neighbors of the current genotype until a local fitness maximum is reached. The landscape is of the rough mount Fuji type, which means that the fitness value assigned to a sequence is the sum of a random and a deterministic component. The random components are independent and identically distributed random variables, and the deterministic component varies linearly with the distance to a reference sequence. The deterministic fitness gradient c is a parameter that interpolates between the limits of an uncorrelated random landscape (c=0) and an effectively additive landscape (c→∞). When the random fitness component is chosen from the Gumbel distribution, explicit expressions for the distribution of the number of steps taken by the greedy walk are obtained, and it is shown that the walk length varies non-monotonically with the strength of the fitness gradient when the starting point is sufficiently close to the reference sequence. Asymptotic results for general distributions of the random fitness component are obtained using extreme value theory, and it is found that the walk length attains a non-trivial limit for L→∞, different from its values for c=0 and c=∞, if c is scaled with L in an appropriate combination.

  7. Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

    PubMed

    Mira, Portia M; Meza, Juan C; Nandipati, Anna; Barlow, Miriam

    2015-10-01

    Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM β-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 β-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of β-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of β-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations.

  8. Hydrologic landscape units and adaptive management of intermountain wetlands

    USGS Publications Warehouse

    Custer, Stephen G.; Sojda, R.S.

    2006-01-01

    daptive management is often proposed to assist in the management of national wildlife refuges and allows the exploration of alternatives as well as the addition of ne w knowledge as it becomes available. The hydrological landscape unit can be a good foundation for such efforts. Red Rock Lakes National Wildlife Refuge (NWR) is in an intermountain basin dominated by vertical tectonics in the Northern Rocky Mountains. A geographic information system was used to define the boundaries for the hydrologic landscape units there. Units identified include alluvial fan, interfan, stream alluvi um and basin flat. Management alternatives can be informed by ex amination of processes that occu r on the units. For example, an ancient alluvial fan unit related to Red Rock Creek appear s to be isolated from stream flow today, with recharge dominated by precipitation and bedrock springs; while other alluvial fan units in the area have shallow ground water recharged from mountain streams and precipitation. The scale of hydrologic processes in interfan units differs from that in alluvial fan hydrologic landscape units. These differences are important when the refuge is evaluating habitat management activities. Hydrologic landscape units provide scientific unde rpinnings for the refuge’s comprehensive planning process. New geologic, hydrologic, and biologic knowledge can be integrated into the hydrologic landscape unit definition and improve adaptive management.

  9. Plasticity, memory and the adaptive landscape of the genotype

    PubMed Central

    l, C. P

    1998-01-01

    The adaptive value of epigenetic inheritance systems is investigated in a simple mathematical framework. These systems enable the environmentally induced phenotypes to be transmitted between generations. The frequencies of the different epigenetic variants are determined by the plasticity and the efficiency of transmission (called memory). Plasticity and memory are genetically determined. This paper studies the evolution of a quantitative character, its plasticity and memory, on the adaptive landscape. Due to the dual inheritance of the character, selection acts on two levels: on the phenotypes of the same genotype, and on the different genotypes. Plasticity generates the raw material, and memory increases the strength of phenotypic selection. If the character is far from the peak of the landscape, then dual inheritance of the character can be advantageous for the genotype. Near the peak it is more favourable to suppress phenotypic variation. This would lead to genetic assimilation.

  10. Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

    EPA Science Inventory

    Classification can allow assessments of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to assess vulnerability to potential future climate change at statewide and basin scales. The HL ...

  11. Adaptation in protein fitness landscapes is facilitated by indirect paths

    PubMed Central

    Wu, Nicholas C; Dai, Lei; Olson, C Anders; Lloyd-Smith, James O; Sun, Ren

    2016-01-01

    The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20L) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 204 = 160,000 variants. We found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve. DOI: http://dx.doi.org/10.7554/eLife.16965.001 PMID:27391790

  12. Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

    PubMed Central

    Moran, Emilio Federico.

    2010-01-01

    High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

  13. Use of hydrologic landscape classification to diagnose streamflow predictability in Oregon

    EPA Science Inventory

    We implement a spatially lumped rainfall-runoff model to predict daily streamflow at 88 catchments within Oregon, USA and analyze its performance within the context of Oregon Hydrologic Landscapes (OHL) classification. OHL classification is used to characterize the physio-climat...

  14. Prospects for hydrologic classification of landscapes and watersheds

    EPA Science Inventory

    The ecological functions of streams and associated riparian zones are strongly influenced by the hydrological attributes of watersheds and landscapes in which they occur. Oregon hydrologic landscape regions (HLRs) have been defined based on four types of GIS data: 1) climate, 2) ...

  15. Mapping agricultural landscapes and characterizing adaptive capacity in Central America

    NASA Astrophysics Data System (ADS)

    Holland, M. B.; Imbach, P. A.; Bouroncle, C.; Donatti, C.; Leguia, E.; Martinez, M.; Medellin, C.; Saborio-Rodriguez, M.; Shamer, S.; Zamora, J.

    2013-12-01

    One of the key challenges in developing adaptation strategies for smallholder farmers in developing countries is that of a data-poor environment, where spatially-explicit information about where the most vulnerable smallholder communities are located is lacking. Developing countries tend to lack consistent and reliable maps on agricultural land use, and have limited information available on smallholder adaptive capacity. We developed a novel participatory and expert mapping process to overcome these barriers and develop detailed national-scale maps that allow for a characterization of unique agricultural landscapes based on profiles of adaptive capacity for smallholder agriculture in each area. This research focuses specifically on the Central American nations of Costa Rica, Guatemala, and Honduras, where our focus is on coffee and basic grains as the two main cropping systems. Here we present the methodology and results of a series of in-depth interviews and participatory mapping sessions with experts working within the broader agricultural sector in each country. We held individual interviews and mapping sessions with approximately thirty experts from each country, and used a detailed survey instrument for each mapping session to both spatially identify distinct agricultural landscapes, and to further characterize each area based on specific farm practices and social context. The survey also included a series of questions to help us assess the relative adaptive capacity of smallholder agriculture within each landscape. After all expert mapping sessions were completed in each country we convened an expert group to assist in both validating and refining the set of landscapes already defined. We developed a characterization of adaptive capacity by aggregating indicators into main assets-based criteria (e.g. land tenure, access to credit, access to technical assistance, sustainable farm practices) derived from further expert weighting of indicators through an online

  16. Fitness of multidimensional phenotypes in dynamic adaptive landscapes.

    PubMed

    Laughlin, Daniel C; Messier, Julie

    2015-08-01

    Phenotypic traits influence species distributions, but ecology lacks established links between multidimensional phenotypes and fitness for predicting species responses to environmental change. The common focus on single traits rather than multiple trait combinations limits our understanding of their adaptive value, and intraspecific trait covariation has been neglected in ecology despite its importance in evolutionary theory and its likely impact on species distributions. Here, we extend the adaptive landscape framework to ecological sorting of multidimensional phenotypes across environments and discuss how two analytical approaches can be used to quantify fitness as a function of the interaction between the phenotype and the environment. We encourage ecologists to consider how phenotypic integration will constrain species responses to environmental change.

  17. Host coevolution alters the adaptive landscape of a virus

    PubMed Central

    2016-01-01

    The origin of new and complex structures and functions is fundamental for shaping the diversity of life. Such key innovations are rare because they require multiple interacting changes. We sought to understand how the adaptive landscape led to an innovation whereby bacteriophage λ evolved the new ability to exploit a receptor, OmpF, on Escherichia coli cells. Previous work showed that this ability evolved repeatedly, despite requiring four mutations in one virus gene. Here, we examine how this innovation evolved by studying six intermediate genotypes of λ isolated during independent transitions to exploit OmpF and comparing them to their ancestor. All six intermediates showed large increases in their adsorption rates on the ancestral host. Improvements in adsorption were offset, in large part, by the evolution of host resistance, which occurred by reduced expression of LamB, the usual receptor for λ. As a consequence of host coevolution, the adaptive landscape of the virus changed such that selection favouring four of the six virus intermediates became stronger after the host evolved resistance, thereby accelerating virus populations along the path to using the new OmpF receptor. This dependency of viral fitness on host genotype thus shows an important role for coevolution in the origin of the new viral function. PMID:27683370

  18. Classification of EEG for Affect Recognition: An Adaptive Approach

    NASA Astrophysics Data System (ADS)

    Alzoubi, Omar; Calvo, Rafael A.; Stevens, Ronald H.

    Research on affective computing is growing rapidly and new applications are being developed more frequently. They use information about the affective/mental states of users to adapt their interfaces or add new functionalities. Face activity, voice, text physiology and other information about the user are used as input to affect recognition modules, which are built as classification algorithms. Brain EEG signals have rarely been used to build such classifiers due to the lack of a clear theoretical framework. We present here an evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment. Our results show that affect recognition from EEG signals might be possible and an adaptive algorithm improves the performance of the classification task.

  19. [Application of SPOT 4 remote sensing imagine in classification of Qira oasis landscape in China].

    PubMed

    Wang, Xizhi; Wang, Gang; Bruelheide, Helge; Runge, Michael

    2002-09-01

    The landscape surounding Qira osais at the southern fringe of the Taklamakan Desert in China was analyzed by using the SPOT 4 multispectrum remote sensing data and GPS (global positioning system) data. The SPOT 4 scene was projected into an UTM grid, and a supervised classification by ERDAS IMAGINE software was applied. In total, 13 landscape units could be distinguished, and a "frame of reference" was set up for establishing monitoring system of landscape patterns dynamics. Limitations, possible improvements and further applications of the approach were discussed.

  20. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification.

  1. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. PMID:26055435

  2. Adaptive classification on brain-computer interfaces using reinforcement signals.

    PubMed

    Llera, A; Gómez, V; Kappen, H J

    2012-11-01

    We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.

  3. Length of adaptive walk on uncorrelated and correlated fitness landscapes.

    PubMed

    Seetharaman, Sarada; Jain, Kavita

    2014-09-01

    We consider the adaptation dynamics of an asexual population that walks uphill on a rugged fitness landscape which is endowed with a large number of local fitness peaks. We work in a parameter regime where only those mutants that are a single mutation away are accessible, as a result of which the population eventually gets trapped at a local fitness maximum and the adaptive walk terminates. We study how the number of adaptive steps taken by the population before reaching a local fitness peak depends on the initial fitness of the population, the extreme value distribution of the beneficial mutations, and correlations among the fitnesses. Assuming that the relative fitness difference between successive steps is small, we analytically calculate the average walk length for both uncorrelated and correlated fitnesses in all extreme value domains for a given initial fitness. We present numerical results for the model where the fitness differences can be large and find that the walk length behavior differs from that in the former model in the Fréchet domain of extreme value theory. We also discuss the relevance of our results to microbial experiments.

  4. Rapid object category adaptation during unlabelled classification.

    PubMed

    Hadas, David; Intrator, Nathan; Yovel, Galit

    2010-01-01

    Recent reports from electrophysiological and psychophysical experiments provide evidence that repeated exposure to an ordered sequence of morphed stimuli may over time adapt a prelearned object category such that the category may generalise the entire sequence as belonging to the same object. Here, a new protocol that includes a single exposure to a morphing sequence is presented. Subjects exposed to the new protocol replaced a prelearned face with an entirely different face within just 3 days, significantly faster than in previous reports.

  5. Oregon Hydrologic Landscapes: An Approach for Broadscale Hydrologic Classification

    EPA Science Inventory

    Gaged streams represent only a small percentage of watershed hydrologic conditions throughout the Unites States and globe, but there is a growing need for hydrologic classification systems that can serve as the foundation for broad-scale assessments of the hydrologic functions of...

  6. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.

  7. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  8. Classification tree models for predicting distributions of michigan stream fish from landscape variables

    USGS Publications Warehouse

    Steen, P.J.; Zorn, T.G.; Seelbach, P.W.; Schaeffer, J.S.

    2008-01-01

    Traditionally, fish habitat requirements have been described from local-scale environmental variables. However, recent studies have shown that studying landscape-scale processes improves our understanding of what drives species assemblages and distribution patterns across the landscape. Our goal was to learn more about constraints on the distribution of Michigan stream fish by examining landscape-scale habitat variables. We used classification trees and landscape-scale habitat variables to create and validate presence-absence models and relative abundance models for Michigan stream fishes. We developed 93 presence-absence models that on average were 72% correct in making predictions for an independent data set, and we developed 46 relative abundance models that were 76% correct in making predictions for independent data. The models were used to create statewide predictive distribution and abundance maps that have the potential to be used for a variety of conservation and scientific purposes. ?? Copyright by the American Fisheries Society 2008.

  9. Adaptive color correction based on object color classification

    NASA Astrophysics Data System (ADS)

    Kotera, Hiroaki; Morimoto, Tetsuro; Yasue, Nobuyuki; Saito, Ryoichi

    1998-09-01

    An adaptive color management strategy depending on the image contents is proposed. Pictorial color image is classified into different object areas with clustered color distribution. Euclidian or Mahalanobis color distance measures, and maximum likelihood method based on Bayesian decision rule, are introduced to the classification. After the classification process, each clustered pixels are projected onto principal component space by Hotelling transform and the color corrections are performed for the principal components to be matched each other in between the individual clustered color areas of original and printed images.

  10. Classification of pasture habitats by Hungarian herders in a steppe landscape (Hungary)

    PubMed Central

    2012-01-01

    Background Landscape ethnoecology focuses on the ecological features of the landscape, how the landscape is perceived, and used by people who live in it. Though studying folk classifications of species has a long history, the comparative study of habitat classifications is just beginning. I studied the habitat classification of herders in a Hungarian steppe, and compared it to classifications of botanists and laymen. Methods For a quantitative analysis the picture sort method was used. Twenty-three pictures of 7-11 habitat types were sorted by 25 herders.’Density’ of pictures along the habitat gradient of the Hortobágy salt steppe was set as equal as possible, but pictures differed in their dominant species, wetness, season, etc. Before sorts, herders were asked to describe pictures to assure proper recognition of habitats. Results Herders classified the images into three main groups: (1) fertile habitats at the higher parts of the habitat gradient (partos, lit. on the shore); (2) saline habitats (szík, lit. salt or saline place), and (3) meadows and marshes (lapos, lit. flooded) at the lower end of the habitat gradient. Sharpness of delimitation changed along the gradient. Saline habitats were the most isolated from the rest. Botanists identified 6 groups. Laymen grouped habitats in a less coherent way. As opposed to my expectations, botanical classification was not more structured than that done by herders. I expected and found high correspondence between the classifications by herders, botanists and laymen. All tended to recognize similar main groups: wetlands, ”good grass” and dry/saline habitats. Two main factors could have been responsible for similar classifications: salient features correlated (e.g. salinity recognizable by herders and botanists but not by laymen correlated with the density of grasslands or height of vegetation recognizable also for laymen), or the same salient features were used as a basis for sorting (wetness, and abiotic stress

  11. How does the selection of landscape classification schemes affect the spatial pattern of natural landscapes? An assessment on a coastal wetland site in southern Italy.

    PubMed

    Tomaselli, V; Veronico, G; Sciandrello, S; Blonda, P

    2016-06-01

    It is widely known that thematic resolution affects spatial pattern and landscape metrics performances. In literature, data dealing with this issue usually refer to a specific class scheme with its thematic levels. In this paper, the effects of different land cover (LC) and habitat classification schemes on the spatial pattern of a coastal landscape were compared. One of the largest components of the Mediterranean wetland system was considered as the study site, and different schemes widely used in the EU were selected and harmonized with a common thematic resolution, suitable for habitat discrimination and monitoring. For each scheme, a thematic map was produced and, for each map, 28 landscape metrics were calculated. The landscape composition, already in terms of number of classes, class area, and number of patches, changes significantly among different classification schemes. Landscape complexity varies according to the class scheme considered and its underlying semantics, depending on how the different types aggregate or split when changing class scheme. Results confirm that the selection of a specific class scheme affects the spatial pattern of the derived landscapes and consequently the landscape metrics, especially at class level. Moreover, among the classification schemes considered, EUNIS seems to be the best choice for a comprehensive representation of both natural and anthropogenic classes.

  12. Implementation of Multispectral Image Classification on a Remote Adaptive Computer

    NASA Technical Reports Server (NTRS)

    Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

    1999-01-01

    As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

  13. The effect of bacterial recombination on adaptation on fitness landscapes with limited peak accessibility.

    PubMed

    Moradigaravand, Danesh; Engelstädter, Jan

    2012-01-01

    There is ample empirical evidence revealing that fitness landscapes are often complex: the fitness effect of a newly arisen mutation can depend strongly on the allelic state at other loci. However, little is known about the effects of recombination on adaptation on such fitness landscapes. Here, we investigate how recombination influences the rate of adaptation on a special type of complex fitness landscapes. On these landscapes, the mutational trajectories from the least to the most fit genotype are interrupted by genotypes with low relative fitness. We study the dynamics of adapting populations on landscapes with different compositions and numbers of low fitness genotypes, with and without recombination. Our results of the deterministic model (assuming an infinite population size) show that recombination generally decelerates adaptation on these landscapes. However, in finite populations, this deceleration is outweighed by the accelerating Fisher-Muller effect under certain conditions. We conclude that recombination has complex effects on adaptation that are highly dependent on the particular fitness landscape, population size and recombination rate.

  14. Classification of transient signals using sparse representations over adaptive dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.

    2011-06-01

    Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

  15. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function

    PubMed Central

    Groenendyk, Derek G.; Ferré, Ty P.A.; Thorp, Kelly R.; Rice, Amy K.

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth’s surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape

  16. Hydrologic-Process-Based Soil Texture Classifications for Improved Visualization of Landscape Function.

    PubMed

    Groenendyk, Derek G; Ferré, Ty P A; Thorp, Kelly R; Rice, Amy K

    2015-01-01

    Soils lie at the interface between the atmosphere and the subsurface and are a key component that control ecosystem services, food production, and many other processes at the Earth's surface. There is a long-established convention for identifying and mapping soils by texture. These readily available, georeferenced soil maps and databases are used widely in environmental sciences. Here, we show that these traditional soil classifications can be inappropriate, contributing to bias and uncertainty in applications from slope stability to water resource management. We suggest a new approach to soil classification, with a detailed example from the science of hydrology. Hydrologic simulations based on common meteorological conditions were performed using HYDRUS-1D, spanning textures identified by the United States Department of Agriculture soil texture triangle. We consider these common conditions to be: drainage from saturation, infiltration onto a drained soil, and combined infiltration and drainage events. Using a k-means clustering algorithm, we created soil classifications based on the modeled hydrologic responses of these soils. The hydrologic-process-based classifications were compared to those based on soil texture and a single hydraulic property, Ks. Differences in classifications based on hydrologic response versus soil texture demonstrate that traditional soil texture classification is a poor predictor of hydrologic response. We then developed a QGIS plugin to construct soil maps combining a classification with georeferenced soil data from the Natural Resource Conservation Service. The spatial patterns of hydrologic response were more immediately informative, much simpler, and less ambiguous, for use in applications ranging from trafficability to irrigation management to flood control. The ease with which hydrologic-process-based classifications can be made, along with the improved quantitative predictions of soil responses and visualization of landscape

  17. An adaptive unsupervised hyperspectral classification method based on Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Yue, Jiang; Wu, Jing-wei; Zhang, Yi; Bai, Lian-fa

    2014-11-01

    In order to achieve adaptive unsupervised clustering in the high precision, a method using Gaussian distribution to fit the similarity of the inter-class and the noise distribution is proposed in this paper, and then the automatic segmentation threshold is determined by the fitting result. First, according with the similarity measure of the spectral curve, this method assumes that the target and the background both in Gaussian distribution, the distribution characteristics is obtained through fitting the similarity measure of minimum related windows and center pixels with Gaussian function, and then the adaptive threshold is achieved. Second, make use of the pixel minimum related windows to merge adjacent similar pixels into a picture-block, then the dimensionality reduction is completed and the non-supervised classification is realized. AVIRIS data and a set of hyperspectral data we caught are used to evaluate the performance of the proposed method. Experimental results show that the proposed algorithm not only realizes the adaptive but also outperforms K-MEANS and ISODATA on the classification accuracy, edge recognition and robustness.

  18. Polytomous Adaptive Classification Testing: Effects of Item Pool Size, Test Termination Criterion, and Number of Cutscores

    ERIC Educational Resources Information Center

    Gnambs, Timo; Batinic, Bernad

    2011-01-01

    Computer-adaptive classification tests focus on classifying respondents in different proficiency groups (e.g., for pass/fail decisions). To date, adaptive classification testing has been dominated by research on dichotomous response formats and classifications in two groups. This article extends this line of research to polytomous classification…

  19. Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance.

    PubMed

    Ogbunugafor, C Brandon; Wylie, C Scott; Diakite, Ibrahim; Weinreich, Daniel M; Hartl, Daniel L

    2016-01-01

    The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary "forks in the road" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their

  20. Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

    PubMed Central

    Ogbunugafor, C. Brandon; Wylie, C. Scott; Diakite, Ibrahim; Weinreich, Daniel M.; Hartl, Daniel L.

    2016-01-01

    The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with

  1. Adaptive random renormalization group classification of multiscale dispersive processes

    NASA Astrophysics Data System (ADS)

    Cushman, John; O'Malley, Dan

    2013-04-01

    Renormalization group operators provide a detailed classification tool for dispersive processes. We begin by reviewing a two-scale renormalization group classification scheme. Repeated application of one operator is associated with long time behavior of the process while repeated application of the other is associated with short time behavior. This approach is shown to be robust even in the presence of non-stationary increments and/or infinite second moments. Fixed points of the operators can be used for further sub classification of the processes when appropriate limits exist. As an example we look at advective dispersion in an ergodic velocity field. Let X(t) be a fixed point of the long-time renormalization group operator (RGO) RX(t)=X(rt)/r^p. Scaling laws for the probability density, mean first passage times, and finite-size Lyapunov exponents of such fixed points are reviewed in anticipation of more general results. A generalized RGO, Rp, where the exponent in R above is now a random variable is introduced. Scaling laws associated with these random RGOs (RRGOs) are demonstrated numerically and applied to a process modeling the transition from sub-dispersion to Fickian dispersion. The scaling laws for the RRGO are not simple power laws, but instead are a weighted average of power laws. The weighting in the scaling laws can be determined adaptively via Bayes' theorem.

  2. Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution

    EPA Science Inventory

    This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...

  3. Context dependence in complex adaptive landscapes: frequency and trait-dependent selection surfaces within an adaptive radiation of Caribbean pupfishes.

    PubMed

    Martin, Christopher H

    2016-06-01

    The adaptive landscape provides the foundational bridge between micro- and macroevolution. One well-known caveat to this perspective is that fitness surfaces depend on ecological context, including competitor frequency, traits measured, and resource abundance. However, this view is based largely on intraspecific studies. It is still unknown how context-dependence affects the larger features of peaks and valleys on the landscape which ultimately drive speciation and adaptive radiation. Here, I explore this question using one of the most complex fitness landscapes measured in the wild in a sympatric pupfish radiation endemic to San Salvador Island, Bahamas by tracking survival and growth of laboratory-reared F2 hybrids. I present new analyses of the effects of competitor frequency, dietary isotopes, and trait subsets on this fitness landscape. Contrary to expectations, decreasing competitor frequency increased survival only among very common phenotypes, whereas less common phenotypes rarely survived despite few competitors, suggesting that performance, not competitor frequency, shapes large-scale features of the fitness landscape. Dietary isotopes were weakly correlated with phenotype and growth, but did not explain additional survival variation. Nonlinear fitness surfaces varied substantially among trait subsets, revealing one-, two-, and three-peak landscapes, demonstrating the complexity of selection in the wild, even among similar functional traits. PMID:27130447

  4. Achieving effective landscape conservation: evolving demands adaptive metrics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Rapid changes in demographics and on- and off-farm land use limit the impacts of U.S. conservation programs and present particular challenges to future conservation efforts. The fragmentation of landscape through urban, suburban, and peri-urban development, coincident with demographic shifts, has s...

  5. Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

    NASA Astrophysics Data System (ADS)

    Alexander, Cici; Deák, Balázs; Kania, Adam; Mücke, Werner; Heilmeier, Hermann

    2015-09-01

    Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels - Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) - based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen's kappa coefficient, κ). The accuracies at Levels 2-4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution

  6. A New Real-coded Genetic Algorithm with an Adaptive Mating Selection for UV-landscapes

    NASA Astrophysics Data System (ADS)

    Oshima, Dan; Miyamae, Atsushi; Nagata, Yuichi; Kobayashi, Shigenobu; Ono, Isao; Sakuma, Jun

    The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes but not deceptive ones called UV-landscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes.

  7. Landscape change and ecosystem classification in a municipal district of a small city (Isernia, Central Italy).

    PubMed

    Acosta, Alicia; Carranza, M Laura; Giancola, Michela

    2005-09-01

    Landscape changes taking place from 1954 to 1992 in the muncipal district of Isernia city (Central Italy) were described in relation to a system of ecosystem classification. Isernia municipal district was selected for study because recent historic changes in this area represent a typical example of landscape transformation similar to many small cities of Italy and other Mediterranean countries. To assess overall changes, three land cover maps (scale 1:25,000) were derived from panchromatic aerial photographs and field surveys. These were then digitalised in a Geographic Information System. A Land Facet (LF) map was derived by combining a phytoclimatic, a lithostatigrafic and a topographic map, and then digitalised as data layers in the same GIS. Results demonstrated two main landscape transformation trends: forest and semi-natural areas increased (8%), whereas agricultural areas decreased (12%). The urban area was relatively small during the entire analysed period, growing from 1% in 1954, to just 5% in 1992. Forest coverage was significant on reliefs, on hillside ecosystems such as limestone and on clay and marl hills LF. Arable land was particularly significant in flat ecosystems with deeper soils, such as on recent alluvial plain LF. These temporal changes were interpreted as being related to the replacement of traditional farming methods (grazing pastures) with more intensive methods (crop fields), especially on alluvial plains.

  8. Convergent Evolution During Local Adaptation to Patchy Landscapes

    PubMed Central

    2015-01-01

    Species often encounter, and adapt to, many patches of similar environmental conditions across their range. Such adaptation can occur through convergent evolution if different alleles arise in different patches, or through the spread of shared alleles by migration acting to synchronize adaptation across the species. The tension between the two reflects the constraint imposed on evolution by the underlying genetic architecture versus how effectively selection and geographic isolation act to inhibit the geographic spread of locally adapted alleles. This paper studies the balance between these two routes to adaptation in a model of continuous environments with patchy selection pressures. We address the following questions: How long does it take for a novel allele to appear in a patch where it is locally adapted through mutation? Or, through migration from another, already adapted patch? Which is more likely to occur, as a function of distance between the patches? What population genetic signal is left by the spread of migrant alleles? To answer these questions we examine the family structure underlying migration–selection equilibrium surrounding an already adapted patch, treating those rare families that reach new patches as spatial branching processes. A main result is that patches further apart than a critical distance will likely evolve independent locally adapted alleles; this distance is proportional to the spatial scale of selection (σ/sm, where σ is the dispersal distance and s m is the selective disadvantage of these alleles between patches), and depends linearly on log(s m/μ), where μ is the mutation rate. This provides a way to understand the role of geographic separation between patches in promoting convergent adaptation and the genomic signals it leaves behind. We illustrate these ideas using the convergent evolution of cryptic coloration in the rock pocket mouse, Chaetodipus intermedius, as an empirical example. PMID:26571125

  9. Convergent Evolution During Local Adaptation to Patchy Landscapes.

    PubMed

    Ralph, Peter L; Coop, Graham

    2015-11-01

    Species often encounter, and adapt to, many patches of similar environmental conditions across their range. Such adaptation can occur through convergent evolution if different alleles arise in different patches, or through the spread of shared alleles by migration acting to synchronize adaptation across the species. The tension between the two reflects the constraint imposed on evolution by the underlying genetic architecture versus how effectively selection and geographic isolation act to inhibit the geographic spread of locally adapted alleles. This paper studies the balance between these two routes to adaptation in a model of continuous environments with patchy selection pressures. We address the following questions: How long does it take for a novel allele to appear in a patch where it is locally adapted through mutation? Or, through migration from another, already adapted patch? Which is more likely to occur, as a function of distance between the patches? What population genetic signal is left by the spread of migrant alleles? To answer these questions we examine the family structure underlying migration-selection equilibrium surrounding an already adapted patch, treating those rare families that reach new patches as spatial branching processes. A main result is that patches further apart than a critical distance will likely evolve independent locally adapted alleles; this distance is proportional to the spatial scale of selection ([Formula: see text], where σ is the dispersal distance and sm is the selective disadvantage of these alleles between patches), and depends linearly on log(sm/μ), where μ is the mutation rate. This provides a way to understand the role of geographic separation between patches in promoting convergent adaptation and the genomic signals it leaves behind. We illustrate these ideas using the convergent evolution of cryptic coloration in the rock pocket mouse, Chaetodipus intermedius, as an empirical example. PMID:26571125

  10. Spatial variation, mapping, and classification of moss families in semi-arid landscapes in NW Turkey.

    PubMed

    Abay, Gökhan; Gül, Ebru; Günlü, Alkan; Erşahin, Sabit; Ursavaş, Serhat

    2015-03-01

    Geostatistics and remote sensing techniques are frequently used in analyzing the spatial variability of terrestrial ecosystems. We analyzed spatial variation of moss families by geostatistics and Landsat imagery in a typical semi-arid landscape in North Central Anatolia, Turkey. We sampled 49 sites, chosen based on elevation, slope steepness, and slope aspect. Moss families were determined in a 10-m(2) representative area at each sampling site. The samples were transported to a laboratory and identified for moss families. In total, 10 families were found. Semivariogram analysis was performed to analyze the spatial structure of these families. The semivariogram analysis showed that the moss families were spatially dependent within 117 m in the study area. Thirteen thematic classes were categorized by Landsat Thematic Mapper (TM) image in the study area. The classification resulted in an overall kappa statistic of 0.8535, producer accuracy of 74.29, and user accuracy of 86.67. The family with the lowest classification accuracy was Orthotrichaceae (kappa of 0.6379, producer accuracy of 64.52, and user accuracy of 66.67). The moss families and the other classes were identified with a 0.78 kappa statistic value and an 80.74 % accuracy level by using the Landsat TM. The classification showed that Brachytheciaceae, Pottiaceae, Bryaceae, and Grimmiaceae were the most abundant moss families in this semi-arid environment.

  11. Comparing the adaptive landscape across trait types: larger QTL effect size in traits under biotic selection

    PubMed Central

    2011-01-01

    Background In a spatially and temporally variable adaptive landscape, mutations operating in opposite directions and mutations of large effect should be commonly fixed due to the shifting locations of phenotypic optima. Similarly, an adaptive landscape with multiple phenotypic optima and deep valleys of low fitness between peaks will favor mutations of large effect. Traits under biotic selection should experience a more spatially and temporally variable adaptive landscape with more phenotypic optima than that experienced by traits under abiotic selection. To test this hypothesis, we assemble information from QTL mapping studies conducted in plants, comparing effect directions and effect sizes of detected QTL controlling traits putatively under abiotic selection to those controlling traits putatively under biotic selection. Results We find no differences in the fraction of antagonistic QTL in traits under abiotic and biotic selection, suggesting similar consistency in selection pressure on these two types of traits. However, we find that QTL controlling traits under biotic selection have a larger effect size than those under abiotic selection, supporting our hypothesis that QTL of large effect are more commonly detected in traits under biotic selection than in traits under abiotic selection. For traits under both abiotic and biotic selection, we find a large number of QTL of large effect, with 10.7% of all QTLs detected controlling more than 20% of the variance in phenotype. Conclusion These results suggest that mutations of large effect are more common in adaptive landscapes strongly determined by biotic forces, but that these types of adaptive landscapes do not result in a higher fraction of mutations acting in opposite directions. The high number of QTL of large effect detected shows that QTL of large effect are more common than predicted by the infinitesimal model of genetic adaptation. PMID:21385379

  12. Cherokee Adaptation to the Landscape of the West and Overcoming the Loss of Culturally Significant Plants

    ERIC Educational Resources Information Center

    Vick, R. Alfred

    2011-01-01

    Plant species utilized by Cherokees have been documented by several authors. However, many of the traditional uses of plants were lost or forgotten in the generations following the Trail of Tears. The pressures of overcoming the physical and psychological impact of the removal, adapting to a new landscape, rebuilding a government, rebuilding…

  13. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.

    PubMed

    Beare, Richard J; Chen, Jian; Kelly, Claire E; Alexopoulos, Dimitrios; Smyser, Christopher D; Rogers, Cynthia E; Loh, Wai Y; Matthews, Lillian G; Cheong, Jeanie L Y; Spittle, Alicia J; Anderson, Peter J; Doyle, Lex W; Inder, Terrie E; Seal, Marc L; Thompson, Deanne K

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T 2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T 2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T 2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical

  14. Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

    PubMed Central

    Beare, Richard J.; Chen, Jian; Kelly, Claire E.; Alexopoulos, Dimitrios; Smyser, Christopher D.; Rogers, Cynthia E.; Loh, Wai Y.; Matthews, Lillian G.; Cheong, Jeanie L. Y.; Spittle, Alicia J.; Anderson, Peter J.; Doyle, Lex W.; Inder, Terrie E.; Seal, Marc L.; Thompson, Deanne K.

    2016-01-01

    Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray

  15. Adaptive codebook selection schemes for image classification in correlated channels

    NASA Astrophysics Data System (ADS)

    Hu, Chia Chang; Liu, Xiang Lian; Liu, Kuan-Fu

    2015-09-01

    The multiple-input multiple-output (MIMO) system with the use of transmit and receive antenna arrays achieves diversity and array gains via transmit beamforming. Due to the absence of full channel state information (CSI) at the transmitter, the transmit beamforming vector can be quantized at the receiver and sent back to the transmitter by a low-rate feedback channel, called limited feedback beamforming. One of the key roles of Vector Quantization (VQ) is how to generate a good codebook such that the distortion between the original image and the reconstructed image is the minimized. In this paper, a novel adaptive codebook selection scheme for image classification is proposed with taking both spatial and temporal correlation inherent in the channel into consideration. The new codebook selection algorithm is developed to select two codebooks from the discrete Fourier transform (DFT) codebook, the generalized Lloyd algorithm (GLA) codebook and the Grassmannian codebook to be combined and used as candidates of the original image and the reconstructed image for image transmission. The channel is estimated and divided into four regions based on the spatial and temporal correlation of the channel and an appropriate codebook is assigned to each region. The proposed method can efficiently reduce the required information of feedback under the spatially and temporally correlated channels, where each region is adaptively. Simulation results show that in the case of temporally and spatially correlated channels, the bit-error-rate (BER) performance can be improved substantially by the proposed algorithm compared to the one with only single codebook.

  16. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms. PMID:21968733

  17. Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.

    PubMed

    Neidhart, Johannes; Szendro, Ivan G; Krug, Joachim

    2014-10-01

    Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage.

  18. Adaptation in Tunably Rugged Fitness Landscapes: The Rough Mount Fuji Model

    PubMed Central

    Neidhart, Johannes; Szendro, Ivan G.; Krug, Joachim

    2014-01-01

    Much of the current theory of adaptation is based on Gillespie’s mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage. PMID:25123507

  19. Recombination accelerates adaptation on a large-scale empirical fitness landscape in HIV-1.

    PubMed

    Moradigaravand, Danesh; Kouyos, Roger; Hinkley, Trevor; Haddad, Mojgan; Petropoulos, Christos J; Engelstädter, Jan; Bonhoeffer, Sebastian

    2014-06-01

    Recombination has the potential to facilitate adaptation. In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations, empirical evidence to test these theories is still scarce. We examined the effect of recombination on adaptation on a large-scale empirical fitness landscape in HIV-1 based on in vitro fitness measurements. Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size, mutation rate and recombination rate. The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined. We also found that both fitness effects of individual mutations and epistatic fitness interactions cause recombination to accelerate adaptation. The estimated epistasis in the adapting populations is significantly negative. Our results highlight the importance of recombination in the evolution of HIV-I.

  20. Adaptation and Coevolution on an Emergent Global Competitive Landscape

    NASA Astrophysics Data System (ADS)

    Fellman, Philip Vos; Post, Jonathan Vos; Wright, Roxana; Dasari, Usha

    Notions of Darwinian selection have been implicit in economic theory for at least sixty years. Richard Nelson and Sidney Winter have argued that while evolutionary thinking was prevalent in prewar economics, the postwar Neoclassical school became almost entirely preoccupied with equilibrium conditions and their mathematical conditions. One of the problems with the economic interpretation of firm selection through competition has been a weak grasp on an incomplete scientific paradigm. As I.F. Price notes: "The biological metaphor has long lurked in the background of management theory largely because the message of 'survival of the fittest' (usually wrongly attributed to Charles Darwin rather than Herbert Spencer) provides a seemingly natural model for market competition (e.g. Alchian 1950, Merrell 1984, Henderson 1989, Moore 1993), without seriously challenging the underlying paradigms of what an organisation is." [1] In this paper we examine the application of dynamic fitness landscape models to economic theory, particularly the theory of technology substitution, drawing on recent work by Kauffman, Arthur, McKelvey, Nelson and Winter, and Windrum and Birchenhall. In particular we use Professor Post's early work with John Holland on the genetic algorithm to explain some of the key differences between static and dynamic approaches to economic modeling.

  1. Adaptive social recommendation in a multiple category landscape

    NASA Astrophysics Data System (ADS)

    Chen, Duanbing; Zeng, An; Cimini, Giulio; Zhang, Yi-Cheng

    2013-02-01

    People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users' preferences. In this work we introduce a more realistic assumption that users' tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users' taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations' diversity while preserving their accuracy.

  2. Experimental demonstration of an adaptive architecture for direct spectral imaging classification.

    PubMed

    Dunlop-Gray, Matthew; Poon, Phillip K; Golish, Dathon; Vera, Esteban; Gehm, Michael E

    2016-08-01

    Spectral imaging is a powerful tool for providing in situ material classification across a spatial scene. Typically, spectral imaging analyses are interested in classification, though often the classification is performed only after reconstruction of the spectral datacube. We present a computational spectral imaging system, the Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C), which yields direct classification across the spatial scene without reconstruction of the source datacube. With a dual disperser architecture and a programmable spatial light modulator, the AFSSI-C measures specific projections of the spectral datacube which are generated by an adaptive Bayesian classification and feature design framework. We experimentally demonstrate multiple order-of-magnitude improvement of classification accuracy in low signal-to-noise (SNR) environments when compared to legacy spectral imaging systems.

  3. Social and natural sciences differ in their research strategies, adapted to work for different knowledge landscapes.

    PubMed

    Jaffe, Klaus

    2014-01-01

    Do different fields of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed two diverging optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in many journals containing a small number of articles. The natural sciences seem to adapt their research strategies to landscapes with large concentrated knowledge clusters, whereas social sciences seem to have adapted to search in landscapes with many small isolated knowledge clusters. Similar bipolar distributions were obtained when comparing levels of insularity estimated by indicators of international collaboration and levels of country-self citations: researchers in academic areas with many journals such as social sciences, arts and humanities, were the most isolated, and that was true in different regions of the world. The work shows that quantitative measures estimating differences between academic disciplines improve our understanding of different research strategies, eventually helping interdisciplinary research and may be also help improve science policies worldwide.

  4. Social and Natural Sciences Differ in Their Research Strategies, Adapted to Work for Different Knowledge Landscapes

    PubMed Central

    Jaffe, Klaus

    2014-01-01

    Do different fields of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed two diverging optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in many journals containing a small number of articles. The natural sciences seem to adapt their research strategies to landscapes with large concentrated knowledge clusters, whereas social sciences seem to have adapted to search in landscapes with many small isolated knowledge clusters. Similar bipolar distributions were obtained when comparing levels of insularity estimated by indicators of international collaboration and levels of country-self citations: researchers in academic areas with many journals such as social sciences, arts and humanities, were the most isolated, and that was true in different regions of the world. The work shows that quantitative measures estimating differences between academic disciplines improve our understanding of different research strategies, eventually helping interdisciplinary research and may be also help improve science policies worldwide. PMID:25426723

  5. On-board multispectral classification study. Volume 2: Supplementary tasks. [adaptive control

    NASA Technical Reports Server (NTRS)

    Ewalt, D.

    1979-01-01

    The operational tasks of the onboard multispectral classification study were defined. These tasks include: sensing characteristics for future space applications; information adaptive systems architectural approaches; data set selection criteria; and onboard functional requirements for interfacing with global positioning satellites.

  6. Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

    ERIC Educational Resources Information Center

    Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

    2006-01-01

    The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

  7. Diagnostic Classification Models and Multidimensional Adaptive Testing: A Commentary on Rupp and Templin

    ERIC Educational Resources Information Center

    Frey, Andreas; Carstensen, Claus H.

    2009-01-01

    On a general level, the objective of diagnostic classifications models (DCMs) lies in a classification of individuals regarding multiple latent skills. In this article, the authors show that this objective can be achieved by multidimensional adaptive testing (MAT) as well. The authors discuss whether or not the restricted applicability of DCMs can…

  8. Hydrologic landscape classification assesses streamflow vulnerability to climate change in Oregon, USA

    NASA Astrophysics Data System (ADS)

    Leibowitz, S. G.; Comeleo, R. L.; Wigington, P. J., Jr.; Weaver, C. P.; Morefield, P. E.; Sproles, E. A.; Ebersole, J. L.

    2014-03-01

    Classification can allow assessments of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to assess vulnerability to potential future climate change at statewide and basin scales. The HL classification has five components: climate, seasonality, aquifer permeability, terrain, and soil permeability. We evaluate changes when the 1971-2000 HL climate indices are recalculated using 2041-2070 simulation results from the ECHAM and PCM climate models with the A2, A1b, and B1 emission scenarios. Changes in climate class were modest (4-18%) statewide. However, there were major changes in seasonality class for five of the six realizations (excluding PCM_B1): Oregon shifts from being 13% snow-dominated to 4-6% snow-dominated under these five realizations, representing a 56-68% reduction in snowmelt-dominated area. At the basin scale, projected changes for the Siletz basin, in Oregon's coast range, include a small switch from very wet to wet climate, with no change in seasonality. However, there is a modest increase in fall and winter water due to increased precipitation. For the Sandy basin, on the western slope of the Cascades, HL climate class does not change, but there are major changes in seasonality, especially for areas with low aquifer permeability, which experiences a 100% loss of spring seasonality. This would reduce summer baseflow, but impacts could potentially be mitigated by streamflow buffering effects provided by groundwater in the high aquifer permeability portions of the upper Sandy. The Middle Fork John Day basin (MFJD), in northeastern Oregon, is snowmelt-dominated. The basin experiences a net loss of wet and moist climate area, along with an increase in dry climate area. The MFJD also experiences major shifts from spring to winter seasonality, representing a 20-60% reduction in snowmelt-dominated area. Altered seasonality and/or magnitude of seasonal streamflows could

  9. Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA

    NASA Astrophysics Data System (ADS)

    Leibowitz, S. G.; Comeleo, R. L.; Wigington, P. J., Jr.; Weaver, C. P.; Morefield, P. E.; Sproles, E. A.; Ebersole, J. L.

    2014-09-01

    Classification can allow for evaluations of the hydrologic functions of landscapes and their responses to stressors. Here we demonstrate the use of a hydrologic landscape (HL) approach to evaluate vulnerability to potential future climate change at statewide and basin scales in the state of Oregon. The HL classification has five components: climate, seasonality, aquifer permeability, terrain, and soil permeability. We evaluate changes when the 1971-2000 HL climate indices are recalculated using 2041-2070 simulation results from the ECHAM (European Centre HAMburg) and PCM (Parallel Climate Model) climate models with the A2, A1b, and B1 emission scenarios. Changes in climate class were modest (4-18%) statewide. However, there were major changes in seasonality class for five of the six realizations (excluding PCM_B1): Oregon shifts from being 13% snow-dominated to 4-6% snow-dominated under these five realizations, representing a 56-68% reduction in snowmelt-dominated area. At the basin scale, simulated changes for the Siletz Basin, in Oregon's Coast Range, include a small switch from very wet to wet climate, with no change in seasonality. However, there is a modest increase in fall and winter water due to increased precipitation. For the Sandy Basin, on the western slope of the Cascades, HL climate class does not change, but there are major changes in seasonality, especially for areas with low aquifer permeability, which experiences a 100% loss of spring seasonality. This would reduce summer baseflow, but effects could potentially be mitigated by streamflow buffering effects provided by groundwater in the high aquifer permeability portions of the upper Sandy. The Middle Fork John Day Basin (MFJD), in northeastern Oregon, is snowmelt-dominated. The basin experiences a net loss of wet and moist climate area, along with an increase in dry climate area. The MFJD also experiences major shifts from spring to winter seasonality, representing a 20-60% reduction in snowmelt

  10. Adaptive Capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes

    PubMed Central

    Riosmena, Fernando

    2014-01-01

    This study examines the ways in which the adaptive capacity of households to climatic events varies within communities and is mediated by institutional and landscape changes. We present qualitative and quantitative data from two Maasai communities differentially exposed to the devastating drought of 2009 in Northern Tanzania. We show how rangeland fragmentation combined with the decoupling of institutions and landscapes are affecting pastoralists ability to cope with drought. Our data highlight that mobility remains a key coping mechanism for pastoralists to avoid cattle loss during a drought. However, mobility is now happening in new ways that require not only large amounts of money but new forms of knowledge and connections outside of customary reciprocity networks. Those least affected by the drought, in terms of cattle lost, were those with large herds who were able to sell some of their cattle and to pay for private access to pastures outside of Maasai areas. Drawing on an entitlements framework, we argue that the new coping mechanisms are not available to all, could be making some households more vulnerable to climate change, and reduce the adaptive capacity of the overall system as reciprocity networks and customary institutions are weakened. As such, we posit that adaptive capacity to climate change is uneven within and across communities, is scale-dependent, and is intimately tied to institutional and landscape changes. PMID:25400331

  11. Exploring indigenous landscape classification across different dimensions: a case study from the Bolivian Amazon

    PubMed Central

    Riu-Bosoms, Carles; Vidal-Amat, Teresa; Duane, Andrea; Fernandez-Llamazares, Alvaro; Guèze, Maximilien; Luz, Ana C.; Macía, Manuel J.; Paneque-Gálvez, Jaime; Reyes-García, Victoria

    2013-01-01

    Decisions on landscape management are often dictated by government officials based on their own understandings of how landscape should be used and managed, but rarely considering local peoples’ understandings of the landscape they inhabit. We use data collected through free listings, field transects, and interviews to describe how an Amazonian group of hunter-horticulturalists, the Tsimane’, classify and perceive the importance of different elements of the landscape across the ecological, socioeconomic, and spiritual dimensions. The Tsimane’ recognize nine folk ecotopes (i.e., culturally-recognized landscape units) and use a variety of criteria (including geomorphological features and landscape uses) to differentiate ecotopes from one another. The Tsimane’ rank different folk ecotopes in accordance with their perceived ecological, socioeconomic, and spiritual importance. Understanding how local people perceive their landscape contributes towards a landscape management planning paradigm that acknowledges the continuing contributions to management of landscape inhabitants, as well as their cultural and land use rights. PMID:25821282

  12. EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

    EPA Science Inventory

    Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

  13. Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

    PubMed Central

    Hosseini, Monireh Sheikh; Zekri, Maryam

    2012-01-01

    Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

  14. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

    PubMed

    Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

    2015-11-01

    One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation.

  15. Testing Adaptive Hypotheses of Convergence with Functional Landscapes: A Case Study of Bone-Cracking Hypercarnivores

    PubMed Central

    Tseng, Zhijie Jack

    2013-01-01

    Morphological convergence is a well documented phenomenon in mammals, and adaptive explanations are commonly employed to infer similar functions for convergent characteristics. I present a study that adopts aspects of theoretical morphology and engineering optimization to test hypotheses about adaptive convergent evolution. Bone-cracking ecomorphologies in Carnivora were used as a case study. Previous research has shown that skull deepening and widening are major evolutionary patterns in convergent bone-cracking canids and hyaenids. A simple two-dimensional design space, with skull width-to-length and depth-to-length ratios as variables, was used to examine optimized shapes for two functional properties: mechanical advantage (MA) and strain energy (SE). Functionality of theoretical skull shapes was studied using finite element analysis (FEA) and visualized as functional landscapes. The distribution of actual skull shapes in the landscape showed a convergent trend of plesiomorphically low-MA and moderate-SE skulls evolving towards higher-MA and moderate-SE skulls; this is corroborated by FEA of 13 actual specimens. Nevertheless, regions exist in the landscape where high-MA and lower-SE shapes are not represented by existing species; their vacancy is observed even at higher taxonomic levels. Results highlight the interaction of biomechanical and non-biomechanical factors in constraining general skull dimensions to localized functional optima through evolution. PMID:23734244

  16. Image subband coding using context-based classification and adaptive quantization.

    PubMed

    Yoo, Y; Ortega, A; Yu, B

    1999-01-01

    Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.

  17. Bayesian classification of polarimetric SAR images using adaptive a priori probabilities

    NASA Technical Reports Server (NTRS)

    Van Zyl, J. J.; Burnette, C. F.

    1992-01-01

    The problem of classifying earth terrain by observed polarimetric scattering properties is tackled with an iterative Bayesian scheme using a priori probabilities adaptively. The first classification is based on the use of fixed and not necessarily equal a priori probabilities, and successive iterations change the a priori probabilities adaptively. The approach is applied to an SAR image in which a single water body covers 10 percent of the image area. The classification accuracy for ocean, urban, vegetated, and total area increase, and the percentage of reclassified pixels decreases greatly as the iteration number increases. The iterative scheme is found to improve the a posteriori classification accuracy of maximum likelihood classifiers by iteratively using the local homogeneity in polarimetric SAR images. A few iterations can improve the classification accuracy significantly without sacrificing key high-frequency detail or edges in the image.

  18. Support vector machine with adaptive composite kernel for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Li, Wei; Du, Qian

    2015-05-01

    With the improvement of spatial resolution of hyperspectral imagery, it is more reasonable to include spatial information in classification. The resulting spectral-spatial classification outperforms the traditional hyperspectral image classification with spectral information only. Among many spectral-spatial classifiers, support vector machine with composite kernel (SVM-CK) can provide superior performance, with one kernel for spectral information and the other for spatial information. In the original SVM-CK, the spatial information is retrieved by spatial averaging of pixels in a local neighborhood, and used in classifying the central pixel. Obviously, not all the pixels in such a local neighborhood may belong to the same class. Thus, we investigate the performance of Gaussian lowpass filter and an adaptive filter with weights being assigned based on the similarity to the central pixel. The adaptive filter can significantly improve classification accuracy while the Gaussian lowpass filter is less time-consuming and less sensitive to the window size.

  19. Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification.

    PubMed

    Algamal, Zakariya Yahya; Lee, Muhammad Hisyam

    2015-12-01

    Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.

  20. Synthetic shuffling and in vitro selection reveal the rugged adaptive fitness landscape of a kinase ribozyme

    PubMed Central

    Curtis, Edward A.; Bartel, David P.

    2013-01-01

    The relationship between genotype and phenotype is often described as an adaptive fitness landscape. In this study, we used a combination of recombination, in vitro selection, and comparative sequence analysis to characterize the fitness landscape of a previously isolated kinase ribozyme. Point mutations present in improved variants of this ribozyme were recombined in vitro in more than 1014 different arrangements using synthetic shuffling, and active variants were isolated by in vitro selection. Mutual information analysis of 65 recombinant ribozymes isolated in the selection revealed a rugged fitness landscape in which approximately one-third of the 91 pairs of positions analyzed showed evidence of correlation. Pairs of correlated positions overlapped to form densely connected networks, and groups of maximally connected nucleotides occurred significantly more often in these networks than they did in randomized control networks with the same number of links. The activity of the most efficient recombinant ribozyme isolated from the synthetically shuffled pool was 30-fold greater than that of any of the ribozymes used to build it, which indicates that synthetic shuffling can be a rich source of ribozyme variants with improved properties. PMID:23798664

  1. Research on adaptive segmentation and activity classification method of filamentous fungi image in microbe fermentation

    NASA Astrophysics Data System (ADS)

    Cai, Xiaochun; Hu, Yihua; Wang, Peng; Sun, Dujuan; Hu, Guilan

    2009-10-01

    The paper presents an adaptive segmentation and activity classification method for filamentous fungi image. Firstly, an adaptive structuring element (SE) construction algorithm is proposed for image background suppression. Based on watershed transform method, the color labeled segmentation of fungi image is taken. Secondly, the fungi elements feature space is described and the feature set for fungi hyphae activity classification is extracted. The growth rate evaluation of fungi hyphae is achieved by using SVM classifier. Some experimental results demonstrate that the proposed method is effective for filamentous fungi image processing.

  2. Classification of local- and landscape-scale ecological types in the Southern Appalachian mountains

    SciTech Connect

    McNab, W.H.

    1996-12-31

    Five local ecological types based on vegetative communities and two landscape types based on groups of communities, were identified by integrating landform, soil, and vegetation components using multivariate techniques. Evaluation and several topographic and soil variables were highly correlated with types of both scales. Landscape ecological types based only on landform and soil variables without vegetation did not correspond with types developed using vegetation.

  3. Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

    NASA Astrophysics Data System (ADS)

    Paneque-Gálvez, Jaime; Mas, Jean-François; Moré, Gerard; Cristóbal, Jordi; Orta-Martínez, Martí; Luz, Ana Catarina; Guèze, Maximilien; Macía, Manuel J.; Reyes-García, Victoria

    2013-08-01

    Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines - SVM), and hybrid (unsupervised-supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different

  4. Adaptive landscape and functional diversity of Neotropical cichlids: implications for the ecology and evolution of Cichlinae (Cichlidae; Cichliformes).

    PubMed

    Arbour, J H; López-Fernández, H

    2014-11-01

    Morphological, lineage and ecological diversity can vary substantially even among closely related lineages. Factors that influence morphological diversification, especially in functionally relevant traits, can help to explain the modern distribution of disparity across phylogenies and communities. Multivariate axes of feeding functional morphology from 75 species of Neotropical cichlid and a stepwise-AIC algorithm were used to estimate the adaptive landscape of functional morphospace in Cichlinae. Adaptive landscape complexity and convergence, as well as the functional diversity of Cichlinae, were compared with expectations under null evolutionary models. Neotropical cichlid feeding function varied primarily between traits associated with ram feeding vs. suction feeding/biting and secondarily with oral jaw muscle size and pharyngeal crushing capacity. The number of changes in selective regimes and the amount of convergence between lineages was higher than expected under a null model of evolution, but convergence was not higher than expected under a similarly complex adaptive landscape. Functional disparity was compatible with an adaptive landscape model, whereas the distribution of evolutionary change through morphospace corresponded with a process of evolution towards a single adaptive peak. The continentally distributed Neotropical cichlids have evolved relatively rapidly towards a number of adaptive peaks in functional trait space. Selection in Cichlinae functional morphospace is more complex than expected under null evolutionary models. The complexity of selective constraints in feeding morphology has likely been a significant contributor to the diversity of feeding ecology in this clade. PMID:25302771

  5. Application of adaptive and neural network computational techniques to Traffic Volume and Classification Monitoring

    SciTech Connect

    Mead, W.C.; Fisher, H.N.; Jones, R.D.; Bisset, K.R.; Lee, L.A.

    1993-09-01

    We are developing a Traffic Volume and Classification Monitoring (TVCM) system based on adaptive and neural network computational techniques. The value of neutral networks in this application lies in their ability to learn from data and to form a mapping of arbitrary topology. The piezoelectric strip and magnetic loop sensors typically used for TVCM provide signals that are complicated and variable, and that correspond in indirect ways with the desired FWHA 13-class classification system. Further, the wide variety of vehicle configurations adds to the complexity of the classification task. Our goal is to provide a TVCM system featuring high accuracy, adaptability to wide sensor and envirorunental variations, and continuous fault detection. We have instrumented an experimental TVCM site, developed PC-based on-line data acquisition software, collected a large database of vehicles` signals together with accurate ground truth determination, and analyzed the data off-line with a neural net classification system that can distinguish between class 2 (automobiles) and class 3 (utility vehicles) with better than 90% accuracy. The neural network used, called the Connectionist Hyperprism Classification (CHC) network, features simple basis functions; rapid, linear training algorithms for basis function amplitudes and widths; and basis function elimination that enhances network speed and accuracy. Work is in progress to extend the system to other classes, to quantify the system`s adaptability, and to develop automatic fault detection techniques.

  6. Energy Landscape Reveals That the Budding Yeast Cell Cycle Is a Robust and Adaptive Multi-stage Process

    PubMed Central

    Lv, Cheng; Li, Xiaoguang; Li, Fangting; Li, Tiejun

    2015-01-01

    Quantitatively understanding the robustness, adaptivity and efficiency of cell cycle dynamics under the influence of noise is a fundamental but difficult question to answer for most eukaryotic organisms. Using a simplified budding yeast cell cycle model perturbed by intrinsic noise, we systematically explore these issues from an energy landscape point of view by constructing an energy landscape for the considered system based on large deviation theory. Analysis shows that the cell cycle trajectory is sharply confined by the ambient energy barrier, and the landscape along this trajectory exhibits a generally flat shape. We explain the evolution of the system on this flat path by incorporating its non-gradient nature. Furthermore, we illustrate how this global landscape changes in response to external signals, observing a nice transformation of the landscapes as the excitable system approaches a limit cycle system when nutrients are sufficient, as well as the formation of additional energy wells when the DNA replication checkpoint is activated. By taking into account the finite volume effect, we find additional pits along the flat cycle path in the landscape associated with the checkpoint mechanism of the cell cycle. The difference between the landscapes induced by intrinsic and extrinsic noise is also discussed. In our opinion, this meticulous structure of the energy landscape for our simplified model is of general interest to other cell cycle dynamics, and the proposed methods can be applied to study similar biological systems. PMID:25794282

  7. Developmental Structuralist Approach to the Classification of Adaptive and Pathologic Personality Organizations: Infancy and Early Childhood.

    ERIC Educational Resources Information Center

    Greenspan, Stanley I.; Lourie, Reginald S.

    This paper applies a developmental structuralist approach to the classification of adaptive and pathologic personality organizations and behavior in infancy and early childhood, and it discusses implications of this approach for preventive intervention. In general, as development proceeds, the structural capacity of the developing infant and child…

  8. Verification of Hydrologic Landscape Derived Basin-Scale Classifications in the Pacific Northwest

    EPA Science Inventory

    The interaction between the physical properties of a catchment (form) and climatic forcing of precipitation and energy control how water is partitioned, stored, and conveyed through a catchment (function). Hydrologic Landscapes (HLs) were previously developed across Oregon and de...

  9. Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes.

    PubMed

    Goulart, Christiane P; Mahmudi, Mentar; Crona, Kristina A; Jacobs, Stephen D; Kallmann, Marcelo; Hall, Barry G; Greene, Devin C; Barlow, Miriam

    2013-01-01

    The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2], [3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM β-lactamase (bla(TEM)) varied greatly among 15 different β-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles bla(TEM-50) and bla(TEM-85), each of which differs from its ancestor bla(TEM-1) by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV.

  10. Targeting the adaptive molecular landscape of castration-resistant prostate cancer

    PubMed Central

    Wyatt, Alexander W; Gleave, Martin E

    2015-01-01

    Castration and androgen receptor (AR) pathway inhibitors induce profound and sustained responses in advanced prostate cancer. However, the inevitable recurrence is associated with reactivation of the AR and progression to a more aggressive phenotype termed castration-resistant prostate cancer (CRPC). AR reactivation can occur directly through genomic modification of the AR gene, or indirectly via co-factor and co-chaperone deregulation. This mechanistic heterogeneity is further complicated by the stress-driven induction of a myriad of overlapping cellular survival pathways. In this review, we describe the heterogeneous and evolvable molecular landscape of CRPC and explore recent successes and failures of therapeutic strategies designed to target AR reactivation and adaptive survival pathways. We also discuss exciting areas of burgeoning anti-tumour research, and their potential to improve the survival and management of patients with CRPC. PMID:25896606

  11. Adaptive Road Crack Detection System by Pavement Classification

    PubMed Central

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  12. Climate Change Impact Assessment and Adaptation Options in Vulnerable Agro-Landscapes in East-Africa

    NASA Astrophysics Data System (ADS)

    Manful, D.; Tscherning, K.; Kersebaum, K.; Dietz, J.; Dietrich, O.; Gomani, C.; Böhm, H.; Büchner, M.; Lischeid, G.,; Ojoyi, M.,

    2009-04-01

    Climate change poses a risk to the livelihoods of large populations in the developing world, especially in Africa. In East Africa, climate change is expected to affect the spatial distribution and quantity of precipitation. The proposed project will assess aspects of climate impacts and adaptation options in Tanzania. The project will attempt to quantify (1) projected impacts including: variability in temperature, rainfall, flooding and drought (2) the affect changes in 1. will have on specific sectors namely agriculture (food security), water resources and ecosystem services. The cumulative effects of diminished surface and ground water flow on agricultural production coupled with increasing demand for food due to increase in human pressure will also be evaluated. Expected outputs of the project include (1) downscaled climate change scenarios for different IPCC emission scenarios (2) model based estimations of climate change impacts on hydrological cycle and assessment of land use options (3) scenarios of sustainable livelihoods and resilient agro-landscapes under climate change (4) assessment of adaptive practices and criteria for best adaptation practices. The presentation will focus on novel approaches that focus on the use of agro-ecosystem models to predict local and regional impacts of climate variability on food with specific needs of the end-user factored into model set-up process. In other words, model configurations adapted to the information needs of a specific end-user or audience are evaluated. The perception of risk within different end-users (small scale farmer versus a regional or state level policy maker) are explicitly taken into consideration with the overarching aim of maximizing the impact of the results obtained from computer-based simulations.

  13. Quantitative modeling of virus evolutionary dynamics and adaptation in serial passages using empirically inferred fitness landscapes.

    PubMed

    Woo, Hyung Jun; Reifman, Jaques

    2014-01-01

    We describe a stochastic virus evolution model representing genomic diversification and within-host selection during experimental serial passages under cell culture or live-host conditions. The model incorporates realistic descriptions of the virus genotypes in nucleotide and amino acid sequence spaces, as well as their diversification from error-prone replications. It quantitatively considers factors such as target cell number, bottleneck size, passage period, infection and cell death rates, and the replication rate of different genotypes, allowing for systematic examinations of how their changes affect the evolutionary dynamics of viruses during passages. The relative probability for a viral population to achieve adaptation under a new host environment, quantified by the rate with which a target sequence frequency rises above 50%, was found to be most sensitive to factors related to sequence structure (distance from the wild type to the target) and selection strength (host cell number and bottleneck size). For parameter values representative of RNA viruses, the likelihood of observing adaptations during passages became negligible as the required number of mutations rose above two amino acid sites. We modeled the specific adaptation process of influenza A H5N1 viruses in mammalian hosts by simulating the evolutionary dynamics of H5 strains under the fitness landscape inferred from multiple sequence alignments of H3 proteins. In light of comparisons with experimental findings, we observed that the evolutionary dynamics of adaptation is strongly affected not only by the tendency toward increasing fitness values but also by the accessibility of pathways between genotypes constrained by the genetic code.

  14. An Analysis of Historic and Projected Climate Scenarios in the Western United States Using Hydrologic Landscape Classification

    NASA Astrophysics Data System (ADS)

    Jones, C., Jr.; Leibowitz, S. G.; Comeleo, R. L.; Stratton, L. E.; Sawicz, K. A.; Wigington, P. J., Jr.

    2015-12-01

    Identifying areas of similar hydrology within the United States and its regions (hydrologic landscapes - HLs) is an active area of research. HLs are being used to construct spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, and the Pacific Northwest. HLs are currently being applied across the Western U.S. to assess historic and projected climatic impacts. During the HL classification process, we analyze climate, seasonality, aquifer permeability, terrain, and soil permeability as the primary hydrologic drivers (and precipitation intensity as a secondary driver) associated with large scale hydrologic processes (storage, conveyance, and flow of water into or out of the watershed) in the West. Hypotheses regarding the dominant hydrologic pathways derived from the HL classification system are tested to corroborate or falsify these assumptions. Changes in climate are more likely to affect certain hydrogeologic parameters than others. For instance, changes in climate may result in changes in the magnitude, timing, or type of precipitation (snow vs. rain). Air temperature and the seasonality of dominant hydrologic processes may also be impacted. However, the effect of these changes on streamflow will depend on soil and aquifer permeability. In this analysis, we summarize (1) the HL classification methodology and (2) the use of historic (1900-present) PRISM climate data and climate projections to assess how changes in climate affect hydrologic processes and their associated impacts (e.g. water resource availability, ecological impacts, etc.) in the Western U.S.

  15. Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data

    NASA Astrophysics Data System (ADS)

    Piiroinen, Rami; Heiskanen, Janne; Mõttus, Matti; Pellikka, Petri

    2015-07-01

    Land use practices are changing at a fast pace in the tropics. In sub-Saharan Africa forests, woodlands and bushlands are being transformed for agricultural use to produce food for the rapidly growing population. The objective of this study was to assess the prospects of mapping the common agricultural crops in highly heterogeneous study area in south-eastern Kenya using high spatial and spectral resolution AisaEAGLE imaging spectroscopy data. Minimum noise fraction transformation was used to pack the coherent information in smaller set of bands and the data was classified with support vector machine (SVM) algorithm. A total of 35 plant species were mapped in the field and seven most dominant ones were used as classification targets. Five of the targets were agricultural crops. The overall accuracy (OA) for the classification was 90.8%. To assess the possibility of excluding the remaining 28 plant species from the classification results, 10 different probability thresholds (PT) were tried with SVM. The impact of PT was assessed with validation polygons of all 35 mapped plant species. The results showed that while PT was increased more pixels were excluded from non-target polygons than from the polygons of the seven classification targets. This increased the OA and reduced salt-and-pepper effects in the classification results. Very high spatial resolution imagery and pixel-based classification approach worked well with small targets such as maize while there was mixing of classes on the sides of the tree crowns.

  16. EVALUATION OF THE USE OF LANDSCAPE CLASSIFICATIONS FOR THE PREDICTION OF FRESHWATER BIOTA: SYNTHESIS AND RECOMMENDATIONS

    EPA Science Inventory

    This paper summarizes and synthesizes the collective results that emerged from the series of papers published in this issue of J-NABS, and places these results in the context of previously published literature describing variation in aquatic biota at landscape spatial scales. Cla...

  17. Using Hydrologic Landscape Classification to Evaluate the Hydrologic Effects of Climate in the Southwestern United States

    EPA Science Inventory

    Hydrologic landscapes (HLs) have been an active area of research at regional and national scales in the United States. The concept has been used to make spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, and the Pacific Northwe...

  18. Improvement of SVM-Based Speech/Music Classification Using Adaptive Kernel Technique

    NASA Astrophysics Data System (ADS)

    Lim, Chungsoo; Chang, Joon-Hyuk

    In this paper, we propose a way to improve the classification performance of support vector machines (SVMs), especially for speech and music frames within a selectable mode vocoder (SMV) framework. A myriad of techniques have been proposed for SVMs, and most of them are employed during the training phase of SVMs. Instead, the proposed algorithm is applied during the test phase and works with existing schemes. The proposed algorithm modifies a kernel parameter in the decision function of SVMs to alter SVM decisions for better classification accuracy based on the previous outputs of SVMs. Since speech and music frames exhibit strong inter-frame correlation, the outputs of SVMs can guide the kernel parameter modification. Our experimental results show that the proposed algorithm has the potential for adaptively tuning classifications of support vector machines for better performance.

  19. A tortoise-hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria.

    PubMed

    Nahum, Joshua R; Godfrey-Smith, Peter; Harding, Brittany N; Marcus, Joseph H; Carlson-Stevermer, Jared; Kerr, Benjamin

    2015-06-16

    In the context of Wright's adaptive landscape, genetic epistasis can yield a multipeaked or "rugged" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such "smooth" topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise-hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise-hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright's shifting balance process.

  20. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

    PubMed

    Ubeyli, Elif Derya

    2009-03-01

    This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286

  1. Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification

    EPA Science Inventory

    A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

  2. Where and why do models fail? Perspectives from Oregon Hydrologic Landscape classification

    EPA Science Inventory

    A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

  3. Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

    USGS Publications Warehouse

    Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

    2016-01-01

    The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

  4. Adaptive landscapes and emergent phenotypes: why do cancers have high glycolysis?

    PubMed

    Gillies, Robert J; Gatenby, Robert A

    2007-06-01

    Investigating the causes of increased aerobic glycolysis in tumors (Warburg Effect) has gone in and out of fashion many times since it was first described almost a century ago. The field is currently in ascendance due to two factors. Over a million FDG-PET studies have unequivocally identified increased glucose uptake as a hallmark of metastatic cancer in humans. These observations, combined with new molecular insights with HIF-1alpha and c-myc, have rekindled an interest in this important phenotype. A preponderance of work has been focused on the molecular mechanisms underlying this effect, with the expectation that a mechanistic understanding may lead to novel therapeutic approaches. There is also an implicit assumption that a mechanistic understanding, although fundamentally reductionist, will nonetheless lead to a more profound teleological understanding of the need for altered metabolism in invasive cancers. In this communication, we describe an alternative approach that begins with teleology; i.e. adaptive landscapes and selection pressures that promote emergence of aerobic glycolysis during the somatic evolution of invasive cancer. Mathematical models and empirical observations are used to define the adaptive advantage of aerobic glycolysis that would explain its remarkable prevalence in human cancers. These studies have led to the hypothesis that increased consumption of glucose in metastatic lesions is not used for substantial energy production via Embden-Meyerhoff glycolysis, but rather for production of acid, which gives the cancer cells a competitive advantage for invasion. Alternative hypotheses, wherein the glucose is used for generation of reducing equivalents (NADPH) or anabolic precursors (ribose) are also discussed. PMID:17624581

  5. Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

    PubMed

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-03-19

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

  6. Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

    PubMed Central

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

  7. Wavelength-adaptive dehazing using histogram merging-based classification for UAV images.

    PubMed

    Yoon, Inhye; Jeong, Seokhwa; Jeong, Jaeheon; Seo, Doochun; Paik, Joonki

    2015-01-01

    Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results. PMID:25808767

  8. A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification

    PubMed Central

    Hegenbart, Sebastian; Uhl, Andreas

    2015-01-01

    Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extraction level, using a robust estimate of global orientation. Scale-adapted features are computed in reference to the estimated scale of an image, based on the distribution of scale normalized Laplacian responses in a scale-space representation. Intrinsic-scale-adaption is performed to compute features, independent of the intrinsic texture scale, leading to a significantly increased discriminative power for a large amount of texture classes. In a final step, the rotation- and scale-invariant features are combined in a multi-resolution representation, which improves the classification accuracy in texture classification scenarios with scaling and rotation significantly. PMID:26240440

  9. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  10. A waveguide invariant adaptive matched filter for active sonar target depth classification.

    PubMed

    Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey

    2011-04-01

    This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions. PMID:21476638

  11. A waveguide invariant adaptive matched filter for active sonar target depth classification.

    PubMed

    Goldhahn, Ryan; Hickman, Granger; Krolik, Jeffrey

    2011-04-01

    This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.

  12. Adaptive polarimetric sensing for optimum radar signature classification using a genetic search algorithm.

    PubMed

    Sadjadi, Firooz A

    2006-08-01

    An automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses a probabilistic pattern separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions--the Rayleigh quotient, the Bhattacharyya, divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian probability of error--are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angles and the angle of polarization ellipse. The results indicate that, based on the majority of the distance functions used, there is a unique set of state of polarization angles whose use will lead to improved classification performance.

  13. Domain adaptation of image classification based on collective target nearest-neighbor representation

    NASA Astrophysics Data System (ADS)

    Tang, Song; Ye, Mao; Liu, Qihe; Li, Fan

    2016-05-01

    In many practical applications, we frequently face the awkward problem in which an image classifier trained in a scenario is difficult to use in a new scenario. Traditionally, the probability inference-based methods are used to solve this problem. From the point of image representation, we propose an approach for domain adaption of image classification. First, all source samples are supposed to form the dictionary. Then, we encode the target sample by combining this dictionary and the local geometric information. Based on this new representation, called target nearest-neighbor representation, image classification can obtain good performance in the target domain. Our core contribution is that the nearest-neighbor information of the target sample is technically exploited to form more robust representation. Experimental results confirm the effectiveness of our method.

  14. Hand movements classification for myoelectric control system using adaptive resonance theory.

    PubMed

    Jahani Fariman, H; Ahmad, Siti A; Hamiruce Marhaban, M; Alijan Ghasab, M; Chappell, Paul H

    2016-03-01

    This research proposes an exploratory study of a simple, accurate, and computationally efficient movement classification technique for prosthetic hand application. Surface myoelectric signals were acquired from the four muscles, namely, flexor carpi ulnaris, extensor carpi radialis, biceps brachii, and triceps brachii, of four normal-limb subjects. The signals were segmented, and the features were extracted with a new combined time-domain feature extraction method. Fuzzy C-means clustering method and scatter plot were used to evaluate the performance of the proposed multi-feature versus Hudgins' multi-feature. The movements were classified with a hybrid Adaptive Resonance Theory-based neural network. Comparative results indicate that the proposed hybrid classifier not only has good classification accuracy (89.09%) but also a significantly improved computation time. PMID:26581764

  15. Adaptation of motor imagery EEG classification model based on tensor decomposition

    NASA Astrophysics Data System (ADS)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Keng Ang, Kai; Ong, Sim Heng

    2014-10-01

    Objective. Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch. Approach. We employ a tensor model to estimate the mismatch in a semi-supervised manner, and the estimate is regularized in the discriminative objective function. Main results. The performance of the proposed adaptation method was evaluated on a dataset recorded from 16 subjects performing motor imagery tasks on different days. The classification results validated the advantage of the proposed method in comparison with other regularization-based or spatial filter adaptation approaches. Experimental results also showed that there is a significant correlation between the quantified mismatch and the classification accuracy. Significance. The proposed method approached the nonstationarity issue from the perspective of data-model mismatch, which is more direct than data variation measurement. The results also demonstrated that the proposed method is effective in enhancing the performance of the feature extraction model.

  16. Multi-source adaptation joint kernel sparse representation for visual classification.

    PubMed

    Tao, JianWen; Hu, Wenjun; Wen, Shiting

    2016-04-01

    Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source domains of information for establishing an adaptation model have been widely recognized, how to boost the robustness of the computational model for multi-source adaptation learning has only recently received attention. To address this issue for achieving enhanced performance, we propose in this paper a novel algorithm called multi-source Adaptation Regularization Joint Kernel Sparse Representation (ARJKSR) for robust visual classification problems. Specifically, ARJKSR jointly represents target dataset by a sparse linear combination of training data of each source domain in some optimal Reproduced Kernel Hilbert Space (RKHS), recovered by simultaneously minimizing the inter-domain distribution discrepancy and maximizing the local consistency, whilst constraining the observations from both target and source domains to share their sparse representations. The optimization problem of ARJKSR can be solved using an efficient alternative direction method. Under the framework ARJKSR, we further learn a robust label prediction matrix for the unlabeled instances of target domain based on the classical graph-based semi-supervised learning (GSSL) diagram, into which multiple Laplacian graphs constructed with the ARJKSR are incorporated. The validity of our method is examined by several visual classification problems. Results demonstrate the superiority of our method in comparison to several state-of-the-arts. PMID:26894961

  17. Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions

    NASA Astrophysics Data System (ADS)

    González-Castro, Víctor; Debayle, Johan; Wazaefi, Yanal; Rahim, Mehdi; Gaudy-Marqueste, Caroline; Grob, Jean-Jacques; Fertil, Bernard

    2015-11-01

    Different texture descriptors are proposed for the automatic classification of skin lesions from dermoscopic images. They are based on color texture analysis obtained from (1) color mathematical morphology (MM) and Kohonen self-organizing maps (SOMs) or (2) local binary patterns (LBPs), computed with the use of local adaptive neighborhoods of the image. Neither of these two approaches needs a previous segmentation process. In the first proposed descriptor, the adaptive neighborhoods are used as structuring elements to carry out adaptive MM operations which are further combined by using Kohonen SOM; this has been compared with a nonadaptive version. In the second one, the adaptive neighborhoods enable geometrical feature maps to be defined, from which LBP histograms are computed. This has also been compared with a classical LBP approach. A receiver operating characteristics analysis of the experimental results shows that the adaptive neighborhood-based LBP approach yields the best results. It outperforms the nonadaptive versions of the proposed descriptors and the dermatologists' visual predictions.

  18. A tortoise–hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria

    PubMed Central

    Nahum, Joshua R.; Godfrey-Smith, Peter; Harding, Brittany N.; Marcus, Joseph H.; Carlson-Stevermer, Jared; Kerr, Benjamin

    2015-01-01

    In the context of Wright’s adaptive landscape, genetic epistasis can yield a multipeaked or “rugged” topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such “smooth” topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise–hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise–hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright’s shifting balance process. PMID:25964348

  19. Human action classification using adaptive key frame interval for feature extraction

    NASA Astrophysics Data System (ADS)

    Lertniphonphan, Kanokphan; Aramvith, Supavadee; Chalidabhongse, Thanarat H.

    2016-01-01

    Human action classification based on the adaptive key frame interval (AKFI) feature extraction is presented. Since human movement periods are different, the action intervals that contain the intensive and compact motion information are considered in this work. We specify AKFI by analyzing an amount of motion through time. The key frame is defined to be the local minimum interframe motion, which is computed by using frame differencing between consecutive frames. Once key frames are detected, the features within a segmented period are encoded by adaptive motion history image and key pose history image. The action representation consists of the local orientation histogram of the features during AKFI. The experimental results on Weizmann dataset, KTH dataset, and UT Interaction dataset demonstrate that the features can effectively classify action and can classify irregular cases of walking compared to other well-known algorithms.

  20. Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification.

    PubMed

    Liu, Hui; Zhang, Cai-Ming; Su, Zhi-Yuan; Wang, Kai; Deng, Kai

    2015-01-01

    The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.

  1. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  2. Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

    PubMed Central

    Luo, Gang; Min, Wanli

    2007-01-01

    Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject snew with limited training data Dnew, we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from Dnew a regulated estimate of CRF’s parameters. Using sleep recordings from human subjects, we show that even without any Dnew, our sleep stager can achieve an average classification accuracy of 70% on snew. This accuracy increases with the size of Dnew and eventually becomes close to the theoretical limit. PMID:18693884

  3. Meeting review. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics.

    PubMed

    Joost, Stéphane; Vuilleumier, Séverine; Jensen, Jeffrey D; Schoville, Sean; Leempoel, Kevin; Stucki, Sylvie; Widmer, Ivo; Melodelima, Christelle; Rolland, Jonathan; Manel, Stéphanie

    2013-07-01

    A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future. PMID:24003454

  4. Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

    PubMed

    Kireeva, Natalia V; Ovchinnikova, Svetlana I; Kuznetsov, Sergey L; Kazennov, Andrey M; Tsivadze, Aslan Yu

    2014-02-01

    This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

  5. Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

    NASA Astrophysics Data System (ADS)

    Shi, Junfei; Li, Lingling; Liu, Fang; Jiao, Licheng; Liu, Hongying; Yang, Shuyuan; Liu, Lu; Hao, Hongxia

    2016-04-01

    Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.

  6. Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.

    PubMed

    Ibrahim, Sulaimon; Chowriappa, Pradeep; Dua, Sumeet; Acharya, U Rajendra; Noronha, Kevin; Bhandary, Sulatha; Mugasa, Hatwib

    2015-12-01

    Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%.

  7. Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

    PubMed Central

    Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

    2015-01-01

    The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential. PMID:26098107

  8. Detection and Classification of Motor Vehicle Noise in a Forested Landscape

    NASA Astrophysics Data System (ADS)

    Brown, Casey L.; Reed, Sarah E.; Dietz, Matthew S.; Fristrup, Kurt M.

    2013-11-01

    Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

  9. Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification.

    PubMed

    Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

    2015-01-01

    The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.

  10. Detection and classification of motor vehicle noise in a forested landscape.

    PubMed

    Brown, Casey L; Reed, Sarah E; Dietz, Matthew S; Fristrup, Kurt M

    2013-11-01

    Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

  11. Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification.

    PubMed

    Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

    2015-01-01

    The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential. PMID:26098107

  12. Collaborative adaptive landscape management (CALM) in rangelands: Discussion of general principles

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The management of rangeland landscapes involves broad spatial extents, mixed land ownership, and multiple resource objectives. Management outcomes depend on biophysical heterogeneity, highly variable weather conditions, land use legacies, and spatial processes such as wildlife movement, hydrological...

  13. Sampling network stratification by terrain classification in eroded agricultural landscapes at plot scale

    NASA Astrophysics Data System (ADS)

    Penížek, Vít; Zádorová, Tereza; Kodešová, Radka

    2015-04-01

    The description of soil properties variability is important aspect in land management and food production at plot scale. We describe novelty approach for design of sampling network on agricultural plots with high relief variability. The terrain properties were used for improved spatial prediction of soil properties including design of the sampling network. Regular sampling network, random sampling network, systematic unaligned sampling network and stratified sampling network schemes were compared to prove the advantages of relief based stratified sampling networks. The study was performed for humus horizon depth prediction on agriculture plot of 6.5 ha with dissected relief where originally homogenous soil cover was differentiated by erosion and sedimentation into mosaic of Chernozem, Regosol and colluvial soils. Moreover the comparison was done on three levels of sampling density (65, 40 and 24 sampling points). The stratification of sampling network was based on unsupervised relief classification. The performance of the soil properties prediction based on different sampling network was assesed by RMSE calculation based on predicted values versus validation dataset. According the RMSE, the stratified sampling network performed the best (7.4 cm) comparing the regular sampling network (10.8 cm), random sampling network (17.7 cm) and systematic unaligned sampling network (11.2 cm). The accuracy of the soil properties spatial prediction decreased with the decreasing number of sampling points, but the stratified network performed significantly better that other used methods. The study showed that, for soil properties spatial variability description at certain accuracy level, relief-based stratified network can contain 25 % less sampling points comparing to regular network. This leads to potential financial and person cost reduction for the soil survey. The study was supported by grant nr. 13-07516P of the Czech science foundation and by grant nr. QJ1230319 of the

  14. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

    PubMed Central

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other

  15. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  16. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  17. EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

    PubMed Central

    Mangia, Anna Lisa; Cappello, Angelo

    2016-01-01

    Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

  18. EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

    PubMed Central

    Mangia, Anna Lisa; Cappello, Angelo

    2016-01-01

    Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback. PMID:27635129

  19. EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures.

    PubMed

    Mondini, Valeria; Mangia, Anna Lisa; Cappello, Angelo

    2016-01-01

    Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's "flexibility" and "customizability," namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

  20. SSD-Optimized Workload Placement with Adaptive Learning and Classification in HPC Environments

    SciTech Connect

    Wan, Lipeng; Lu, Zheng; Cao, Qing; Wang, Feiyi; Oral, H Sarp; Settlemyer, Bradley W

    2014-01-01

    In recent years, non-volatile memory devices such as SSD drives have emerged as a viable storage solution due to their increasing capacity and decreasing cost. Due to the unique capability and capacity requirements in large scale HPC (High Performance Computing) storage environment, a hybrid config- uration (SSD and HDD) may represent one of the most available and balanced solutions considering the cost and performance. Under this setting, effective data placement as well as movement with controlled overhead become a pressing challenge. In this paper, we propose an integrated object placement and movement framework and adaptive learning algorithms to address these issues. Specifically, we present a method that shuffle data objects across storage tiers to optimize the data access performance. The method also integrates an adaptive learning algorithm where real- time classification is employed to predict the popularity of data object accesses, so that they can be placed on, or migrate between SSD or HDD drives in the most efficient manner. We discuss preliminary results based on this approach using a simulator we developed to show that the proposed methods can dynamically adapt storage placements and access pattern as workloads evolve to achieve the best system level performance such as throughput.

  1. EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures.

    PubMed

    Mondini, Valeria; Mangia, Anna Lisa; Cappello, Angelo

    2016-01-01

    Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's "flexibility" and "customizability," namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback. PMID:27635129

  2. Adapted Verbal Feedback, Instructor Interaction and Student Emotions in the Landscape Architecture Studio

    ERIC Educational Resources Information Center

    Smith, Carl A.; Boyer, Mark E.

    2015-01-01

    In light of concerns with architectural students' emotional jeopardy during traditional desk and final-jury critiques, the authors pursue alternative approaches intended to provide more supportive and mentoring verbal assessment in landscape architecture studios. In addition to traditional studio-based critiques throughout a semester, we provide…

  3. Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution.

    PubMed

    Messier, Kyle P; Jackson, Laura E; White, Jennifer L; Hilborn, Elizabeth D

    2015-01-01

    This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution.

  4. Adaptive three-dimensional range-crossrange-frequency filter processing string for sea mine classification in side scan sonar imagery

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Fernandez, Manuel F.; Dobeck, Gerald J.

    1997-07-01

    An automatic, robust, adaptive clutter suppression, predetection level fusion, sea mine detection and classification processing string has been developed and applied to shallow water side-scan sonar imagery data. The overall processing string includes pre-processing string includes pre-processing, adaptive clutter filtering (ACF), 2D normalization, detection, feature extraction and classification processing blocks. The pre-processing block contains automatic gain control, data decimation and data alignment processing. The ACF is a multi-dimensional adaptive linear FIR filter, optimal in the least squares sense, for simultaneous background clutter suppression and preservation of an average peak target signature. After data alignment, using a 3D ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range-crossrange domain. Following 2D normalization, the detection consists of thresholding, clustering of exceedances and limiting their number. Finally, features are extracted and a orthogonalization transformation is applied to the data, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. The utility of the overall processing string was demonstrated with two side-scan sonar data sets. The ACF, feature orthogonalization, LLRT-based classification processing string provided average probability of correct mine classification and false alarm rate performance exceeding the one obtained when utilizing an expert sonar operator. The overall processing string can be easily implemented in real-time using COTS technology.

  5. An adaptive threshold based image processing technique for improved glaucoma detection and classification.

    PubMed

    Issac, Ashish; Partha Sarathi, M; Dutta, Malay Kishore

    2015-11-01

    Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR), neuro retinal rim (NRR) area and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis. These features which have discriminatory changes with the occurrence of glaucoma are strategically used for training the classifiers to improve the accuracy of identification. The segmentation of optic disc and cup based on adaptive threshold of the pixel intensities lying in the optic nerve head region. Unlike existing methods the proposed algorithm is based on an adaptive threshold that uses local features from the fundus image for segmentation of optic cup and optic disc making it invariant to the quality of the image and noise content which may find wider acceptability. The experimental results indicate that such features are more significant in comparison to the statistical or textural features as considered in existing works. The proposed work achieves an accuracy of 94.11% with a sensitivity of 100%. A comparison of the proposed work with the existing methods indicates that the proposed approach has improved accuracy of classification glaucoma from a digital fundus which may be considered clinically significant.

  6. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

    PubMed

    Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

    2016-05-01

    Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches. PMID:27058283

  7. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.

    PubMed

    Liu, Dong; Wang, Shengsheng; Huang, Dezhi; Deng, Gang; Zeng, Fantao; Chen, Huiling

    2016-05-01

    Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.

  8. Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

    PubMed Central

    Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  9. Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.

    PubMed

    Gaspar-Cunha, A; Recio, G; Costa, L; Estébanez, C

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  10. Factors shaping the adaptive landscape for arboviruses: implications for the emergence of disease

    PubMed Central

    Coffey, Lark L; Forrester, Naomi; Tsetsarkin, Konstantin; Vasilakis, Nikos; Weaver, Scott C

    2013-01-01

    Many examples of the emergence or re-emergence of infectious diseases involve the adaptation of zoonotic viruses to new amplification hosts or to humans themselves. These include several instances of simple mutational adaptations, often to hosts closely related to the natural reservoirs. However, based on theoretical grounds, arthropod-borne viruses, or arboviruses, may face several challenges for adaptation to new hosts. Here, we review recent findings regarding adaptive evolution of arboviruses and its impact on disease emergence. We focus on the zoonotic alphaviruses Venezuelan equine encephalitis and chikungunya viruses, which have undergone adaptive evolution that mediated recent outbreaks of disease, as well as the flaviviruses dengue and West Nile viruses, which have emerged via less dramatic adaptive mechanisms. PMID:23374123

  11. Social Networks and Adaptation to Environmental Change: The Case of Central Oregon's Fire-Prone Forest Landscape

    NASA Astrophysics Data System (ADS)

    Fischer, A.

    2012-12-01

    Social networks are the patterned interactions among individuals and organizations through which people refine their beliefs and values, negotiate meanings for things and develop behavioral intentions. The structure of social networks has bearing on how people communicate information, generate and retain knowledge, make decisions and act collectively. Thus, social network structure is important for how people perceive, shape and adapt to the environment. We investigated the relationship between social network structure and human adaptation to wildfire risk in the fire-prone forested landscape of Central Oregon. We conducted descriptive and non-parametric social network analysis on data gathered through interviews to 1) characterize the structure of the network of organizations involved in forest and wildfire issues and 2) determine whether network structure is associated with organizations' beliefs, values and behaviors regarding fire and forest management. Preliminary findings indicate that fire protection and forest-related organizations do not frequently communicate or cooperate, suggesting that opportunities for joint problem-solving, innovation and collective action are limited. Preliminary findings also suggest that organizations with diverse partners are more likely to hold adaptive beliefs about wildfire and work cooperatively. We discuss the implications of social network structure for adaptation to changing environmental conditions such as wildfire risk.

  12. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation.

    PubMed

    Fitzpatrick, Matthew C; Keller, Stephen R

    2015-01-01

    Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism-environment relationships that combines genomic data and community-level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA-5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community-level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species-level assessments of climate change vulnerability.

  13. Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images.

    PubMed

    Quirós, Elia; Felicísimo, Angel M; Cuartero, Aurora

    2009-01-01

    This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test. PMID:22291550

  14. Development of a Digital Aquifer Permeability Map for the Pacific Southwest in Support of Hydrologic Landscape Classification: Methods

    EPA Science Inventory

    Researchers at the U.S. Environmental Protection Agency’s Western Ecology Division have been developing hydrologic landscape maps for selected U.S. states in an effort to create a method to identify the intrinsic watershed attributes of landscapes in regions with little dat...

  15. Pathogen population bottlenecks and adaptive landscapes: overcoming the barriers to disease emergence.

    PubMed

    Geoghegan, Jemma L; Senior, Alistair M; Holmes, Edward C

    2016-08-31

    Emerging diseases are a major challenge to public health. Revealing the evolutionary processes that allow novel pathogens to adapt to new hosts, also the potential barriers to host adaptation, is central to understanding the drivers of disease emergence. In particular, it is unclear how the genetics and ecology of pathogens interact to shape the likelihood of successful cross-species transmission. To better understand the determinants of host adaptation and emergence, we modelled key aspects of pathogen evolutionary dynamics at both intra- and inter-host scales, using parameter values similar to those observed in influenza virus. We considered the possibility of acquiring the necessary host adaptive mutations both before ('off-the-shelf' emergence) and after ('tailor-made' emergence) a virus is transmitted from a donor to a new recipient species. Under both scenarios, population bottlenecks at inter-host transmission act as a major barrier to host adaptation, greatly limiting the number of adaptive mutations that are able to cross the species barrier. In addition, virus emergence is hindered if the fitness valley between the donor and recipient hosts is either too steep or too shallow. Overall, our results reveal where in evolutionary parameter space a virus could adapt to and become transmissible in a new species. PMID:27581875

  16. Classification

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  17. Statistical analysis of multilook polarimetric SAR data and terrain classification with adaptive distribution

    NASA Astrophysics Data System (ADS)

    Liu, Guoqing; Huang, ShunJi; Torre, Andrea; Rubertone, Franco S.

    1995-11-01

    This paper deals with analysis of statistical properties of multi-look processed polarimetric SAR data. Based on an assumption that the multi-look polarimetric measurement is a product between a Gamma-distributed texture variable and a Wishart-distributed polarimetric speckle variable, it is shown that the multi-look polarimetric measurement from a nonhomogeneous region obeys a generalized K-distribution. In order to validate this statistical model, two of its varied versions, multi-look intensity and amplitude K-distributions are particularly compared with histograms of the observed multi-look SAR data of three terrain types, ocean, forest-like and city regions, and with four empirical distribution models, Gaussian, log-normal, gamma and Weibull models. A qualitative relation between the degree of nonhomogeneity of a textured scene and the well-fitting statistical model is then empirically established. Finally, a classifier with adaptive distributions guided by the order parameter of the texture distribution estimated with local statistics is introduced to perform terrain classification, experimental results with both multi-look fully polarimetric data and multi-look single-channel intensity/amplitude data indicate its effectiveness.

  18. Classification and assessment of water bodies as adaptive structural measures for flood risk management planning.

    PubMed

    McMinn, William R; Yang, Qinli; Scholz, Miklas

    2010-09-01

    Severe rainfall events have become increasingly common in Europe. Flood defence engineering works are highly capital intensive and can be limited by land availability, leaving land and communities exposed to repeated flooding. Any adaptive drainage structure must have engineered inlets and outlets that control the water level and the rate of release. In Scotland, there are a relatively high number of drinking water reservoirs (operated by Scottish Water), which fall within this defined category and could contribute to flood management control. Reducing the rate of runoff from the upper reaches of a catchment will reduce the volume and peak flows of flood events downstream, thus allowing flood defences to be reduced in size, decreasing the corresponding capital costs. A database of retention basins with flood control potential has been developed for Scotland. The research shows that the majority of small and former drinking water reservoirs are kept full and their spillways are continuously in operation. Utilising some of the available capacity to contribute to flood control could reduce the costs of complying with the EU Flood Directive. Furthermore, the application of a previously developed classification model for Baden in Germany for the Scottish data set showed a lower diversity for basins in Scotland due to less developed infrastructure. The principle value of this approach is a clear and unambiguous categorisation, based on standard variables, which can help to promote communication and understanding between stakeholders.

  19. Expanding the prevention armamentarium portfolio: A framework for promoting HIV-Conversant Communities within a complex, adaptive epidemiological landscape

    PubMed Central

    Burman, Christopher J.; Aphane, Marota; Mtapuri, Oliver; Delobelle, Peter

    2015-01-01

    Abstract The article describes a design journey that culminated in an HIV-Conversant Community Framework that is now being piloted in the Limpopo Province of South Africa. The objective of the initiative is to reduce the aggregate community viral load by building capacity at multiple scales that strengthens peoples' HIV-related navigational skill sets—while simultaneously opening a ‘chronic situation’ schema. The framework design is based upon a transdisciplinary methodological combination that synthesises ideas and constructs from complexity science and the management sciences as a vehicle through which to re-conceptualise HIV prevention. This resulted in a prototype that included the following constructs: managing HIV-prevention in a complex, adaptive epidemiological landscape; problematising and increasing the scope of the HIV knowledge armamentarium through education that focuses on the viral load and Langerhans cells; disruptive innovation and safe-fail probes followed by the facilitation of path creations and pattern management implementation techniques. These constructs are underpinned by a ‘middle-ground’ prevention approach which is designed to bridge the prevention ‘fault line’, enabling a multi-ontology conceptualisation of the challenge to be developed. The article concludes that stepping outside of the ‘ordered’ epistemological parameters of the existing prevention ‘messaging’ mind-set towards a more systemic approach that emphasises agency, structure and social practices as a contribution to ‘ending AIDS by 2030’ is worthy of further attention if communities are to engage more adaptively with the dynamic HIV landscape in South Africa. PMID:25888256

  20. Landscape genomics reveal signatures of local adaptation in barley (Hordeum vulgare L.)

    PubMed Central

    Abebe, Tiegist D.; Naz, Ali A.; Léon, Jens

    2015-01-01

    Land plants are sessile organisms that cannot escape the adverse climatic conditions of a given environment. Hence, adaptation is one of the solutions to surviving in a challenging environment. This study was aimed at detecting adaptive loci in barley landraces that are affected by selection. To that end, a diverse population of barley landraces was analyzed using the genotyping by sequencing approach. Climatic data for altitude, rainfall and temperature were collected from 61 weather sites near the origin of selected landraces across Ethiopia. Population structure analysis revealed three groups whereas spatial analysis accounted significant similarities at shorter geographic distances (< 40 Km) among barley landraces. Partitioning the variance between climate variables and geographic distances indicated that climate variables accounted for most of the explainable genetic variation. Markers by climatic variables association analysis resulted in altogether 18 and 62 putative adaptive loci using Bayenv and latent factor mixed model (LFMM), respectively. Subsequent analysis of the associated SNPs revealed putative candidate genes for plant adaptation. This study highlights the presence of putative adaptive loci among barley landraces representing original gene pool of the farming communities. PMID:26483825

  1. Adaptable Neighbours: Movement Patterns of GPS-Collared Leopards in Human Dominated Landscapes in India

    PubMed Central

    Odden, Morten; Athreya, Vidya; Rattan, Sandeep; Linnell, John D. C.

    2014-01-01

    Understanding the nature of the interactions between humans and wildlife is of vital importance for conflict mitigation. We equipped five leopards with GPS-collars in Maharashtra (4) and Himachal Pradesh (1), India, to study movement patterns in human-dominated landscapes outside protected areas. An adult male and an adult female were both translocated 52 km, and exhibited extensive, and directional, post release movements (straight line movements: male  = 89 km in 37 days, female  = 45 km in 5 months), until they settled in home ranges of 42 km2 (male) and 65 km2 (female). The three other leopards, two adult females and a young male were released close to their capture sites and used small home ranges of 8 km2 (male), 11 km2 and 15 km2 (females). Movement patterns were markedly nocturnal, with hourly step lengths averaging 339±9.5 m (SE) during night and 60±4.1 m during day, and night locations were significantly closer to human settlements than day locations. However, more nocturnal movements were observed among those three living in the areas with high human population densities. These visited houses regularly at nighttime (20% of locations <25 m from houses), but rarely during day (<1%). One leopard living in a sparsely populated area avoided human settlements both day and night. The small home ranges of the leopards indicate that anthropogenic food resources may be plentiful although wild prey is absent. The study provides clear insights into the ability of leopards to live and move in landscapes that are extremely modified by human activity. PMID:25390067

  2. Adaptable neighbours: movement patterns of GPS-collared leopards in human dominated landscapes in India.

    PubMed

    Odden, Morten; Athreya, Vidya; Rattan, Sandeep; Linnell, John D C

    2014-01-01

    Understanding the nature of the interactions between humans and wildlife is of vital importance for conflict mitigation. We equipped five leopards with GPS-collars in Maharashtra (4) and Himachal Pradesh (1), India, to study movement patterns in human-dominated landscapes outside protected areas. An adult male and an adult female were both translocated 52 km, and exhibited extensive, and directional, post release movements (straight line movements: male = 89 km in 37 days, female = 45 km in 5 months), until they settled in home ranges of 42 km2 (male) and 65 km2 (female). The three other leopards, two adult females and a young male were released close to their capture sites and used small home ranges of 8 km2 (male), 11 km2 and 15 km2 (females). Movement patterns were markedly nocturnal, with hourly step lengths averaging 339±9.5 m (SE) during night and 60±4.1 m during day, and night locations were significantly closer to human settlements than day locations. However, more nocturnal movements were observed among those three living in the areas with high human population densities. These visited houses regularly at nighttime (20% of locations <25 m from houses), but rarely during day (<1%). One leopard living in a sparsely populated area avoided human settlements both day and night. The small home ranges of the leopards indicate that anthropogenic food resources may be plentiful although wild prey is absent. The study provides clear insights into the ability of leopards to live and move in landscapes that are extremely modified by human activity.

  3. Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.

    PubMed

    Vidaurre, C; Schlögl, A; Cabeza, R; Scherer, R; Pfurtscheller, G

    2005-11-01

    We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.

  4. A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification.

    PubMed

    Li, Pu; Chen, Bing; Li, Zelin; Zheng, Xiao; Wu, Hongjing; Jing, Liang; Lee, Kenneth

    2014-09-15

    In this paper, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) model was developed to classify a given site into distinguished zones representing different levels of offshore Oil Spill Vulnerability Index (OSVI). It consisted of an adaptive resonance theory (ART) module, an ART Mapping module, and a centroid determination module. Monte Carlo simulation was integrated with the TSAM approach to address uncertainties that widely exist in site conditions. The applicability of the proposed model was validated by classifying a large coastal area, which was surrounded by potential oil spill sources, based on 12 features. Statistical analysis of the results indicated that the classification process was affected by multiple features instead of one single feature. The classification results also provided the least or desired number of zones which can sufficiently represent the levels of offshore OSVI in an area under uncertainty and complexity, saving time and budget in spill monitoring and response. PMID:25044043

  5. ADAPT: building conceptual models of the physical and biological processes across permafrost landscapes

    NASA Astrophysics Data System (ADS)

    Allard, M.; Vincent, W. F.; Lemay, M.

    2012-12-01

    Fundamental and applied permafrost research is called upon in Canada in support of environmental protection, economic development and for contributing to the international efforts in understanding climatic and ecological feedbacks of permafrost thawing under a warming climate. The five year "Arctic Development and Adaptation to Permafrost in Transition" program (ADAPT) funded by NSERC brings together 14 scientists from 10 Canadian universities and involves numerous collaborators from academia, territorial and provincial governments, Inuit communities and industry. The geographical coverage of the program encompasses all of the permafrost regions of Canada. Field research at a series of sites across the country is being coordinated. A common protocol for measuring ground thermal and moisture regime, characterizing terrain conditions (vegetation, topography, surface water regime and soil organic matter contents) is being applied in order to provide inputs for designing a general model to provide an understanding of transfers of energy and matter in permafrost terrain, and the implications for biological and human systems. The ADAPT mission is to produce an 'Integrated Permafrost Systems Science' framework that will be used to help generate sustainable development and adaptation strategies for the North in the context of rapid socio-economic and climate change. ADAPT has three major objectives: to examine how changing precipitation and warming temperatures affect permafrost geosystems and ecosystems, specifically by testing hypotheses concerning the influence of the snowpack, the effects of water as a conveyor of heat, sediments, and carbon in warming permafrost terrain and the processes of permafrost decay; to interact directly with Inuit communities, the public sector and the private sector for development and adaptation to changes in permafrost environments; and to train the new generation of experts and scientists in this critical domain of research in Canada

  6. Landscape genetics and hierarchical genetic structure in Atlantic salmon: the interaction of gene flow and local adaptation.

    PubMed

    Dionne, Mélanie; Caron, François; Dodson, Julian J; Bernatchez, Louis

    2008-05-01

    Disentangling evolutionary forces that may interact to determine the patterns of genetic differentiation within and among wild populations is a major challenge in evolutionary biology. The objective of this study was to assess the genetic structure and the potential influence of several ecological variables on the extent of genetic differentiation at multiple spatial scales in a widely distributed species, the Atlantic salmon, Salmo salar. A total of 2775 anadromous fish were sampled from 51 rivers along the North American Atlantic coast and were genotyped using 13 microsatellites. A Bayesian analysis clustered these populations into seven genetically and geographically distinct groups, characterized by different environmental and ecological factors, mainly temperature. These groups were also characterized by different extent of genetic differentiation among populations. Dispersal was relatively high and of the same magnitude within compared to among regional groups, which contrasted with the maintenance of a regional genetic structure. However, genetic differentiation was lower among populations exchanging similar rates of local as opposed to inter-regional migrants, over the same geographical scale. This raised the hypothesis that gene flow could be constrained by local adaptation at the regional scale. Both coastal distance and temperature regime were found to influence the observed genetic structure according to landscape genetic analyses. The influence of other factors such as latitude, river length and altitude, migration tactic, and stocking was not significant at any spatial scale. Overall, these results suggested that the interaction between gene flow and thermal regime adaptation mainly explained the hierarchical genetic structure observed among Atlantic salmon populations.

  7. Adaptive landscapes and density-dependent selection in declining salmonid populations: going beyond numerical responses to human disturbance

    PubMed Central

    Einum, Sigurd; Robertsen, Grethe; Fleming, Ian A

    2008-01-01

    Theory suggests an important role for population density in shaping adaptive landscapes through density-dependent selection. Here, we identify five methodological approaches for studying such selection, review the existing empirical evidence for it, and ask whether current declines in abundance can be expected to trigger evolutionary responses in salmonid fishes. Across taxa we find substantial amounts of evidence for population density influencing the location of adaptive peaks for a range of traits, and, in the presence of frequency dependence, changing the shape of selection (stabilizing versus disruptive). For salmonids, biological and theoretical considerations suggest that the optimal value of a number of traits associated with juvenile competitive ability (e.g. egg size, timing of emergence from nests, dominance ability), may depend on population density. For adults, more direct experimental and comparative evidence suggest that secondary sexual traits can be subject to density-dependent selection. There is also evidence that density affects the frequency-dependent selection likely responsible for the expression of alternative male reproductive phenotypes in salmon. Less is known however about the role of density in maintaining genetic variation among juveniles. Further efforts are required to elucidate the indirect evolutionary effects of declining population abundances, both in salmonids and in other anthropogenically challenged organisms. PMID:25567629

  8. Two are better than one: combining landscape genomics and common gardens for detecting local adaptation in forest trees.

    PubMed

    Lepais, Olivier; Bacles, Cecile F

    2014-10-01

    Predicting likely species responses to an alteration of their local environment is key to decision-making in resource management, ecosystem restoration and biodiversity conservation practice in the face of global human-induced habitat disturbance. This is especially true for forest trees which are a dominant life form on Earth and play a central role in supporting diverse communities and structuring a wide range of ecosystems. In Europe, it is expected that most forest tree species will not be able to migrate North fast enough to follow the estimated temperature isocline shift given current predictions for rapid climate warming. In this context, a topical question for forest genetics research is to quantify the ability for tree species to adapt locally to strongly altered environmental conditions (Kremer et al. ). Identifying environmental factors driving local adaptation is, however, a major challenge for evolutionary biology and ecology in general but is particularly difficult in trees given their large individual and population size and long generation time. Empirical evaluation of local adaptation in trees has traditionally relied on fastidious long-term common garden experiments (provenance trials) now supplemented by reference genome sequence analysis for a handful of economically valuable species. However, such resources have been lacking for most tree species despite their ecological importance in supporting whole ecosystems. In this issue of Molecular Ecology, De Kort et al. () provide original and convincing empirical evidence of local adaptation to temperature in black alder, Alnus glutinosa L. Gaertn, a surprisingly understudied keystone species supporting riparian ecosystems. Here, De Kort et al. () use an innovative empirical approach complementing state-of-the-art landscape genomics analysis of A. glutinosa populations sampled in natura across a regional climate gradient with phenotypic trait assessment in a common garden experiment (Fig. ). By

  9. Classification of short-lived objects using an interactive adaptable assistance system

    NASA Astrophysics Data System (ADS)

    El Bekri, Nadia; Angele, Susanne; Peinsipp-Byma, Elisabeth

    2015-05-01

    "Although we know that it is not a familiar object, after a while we can say what it resembles". The core task of an aerial image analyst is to recognize different object types based on certain clearly classified characteristics from aerial or satellite images. An interactive recognition assistance system compares selected features with a fixed set of reference objects (core data set). Therefore it is mainly designed to evaluate durable single objects like a specific type of ship or vehicle. Aerial image analysts on missions realized a changed warfare over the time. The task was not anymore to classify and thereby recognize a single durable object. The problem was that they had to classify strong variable objects and the reference set did not match anymore. In order to approach this new scope we introduce a concept to a further development of the interactive assistance system to be able to handle also short-lived, not clearly classifiable and strong variable objects like for example dhows. Dhows are the type of ships that are often used during pirate attacks at the coast of West Africa. Often these ships were build or extended by the pirates themselves. They follow no particular pattern as the standard construction of a merchant ship. In this work we differ between short-lived and durable objects. The interactive adaptable assistance system is supposed to assist image analysts with the classification of objects, which are new and not listed in the reference set of objects yet. The human interaction and perception is an important factor in order to realize this task and achieve the goal of recognition. Therefore we had to model the possibility to classify short-lived objects with appropriate procedures taking into consideration all aspects of short-lived objects. In this paper we will outline suitable measures and the possibilities to categorize short-lived objects via simple basic shapes as well as a temporary data storage concept for shortlived objects. The

  10. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance.

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

    Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M. PMID:24219635

  11. The peaks and geometry of fitness landscapes.

    PubMed

    Crona, Kristina; Greene, Devin; Barlow, Miriam

    2013-01-21

    Fitness landscapes are central in the theory of adaptation. Recent work compares global and local properties of fitness landscapes. It has been shown that multi-peaked fitness landscapes have a local property called reciprocal sign epistasis interactions. The converse is not true. We show that no condition phrased in terms of reciprocal sign epistasis interactions only, implies multiple peaks. We give a sufficient condition for multiple peaks phrased in terms of two-way interactions. This result is surprising since it has been claimed that no sufficient local condition for multiple peaks exist. We show that our result cannot be generalized to sufficient conditions for three or more peaks. Our proof depends on fitness graphs, where nodes represent genotypes and where arrows point toward more fit genotypes. We also use fitness graphs in order to give a new brief proof of the equivalent characterizations of fitness landscapes lacking genetic constraints on accessible mutational trajectories. We compare a recent geometric classification of fitness landscape based on triangulations of polytopes with qualitative aspects of gene interactions. One observation is that fitness graphs provide information that are not contained in the geometric classification. We argue that a qualitative perspective may help relating theory of fitness landscapes and empirical observations.

  12. An analysis of historic and projected climate scenarios in the Western United States using hydrologic landscape classification.

    EPA Science Inventory

    : Identifying areas of similar hydrology within the United States and its regions (hydrologic landscapes - HLs) is an active area of research. HLs are being used to construct spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, a...

  13. An analysis of historic and projected climate scenarios in the Western united States using hydrologic landscape classification

    EPA Science Inventory

    Identifying areas of similar hydrology within the United States and its regions (Hydrologic landscapes - HLs) is an active area of research. HLs have been used to make spatially distributed assessments of variability in streamflow and climatic response in Oregon, Alaska, and the ...

  14. Key landscape ecology metrics for assessing climate change adaptation options: rate of change and patchiness of impacts

    USGS Publications Warehouse

    López-Hoffman, Laura; Breshears, David D.; Allen, Craig D.; Miller, Marc L.

    2013-01-01

    Under a changing climate, devising strategies to help stakeholders adapt to alterations to ecosystems and their services is of utmost importance. In western North America, diminished snowpack and river flows are causing relatively gradual, homogeneous (system-wide) changes in ecosystems and services. In addition, increased climate variability is also accelerating the incidence of abrupt and patchy disturbances such as fires, floods and droughts. This paper posits that two key variables often considered in landscape ecology—the rate of change and the degree of patchiness of change—can aid in developing climate change adaptation strategies. We use two examples from the “borderland” region of the southwestern United States and northwestern Mexico. In piñon-juniper woodland die-offs that occurred in the southwestern United States during the 2000s, ecosystem services suddenly crashed in some parts of the system while remaining unaffected in other locations. The precise timing and location of die-offs was uncertain. On the other hand, slower, homogeneous change, such as the expected declines in water supply to the Colorado River delta, will likely impact the entire ecosystem, with ecosystem services everywhere in the delta subject to alteration, and all users likely exposed. The rapidity and spatial heterogeneity of faster, patchy climate change exemplified by tree die-off suggests that decision-makers and local stakeholders would be wise to operate under a Rawlsian “veil of ignorance,” and implement adaptation strategies that allow ecosystem service users to equitably share the risk of sudden loss of ecosystem services before actual ecosystem changes occur. On the other hand, in the case of slower, homogeneous, system-wide impacts to ecosystem services as exemplified by the Colorado River delta, adaptation strategies can be implemented after the changes begin, but will require a fundamental rethinking of how ecosystems and services are used and valued. In

  15. Who runs fastest in an adaptive landscape: sexual versus asexual reproduction

    NASA Astrophysics Data System (ADS)

    Holmström, Kerstin; Jensen, Henrik Jeldtoft

    2004-06-01

    We compare the speed with which a sexual, respectively, an asexual, population is able to respond to a biased selective pressure. Our model focuses on the Weismann hypothesis that the extra variation caused by crossing-over and recombination during sexual reproduction allows a sexual population to adapt faster. We find, however, that the extra variation amongst the progeny produced during sexual reproduction for most model parameters is unable to overcome the effect that parents with a high individual fitness in general must mate with individuals of lower individual fitness resulting in a moderate reproductive fitness for the pair.

  16. Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK

    NASA Astrophysics Data System (ADS)

    O'Connell, Jerome; Bradter, Ute; Benton, Tim G.

    2013-10-01

    With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

  17. Adaptation of neurological practice and policy to a changing US health-care landscape.

    PubMed

    Gorelick, Philip B

    2016-04-01

    Health care in the USA is undergoing a drastic transformation under the Patient Protection and Affordable Care Act. The Patient Protection and Affordable Care Act is driving major health-care policy changes by connecting payment for traditional health-care services to value-based care initiatives and emphasising population health and innovative mechanisms to deliver care. Under the Patient Protection and Affordable Care Act, neurological practice will need to adapt and transform. Therefore, neurological policy should consider employing a new framework for neurological residency training, developing interdisciplinary team approaches to neurological subspecialty care, and strengthening the primary care-neurological specialty care interface to avoid redundancies and other medical waste. Additionally, neurological policy will need to support a more robust review of diagnostic and care pathway use to reduce avoidable expenditures, and test and implement bundled payments for key neurological diagnoses. In view of an anticipated 19% shortage of US neurologists in the next 10 years, development of new neurological policy under the Patient Protection and Affordable Care Act is paramount.

  18. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  19. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    PubMed Central

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  20. Field heritability of a plant adaptation to fire in heterogeneous landscapes.

    PubMed

    Castellanos, M C; González-Martínez, S C; Pausas, J G

    2015-11-01

    The strong association observed between fire regimes and variation in plant adaptations to fire suggests a rapid response to fire as an agent of selection. It also suggests that fire-related traits are heritable, a precondition for evolutionary change. One example is serotiny, the accumulation of seeds in unopened fruits or cones until the next fire, an important strategy for plant population persistence in fire-prone ecosystems. Here, we evaluate the potential of this trait to respond to natural selection in its natural setting. For this, we use a SNP marker approach to estimate genetic variance and heritability of serotiny directly in the field for two Mediterranean pine species. Study populations were large and heterogeneous in climatic conditions and fire regime. We first estimated the realized relatedness among trees from genotypes, and then partitioned the phenotypic variance in serotiny using Bayesian animal models that incorporated environmental predictors. As expected, field heritability was smaller (around 0.10 for both species) than previous estimates under common garden conditions (0.20). An estimate on a subset of stands with more homogeneous environmental conditions was not different from that in the complete set of stands, suggesting that our models correctly captured the environmental variation at the spatial scale of the study. Our results highlight the importance of measuring quantitative genetic parameters in natural populations, where environmental heterogeneity is a critical aspect. The heritability of serotiny, although not high, combined with high phenotypic variance within populations, confirms the potential of this fire-related trait for evolutionary change in the wild.

  1. The population genomic landscape of human genetic structure, admixture history and local adaptation in Peninsular Malaysia.

    PubMed

    Deng, Lian; Hoh, Boon Peng; Lu, Dongsheng; Fu, Ruiqing; Phipps, Maude E; Li, Shilin; Nur-Shafawati, Ab Rajab; Hatin, Wan Isa; Ismail, Endom; Mokhtar, Siti Shuhada; Jin, Li; Zilfalil, Bin Alwi; Marshall, Christian R; Scherer, Stephen W; Al-Mulla, Fahd; Xu, Shuhua

    2014-09-01

    Peninsular Malaysia is a strategic region which might have played an important role in the initial peopling and subsequent human migrations in Asia. However, the genetic diversity and history of human populations--especially indigenous populations--inhabiting this area remain poorly understood. Here, we conducted a genome-wide study using over 900,000 single nucleotide polymorphisms (SNPs) in four major Malaysian ethnic groups (MEGs; Malay, Proto-Malay, Senoi and Negrito), and made comparisons of 17 world-wide populations. Our data revealed that Peninsular Malaysia has greater genetic diversity corresponding to its role as a contact zone of both early and recent human migrations in Asia. However, each single Orang Asli (indigenous) group was less diverse with a smaller effective population size (N(e)) than a European or an East Asian population, indicating a substantial isolation of some duration for these groups. All four MEGs were genetically more similar to Asian populations than to other continental groups, and the divergence time between MEGs and East Asian populations (12,000--6,000 years ago) was also much shorter than that between East Asians and Europeans. Thus, Malaysian Orang Asli groups, despite their significantly different features, may share a common origin with the other Asian groups. Nevertheless, we identified traces of recent gene flow from non-Asians to MEGs. Finally, natural selection signatures were detected in a batch of genes associated with immune response, human height, skin pigmentation, hair and facial morphology and blood pressure in MEGs. Notable examples include SYN3 which is associated with human height in all Orang Asli groups, a height-related gene (PNPT1) and two blood pressure-related genes (CDH13 and PAX5) in Negritos. We conclude that a long isolation period, subsequent gene flow and local adaptations have jointly shaped the genetic architectures of MEGs, and this study provides insight into the peopling and human migration

  2. Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

    PubMed Central

    Anifah, Lilik; Purnama, I Ketut Eddy; Hariadi, Mochamad; Purnomo, Mauridhi Hery

    2013-01-01

    Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4. PMID:23525188

  3. FMRI-adaptation to highly-rendered color photographs of animals and manipulable artifacts during a classification task.

    PubMed

    Chouinard, Philippe A; Goodale, Melvyn A

    2012-02-01

    We used fMRI to identify brain areas that adapted to either animals or manipulable artifacts while participants classified highly-rendered color photographs into subcategories. Several key brain areas adapted more strongly to one class of objects compared to the other. Namely, we observed stronger adaptation for animals in the lingual gyrus bilaterally, which are known to analyze the color of objects, and in the right frontal operculum and in the anterior insular cortex bilaterally, which are known to process emotional content. In contrast, the left anterior intraparietal sulcus, which is important for configuring the hand to match the three-dimensional structure of objects during grasping, adapted more strongly to manipulable artifacts. Contrary to what a previous study has found using gray-scale photographs, we did not replicate categorical-specific adaptation in the lateral fusiform gyrus for animals and categorical-specific adaptation in the medial fusiform gyrus for manipulable artifacts. Both categories of objects adapted strongly in the fusiform gyrus without any clear preference in location along its medial-lateral axis. We think that this is because the fusiform gyrus has an important role to play in color processing and hence its responsiveness to color stimuli could be very different than its responsiveness to gray-scale photographs. Nevertheless, on the basis of what we found, we propose that the recognition and subsequent classification of animals may depend primarily on perceptual properties, such as their color, and on their emotional content whereas other factors, such as their function, may play a greater role for classifying manipulable artifacts.

  4. Domain adaptation for land use classification: A spatio-temporal knowledge reusing method

    NASA Astrophysics Data System (ADS)

    Liu, Yilun; Li, Xia

    2014-12-01

    Land use classification requires a significant amount of labeled data, which may be difficult and time consuming to obtain. On the other hand, without a sufficient number of training samples, conventional classifiers are unable to produce satisfactory classification results. This paper aims to overcome this issue by proposing a new model, TrCbrBoost, which uses old domain data to successfully train a classifier for mapping the land use types of target domain when new labeled data are unavailable. TrCbrBoost adopts a fuzzy CBR (Case Based Reasoning) model to estimate the land use probabilities for the target (new) domain, which are subsequently used to estimate the classifier performance. Source (old) domain samples are used to train the classifiers of a revised TrAdaBoost algorithm in which the weight of each sample is adjusted according to the classifier's performance. This method is tested using time-series SPOT images for land use classification. Our experimental results indicate that TrCbrBoost is more effective than traditional classification models, provided that sufficient amount of old domain data is available. Under these conditions, the proposed method is 9.19% more accurate.

  5. Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape

    NASA Astrophysics Data System (ADS)

    Mueller-Warrant, George W.; Whittaker, Gerald W.; Banowetz, Gary M.; Griffith, Stephen M.; Barnhart, Bradley L.

    2015-06-01

    Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region. Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud- and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Final classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95% overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an

  6. Response and adaptation of grapevine cultivars to hydrological conditions forced by a changing climate in a complex landscape

    NASA Astrophysics Data System (ADS)

    De Lorenzi, Francesca; Bonfante, Antonello; Alfieri, Silvia Maria; Monaco, Eugenia; De Mascellis, Roberto; Manna, Piero; Menenti, Massimo

    2014-05-01

    Soil water availability is one of the main components of the terroir concept, influencing crop yield and fruit composition in grapes. The aim of this work is to analyze some elements of the "natural environment" of terroir (climate and soil) in combination with the intra-specific biodiversity of yield responses of grapevine to water availability. From a reference (1961-90) to a future (2021-50) climate case, the effects of climate evolution on soil water availability are assessed and, regarding soil water regime as a predictor variable, the potential spatial distribution of wine-producing cultivars is determined. In a region of Southern Italy (Valle Telesina, 20,000 ha), where a terroir classification has been produced (Bonfante et al., 2011), we applied an agro-hydrological model to determine water availability indicators. Simulations were performed in 60 soil typological units, over the entire study area, and water availability (= hydrological) indicators were determined. Two climate cases were considered: reference (1961-90) and future (2021-2050), the former from climatic statistics on observed variables, and the latter from statistical downscaling of predictions by general circulation models (AOGCM) under A1B SRES scenario. Climatic data consist of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. Spatial and temporal variability of hydrological indicators was addressed. With respect to temporal variability, both inter-annual and intra-annual (i.e. at different stages of crop cycle) variability were analyzed. Some cultivar-specific relations between hydrological indicators and characteristics of must quality were established. Moreover, for several wine-producing cultivars, hydrological requirements were determined by means of yield response functions to soil water availability, through the re-analysis of experimental data derived from scientific literature. The standard errors of estimated

  7. Adaptive probabilistic neural networks for pattern classification in time-varying environment.

    PubMed

    Rutkowski, Leszek

    2004-07-01

    In this paper, we propose a new class of probabilistic neural networks (PNNs) working in nonstationary environment. The novelty is summarized as follows: 1) We formulate the problem of pattern classification in nonstationary environment as the prediction problem and design a probabilistic neural network to classify patterns having time-varying probability distributions. We note that the problem of pattern classification in the nonstationary case is closely connected with the problem of prediction because on the basis of a learning sequence of the length n, a pattern in the moment n + k, k > or = 1 should be classified. 2) We present, for the first time in literature, definitions of optimality of PNNs in time-varying environment. Moreover, we prove that our PNNs asymptotically approach the Bayes-optimal (time-varying) decision surface. 3) We investigate the speed of convergence of constructed PNNs. 4) We design in detail PNNs based on Parzen kernels and multivariate Hermite series.

  8. Testing the Hydrological Landscape Unit Classification System and Other Terrain Analysis Measures for Predicting Low-Flow Nitrate and Chloride in Watersheds

    NASA Astrophysics Data System (ADS)

    Poor, Cara J.; McDonnell, Jeffrey J.; Bolte, John

    2008-11-01

    Elevated nitrate concentrations in streamwater are a major environmental management problem. While land use exerts a large control on stream nitrate, hydrology often plays an equally important role. To date, predictions of low-flow nitrate in ungauged watersheds have been poor because of the difficulty in describing the uniqueness of watershed hydrology over large areas. Clearly, hydrologic response varies depending on the states and stocks of water, flow pathways, and residence times. How to capture the dominant hydrological controls that combine with land use to define streamwater nitrate concentration is a major research challenge. This paper tests the new Hydrologic Landscape Regions (HLRs) watershed classification scheme of Wolock and others (Environmental Management 34:S71-S88, 2004) to address the question: Can HLRs be used as a way to predict low-flow nitrate? We also test a number of other indexes including inverse-distance weighting of land use and the well-known topographic index (TI) to address the question: How do other terrain and land use measures compare to HLR in terms of their ability to predict low-flow nitrate concentration? We test this for 76 watersheds in western Oregon using the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program and Regional Environmental Monitoring and Assessment Program data. We found that HLRs did not significantly improve nitrate predictions beyond the standard TI and land-use metrics. Using TI and inverse-distance weighting did not improve nitrate predictions; the best models were the percentage land use—elevation models. We did, however, see an improvement of chloride predictions using HLRs, TI, and inverse-distance weighting; adding HLRs and TI significantly improved model predictions and the best models used inverse-distance weighting and elevation. One interesting result of this study is elevation consistently predicted nitrate better than TI or the hydrologic classification

  9. Adaptive filter for mine detection and classification in side-scan sonar imagery

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Antoni, Diana; Fernandez, Manuel F.; Dobeck, Gerald J.

    1995-06-01

    A need exists to develop robust automatic techniques for discriminating between minelike target and clutter returns in sonar imagery. To address this need, an adaptive clutter suppression linear FIR filtering technique has been developed and applied to side scan sonar imagery data. The adaptive filtering procedure consists of four stages. First, a normalized average target signature (shape) within the filter window is computed using training set data. Second, the background clutter covariance matrix is computed by scanning the filter window over the data. Third, following substitutions of the average target signature and covariance expressions into a set of normal equations, an adaptive filter is computed which simultaneously suppresses the background clutter while preserving the peak of the average target signature. Finally, the data is filtered using the 2D adaptive range-crossrange filter. The overall mine detection processing string includes automatic gain control, data decimation, adaptive clutter filtering (ACF), 2D normalization, thresholding, exceedance clustering, limiting the number of exceedances and secondary thresholding processing blocks. The utility of the ACF processing string was demonstrated with three side scan sonar datasets. The ACF algorithm provided average probability of detection and false alarm rate performance similar to that obtained when utilizing an expert sonar operator.

  10. Travelling in the eastern Mediterranean with landscape character assessment

    NASA Astrophysics Data System (ADS)

    Abu Jaber, N.; Abunnasr, Y.; Abu Yahya, A.; Boulad, N.; Christou, O.; Dimitropoulos, G.; Dimopoulos, T.; Gkoltsiou, K.; Khreis, N.; Manolaki, P.; Michael, K.; Odeh, T.; Papatheodoulou, A.; Sorotou, A.; Sinno, S.; Suliman, O.; Symons, N.; Terkenli, T.; Trigkas, Vassilis; Trovato, M. G.; Victora, M.; Zomeni, M.; Vogiatzakis, I. N.

    2015-06-01

    Following its application in Northern Europe, Landscape Character Assessment has also been implemented in Euro-Mediterranean countries as a tool for classifying, describing and assessing landscapes. Many landscape classifications employed in the Euro-Mediterranean area are similar in philosophy and application to the ones developed in Northern Europe. However, many aspects of landform, climate, land-use and ecology, as well as socio-economic context are distinctive of Mediterranean landscapes. The paper discusses the conceptual and methodological issues faced during landscape mapping and characterisation in four East-Mediterranean countries (within the MEDSCAPES project): Cyprus, Greece, Jordan and Lebanon. The major hurdles to overcome during the first phase of methodology development include variation in availability, quality, scale and coverage of spatial datasets between countries and also terminology semantics around landscapes. For example, the concept of landscape - a well-defined term in Greek and English - did not exist in Arabic. Another issue is the use of relative terms like 'high mountains,' `uplands' `lowlands' or ' hills'. Such terms, which are regularly used in landscape description, were perceived slightly differently in the four participating countries. In addition differences exist in nomenclature and classification systems used by each country for the dominant landscape-forming factors i.e. geology, soils and land use- but also in the cultural processes shaping the landscapes - compared both to each other and to the Northern-European norms. This paper argues for the development of consistent, regionally adapted, relevant and standardised methodologies if the results and application of LCA in the eastern Mediterranean region are to be transferable and comparable between countries.

  11. Classification of the Hearing Impaired for Independent Living Using the Vineland Adaptive Behavior Scale.

    ERIC Educational Resources Information Center

    Dunlap, William R.; Sands, Deanna Iceman

    1990-01-01

    The Vineland Adaptive Behavior Scale was used to classify 118 hearing-impaired persons (88 percent were ages 16-21) into groups based on their ability to be trained in independent living skills. Using cluster analysis, the subjects were placed into three groups according to four domains: communication, daily living, socialization, and maladaptive…

  12. CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems.

    PubMed

    Lange, Sita J; Alkhnbashi, Omer S; Rose, Dominic; Will, Sebastian; Backofen, Rolf

    2013-09-01

    Central to Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas systems are repeated RNA sequences that serve as Cas-protein-binding templates. Classification is based on the architectural composition of associated Cas proteins, considering repeat evolution is essential to complete the picture. We compiled the largest data set of CRISPRs to date, performed comprehensive, independent clustering analyses and identified a novel set of 40 conserved sequence families and 33 potential structure motifs for Cas-endoribonucleases with some distinct conservation patterns. Evolutionary relationships are presented as a hierarchical map of sequence and structure similarities for both a quick and detailed insight into the diversity of CRISPR-Cas systems. In a comparison with Cas-subtypes, I-C, I-E, I-F and type II were strongly coupled and the remaining type I and type III subtypes were loosely coupled to repeat and Cas1 evolution, respectively. Subtypes with a strong link to CRISPR evolution were almost exclusive to bacteria; nevertheless, we identified rare examples of potential horizontal transfer of I-C and I-E systems into archaeal organisms. Our easy-to-use web server provides an automated assignment of newly sequenced CRISPRs to our classification system and enables more informed choices on future hypotheses in CRISPR-Cas research: http://rna.informatik.uni-freiburg.de/CRISPRmap.

  13. CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems

    PubMed Central

    Lange, Sita J.; Alkhnbashi, Omer S.; Rose, Dominic; Will, Sebastian; Backofen, Rolf

    2013-01-01

    Central to Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas systems are repeated RNA sequences that serve as Cas-protein–binding templates. Classification is based on the architectural composition of associated Cas proteins, considering repeat evolution is essential to complete the picture. We compiled the largest data set of CRISPRs to date, performed comprehensive, independent clustering analyses and identified a novel set of 40 conserved sequence families and 33 potential structure motifs for Cas-endoribonucleases with some distinct conservation patterns. Evolutionary relationships are presented as a hierarchical map of sequence and structure similarities for both a quick and detailed insight into the diversity of CRISPR-Cas systems. In a comparison with Cas-subtypes, I-C, I-E, I-F and type II were strongly coupled and the remaining type I and type III subtypes were loosely coupled to repeat and Cas1 evolution, respectively. Subtypes with a strong link to CRISPR evolution were almost exclusive to bacteria; nevertheless, we identified rare examples of potential horizontal transfer of I-C and I-E systems into archaeal organisms. Our easy-to-use web server provides an automated assignment of newly sequenced CRISPRs to our classification system and enables more informed choices on future hypotheses in CRISPR-Cas research: http://rna.informatik.uni-freiburg.de/CRISPRmap. PMID:23863837

  14. CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems.

    PubMed

    Lange, Sita J; Alkhnbashi, Omer S; Rose, Dominic; Will, Sebastian; Backofen, Rolf

    2013-09-01

    Central to Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR)-Cas systems are repeated RNA sequences that serve as Cas-protein-binding templates. Classification is based on the architectural composition of associated Cas proteins, considering repeat evolution is essential to complete the picture. We compiled the largest data set of CRISPRs to date, performed comprehensive, independent clustering analyses and identified a novel set of 40 conserved sequence families and 33 potential structure motifs for Cas-endoribonucleases with some distinct conservation patterns. Evolutionary relationships are presented as a hierarchical map of sequence and structure similarities for both a quick and detailed insight into the diversity of CRISPR-Cas systems. In a comparison with Cas-subtypes, I-C, I-E, I-F and type II were strongly coupled and the remaining type I and type III subtypes were loosely coupled to repeat and Cas1 evolution, respectively. Subtypes with a strong link to CRISPR evolution were almost exclusive to bacteria; nevertheless, we identified rare examples of potential horizontal transfer of I-C and I-E systems into archaeal organisms. Our easy-to-use web server provides an automated assignment of newly sequenced CRISPRs to our classification system and enables more informed choices on future hypotheses in CRISPR-Cas research: http://rna.informatik.uni-freiburg.de/CRISPRmap. PMID:23863837

  15. Crop classification by forward neural network with adaptive chaotic particle swarm optimization.

    PubMed

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10(-7) s.

  16. Supervised adaptive Hamming net for classification of multiple-valued patterns.

    PubMed

    Hung, C A; Lin, S F

    1997-04-01

    A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequences of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code. PMID:9327274

  17. Studying the Effect of Adaptive Momentum in Improving the Accuracy of Gradient Descent Back Propagation Algorithm on Classification Problems

    NASA Astrophysics Data System (ADS)

    Rehman, Muhammad Zubair; Nawi, Nazri Mohd.

    Despite being widely used in the practical problems around the world, Gradient Descent Back-propagation algorithm comes with problems like slow convergence and convergence to local minima. Previous researchers have suggested certain modifications to improve the convergence in gradient Descent Back-propagation algorithm such as careful selection of input weights and biases, learning rate, momentum, network topology, activation function and value for 'gain' in the activation function. This research proposed an algorithm for improving the working performance of back-propagation algorithm which is 'Gradient Descent with Adaptive Momentum (GDAM)' by keeping the gain value fixed during all network trials. The performance of GDAM is compared with 'Gradient Descent with fixed Momentum (GDM)' and 'Gradient Descent Method with Adaptive Gain (GDM-AG)'. The learning rate is fixed to 0.4 and maximum epochs are set to 3000 while sigmoid activation function is used for the experimentation. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like Wine Quality, Mushroom and Thyroid disease.

  18. An adaptive image segmentation process for the classification of lung biopsy images

    NASA Astrophysics Data System (ADS)

    McKee, Daniel W.; Land, Walker H., Jr.; Zhukov, Tatyana; Song, Dansheng; Qian, Wei

    2006-03-01

    The purpose of this study was to develop a computer-based second opinion diagnostic tool that could read microscope images of lung tissue and classify the tissue sample as normal or cancerous. This problem can be broken down into three areas: segmentation, feature extraction and measurement, and classification. We introduce a kernel-based extension of fuzzy c-means to provide a coarse initial segmentation, with heuristically-based mechanisms to improve the accuracy of the segmentation. The segmented image is then processed to extract and quantify features. Finally, the measured features are used by a Support Vector Machine (SVM) to classify the tissue sample. The performance of this approach was tested using a database of 85 images collected at the Moffitt Cancer Center and Research Institute. These images represent a wide variety of normal lung tissue samples, as well as multiple types of lung cancer. When used with a subset of the data containing images from the normal and adenocarcinoma classes, we were able to correctly classify 78% of the images, with a ROC A Z of 0.758.

  19. Improved Correlation of the Neuropathologic Classification According to Adapted World Health Organization Classification and Outcome After Radiotherapy in Patients With Atypical and Anaplastic Meningiomas

    SciTech Connect

    Combs, Stephanie E.; Schulz-Ertner, Daniela; Debus, Juergen; Deimling, Andreas von; Hartmann, Christian

    2011-12-01

    Purpose: To evaluate the correlation between the 1993 and 2000/2007 World Health Organization (WHO) classification with the outcome in patients with high-grade meningiomas. Patients and Methods: Between 1985 and 2004, 73 patients diagnosed with atypical or anaplastic meningiomas were treated with radiotherapy. Sections from the paraffin-embedded tumor material from 66 patients (90%) from 13 different pathology departments were re-evaluated according to the first revised WHO classification from 1993 and the revised classifications from 2000/2007. In 4 cases, the initial diagnosis meningioma was not reproducible (5%). Therefore, 62 patients with meningiomas were analyzed. Results: All 62 tumors were reclassified according to the 1993 and 2000/2007 WHO classification systems. Using the 1993 system, 7 patients were diagnosed with WHO grade I meningioma (11%), 23 with WHO grade II (37%), and 32 with WHO grade III meningioma (52%). After scoring using the 2000/2007 system, we found 17 WHO grade I meningiomas (27%), 32 WHO grade II meningiomas (52%), and 13 WHO grade III meningiomas (21%). According to the 1993 classification, the difference in overall survival was not statistically significant among the histologic subgroups (p = .96). Using the 2000/2007 WHO classifications, the difference in overall survival became significant (p = .02). Of the 62 reclassified patients 29 developed tumor progression (47%). No difference in progression-free survival was observed among the histologic subgroups (p = .44). After grading according to the 2000/2007 WHO classifications, significant differences in progression-free survival were observed among the three histologic groups (p = .005). Conclusion: The new 2000/2007 WHO classification for meningiomas showed an improved correlation between the histologic grade and outcome. This classification therefore provides a useful basis to determine the postoperative indication for radiotherapy. According to our results, a comparison of the

  20. The 'Functional Landscape Approach': Building a socio-ecological evidence base for its contribution to adaptation and resilience in wetland catchments.

    NASA Astrophysics Data System (ADS)

    Carrie, Rachael; Dixon, Alan

    2015-04-01

    Sustainable land management is increasingly taking a landscape approach to advocate simultaneously for local and multiple stakeholder-negotiated development and environmental objectives. Landscape approaches advance earlier frameworks that failed to acknowledge or reconcile either biodiversity or societal trade-offs, and that often tended toward externally-derived or imposed management interventions. Most recently, the management of land to balance biodiversity, food security and ecosystem services outcomes has been informed by socio-ecological systems thinking that seeks to promote an interdisciplinary understanding of any given 'landscape' where environmental and social factors continually interact in complex, adaptive and resilient ways. Reflecting these concepts, and integrating local and external scientific knowledge, the Functional Landscape Approach (FLA) was developed by Wetland Action, focussing on wetland systems in rural sub-Saharan Africa to contribute to environmentally sensitive and climate resilient strategies for safeguarding essential ecosystem services and improving livelihoods and well-being. In particular, the FLA stresses the ways in which land productivity can be improved through supporting, strengthening or re-establishing functional linkages between wetlands and their catchments and provides a basis for local identification of specific interventions to improve the sustainability of land use. Crucially, it also emphasises the need for community-based institutional support and the importance of incentives through market linkages and value-chain development. In this paper we will describe our experiences of developing and implementing the FLA in Ethiopia, Zambia and Malawi over the past two decades. Drawing on successful and less-successful elements of participatory planning, monitoring and evaluation, and the facilitation of long-term sustainable benefits, we will discuss some of the accomplishments and challenges that can be associated with

  1. Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics.

    PubMed

    Hand, Brian K; Muhlfeld, Clint C; Wade, Alisa A; Kovach, Ryan P; Whited, Diane C; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W; Garner, Brittany A; Kimball, John S; Stanford, Jack A; Luikart, Gordon

    2016-02-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics. PMID:26677031

  2. Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics

    USGS Publications Warehouse

    Hand, Brian K; Muhlfeld, Clint C.; Wade, Alisa A.; Kovach, Ryan; Whited, Diane C.; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W.; Garner, B. A.; Kimball, John S; Stanford, Jack A.; Luikart, Gordon

    2016-01-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

  3. Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics.

    PubMed

    Hand, Brian K; Muhlfeld, Clint C; Wade, Alisa A; Kovach, Ryan P; Whited, Diane C; Narum, Shawn R; Matala, Andrew P; Ackerman, Michael W; Garner, Brittany A; Kimball, John S; Stanford, Jack A; Luikart, Gordon

    2016-02-01

    Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

  4. Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

    NASA Astrophysics Data System (ADS)

    Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

    2008-08-01

    SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise

  5. Luminous Landscapes.

    ERIC Educational Resources Information Center

    Okrent, Inez

    2000-01-01

    Describes an activity for third-grade students in which they learn about early American landscape painters, specifically Frederick Church, Thomas Moran, and Albert Bierstadt. Students create natural landscapes, using the basic elements of landscape compositions. Discusses the process. (CMK)

  6. Functional decoupling between flowers and leaves in the Ameroglossum pernambucense complex can facilitate local adaptation across a pollinator and climatic heterogeneous landscape.

    PubMed

    Wanderley, A M; Galetto, L; Machado, I C S

    2016-03-01

    Decoupling between floral and leaf traits is expected in plants with specialized pollination systems to assure a precise flower-pollinator fit, irrespective of leaf variation associated with environmental heterogeneity (functional modularity). Nonetheless, developmental interactions among floral traits also decouple flowers from leaves regardless of selection pressures (developmental modularity). We tested functional modularity in the hummingbird-pollinated flowers of the Ameroglossum pernambucense complex while controlling for developmental modularity. Using two functional traits responsible for flower-pollinator fit [floral tube length (TL) and anther-nectary distance (AN)], one floral trait not linked to pollination [sepal length (SL), control for developmental modularity] and one leaf trait [leaf length (LL)], we found evidence of flower functional modularity. Covariation between TL and AN was ca. two-fold higher than the covariation of either of these traits with sepal and leaf lengths, and variations in TL and AN, important for a precise flower-pollinator fit, were smaller than SL and LL variations. Furthermore, we show that previously reported among-population variation of flowers associated with local pollinator phenotypes was independent from SL and LL variations. These results suggest that TL and AN are functionally linked to fit pollinators and sufficiently decoupled from developmentally related floral traits (SL) and vegetative traits (LL). These results support previous evidences of population differentiation due to local adaptation in the A. pernambucense complex and shed light on the role of flower-leaf decoupling for local adaptation in species distributed across biotic and abiotic heterogeneous landscapes. PMID:26663030

  7. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery

    EPA Science Inventory

    Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional ...

  8. Multi-temporal land cover classification of the Konya Basin, south-central Turkey, based on a LANDSAT TM-derived NDVI/NDMI time series: satellite remote sensing in support of landscape-scale soil biogeochemistry research

    NASA Astrophysics Data System (ADS)

    Mayes, M. T.; Ozdogan, M.; Marin-Spiotta, E.

    2010-12-01

    Recently, terrestrial biogeochemists and soil scientists have called for new approaches to study human impacts on soil biogeochemical properties at landscape-wide, 100-1000 km2 spatial scales (Trumbore and Czimczik 2008). Here, we use satellite remote sensing to map land cover across a 16,000 km2 region in the Konya Basin, south-central Turkey, in support of research into agricultural and pastoral land use impacts on soil biogeochemistry. Our land cover classification is based on time series analysis of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) data, derived from eight LANDSAT TM images spanning the 2006-2007 growing seasons. Using a hierarchical, binary-split classification approach and a support vector machine (SVM) algorithm, we map five land cover classes that correspond with the following dominant land-use categories: 1) annual cultivated row-crops, 2) perennial orchards/cultivated woody vegetation, 3) fallow fields, 4) uncultivated woody vegetation, 5) steppe vegetation/rangeland. The final map has an overall classification accuracy of 87.4% (kappa = 0.842), determined via traditional confusion-matrix analysis and over 150 site visits during summer 2010. Classes 1 and 2, which have the highest per-pixel NDVI and NDMI sums across image dates, attain the highest producer and consumer accuracies (>95%). We also compare the relative contributions and efficacy of NDVI and NDMI in separating land cover classes, and the influence of radiometric correction and calibration across image dates on classification accuracies. Our results support previous research showing that NDVI time series can effectively classify agricultural landscapes in semi-arid to arid environments (Simonneaux et al. 2008; Pax-Lenny et al. 1996). By combining our land cover map with other geospatial information in a GIS, we demonstrate how satellite remote sensing can help expand spatial scales of terrestrial biogeochemistry research from

  9. Reducing the overall HIV-burden in South Africa: is 'reviving ABC' an appropriate fit for a complex, adaptive epidemiological HIV landscape?

    PubMed

    Burman, Christopher J; Aphane, Marota; Delobelle, Peter

    2015-01-01

    This article questions the recommendations to 'revive ABC (abstain, be faithful, condomise)' as a mechanism to 'educate' people in South Africa about HIV prevention as the South African National HIV Prevalence, Incidence and Behaviour Survey, 2012, suggests. We argue that ABC was designed as a response to a particular context which has now radically changed. In South Africa the contemporary context reflects the mass roll-out of antiretroviral treatment; significant bio-medical knowledge gains; a generalised population affected by HIV that has made sense of and embodied those diverse experiences; and a government committed to confronting the epidemic. We suggest that the situation can now be plausibly conceptualised as a complex, adaptive epidemiological landscape that could benefit from an expansion of the existing, 'descriptive' prevention paradigm towards strategies that focus on the dynamics of transmission. We argue for this shift by proposing a theoretical framework based on complexity theory and pattern management. We interrogate one educational prevention heuristic that emphasises the importance of risk-reduction through the lens of transmission, called A-3B-4C-T. We argue that this type of approach provides expansive opportunities for people to engage with the epidemic in contextualised, innovative ways that supersede the opportunities afforded by ABC. We then suggest that framing the prevention imperative through the lens of 'dynamic prevention' at scale opens more immediate opportunities, as well as developing a future-oriented mind-set, than the 'descriptive prevention' parameters can facilitate. The parameters of the 'descriptive prevention' paradigm, that maintain - and partially reinforce - the presence of ABC, do not have the flexibility required to develop the armamentarium of tools required to contribute to the management of a complex epidemiological landscape. Uncritically adhering to both the 'descriptive paradigm', and ABC, represents an

  10. Quasispecies on Fitness Landscapes.

    PubMed

    Schuster, Peter

    2016-01-01

    Selection-mutation dynamics is studied as adaptation and neutral drift on abstract fitness landscapes. Various models of fitness landscapes are introduced and analyzed with respect to the stationary mutant distributions adopted by populations upon them. The concept of quasispecies is introduced, and the error threshold phenomenon is analyzed. Complex fitness landscapes with large scatter of fitness values are shown to sustain error thresholds. The phenomenological theory of the quasispecies introduced in 1971 by Eigen is compared to approximation-free numerical computations. The concept of strong quasispecies understood as mutant distributions, which are especially stable against changes in mutations rates, is presented. The role of fitness neutral genotypes in quasispecies is discussed.

  11. [Landscape and ecological genomics].

    PubMed

    2013-10-01

    Landscape genomics is the modern version of landscape genetics, a discipline that arose approximately 10 years ago as a combination of population genetics, landscape ecology, and spatial statistics. It studies the effects of environmental variables on gene flow and other microevolutionary processes that determine genetic connectivity and variations in populations. In contrast to population genetics, it operates at the level of individual specimens rather than at the level of population samples. Another important difference between landscape genetics and genomics and population genetics is that, in the former, the analysis of gene flow and local adaptations takes quantitative account of landforms and features of the matrix, i.e., hostile spaces that separate species habitats. Landscape genomics is a part of population ecogenomics, which, along with community genomics, is a major part of ecological genomics. One of the principal purposes of landscape genomics is the identification and differentiation of various genome-wide and locus-specific effects. The approaches and computation tools developed for combined analysis of genomic and landscape variables make it possible to detect adaptation-related genome fragments, which facilitates the planning of conservation efforts and the prediction of species' fate in response to expected changes in the environment. PMID:25508669

  12. [Landscape and ecological genomics].

    PubMed

    Tetushkin, E Ia

    2013-10-01

    Landscape genomics is the modern version of landscape genetics, a discipline that arose approximately 10 years ago as a combination of population genetics, landscape ecology, and spatial statistics. It studies the effects of environmental variables on gene flow and other microevolutionary processes that determine genetic connectivity and variations in populations. In contrast to population genetics, it operates at the level of individual specimens rather than at the level of population samples. Another important difference between landscape genetics and genomics and population genetics is that, in the former, the analysis of gene flow and local adaptations takes quantitative account of landforms and features of the matrix, i.e., hostile spaces that separate species habitats. Landscape genomics is a part of population ecogenomics, which, along with community genomics, is a major part of ecological genomics. One of the principal purposes of landscape genomics is the identification and differentiation of various genome-wide and locus-specific effects. The approaches and computation tools developed for combined analysis of genomic and landscape variables make it possible to detect adaptation-related genome fragments, which facilitates the planning of conservation efforts and the prediction of species' fate in response to expected changes in the environment. PMID:25474890

  13. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  14. Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

    NASA Astrophysics Data System (ADS)

    Conrad, Christopher; Machwitz, Miriam; Schorcht, Gunther; Löw, Fabian; Fritsch, Sebastian; Dech, Stefan

    2011-11-01

    In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

  15. The farmer as a landscape steward: Comparing local understandings of landscape stewardship, landscape values, and land management actions.

    PubMed

    Raymond, Christopher M; Bieling, Claudia; Fagerholm, Nora; Martin-Lopez, Berta; Plieninger, Tobias

    2016-03-01

    We develop a landscape stewardship classification which distinguishes between farmers' understanding of landscape stewardship, their landscape values, and land management actions. Forty semi-structured interviews were conducted with small-holder (<5 acres), medium-holders (5-100 acres), and large-holders (>100 acres) in South-West Devon, UK. Thematic analysis revealed four types of stewardship understandings: (1) an environmental frame which emphasized the farmers' role in conserving or restoring wildlife; (2) a primary production frame which emphasized the farmers' role in taking care of primary production assets; (3) a holistic frame focusing on farmers' role as a conservationist, primary producer, and manager of a range of landscape values, and; (4) an instrumental frame focusing on the financial benefits associated with compliance with agri-environmental schemes. We compare the landscape values and land management actions that emerged across stewardship types, and discuss the global implications of the landscape stewardship classification for the engagement of farmers in landscape management.

  16. Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

    PubMed

    Van Beurden, Eric K; Kia, Annie M; Zask, Avigdor; Dietrich, Uta; Rose, Lauren

    2013-03-01

    Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches. PMID:22128193

  17. Making sense in a complex landscape: how the Cynefin Framework from Complex Adaptive Systems Theory can inform health promotion practice.

    PubMed

    Van Beurden, Eric K; Kia, Annie M; Zask, Avigdor; Dietrich, Uta; Rose, Lauren

    2013-03-01

    Health promotion addresses issues from the simple (with well-known cause/effect links) to the highly complex (webs and loops of cause/effect with unpredictable, emergent properties). Yet there is no conceptual framework within its theory base to help identify approaches appropriate to the level of complexity. The default approach favours reductionism--the assumption that reducing a system to its parts will inform whole system behaviour. Such an approach can yield useful knowledge, yet is inadequate where issues have multiple interacting causes, such as social determinants of health. To address complex issues, there is a need for a conceptual framework that helps choose action that is appropriate to context. This paper presents the Cynefin Framework, informed by complexity science--the study of Complex Adaptive Systems (CAS). It introduces key CAS concepts and reviews the emergence and implications of 'complex' approaches within health promotion. It explains the framework and its use with examples from contemporary practice, and sets it within the context of related bodies of health promotion theory. The Cynefin Framework, especially when used as a sense-making tool, can help practitioners understand the complexity of issues, identify appropriate strategies and avoid the pitfalls of applying reductionist approaches to complex situations. The urgency to address critical issues such as climate change and the social determinants of health calls for us to engage with complexity science. The Cynefin Framework helps practitioners make the shift, and enables those already engaged in complex approaches to communicate the value and meaning of their work in a system that privileges reductionist approaches.

  18. Laser Raman detection of platelets for early and differential diagnosis of Alzheimer’s disease based on an adaptive Gaussian process classification algorithm

    NASA Astrophysics Data System (ADS)

    Luo, Yusheng; Du, Z. W.; Yang, Y. J.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.

    2013-04-01

    Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.

  19. Image-based ATR utilizing adaptive clutter filter detection, LLRT classification, and Volterra fusion with application to side-looking sonar

    NASA Astrophysics Data System (ADS)

    Aridgides, Tom; Fernández, Manuel

    2010-04-01

    An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, detection, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by three distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. These three ATR processing strings were individually developed and tuned by researchers from different companies. The utility of the overall processing strings and their fusion was demonstrated with an extensive side-looking sonar dataset. In this paper we describe a new processing improvement: six additional classification features are extracted, using primarily target shadow information and a feature extraction window whose length is now made variable as a function of range. This new ATR processing improvement resulted in a 3:1 reduction in false alarms. Two advanced fusion algorithms are subsequently applied: First, a nonlinear Volterra expansion (2nd order) feature-LLRT fusion algorithm is employed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block is utilized. It is shown that cascaded Volterra feature- LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Volterra feature-LLRT fusion algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.

  20. Hydrologic Landscape Characterization for the Pacific Northwest, USA

    EPA Science Inventory

    Hydrologic classification can help address some of the challenges facing catchment hydrology. Wigington et al. (2013) developed a hydrologic landscape (HL) approach to classification that was applied to the state of Oregon. Several characteristics limited its applicability outs...

  1. Do Ecological Niche Model Predictions Reflect the Adaptive Landscape of Species?: A Test Using Myristica malabarica Lam., an Endemic Tree in the Western Ghats, India

    PubMed Central

    Nagaraju, Shivaprakash K.; Gudasalamani, Ravikanth; Barve, Narayani; Ghazoul, Jaboury; Narayanagowda, Ganeshaiah Kotiganahalli; Ramanan, Uma Shaanker

    2013-01-01

    Ecological niche models (ENM) have become a popular tool to define and predict the “ecological niche” of a species. An implicit assumption of the ENMs is that the predicted ecological niche of a species actually reflects the adaptive landscape of the species. Thus in sites predicted to be highly suitable, species would have maximum fitness compared to in sites predicted to be poorly suitable. As yet there are very few attempts to address this assumption. Here we evaluate this assumption. We used Bioclim (DIVA GIS version 7.3) and Maxent (version 3.3.2) to predict the habitat suitability of Myristica malabarica Lam., an economically important tree occurring in the Western Ghats, India. We located populations of the trees naturally occurring in different habitat suitability regimes (from highly suitable to poorly suitable) and evaluated them for their regeneration ability and genetic diversity. We also evaluated them for two plant functional traits, fluctuating asymmetry – an index of genetic homeostasis, and specific leaf weight – an index of primary productivity, often assumed to be good surrogates of fitness. We show a significant positive correlation between the predicted habitat quality and plant functional traits, regeneration index and genetic diversity of populations. Populations at sites predicted to be highly suitable had a higher regeneration and gene diversity compared to populations in sites predicted to be poor or unsuitable. Further, individuals in the highly suitable sites exhibited significantly less fluctuating asymmetry and significantly higher specific leaf weight compared to individuals in the poorly suitable habitats. These results for the first time provide an explicit test of the ENM with respect to the plant functional traits, regeneration ability and genetic diversity of populations along a habitat suitability gradient. We discuss the implication of these resultsfor designing viable species conservation and restoration programs. PMID

  2. Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

    NASA Astrophysics Data System (ADS)

    Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

    2013-03-01

    The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

  3. Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation

    USGS Publications Warehouse

    Soranno, Patricia A.; Cheruvelil, Kendra Spence; Webster, Katherine E.; Bremigan, Mary T.; Wagner, Tyler; Stow, Craig A.

    2010-01-01

    Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

  4. Study of settlement distribution pattern in the Kolkheti lowland (Black Sea coast of Georgia) starting from early Bronze Age - natural and human influence and adaptation to landscape evolution

    NASA Astrophysics Data System (ADS)

    Elashvili, Mikheil; Akhvlediani, Dimitri; Navrozashvili, Levan; Sukhishvili, Lasha; Kirkitadze, Giorgi; Kelterbaum, Daniel; Laermans, Hannes

    2015-04-01

    archaeological datasets are collected in the joint-venture project and in addition with known historical and old topographic maps of the region they represent a good start for the research. There are typical ancient settlements in the Kolkheti lowland, called locally "Dikhagudzuba", which are still identifiable on aerial imagery. Their structure, physical dimensions and locations were analyzed from aerial and on site studies. Data from existing archaeological studies and recent field works were analyzed to create a reliable database on the distribution of Bronze Age settlements. Changes in paleoclimate, sea level and river deltas represent the main components to form a paleolandscape of the study area. Based on the results of recent fieldwork and the analyses of regional historical maps in addition with the general geological and geomorphological settings paleogeographical scenarios were constructed. Proposed models of past landscape changes and human settlement pattern were merged and analyzed. From one hand the human settlement distribution (taking into account tells relation with the local landscape of the same period) help us to identify the best suitable scenario from the set of paleolandscape patterns. Moreover, paleogeographical scenarios provide a better understanding on the erection of human settlements in the past, and their influence and adaptation to ongoing changes.

  5. Equivalent Diagnostic Classification Models

    ERIC Educational Resources Information Center

    Maris, Gunter; Bechger, Timo

    2009-01-01

    Rupp and Templin (2008) do a good job at describing the ever expanding landscape of Diagnostic Classification Models (DCM). In many ways, their review article clearly points to some of the questions that need to be answered before DCMs can become part of the psychometric practitioners toolkit. Apart from the issues mentioned in this article that…

  6. Adapting Landscape Mosaics of medIteranean Rainfed Agrosystems for a sustainable management of crop production, water and soil resources: the ALMIRA project.

    NASA Astrophysics Data System (ADS)

    Jacob, Frédéric; Mekki, Insaf; Chikhaoui, Mohamed

    2014-05-01

    In the context of mitigating the pressures induced by global change combined with demography and market pressures, there is increasing societal demand and scientific need to understand the functioning of Mediterranean Rainfed Agrosystems (MRAs) for their potential to provide various environmental and economic services of importance such as food production, preservation of employment and local knowhow, downstream water delivery or mitigation of rural exodus. Efficient MRAs management strategies that allow for compromises between economic development and natural resources preservation are needed. Such strategies require innovative system based research, integration across approaches and scales. One of the major challenges is to make all contributions from different disciplines converging towards a reproducible transdisciplinary approach. The objective of this communication is to present the ALMIRA project, a Tunisian - Moroccan - French project which lasts four years (2014 - 2017). The communication details the societal context, the scientific positioning and the related work hypothesis, the study areas, the project structure, the expected outcomes and the partnership which capitalizes on long term collaborations. ALMIRA aims to explore the modulation of landscape mosaics within MRAs to optimize landscape services. To explore this new lever, ALMIRA proposes to design, implement and test a new Integrated Assessment Modelling approach that explicitly i) includes innovations and action means into prospective scenarii for landscape evolutions, and ii) addresses landscape mosaics and processes of interest from the agricultural field to the resource governance catchment. This requires tackling methodological challenges in relation to i) the design of spatially explicit landscape evolution scenarii, ii) the coupling of biophysical processes related to agricultural catchment hydrology, iii) the digital mapping of landscape properties and iv) the economic assessment of the

  7. Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

    NASA Astrophysics Data System (ADS)

    Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

    2016-06-01

    The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

  8. Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Abreu, Carlos; Miranda, Francisco; Mendes, Paulo M.

    2016-06-01

    The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular, they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless, due to the critical nature of the data conveyed by such patient monitoring applications, they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context, vis-à-vis the quality of service being provided by the wireless sensor network, this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric's value.

  9. Landscape Architecture.

    ERIC Educational Resources Information Center

    American School and University, 1985

    1985-01-01

    Members of the American Society of Landscape Architects shape open spaces on the campuses of Georgetown University, District of Columbia; the University of Missouri; Auraria Higher Education Center, Colorado; and the University of Michigan. (MLF)

  10. Mars Landscapes

    NASA Video Gallery

    Spacecraft have studied the Martian surface for decades, giving Earthlings insights into the history, climate and geology of our nearest neighbor, Mars. These images are from "Mars Landscapes," a v...

  11. Applying landscape genetics to the microbial world.

    PubMed

    Dudaniec, Rachael Y; Tesson, Sylvie V M

    2016-07-01

    Landscape genetics, which explicitly quantifies landscape effects on gene flow and adaptation, has largely focused on macroorganisms, with little attention given to microorganisms. This is despite overwhelming evidence that microorganisms exhibit spatial genetic structuring in relation to environmental variables. The increasing accessibility of genomic data has opened up the opportunity for landscape genetics to embrace the world of microorganisms, which may be thought of as 'the invisible regulators' of the macroecological world. Recent developments in bioinformatics and increased data accessibility have accelerated our ability to identify microbial taxa and characterize their genetic diversity. However, the influence of the landscape matrix and dynamic environmental factors on microorganism genetic dispersal and adaptation has been little explored. Also, because many microorganisms coinhabit or codisperse with macroorganisms, landscape genomic approaches may improve insights into how micro- and macroorganisms reciprocally interact to create spatial genetic structure. Conducting landscape genetic analyses on microorganisms requires that we accommodate shifts in spatial and temporal scales, presenting new conceptual and methodological challenges not yet explored in 'macro'-landscape genetics. We argue that there is much value to be gained for microbial ecologists from embracing landscape genetic approaches. We provide a case for integrating landscape genetic methods into microecological studies and discuss specific considerations associated with the novel challenges this brings. We anticipate that microorganism landscape genetic studies will provide new insights into both micro- and macroecological processes and expand our knowledge of species' distributions, adaptive mechanisms and species' interactions in changing environments.

  12. Monitoring of Agricultural Landscape in Norway

    NASA Astrophysics Data System (ADS)

    Wallin, H. G.; Engan, G.

    2012-07-01

    An overall societal aim is to ensure a sustainable use and management of agricultural landscapes. This requires continuous delivery of reliable and up-to-date information to decision-makers. To be able to deliver this information, a monitoring program for agricultural landscapes was initiated in Norway 13 years ago. The program documents and reports on land use / land cover changes from data captured through interpretation of true colour aerial photos using stereo instruments. The monitoring programme is based on a sample of 1000 squares of 1 × 1 km and the entire sample of squares is photographed over a five-year period. Each square is then mapped repeatedly every fifth year to record changes. Aerial photo interpretation is based on a custom classification system which is built up hierarchically, with three levels. The first level comprises seven land type classes: Agricultural land, Bare ground, Semi-natural open vegetation, Unforested wetland vegetation, Forest, Urban areas and Water. These land classes are further divided into 24 land types at level two, and approximately 100 land types at level 3. In addition to land type units we map both line elements like stone fences and point elements like buildings and solitary threes. By use of indicators that describe status and change focusing on themes of particular policy interest, we can report on whether policy aims are being fulfilled or not. Four indicator themes have been in focus hitherto: landscape spatial structure, biological diversity, cultural heritage and accessibility. Our data is stored in databases and most of the data quality check/structure process and analyses are now being made in open source software like PostGIS and PostSQL. To assess the accuracy of the photo-interpretation, ground truthing is carried out on 10 % of the squares. The results of this operation document the benefits of having access to photos of the same area from two different years. The program is designed first and foremost to

  13. Learning Landscapes

    ERIC Educational Resources Information Center

    Noyes, Andrew

    2004-01-01

    This article explores the metaphor of learning landscapes, a tool developed in order to map children's experiences of, and attitudes to, learning (mathematics) before and after the transfer from primary to secondary school. Firstly, the continuing problems surrounding school transfer and why a re-examination of this is required are considered.…

  14. Design Issues Adapting a Visual Paper-and-Pencil Test to the Computer: A Case Study--The Figure Classification Test.

    ERIC Educational Resources Information Center

    Washington, James M., Jr.

    This paper documents issues in converting the Figure Classification Test to the computer. The purpose of the test, which is almost entirely visual, is to determine the subject's ability to discover rules via the visual/spatial environment. The methodology of the paper-and-pencil Figure Classification Test is as follows: the subject views a series…

  15. Climates, Landscapes, and Civilizations

    NASA Astrophysics Data System (ADS)

    Schultz, Colin

    2013-10-01

    Humans are now the dominant driver of global climate change. From ocean acidification to sea level rise, changes in precipitation patterns, and rising temperatures, global warming is presenting us with an uncertain future. However, this is not the first time human civilizations have faced a changing world. In the AGU monograph Climates, Landscapes, and Civilizations, editors Liviu Giosan, Dorian Q. Fuller, Kathleen Nicoll, Rowan K. Flad, and Peter C. Clift explore how some ancient peoples weathered the shifting storms while some faded away. In this interview, Eos speaks with Liviu Giosan about the decay of civilizations, ancient adaptation, and the surprisingly long history of humanity's effect on the Earth.

  16. Quantitative analyses of empirical fitness landscapes

    NASA Astrophysics Data System (ADS)

    Szendro, Ivan G.; Schenk, Martijn F.; Franke, Jasper; Krug, Joachim; de Visser, J. Arjan G. M.

    2013-01-01

    The concept of a fitness landscape is a powerful metaphor that offers insight into various aspects of evolutionary processes and guidance for the study of evolution. Until recently, empirical evidence on the ruggedness of these landscapes was lacking, but since it became feasible to construct all possible genotypes containing combinations of a limited set of mutations, the number of studies has grown to a point where a classification of landscapes becomes possible. The aim of this review is to identify measures of epistasis that allow a meaningful comparison of fitness landscapes and then apply them to the empirical landscapes in order to discern factors that affect ruggedness. The various measures of epistasis that have been proposed in the literature appear to be equivalent. Our comparison shows that the ruggedness of the empirical landscape is affected by whether the included mutations are beneficial or deleterious and by whether intragenic or intergenic epistasis is involved. Finally, the empirical landscapes are compared to landscapes generated with the rough Mt Fuji model. Despite the simplicity of this model, it captures the features of the experimental landscapes remarkably well.

  17. Vulnerability of Oregon hydrologic landscapes and streamflow to climate change

    EPA Science Inventory

    Hydrologic classification systems can provide a basis for broadscale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors. Such assessments could be particularly useful in determining hydrologic vulnerability from climate change. ...

  18. Improving classification of psychoses.

    PubMed

    Lawrie, Stephen M; O'Donovan, Michael C; Saks, Elyn; Burns, Tom; Lieberman, Jeffrey A

    2016-04-01

    Psychosis has been recognised as an abnormal state in need of care throughout history and by diverse cultures. Present classifications of psychotic disorder remain based on the presence of specific psychotic symptoms, relative to affective and other symptoms, and their sequence and duration. Although extant diagnostic classifications have restricted validity, they have proven reliability and most clinicians and some patients find them useful. Moreover, these classifications have yet to be replaced by anything better. We propose that an expansion of the subgrouping approach inherent to classification will provide incremental improvement to present diagnostic constructs-as has worked in the rest of medicine. We also propose that subgroups could be created both within and across present diagnostic classifications, taking into consideration the potential value of continuous measures (eg, duration of psychotic symptoms and intelligence quotient). Health-care workers also need to work with service users and carers to develop and adapt approaches to diagnosis that are seen as helpful.

  19. Improving classification of psychoses.

    PubMed

    Lawrie, Stephen M; O'Donovan, Michael C; Saks, Elyn; Burns, Tom; Lieberman, Jeffrey A

    2016-04-01

    Psychosis has been recognised as an abnormal state in need of care throughout history and by diverse cultures. Present classifications of psychotic disorder remain based on the presence of specific psychotic symptoms, relative to affective and other symptoms, and their sequence and duration. Although extant diagnostic classifications have restricted validity, they have proven reliability and most clinicians and some patients find them useful. Moreover, these classifications have yet to be replaced by anything better. We propose that an expansion of the subgrouping approach inherent to classification will provide incremental improvement to present diagnostic constructs-as has worked in the rest of medicine. We also propose that subgroups could be created both within and across present diagnostic classifications, taking into consideration the potential value of continuous measures (eg, duration of psychotic symptoms and intelligence quotient). Health-care workers also need to work with service users and carers to develop and adapt approaches to diagnosis that are seen as helpful. PMID:27063387

  20. Multistage Adaptive Testing for a Large-Scale Classification Test: Design, Heuristic Assembly, and Comparison with Other Testing Modes. ACT Research Report Series, 2012 (6)

    ERIC Educational Resources Information Center

    Zheng, Yi; Nozawa, Yuki; Gao, Xiaohong; Chang, Hua-Hua

    2012-01-01

    Multistage adaptive tests (MSTs) have gained increasing popularity in recent years. MST is a balanced compromise between linear test forms (i.e., paper-and-pencil testing and computer-based testing) and traditional item-level computer-adaptive testing (CAT). It combines the advantages of both. On one hand, MST is adaptive (and therefore more…

  1. Landscape complexity and vegetation dynamics in Riding Mountain National Park, Canada

    NASA Astrophysics Data System (ADS)

    Walker, David John

    spatial complexity over time. Fragmentation and habitat losses in the region surrounding RMNP were found to be high, with only half of the forest present in 1950 remaining in the 1990's. Scale-invariant spatial dispersion of forest fragments decreased between the 1950's and 1990's. Thus, the study area is becoming increasingly isolated from other natural forested areas within the region. In creating maps of land cover for these analyses, it was found that structural composition of the canopy was often more important than floristics in determining spectral reflectance in Landsat data. A rule-based optimization procedure using multivariate analysis was developed to maximize the relationship between vegetation on the ground and spectral reflectance. Because of the high degree of spatial complexity in these systems, an alternative approach to map accuracy assessment utilizing multiple discriminant analysis (MDA) was developed. It was found that closed conifer stands composed of different softwood species were not easily discriminated during classification because of identical spectral signatures at the stand-level. It is suggested that the highly structured architecture and conical form of conifer stands results in the anechoic interception and absorption of light. This light interception strategy may have adaptive advantages in regions where sun angle is low, or where cloud cover is high, such as in the boreal forest and montane environments. The results of these investigations into landscape pattern suggest that ecosystem dynamics in the boreal forest produce scale-invariant landscape complexity.

  2. Improving land cover classification using input variables derived from a geographically weighted principal components analysis

    NASA Astrophysics Data System (ADS)

    Comber, Alexis J.; Harris, Paul; Tsutsumida, Narumasa

    2016-09-01

    This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.

  3. Classification Options

    ERIC Educational Resources Information Center

    Exceptional Children, 1978

    1978-01-01

    The interview presents opinions of Nicholas Hobbs on the classification of exceptional children, including topics such as ecologically oriented classification systems, the role of parents, and need for revision of teacher preparation programs. (IM)

  4. Landscaping for energy efficiency

    SciTech Connect

    1995-04-01

    This publication by the National Renewable Energy Laboratory addresses the use of landscaping for energy efficiency. The topics of the publication include minimizing energy expenses; landscaping for a cleaner environment; climate, site, and design considerations; planning landscape; and selecting and planting trees and shrubs. A source list for more information on landscaping for energy efficiency and a reading list are included.

  5. [Characteristic study on village landscape patterns in Sichuan Basin hilly region based on high resolution IKONOS remote sensing].

    PubMed

    Li, Shoucheng; Liu, Wenquan; Cheng, Xu; Ellis, Erle C

    2005-10-01

    To realize the landscape programming of agro-ecosystem management, landscape-stratification can provide us the best understanding of landscape ecosystem at very detailed scales. For this purpose, the village landscapes in densely populated Jintang and Jianyang Counties of Sichuan Basin hilly region were mapped from high resolution (1 m) IKONOS satellite imagery by using a standardized 4 level ecological landscape classification and mapping system in a regionally-representative sample of five 500 x 500 m2 landscape quadrats (sample plots). Based on these maps, the spatial patterns were analyzed by landscape indicators, which demonstrated a large variety of landscape types or ecotopes across the village landscape of this region, with diversity indexes ranging from 1.08 to 2.26 at different levels of the landscape classification system. The richness indices ranged from 42.2% to 58.6 %, except that for the landcover at 85 %. About 12.5 % of the ecotopes were distributed in the same way in each landscape sample, and the remaining 87.5% were distributed differently. The landscape fragmentation indices varied from 2.93 to 4.27 across sample plots, and from 2.86 to 5.63 across classification levels. The population density and the road and hamlet areas had strong linear correlations with some landscape indicators, and especially, the correlation coefficients of hamlet areas with fractal indexes and fragmental dimensions were 0.957* and 0.991**, respectively. The differences in most landscape pattern indices across sample plots and landscape classes were statistically significant, indicating that cross-scale mapping and classification of village landscapes could provide more detailed information on landscape patterns than those from a single level of classification.

  6. Adaptive structures. [for space applications

    NASA Technical Reports Server (NTRS)

    Wada, B. K.; Fanson, J. L.; Crawley, E. F.

    1990-01-01

    Current research in the field of advanced adaptive structures for space applications is reviewed. A classification of adaptive structures is proposed whereby such structures are subdivided into adaptive, sensory, controlled, active, and intelligent structures. The definition and properties of each type of adaptive structures are presented, and methods of structure control are discussed.

  7. Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher's Geometric Model?

    PubMed

    Blanquart, François; Bataillon, Thomas

    2016-06-01

    The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes-including the mean and SD of selection and epistasis coefficients-it was often unable to explain the full structure of fitness landscapes.

  8. Multi-scale curvature for automated identification of glaciated mountain landscapes.

    PubMed

    Prasicek, Günther; Otto, Jan-Christoph; Montgomery, David R; Schrott, Lothar

    2014-03-15

    Erosion by glacial and fluvial processes shapes mountain landscapes in a long-recognized and characteristic way. Upland valleys incised by fluvial processes typically have a V-shaped cross-section with uniform and moderately steep slopes, whereas glacial valleys tend to have a U-shaped profile with a changing slope gradient. We present a novel regional approach to automatically differentiate between fluvial and glacial mountain landscapes based on the relation of multi-scale curvature and drainage area. Sample catchments are delineated and multiple moving window sizes are used to calculate per-cell curvature over a variety of scales ranging from the vicinity of the flow path at the valley bottom to catchment sections fully including valley sides. Single-scale curvature can take similar values for glaciated and non-glaciated catchments but a comparison of multi-scale curvature leads to different results according to the typical cross-sectional shapes. To adapt these differences for automated classification of mountain landscapes into areas with V- and U-shaped valleys, curvature values are correlated with drainage area and a new and simple morphometric parameter, the Difference of Minimum Curvature (DMC), is developed. At three study sites in the western United States the DMC thresholds determined from catchment analysis are used to automatically identify 5 × 5 km quadrats of glaciated and non-glaciated landscapes and the distinctions are validated by field-based geological and geomorphological maps. Our results demonstrate that DMC is a good predictor of glacial imprint, allowing automated delineation of glacially and fluvially incised mountain landscapes. PMID:24748703

  9. Multi-scale curvature for automated identification of glaciated mountain landscapes.

    PubMed

    Prasicek, Günther; Otto, Jan-Christoph; Montgomery, David R; Schrott, Lothar

    2014-03-15

    Erosion by glacial and fluvial processes shapes mountain landscapes in a long-recognized and characteristic way. Upland valleys incised by fluvial processes typically have a V-shaped cross-section with uniform and moderately steep slopes, whereas glacial valleys tend to have a U-shaped profile with a changing slope gradient. We present a novel regional approach to automatically differentiate between fluvial and glacial mountain landscapes based on the relation of multi-scale curvature and drainage area. Sample catchments are delineated and multiple moving window sizes are used to calculate per-cell curvature over a variety of scales ranging from the vicinity of the flow path at the valley bottom to catchment sections fully including valley sides. Single-scale curvature can take similar values for glaciated and non-glaciated catchments but a comparison of multi-scale curvature leads to different results according to the typical cross-sectional shapes. To adapt these differences for automated classification of mountain landscapes into areas with V- and U-shaped valleys, curvature values are correlated with drainage area and a new and simple morphometric parameter, the Difference of Minimum Curvature (DMC), is developed. At three study sites in the western United States the DMC thresholds determined from catchment analysis are used to automatically identify 5 × 5 km quadrats of glaciated and non-glaciated landscapes and the distinctions are validated by field-based geological and geomorphological maps. Our results demonstrate that DMC is a good predictor of glacial imprint, allowing automated delineation of glacially and fluvially incised mountain landscapes.

  10. Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

    NASA Astrophysics Data System (ADS)

    Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

    2016-06-01

    This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

  11. [Land use and land cover charnge (LUCC) and landscape service: Evaluation, mapping and modeling].

    PubMed

    Song, Zhang-jian; Cao, Yu; Tan, Yong-zhong; Chen, Xiao-dong; Chen, Xian-peng

    2015-05-01

    Studies on ecosystem service from landscape scale aspect have received increasing attention from researchers all over the world. Compared with ecosystem scale, it should be more suitable to explore the influence of human activities on land use and land cover change (LUCC), and to interpret the mechanisms and processes of sustainable landscape dynamics on landscape scale. Based on comprehensive and systematic analysis of researches on landscape service, this paper firstly discussed basic concepts and classification of landscape service. Then, methods of evaluation, mapping and modeling of landscape service were analyzed and concluded. Finally, future trends for the research on landscape service were proposed. It was put forward that, exploring further connotation and classification system of landscape service, improving methods and quantitative indicators for evaluation, mapping and modelling of landscape service, carrying out long-term integrated researches on landscape pattern-process-service-scale relationships and enhancing the applications of theories and methods on landscape economics and landscape ecology are very important fields of the research on landscape service in future.

  12. Soil erosion dynamics response to landscape pattern.

    PubMed

    Ouyang, Wei; Skidmore, Andrew K; Hao, Fanghua; Wang, Tiejun

    2010-02-15

    Simulating soil erosion variation with a temporal land use database reveals long-term fluctuations in landscape patterns, as well as priority needs for soil erosion conservation. The application of a multi-year land use database in support of a Soil Water Assessment Tool (SWAT) led to an accurate assessment, from 1977 to 2006, of erosion in the upper watershed of the Yellow River. At same time, the impacts of land use and landscape service features on soil erosion load were assessed. A series of supervised land use classifications of Landsat images characterized variations in land use and landscape patterns over three decades. The SWAT database was constructed with soil properties, climate and elevation data. Using water flow and sand density data as parameters, regional soil erosion load was simulated. A numerical statistical model was used to relate soil erosion to land use and landscape. The results indicated that decadal decrease of grassland areas did not pose a significant threat to soil erosion, while the continual increase of bare land, water area and farmland increased soil erosion. Regional landscape variation also had a strong relationship with erosion. Patch level landscape analyses demonstrated that larger water area led to more soil erosion. The patch correlation indicated that contagious grassland patches reduced soil erosion yield. The increased grassland patches led to more patch edges, in turn increasing the sediment transportation from the patch edges. The findings increase understanding of the temporal variation in soil erosion processes, which is the basis for preventing local pollution.

  13. Landscape analysis: Theoretical considerations and practical needs

    USGS Publications Warehouse

    Godfrey, A.E.; Cleaves, E.T.

    1991-01-01

    Numerous systems of land classification have been proposed. Most have led directly to or have been driven by an author's philosophy of earth-forming processes. However, the practical need of classifying land for planning and management purposes requires that a system lead to predictions of the results of management activities. We propose a landscape classification system composed of 11 units, from realm (a continental mass) to feature (a splash impression). The classification concerns physical aspects rather than economic or social factors; and aims to merge land inventory with dynamic processes. Landscape units are organized using a hierarchical system so that information may be assembled and communicated at different levels of scale and abstraction. Our classification uses a geomorphic systems approach that emphasizes the geologic-geomorphic attributes of the units. Realm, major division, province, and section are formulated by subdividing large units into smaller ones. For the larger units we have followed Fenneman's delineations, which are well established in the North American literature. Areas and districts are aggregated into regions and regions into sections. Units smaller than areas have, in practice, been subdivided into zones and smaller units if required. We developed the theoretical framework embodied in this classification from practical applications aimed at land use planning and land management in Maryland (eastern Piedmont Province near Baltimore) and Utah (eastern Uinta Mountains). ?? 1991 Springer-Verlag New York Inc.

  14. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

    NASA Astrophysics Data System (ADS)

    Pham, Lien T. H.; Brabyn, Lars; Ashraf, Salman

    2016-08-01

    There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.

  15. Hubble Classification

    NASA Astrophysics Data System (ADS)

    Murdin, P.

    2000-11-01

    A classification scheme for galaxies, devised in its original form in 1925 by Edwin P Hubble (1889-1953), and still widely used today. The Hubble classification recognizes four principal types of galaxy—elliptical, spiral, barred spiral and irregular—and arranges these in a sequence that is called the tuning-fork diagram....

  16. Consensus in landscape preference judgments: the effects of landscape visual aesthetic quality and respondents' characteristics.

    PubMed

    Kalivoda, Ondřej; Vojar, Jiří; Skřivanová, Zuzana; Zahradník, Daniel

    2014-05-01

    Landscape's visual aesthetic quality (VAQ) has been widely regarded as a valuable resource worthy of protection. Although great effort has been devoted to determining the factors driving aesthetic preferences, public consensus in judgments has been neglected in the vast majority of such studies. Therefore, the aim of our study was to analyze three main possible sources of judgment variance: landscape VAQ, landscape type, and variability among respondents. Based upon an extensive perception-based investigation including more than 400 hikers as respondents, we found that variance in respondents' judgments differed significantly among assessed landscape scenes. We discovered a significant difference in judgment variances within each investigated respondent characteristic (gender, age, education level, occupational classification, and respondent's type of residence). Judgment variance was at the same time affected by landscape VAQ itself - the higher the VAQ, the better the consensus. While differences caused by characteristics indicate subjectivity of aesthetic values, the knowledge that people better find consensus for positively perceived landscapes provides a cogent argument for legal protection of valuable landscape scenes.

  17. The Campus Landscape.

    ERIC Educational Resources Information Center

    du Von, Jay

    1966-01-01

    All across the country, landscaping and site development are coming to the fore as essential and integral parts of university planning and development. This reprint concentrates on the function of landscape architecture, and briefly examines some of the major responsibilities of the landscape architect in planning a campus. Included are--(1)…

  18. Using remote sensing products to classify landscape. A multi-spatial resolution approach

    NASA Astrophysics Data System (ADS)

    García-Llamas, Paula; Calvo, Leonor; Álvarez-Martínez, José Manuel; Suárez-Seoane, Susana

    2016-08-01

    The European Landscape Convention encourages the inventory and characterization of landscapes for environmental management and planning actions. Among the range of data sources available for landscape classification, remote sensing has substantial applicability, although difficulties might arise when available data are not at the spatial resolution of operational interest. We evaluated the applicability of two remote sensing products informing on land cover (the categorical CORINE map at 30 m resolution and the continuous NDVI spectral index at 1 km resolution) in landscape classification across a range of spatial resolutions (30 m, 90 m, 180 m, 1 km), using the Cantabrian Mountains (NW Spain) as study case. Separate landscape classifications (using topography, urban influence and land cover as inputs) were accomplished, one per each land cover dataset and spatial resolution. Classification accuracy was estimated through confusion matrixes and uncertainty in terms of both membership probability and confusion indices. Regarding landscape classifications based on CORINE, both typology and number of landscape classes varied across spatial resolutions. Classification accuracy increased from 30 m (the original resolution of CORINE) to 90m, decreasing towards coarser resolutions. Uncertainty followed the opposite pattern. In the case of landscape classifications based on NDVI, the identified landscape patterns were geographically structured and showed little sensitivity to changes across spatial resolutions. Only the change from 1 km (the original resolution of NDVI) to 180 m improved classification accuracy. The value of confusion indices increased with resolution. We highlight the need for greater effort in selecting data sources at the suitable spatial resolution, matching regional peculiarities and minimizing error and uncertainty.

  19. Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

    NASA Astrophysics Data System (ADS)

    Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan

    2001-07-01

    A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.

  20. [Dynamic changes of landscape pattern and hemeroby in Ximen Island wetland, Zhejiang Province, China].

    PubMed

    Xiao, Cui; Xie, Xue-Fen; Wu, Tao; Jiang, Guo-Jun; Bian, Hua-Jing; Xu, Wei

    2014-11-01

    Abstract: The hemeroby type classification system of Ximen Island wetland of Zhejiang Province was established based on the multiple datasets: SOPT-5 image data with a spatial resolution of 5 m in 2007 and 2010, its wetland land cover and land use status, the National Land Use Classification (on trail), and sea area use classification of marine industry standards as well as remote sensing data features. Meanwhile, the dynamic relationship between the landscape pattern and the degree of hemeroby in Ximen Island was investigated with the landscape indices and hemeroby index (HI) derived from the landscape pattern index and GIS spatial analysis. The results showed that the wetland landscape spatial heterogeneity, fragmentation and dominance index dropped, and the landscape shape index complexity was low. The human disturbance center developed from a dispersion type to a concentration type. The landscape type of the disturbance center was bare land and settlement. The HI rose up from the sea to the land. Settlement, wharf and traffic land had the highest HI. The HI of the mudflat cultivation, mudflats and raft-cultivation dramatically changed. Marine-terrestrial interlaced zone showed a low total HI with unstable characteristics. The number of patches declined of undisturbed, partially disturbed and completely disturbed landscapes. Mean patch areas of partially disturbed and completely disturbed landscapes increased, and that of the undisturbed decreased. Mean shape index of the undisturbed landscape decreased, while the partially disturbed and completely disturbed landscapes showed a trend of shape complication. PMID:25898624

  1. Combinatorial vector fields and the valley structure of fitness landscapes.

    PubMed

    Stadler, Bärbel M R; Stadler, Peter F

    2010-12-01

    Adaptive (downhill) walks are a computationally convenient way of analyzing the geometric structure of fitness landscapes. Their inherently stochastic nature has limited their mathematical analysis, however. Here we develop a framework that interprets adaptive walks as deterministic trajectories in combinatorial vector fields and in return associate these combinatorial vector fields with weights that measure their steepness across the landscape. We show that the combinatorial vector fields and their weights have a product structure that is governed by the neutrality of the landscape. This product structure makes practical computations feasible. The framework presented here also provides an alternative, and mathematically more convenient, way of defining notions of valleys, saddle points, and barriers in landscape. As an application, we propose a refined approximation for transition rates between macrostates that are associated with the valleys of the landscape.

  2. Classification of Fuel Types Using Envisat Data

    NASA Astrophysics Data System (ADS)

    Wozniak, Edyta; Nasilowska, Sylwia

    2010-12-01

    Forest fires have an important impact on landscape structure and ecosystems biodiversity. Moreover, wild land fires have strong influence on forest planning and management. Furthermore, forest fires affect not only woodworking industry but also arable fields and inhabitants life too. A precise knowledge of the spatial distribution of fuels is necessary to predict, analyse and model fire behaviour. Modelling of fire spread is difficult and complicated because it depends on many factors. First of all, it depends on undergrowth and brushwood moisture and thickness, and tree species. There are many fuel types classification developed for regional environmental condition. The main drawback of implemented systems is utility for particular region of interest. That causes a need of permanent, consequent and more accurate researches in specific habitat not only in continental scale. In this paper a new system is proposed. It organizes fuels into three major groups (coniferous, deciduous wood and open) and four subcategories which describes a fuel structure (trees lower then 4m, trees higher than 4 m: without bushes; with low bushes lower them 2m; with high bushes higher then 2m). This classification is adapted into Polish lowlands environmental condition. The classification was carried out on the base of 120 training plots, which were determinate during a field experiment in north-eastern Poland. The plots discriminate homogeneous parts of forest which correspond to fuel classes. In the study we used the ENVISAT Alternating Polarization (HH/HV) image. The most popular classifiers were tried out and the maximum likelihood method resulted the most efficient. To map fuel types many methods are employed. The use of remote sensing systems gives the possibility of low- costs and time-consuming fuels mapping and updating. The employ of SAR systems permits mapping independently of weather condition. The microwave data has the potential to estimate fuel loads and map fuel types. The

  3. Applicability of Hydrologic Landscapes for Model Calibration at the Watershed Scale in the Pacific Northwest

    EPA Science Inventory

    The Pacific Northwest Hydrologic Landscapes (PNW HL) at the assessment unit scale has provided a solid conceptual classification framework to relate and transfer hydrologically meaningful information between watersheds without access to streamflow time series. A collection of tec...

  4. Vulnerability of Oregon Hydrologic Landscapes and Streamflow to Climate Change - 5/20/2014

    EPA Science Inventory

    Hydrologic classification systems can provide a basis for broadscale assessments of the hydrologic functions of landscapes and watersheds and their responses to stressors. Such assessments could be particularly useful in determining hydrologic vulnerability from climate change. A...

  5. Landscape ecological security assessment based on projection pursuit in Pearl River Delta.

    PubMed

    Gao, Yang; Wu, Zhifeng; Lou, Quansheng; Huang, Huamei; Cheng, Jiong; Chen, Zhangli

    2012-04-01

    Regional landscape ecological security is an important issue for ecological security, and has a great influence on national security and social sustainable development. The Pearl River Delta (PRD) in southern China has experienced rapid economic development and intensive human activities in recent years. This study, based on landscape analysis, provides a method to discover the alteration of character among different landscape types and to understand the landscape ecological security status. Based on remotely sensed products of the Landsat 5 TM images in 1990 and the Landsat 7 ETM+ images in 2005, landscape classification maps of nine cities in the PRD were compiled by implementing Remote Sensing and Geographic Information System technology. Several indices, including aggregation, crush index, landscape shape index, Shannon's diversity index, landscape fragile index, and landscape security adjacent index, were applied to analyze spatial-temporal characteristics of landscape patterns in the PRD. A landscape ecological security index based on these outcomes was calculated by projection pursuit using genetic algorithm. The landscape ecological security of nine cities in the PRD was thus evaluated. The main results of this research are listed as follows: (1) from 1990 to 2005, the aggregation index, crush index, landscape shape index, and Shannon's diversity index of nine cities changed little in the PRD, while the landscape fragile index and landscape security adjacent index changed obviously. The landscape fragile index of nine cities showed a decreasing trend; however, the landscape security adjacent index has been increasing; (2) from 1990 to 2005, landscape ecology of the cities of Zhuhai and Huizhou maintained a good security situation. However, there was a relatively low value of ecological security in the cities of Dongguan and Foshan. Except for Foshan and Guangzhou, whose landscape ecological security situation were slightly improved, the cities had reduced

  6. Landscape ecological security assessment based on projection pursuit in Pearl River Delta.

    PubMed

    Gao, Yang; Wu, Zhifeng; Lou, Quansheng; Huang, Huamei; Cheng, Jiong; Chen, Zhangli

    2012-04-01

    Regional landscape ecological security is an important issue for ecological security, and has a great influence on national security and social sustainable development. The Pearl River Delta (PRD) in southern China has experienced rapid economic development and intensive human activities in recent years. This study, based on landscape analysis, provides a method to discover the alteration of character among different landscape types and to understand the landscape ecological security status. Based on remotely sensed products of the Landsat 5 TM images in 1990 and the Landsat 7 ETM+ images in 2005, landscape classification maps of nine cities in the PRD were compiled by implementing Remote Sensing and Geographic Information System technology. Several indices, including aggregation, crush index, landscape shape index, Shannon's diversity index, landscape fragile index, and landscape security adjacent index, were applied to analyze spatial-temporal characteristics of landscape patterns in the PRD. A landscape ecological security index based on these outcomes was calculated by projection pursuit using genetic algorithm. The landscape ecological security of nine cities in the PRD was thus evaluated. The main results of this research are listed as follows: (1) from 1990 to 2005, the aggregation index, crush index, landscape shape index, and Shannon's diversity index of nine cities changed little in the PRD, while the landscape fragile index and landscape security adjacent index changed obviously. The landscape fragile index of nine cities showed a decreasing trend; however, the landscape security adjacent index has been increasing; (2) from 1990 to 2005, landscape ecology of the cities of Zhuhai and Huizhou maintained a good security situation. However, there was a relatively low value of ecological security in the cities of Dongguan and Foshan. Except for Foshan and Guangzhou, whose landscape ecological security situation were slightly improved, the cities had reduced

  7. Predictability of evolution in complex fitness landscapes

    NASA Astrophysics Data System (ADS)

    Krug, Joachim

    2013-03-01

    Evolutionary adaptations arise from an intricate interplay of deterministic selective forces and random reproductive or mutational events, and the relative roles of these two types of influences is the subject of a long-standing controversy. In general, the predictability of adaptive trajectories is governed by the genetic constraints imposed by the structure of the underlying fitness landscape as well as by the supply rate and effect size of beneficial mutations. On the level of single mutational steps, evolutionary predictability depends primarily on the distribution of fitness effects, with heavy-tailed distributions giving rise to highly predictable behavior. The genetic constraints imposed by the fitness landscape can be quantified through the statistical properties of accessible mutational pathways along which fitness increases monotonically. I will report on recent progress in the understanding of evolutionary accessibility in model landscapes and compare the predictions of the models to empirical data. Finally, I will describe extensive Wright-Fisher-type simulations of asexual adaptation on an empirical fitness landscape. By quantifying predictability through the entropies of the distributions of evolutionary trajectories and endpoints we show that, contrary to common wisdom, the predictability of evolution depends non-monotonically on population size. Supported by DFG within SFB 680 and SPP 1590.

  8. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    DOE PAGESBeta

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

  9. Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

    SciTech Connect

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.

    2014-12-09

    We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  10. Landscape ecology of eastern coyotes based on large-scale estimates of abundance.

    PubMed

    Kays, Roland W; Gompper, Matthew E; Ray, Justina C

    2008-06-01

    Since their range expansion into eastern North America in the mid-1900s, coyotes (Canis latrans) have become the region's top predator. Although widespread across the region, coyote adaptation to eastern forests and use of the broader landscape are not well understood. We studied the distribution and abundance of coyotes by collecting coyote feces from 54 sites across a diversity of landscapes in and around the Adirondacks of northern New York. We then genotyped feces with microsatellites and found a close correlation between the number of detected individuals and the total number of scats at a site. We created habitat models predicting coyote abundance using multi-scale vegetation and landscape data and ranked them with an information-theoretic model selection approach. These models allow us to reject the hypothesis that eastern forests are unsuitable habitat for coyotes as their abundance was positively correlated with forest cover and negatively correlated with measures of rural non-forest landscapes. However, measures of vegetation structure turned out to be better predictors of coyote abundance than generalized "forest vs. open" classification. The best supported models included those measures indicative of disturbed forest, especially more open canopies found in logged forests, and included natural edge habitats along water courses. These forest types are more productive than mature forests and presumably host more prey for coyotes. A second model with only variables that could be mapped across the region highlighted the lower density of coyotes in areas with high human settlement, as well as positive relationships with variables such as snowfall and lakes that may relate to increased numbers and vulnerability of deer. The resulting map predicts coyote density to be highest along the southwestern edge of the Adirondack State Park, including Tug Hill, and lowest in the mature forests and more rural areas of the central and eastern Adirondacks. Together, these

  11. Another Paper Landscape?

    ERIC Educational Resources Information Center

    Radlak, Ted

    2001-01-01

    Describes the University of Toronto's extensive central campus revitalization plan to create lush landscapes that add to the school's image and attractiveness. Drawings and photographs are included. (GR)

  12. Computerized Classification Testing with the Rasch Model

    ERIC Educational Resources Information Center

    Eggen, Theo J. H. M.

    2011-01-01

    If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald,…

  13. The classification of asteroids

    NASA Astrophysics Data System (ADS)

    Davies, J. K.; Eaton, N.; Green, S. F.; McCheyne, R. S.; Meadows, A. J.

    A numerical taxonomy of asteroids is proposed and illustrated for a sample of 82 well-characterized asteroids in the TRIAD file described by Zellner (1979). The growth of different classification schemes, reflecting the rapid increase in knowledge of the physical and orbital properties of asteroids, is traced since about 1970. The proposed system is adapted from a microbiological taxonomy program and uses only physical parameters: albedo, red/blue ratio, visible-spectrum curvature, Fe(2+) absorption near 0.9 microns, U-B, and B-V. A dendrogram is presented and interpreted, and the scheme is found to agree reasonably well with conventional classifications, to allow the incorporation of new kinds of data, and to facilitate the identification of objects with particular charcteristics to plan future observations.

  14. AEDS Property Classification Code Manual.

    ERIC Educational Resources Information Center

    Association for Educational Data Systems, Washington, DC.

    The control and inventory of property items using data processing machines requires a form of numerical description or code which will allow a maximum of description in a minimum of space on the data card. An adaptation of a standard industrial classification system is given to cover any expendable warehouse item or non-expendable piece of…

  15. Land use classification in Bolivia

    NASA Technical Reports Server (NTRS)

    Brockmann, C. E.; Brooner, W. G.

    1975-01-01

    The Bolivian LANDSAT Program is an integrated, multidisciplinary project designed to provide thematic analysis of LANDSAT, Skylab, and other remotely sensed data for natural resource management and development in Bolivia, is discussed. Among the first requirements in the program is the development of a legend, and appropriate methodologies, for the analysis and classification of present land use based on landscape cover. The land use legend for Bolivia consists of approximately 80 categories in a hierarchical organization which may be collapsed for generalization, or expanded for greater detail. The categories, and their definitions, provide for both a graphic and textual description of the complex and diverse landscapes found in Bolivia, and are designed for analysis from LANDSAT and other remotely sensed data at scales of 1:1,000,000 and 1:250,000. Procedures and example products developed are described and illustrated, for the systematic analysis and mapping of present land use for all of Bolivia.

  16. Exploring the fitness landscape of poliovirus

    NASA Astrophysics Data System (ADS)

    Bianco, Simone; Acevedo, Ashely; Andino, Raul; Tang, Chao

    2012-02-01

    RNA viruses are known to display extraordinary adaptation capabilities to different environments, due to high mutation rates. Their very dynamical evolution is captured by the quasispecies concept, according to which the viral population forms a swarm of genetic variants linked through mutation, which cooperatively interact at a functional level and collectively contribute to the characteristics of the population. The description of the viral fitness landscape becomes paramount towards a more thorough understanding of the virus evolution and spread. The high mutation rate, together with the cooperative nature of the quasispecies, makes it particularly challenging to explore its fitness landscape. I will present an investigation of the dynamical properties of poliovirus fitness landscape, through both the adoption of new experimental techniques and theoretical models.

  17. Landscape epidemiology of plant diseases.

    PubMed

    Plantegenest, Manuel; Le May, Christophe; Fabre, Frédéric

    2007-10-22

    Many agricultural landscapes are characterized by a high degree of heterogeneity and fragmentation. Landscape ecology focuses on the influence of habitat heterogeneity in space and time on ecological processes. Landscape epidemiology aims at applying concepts and approaches originating from landscape ecology to the study of pathogen dynamics at the landscape scale. However, despite the strong influence that the landscape properties may have on the spread of plant diseases, landscape epidemiology has still received little attention from plant pathologists. Some recent methodological and technological progress provides new and powerful tools to describe and analyse the spatial patterns of host-pathogen interactions. Here, we review some important topics in plant pathology that may benefit from a landscape perspective. These include the influence of: landscape composition on the global inoculum pressure; landscape heterogeneity on pathogen dynamics; landscape structure on pathogen dispersal; and landscape properties on the emergence of pathogens and on their evolution.

  18. BATS AND BT INSECT RESISTANCE ON AGRICULTURAL LANDSCAPES

    EPA Science Inventory

    A landscape model that utilizes land cover classification data, insect life history, insect movement, and bat foraging pressure is developed that addresses the implementation of genetically modified crops in the Winter Garden region of Texas. The principal strategy for delaying r...

  19. Subject Classification.

    ERIC Educational Resources Information Center

    Thompson, Gayle; And Others

    Three newspaper librarians described how they manage the files of newspaper clippings which are a necessary part of their collections. The development of a new subject classification system for the clippings files was outlined. The new subject headings were based on standard subject heading lists and on local need. It was decided to use a computer…

  20. Classifying Classification

    ERIC Educational Resources Information Center

    Novakowski, Janice

    2009-01-01

    This article describes the experience of a group of first-grade teachers as they tackled the science process of classification, a targeted learning objective for the first grade. While the two-year process was not easy and required teachers to teach in a new, more investigation-oriented way, the benefits were great. The project helped teachers and…

  1. Towards a Numerical Approach to Identify Hydrogeological Landscape Units

    NASA Astrophysics Data System (ADS)

    Akter, F.; Vervoort, R. W.; Bishop, T. F. A.

    2014-12-01

    Groundwater salinity remains a major issue due to its impact on agriculture and infrastructure. In Australia, it is recognized that groundwater salinity varies significantly across space and time. In NSW, the state government has developed a landscape classification for management based on hydrogeology, landuse and landscape aspects, mainly derived from GIS overlays and operator experience. In this study, we use historical water quality data, geology and drilling logs to develop a more rigorous numerical approach to landscape classification. A combination of statistical methods (Generalised additive model (GAM) and Semi-variogram analysis) was used to identify the significant spatio-temporal factors that induce the variability of groundwater salinity across the Muttama catchment (1059km2) in the southern part of NSW, Australia. The statistical model explained 57% of the variance in the electrical conductivity levels in the groundwater across the landscape. Geology and lag rainfall were the key factors that explained overall catchment groundwater salinity, thus defining the hydrogeological landscape units. Semi-variogram analysis revealed the remaining residuals did not indicate further spatial organisation. Current work focusses on also predicting groundwater response times. Therefore, the results of this study highlighted framework to numerically develop hydrogeological units based on the geological landscape characteristics.

  2. Geomorpho-Landscapes

    NASA Astrophysics Data System (ADS)

    Farabollini, Piero; Lugeri, Francesca; Amadio, Vittorio

    2014-05-01

    Landscape is the object of human perceptions, being the image of spatial organization of elements and structures: mankind lives the first approach with the environment, viewing and feeling the landscape. Many definitions of landscape have been given over time: in this case we refer to the Landscape defined as the result of interaction among physical, biotic and anthropic phenomena acting in a different spatial-temporal scale (Foreman & Godron) Following an Aristotelic approach in studying nature, we can assert that " Shape is synthesis": so it is possible to read the land features as the expression of the endogenous and exogenous processes that mould earth surfaces; moreover, Landscape is the result of the interaction of natural and cultural components, and conditions the spatial-temporal development of a region. The study of the Landscape offers results useful in order to promote sustainable development, ecotourism, enhancement of natural and cultural heritage, popularization of the scientific knowledge. In Italy, a very important GIS-based tool to represent the territory is the "Carta della Natura" ("Map of Nature", presently coordinated by the ISPRA) that aims at assessing the state of the whole Italian territory, analyzing Landscape. The methodology follows a holistic approach, taking into consideration all the components of a landscape and then integrating the information. Each individual landscape, studied at different scales, shows distinctive elements: structural, which depend on physical form and specific spatial organization; functional, which depend on relationships created between biotic and abiotic elements, and dynamic, which depend on the successive evolution of the structure. The identification of the landscape units, recognized at different scales of analysis, allows an evaluation of the state of the land, referring to the dual risk/resource which characterizes the Italian country. An interesting opportunity is to discover those areas of unusual

  3. Properties of selected mutations and genotypic landscapes under Fisher's geometric model.

    PubMed

    Blanquart, François; Achaz, Guillaume; Bataillon, Thomas; Tenaillon, Olivier

    2014-12-01

    The fitness landscape-the mapping between genotypes and fitness-determines properties of the process of adaptation. Several small genotypic fitness landscapes have recently been built by selecting a handful of beneficial mutations and measuring fitness of all combinations of these mutations. Here, we generate several testable predictions for the properties of these small genotypic landscapes under Fisher's geometric model of adaptation. When the ancestral strain is far from the fitness optimum, we analytically compute the fitness effect of selected mutations and their epistatic interactions. Epistasis may be negative or positive on average depending on the distance of the ancestral genotype to the optimum and whether mutations were independently selected, or coselected in an adaptive walk. Simulations show that genotypic landscapes built from Fisher's model are very close to an additive landscape when the ancestral strain is far from the optimum. However, when it is close to the optimum, a large diversity of landscape with substantial roughness and sign epistasis emerged. Strikingly, small genotypic landscapes built from several replicate adaptive walks on the same underlying landscape were highly variable, suggesting that several realizations of small genotypic landscapes are needed to gain information about the underlying architecture of the fitness landscape.

  4. How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

    PubMed Central

    du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian

    2016-01-01

    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564

  5. How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

    PubMed

    du Plessis, Louis; Leventhal, Gabriel E; Bonhoeffer, Sebastian

    2016-09-01

    Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.

  6. A checklist for ecological management of landscapes for conservation.

    PubMed

    Lindenmayer, David; Hobbs, Richard J; Montague-Drake, Rebecca; Alexandra, Jason; Bennett, Andrew; Burgman, Mark; Cale, Peter; Calhoun, Aram; Cramer, Viki; Cullen, Peter; Driscoll, Don; Fahrig, Lenore; Fischer, Joern; Franklin, Jerry; Haila, Yrjo; Hunter, Malcolm; Gibbons, Philip; Lake, Sam; Luck, Gary; MacGregor, Chris; McIntyre, Sue; Nally, Ralph Mac; Manning, Adrian; Miller, James; Mooney, Hal; Noss, Reed; Possingham, Hugh; Saunders, Denis; Schmiegelow, Fiona; Scott, Michael; Simberloff, Dan; Sisk, Tom; Tabor, Gary; Walker, Brian; Wiens, John; Woinarski, John; Zavaleta, Erika

    2008-01-01

    The management of landscapes for biological conservation and ecologically sustainable natural resource use are crucial global issues. Research for over two decades has resulted in a large literature, yet there is little consensus on the applicability or even the existence of general principles or broad considerations that could guide landscape conservation. We assess six major themes in the ecology and conservation of landscapes. We identify 13 important issues that need to be considered in developing approaches to landscape conservation. They include recognizing the importance of landscape mosaics (including the integration of terrestrial and aquatic areas), recognizing interactions between vegetation cover and vegetation configuration, using an appropriate landscape conceptual model, maintaining the capacity to recover from disturbance and managing landscapes in an adaptive framework. These considerations are influenced by landscape context, species assemblages and management goals and do not translate directly into on-the-ground management guidelines but they should be recognized by researchers and resource managers when developing guidelines for specific cases. Two crucial overarching issues are: (i) a clearly articulated vision for landscape conservation and (ii) quantifiable objectives that offer unambiguous signposts for measuring progress.

  7. Planetary Landscape Geography

    NASA Astrophysics Data System (ADS)

    Hargitai, H.

    INTRODUCTION Landscape is one of the most often used category in physical ge- ography. The term "landshap" was introduced by Dutch painters in the 15-16th cen- tury. [1] The elements that build up a landscape (or environment) on Earth consists of natural (biogenic and abiogenic - lithologic, atmospheric, hydrologic) and artificial (antropogenic) factors. Landscape is a complex system of these different elements. The same lithology makes different landscapes under different climatic conditions. If the same conditions are present, the same landscape type will appear. Landscapes build up a hierarchic system and cover the whole surface. On Earth, landscapes can be classified and qualified according to their characteristics: relief forms (morphology), and its potential economic value. Aesthetic and subjective parameters can also be considered. Using the data from landers and data from orbiters we can now classify planetary landscapes (these can be used as geologic mapping units as well). By looking at a unknown landscape, we can determine the processes that created it and its development history. This was the case in the Pathfinder/Sojourner panoramas. [2]. DISCUSSION Planetary landscape evolution. We can draw a raw landscape develop- ment history by adding the different landscape building elements to each other. This has a strong connection with the planet's thermal evolution (age of the planet or the present surface materials) and with orbital parameters (distance from the central star, orbit excentricity etc). This way we can build a complex system in which we use differ- ent evolutional stages of lithologic, atmospheric, hydrologic and biogenic conditions which determine the given - Solar System or exoplanetary - landscape. Landscape elements. "Simple" landscapes can be found on asteroids: no linear horizon is present (not differentiated body, only impact structures), no atmosphere (therefore no atmospheric scattering - black sky as part of the landscape) and no

  8. Union of phylogeography and landscape genetics.

    PubMed

    Rissler, Leslie J

    2016-07-19

    Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the "microgeographic races" of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography. PMID:27432989

  9. Union of phylogeography and landscape genetics

    PubMed Central

    Rissler, Leslie J.

    2016-01-01

    Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the “microgeographic races” of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography. PMID:27432989

  10. Union of phylogeography and landscape genetics.

    PubMed

    Rissler, Leslie J

    2016-07-19

    Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the "microgeographic races" of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography.

  11. Termination Criteria for Computerized Classification Testing

    ERIC Educational Resources Information Center

    Thompson, Nathan A.

    2011-01-01

    Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the termination criterion,…

  12. Seismic event classification system

    DOEpatents

    Dowla, Farid U.; Jarpe, Stephen P.; Maurer, William

    1994-01-01

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities.

  13. Seismic event classification system

    DOEpatents

    Dowla, F.U.; Jarpe, S.P.; Maurer, W.

    1994-12-13

    In the computer interpretation of seismic data, the critical first step is to identify the general class of an unknown event. For example, the classification might be: teleseismic, regional, local, vehicular, or noise. Self-organizing neural networks (SONNs) can be used for classifying such events. Both Kohonen and Adaptive Resonance Theory (ART) SONNs are useful for this purpose. Given the detection of a seismic event and the corresponding signal, computation is made of: the time-frequency distribution, its binary representation, and finally a shift-invariant representation, which is the magnitude of the two-dimensional Fourier transform (2-D FFT) of the binary time-frequency distribution. This pre-processed input is fed into the SONNs. These neural networks are able to group events that look similar. The ART SONN has an advantage in classifying the event because the types of cluster groups do not need to be pre-defined. The results from the SONNs together with an expert seismologist's classification are then used to derive event classification probabilities. 21 figures.

  14. Elaboration of the third-generation world map of terrestrial landscapes as a model of the landscape sphere of the Earth

    NASA Astrophysics Data System (ADS)

    Romanova, Emma; Alexeeva, Nina; Arshinova, Marina; Klimanova, Oksana; Kovaleva, Tatiana; Kondratieva, Tatiana; Alyautdinov, Ali

    2016-04-01

    The first fundamental investigation aimed at the elaboration of the global map of terrestrial landscapes has resulted in a series of maps for the Physical-Geographical Atlas of the World (1964). Typological classification of landscapes and the concept of the zonal differentiation of terrestrial landscapes of the Earth became a basis for the maps of physical-geographical regions of individual continents and the global map of landscape types at the scale of 1:80 Mln. The next stage of research in the sphere of small-scale landscape regionalization and mapping of both natural and natural-anthropogenic landscapes has produced the global maps of Geographical Belts and Zonal Types of Terrestrial Landscapes (1988) and Present-Day Landscapes of the World (1992) at the scale of 1:15 Mln. By the end of the 1990-s similar maps of individual continents were compiled for the Nature and Resources of the Earth digital atlas. Recent decades saw further development of the idea of zone - sector - belt structure of the Earth's landscape sphere which includes several hierarchically subordinated natural-territorial levels. New theoretical studies and emergence of extensive information materials allowed starting the elaboration of a new (third-generation) map at the scales of 1:15 Mln to 1:5 Mln. A new classification of landscape units was suggested basing on the analysis of principal landscape-forming factors (climatic, lithogene and biogenic). A new cartographical model was developed specifying the following hierarchical levels: geographical belts, sectors, natural zones and sub-zones, classes and subclasses of landscapes. Classification criteria used for landscape systematization and mapping include both natural parameters (radiation balance, heat and moisture supply, structure of the vegetative period, biological productivity of vegetation, etc.) and anthropogenic indicators, thus providing for the evaluation of the geoecological state of landscapes (ecosystems of regional dimension

  15. Appendix E: Research papers. Use of remote sensing in landscape stratification for environmental impact assessment

    NASA Technical Reports Server (NTRS)

    Stanturf, J. A.; Heimbuch, D. G.

    1980-01-01

    A refinement to the matrix approach to environmental impact assessment is to use landscape units in place of separate environmental elements in the analysis. Landscape units can be delineated by integrating remotely sensed data and available single-factor data. A remote sensing approach to landscape stratification is described and the conditions under which it is superior to other approaches that require single-factor maps are indicated. Flowcharts show the steps necessary to develop classification criteria, delineate units and a map legend, and use the landscape units in impact assessment. Application of the approach to assessing impacts of a transmission line in Montana is presented to illustrate the method.

  16. Application of ant colony optimization to image classification using a Markov model with non-stationary neighborhoods

    NASA Astrophysics Data System (ADS)

    Le Hégarat-Mascle, S.; Kallel, A.; Descombes, X.

    2005-10-01

    In global classifications using Markov Random Field (MRF) modelling, the neighbourhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighbourhood. However this non-stationary neighbourhood modelling is useful only if an efficient heuristic can be defined to perform the optimization. Ant colony optimization (ACO) is currently a popular algorithm. It models upon the behavior of social insects for computing strategies: the information gathered by simple autonomous mobile agents, called ants, is shared and exploited for problem solving. Here we propose to use the ACO and to exploit its ability of self-organization. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favouring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighbourhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images, SPOT4/HRV, representing agricultural areas. In the studied examples, we found that it outperforms the fixed-form neighbourhood used in classical MRF classifications. The advantage of having a neighborhood shape that automatically adapts to the image segment clearly appears in these cases of images containing fine elements, lanes or thin fields, but also complex natural landscape structures.

  17. Landscape evolution (A Review)

    PubMed Central

    Sharp, Robert P.

    1982-01-01

    Landscapes are created by exogenic and endogenic processes acting along the interface between the lithosphere and the atmosphere and hydrosphere. Various landforms result from the attack of weathering and erosion upon the highly heterogeneous lithospheric surface. Landscapes are dynamic, acutely sensitive to natural and artificial perturbation. Undisturbed, they can evolve through a succession of stages to a plain of low relief. Often, the progression of an erosion cycle is interrupted by tectonic or environmental changes; thus, many landscapes preserve vestiges of earlier cycles useful in reconstructing the recent history of Earth's surface. Landforms are bounded by slopes, so their evolution is best understood through study of slopes and the complex of factors controlling slope character and development. The substrate, biosphere, climatic environment, and erosive processes are principal factors. Creep of the disintegrated substrate and surface wash by water are preeminent. Some slopes attain a quasisteady form and recede parallel to themselves (backwearing); others become ever gentler with time (downwearing). The lovely convex/rectilinear/concave profile of many debris-mantled slopes reflects an interplay between creep and surface wash. Landscapes of greatest scenic attraction are usually those in which one or two genetic factors have strongly dominated or those perturbed by special events. Nature has been perturbing landscapes for billions of years, so mankind can learn about landscape perturbation from natural examples. Images

  18. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes

    PubMed Central

    Baym, Michael; Kim, Seungsoo; Veres, Adrian; Bershtein, Shimon; Kishony, Roy

    2015-01-01

    Predicting evolutionary paths to antibiotic resistance is key for understanding and controlling drug resistance. When considering a single final resistant genotype, epistatic contingencies among mutations restricts evolution to a small number of adaptive paths. Less attention has been given to multi-peak landscapes, and while specific peaks can be favored, it is unknown whether and how early a commitment to final fate is made. Here we characterized a multi-peaked adaptive landscape for trimethoprim resistance by constructing all combinatorial alleles of seven resistance-conferring mutations in dihydrofolate reductase. We observe that epistatic interactions increase rather than decrease the accessibility of each peak; while they restrict the number of direct paths, they generate more indirect paths, where mutations are adaptively gained and later adaptively lost or changed. This enhanced accessibility allows evolution to proceed through many adaptive steps while delaying commitment to genotypic fate, hindering our ability to predict or control evolutionary outcomes. PMID:26060115

  19. Quantifying the similarity of monotonic trajectories in rough and smooth fitness landscapes.

    PubMed

    Lobkovsky, Alexander E; Wolf, Yuri I; Koonin, Eugene V

    2013-07-01

    When selection is strong and mutations are rare, evolution can be thought of as an uphill trajectory in a rugged fitness landscape. In this context the fitness landscape is a directed acyclic graph in which nodes are genotypes and edges lead from lower to higher fitness genotypes that differ by a single mutation. Because the space of genotypes is vastly multi-dimensional, classification of fitness landscapes is challenging. Many proposed summary characteristics of fitness landscapes attempt to quantify biologically relevant and intuitive notions such as roughness or peak accessibility in alternative ways. Here we explore, in different types of landscapes, the behavior of the recently introduced mean path divergence which quantifies the degree of similarity among evolutionary trajectories with the same endpoints. We find that monotonic trajectories in empirical and model fitness landscapes are significantly more constrained, with low median path divergence, than those in purely additive landscapes. By contrast, transcription factor sequence specificity (aptamer binding affinity) landscapes are markedly smoother and allow substantial variability in monotonic paths that can be greater than that in fully additive landscapes. We propose that the smoothness of the specificity landscapes is a consequence of the simple dependence of the transcription factor binding affinity on the aptamer sequence in contrast to the complex sequence-fitness mapping in folding landscapes.

  20. Quantifying landscape resilience using vegetation indices

    NASA Astrophysics Data System (ADS)

    Eddy, I. M. S.; Gergel, S. E.

    2014-12-01

    Landscape resilience refers to the ability of systems to adapt to and recover from disturbance. In pastoral landscapes, degradation can be measured in terms of increased desertification and/or shrub encroachment. In many countries across Central Asia, the use and resilience of pastoral systems has changed markedly over the past 25 years, influenced by centralized Soviet governance, private property rights and recently, communal resource governance. In Kyrgyzstan, recent governance reforms were in response to the increasing degradation of pastures attributed to livestock overgrazing. Our goal is to examine and map the landscape-level factors that influence overgrazing throughout successive governance periods. Here, we map and examine some of the spatial factors influencing landscape resilience in agro-pastoral systems in the Kyrgyzstan Republic where pastures occupy >50% of the country's area. We ask three questions: 1) which mechanisms of pasture degradation (desertification vs. shrub encroachment), are detectable using remote sensing vegetation indices?; 2) Are these degraded pastures associated with landscape features that influence herder mobility and accessibility (e.g., terrain, distance to other pastures)?; and 3) Have these patterns changed through successive governance periods? Using a chronosequence of Landsat imagery (1999-2014), NDVI and other VIs were used to identify trends in pasture condition during the growing season. Least-cost path distances as well as graph theoretic indices were derived from topographic factors to assess landscape connectivity (from villages to pastures and among pastures). Fieldwork was used to assess the feasibility and accuracy of this approach using the most recent imagery. Previous research concluded that low herder mobility hindered pasture use, thus we expect the distance from pasture to village to be an important predictor of pasture condition. This research will quantify the magnitude of pastoral degradation and test

  1. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning.

    PubMed

    Theobald, David M; Harrison-Atlas, Dylan; Monahan, William B; Albano, Christine M

    2015-01-01

    Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings.

  2. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning

    PubMed Central

    Theobald, David M.; Harrison-Atlas, Dylan; Monahan, William B.; Albano, Christine M.

    2015-01-01

    Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings. PMID:26641818

  3. Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning.

    PubMed

    Theobald, David M; Harrison-Atlas, Dylan; Monahan, William B; Albano, Christine M

    2015-01-01

    Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings. PMID:26641818

  4. Probabilistic drought classification using gamma mixture models

    NASA Astrophysics Data System (ADS)

    Mallya, Ganeshchandra; Tripathi, Shivam; Govindaraju, Rao S.

    2015-07-01

    Drought severity is commonly reported using drought classes obtained by assigning pre-defined thresholds on drought indices. Current drought classification methods ignore modeling uncertainties and provide discrete drought classification. However, the users of drought classification are often interested in knowing inherent uncertainties in classification so that they can make informed decisions. Recent studies have used hidden Markov models (HMM) for quantifying uncertainties in drought classification. The HMM method conceptualizes drought classes as distinct hydrological states that are not observed (hidden) but affect observed hydrological variables. The number of drought classes or hidden states in the model is pre-specified, which can sometimes result in model over-specification problem. This study proposes an alternate method for probabilistic drought classification where the number of states in the model is determined by the data. The proposed method adapts Standard Precipitation Index (SPI) methodology of drought classification by employing gamma mixture model (Gamma-MM) in a Bayesian framework. The method alleviates the problem of choosing a suitable distribution for fitting data in SPI analysis, quantifies modeling uncertainties, and propagates them for probabilistic drought classification. The method is tested on rainfall data over India. Comparison of the results with standard SPI show important differences particularly when SPI assumptions on data distribution are violated. Further, the new method is simpler and more parsimonious than HMM based drought classification method and can be a viable alternative for probabilistic drought classification.

  5. Epistasis and the Structure of Fitness Landscapes: Are Experimental Fitness Landscapes Compatible with Fisher’s Geometric Model?

    PubMed Central

    Blanquart, François; Bataillon, Thomas

    2016-01-01

    The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher’s model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher’s model was able to explain several statistical properties of the landscapes—including the mean and SD of selection and epistasis coefficients—it was often unable to explain the full structure of fitness landscapes. PMID:27052568

  6. Differential responses of cryptic bat species to the urban landscape.

    PubMed

    Lintott, Paul R; Barlow, Kate; Bunnefeld, Nils; Briggs, Philip; Gajas Roig, Clara; Park, Kirsty J

    2016-04-01

    Urbanization is a key global driver in the modification of land use and has been linked to population declines even in widespread and relatively common species. Cities comprise a complex assortment of habitat types yet we know relatively little about the effects of their composition and spatial configuration on species distribution. Although many bat species exploit human resources, the majority of species are negatively impacted by urbanization. Here, we use data from the National Bat Monitoring Programme, a long-running citizen science scheme, to assess how two cryptic European bat species respond to the urban landscape. A total of 124 × 1 km(2) sites throughout Britain were surveyed. The landscape surrounding each site was mapped and classified into discrete biotope types (e.g., woodland). Generalized linear models were used to assess differences in the response to the urban environment between the two species, and which landscape factors were associated with the distributions of P. pipistrellus and P. pygmaeus. The relative prevalence of P. pygmaeus compared to P. pipistrellus was greater in urban landscapes with a higher density of rivers and lakes, whereas P. pipistrellus was frequently detected in landscapes comprising a high proportion of green space (e.g., parklands). Although P. pipistrellus is thought to be well adapted to the urban landscape, we found a strong negative response to urbanization at a relatively local scale (1 km), whilst P. pygmaeus was detected more regularly in wooded urban landscapes containing freshwater. These results show differential habitat use at a landscape scale of two morphologically similar species, indicating that cryptic species may respond differently to anthropogenic disturbance. Even species considered relatively common and well adapted to the urban landscape may respond negatively to the built environment highlighting the future challenges involved in maintaining biodiversity within an increasingly urbanized

  7. Differential responses of cryptic bat species to the urban landscape.

    PubMed

    Lintott, Paul R; Barlow, Kate; Bunnefeld, Nils; Briggs, Philip; Gajas Roig, Clara; Park, Kirsty J

    2016-04-01

    Urbanization is a key global driver in the modification of land use and has been linked to population declines even in widespread and relatively common species. Cities comprise a complex assortment of habitat types yet we know relatively little about the effects of their composition and spatial configuration on species distribution. Although many bat species exploit human resources, the majority of species are negatively impacted by urbanization. Here, we use data from the National Bat Monitoring Programme, a long-running citizen science scheme, to assess how two cryptic European bat species respond to the urban landscape. A total of 124 × 1 km(2) sites throughout Britain were surveyed. The landscape surrounding each site was mapped and classified into discrete biotope types (e.g., woodland). Generalized linear models were used to assess differences in the response to the urban environment between the two species, and which landscape factors were associated with the distributions of P. pipistrellus and P. pygmaeus. The relative prevalence of P. pygmaeus compared to P. pipistrellus was greater in urban landscapes with a higher density of rivers and lakes, whereas P. pipistrellus was frequently detected in landscapes comprising a high proportion of green space (e.g., parklands). Although P. pipistrellus is thought to be well adapted to the urban landscape, we found a strong negative response to urbanization at a relatively local scale (1 km), whilst P. pygmaeus was detected more regularly in wooded urban landscapes containing freshwater. These results show differential habitat use at a landscape scale of two morphologically similar species, indicating that cryptic species may respond differently to anthropogenic disturbance. Even species considered relatively common and well adapted to the urban landscape may respond negatively to the built environment highlighting the future challenges involved in maintaining biodiversity within an increasingly urbanized

  8. Geomorpho-Landscapes

    NASA Astrophysics Data System (ADS)

    Farabollini, Piero; Lugeri, Francesca; Amadio, Vittorio

    2014-05-01

    Landscape is the object of human perceptions, being the image of spatial organization of elements and structures: mankind lives the first approach with the environment, viewing and feeling the landscape. Many definitions of landscape have been given over time: in this case we refer to the Landscape defined as the result of interaction among physical, biotic and anthropic phenomena acting in a different spatial-temporal scale (Foreman & Godron) Following an Aristotelic approach in studying nature, we can assert that " Shape is synthesis": so it is possible to read the land features as the expression of the endogenous and exogenous processes that mould earth surfaces; moreover, Landscape is the result of the interaction of natural and cultural components, and conditions the spatial-temporal development of a region. The study of the Landscape offers results useful in order to promote sustainable development, ecotourism, enhancement of natural and cultural heritage, popularization of the scientific knowledge. In Italy, a very important GIS-based tool to represent the territory is the "Carta della Natura" ("Map of Nature", presently coordinated by the ISPRA) that aims at assessing the state of the whole Italian territory, analyzing Landscape. The methodology follows a holistic approach, taking into consideration all the components of a landscape and then integrating the information. Each individual landscape, studied at different scales, shows distinctive elements: structural, which depend on physical form and specific spatial organization; functional, which depend on relationships created between biotic and abiotic elements, and dynamic, which depend on the successive evolution of the structure. The identification of the landscape units, recognized at different scales of analysis, allows an evaluation of the state of the land, referring to the dual risk/resource which characterizes the Italian country. An interesting opportunity is to discover those areas of unusual

  9. Relationship between tourism development and vegetated landscapes in Luya Mountain Nature Reserve, Shanxi, China.

    PubMed

    Cheng, Zhan-Hong; Zhang, Jin-Tun

    2005-09-01

    The relationship between tourism development and vegetated landscapes is analyzed for the Luya Mountain Nature Reserve (LMNR), Shanxi, China, in this study. Indices such as Sensitive Level (SL), Landscape Importance Value (LIV), information index of biodiversity (H'), Shade-tolerant Species Proportion (SSP), and Tourism Influencing Index (TII) are used to characterize vegetated landscapes, the impact of tourism, and their relationship. Their relationship is studied by Two-Way Indicator Species Analysis (TWINSPAN) and Detrended Correspondence Analysis (DCA). TWINSPAN gives correct and rapid partition to the classification, and DCA ordination shows the changing tendency of all vegetation types based on tourism development. These results reflect the ecological relationship between tourism development and vegetated landscapes. In Luya Mountain Nature Reserve, most plant communities are in good or medium condition, which shows that these vegetated landscapes can support more tourism. However, the occurrence of the bad condition shows that there is a severe contradiction between tourism development and vegetated landscapes.

  10. Contextualizing Object Detection and Classification.

    PubMed

    Chen, Qiang; Song, Zheng; Dong, Jian; Huang, Zhongyang; Hua, Yang; Yan, Shuicheng

    2015-01-01

    We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate on co-occurrence relationship within classes and few of them focus on contextualization from a top-down perspective, i.e. high-level task context. In this paper, our system adopts a new method for adaptive context modeling and iterative boosting. First, the contextualized support vector machine (Context-SVM) is proposed, where the context takes the role of dynamically adjusting the classification score based on the sample ambiguity, and thus the context-adaptive classifier is achieved. Then, an iterative training procedure is presented. In each step, Context-SVM, associated with the output context from one task (object classification or detection), is instantiated to boost the performance for the other task, whose augmented outputs are then further used to improve the former task by Context-SVM. The proposed solution is evaluated on the object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets, and achieves the state-of-the-art performance.

  11. Effects of landscape structure on movement patterns of the flightless bush cricket Pholidoptera griseoaptera.

    PubMed

    Diekötter, Tim; Speelmans, Marjan; Dusoulier, François; Van Wingerden, Walter K R E; Malfait, Jean-Pierre; Crist, Thomas O; Edwards, Peter J; Dietz, Hansjörg

    2007-02-01

    Because the viability of a population may depend on whether individuals can disperse, it is important for conservation planning to understand how landscape structure affects movement behavior. Some species occur in a wide range of landscapes differing greatly in structure, and the question arises of whether these species are particularly versatile in their dispersal or whether they are composed of genetically distinct populations adapted to contrasting landscapes. We performed a capture-mark-resight experiment to study movement patterns of the flightless bush cricket Pholidoptera griseoaptera (De Geer 1773) in two contrasting agricultural landscapes in France and Switzerland. The mean daily movement of P. griseoaptera was significantly higher in the landscape with patchily distributed habitat (Switzerland) than in the landscape with greater habitat connectivity (France). Net displacement rate did not differ between the two landscapes, which we attributed to the presence of more linear elements in the connected landscape, resulting in a more directed pattern of movement by P. griseoaptera. Significant differences in the movement patterns between landscapes with contrasting structure suggest important effects of landscape structure on movement and dispersal success. The possibility of varying dispersal ability within the same species needs to be studied in more detail because this may provide important information for sustainable landscape planning aimed at maintaining viable metapopulations, especially in formerly well-connected landscapes.

  12. Development of a reproducible method for determining quantity of water and its configuration in a marsh landscape

    USGS Publications Warehouse

    Suir, Glenn M.; Evers, D. Elaine; Steyer, Gregory D.; Sasser, Charles E.

    2013-01-01

    Coastal Louisiana is a dynamic and ever-changing landscape. From 1956 to 2010, over 3,734 km2 of Louisiana's coastal wetlands have been lost due to a combination of natural and human-induced activities. The resulting landscape constitutes a mosaic of conditions from highly deteriorated to relatively stable with intact landmasses. Understanding how and why coastal landscapes change over time is critical to restoration and rehabilitation efforts. Historically, changes in marsh pattern (i.e., size and spatial distribution of marsh landmasses and water bodies) have been distinguished using visual identification by individual researchers. Difficulties associated with this approach include subjective interpretation, uncertain reproducibility, and laborious techniques. In order to minimize these limitations, this study aims to expand existing tools and techniques via a computer-based method, which uses geospatial technologies for determining shifts in landscape patterns. Our method is based on a raster framework and uses landscape statistics to develop conditions and thresholds for a marsh classification scheme. The classification scheme incorporates land and water classified imagery and a two-part classification system: (1) ratio of water to land, and (2) configuration and connectivity of water within wetland landscapes to evaluate changes in marsh patterns. This analysis system can also be used to trace trajectories in landscape patterns through space and time. Overall, our method provides a more automated means of quantifying landscape patterns and may serve as a reliable landscape evaluation tool for future investigations of wetland ecosystem processes in the northern Gulf of Mexico.

  13. Modeling animal landscapes.

    PubMed

    Porter, W P; Ostrowski, S; Williams, J B

    2010-01-01

    There is an increasing need to assess the effects of climate and land-use change on habitat quality, ideally from a mechanistic basis. The symposium "Molecules to Migration: Pressures of Life" at the Fourth International Conference in Africa for Comparative Physiology and Biochemistry, Maasai Mara National Reserve, Kenya, 2008, illustrated how the principles of biophysical ecology can capture the mechanistic links between organisms, climate, and other habitat features. These principles provide spatially explicit assessments of habitat quality from a physiological perspective (i.e., "animal landscapes") that can be validated independently of the data used to derive and parameterize them. The contents of this symposium showcased how the modeling of animal landscapes can be used to assess key issues in applied and theoretical ecology. The presentations included applications to amphibians, reptiles, birds, and mammals. The rare Arabian oryx on the Arabian Peninsula is used as an example for energetic calculations and their implications for behavior on the landscape. PMID:20670170

  14. An Object-Based Method for Chinese Landform Types Classification

    NASA Astrophysics Data System (ADS)

    Ding, Hu; Tao, Fei; Zhao, Wufan; Na, Jiaming; Tang, Guo'an

    2016-06-01

    Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

  15. Remote Sensing Information Classification

    NASA Technical Reports Server (NTRS)

    Rickman, Douglas L.

    2008-01-01

    This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.

  16. Classification and knowledge

    NASA Technical Reports Server (NTRS)

    Kurtz, Michael J.

    1989-01-01

    Automated procedures to classify objects are discussed. The classification problem is reviewed, and the relation of epistemology and classification is considered. The classification of stellar spectra and of resolved images of galaxies is addressed.

  17. Sampling in landscape genomics.

    PubMed

    Manel, Stéphanie; Albert, Cécile H; Yoccoz, Nigel G

    2012-01-01

    Landscape genomics, based on the sampling of individuals genotyped for a large number of markers, may lead to the identification of regions of the genome correlated to selection pressures caused by the environment. In this chapter, we discuss sampling strategies to be used in a landscape genomics approach. We suggest that designs based on model-based stratification using the climatic and/or biological spaces are in general more efficient than designs based on the geographic space. More work is needed to identify designs that allow disentangling environmental selection pressures versus other processes such as range expansions or hierarchical population structure.

  18. Labyrinthine granular landscapes.

    PubMed

    Caps, H; Vandewalle, N

    2001-11-01

    We have numerically studied a model of granular landscape eroded by wind. We show the appearance of labyrinthic patterns when the wind orientation turns by 90 degrees. The occurrence of such structures is discussed. Moreover, we introduce the density n(k) of "defects" as the dynamic parameter governing the landscape evolution. A power-law behavior of n(k) is found as a function of time. In the case of wind variations, the exponent (drastically) shifts from two to one. The presence of two asymptotic values of n(k) implies the irreversibility of the labyrinthic formation process.

  19. FLEX-TOPO: Proof of concept in a central European landscape

    NASA Astrophysics Data System (ADS)

    Gharari, Shervan; Hrachowitz, Markus; Fenicia, Fabrizio; Gao, Hongkai; Euser, Tanja; Savenije, Huub

    2013-04-01

    algorithm was adapted and applied in a way that in subsequent steps more and more comparative criteria are forced to be satisfied. Including landscape classification into hydrological models appears to be a powerful tool to achieve higher realism. Not only does it allow to consider and to make use of crucial feedback processes between the hydrological system and the eco-system, it also leads to more detailed information on how a catchment may work than would be the case in a lumped model.

  20. Classification of Physical Activity

    PubMed Central

    Turksoy, Kamuran; Paulino, Thiago Marques Luz; Zaharieva, Dessi P.; Yavelberg, Loren; Jamnik, Veronica; Riddell, Michael C.; Cinar, Ali

    2015-01-01

    Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise. PMID:26443291

  1. Design of multitarget activity landscapes that capture hierarchical activity cliff distributions.

    PubMed

    Dimova, Dilyana; Wawer, Mathias; Wassermann, Anne Mai; Bajorath, Jürgen

    2011-02-28

    An activity landscape model of a compound data set can be rationalized as a graphical representation that integrates molecular similarity and potency relationships. Activity landscape representations of different design are utilized to aid in the analysis of structure-activity relationships and the selection of informative compounds. Activity landscape models reported thus far focus on a single target (i.e., a single biological activity) or at most two targets, giving rise to selectivity landscapes. For compounds active against more than two targets, landscapes representing multitarget activities are difficult to conceptualize and have not yet been reported. Herein, we present a first activity landscape design that integrates compound potency relationships across multiple targets in a formally consistent manner. These multitarget activity landscapes are based on a general activity cliff classification scheme and are visualized in graph representations, where activity cliffs are represented as edges. Furthermore, the contributions of individual compounds to structure-activity relationship discontinuity across multiple targets are monitored. The methodology has been applied to derive multitarget activity landscapes for compound data sets active against different target families. The resulting landscapes identify single-, dual-, and triple-target activity cliffs and reveal the presence of hierarchical cliff distributions. From these multitarget activity landscapes, compounds forming complex activity cliffs can be readily selected.

  2. Accurate statistical tests for smooth classification images.

    PubMed

    Chauvin, Alan; Worsley, Keith J; Schyns, Philippe G; Arguin, Martin; Gosselin, Frédéric

    2005-10-05

    Despite an obvious demand for a variety of statistical tests adapted to classification images, few have been proposed. We argue that two statistical tests based on random field theory (RFT) satisfy this need for smooth classification images. We illustrate these tests on classification images representative of the literature from F. Gosselin and P. G. Schyns (2001) and from A. B. Sekuler, C. M. Gaspar, J. M. Gold, and P. J. Bennett (2004). The necessary computations are performed using the Stat4Ci Matlab toolbox.

  3. Mapping the Ancient Maya Landscape from Space

    NASA Technical Reports Server (NTRS)

    Sever, Tom

    2003-01-01

    This project uses new satellite and airborne imagery in combination with remote sensing, GIS, and GPS technology to understand the dynamics of how the Maya successfully interacted with their karst topographic landscape for several centuries in the northern Peten region of Guatemala. The ancient Maya attained one of the greatest population densities in human history in the tropical forest of the Peten, Guatemala, and it was in this region that the Maya civilization began, flourished, and abruptly disappeared for unknown reasons around AD 800. How the Maya were able to successfully manage water and feed this dense population is not known at this time. However, a recent NASA-funded project was the first to investigate large seasonal swamps (bajos) that make up 40 percent of the landscape. Through the use of remote sensing, ancient Maya features such as cities, roadways, canals and water reservoirs have been detected and verified through ground reconnaissance. The results of this research cast new light on the adaptation of the ancient Maya to their environment. Micro-environmental variation within the wetlands was elucidated and the different vegetational associations identified in the satellite imagery. More than 70 new archeological sites within and at the edges of the bajo were mapped and tested. Modification of the landscape by the Maya in the form of dams and reservoirs in the Holmul River and its tributaries and possible drainage canals in bajos was demonstrated. The recent acquisition of one-meter IKONOS imagery and high resolution STAR-3i radar imagery (2.5m backscatter/ 10m DEM), opens new possibilities for understanding how a civilization was able to survive for centuries upon a karst topographic landscape and their human-induced effects upon the local climate. This understanding is critical for the current population that is presently experiencing rapid population growth and destroying the landscape through non-traditional farming and grazing techniques

  4. Sharing a Disparate Landscape

    ERIC Educational Resources Information Center

    Ali-Khan, Carolyne

    2010-01-01

    Working across boundaries of power, identity, and political geography is fraught with difficulties and contradictions. In Tali Tal and Iris Alkaher's, "Collaborative environmental projects in a multicultural society: Working from within separate or mutual landscapes?" the authors describe their efforts to do this in the highly charged atmosphere…

  5. Landscape Management: Field Specialist.

    ERIC Educational Resources Information Center

    Newton, Deborah; Newton, Steve

    This module is the second volume in a series of three publications on landscape management. The module contains five instructional units that cover the following topics: orientation; equipment; irrigation systems and maintenance; plant material identification and pests; and turf identification and pests. Each instructional unit follows a standard…

  6. Biofuels from urban landscapes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biomass from urban landscapes is an untapped resource. Lawn thatch and clippings, fallen leaves and tree limbs are all potential sources of biofuels. Most cities already collect and transport these materials to disposal sites; but, alternatively could collect and transport these materials to a loc...

  7. Moving into Landscapes

    ERIC Educational Resources Information Center

    Nelson, Cindy

    2008-01-01

    This article describes a lesson, designed for second graders, that begins with the teacher showing and talking about a few landscape fundamentals: horizon line, depth, and the mood or feeling that a work of art inspires. A class discussion ensues about how an artist's images can make one feel, how they can convey calmness, warmth, anxiety, or a…

  8. LANDSCAPE MANAGEMENT PRACTICES

    EPA Science Inventory

    USDA Conservation Practices are applied at various scales ranging from a portion of a field or a specific farm operation to the watershed or landscape scale. The Conservation Effects Assessment Project is a joint effort of USDA Conservation and Research agencies to determine the...

  9. Shaping the Landscape.

    ERIC Educational Resources Information Center

    Naturescope, 1987

    1987-01-01

    Provides background information on various agents that change the landscape. Includes teaching activities on weathering, water, wind and ice erosion, plate tectonics, sedimentation, deposition, mountain building, and determining contour lines. Contains reproducible handouts and worksheets for two of the activities. (TW)

  10. A Curious Landscape

    NASA Technical Reports Server (NTRS)

    2004-01-01

    This 'postcard' from the panoramic camera on the Mars Exploration Rover Opportunity shows the view of the martian landscape southwest of the rover. The image was taken in the late martian afternoon at Meridiani Planum on Mars, where Opportunity landed at approximately 9:05 p.m. PST on Saturday, Jan. 24.

  11. Landscapes. Artists' Workshop Series.

    ERIC Educational Resources Information Center

    King, Penny; Roundhill, Clare

    This instructional resource, designed to be used by and with elementary level students, provides inspiration for landscape painting by presenting the work of six different artists. These include: "Fuji in Clear Weather" (Katsushika Hokusai, 1823-29); "The Tree of Life" (Gustav Klimt, c. 1905-1909); "The Waterlily Pond" (Claude Monet, 1899);…

  12. Desert landscape irrigation

    SciTech Connect

    Quinones, R.

    1995-06-01

    Industrialization can take place in an arid environment if a long term, overall water management program is developed. The general rule to follow is that recharge must equal or exceed use. The main problem encountered in landscape projects is that everyone wants a lush jungle setting, tall shade trees, ferns, with a variety of floral arrangements mixed in. What we want, what we can afford, and what we get are not always the same. Vegetation that requires large quantities of water are not native to any desert. Surprisingly; there are various types of fruit trees, and vegetables that will thrive in the desert. Peaches, plums, nut trees, do well with drip irrigation as well as tomatoes. Shaded berry plans will also do well, the strawberry being one. In summary; if we match our landscape to our area, we can then design our irrigation system to maintain our landscape and grow a variety of vegetation in any arid or semiarid environment. The application of science and economics to landscaping has now come of age.

  13. Rivers and landscape

    SciTech Connect

    Petts, G.; Foster, I.

    1985-01-01

    This book provides readers with a knowledge of river systems, emphasising functional relationships between forms and processes, and the historical change of fluvial landscapes including evidence from valley fills and lake sediments. In explaining the properties and dynamics of river systems, the authors focus on new approaches, ideas and interpretations.

  14. Campus Landscape: Functions, Forms, Features.

    ERIC Educational Resources Information Center

    Dober, Richard P.

    This guide provides information, instruction, and ideas on planning and designing every aspect of the campus landscape, from parking lots to playing fields. Using real-world examples of classic and contemporary campus landscapes, it features coverage of landscape restoration and regeneration; provides an assessment matrix for consistent, effective…

  15. Geomorphology of anthropogenic landscapes

    NASA Astrophysics Data System (ADS)

    Sofia, Giulia; Tarolli, Paolo

    2015-04-01

    The construction of urban areas and the development of road networks leave a significant signature on the Earth surface, providing a geomorphological evidence to support the idea that humans are nowadays a geomorphic agent having deep effects on the morphological organization of the landscape. The reconstruction or identification of anthropogenic topographies, therefore, provides a mechanism for quantifying anthropogenic changes to the landscape systems in the Anthropocene. Following this research line, the present study tests the effectiveness of a recently published topographic index, the Slope Local Length of Autocorrelation (SLLAC, Sofia et al. 2014) to portrait anthropogenic geomorphology, focusing in particular on road network density, and urban complexity (UCI). At first, the research considers the increasing of anthropic structures and the resulting changes in the SLLAC and in two derived parameters (mean SLLAC per km2 and SLLAC roughness, or Surface Peak Curvature -Spc). As a second step, considering the SLLAC derived indices, the anthropogenic geomorphology is automatically depicted using a k-means clustering algorithm. In general, the increasing of road network density or of the UCI is positively correlated to the mean SLLAC per km2, while the Spc is negatively correlated to the increasing of the anthropic structures. Areas presenting different road network organization are effectively captured considering multiple combinations of the defined parameters. Landscapes with small scattered towns, and a network with long roads in a dendritic shape (with hierarchical branching) are characterized simultaneously by high mean SLLAC and low Spc. Large and complex urban areas served by rectilinear networks with numerous short straight lines and right angles, have either a maximized mean SLLAC or a minimized Spc or both. In all cases, the anthropogenic landscape identified by the procedure is comparable to the ones identified manually from orthophoto, with the

  16. Analyzing landscape changes in the Bafa Lake Nature Park of Turkey using remote sensing and landscape structure metrics.

    PubMed

    Esbah, Hayriye; Deniz, Bulent; Kara, Baris; Kesgin, Birsen

    2010-06-01

    Bafa Lake Nature Park is one of Turkey's most important legally protected areas. This study aimed at analyzing spatial change in the park environment by using object-based classification technique and landscape structure metrics. SPOT 2X (1994) and ASTER (2005) images are the primary research materials. Results show that artificial surfaces, low maqui, garrigue, and moderately high maqui covers have increased and coniferous forests, arable lands, permanent crop, and high maqui covers have decreased; coniferous forest, high maqui, grassland, and saline areas are in a disappearance stage of the land transformation; and the landscape pattern is more fragmented outside the park boundaries. The management actions should support ongoing vegetation regeneration, mitigate transformation of vegetation structure to less dense and discontinuous cover, control the dynamics at the agricultural-natural landscape interface, and concentrate on relatively low but steady increase of artificial surfaces.

  17. The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments.

    PubMed

    Ramsey, Elijah W; Nelson, Gene A; Echols, Darrell; Sapkota, Sijan K

    2002-05-01

    The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

  18. Using graph approach for managing connectivity in integrative landscape modelling

    NASA Astrophysics Data System (ADS)

    Rabotin, Michael; Fabre, Jean-Christophe; Libres, Aline; Lagacherie, Philippe; Crevoisier, David; Moussa, Roger

    2013-04-01

    FLUID-landr library has been developed in order i) to be used with no GIS expert skills needed (common gis formats can be read and simplified spatial management is provided), ii) to easily develop adapted rules of landscape discretization and graph creation to follow spatialized model requirements and iii) to allow model developers to manage dynamic and complex spatial topology. Graph management in OpenFLUID are shown with i) examples of hydrological modelizations on complex farmed landscapes and ii) the new implementation of Geo-MHYDAS tool based on the OpenFLUID-landr library, which allows to discretize a landscape and create graph structure for the MHYDAS model requirements.

  19. Feature Space Mapping as a universal adaptive system

    NASA Astrophysics Data System (ADS)

    Duch, Włodzisław; Diercksen, Geerd H. F.

    1995-06-01

    The most popular realizations of adaptive systems are based on the neural network type of algorithms, in particular feedforward multilayered perceptrons trained by backpropagation of error procedures. In this paper an alternative approach based on multidimensional separable localized functions centered at the data clusters is proposed. In comparison with the neural networks that use delocalized transfer functions this approach allows for full control of the basins of attractors of all stationary points. Slow learning procedures are replaced by the explicit construction of the landscape function followed by the optimization of adjustable parameters using gradient techniques or genetic algorithms. Retrieving information does not require searches in multidimensional subspaces but it is factorized into a series of one-dimensional searches. Feature Space Mapping is applicable to learning not only from facts but also from general laws and may be treated as a fuzzy expert system (neurofuzzy system). The number of nodes (fuzzy rules) is growing as the network creates new nodes for novel data but the search time is sublinear in the number of rules or data clusters stored. Such a system may work as a universal classificator, approximator and reasoning system. Examples of applications for the identification of spectra (classification), intelligent databases (association) and for the analysis of simple electrical circuits (expert system type) are given.

  20. Optimization of landscape services under uncoordinated management by multiple landowners.

    PubMed

    Porto, Miguel; Correia, Otília; Beja, Pedro

    2014-01-01

    objectives. It may thus be adapted to other socio-ecological systems, particularly where specific patterns of landscape heterogeneity are to be maintained despite imperfect management by multiple landowners.

  1. Learning and Domain Adaptation

    NASA Astrophysics Data System (ADS)

    Mansour, Yishay

    Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition).

  2. A Novel Modulation Classification Approach Using Gabor Filter Network

    PubMed Central

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

  3. Adaptive processing for LANDSAT data

    NASA Technical Reports Server (NTRS)

    Crane, R. B.; Reyer, J. F.

    1975-01-01

    Analytical and test results on the use of adaptive processing on LANDSAT data are presented. The Kalman filter was used as a framework to contain different adapting techniques. When LANDSAT MSS data were used all of the modifications made to the Kalman filter performed the functions for which they were designed. It was found that adaptive processing could provide compensation for incorrect signature means, within limits. However, if the data were such that poor classification accuracy would be obtained when the correct means were used, then adaptive processing would not improve the accuracy and might well lower it even further.

  4. Simulations of Fluvial Landscapes

    NASA Astrophysics Data System (ADS)

    Cattan, D.; Birnir, B.

    2013-12-01

    The Smith-Bretherton-Birnir (SBB) model for fluvial landsurfaces consists of a pair of partial differential equations, one governing water flow and one governing the sediment flow. Numerical solutions of these equations have been shown to provide realistic models in the evolution of fluvial landscapes. Further analysis of these equations shows that they possess scaling laws (Hack's Law) that are known to exist in nature. However, the simulations are highly dependent on the numerical methods used; with implicit methods exhibiting the correct scaling laws, but the explicit methods fail to do so. These equations, and the resulting models, help to bridge the gap between the deterministic and the stochastic theories of landscape evolution. Slight modifications of the SBB equations make the results of the model more realistic. By modifying the sediment flow equation, the model obtains more pronounced meandering rivers. Typical landsurface with rivers.

  5. Wildfire and landscape change

    USGS Publications Warehouse

    Santi, P.; Cannon, S.; DeGraff, J.

    2013-01-01

    Wildfire is a worldwide phenomenon that is expected to increase in extent and severity in the future, due to fuel accumulations, shifting land management practices, and climate change. It immediately affects the landscape by removing vegetation, depositing ash, influencing water-repellent soil formation, and physically weathering boulders and bedrock. These changes typically lead to increased erosion through sheetwash, rilling, dry ravel, and increased mass movement in the form of floods, debris flow, rockfall, and landslides. These process changes bring about landform changes as hillslopes are lowered and stream channels aggrade or incise at increased rates. Furthermore, development of alluvial fans, debris fans, and talus cones are enhanced. The window of disturbance to the landscape caused by wildfire is typically on the order of three to four years, with some effects persisting up to 30 years.

  6. Sharing a disparate landscape

    NASA Astrophysics Data System (ADS)

    Ali-Khan, Carolyne

    2010-06-01

    Working across boundaries of power, identity, and political geography is fraught with difficulties and contradictions. In Tali Tal and Iris Alkaher's, " Collaborative environmental projects in a multicultural society: Working from within separate or mutual landscapes?" the authors describe their efforts to do this in the highly charged atmosphere of Israel. This forum article offers a response to their efforts. Writing from a framework of critical pedagogy, I use the concepts of space and time to anchor my analysis, as I examine the issue of power in this Jew/Arab collaborative environmental project. This response problematizes "sharing" in a landscape fraught with disparities. It also looks to further Tal and Alkaher's work by geographically and politically grounding it in the broader current conflict and by juxtaposing sustainability with equity.

  7. Characterizing biotic and abiotic properties of landscape and their implications for ecohydrological processes across scales

    NASA Astrophysics Data System (ADS)

    Kumar, J.; Langford, Z.; Hoffman, F. M.

    2015-12-01

    Ecohydrological processes governing the dynamics of terrestrial ecosystems and its response and feedback to climate change occur at diverse spatial and temporal scales. To accurately capture the dynamics of ecohydrological processes in the model, its critically important to capture the subgrid scale heterogeneity of the landscape and develop scale aware process representation and parameterization. This study focused on the Arctic tundra landscape at Seward Peninsula of Alaska. Ecohydrological processes in this sensitive landscape are strongly governed by the physical and structural properties (like topography, soil, permafrost, geomorphology etc.) of the landscape, environmental conditions (like temperature, precipitation, light, radiation) and biotic conditions (vegetation, above/below biomass and organic matter, disturbance history etc.). From site to watershed to regional (scale at which models often operate), landscape is a complex mosaic of a range of biotic and abiotic properties. We have developed and applied a hierarchical characterization and classification approach to segment the landscape in distinct units which can be used to develop and parameterize process models at local scale. We also analyze how the distribution and organization of the landscape units as building blocks influence and interact with ecosystem processes across scales. Our goals is understand the landscape organization principles and their roles to inform and improve process based models of ecohydrological processes in Arctic tundra landscape.

  8. [Landscape character assessment framework in rural area: A case study in Qiaokou, Chang-sha, China].

    PubMed

    Zhang, Qian; Liu, Wen-ping; Yu, Zhen-rong

    2015-05-01

    Based on the concept and methods of landscape character assessment (LCA) in England, this paper applied a complete process of landscape character assessment with a case study in Qiaokou Town, which is located in a typical southern paddy fields area in Changsha City. We drew the landscape character map of Qiaokou Town through desk classification and field survey, identified and compared the key characters of each character area, and proposed suggestions on the improvement and stewardship of landscape characters. The results showed that Qiaokou could be divided into 2 landscape character types and 7 landscape character areas with the main differences in cropland and vegetation pattern as well as aesthetic characters. The case study indicated that LCA could be a critical tool to identify the characteristics in rural area, and provide helpful guidance to protect, restore and maintain the unique culture and characters of rural landscape, which is useful for targeted rural landscape development. In the future, we suggested that the assessment on the effects of landscape construction measures on the ecosystem services should be incorporated in LCA research as well. PMID:26571675

  9. [Landscape character assessment framework in rural area: A case study in Qiaokou, Chang-sha, China].

    PubMed

    Zhang, Qian; Liu, Wen-ping; Yu, Zhen-rong

    2015-05-01

    Based on the concept and methods of landscape character assessment (LCA) in England, this paper applied a complete process of landscape character assessment with a case study in Qiaokou Town, which is located in a typical southern paddy fields area in Changsha City. We drew the landscape character map of Qiaokou Town through desk classification and field survey, identified and compared the key characters of each character area, and proposed suggestions on the improvement and stewardship of landscape characters. The results showed that Qiaokou could be divided into 2 landscape character types and 7 landscape character areas with the main differences in cropland and vegetation pattern as well as aesthetic characters. The case study indicated that LCA could be a critical tool to identify the characteristics in rural area, and provide helpful guidance to protect, restore and maintain the unique culture and characters of rural landscape, which is useful for targeted rural landscape development. In the future, we suggested that the assessment on the effects of landscape construction measures on the ecosystem services should be incorporated in LCA research as well.

  10. Using multitemporal Landsat imagery to monitor and model the influences of landscape pattern on urban expansion in a metropolitan region

    NASA Astrophysics Data System (ADS)

    Yang, Yetao; Wong, Louis Ngai Yuen; Chen, Chao; Chen, Tao

    2014-01-01

    Studying the interaction between landscape patterns and temporal land-use changes in a metropolitan area can improve understanding of the urbanization process. Multitemporal remote sensing imagery is widely used to map the urbanization-caused temporal land-use dynamics, which mainly appear as built-up growth. Remote sensing integrated with landscape metrics is also used to quantitatively describe the landscape pattern of the urban area in recent literature. However, few studies have focused on the interaction between the pattern and the process of urbanization in a metropolitan area. We propose a grid-based framework to analyze the influence of the landscape pattern on the built-up growth by using the multitemporal Landsat imagery. Remote sensing classification method is used to obtain thematic land-use maps. Built-up growth is then extracted from the multitemporal classification results by a postclassification change detection. Landscape pattern, which is quantitatively described by landscape metrics, is derived from the thematic land-use maps. A grid-based method is used to analyze the spatial variation of landscape pattern and its related built-up growth. Finally, the spatial relationship between the landscape pattern and the built-up growth characters is assessed and modeled by using the mathematical regression method. The present study shows that an apparent correlation between landscape pattern and built-up growth exists. The correlation reflects the inherent influences of landscape pattern on urban expansion. The landscape pattern indicates the land development stage, while the urbanization stage determines the speed and style of the following built-up growth. Scales, including temporal scale and spatial scale, are important to modeling the landscape pattern effects on the built-up growth. The proposed analysis framework is efficient in detecting and modeling the landscape pattern effects on the built-up growth.

  11. Multitemporal spatial pattern analysis of Tulum's tropical coastal landscape

    NASA Astrophysics Data System (ADS)

    Ramírez-Forero, Sandra Carolina; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

    2011-11-01

    The tropical coastal landscape of Tulum in Quintana Roo, Mexico has a high ecological, economical, social and cultural value, it provides environmental and tourism services at global, national, regional and local levels. The landscape of the area is heterogeneous and presents random fragmentation patterns. In recent years, tourist services of the region has been increased promoting an accelerate expansion of hotels, transportation and recreation infrastructure altering the complex landscape. It is important to understand the environmental dynamics through temporal changes on the spatial patterns and to propose a better management of this ecological area to the authorities. This paper addresses a multi-temporal analysis of land cover changes from 1993 to 2000 in Tulum using Thematic Mapper data acquired by Landsat-5. Two independent methodologies were applied for the analysis of changes in the landscape and for the definition of fragmentation patterns. First, an Iteratively Multivariate Alteration Detection (IR-MAD) algorithm was used to detect and localize land cover change/no-change areas. Second, the post-classification change detection evaluated using the Support Vector Machine (SVM) algorithm. Landscape metrics were calculated from the results of IR-MAD and SVM. The analysis of the metrics indicated, among other things, a higher fragmentation pattern along roadways.

  12. THE EFFECTS OF HABITAT RESOLUTION ON MODELS OF AVIAN DIVERSITY AND DISTRIBUTIONS: A COMPARISON OF TWO LAND-COVER CLASSIFICATIONS

    EPA Science Inventory

    The quantification of pattern is a key element of landscape analyses. One aspect of this quantification of particular importance to landscape ecologists regards the classification of continuous variables to produce categorical variables such as land-cover type or elevation strat...

  13. I-CAN: The Classification and Prediction of Support Needs

    ERIC Educational Resources Information Center

    Arnold, Samuel R. C.; Riches, Vivienne C.; Stancliffe, Roger J.

    2014-01-01

    Background: Since 1992, the diagnosis and classification of intellectual disability has been dependent upon three constructs: intelligence, adaptive behaviour and support needs (Luckasson "et al." 1992. Mental Retardation: Definition, Classification and Systems of Support. American Association on Intellectual and Developmental…

  14. Incorporating Bioenergy in Sustainable Landscape Designs Workshop Two: Agricultural Landscapes

    SciTech Connect

    Negri, M. Cristina; Ssegane, H.

    2015-08-01

    The Bioenergy Technologies Office hosted two workshops on Incorporating Bioenergy in Sustainable Landscape Designs with Oak Ridge and Argonne National Laboratories in 2014. The second workshop focused on agricultural landscapes and took place in Argonne, IL from June 24—26, 2014. The workshop brought together experts to discuss how landscape design can contribute to the deployment and assessment of sustainable bioenergy. This report summarizes the discussions that occurred at this particular workshop.

  15. Pathogen population dynamics in agricultural landscapes: the Ddal modelling framework.

    PubMed

    Papaïx, Julien; Adamczyk-Chauvat, Katarzyna; Bouvier, Annie; Kiêu, Kiên; Touzeau, Suzanne; Lannou, Christian; Monod, Hervé

    2014-10-01

    Modelling processes that occur at the landscape scale is gaining more and more attention from theoretical ecologists to agricultural managers. Most of the approaches found in the literature lack applicability for managers or, on the opposite, lack a sound theoretical basis. Based on the metapopulation concept, we propose here a modelling approach for landscape epidemiology that takes advantage of theoretical results developed in the metapopulation context while considering realistic landscapes structures. A landscape simulator makes it possible to represent both the field pattern and the spatial distribution of crops. The pathogen population dynamics are then described through a matrix population model both stage- and space-structured. In addition to a classical invasion analysis we present a stochastic simulation experiment and provide a complete framework for performing a sensitivity analysis integrating the landscape as an input factor. We illustrate our approach using an example to evaluate whether the agricultural landscape composition and structure may prevent and mitigate the development of an epidemic. Although designed for a fungal foliar disease, our modelling approach is easily adaptable to other organisms.

  16. The potential and flux landscape theory of evolution

    NASA Astrophysics Data System (ADS)

    Zhang, Feng; Xu, Li; Zhang, Kun; Wang, Erkang; Wang, Jin

    2012-08-01

    We established the potential and flux landscape theory for evolution. We found explicitly the conventional Wright's gradient adaptive landscape based on the mean fitness is inadequate to describe the general evolutionary dynamics. We show the intrinsic potential as being Lyapunov function(monotonically decreasing in time) does exist and can define the adaptive landscape for general evolution dynamics for studying global stability. The driving force determining the dynamics can be decomposed into gradient of potential landscape and curl probability flux. Non-zero flux causes detailed balance breaking and measures how far the evolution from equilibrium state. The gradient of intrinsic potential and curl flux are perpendicular to each other in zero fluctuation limit resembling electric and magnetic forces on electrons. We quantified intrinsic energy, entropy and free energy of evolution and constructed non-equilibrium thermodynamics. The intrinsic non-equilibrium free energy is a Lyapunov function. Both intrinsic potential and free energy can be used to quantify the global stability and robustness of evolution. We investigated an example of three allele evolutionary dynamics with frequency dependent selection (detailed balance broken). We uncovered the underlying single, triple, and limit cycle attractor landscapes. We found quantitative criterions for stability through landscape topography. We also quantified evolution pathways and found paths do not follow potential gradient and are irreversible due to non-zero flux. We generalized the original Fisher's fundamental theorem to the general (i.e., frequency dependent selection) regime of evolution by linking the adaptive rate with not only genetic variance related to the potential but also the flux. We show there is an optimum potential where curl flux resulting from biotic interactions of individuals within a species or between species can sustain an endless evolution even if the physical environment is unchanged. We

  17. Estimating methane fluxes at a landscape scale

    NASA Astrophysics Data System (ADS)

    Stockdale, James; MacBean, Natasha

    2010-05-01

    Terrestrial methane fluxes are an important component of peatland carbon budgets. Using a well-studied peatland site in Wales as a case study, we present a variety of approaches to quantifying annual methane fluxes at a landscape scale, with a focus on the comparison between a simple stratification method, an empirical regression-based method and a process-based method. The simplest approach relies on in situ methane flux measurements which, due to the indirect effects on methane flux from the vascular transport mechanism and co-variation with hydrological conditions, were stratified by vegetation type. Aside from this initial classification, an annual landscape flux was produced through a linear scaling model without attempting to consider any physical, chemical or biological processes known to control methane fluxes. The regression-based approach attempted to model fluxes using repeated measurements from across the study site over a 12 months period, together with environmental variables from associated locations. This method classifies the landscape by vegetation in a similar way to the first method and also takes into consideration variables commonly known to influence methane flux such as temperature and water table. However, no direct consideration of methane production or consumption is included in this empirical regression model. In contrast to both the preceding methods, estimates of methane flux using a process-based model were constructed for the same landscape. This method uses the Carnegie-Ames-Stanford Approach (CASA) model (Potter et al., 1993), which has been modified to include a representation of methane dynamics. The model is calibrated with ground-based measurements of net CH4 flux and water table depth using a Metropolis Hastings Markov Chain Monte Carlo approach. Comparison of these approaches shows that, while simple methods of stratification and scaling are computationally inexpensive and quick to perform, they are least successful when

  18. Understanding Patchy Landscape Dynamics: Towards a Landscape Language

    PubMed Central

    Gaucherel, Cédric; Boudon, Frédéric; Houet, Thomas; Castets, Mathieu; Godin, Christophe

    2012-01-01

    Patchy landscapes driven by human decisions and/or natural forces are still a challenge to be understood and modelled. No attempt has been made up to now to describe them by a coherent framework and to formalize landscape changing rules. Overcoming this lacuna was our first objective here, and this was largely based on the notion of Rewriting Systems, also called Formal Grammars. We used complicated scenarios of agricultural dynamics to model landscapes and to write their corresponding driving rule equations. Our second objective was to illustrate the relevance of this landscape language concept for landscape modelling through various grassland managements, with the final aim to assess their respective impacts on biological conservation. For this purpose, we made the assumptions that a higher grassland appearance frequency and higher land cover connectivity are favourable to species conservation. Ecological results revealed that dairy and beef livestock production systems are more favourable to wild species than is hog farming, although in different ways. Methodological results allowed us to efficiently model and formalize these landscape dynamics. This study demonstrates the applicability of the Rewriting System framework to the modelling of agricultural landscapes and, hopefully, to other patchy landscapes. The newly defined grammar is able to explain changes that are neither necessarily local nor Markovian, and opens a way to analytical modelling of landscape dynamics. PMID:23049935

  19. Economic linkages to changing landscapes.

    PubMed

    Peterson, Jeffrey M; Caldas, Marcellus M; Bergtold, Jason S; Sturm, Belinda S; Graves, Russell W; Earnhart, Dietrich; Hanley, Eric A; Brown, J Christopher

    2014-01-01

    Many economic processes are intertwined with landscape change. A large number of individual economic decisions shape the landscape, and in turn the changes in the landscape shape economic decisions. This article describes key research questions about the economics of landscape change and reviews the state of research knowledge. The rich and varied economic-landscape interactions are an active area of research by economists, geographers, and others. Because the interactions are numerous and complex, disentangling the causal relationships in any given landscape system is a formidable research challenge. Limited data with mismatched temporal and spatial scales present further obstacles. Nevertheless, the growing body of economic research on these topics is advancing and shares fundamental challenges, as well as data and methods, with work in other disciplines.

  20. Using community archetypes to better understand differential community adaptation to wildfire risk.

    PubMed

    Carroll, Matthew; Paveglio, Travis

    2016-06-01

    One of the immediate challenges of wildfire management concerns threats to human safety and property in residential areas adjacent to non-cultivated vegetation. One approach for relieving this problem is to increase human community 'adaptiveness' to deal with the risk and reality of fire in a variety of landscapes. The challenge in creating 'fire-adapted communities' (FACs) is the great diversity in character and make-up of populations at risk from wildfire. This paper outlines a recently developed categorization scheme for Wildland-Urban Interface (WUI) communities based on a larger conceptual approach for understanding how social diversity is likely to influence the creation of FACs. The WUI categorization scheme situates four community archetypes on a continuum that recognizes dynamic change in human community functioning. We use results from the WUI classification scheme to outline key characteristics associated with each archetype and results from recent case studies to demonstrate the diversity across WUI communities. Differences among key characteristics of local social context will likely result in the need for different adaptation strategies to wildfire. While the WUI archetypes described here may not be broadly applicable to other parts of the world, we argue that the conceptual approach and strategies for systematically documenting local influences on wildfire adaptation have potential for broad application.This article is part of the themed issue 'The interaction of fire and mankind'.

  1. Using community archetypes to better understand differential community adaptation to wildfire risk.

    PubMed

    Carroll, Matthew; Paveglio, Travis

    2016-06-01

    One of the immediate challenges of wildfire management concerns threats to human safety and property in residential areas adjacent to non-cultivated vegetation. One approach for relieving this problem is to increase human community 'adaptiveness' to deal with the risk and reality of fire in a variety of landscapes. The challenge in creating 'fire-adapted communities' (FACs) is the great diversity in character and make-up of populations at risk from wildfire. This paper outlines a recently developed categorization scheme for Wildland-Urban Interface (WUI) communities based on a larger conceptual approach for understanding how social diversity is likely to influence the creation of FACs. The WUI categorization scheme situates four community archetypes on a continuum that recognizes dynamic change in human community functioning. We use results from the WUI classification scheme to outline key characteristics associated with each archetype and results from recent case studies to demonstrate the diversity across WUI communities. Differences among key characteristics of local social context will likely result in the need for different adaptation strategies to wildfire. While the WUI archetypes described here may not be broadly applicable to other parts of the world, we argue that the conceptual approach and strategies for systematically documenting local influences on wildfire adaptation have potential for broad application.This article is part of the themed issue 'The interaction of fire and mankind'. PMID:27216514

  2. Landscape Construction in Dynamical Systems

    NASA Astrophysics Data System (ADS)

    Tang, Ying; Yuan, Ruoshi; Wang, Gaowei; Ao, Ping

    The idea of landscape has been recently applied to study various of biological problems. We demonstrate that a dynamical structure built into nonlinear dynamical systems allows us to construct such a global optimization landscape, which serves as the Lyapunov function for the ordinary differential equation. We find exact constructions on the landscape for a class of dynamical systems, including a van der Pol type oscillator, competitive Lotka-Volterra systems, and a chaotic system. The landscape constructed provides a new angle for understanding and modelling biological network dynamics.

  3. The concept of hydrologic landscapes

    USGS Publications Warehouse

    Winter, T.C.

    2001-01-01

    Hydrologic landscapes are multiples or variations of fundamental hydrologic landscape units. A fundamental hydrologic landscape unit is defined on the basis of land-surface form, geology, and climate. The basic land-surface form of a fundamental hydrologic landscape unit is an upland separated from a lowland by an intervening steeper slope. Fundamental hydrologic landscape units have a complete hydrologic system consisting of surface runoff, ground-water flow, and interaction with atmospheric water. By describing actual landscapes in terms of land-surface slope, hydraulic properties of soils and geologic framework, and the difference between precipitation and evapotranspiration, the hydrologic system of actual landscapes can be conceptualized in a uniform way. This conceptual framework can then be the foundation for design of studies and data networks, syntheses of information on local to national scales, and comparison of process research across small study units in a variety of settings. The Crow Wing River watershed in central Minnesota is used as an example of evaluating stream discharge in the context of hydrologic landscapes. Lake-research watersheds in Wisconsin, Minnesota, North Dakota, and Nebraska are used as an example of using the hydrologic-landscapes concept to evaluate the effect of ground water on the degree of mineralization and major-ion chemistry of lakes that lie within ground-water flow systems.

  4. Stonehenge and its Landscape

    NASA Astrophysics Data System (ADS)

    Ruggles, Clive L. N.

    In the 1960s and 1970s, Stonehenge polarized academic opinion between those (mainly astronomers) who claimed it demonstrated great astronomical sophistication and those (mainly archaeologists) who denied it had anything to do with astronomy apart from the solstitial alignment of its main axis. Now, several decades later, links to the annual passage of the sun are generally recognized as an essential part of the function and meaning not only of Stonehenge but also of several other nearby monuments, giving us important insights into beliefs and actions relating to the seasonal cycle by the prehistoric communities who populated this chalkland landscape in the third millennium BC Links to the moon remain more debatable.

  5. Wind-Eroded Landscape

    NASA Technical Reports Server (NTRS)

    2005-01-01

    5 August 2005 This Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image shows a dust-mantled, wind-eroded landscape in the Medusae Sulci region of Mars. Wind eroded the bedrock in this region, and then, later, windblown dust covered much of the terrain.

    Location near: 5.7oS, 160.2oW Image width: width: 3 km (1.9 mi) Illumination from: lower left Season: Southern Spring

  6. Probing the String Landscape

    ScienceCinema

    Keith Dienes

    2016-07-12

    We are currently in the throes of a potentially huge paradigm shift in physics. Motivated by recent developments in string theory and the discovery of the so-called "string landscape", physicists are beginning to question the uniqueness of fundamental theories of physics and the methods by which such theories might be understood and investigated. In this colloquium, I will give a non-technical introduction to the nature of this paradigm shift and how it developed. I will also discuss some of the questions to which it has led, and the nature of the controversies it has spawned.

  7. Probing the String Landscape

    SciTech Connect

    Keith Dienes

    2009-12-01

    We are currently in the throes of a potentially huge paradigm shift in physics. Motivated by recent developments in string theory and the discovery of the so-called "string landscape", physicists are beginning to question the uniqueness of fundamental theories of physics and the methods by which such theories might be understood and investigated. In this colloquium, I will give a non-technical introduction to the nature of this paradigm shift and how it developed. I will also discuss some of the questions to which it has led, and the nature of the controversies it has spawned.

  8. Classification Shell Game.

    ERIC Educational Resources Information Center

    Etzold, Carol

    1983-01-01

    Discusses shell classification exercises. Through keying students advanced from the "I know what a shell looks like" stage to become involved in the classification process: observing, labeling, making decisions about categories, and identifying marine animals. (Author/JN)

  9. Landscapes Impacted by Light

    NASA Astrophysics Data System (ADS)

    Arellano, B.; Roca, J.

    2016-06-01

    The gradual spread of urbanization, the phenomenon known under the term urban sprawl, has become one of the paradigms that have characterized the urban development since the second half of the twentieth century and early twenty-first century. However, there is no unanimous consensus about what means "urbanization". The plurality of forms of human settlement on the planet difficult to identify the urbanization processes. The arrival of electrification to nearly every corner of the planet is certainly the first and more meaningful indicator of artificialization of land. In this sense, the paper proposes a new methodology based on the analysis of the satellite image of nighttime lights designed to identify the highly impacted landscapes worldwide and to build an index of Land Impacted by Light per capita (LILpc) as an indicator of the level of urbanization. The used methodology allows the identification of different typologies of urbanized areas (villages, cities or metropolitan areas), as well as "rural", "rurban", "periurban" and "central" landscapes. The study identifies 186,134 illuminated contours (urbanized areas). In one hand, 404 of these contours could be consider as real "metropolitan areas"; and in the other hand, there are 161,821 contours with less than 5,000 inhabitants, which could be identify as "villages". Finally, the paper shows that 44.5 % live in rural areas, 15.5 % in rurban spaces, 26.2 % in suburban areas and only 18.4 % in central areas.

  10. Intrinsically Disordered Energy Landscapes

    NASA Astrophysics Data System (ADS)

    Chebaro, Yassmine; Ballard, Andrew J.; Chakraborty, Debayan; Wales, David J.

    2015-05-01

    Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such ‘intrinsically disordered’ landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an -helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium.

  11. The Sahara's Diverse Landscape

    NASA Technical Reports Server (NTRS)

    2002-01-01

    Vast stretches of uninterrupted sand are only one kind of Saharan landscape. This true-color MODIS image from November 9, 2001, reveals a diversity of land surface features, including ancient lava flows and volcanoes. Beginning at upper left and moving clockwise are the countries of Algeria, Tunisia, Libya, Chad, and Niger. Evidence of previous volcanic activity in the Sahara can be found in northeastern Chad, in particular, in a region known as Tibesti. Reaching up out of the surrounding desert, the dark rock of the Tibesti Plateau stands out in dark brown against the sand. Scattered throughout the region are the circular cones and calderas of several volcanoes. The dark remains of a lava flow mark the location of the Tousside volcano. North of Tibesti, in Libya, more dark-colored lava beds leave their mark on the landscape. Variety exists in Algeria, where the Grand Erg Oriental desert (far upper left) is hemmed in to the south by the Tinrhert Plateau. South of the Plateau, desert resumes briefly, only to give way to a mountainous region traced with impermanent rivers. In northern Niger, a sinuous gray-green line marks the edge of an escarpment that separates the Mangueni Plateau to the north from the rock deserts to the south. Image courtesy Jacques Descloitres, MODIS Land Rapid Response Team at NASA GSFC

  12. Landscape Evolution of Titan

    NASA Technical Reports Server (NTRS)

    Moore, Jeffrey

    2012-01-01

    Titan may have acquired its massive atmosphere relatively recently in solar system history. The warming sun may have been key to generating Titan's atmosphere over time, starting from a thin atmosphere with condensed surface volatiles like Triton, with increased luminosity releasing methane, and then large amounts of nitrogen (perhaps suddenly), into the atmosphere. This thick atmosphere, initially with much more methane than at present, resulted in global fluvial erosion that has over time retreated towards the poles with the removal of methane from the atmosphere. Basement rock, as manifested by bright, rough, ridges, scarps, crenulated blocks, or aligned massifs, mostly appears within 30 degrees of the equator. This landscape was intensely eroded by fluvial processes as evidenced by numerous valley systems, fan-like depositional features and regularly-spaced ridges (crenulated terrain). Much of this bedrock landscape, however, is mantled by dunes, suggesting that fluvial erosion no longer dominates in equatorial regions. High midlatitude regions on Titan exhibit dissected sedimentary plains at a number of localities, suggesting deposition (perhaps by sediment eroded from equatorial regions) followed by erosion. The polar regions are mainly dominated by deposits of fluvial and lacustrine sediment. Fluvial processes are active in polar areas as evidenced by alkane lakes and occasional cloud cover.

  13. Norwegian millstone quarry landscapes

    NASA Astrophysics Data System (ADS)

    Heldal, Tom; Meyer, Gurli; Grenne, Tor

    2013-04-01

    Rotary querns and millstones were used in Norway since just after the Roman Period until the last millstone was made in the 1930s. Throughout all this time millstone mining was fundamental for daily life: millstones were needed to grind grain, our most important food source. We can find millstone quarries in many places in the country from coast to mountain. Some of them cover many square kilometers and count hundreds of quarries as physical testimonies of a long and great production history. Other quarries are small and hardly visible. Some of this history is known through written and oral tradition, but most of it is hidden and must be reconstructed from the traces we can find in the landscape today. The Millstone project has put these quarry landscapes on the map, and conducted a range of case studies, including characterization of archaeological features connected to the quarrying, interpretation of quarrying techniques and evolution of such and establishing distribution and trade patterns by the aid of geological provenance. The project also turned out to be a successful cooperation between different disciplines, in particular geology and archaeology.

  14. Intrinsically disordered energy landscapes.

    PubMed

    Chebaro, Yassmine; Ballard, Andrew J; Chakraborty, Debayan; Wales, David J

    2015-01-01

    Analysis of an intrinsically disordered protein (IDP) reveals an underlying multifunnel structure for the energy landscape. We suggest that such 'intrinsically disordered' landscapes, with a number of very different competing low-energy structures, are likely to characterise IDPs, and provide a useful way to address their properties. In particular, IDPs are present in many cellular protein interaction networks, and several questions arise regarding how they bind to partners. Are conformations resembling the bound structure selected for binding, or does further folding occur on binding the partner in a induced-fit fashion? We focus on the p53 upregulated modulator of apoptosis (PUMA) protein, which adopts an α-helical conformation when bound to its partner, and is involved in the activation of apoptosis. Recent experimental evidence shows that folding is not necessary for binding, and supports an induced-fit mechanism. Using a variety of computational approaches we deduce the molecular mechanism behind the instability of the PUMA peptide as a helix in isolation. We find significant barriers between partially folded states and the helix. Our results show that the favoured conformations are molten-globule like, stabilised by charged and hydrophobic contacts, with structures resembling the bound state relatively unpopulated in equilibrium. PMID:25999294

  15. On the Usefulness of Hydrologic Landscapes on Hydrologic Model calibration and Selection

    EPA Science Inventory

    Hydrologic Landscapes (HLs) are units that can be used in aggregate to describe the watershed-scale hydrologic response of an area through use of physical and climatic properties. The HL assessment unit is a useful classification tool to relate and transfer hydrologically meaning...

  16. On the Usefulness of Hydrologic Landscapes for Hydrologic Modeling and Water Management

    EPA Science Inventory

    Hydrologic Landscapes (HLs) are units that can be used in aggregate to describe the watershed-scale hydrologic response of an area through use of physical and climatic properties. The HL assessment unit is a useful classification tool to relate and transfer hydrologically meaning...

  17. Fantasy Landscapes with a Message

    ERIC Educational Resources Information Center

    D'Amico, Elizabeth

    2005-01-01

    The author of this article describes using a Fantasy Landscapes lesson to get students expressing environmental issues through art. The Fantasy Landscapes lesson is an exploration of art elements and design principles through visual problem solving that links ideas, language, and theory to art. To get students thinking specifically about…

  18. Landscape in a Lacquer Box

    ERIC Educational Resources Information Center

    Savage, Martha

    2010-01-01

    A symbolic dry landscape garden of Eastern origin holds a special fascination for the author's middle-school students, which is why the author chose to create a project exploring this view of nature. A dry landscape garden, or "karesansui," is an arrangement of rocks, worn by nature and surrounded by a "sea" of sand, raked into patterns…

  19. Landscaping With Maintenance in Mind.

    ERIC Educational Resources Information Center

    Sorensen, Randy

    2000-01-01

    Examines school ground landscape design that enhances attractive of the school and provides for easier maintenance. Landscape design issues discussed include choice of grass, trees, and shrubs; irrigation; and safety and access. Other considerations for lessening maintenance problems for facility managers are also highlighted. (GR)

  20. Singularities of quantum control landscapes

    NASA Astrophysics Data System (ADS)

    Wu, Re-Bing; Long, Ruixing; Dominy, Jason; Ho, Tak-San; Rabitz, Herschel

    2012-07-01

    Quantum control landscape theory was formulated to assess the ease of finding optimal control fields in simulations and in the laboratory. The landscape is the observable as a function of the controls, and a primary goal of the theory is the analysis of landscape features. In what is referred to as the kinematic picture of the landscape, prior work showed that the landscapes are generally free of traps that could halt the search for an optimal control at a suboptimal observable value. The present paper considers the dynamical picture of the landscape, seeking the existence of singular controls, especially of a nonkinematic nature along with an assessment of whether they correspond to traps. We analyze the necessary and sufficient conditions for singular controls to be kinematic or nonkinematic critical solutions and the likelihood of their being encountered while maximizing an observable. An algorithm is introduced to seek singular controls on the landscape in simulations along with an associated Hessian landscape analysis. Simulations are performed for a large number of model finite-level quantum systems, showing that all the numerically identified kinematic and nonkinematic singular critical controls are not traps, in support of the prior empirical observations on the ease of finding high-quality optimal control fields.

  1. Complex Landscape Terms in Seri

    ERIC Educational Resources Information Center

    O'Meara, Carolyn; Bohnemeyer, Jurgen

    2008-01-01

    The nominal lexicon of Seri is characterized by a prevalence of analytical descriptive terms. We explore the consequences of this typological trait in the landscape domain. The complex landscape terms of Seri classify geographic entities in terms of their material make-up and spatial properties such as shape, orientation, and merological…

  2. Management and development of land in the name of the Green Economy: planning, landscape, efficiency, biodiversity

    NASA Astrophysics Data System (ADS)

    Benvenuti, Paolo

    2016-04-01

    preservation of landscapes; • relationship between the wine areas with the territory and its infrastructure; • participation in the process of territorial planning with operators and administrations; • relationship between wine and landscape, adaptation to climatic deterioration, renewable energy sources and energy efficiency; • new skills and new forms to manage the vineyard Depth knowledge of the characteristics of the territory wine passes through: • wine zoning, i.e. the identification of the more suitable terroirs for a wine: study of the climate, the soil, the vines of the interactions with the environment (Chart of vocations agroforestry); • soil classification; • analysis of the ecosystems (flora and fauna, biodiversity, forest, grasslands, crops); • identification of landscapes, from the analysis of types, morphology of the urban and rural landscape, processes of contextualization, etc. (Chart of landscape values) . The results achieved so far by the Italian municipalities that have adopted them, will soon be enhanced by enlargement of the methodological lines to new issues such as accessibility in wine territory, strengthening of local participation and the presence, promotion of wine as an integral part the local food planning, development of planning practices in the process of institutional reform.

  3. Fitness Landscapes of Functional RNAs.

    PubMed

    Kun, Ádám; Szathmáry, Eörs

    2015-08-21

    The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world.

  4. Fitness Landscapes of Functional RNAs

    PubMed Central

    Kun, Ádám; Szathmáry, Eörs

    2015-01-01

    The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world. PMID:26308059

  5. Issues in using landscape indicators to assess land changes

    SciTech Connect

    Kline, Keith L; Dale, Virginia H

    2012-01-01

    Landscape indicators, when combined with information about environmental conditions (such as habitat potential, biodiversity, carbon and nutrient cycling, and erosion) and socioeconomic forces, can provide insights about changing ecosystem services. They also provide information about opportunities for improving natural resources management. Landscape indicators rely on data regarding land cover, land management and land functionality. Challenges in using landscape indicators to assess change and effects include (1) measures of land management and attributes that are reliable, robust and consistent for all areas on the Earth do not exist, and thus land cover is more frequently utilized; (2) multiple types of land cover and management are often found within a single landscape and are constantly changing, which complicates measurement and interpretation; and (3) while causal analysis is essential for understanding and interpreting changes in indicator values, the interactions among multiple causes and effects over time make accurate attribution among many drivers of change particularly difficult. Because of the complexity, sheer number of variables, and limitations of empirical data on land changes, models are often used to illustrate and estimate values for landscape indicators, and those models have several problems. Recommendations to improve our ability to assess the effects of changes in land management include refinement of questions to be more consistent with available information and the development of data sets based on systematic measurement over time of spatially explicit land qualities such as carbon and nutrient stocks, water and soil quality, net primary productivity, habitat and biodiversity. Well-defined and consistent land-classification systems that are capable of tracking changes in these and other qualities that matter to society need to be developed and deployed. Because landscapes are so dynamic, it is crucial to develop ways for the scientific

  6. Classification, disease, and diagnosis.

    PubMed

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study. PMID:21532133

  7. Classification, disease, and diagnosis.

    PubMed

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  8. Landscape structure affects specialists but not generalists in naturally fragmented grasslands

    USGS Publications Warehouse

    Miller, Jesse E.D.; Damschen, Ellen Ingman; Harrison, Susan P.; Grace, James B.

    2015-01-01

    Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural landscapes become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. In this study, we asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure-defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index)-had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities, and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats.

  9. Buildings Interoperability Landscape

    SciTech Connect

    Hardin, Dave; Stephan, Eric G.; Wang, Weimin; Corbin, Charles D.; Widergren, Steven E.

    2015-12-31

    Through its Building Technologies Office (BTO), the United States Department of Energy’s Office of Energy Efficiency and Renewable Energy (DOE-EERE) is sponsoring an effort to advance interoperability for the integration of intelligent buildings equipment and automation systems, understanding the importance of integration frameworks and product ecosystems to this cause. This is important to BTO’s mission to enhance energy efficiency and save energy for economic and environmental purposes. For connected buildings ecosystems of products and services from various manufacturers to flourish, the ICT aspects of the equipment need to integrate and operate simply and reliably. Within the concepts of interoperability lie the specification, development, and certification of equipment with standards-based interfaces that connect and work. Beyond this, a healthy community of stakeholders that contribute to and use interoperability work products must be developed. On May 1, 2014, the DOE convened a technical meeting to take stock of the current state of interoperability of connected equipment and systems in buildings. Several insights from that meeting helped facilitate a draft description of the landscape of interoperability for connected buildings, which focuses mainly on small and medium commercial buildings. This document revises the February 2015 landscape document to address reviewer comments, incorporate important insights from the Buildings Interoperability Vision technical meeting, and capture thoughts from that meeting about the topics to be addressed in a buildings interoperability vision. In particular, greater attention is paid to the state of information modeling in buildings and the great potential for near-term benefits in this area from progress and community alignment.

  10. Connecting Managers and Scientists Through the California Landscape Conservation Cooperative

    NASA Astrophysics Data System (ADS)

    Ballard, G.; Schlafmann, D.

    2015-12-01

    The science-management partnership of the California Landscape Conservation Cooperative (CA LCC) works to address the landscape-scale impacts of climate change on California's ecosystems. This presentation focuses on models of successful science translation and co-production demonstrated by Point Blue Conservation Science, a member of the CA LCC. These models have all brought scientists and managers together for the common goal of integrating climate science into natural resource management decisions. These efforts include the intentional and deliberate consideration of climate change, addressing both mitigation and adaptation, referred to as Climate-Smart Conservation. These Climate-Smart Conservation efforts are strengthened through collaboratively adopting forward-looking goals and explicitly linking strategies to key climate impacts and vulnerabilities. Successes include integrating climate modeling into decision making and planning efforts, establishment of citizen science networks that inform management actions, and collaborative development of climate adaptation strategies with natural resource managers from the Sierra to the sea.

  11. Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points

    EPA Science Inventory

    Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two convention...

  12. Security classification of information

    SciTech Connect

    Quist, A.S.

    1989-09-01

    Certain governmental information must be classified for national security reasons. However, the national security benefits from classifying information are usually accompanied by significant costs -- those due to a citizenry not fully informed on governmental activities, the extra costs of operating classified programs and procuring classified materials (e.g., weapons), the losses to our nation when advances made in classified programs cannot be utilized in unclassified programs. The goal of a classification system should be to clearly identify that information which must be protected for national security reasons and to ensure that information not needing such protection is not classified. This document was prepared to help attain that goal. This document is the first of a planned four-volume work that comprehensively discusses the security classification of information. Volume 1 broadly describes the need for classification, the basis for classification, and the history of classification in the United States from colonial times until World War 2. Classification of information since World War 2, under Executive Orders and the Atomic Energy Acts of 1946 and 1954, is discussed in more detail, with particular emphasis on the classification of atomic energy information. Adverse impacts of classification are also described. Subsequent volumes will discuss classification principles, classification management, and the control of certain unclassified scientific and technical information. 340 refs., 6 tabs.

  13. Security classification of information

    SciTech Connect

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  14. Recursive heuristic classification

    NASA Technical Reports Server (NTRS)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  15. Landscape characterization and biodiversity research

    SciTech Connect

    Dale, V.H.; Offerman, H.; Frohn, R.; Gardner, R.H.

    1995-03-01

    Rapid deforestation often produces landscape-level changes in forest characteristics and structure, including area, distribution, and forest habitat types. Changes in landscape pattern through fragmentation or aggregation of natural habitats can alter patterns of abundance for single species and entire communities. Examples of single-species effects include increased predation along the forest edge, the decline in the number of species with poor dispersal mechanisms, and the spread of exotic species that have deleterious effects (e.g., gypsy moth). A decrease in the size and number of natural habitat patches increases the probability of local extirpation and loss of diversity of native species, whereas a decline in connectivity between habitat patches can negatively affect species persistence. Thus, there is empirical justification for managing entire landscapes, not just individual habitat types, in order to insure that native plant and animal diversity is maintained. A landscape is defined as an area composed of a mosaic of interacting ecosystems, or patches, with the heterogeneity among the patches significantly affecting biotic and abiotic processes in the landscape. Patches comprising a landscape are usually composed of discrete areas of relatively homogeneous environmental conditions and must be defined in terms of the organisms of interest. A large body of theoretical work in landscape ecology has provided a wealth of methods for quantifying spatial characteristics of landscapes. Recent advances in remote sensing and geographic information systems allow these methods to be applied over large areas. The objectives of this paper are to present a brief overview of common measures of landscape characteristics, to explore the new technology available for their calculation, to provide examples of their application, and to call attention to the need for collection of spatially-explicit field data.

  16. Investigations in adaptive processing of multispectral data

    NASA Technical Reports Server (NTRS)

    Kriegler, F. J.; Horwitz, H. M.

    1973-01-01

    Adaptive data processing procedures are applied to the problem of classifying objects in a scene scanned by multispectral sensor. These procedures show a performance improvement over standard nonadaptive techniques. Some sources of error in classification are identified and those correctable by adaptive processing are discussed. Experiments in adaptation of signature means by decision-directed methods are described. Some of these methods assume correlation between the trajectories of different signature means; for others this assumption is not made.

  17. Neighbourhood-scale urban forest ecosystem classification.

    PubMed

    Steenberg, James W N; Millward, Andrew A; Duinker, Peter N; Nowak, David J; Robinson, Pamela J

    2015-11-01

    Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape ecosystem conditions into logical and relatively homogeneous management units, making the potential for ecosystem-based decision support available to urban planners. The purpose of this study is to develop and propose a framework for urban forest ecosystem classification (UFEC). The multifactor framework integrates 12 ecosystem components that characterize the biophysical landscape, built environment, and human population. This framework is then applied at the neighbourhood scale in Toronto, Canada, using hierarchical cluster analysis. The analysis used 27 spatially-explicit variables to quantify the ecosystem components in Toronto. Twelve ecosystem classes were identified in this UFEC application. Across the ecosystem classes, tree canopy cover was positively related to economic wealth, especially income. However, education levels and homeownership were occasionally inconsistent with the expected positive relationship with canopy cover. Open green space and stocking had variable relationships with economic wealth and were more closely related to population density, building intensity, and land use. The UFEC can provide ecosystem-based information for greening initiatives, tree planting, and the maintenance of the existing canopy. Moreover, its use has the potential to inform the prioritization of limited municipal resources according to ecological conditions and to concerns of social equity in the access to nature and distribution of ecosystem service supply.

  18. Adaptive Management

    EPA Science Inventory

    Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive managem...

  19. How soil shapes the landscape

    NASA Astrophysics Data System (ADS)

    Minasny, Budiman; Finke, Peter; Vanwalleghem, Tom Tom; Stockmann, Uta; McBratney, Alex

    2014-05-01

    There has been an increase in interest in quantitative modelling of soil genesis, which can provide prediction of environmental changes through numerical models. Modelling soil formation is a difficult task because soil itself is highly complex with interactions between water, inorganic materials and organic matter. This paper will provide a review on the research efforts of modelling soil genesis, their connection with landscape models and the inexorable genesis of the IUSS soil landscape modelling working group. Quantitative modelling soil formation using mechanistic models have begun in the 1980s such as the 'soil deficit' model by Kirkby (1985), Hoosbeek & Bryant's pedodynamic model (1992), and recently the SoilGen model by Finke (2008). These profile models considered the chemical reactions and physical processes in the soil at the horizon and pedon scale. The SoilGen model is an integration of sub-models, such as water and solute movement, heat transport, soil organic matter decomposition, mineral dissolution, ion exchange, adsorption, speciation, complexation and precipitation. The model can calculate with detail the chemical changes and materials fluxes in a profile and has been successfully applied. While they can simulate soil profile development in detail, there is still a gap how the processes act in the landscape. Meanwhile research in landscape formation in geomorphology is progressing steadily over time, slope development models model have been developed since 1970s (Ahnert, 1977). Soil was also introduced in a landscape, however soil processes are mainly modelled through weathering and transport processes (Minasny & McBratney 1999, 2001). Recently, Vanwalleghem et al. (2013) are able to combine selected physical, chemical and biological processes to simulate a full 3-D soil genesis in the landscape. Now there are research gaps between the 2 approaches: the landscape modellers increasingly recognise the importance of soil and need more detailed soil

  20. Connecting Brabant's cover sand landscapes through landscape history

    NASA Astrophysics Data System (ADS)

    Heskes, Erik; van den Ancker, Hanneke; Jungerius, Pieter Dirk; Harthoorn, Jaap; Maes, Bert; Leenders, Karel; de Jongh, Piet; Kluiving, Sjoerd; van den Oetelaar, Ger

    2015-04-01

    Noord-Brabant has the largest variety of cover sand landscapes in The Netherlands, and probably in Western Europe. During the Last Ice Age the area was not covered by land ice and a polar desert developed in which sand dunes buried the existing river landscapes. Some of these polar dune landscapes experienced a geomorphological and soil development that remained virtually untouched up to the present day, such as the low parabolic dunes of the Strabrechtse Heide or the later and higher dunes of the Oisterwijkse Vennen. As Noord-Brabant lies on the fringe of a tectonic basin, the thickness of cover sand deposits in the Centrale Slenk, part of a rift through Europe, amounts up to 20 metres. Cover sand deposits along the fault lines cause the special phenomenon of 'wijst' to develop, in which the higher grounds are wetter than the boarding lower grounds. Since 4000 BC humans settled in these cover sand landscapes and made use of its small-scale variety. An example are the prehistoric finds on the flanks and the historic towns on top of the 'donken' in northwest Noord-Brabant, where the cover sand landscapes are buried by river and marine deposits and only the peaks of the dunes protrude as donken. Or the church of Handel that is built beside a 'wijst' source and a site of pilgrimage since living memory. Or the 'essen' and plaggen agriculture that developed along the stream valleys of Noord-Brabant from 1300 AD onwards, giving rise to geomorphological features as 'randwallen' and plaggen soils of more than a metre thickness. Each region of Brabant each has its own approach in attracting tourists and has not yet used this common landscape history to connect, manage and promote their territories. We propose a landscape-historical approach to develop a national or European Geopark Brabants' cover sand landscapes, in which each region focuses on a specific part of the landscape history of Brabant, that stretches from the Late Weichselian polar desert when the dune

  1. Evolutionary Accessibility of Modular Fitness Landscapes

    NASA Astrophysics Data System (ADS)

    Schmiegelt, B.; Krug, J.

    2013-10-01

    A fitness landscape is a mapping from the space of genetic sequences, which is modeled here as a binary hypercube of dimension L, to the real numbers. We consider random models of fitness landscapes, where fitness values are assigned according to some probabilistic rule, and study the statistical properties of pathways to the global fitness maximum along which fitness increases monotonically. Such paths are important for evolution because they are the only ones that are accessible to an adapting population when mutations occur at a low rate. The focus of this work is on the block model introduced by A.S. Perelson and C.A. Macken (Proc. Natl. Acad. Sci. USA 92:9657, 1995) where the genome is decomposed into disjoint sets of loci (`modules') that contribute independently to fitness, and fitness values within blocks are assigned at random. We show that the number of accessible paths can be written as a product of the path numbers within the blocks, which provides a detailed analytic description of the path statistics. The block model can be viewed as a special case of Kauffman's NK-model, and we compare the analytic results to simulations of the NK-model with different genetic architectures. We find that the mean number of accessible paths in the different versions of the model are quite similar, but the distribution of the path number is qualitatively different in the block model due to its multiplicative structure. A similar statement applies to the number of local fitness maxima in the NK-models, which has been studied extensively in previous works. The overall evolutionary accessibility of the landscape, as quantified by the probability to find at least one accessible path to the global maximum, is dramatically lowered by the modular structure.

  2. Mapping resource use over a Russian landscape: an integrated look at harvesting of a non-timber forest product in central Kamchatka

    NASA Astrophysics Data System (ADS)

    Hitztaler, Stephanie K.; Bergen, Kathleen M.

    2013-12-01

    Small-scale resource use became an important adaptive mechanism in remote logging communities in Russia at the onset of the post-Soviet period in 1991. We focused on harvesting of a non-timber forest product, lingonberry (Vaccinium vitis-idaea), in the forests of the Kamchatka Peninsula (Russian Far East). We employed an integrated geographical approach to make quantifiable connections between harvesting and the landscape, and to interpret these relationships in their broader contexts. Landsat TM images were used for a new classification; the resulting land-cover map was the basis for linking non-spatial data on harvesters’ gathering behaviors to spatial data within delineated lingonberry gathering sites. Several significant relationships emerged: (1) mature forests negatively affected harvesters’ initial choice to gather in a site, while young forests had a positive effect; (2) land-cover type was critical in determining how and why gathering occurred: post-disturbance young and maturing forests were significantly associated with higher gathering intensity and with the choice to market harvests; and (3) distance from gathering sites to villages and main roads also mattered: longer distances were significantly correlated to more time spent gathering and to increased marketing of harvests. We further considered our findings in light of the larger ecological and social dynamics at play in central Kamchatka. This unique study is an important starting point for conservation- and sustainable development-based work, and for additional research into the drivers of human-landscape interactions in the Russian Far East.

  3. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. |

    1993-06-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  4. Adaptive Transfer Function Networks

    SciTech Connect

    Goulding, J.R. Portland State Univ., OR . Dept. of Electrical Engineering)

    1993-01-01

    Real-time pattern classification and time-series forecasting applications continue to drive artificial neural network (ANN) technology. As ANNs increase in complexity, the throughput of digital computer simulations decreases. A novel ANN, the Adaptive Transfer Function Network (ATF-Net), directly addresses the issue of throughput. ATF-Nets are global mapping equations generated by the superposition of ensembles of neurodes having arbitrary continuous functions receiving encoded input data. ATF-Nets may be implemented on parallel digital computers. An example is presented which illustrates a four-fold increase in computational throughput.

  5. The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

    USGS Publications Warehouse

    Ramsey, Elijah W.; Nelson, G.A.; Echols, D.; Sapkota, S.K.

    2002-01-01

    The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

  6. Managing for Climate Change in Western Forest Ecosystems; The Role of Refugia in Adaptation Strategies (Invited)

    NASA Astrophysics Data System (ADS)

    Millar, C. I.; Morelli, T.

    2009-12-01

    Managing forested ecosystems in western North America for adaptation to climate change involves options that depend on resource objectives, landscape conditions, sensitivity to change, and social desires. Strategies range from preserving species and ecosystems in the face of change (resisting change); managing for resilience to change; realigning ecosystems that have been severely altered so that they can adapt successfully; and enabling species to respond to climate changes. We are exploring one extreme in this range of strategies, that is, to manage locations, species, communities, or ecosystems as refugia. This concept is familiar from the Quaternary literature as isolated locations where climates remained warm during cold glacial intervals and wherein species contracted and persisted in small populations. References to refugia have been made in the climate-adaptation literature but little elaborated, and applications have not been described. We are addressing this gap conceptually and in case-studies from national forest and national park environments in California. Using a classification of refugium categories, we extend the concept beyond the original use to include diverse locations and conditions where plant or animal species, or ecosystems of concern, would persist during future changing climatic backgrounds. These locations may be determined as refugial for reasons of local microclimate, substrate, elevation, topographic context, paleohistory, species ecology, or management capacity. Recognizing that species and ecosystems respond to climate change differently, refugium strategies are appropriate in some situations and not others. We describe favorable conditions for using refugium strategies and elaborate specific approaches in Sierra Nevada case studies.

  7. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 23 Highways 1 2011-04-01 2011-04-01 false Landscape development. 752.4 Section 752.4 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RIGHT-OF-WAY AND ENVIRONMENT LANDSCAPE AND ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes...

  8. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 23 Highways 1 2013-04-01 2013-04-01 false Landscape development. 752.4 Section 752.4 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RIGHT-OF-WAY AND ENVIRONMENT LANDSCAPE AND ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes...

  9. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 23 Highways 1 2014-04-01 2014-04-01 false Landscape development. 752.4 Section 752.4 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RIGHT-OF-WAY AND ENVIRONMENT LANDSCAPE AND ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes...

  10. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 23 Highways 1 2012-04-01 2012-04-01 false Landscape development. 752.4 Section 752.4 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RIGHT-OF-WAY AND ENVIRONMENT LANDSCAPE AND ROADSIDE DEVELOPMENT § 752.4 Landscape development. (a) Landscape development, which includes...

  11. Classification of Itch.

    PubMed

    Ständer, Sonja

    2016-01-01

    Chronic pruritus has diverse forms of presentation and can appear not only on normal skin [International Forum for the Study of Itch (IFSI) classification group II], but also in the company of dermatoses (IFSI classification group I). Scratching, a natural reflex, begins in response to itch. Enough damage can be done to the skin by scratching to cause changes in the primary clinical picture, often leading to a clinical picture predominated by the development of chronic scratch lesions (IFSI classification group III). An internationally recognized, standardized classification system was created by the IFSI to not only aid in clarifying terms and definitions, but also to harmonize the global nomenclature for itch. PMID:27578063

  12. Spatiotemporal microbial evolution on antibiotic landscapes.

    PubMed

    Baym, Michael; Lieberman, Tami D; Kelsic, Eric D; Chait, Remy; Gross, Rotem; Yelin, Idan; Kishony, Roy

    2016-09-01

    A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics. PMID:27609891

  13. Accidental inflation in the landscape

    SciTech Connect

    Blanco-Pillado, Jose J.; Metallinos, Konstantinos; Gomez-Reino, Marta E-mail: marta.gomez-reino.perez@cern.ch

    2013-02-01

    We study some aspects of fine tuning in inflationary scenarios within string theory flux compactifications and, in particular, in models of accidental inflation. We investigate the possibility that the apparent fine-tuning of the low energy parameters of the theory needed to have inflation can be generically obtained by scanning the values of the fluxes over the landscape. Furthermore, we find that the existence of a landscape of eternal inflation in this model provides us with a natural theory of initial conditions for the inflationary period in our vacuum. We demonstrate how these two effects work in a small corner of the landscape associated with the complex structure of the Calabi-Yau manifold P{sup 4}{sub [1,1,1,6,9]} by numerically investigating the flux vacua of a reduced moduli space. This allows us to obtain the distribution of observable parameters for inflation in this mini-landscape directly from the fluxes.

  14. Studying Landforms through Landscape Painting.

    ERIC Educational Resources Information Center

    Glenn, William H.

    1981-01-01

    Using three specific works of art, the author demonstrates how a study of selected landscape paintings can be integrated into units on landforms in secondary school earth science and general science courses. (Author/SJL)

  15. Planetary landscape: a new synthesis

    NASA Astrophysics Data System (ADS)

    Hargitai, H.

    The elements that build up a landscape on Earth consists of natural (biogenic and abiogenic - lithologic, atmospheric, hydrologic) and artificial (antropogenic) factors. Landscape is a complex system of these different elements, which interact with one another. For example the same lithology makes different landscapes under different climatic conditions. If the same conditions are present, the same landscape type will appear. The mosaic of ecotopes (topical) units, which are the system of homogenous caharacteristic areas of various geotopes makes up different level geochores (chorical unit). Geochores build up a hierarchic system and cover the whole surface.On Earth, landscapes can be qualified according to their characteristics: relief forms (morphology), and its potential economic value. Aesthetic and subjective parameters can also be considered especially when speaking of a residental area. We now propose the determination of "planetary landscape sets" which can potentially occur on the solid surface of a planetary body during its lifetime. This naturally includes landscapes of the present state of planetary bodies and also paleolandscapes from the past of planets, including Earth. Landscapes occur in the boundary of the planets solid and not solid sphere that is on the solid-vacuum, the solid - gas and on the solid - liquid boundary. Thinking this way a landscape can occurs on the ocean floor as well. We found that for the determination of a planetary landscape system, we can use the experiences from the making of the terminology and nomenclature system of Earth undersea topography. [1] The nomenclature system and the terminology used by astrogeologists could be revised. Common names of features should be defined (nova, tessera, volcano, tholus, lobate ejecta crater etc) with a type example for each. A well defined hierarchy for landscape types should be defined. The Moon is the best example, since it uses many names that originates from the 17th century, mixed

  16. Impacts of Landscape Context on Patterns of Wind Downfall Damage in a Fragmented Amazonian Landscape

    NASA Astrophysics Data System (ADS)

    Schwartz, N.; Uriarte, M.; DeFries, R. S.; Gutierrez-Velez, V. H.; Fernandes, K.; Pinedo-Vasquez, M.

    2015-12-01

    Wind is a major disturbance in the Amazon and has both short-term impacts and lasting legacies in tropical forests. Observed patterns of damage across landscapes result from differences in wind exposure and stand characteristics, such as tree stature, species traits, successional age, and fragmentation. Wind disturbance has important consequences for biomass dynamics in Amazonian forests, and understanding the spatial distribution and size of impacts is necessary to quantify the effects on carbon dynamics. In November 2013, a mesoscale convective system was observed over the study area in Ucayali, Peru, a highly human modified and fragmented forest landscape. We mapped downfall damage associated with the storm in order to ask: how does the severity of damage vary within forest patches, and across forest patches of different sizes and successional ages? We applied spectral mixture analysis to Landsat images from 2013 and 2014 to calculate the change in non-photosynthetic vegetation fraction after the storm, and combined it with C-band SAR data from the Sentinel-1 satellite to predict downfall damage measured in 30 field plots using random forest regression. We then applied this model to map damage in forests across the study area. Using a land cover classification developed in a previous study, we mapped secondary and mature forest, and compared the severity of damage in the two. We found that damage was on average higher in secondary forests, but patterns varied spatially. This study demonstrates the utility of using multiple sources of satellite data for mapping wind disturbance, and adds to our understanding of the sources of variation in wind-related damage. Ultimately, an improved ability to map wind impacts and a better understanding of their spatial patterns can contribute to better quantification of carbon dynamics in Amazonian landscapes.

  17. Protein evolution on rugged landscapes

    SciTech Connect

    Macken, C.A. ); Perelson, A.S. Sante Fe Institute, NM )

    1989-08-01

    The authors analyze a mathematical model of protein evolution in which the evolutionary process is viewed as hill-climbing on a random fitness landscape. In studying the structure of such landscapes, they note that a large number of local optima exist, and they calculate the time and number of mutational changes until a protein gets trapped at a local optimum. Such a hill-climbing process may underlie the evolution of antibody molecules by somatic hypermutation.

  18. Titan Polar Landscape Evolution

    NASA Technical Reports Server (NTRS)

    Moore, Jeffrey M.

    2016-01-01

    With the ongoing Cassini-era observations and studies of Titan it is clear that the intensity and distribution of surface processes (particularly fluvial erosion by methane and Aeolian transport) has changed through time. Currently however, alternate hypotheses substantially differ among specific scenarios with respect to the effects of atmospheric evolution, seasonal changes, and endogenic processes. We have studied the evolution of Titan's polar region through a combination of analysis of imaging, elevation data, and geomorphic mapping, spatially explicit simulations of landform evolution, and quantitative comparison of the simulated landscapes with corresponding Titan morphology. We have quantitatively evaluated alternate scenarios for the landform evolution of Titan's polar terrain. The investigations have been guided by recent geomorphic mapping and topographic characterization of the polar regions that are used to frame hypotheses of process interactions, which have been evaluated using simulation modeling. Topographic information about Titan's polar region is be based on SAR-Topography and altimetry archived on PDS, SAR-based stereo radar-grammetry, radar-sounding lake depth measurements, and superposition relationships between geomorphologic map units, which we will use to create a generalized topographic map.

  19. Nanofiber patent landscape.

    PubMed

    Ngiam, Michelle; Ramakrishna, Seeram; Raghunath, Michael; Chan, Casey K

    2007-01-01

    Despite the large number of publications in peer review literature in the field of nanofibers, there is still uncertainty as to what aspects of these research results have commercial applications. In an effort to better understand the technological progress made in the field of nanofibers, we surveyed the patents issued in the United States from 1976 up to end 2006. The present review will provide an overall view of the current patent landscape including trends and key applications. Key assignees and key inventors were identified and their contributions were discussed. Patents were identified using keywords such as nanofibers, ultrafine, and electrospinning. After patents were downloaded, we reviewed each patent for relevancy and identified 100 patents to be related to nanofibers. 75% of the current issued patents on nanofibers are directed at either fabrication methods or the use of nanofibers in filtration systems. The patent data indicates that medical applications and medical products using nanofibers appear to be the emerging application for nanofibers. We anticipate a growing number of patents on novel applications for nanofiber would originate from academic centers in the future.

  20. PSEUDO-CODEWORD LANDSCAPE

    SciTech Connect

    CHERTKOV, MICHAEL; STEPANOV, MIKHAIL

    2007-01-10

    The authors discuss performance of Low-Density-Parity-Check (LDPC) codes decoded by Linear Programming (LP) decoding at moderate and large Signal-to-Noise-Ratios (SNR). Frame-Error-Rate (FER) dependence on SNR and the noise space landscape of the coding/decoding scheme are analyzed by a combination of the previously introduced instanton/pseudo-codeword-search method and a new 'dendro' trick. To reduce complexity of the LP decoding for a code with high-degree checks, {ge} 5, they introduce its dendro-LDPC counterpart, that is the code performing identifically to the original one under Maximum-A-Posteriori (MAP) decoding but having reduced (down to three) check connectivity degree. Analyzing number of popular LDPC codes and their dendro versions performing over the Additive-White-Gaussian-Noise (AWGN) channel, they observed two qualitatively different regimes: (i) error-floor sets early, at relatively low SNR, and (ii) FER decays with SNR increase faster at moderate SNR than at the largest SNR. They explain these regimes in terms of the pseudo-codeword spectra of the codes.

  1. Fireplace adapters

    SciTech Connect

    Hunt, R.L.

    1983-12-27

    An adapter is disclosed for use with a fireplace. The stove pipe of a stove standing in a room to be heated may be connected to the flue of the chimney so that products of combustion from the stove may be safely exhausted through the flue and outwardly of the chimney. The adapter may be easily installed within the fireplace by removing the damper plate and fitting the adapter to the damper frame. Each of a pair of bolts has a portion which hooks over a portion of the damper frame and a threaded end depending from the hook portion and extending through a hole in the adapter. Nuts are threaded on the bolts and are adapted to force the adapter into a tight fit with the adapter frame.

  2. Remotely-Sensed Urban Wet-Landscapes AN Indicator of Coupled Effects of Human Impact and Climate Change

    NASA Astrophysics Data System (ADS)

    Ji, Wei

    2016-06-01

    This study proposes the concept of urban wet-landscapes (loosely-defined wetlands) as against dry-landscapes (mainly impervious surfaces). The study is to examine whether the dynamics of urban wet-landscapes is a sensitive indicator of the coupled effects of the two major driving forces of urban landscape change - human built-up impact and climate (precipitation) variation. Using a series of satellite images, the study was conducted in the Kansas City metropolitan area of the United States. A rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. The spatial analyses of wetland changes were implemented at the scales of metropolitan, watershed, and sub-watershed as well as based on the size of surface water bodies in order to reveal urban wetland change trends in relation to the driving forces. The study identified that wet-landscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while smaller wetlands decreased mainly due to human development activities. These findings suggest that wet-landscapes, as against the dry-landscapes, can be a more effective indicator of the coupled effects of human impact and climate change.

  3. [Dynamic changes of landscape pattern and eco-disturbance degree in Shuangtai estuary wetland of Liaoning Province, China].

    PubMed

    Chen, Ai-lian; Zhu, Bo-qin; Chen, Li-ding; Wu, Yan-hua; Sun, Ran-hao

    2010-05-01

    The main objective of establishing natural reserve is to protect its natural resources from human disturbances and maintain its critical ecological service values. This paper introduced the concept of hemeroby, and by using remote sensing technology, systematically assessed the dynamic changes of landscape pattern and eco-disturbance degree in Shuangtai estuary wetland of Liaoning Province, China. Firstly, a knowledge-based expert system was used to classify the landscape into three first-level types based on eco-disturbance degree, i.e., undisturbed, partially disturbed, or completely disturbed, which were further classified into 30 second-level categories. Secondly, questionnaire and experts knowledge were adopted to determine the hemeroby index for each landscape type and to formulate a landscape classification system. Finally, the landscape classification maps and hemeroby indices were derived by using the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data acquired on 30 April 1987, 7 June 1995, 12 June 2000, and 11 October 2006. The results indicated that from 1987 to 2006, the landscape patches in the study area became more fragmented, being most obvious for reed marsh. Undisturbed landscape type decreased in area, while partially and completely disturbed types were in adverse. The overall characteristics of the spatial distribution of hemeroby index were of most variable in the areas along the river and surrounding the estuary and being the highest in the areas surrounding the city, largely due to the rapid urbanization and the blooming fishery in the study area. PMID:20707090

  4. Analysis of uncertainty in multi-temporal object-based classification

    NASA Astrophysics Data System (ADS)

    Löw, Fabian; Knöfel, Patrick; Conrad, Christopher

    2015-07-01

    Agricultural management increasingly uses crop maps based on classification of remotely sensed data. However, classification errors can translate to errors in model outputs, for instance agricultural production monitoring (yield, water demand) or crop acreage calculation. Hence, knowledge on the spatial variability of the classier performance is important information for the user. But this is not provided by traditional assessments of accuracy, which are based on the confusion matrix. In this study, classification uncertainty was analyzed, based on the support vector machines (SVM) algorithm. SVM was applied to multi-spectral time series data of RapidEye from different agricultural landscapes and years. Entropy was calculated as a measure of classification uncertainty, based on the per-object class membership estimations from the SVM algorithm. Permuting all possible combinations of available images allowed investigating the impact of the image acquisition frequency and timing, respectively, on the classification uncertainty. Results show that multi-temporal datasets decrease classification uncertainty for different crops compared to single data sets, but there was no "one-image-combination-fits-all" solution. The number and acquisition timing of the images, for which a decrease in uncertainty could be realized, proved to be specific to a given landscape, and for each crop they differed across different landscapes. For some crops, an increase of uncertainty was observed when increasing the quantity of images, even if classification accuracy was improved. Random forest regression was employed to investigate the impact of different explanatory variables on the observed spatial pattern of classification uncertainty. It was strongly influenced by factors related with the agricultural management and training sample density. Lower uncertainties were revealed for fields close to rivers or irrigation canals. This study demonstrates that classification uncertainty estimates

  5. Landscape and climate science and scenarios for Florida

    USGS Publications Warehouse

    Terando, Adam; Traxler, Steve; Collazo, Jaime

    2014-01-01

    The Peninsular Florida Landscape Conservation Cooperative (PFLCC) is part of a network of 22 Landscape Conservation Cooperatives (LCCs) that extend from Alaska to the Caribbean. LCCs are regional-applied conservation-science partnerships among Federal agencies, regional organizations, States, tribes, nongovernmental organizations (NGOs), private stakeholders, universities, and other entities within a geographic area. The goal of these conservation-science partnerships is to help inform managers and decision makers at a landscape scale to further the principles of adaptive management and strategic habitat conservation. A major focus for LCCs is to help conservation managers and decision makers respond to large-scale ecosystem and habitat stressors, such as climate change, habitat fragmentation, invasive species, and water scarcity. The purpose of the PFLCC is to facilitate planning, design, and implementation of conservation strategies for fish and wildlife species at the landscape level using the adaptive management framework of strategic habitat conservation—integrating planning, design, delivery, and evaluation. Florida faces a set of unique challenges when responding to regional and global stressors because of its unique ecosystems and assemblages of species, its geographic location at the crossroads of temperate and tropical climates, and its exposure to both rapid urbanization and rising sea levels as the climate warms. In response to these challenges, several landscape-scale science projects were initiated with the goal of informing decision makers about how potential changes in climate and the built environment could impact habitats and ecosystems of concern in Florida and the Southeast United States. In June 2012, the PFLCC, North Carolina State University, convened a workshop at the U.S. Geological Survey (USGS) Coastal and Marine Science Center in St. Petersburg to assess the results of these integrated assessments and to foster an open dialogue about

  6. Landscape structure affects specialists but not generalists in naturally fragmented grasslands.

    PubMed

    Miller, Jesse E D; Damschen, Ellen I; Harrison, Susan P; Grace, James B

    2015-12-01

    Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural habitats become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. We asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, USA, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure, defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index), had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats. PMID:26909437

  7. Disentangling the effects of historic vs. contemporary landscape structure on population genetic divergence.

    PubMed

    Zellmer, A J; Knowles, L L

    2009-09-01

    Increasing habitat fragmentation poses an immediate threat to population viability, as gene flow patterns are changed in these altered landscapes. Patterns of genetic divergence can potentially reveal the impact of these shifts in landscape connectivity. However, divergence patterns not only carry the signature of altered contemporary landscapes, but also historical ones. When considered separately, both recent and historical landscape structure appear to significantly affect connectivity among 51 wood frog (Rana sylvatica) populations. However, by controlling for correlations among landscape structure from multiple time periods, we show that patterns of genetic divergence reflect recent landscape structure as opposed to landscape structure prior to European settlement of the region (before 1850s). At the same time, within-population genetic diversities remain high and a genetic signature of population bottlenecks is lacking. Together, these results suggest that metapopulation processes - not drift-induced divergence associated with strong demographic bottlenecks following habitat loss - underlie the strikingly rapid consequences of temporally shifting landscape structure on these amphibians. We discuss the implications of these results in the context of understanding the role of population demography in the adaptive variation observed in wood frog populations.

  8. Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis.

    PubMed

    Heckmann, David

    2015-12-01

    How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth 'Mount Fuji' landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants. PMID:26614656

  9. Landscape structure affects specialists but not generalists in naturally fragmented grasslands.

    PubMed

    Miller, Jesse E D; Damschen, Ellen I; Harrison, Susan P; Grace, James B

    2015-12-01

    Understanding how biotic communities respond to landscape spatial structure is critically important for conservation management as natural habitats become increasingly fragmented. However, empirical studies of the effects of spatial structure on plant species richness have found inconsistent results, suggesting that more comprehensive approaches are needed. We asked how landscape structure affects total plant species richness and the richness of a guild of specialized plants in a multivariate context. We sampled herbaceous plant communities at 56 dolomite glades (insular, fire-adapted grasslands) across the Missouri Ozarks, USA, and used structural equation modeling (SEM) to analyze the relative importance of landscape structure, soil resource availability, and fire history for plant communities. We found that landscape spatial structure, defined as the area-weighted proximity of glade habitat surrounding study sites (proximity index), had a significant effect on total plant species richness, but only after we controlled for environmental covariates. Richness of specialist species, but not generalists, was positively related to landscape spatial structure. Our results highlight that local environmental filters must be considered to understand the influence of landscape structure on communities and that unique species guilds may respond differently to landscape structure than the community as a whole. These findings suggest that both local environment and landscape context should be considered when developing management strategies for species of conservation concern in fragmented habitats.

  10. Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis.

    PubMed

    Heckmann, David

    2015-12-01

    How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth 'Mount Fuji' landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants.

  11. Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations.

    PubMed

    Cervera, Héctor; Lalić, Jasna; Elena, Santiago F

    2016-08-17

    Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it. PMID:27534955

  12. Shifting Fitness and Epistatic Landscapes Reflect Trade-offs along an Evolutionary Pathway.

    PubMed

    Steinberg, Barrett; Ostermeier, Marc

    2016-07-01

    Nature repurposes proteins via evolutionary processes. Such adaptation can come at the expense of the original protein's function, which is a trade-off of adaptation. We sought to examine other potential adaptive trade-offs. We measured the effect on ampicillin resistance of ~12,500 unique single amino acid mutants of the TEM-1, TEM-17, TEM-19, and TEM-15 β-lactamase alleles, which constitute an adaptive path in the evolution of cefotaxime resistance. These protein fitness landscapes were compared and used to calculate epistatic interactions between these mutations and the two mutations in the pathway (E104K and G238S). This series of protein fitness landscapes provides a systematic, quantitative description of pairwise/tertiary intragenic epistasis involving adaptive mutations. We find that the frequency of mutations exhibiting epistasis increases along the evolutionary pathway. Adaptation moves the protein to a region in the fitness landscape characterized by decreased mutational robustness and increased ruggedness, as measured by fitness effects of mutations and epistatic interactions for TEM-1's original function. This movement to such a "fitness territory" has evolutionary consequences and is an important adaptive trade-off and cost of adaptation. Our systematic study provides detailed insight into the relationships between mutation, protein structure, protein stability, and epistasis and quantitatively depicts the different costs inherent in the evolution of new functions.

  13. 22 CFR 9.6 - Derivative classification.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... CFR 2001.22. (c) Department of State Classification Guide. The Department of State Classification... classification. (a) Definition. Derivative classification is the incorporating, paraphrasing, restating...

  14. Library Classification 2020

    ERIC Educational Resources Information Center

    Harris, Christopher

    2013-01-01

    In this article the author explores how a new library classification system might be designed using some aspects of the Dewey Decimal Classification (DDC) and ideas from other systems to create something that works for school libraries in the year 2020. By examining what works well with the Dewey Decimal System, what features should be carried…

  15. Genomic landscape of liposarcoma.

    PubMed

    Kanojia, Deepika; Nagata, Yasunobu; Garg, Manoj; Lee, Dhong Hyun; Sato, Aiko; Yoshida, Kenichi; Sato, Yusuke; Sanada, Masashi; Mayakonda, Anand; Bartenhagen, Christoph; Klein, Hans-Ulrich; Doan, Ngan B; Said, Jonathan W; Mohith, S; Gunasekar, Swetha; Shiraishi, Yuichi; Chiba, Kenichi; Tanaka, Hiroko; Miyano, Satoru; Myklebost, Ola; Yang, Henry; Dugas, Martin; Meza-Zepeda, Leonardo A; Silberman, Allan W; Forscher, Charles; Tyner, Jeffrey W; Ogawa, Seishi; Koeffler, H Phillip

    2015-12-15

    Liposarcoma (LPS) is the most common type of soft tissue sarcoma accounting for 20% of all adult sarcomas. Due to absence of clinically effective treatment options in inoperable situations and resistance to chemotherapeutics, a critical need exists to identify novel therapeutic targets. We analyzed LPS genomic landscape using SNP arrays, whole exome sequencing and targeted exome sequencing to uncover the genomic information for development of specific anti-cancer targets. SNP array analysis indicated known amplified genes (MDM2, CDK4, HMGA2) and important novel genes (UAP1, MIR557, LAMA4, CPM, IGF2, ERBB3, IGF1R). Carboxypeptidase M (CPM), recurrently amplified gene in well-differentiated/de-differentiated LPS was noted as a putative oncogene involved in the EGFR pathway. Notable deletions were found at chromosome 1p (RUNX3, ARID1A), chromosome 11q (ATM, CHEK1) and chromosome 13q14.2 (MIR15A, MIR16-1). Significantly and recurrently mutated genes (false discovery rate < 0.05) included PLEC (27%), MXRA5 (21%), FAT3 (24%), NF1 (20%), MDC1 (10%), TP53 (7%) and CHEK2 (6%). Further, in vitro and in vivo functional studies provided evidence for the tumor suppressor role for Neurofibromin 1 (NF1) gene in different subtypes of LPS. Pathway analysis of recurrent mutations demonstrated signaling through MAPK, JAK-STAT, Wnt, ErbB, axon guidance, apoptosis, DNA damage repair and cell cycle pathways were involved in liposarcomagenesis. Interestingly, we also found mutational and copy number heterogeneity within a primary LPS tumor signifying the importance of multi-region sequencing for cancer-genome guided therapy. In summary, these findings provide insight into the genomic complexity of LPS and highlight potential druggable pathways for targeted therapeutic approach.

  16. Genomic landscape of liposarcoma

    PubMed Central

    Kanojia, Deepika; Nagata, Yasunobu; Garg, Manoj; Lee, Dhong Hyun; Sato, Aiko; Yoshida, Kenichi; Sato, Yusuke; Sanada, Masashi; Mayakonda, Anand; Bartenhagen, Christoph; Klein, Hans-Ulrich; Doan, Ngan B.; Said, Jonathan W.; Mohith, S.; Gunasekar, Swetha; Shiraishi, Yuichi; Chiba, Kenichi; Tanaka, Hiroko; Miyano, Satoru; Myklebost, Ola; Yang, Henry; Dugas, Martin; Meza-Zepeda, Leonardo A.; Silberman, Allan W.; Forscher, Charles; Tyner, Jeffrey W.; Ogawa, Seishi; Koeffler, H. Phillip

    2015-01-01

    Liposarcoma (LPS) is the most common type of soft tissue sarcoma accounting for 20% of all adult sarcomas. Due to absence of clinically effective treatment options in inoperable situations and resistance to chemotherapeutics, a critical need exists to identify novel therapeutic targets. We analyzed LPS genomic landscape using SNP arrays, whole exome sequencing and targeted exome sequencing to uncover the genomic information for development of specific anti-cancer targets. SNP array analysis indicated known amplified genes (MDM2, CDK4, HMGA2) and important novel genes (UAP1, MIR557, LAMA4, CPM, IGF2, ERBB3, IGF1R). Carboxypeptidase M (CPM), recurrently amplified gene in well-differentiated/de-differentiated LPS was noted as a putative oncogene involved in the EGFR pathway. Notable deletions were found at chromosome 1p (RUNX3, ARID1A), chromosome 11q (ATM, CHEK1) and chromosome 13q14.2 (MIR15A, MIR16-1). Significantly and recurrently mutated genes (false discovery rate < 0.05) included PLEC (27%), MXRA5 (21%), FAT3 (24%), NF1 (20%), MDC1 (10%), TP53 (7%) and CHEK2 (6%). Further, in vitro and in vivo functional studies provided evidence for the tumor suppressor role for Neurofibromin 1 (NF1) gene in different subtypes of LPS. Pathway analysis of recurrent mutations demonstrated signaling through MAPK, JAK-STAT, Wnt, ErbB, axon guidance, apoptosis, DNA damage repair and cell cycle pathways were involved in liposarcomagenesis. Interestingly, we also found mutational and copy number heterogeneity within a primary LPS tumor signifying the importance of multi-region sequencing for cancer-genome guided therapy. In summary, these findings provide insight into the genomic complexity of LPS and highlight potential druggable pathways for targeted therapeutic approach. PMID:26643872

  17. Genomic landscape of liposarcoma.

    PubMed

    Kanojia, Deepika; Nagata, Yasunobu; Garg, Manoj; Lee, Dhong Hyun; Sato, Aiko; Yoshida, Kenichi; Sato, Yusuke; Sanada, Masashi; Mayakonda, Anand; Bartenhagen, Christoph; Klein, Hans-Ulrich; Doan, Ngan B; Said, Jonathan W; Mohith, S; Gunasekar, Swetha; Shiraishi, Yuichi; Chiba, Kenichi; Tanaka, Hiroko; Miyano, Satoru; Myklebost, Ola; Yang, Henry; Dugas, Martin; Meza-Zepeda, Leonardo A; Silberman, Allan W; Forscher, Charles; Tyner, Jeffrey W; Ogawa, Seishi; Koeffler, H Phillip

    2015-12-15

    Liposarcoma (LPS) is the most common type of soft tissue sarcoma accounting for 20% of all adult sarcomas. Due to absence of clinically effective treatment options in inoperable situations and resistance to chemotherapeutics, a critical need exists to identify novel therapeutic targets. We analyzed LPS genomic landscape using SNP arrays, whole exome sequencing and targeted exome sequencing to uncover the genomic information for development of specific anti-cancer targets. SNP array analysis indicated known amplified genes (MDM2, CDK4, HMGA2) and important novel genes (UAP1, MIR557, LAMA4, CPM, IGF2, ERBB3, IGF1R). Carboxypeptidase M (CPM), recurrently amplified gene in well-differentiated/de-differentiated LPS was noted as a putative oncogene involved in the EGFR pathway. Notable deletions were found at chromosome 1p (RUNX3, ARID1A), chromosome 11q (ATM, CHEK1) and chromosome 13q14.2 (MIR15A, MIR16-1). Significantly and recurrently mutated genes (false discovery rate < 0.05) included PLEC (27%), MXRA5 (21%), FAT3 (24%), NF1 (20%), MDC1 (10%), TP53 (7%) and CHEK2 (6%). Further, in vitro and in vivo functional studies provided evidence for the tumor suppressor role for Neurofibromin 1 (NF1) gene in different subtypes of LPS. Pathway analysis of recurrent mutations demonstrated signaling through MAPK, JAK-STAT, Wnt, ErbB, axon guidance, apoptosis, DNA damage repair and cell cycle pathways were involved in liposarcomagenesis. Interestingly, we also found mutational and copy number heterogeneity within a primary LPS tumor signifying the importance of multi-region sequencing for cancer-genome guided therapy. In summary, these findings provide insight into the genomic complexity of LPS and highlight potential druggable pathways for targeted therapeutic approach. PMID:26643872

  18. 2-Stage Classification Modeling

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using themore » most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.« less

  19. 2-Stage Classification Modeling

    SciTech Connect

    Baltich, L. K.

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using the most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.

  20. Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers

    NASA Astrophysics Data System (ADS)

    Nowakowski, Artur

    2015-10-01

    Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics. The proposed method is employed for the exemplary problem of artificial area identification. Classification of IKONOS multispectral data results in short computational time and overall accuracy of 94.4% comparing to 94.0% obtained by using AdaBoost.M1 with trees and 93.8% achieved using Random Forest. The influence of a manipulation of the final threshold of the strong classifier on classification results is reported.

  1. Using landscape history to predict biodiversity patterns in fragmented landscapes

    PubMed Central

    Ewers, Robert M; Didham, Raphael K; Pearse, William D; Lefebvre, Véronique; Rosa, Isabel M D; Carreiras, João M B; Lucas, Richard M; Reuman, Daniel C

    2013-01-01

    Landscape ecology plays a vital role in understanding the impacts of land-use change on biodiversity, but it is not a predictive discipline, lacking theoretical models that quantitatively predict biodiversity patterns from first principles. Here, we draw heavily on ideas from phylogenetics to fill this gap, basing our approach on the insight that habitat fragments have a shared history. We develop a landscape ‘terrageny’, which represents the historical spatial separation of habitat fragments in the same way that a phylogeny represents evolutionary divergence among species. Combining a random sampling model with a terrageny generates numerical predictions about the expected proportion of species shared between any two fragments, the locations of locally endemic species, and the number of species that have been driven locally extinct. The model predicts that community similarity declines with terragenetic distance, and that local endemics are more likely to be found in terragenetically distinctive fragments than in large fragments. We derive equations to quantify the variance around predictions, and show that ignoring the spatial structure of fragmented landscapes leads to over-estimates of local extinction rates at the landscape scale. We argue that ignoring the shared history of habitat fragments limits our ability to understand biodiversity changes in human-modified landscapes. PMID:23931035

  2. Adaptive SPECT

    PubMed Central

    Barrett, Harrison H.; Furenlid, Lars R.; Freed, Melanie; Hesterman, Jacob Y.; Kupinski, Matthew A.; Clarkson, Eric; Whitaker, Meredith K.

    2008-01-01

    Adaptive imaging systems alter their data-acquisition configuration or protocol in response to the image information received. An adaptive pinhole single-photon emission computed tomography (SPECT) system might acquire an initial scout image to obtain preliminary information about the radiotracer distribution and then adjust the configuration or sizes of the pinholes, the magnifications, or the projection angles in order to improve performance. This paper briefly describes two small-animal SPECT systems that allow this flexibility and then presents a framework for evaluating adaptive systems in general, and adaptive SPECT systems in particular. The evaluation is in terms of the performance of linear observers on detection or estimation tasks. Expressions are derived for the ideal linear (Hotelling) observer and the ideal linear (Wiener) estimator with adaptive imaging. Detailed expressions for the performance figures of merit are given, and possible adaptation rules are discussed. PMID:18541485

  3. Adaptive Computing.

    ERIC Educational Resources Information Center

    Harrell, William

    1999-01-01

    Provides information on various adaptive technology resources available to people with disabilities. (Contains 19 references, an annotated list of 129 websites, and 12 additional print resources.) (JOW)

  4. Contour adaptation.

    PubMed

    Anstis, Stuart

    2013-01-01

    It is known that adaptation to a disk that flickers between black and white at 3-8 Hz on a gray surround renders invisible a congruent gray test disk viewed afterwards. This is contrast adaptation. We now report that adapting simply to the flickering circular outline of the disk can have the same effect. We call this "contour adaptation." This adaptation does not transfer interocularly, and apparently applies only to luminance, not color. One can adapt selectively to only some of the contours in a display, making only these contours temporarily invisible. For instance, a plaid comprises a vertical grating superimposed on a horizontal grating. If one first adapts to appropriate flickering vertical lines, the vertical components of the plaid disappears and it looks like a horizontal grating. Also, we simulated a Cornsweet (1970) edge, and we selectively adapted out the subjective and objective contours of a Kanisza (1976) subjective square. By temporarily removing edges, contour adaptation offers a new technique to study the role of visual edges, and it demonstrates how brightness information is concentrated in edges and propagates from them as it fills in surfaces.

  5. National-level progress on adaptation

    NASA Astrophysics Data System (ADS)

    Lesnikowski, Alexandra; Ford, James; Biesbroek, Robbert; Berrang-Ford, Lea; Heymann, S. Jody

    2016-03-01

    It is increasingly evident that adaptation will figure prominently in the post-2015 United Nations climate change agreement. As adaptation obligations under the United Nations Framework Convention on Climate Change evolve, more rigorous approaches to measuring adaptation progress among parties will be critical. In this Letter we elaborate on an emerging area of research referred to as `adaptation tracking’, which has potential to inform development of a global adaptation monitoring framework. We evaluate this potential by presenting evidence on policy change for 41 high-income countries between 2010 and 2014. We examine whether countries that were in early stages of adaptation planning in 2010 are making progress to close adaptation gaps, and how the landscape of adaptation in these countries has evolved. In total we find an 87% increase in reported adaptation policies and measures, and evidence that implementation of concrete adaptation initiatives is growing. Reflecting on the strengths and challenges of this early methodology, we further discuss how adaptation tracking practices could guide development of a robust framework for monitoring global adaptation progress and inform future research on policy change across countries.

  6. Landscape pattern and transition under natural and anthropogenic disturbance in an arid region of northwestern China

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Wang, Tianwei; Cai, Chongfa; Li, Chongguang; Liu, Yaojun; Bao, Yuze; Guan, Wuhong

    2016-02-01

    There is a pressing need to determine the relationships between driving variables and landscape transformations. Human activities shape landscapes and turn them into complex assemblages of highly diverse structures. Other factors, including climate and topography, also play significant roles in landscape transitions, and identifying the interactions among the variables is critical to environmental management. This study analyzed the configurations and spatial-temporal processes of landscape changes from 1998 to 2011 under different anthropogenic disturbances, identified the main variables that determine the landscape patterns and transitions, and quantified the relationships between pairs of driver sets. Landsat images of Baicheng and Tekes from 1998, 2006 and 2011 were used to classify landscapes by supervised classification. Redundancy analysis (RDA) and variation partitioning were performed to identify the main driving forces and to quantify the unique, shared, and total explained variation of the sets of variables. The results indicate that the proportions of otherwise identical landscapes in Baicheng and Tekes were very different. The area of the grassland in Tekes was much larger than that of the cropland; however, the differences between the grassland and cropland in Baicheng were not as pronounced. Much of the grassland in Tekes was located in an area that was near residents, whereas most of the grassland in Baicheng was far from residents. The slope, elevation, annual precipitation, annual temperature, and distance to the nearest resident were strong driving forces influencing the patterns and transitions of the landscapes. The results of the variation partitioning indicated complex interrelationships among all of the pairs of driver sets. All of the variable sets had significant explanatory roles, most of which had both unique and shared variations with the others. The results of this study can assist policy makers and planners in implementing sustainable

  7. [Conformal radiotherapy: principles and classification].

    PubMed

    Rosenwald, J C; Gaboriaud, G; Pontvert, D

    1999-01-01

    'Conformal radiotherapy' is the name fixed by usage and given to a new form of radiotherapy resulting from the technological improvements observed during, the last ten years. While this terminology is now widely used, no precise definition can be found in the literature. Conformal radiotherapy refers to an approach in which the dose distribution is more closely 'conformed' or adapted to the actual shape of the target volume. However, the achievement of a consensus on a more specific definition is hampered by various difficulties, namely in characterizing the degree of 'conformality'. We have therefore suggested a classification scheme be established on the basis of the tools and the procedures actually used for all steps of the process, i.e., from prescription to treatment completion. Our classification consists of four levels: schematically, at level 0, there is no conformation (rectangular fields); at level 1, a simple conformation takes place, on the basis of conventional 2D imaging; at level 2, a 3D reconstruction of the structures is used for a more accurate conformation; and level 3 includes research and advanced dynamic techniques. We have used our personal experience, contacts with colleagues and data from the literature to analyze all the steps of the planning process, and to define the tools and procedures relevant to a given level. The corresponding tables have been discussed and approved at the European level within the Dynarad concerted action. It is proposed that the term 'conformal radiotherapy' be restricted to procedures where all steps are at least at level 2.

  8. Hidden Randomness between Fitness Landscapes Limits Reverse Evolution

    NASA Astrophysics Data System (ADS)

    Tan, Longzhi; Serene, Stephen; Xiao Chao, Hui; Gore, Jeff

    2012-02-01

    Natural populations must constantly adapt to the ever-changing environment. A fundamental question in evolutionary biology is whether adaptations can be reversed by returning the population to its ancestral environment. Traditionally, reverse evolution is defined as restoring an ancestral phenotype (physical characteristics such as body size), and the classic Dollo's Law has hypothesized the impossibility of reversing complex adaptations. However, this ``law'' remains ambiguous unless reverse evolution can be studied at the level of genotypes (the underlying genome sequence). We measured the fitness landscapes of a bacterial antibiotic-resistance gene and analyzed the reversibility of evolution as a global, statistical feature of the landscapes. In both experiments and simulations, we find that an adaptation's reversibility declines as the number of mutations it involves increases, suggesting a probabilistic form of Dollo's Law at the molecular level. We also show computationally that slowly switching between environments facilitates reverse evolution in small populations, where clonal interference is negligible or moderate. This is an analogy to thermodynamics, where the reversibility of a physical process is maximized when conditions are modified infinitely slowly.

  9. Landscape structure and the genetic effects of a population collapse.

    PubMed

    Caplins, Serena A; Gilbert, Kimberly J; Ciotir, Claudia; Roland, Jens; Matter, Stephen F; Keyghobadi, Nusha

    2014-12-01

    Both landscape structure and population size fluctuations influence population genetics. While independent effects of these factors on genetic patterns and processes are well studied, a key challenge is to understand their interaction, as populations are simultaneously exposed to habitat fragmentation and climatic changes that increase variability in population size. In a population network of an alpine butterfly, abundance declined 60-100% in 2003 because of low over-winter survival. Across the network, mean microsatellite genetic diversity did not change. However, patch connectivity and local severity of the collapse interacted to determine allelic richness change within populations, indicating that patch connectivity can mediate genetic response to a demographic collapse. The collapse strongly affected spatial genetic structure, leading to a breakdown of isolation-by-distance and loss of landscape genetic pattern. Our study reveals important interactions between landscape structure and temporal demographic variability on the genetic diversity and genetic differentiation of populations. Projected future changes to both landscape and climate may lead to loss of genetic variability from the studied populations, and selection acting on adaptive variation will likely occur within the context of an increasing influence of genetic drift.

  10. Landscape structure and the genetic effects of a population collapse

    PubMed Central

    Caplins, Serena A.; Gilbert, Kimberly J.; Ciotir, Claudia; Roland, Jens; Matter, Stephen F.; Keyghobadi, Nusha

    2014-01-01

    Both landscape structure and population size fluctuations influence population genetics. While independent effects of these factors on genetic patterns and processes are well studied, a key challenge is to understand their interaction, as populations are simultaneously exposed to habitat fragmentation and climatic changes that increase variability in population size. In a population network of an alpine butterfly, abundance declined 60–100% in 2003 because of low over-winter survival. Across the network, mean microsatellite genetic diversity did not change. However, patch connectivity and local severity of the collapse interacted to determine allelic richness change within populations, indicating that patch connectivity can mediate genetic response to a demographic collapse. The collapse strongly affected spatial genetic structure, leading to a breakdown of isolation-by-distance and loss of landscape genetic pattern. Our study reveals important interactions between landscape structure and temporal demographic variability on the genetic diversity and genetic differentiation of populations. Projected future changes to both landscape and climate may lead to loss of genetic variability from the studied populations, and selection acting on adaptive variation will likely occur within the context of an increasing influence of genetic drift. PMID:25320176

  11. The complex landscape of pancreatic cancer metabolism

    PubMed Central

    Sousa, Cristovão Marques; Kimmelman, Alec C.

    2014-01-01

    Pancreatic ductal adenocarcinomas (PDA) are extremely aggressive cancers and currently available therapies are only minimally effective in treating this disease. Tackling this devastating cancer has been a major challenge to the scientific and medical communities, in part due to its intense therapeutic resistance. One of the aspects of this tumor that contributes to its aggressive behavior is its altered cellular metabolism. Indeed, PDA cells seem to possess the ability to adapt their metabolism to the particular environment to which they are exposed, including utilizing diverse fuel sources depending on their availability. Moreover, PDA tumors are efficient at recycling various metabolic substrates through activation of different salvage pathways such as autophagy and macropinocytosis. Together, these diverse metabolic adaptations allow PDA cells to survive and thrive in harsh environments that may lack nutrients and oxygen. Not surprisingly, given its central role in the pathogenesis of this tumor, oncogenic Kras plays a critical role in much of the metabolic reprogramming seen in PDA. In this review, we discuss the metabolic landscape of PDA tumors, including the molecular underpinnings of the key regulatory nodes, and describe how such pathways can be exploited for future diagnostic and therapeutic approaches PMID:24743516

  12. Climate adaptation

    NASA Astrophysics Data System (ADS)

    Kinzig, Ann P.

    2015-03-01

    This paper is intended as a brief introduction to climate adaptation in a conference devoted otherwise to the physics of sustainable energy. Whereas mitigation involves measures to reduce the probability of a potential event, such as climate change, adaptation refers to actions that lessen the impact of climate change. Mitigation and adaptation differ in other ways as well. Adaptation does not necessarily have to be implemented immediately to be effective; it only needs to be in place before the threat arrives. Also, adaptation does not necessarily require global, coordinated action; many effective adaptation actions can be local. Some urban communities, because of land-use change and the urban heat-island effect, currently face changes similar to some expected under climate change, such as changes in water availability, heat-related morbidity, or changes in disease patterns. Concern over those impacts might motivate the implementation of measures that would also help in climate adaptation, despite skepticism among some policy makers about anthropogenic global warming. Studies of ancient civilizations in the southwestern US lends some insight into factors that may or may not be important to successful adaptation.

  13. Entropy landscape of solutions in the binary perceptron problem

    NASA Astrophysics Data System (ADS)

    Huang, Haiping; Wong, K. Y. Michael; Kabashima, Yoshiyuki

    2013-09-01

    The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a reference configuration and of solution-pairs separated by a given Hamming distance in the solution space. We evaluate the entropy at the annealed level as well as replica symmetric level and the mean field result is confirmed by the numerical simulations on single instances using the proposed message passing algorithms. From the first landscape (a random configuration as a reference), we see clearly how the solution space shrinks as more constraints are added. From the second landscape of solution-pairs, we deduce the coexistence of clustering and freezing in the solution space.

  14. Correlates of vernal pool occurrence in the Massachusetts USA, landscape

    USGS Publications Warehouse

    Grant, E.H.C.

    2005-01-01

    Vernal pool wetlands are at risk of destruction across the northeast United States, due in part to their diminutive size and short hydroperiods. These characteristics make it difficult to locate vernal pool habitats in the landscape during much of the year, and no efficient method exists for predicting their occurrence. A logistic regression procedure was used to identify large-scale variables that influence the presence of a potential vernal pool, including surficial geology, land use and land cover, soil classification, topography, precipitation, and surficial hydrologic features. The model was validated with locations of field-verified vernal pools. The model demonstrated that the probability of potential vernal pool occurrence is positively related to slope, negatively related to till/bedrock surficial geology, and negatively related to the proportion of cropland, urban/commercial, and high density residential development in the landscape. The relationship between vernal pool occurrence and large-scale variables suggests that these habitats do not occur at random in the landscape, and thus, protection in situ should be considered.

  15. Fitness landscapes emerging from pharmacodynamic functions in the evolution of multidrug resistance.

    PubMed

    Engelstädter, J

    2014-05-01

    Adaptive evolution often involves beneficial mutations at more than one locus. In this case, the trajectory and rate of adaptation is determined by the underlying fitness landscape, that is, the fitness values and mutational connectivity of all genotypes under consideration. Drug resistance, especially resistance to multiple drugs simultaneously, is also often conferred by mutations at several loci so that the concept of fitness landscapes becomes important. However, fitness landscapes underlying drug resistance are not static but dependent on drug concentrations, which means they are influenced by the pharmacodynamics of the drugs administered. Here, I present a mathematical framework for fitness landscapes of multidrug resistance based on Hill functions describing how drug concentrations affect fitness. I demonstrate that these 'pharmacodynamic fitness landscapes' are characterized by pervasive epistasis that arises through (i) fitness costs of resistance (even when these costs are additive), (ii) nonspecificity of resistance mutations to drugs, in particular cross-resistance, and (iii) drug interactions (both synergistic and antagonistic). In the latter case, reciprocal drug suppression may even lead to reciprocal sign epistasis, so that the doubly resistant genotype occupies a local fitness peak that may be difficult to access by evolution. Simulations exploring the evolutionary dynamics on some pharmacodynamic fitness landscapes with both constant and changing drug concentrations confirm the crucial role of epistasis in determining the rate of multidrug resistance evolution.

  16. LORICA - A new model for linking landscape and soil profile evolution: Development and sensitivity analysis

    NASA Astrophysics Data System (ADS)

    Temme, Arnaud J. A. M.; Vanwalleghem, Tom

    2016-05-01

    Soils and landscapes evolve in tandem. Landscape position is a strong determinant of vertical soil development, which has often been formalized in the catena concept. At the same time, soil properties are strong determinants of geomorphic processes such as overland erosion, landsliding and creep. We present a new soilscape evolution model; LORICA, to study these numerous interactions between soil and landscape development. The model is based on the existing landscape evolution model LAPSUS and the soil formation model MILESD. The model includes similar soil formation processes as MILESD, but the main novelties include the consideration of more layers and the dynamic adaption of the number of layers as a function of the soil profile's heterogeneity. New processes in the landscape evolution component include a negative feedback of vegetation and armouring and particle size selectivity of the erosion-deposition process. In order to quantify these different interactions, we present a full sensitivity analysis of the input parameters. First results show that the model successfully simulates various soil-landscape interactions, leading to outputs where the surface changes in the landscape clearly depend on soil development, and soil changes depend on landscape location. Sensitivity analysis of the model confirms that soil and landscape interact: variables controlling amount and position of fine clay have the largest effect on erosion, and erosion variables control among others the amount of chemical weathering. These results show the importance of particle size distribution, and especially processes controlling the presence of finer clay particles that are easily eroded, both for the resulting landscape form as for the resulting soil profiles. Further research will have to show whether this is specific to the boundary conditions of this study or a general phenomenon.

  17. ASSESSING THE HYDROGEOLOGIC CLASSIFICATION SYSTEM IN MID-ATLANTIC COASTAL PLAIN STREAMS USING BENTHIC MACROINVERTEBRATES

    EPA Science Inventory

    Assessing classification systems that describe natural variation across regions is an important first step for developing indicators. We evaluated a hydrogeologic framework for first order streams in the mid-Atlantic Coastal Plain as part of the LIPS-MACS (Landscape Indicators f...

  18. IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory


    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses,...

  19. Predictability of evolutionary trajectories in fitness landscapes.

    PubMed

    Lobkovsky, Alexander E; Wolf, Yuri I; Koonin, Eugene V

    2011-12-01

    Experimental studies on enzyme evolution show that only a small fraction of all possible mutation trajectories are accessible to evolution. However, these experiments deal with individual enzymes and explore a tiny part of the fitness landscape. We report an exhaustive analysis of fitness landscapes constructed with an off-lattice model of protein folding where fitness is equated with robustness to misfolding. This model mimics the essential features of the interactions between amino acids, is consistent with the key paradigms of protein folding and reproduces the universal distribution of evolutionary rates among orthologous proteins. We introduce mean path divergence as a quantitative measure of the degree to which the starting and ending points determine the path of evolution in fitness landscapes. Global measures of landscape roughness are good predictors of path divergence in all studied landscapes: the mean path divergence is greater in smooth landscapes than in rough ones. The model-derived and experimental landscapes are significantly smoother than random landscapes and resemble additive landscapes perturbed with moderate amounts of noise; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. We suggest that smoothness and the substantial deficit of peaks in the fitness landscapes of protein evolution are fundamental consequences of the physics of protein folding.

  20. Energy Landscape of Social Balance

    NASA Astrophysics Data System (ADS)

    Marvel, Seth A.; Strogatz, Steven H.; Kleinberg, Jon M.

    2009-11-01

    We model a close-knit community of friends and enemies as a fully connected network with positive and negative signs on its edges. Theories from social psychology suggest that certain sign patterns are more stable than others. This notion of social “balance” allows us to define an energy landscape for such networks. Its structure is complex: numerical experiments reveal a landscape dimpled with local minima of widely varying energy levels. We derive rigorous bounds on the energies of these local minima and prove that they have a modular structure that can be used to classify them.

  1. Classification accuracy improvement

    NASA Technical Reports Server (NTRS)

    Kistler, R.; Kriegler, F. J.

    1977-01-01

    Improvements made in processing system designed for MIDAS (prototype multivariate interactive digital analysis system) effects higher accuracy in classification of pixels, resulting in significantly-reduced processing time. Improved system realizes cost reduction factor of 20 or more.

  2. Update on diabetes classification.

    PubMed

    Thomas, Celeste C; Philipson, Louis H

    2015-01-01

    This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal.

  3. An Easy Classification System

    ERIC Educational Resources Information Center

    Bryan, R. C.

    1973-01-01

    File folders can be used effectively to develop and retrieve information about animal classification systems. Animal characters are drawn separately on the front side of a file cover and holes are punched next to each character. (PS)

  4. Martian Landscapes in Motion

    NASA Astrophysics Data System (ADS)

    Mattson, Sarah; McEwen, Alfred; Kirk, Randolph; Howington-Kraus, Elpitha; Chojnacki, Matthew; Runyon, Kirby; Cremonese, Gabriele; Re, Cristina

    2014-05-01

    RISE orthorectified image sequences makes it possible to conduct accurate change detection studies of active processes on Mars. Some examples of studies of active landscapes on Mars using HiRISE DTMs and orthoimage sequences include: dune and ripple motion (Bridges et al., 2012, Nature), recurring slope lineae (RSL) (McEwen et al., 2011, Science; McEwen et al., 2013, Nature Geoscience), gully activity (Dundas et al., 2012, Icarus), and polar processes (Hansen et al., 2011, Science; Portyankina et al. 2013, Icarus,). These studies encompass images from multiple Mars years and seasons. Sequences of orthoimages make it possible to generate animated gifs or movies to visualize temporal changes (http://www.uahirise.org/sim/). They can also be brought into geospatial software to quantitatively map and record changes. The ability to monitor the surface of Mars at high spatial resolution with frequent repeat images has opened up our insight into seasonal and interannual changes, further increasing our understanding of Mars as an active planet.

  5. Microfluidic Platform Generates Oxygen Landscapes for Localized Hypoxic Activation

    PubMed Central

    Rexius, Megan L.; Mauleon, Gerardo; Malik, Asrar B.; Rehman, Jalees; Eddington, David T.

    2014-01-01

    An open-well microfluidic platform generates an oxygen landscape using gas-perfused networks which diffuse across a membrane. The device enables real-time analysis of cellular and tissue responses to oxygen tension to define how cells adapt to heterogeneous oxygen conditions found in the physiological setting. We demonstrate that localized hypoxic activation of cells elicited specific metabolic and gene responses in human microvascular endothelial cells and bone marrow-derived mesenchymal stem cells. A robust demonstration of the compatibility of the device with standard laboratory techniques demonstrates the wide utility of the method. This platform is ideally suited to study real-time cell responses and cell-cell interactions within physiologically relevant oxygen landscapes. PMID:25315003

  6. Risk-adaptive radiotherapy

    NASA Astrophysics Data System (ADS)

    Kim, Yusung

    Currently, there is great interest in integrating biological information into intensity-modulated radiotherapy (IMRT) treatment planning with the aim of boosting high-risk tumor subvolumes. Selective boosting of tumor subvolumes can be accomplished without violating normal tissue complication constraints using information from functional imaging. In this work we have developed a risk-adaptive optimization-framework that utilizes a nonlinear biological objective function. Employing risk-adaptive radiotherapy for prostate cancer, it is possible to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing normal tissue complications. Subsequently, we have studied the impact of functional imaging accuracy, and found on the one hand that loss in sensitivity had a large impact on expected local tumor control, which was maximal when a low-risk classification for the remaining low risk PTV was chosen. While on the other hand loss in specificity appeared to have a minimal impact on normal tissue sparing. Therefore, it appears that in order to improve the therapeutic ratio a functional imaging technique with a high sensitivity, rather than specificity, is needed. Last but not least a comparison study between selective boosting IMRT strategies and uniform-boosting IMRT strategies yielding the same EUD to the overall PTV was carried out, and found that selective boosting IMRT considerably improves expected TCP compared to uniform-boosting IMRT, especially when lack of control of the high-risk tumor subvolumes is the cause of expected therapy failure. Furthermore, while selective boosting IMRT, using physical dose-volume objectives, did yield similar rectal and bladder sparing when compared its equivalent uniform-boosting IMRT plan, risk-adaptive radiotherapy, utilizing biological objective functions, did yield a 5.3% reduction in NTCP for the rectum. Hence, in risk-adaptive radiotherapy the

  7. Supernova Photometric Lightcurve Classification

    NASA Astrophysics Data System (ADS)

    Zaidi, Tayeb; Narayan, Gautham

    2016-01-01

    This is a preliminary report on photometric supernova classification. We first explore the properties of supernova light curves, and attempt to restructure the unevenly sampled and sparse data from assorted datasets to allow for processing and classification. The data was primarily drawn from the Dark Energy Survey (DES) simulated data, created for the Supernova Photometric Classification Challenge. This poster shows a method for producing a non-parametric representation of the light curve data, and applying a Random Forest classifier algorithm to distinguish between supernovae types. We examine the impact of Principal Component Analysis to reduce the dimensionality of the dataset, for future classification work. The classification code will be used in a stage of the ANTARES pipeline, created for use on the Large Synoptic Survey Telescope alert data and other wide-field surveys. The final figure-of-merit for the DES data in the r band was 60% for binary classification (Type I vs II).Zaidi was supported by the NOAO/KPNO Research Experiences for Undergraduates (REU) Program which is funded by the National Science Foundation Research Experiences for Undergraduates Program (AST-1262829).

  8. Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

    NASA Astrophysics Data System (ADS)

    Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

    2015-12-01

    Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

  9. Modeling Climate Change Impacts on Landscape Evolution, Fire, and Hydrology

    NASA Astrophysics Data System (ADS)

    Sheppard, B. S.; O Connor, C.; Falk, D. A.; Garfin, G. M.

    2015-12-01

    Landscape disturbances such as wildfire interact with climate variability to influence hydrologic regimes. We coupled landscape, fire, and hydrologic models and forced them using projected climate to demonstrate climate change impacts anticipated at Fort Huachuca in southeastern Arizona, USA. The US Department of Defense (DoD) recognizes climate change as a trend that has implications for military installations, national security and global instability. The goal of this DoD Strategic Environmental Research and Development Program (SERDP) project (RC-2232) is to provide decision making tools for military installations in the southwestern US to help them adapt to the operational realities associated with climate change. For this study we coupled the spatially explicit fire and vegetation dynamics model FireBGCv2 with the Automated Geospatial Watershed Assessment tool (AGWA) to evaluate landscape vegetation change, fire disturbance, and surface runoff in response to projected climate forcing. A projected climate stream for the years 2005-2055 was developed from the Multivariate Adaptive Constructed Analogs (MACA) 4 km statistical downscaling of the CanESM2 GCM using Representative Concentration Pathway (RCP) 8.5. AGWA, an ArcGIS add-in tool, was used to automate the parameterization and execution of the Soil Water Assessment Tool (SWAT) and the KINematic runoff and EROSion2 (KINEROS2) models based on GIS layers. Landscape raster data generated by FireBGCv2 project an increase in fire and drought associated tree mortality and a decrease in vegetative basal area over the years of simulation. Preliminary results from SWAT modeling efforts show an increase to surface runoff during years following a fire, and for future winter rainy seasons. Initial results from KINEROS2 model runs show that peak runoff rates are expected to increase 10-100 fold as a result of intense rainfall falling on burned areas.

  10. Bioenergy in a Multifunctional Landscape

    SciTech Connect

    Watts, Chad; Negri, Cristina; Ssegane, Herbert

    2015-10-23

    How can our landscapes be managed most effectively to produce crops for food, feed, and bioenergy, while also protecting our water resources by preventing the loss of nutrients from the soil? Dr. Cristina Negri and her team at the U.S. Department of Energy’s Argonne National Laboratory are tackling this question at an agricultural research site located in Fairbury, Illinois.

  11. SAVANNAH RIVER BASIN LANDSCAPE ANALYSIS

    EPA Science Inventory

    Scientists from the U.S. Environmental Protection Agency (EPA), Region 4, Science and Ecosystem Support Division, enlisted the assistance of the landscape ecology group of U.S. EPA, Office of Research and Development (ORD), National Exposure Research Laboratory, Environmental Sci...

  12. [Meadow maris: a genetic landscape].

    PubMed

    El'chinova, G I; Startseva, E A; Moshkina, I S; Ginter, E K

    1998-05-01

    The distribution of the most frequent family names was analyzed in five regions of the Marii El republic, and diagrams of their genetic landscape were constructed. Based on the diagrams, conclusions were drawn regarding the genetic subdivision of the corresponding populations and the boundary between elementary populations within them.

  13. Linguistic Landscape and Minority Languages

    ERIC Educational Resources Information Center

    Cenoz, Jasone; Gorter, Durk

    2006-01-01

    This paper focuses on the linguistic landscape of two streets in two multilingual cities in Friesland (Netherlands) and the Basque Country (Spain) where a minority language is spoken, Basque or Frisian. The paper analyses the use of the minority language (Basque or Frisian), the state language (Spanish or Dutch) and English as an international…

  14. Language's Landscape of the Mind.

    ERIC Educational Resources Information Center

    Tracy, Janet

    2000-01-01

    Describes how the author's 6 middle school students living in a village in the Yukon, 100 miles off the road system just below the arctic circle, enthusiastically wrote stories or poems about their lives. The students shared their works via an online electronic conferencing system with students from the unimaginably different landscape of the…

  15. Flowers and Landscape by Serendipity.

    ERIC Educational Resources Information Center

    Pippin, Sandi

    2003-01-01

    Describes an art lesson in which students sketch drawings of flowers and use watercolor paper and other materials to paint a landscape. Explains that the students also learn about impressionism in this lesson. Discusses how the students prepare the paper and create their artwork. (CMK)

  16. Assessing the New Competitive Landscape.

    ERIC Educational Resources Information Center

    Blustain, Harvey; Goldstein, Philip; Lozier, Gregory

    1998-01-01

    Argues that complex forces (new delivery technologies, changing demographics, emergence of corporate universities, global economy) have created a new, competitive landscape for higher education that forces institutions to think methodically about how to respond. A framework for college planning, incorporating three critical components, is…

  17. Selected Landscape Plants. Slide Script.

    ERIC Educational Resources Information Center

    McCann, Kevin

    This slide script, part of a series of slide scripts designed for use in vocational agriculture classes, deals with commercially important woody ornamental landscape plants. Included in the script are narrations for use with a total of 253 slides illustrating 92 different plants. Several slides are used to illustrate each plant: besides a view of…

  18. Ornamental Landscape Grasses. Slide Script.

    ERIC Educational Resources Information Center

    Still, Steven M.; Adams, Denise W.

    This slide script to accompany the slide series, Ornamental Landscape Grasses, contains photographs of the 167 slides and accompanying narrative text intended for use in the study and identification of commercially important ornamental grasses and grasslike plants. Narrative text is provided for slides of 62 different perennial and annual species…

  19. An Analysis of the Landscaping Occupation.

    ERIC Educational Resources Information Center

    Stemple, Lynn L.; Dilley, John E.

    The general purpose of the occupational analysis is to provide workable, basic information dealing with the many and varied duties performed in the landscape services occupation. Depending on the preparation and abilities of the individual student, he may enter the landscape area as (1) nursery worker, (2) landscape planter, (3) landscape…

  20. 23 CFR 752.4 - Landscape development.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... landscaping and environmental design. (b) Landscape development should have provisions for plant establishment periods of a duration sufficient for expected survival in the highway environment. Normal 1-year plant... for natural regeneration of native growth and the management of that growth. (e) Landscaping...