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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

    USDA-ARS?s Scientific Manuscript database

    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. Adaptive Gaussian Pattern Classification

    DTIC Science & Technology

    1988-08-01

    redundant model of the data to be used in classification . There are two classes of learning, or adaptation schemes. The first, unsupervised learning...37, No. 3, pp. 242-247, 1983. [2] E. F. Codd, Cellular Automata , Academic Press, 1968. [31 H. Everett, G. Gilbreath, S. Alderson, D. J. Marchette...Na al Oca aytm aete !JTI FL E COPY AD-A 199 030 Technical Document 1335 August 1988 Adaptive Gaussian Pattern Classif ication C. E. Priebe D. J

  6. [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.

  7. 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...

  8. 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...

  9. Making landscape classification relevant for agriculture

    USDA-ARS?s Scientific Manuscript database

    Most regional and larger landscape classifications have focused on natural vegetation types, but these broad classifications have potential uses in agriculture as well. Defining a site type in terms of the vegetation it can support, whether natural or managed, can provide an extremely flexible frame...

  10. 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...

  11. 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...

  12. Classification of the visual landscape for transmission planning

    Treesearch

    Curtis Miller; Nargis Jetha; Rod MacDonald

    1979-01-01

    The Visual Landscape Type Classification method of the Route and Site Selection Division, Ontario Hydro, defines and delineates the landscape into discrete visual units using parametric and judgmental data. This qualitative and quantitative information is documented in a prescribed format to give each of the approximately 1100 Landscape Types a unique description....

  13. 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...

  14. 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

  15. Environmentally Adaptive UXO Detection and Classification Systems

    DTIC Science & Technology

    2016-04-01

    data used in this study. 3 Environmentally Adaptive UXO Detection and Classification Systems 1 Abstract This final report addresses the problem of...FINAL REPORT Environmentally Adaptive UXO Detection and Classification Systems SERDP Project MR-2417 APRIL 2016 Dr. Nick Klausner Information...Defense Strategic Environmental Research and Development Program (SERDP). The publication of this report does not indicate endorsement by the

  16. 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.

  17. 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

  18. 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…

  19. 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…

  20. Method of classification of integumentary landscape elements

    NASA Astrophysics Data System (ADS)

    Voloshyn, V. I.; Bushuyev, Ye. I.; Parshina, O. I.; Fedorov, O. P.

    We develop the method for the determination of technology for creation of thematic map of landscape elements of the territory of Ukraine using remotely sensed data. The purpose of our investigation is maximum formalization and accessibility of the method for many users.

  1. The Landscape of long noncoding RNA classification.

    PubMed

    St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

    2015-05-01

    Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Regulated superinfection may help HIV adaptation on rugged landscape.

    PubMed

    Leontiev, Vladimir; Hadany, Lilach

    2010-05-01

    Human immunodeficiency virus (HIV) is highly adaptable to a, changing environment, including host immune response and antiviral drugs. Superinfection occurs when several HIV proviruses share the same host cell. We previously proposed that HIV may regulate the rate of its superinfection, which would help the virus to adapt (Leontiev et al., 2008). In this paper we, investigate the effect of regulated superinfection in HIV on complex, adaptation on rugged fitness landscapes. We present the results of our in silico experiments that suggest that regulated superinfection facilitates HIV, adaptation on rugged fitness landscapes and that the advantage of regulated, superinfection increases with the ruggedness of the landscape.

  3. Ecological Landscape Classification Using Astronaut Photography

    NASA Astrophysics Data System (ADS)

    Stefanov, W. L.; Castle, J. V.

    2006-12-01

    Digital astronaut photography acquired from the International Space Station is a potentially useful dataset for ecologic, geologic, and land use/land cover studies as it varies greatly in resolution (6 m/pixel minimum) and temporal frequency (minimum 1 day repeat cycle). The entire digital astronaut dataset is freely available from http://eol.jsc.nasa.gov. The dataset includes imagery from 1961 to present, and includes data for much of the Earth's surface. The National Science Foundation's Long Term Ecological Research (LTER) Network provides an ideal framework for assessment of the quantitative potential of digital astronaut photography. The Network of 26 sites represent a wide range of biomes including temperate and tropical forest, deserts, grasslands, tundra, and urban human-dominated ecosystems. This wide range of sites provides an excellent database for comparison of digital astronaut photography with remotely sensed data (i.e. Landsat) as well as field-based validation and measurement data. Used with remotely-sensed satellite and airborne data, digital astronaut photography can increase the temporal resolution of observed variables such as land cover, land use change, vegetation dynamics, and surface soil processes. In contrast to traditional narrow bandwidth remote sensing instruments, digital astronaut photography is acquired using off-the-shelf digital cameras sensitive to the visible red, green, and blue wavelengths; decisions to acquire imagery are made on-the-fly by the astronaut. The wide bandpasses of the camera make traditional classification approaches difficult as discrete spectral information is not typically obtained. We apply a multilevel, object-oriented image segmentation approach to high resolution digital astronaut photography of LTER sites representing a range of continental and island biomes. This approach emphasizes spatial relationships of similar pixels in addition to spectral information. Results include comparison of classification

  4. 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

  5. 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

  6. Selection Limits to Adaptive Walks on Correlated Landscapes

    PubMed Central

    Heredia, Jorge Pérez; Trubenová, Barbora; Sudholt, Dirk; Paixão, Tiago

    2017-01-01

    Adaptation depends critically on the effects of new mutations and their dependency on the genetic background in which they occur. These two factors can be summarized by the fitness landscape. However, it would require testing all mutations in all backgrounds, making the definition and analysis of fitness landscapes mostly inaccessible. Instead of postulating a particular fitness landscape, we address this problem by considering general classes of landscapes and calculating an upper limit for the time it takes for a population to reach a fitness peak, circumventing the need to have full knowledge about the fitness landscape. We analyze populations in the weak-mutation regime and characterize the conditions that enable them to quickly reach the fitness peak as a function of the number of sites under selection. We show that for additive landscapes there is a critical selection strength enabling populations to reach high-fitness genotypes, regardless of the distribution of effects. This threshold scales with the number of sites under selection, effectively setting a limit to adaptation, and results from the inevitable increase in deleterious mutational pressure as the population adapts in a space of discrete genotypes. Furthermore, we show that for the class of all unimodal landscapes this condition is sufficient but not necessary for rapid adaptation, as in some highly epistatic landscapes the critical strength does not depend on the number of sites under selection; effectively removing this barrier to adaptation. PMID:27881471

  7. 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.

  8. Adaptive learning based heartbeat classification.

    PubMed

    Srinivas, M; Basil, Tony; Mohan, C Krishna

    2015-01-01

    Cardiovascular diseases (CVD) are a leading cause of unnecessary hospital admissions as well as fatalities placing an immense burden on the healthcare industry. A process to provide timely intervention can reduce the morbidity rate as well as control rising costs. Patients with cardiovascular diseases require quick intervention. Towards that end, automated detection of abnormal heartbeats captured by electronic cardiogram (ECG) signals is vital. While cardiologists can identify different heartbeat morphologies quite accurately among different patients, the manual evaluation is tedious and time consuming. In this chapter, we propose new features from the time and frequency domains and furthermore, feature normalization techniques to reduce inter-patient and intra-patient variations in heartbeat cycles. Our results using the adaptive learning based classifier emulate those reported in existing literature and in most cases deliver improved performance, while eliminating the need for labeling of signals by domain experts.

  9. Transcriptome Analysis Reveals Signature of Adaptation to Landscape Fragmentation

    PubMed Central

    Ikonen, Suvi; Auvinen, Petri; Paulin, Lars; Koskinen, Patrik; Holm, Liisa; Taipale, Minna; Duplouy, Anne; Ruokolainen, Annukka; Saarnio, Suvi; Sirén, Jukka; Kohonen, Jukka; Corander, Jukka; Frilander, Mikko J.; Ahola, Virpi; Hanski, Ilkka

    2014-01-01

    We characterize allelic and gene expression variation between populations of the Glanville fritillary butterfly (Melitaea cinxia) from two fragmented and two continuous landscapes in northern Europe. The populations exhibit significant differences in their life history traits, e.g. butterflies from fragmented landscapes have higher flight metabolic rate and dispersal rate in the field, and higher larval growth rate, than butterflies from continuous landscapes. In fragmented landscapes, local populations are small and have a high risk of local extinction, and hence the long-term persistence at the landscape level is based on frequent re-colonization of vacant habitat patches, which is predicted to select for increased dispersal rate. Using RNA-seq data and a common garden experiment, we found that a large number of genes (1,841) were differentially expressed between the landscape types. Hexamerin genes, the expression of which has previously been shown to have high heritability and which correlate strongly with larval development time in the Glanville fritillary, had higher expression in fragmented than continuous landscapes. Genes that were more highly expressed in butterflies from newly-established than old local populations within a fragmented landscape were also more highly expressed, at the landscape level, in fragmented than continuous landscapes. This result suggests that recurrent extinctions and re-colonizations in fragmented landscapes select a for specific expression profile. Genes that were significantly up-regulated following an experimental flight treatment had higher basal expression in fragmented landscapes, indicating that these butterflies are genetically primed for frequent flight. Active flight causes oxidative stress, but butterflies from fragmented landscapes were more tolerant of hypoxia. We conclude that differences in gene expression between the landscape types reflect genomic adaptations to landscape fragmentation. PMID:24988207

  10. Transcriptome analysis reveals signature of adaptation to landscape fragmentation.

    PubMed

    Somervuo, Panu; Kvist, Jouni; Ikonen, Suvi; Auvinen, Petri; Paulin, Lars; Koskinen, Patrik; Holm, Liisa; Taipale, Minna; Duplouy, Anne; Ruokolainen, Annukka; Saarnio, Suvi; Sirén, Jukka; Kohonen, Jukka; Corander, Jukka; Frilander, Mikko J; Ahola, Virpi; Hanski, Ilkka

    2014-01-01

    We characterize allelic and gene expression variation between populations of the Glanville fritillary butterfly (Melitaea cinxia) from two fragmented and two continuous landscapes in northern Europe. The populations exhibit significant differences in their life history traits, e.g. butterflies from fragmented landscapes have higher flight metabolic rate and dispersal rate in the field, and higher larval growth rate, than butterflies from continuous landscapes. In fragmented landscapes, local populations are small and have a high risk of local extinction, and hence the long-term persistence at the landscape level is based on frequent re-colonization of vacant habitat patches, which is predicted to select for increased dispersal rate. Using RNA-seq data and a common garden experiment, we found that a large number of genes (1,841) were differentially expressed between the landscape types. Hexamerin genes, the expression of which has previously been shown to have high heritability and which correlate strongly with larval development time in the Glanville fritillary, had higher expression in fragmented than continuous landscapes. Genes that were more highly expressed in butterflies from newly-established than old local populations within a fragmented landscape were also more highly expressed, at the landscape level, in fragmented than continuous landscapes. This result suggests that recurrent extinctions and re-colonizations in fragmented landscapes select a for specific expression profile. Genes that were significantly up-regulated following an experimental flight treatment had higher basal expression in fragmented landscapes, indicating that these butterflies are genetically primed for frequent flight. Active flight causes oxidative stress, but butterflies from fragmented landscapes were more tolerant of hypoxia. We conclude that differences in gene expression between the landscape types reflect genomic adaptations to landscape fragmentation.

  11. Classification of community types, successional sequences, and landscapes of the Copper River Delta, Alaska.

    Treesearch

    Keith. Boggs

    2000-01-01

    A classification of community types, successional sequences, and landscapes is presented for the piedmont of the Copper River Delta. The classification was based on a sampling of 471 sites. A total of 75 community types, 42 successional sequences, and 6 landscapes are described. The classification of community types reflects the existing vegetation communities on the...

  12. 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.

  13. Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

    NASA Astrophysics Data System (ADS)

    Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  14. Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning.

    PubMed

    Shir, Ofer M; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳10^{4}). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  15. 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.

  16. Rugged adaptive landscapes shape a complex, sympatric radiation

    PubMed Central

    Pfaender, Jobst; Hadiaty, Renny K.; Schliewen, Ulrich K.; Herder, Fabian

    2016-01-01

    Strong disruptive ecological selection can initiate speciation, even in the absence of physical isolation of diverging populations. Species evolving under disruptive ecological selection are expected to be ecologically distinct but, at least initially, genetically weakly differentiated. Strong selection and the associated fitness advantages of narrowly adapted individuals, coupled with assortative mating, are predicted to overcome the homogenizing effects of gene flow. Theoretical plausibility is, however, contrasted by limited evidence for the existence of rugged adaptive landscapes in nature. We found evidence for multiple, disruptive ecological selection regimes that have promoted divergence in the sympatric, incipient radiation of ‘sharpfin’ sailfin silverside fishes in ancient Lake Matano (Sulawesi, Indonesia). Various modes of ecological specialization have led to adaptive morphological differences between the species, and differently adapted morphs display significant but incomplete reproductive isolation. Individual fitness and variation in morphological key characters show that disruptive selection shapes a rugged adaptive landscape in this small but complex incipient lake fish radiation. PMID:26763702

  17. Rugged adaptive landscapes shape a complex, sympatric radiation.

    PubMed

    Pfaender, Jobst; Hadiaty, Renny K; Schliewen, Ulrich K; Herder, Fabian

    2016-01-13

    Strong disruptive ecological selection can initiate speciation, even in the absence of physical isolation of diverging populations. Species evolving under disruptive ecological selection are expected to be ecologically distinct but, at least initially, genetically weakly differentiated. Strong selection and the associated fitness advantages of narrowly adapted individuals, coupled with assortative mating, are predicted to overcome the homogenizing effects of gene flow. Theoretical plausibility is, however, contrasted by limited evidence for the existence of rugged adaptive landscapes in nature. We found evidence for multiple, disruptive ecological selection regimes that have promoted divergence in the sympatric, incipient radiation of 'sharpfin' sailfin silverside fishes in ancient Lake Matano (Sulawesi, Indonesia). Various modes of ecological specialization have led to adaptive morphological differences between the species, and differently adapted morphs display significant but incomplete reproductive isolation. Individual fitness and variation in morphological key characters show that disruptive selection shapes a rugged adaptive landscape in this small but complex incipient lake fish radiation. © 2016 The Author(s).

  18. Mapping land cover in urban residential landscapes using fine resolution imagery and object-oriented classification

    USDA-ARS?s Scientific Manuscript database

    A knowledge of different types of land cover in urban residential landscapes is important for building social and economic city-wide policies including landscape ordinances and water conservation programs. Urban landscapes are typically heterogeneous, so classification of land cover in these areas ...

  19. Adaptation with gene flow across the landscape in a dune sunflower.

    PubMed

    Andrew, Rose L; Ostevik, Katherine L; Ebert, Daniel P; Rieseberg, Loren H

    2012-05-01

    Isolation by adaptation increases divergence at neutral loci when natural selection against immigrants reduces the rate of gene flow between different habitats. This can occur early in the process of adaptive divergence and is a key feature of ecological speciation. Despite the ability of isolation by distance (IBD) and other forms of landscape resistance to produce similar patterns of neutral divergence within species, few studies have used landscape genetics to control for these other forces. We have studied the divergence of Helianthus petiolaris ecotypes living in active sand dunes and adjacent non-dune habitat, using landscape genetics approaches, such as circuit theory and multiple regression of distance matrices, in addition to coalescent modelling. Divergence between habitats was significant, but not strong, and was shaped by IBD. We expected that increased resistance owing to patchy and unfavourable habitat in the dunes would contribute to divergence. Instead, we found that landscape resistance models with lower resistance in the dunes performed well as predictors of genetic distances among subpopulations. Nevertheless, habitat class remained a strong predictor of genetic distance when controlling for isolation by resistance and IBD. We also measured environmental variables at each site and confirmed that specific variables, especially soil nitrogen and vegetation cover, explained a greater proportion of variance in genetic distance than did landscape or the habitat classification alone. Asymmetry in effective population sizes and numbers of migrants per generation was detected using coalescent modelling with Bayesian inference, which is consistent with incipient ecological speciation being driven by the dune habitat.

  20. 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

  1. TUTORIAL: Towards adaptive classification for BCI

    NASA Astrophysics Data System (ADS)

    Shenoy, Pradeep; Krauledat, Matthias; Blankertz, Benjamin; Rao, Rajesh P. N.; Müller, Klaus-Robert

    2006-03-01

    Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance. .

  2. Gap Shape Classification using Landscape Indices and Multivariate Statistics

    NASA Astrophysics Data System (ADS)

    Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

    2016-11-01

    This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

  3. Gap Shape Classification using Landscape Indices and Multivariate Statistics.

    PubMed

    Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

    2016-11-30

    This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

  4. Gap Shape Classification using Landscape Indices and Multivariate Statistics

    PubMed Central

    Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

    2016-01-01

    This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

  5. 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.

  6. 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...

  7. 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

  8. 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 ...

  9. 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 ...

  10. 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...

  11. 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...

  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. 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. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  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. 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) ...

  16. PATTERN RECOGNITION AND CLASSIFICATION USING ADAPTIVE LINEAR NEURON DEVICES

    DTIC Science & Technology

    adaption by an adaptive linear neuron ( Adaline ), as applied to the pattern recognition and classification problem; (2) Four possible iterative adaption...schemes which may be used to train as Adaline ; (3) Use of Multiple Adalines (Madaline) and two logic layers to increase system capability; and (4) Use...of Adaline in the practical fields of Speech Recognition, Weather Forecasting and Adaptive Control Systems and the possible use of Madaline in the Character Recognition field.

  17. 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

  18. Application of Unsupervised Clustering using Sparse Representations on Learned Dictionaries to develop Land Cover Classifications in Arctic Landscapes

    NASA Astrophysics Data System (ADS)

    Rowland, J. C.; Moody, D. I.; Brumby, S.; Gangodagamage, C.

    2012-12-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. Successful application of novel unsupervised feature extraction and clustering algorithms for use in Land Cover Classification requires the ability to determine what landscape attributes are represented by automated clustering. A closely related challenge is learning how to precondition the input data streams to the unsupervised classification algorithms in order to obtain clusters that represent Land Cover category of relevance to landsurface change and modeling applications. We present results from an ongoing effort to apply novel clustering methodologies developed primarily for neuroscience machine vision applications to the environmental sciences. We use a Hebbian learning rule to build spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. These sparse representations of pixel patches are used to perform unsupervised k-means clustering. In our application, we use 8-band multispectral Worldview-2 data from three arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g. soil moisture and inundation), and topographic/geomorphic characteristics. The challenge of developing a meaningful land cover classification includes both learning how optimize the clustering algorithm and successfully interpreting the results. In applying the unsupervised clustering, we have the flexibility of selecting both the window

  19. 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

  20. Host coevolution alters the adaptive landscape of a virus.

    PubMed

    Burmeister, Alita R; Lenski, Richard E; Meyer, Justin R

    2016-09-28

    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. © 2016 The Authors.

  1. a Curvature Based Adaptive Neighborhood for Individual Point Cloud Classification

    NASA Astrophysics Data System (ADS)

    He, E.; Chen, Q.; Wang, H.; Liu, X.

    2017-09-01

    As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.

  2. Development of a technique for classification of integumentary elements of a landscape

    NASA Astrophysics Data System (ADS)

    Voloshyn, V. I.; Bushuyev, Ye. I.; Parshyna, O. I.

    Methodical maintenance for classification of integumentary elements of a landscape is considered on the basis of data of the Earth's remote sensing as the primary goal of management of territory. The classes of integumentary elements and technological operations of satellite data processing are listed.

  3. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Classification of adaptive memetic algorithms: a comparative study.

    PubMed

    Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

    2006-02-01

    Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

  5. Landscape genetics, adaptive diversity and population structure in Phaseolus vulgaris.

    PubMed

    Rodriguez, Monica; Rau, Domenico; Bitocchi, Elena; Bellucci, Elisa; Biagetti, Eleonora; Carboni, Andrea; Gepts, Paul; Nanni, Laura; Papa, Roberto; Attene, Giovanna

    2016-03-01

    Here we studied the organization of genetic variation of the common bean (Phaseolus vulgaris) in its centres of domestication. We used 131 single nucleotide polymorphisms to investigate 417 wild common bean accessions and a representative sample of 160 domesticated genotypes, including Mesoamerican and Andean genotypes, for a total of 577 accessions. By analysing the genetic spatial patterns of the wild common bean, we documented the existence of several genetic groups and the occurrence of variable degrees of diversity in Mesoamerica and the Andes. Moreover, using a landscape genetics approach, we demonstrated that both demographic processes and selection for adaptation were responsible for the observed genetic structure. We showed that the study of correlations between markers and ecological variables at a continental scale can help in identifying local adaptation genes. We also located putative areas of common bean domestication in Mesoamerica, in the Oaxaca Valley, and the Andes, in southern Bolivia-northern Argentina. These observations are of paramount importance for the conservation and exploitation of the genetic diversity preserved within this species and other plant genetic resources. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

  6. 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...

  7. The Landscape of long non-coding RNA classification

    PubMed Central

    St Laurent, Georges; Wahlestedt, Claes; Kapranov, Philipp

    2015-01-01

    Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long non-coding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the non-coding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual un-ambiguous classification framework results in a number of challenges in the annotation and interpretation of non-coding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. PMID:25869999

  8. 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...

  9. 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.

  10. Mutual Information Item Selection in Adaptive Classification Testing

    ERIC Educational Resources Information Center

    Weissman, Alexander

    2007-01-01

    A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local-…

  11. Adaptive stellar spectral subclass classification based on Bayesian SVMs

    NASA Astrophysics Data System (ADS)

    Du, Changde; Luo, Ali; Yang, Haifeng

    2017-02-01

    Stellar spectral classification is one of the most fundamental tasks in survey astronomy. Many automated classification methods have been applied to spectral data. However, their main limitation is that the model parameters must be tuned repeatedly to deal with different data sets. In this paper, we utilize the Bayesian support vector machines (BSVM) to classify the spectral subclass data. Based on Gibbs sampling, BSVM can infer all model parameters adaptively according to different data sets, which allows us to circumvent the time-consuming cross validation for penalty parameter. We explored different normalization methods for stellar spectral data, and the best one has been suggested in this study. Finally, experimental results on several stellar spectral subclass classification problems show that the BSVM model not only possesses good adaptability but also provides better prediction performance than traditional methods.

  12. 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.

  13. An operational framework for object-based land use classification of heterogeneous rural landscapes

    NASA Astrophysics Data System (ADS)

    Watmough, Gary R.; Palm, Cheryl A.; Sullivan, Clare

    2017-02-01

    The characteristics of very high resolution (VHR) satellite data are encouraging development agencies to investigate its use in monitoring and evaluation programmes. VHR data pose challenges for land use classification of heterogeneous rural landscapes as it is not possible to develop generalised and transferable land use classification definitions and algorithms. We present an operational framework for classifying VHR satellite data in heterogeneous rural landscapes using an object-based and random forest classifier. The framework overcomes the challenges of classifying VHR data in anthropogenic landscapes. It does this by using an image stack of RGB-NIR, Normalised Difference Vegetation Index (NDVI) and textural bands in a two-phase object-based classification. The framework can be applied to data acquired by different sensors, with different view and illumination geometries, at different times of the year. Even with these complex input data the framework can produce classification results that are comparable across time. Here we describe the framework and present an example of its application using data from QuickBird (2 images) and GeoEye (1 image) sensors.

  14. Quantitative analysis of lunar crater's landscape: automatic detection, classification and geological applications

    NASA Astrophysics Data System (ADS)

    Li, Ke; Chen, Jianping; He, Shujun; Zhang, Mingchao

    2013-04-01

    Lunar craters are the most important geological tectonic features on the moon; they are among the most studied subjects when it comes to the analysis of the surface of the moon since they provide us with the relative age of the surface unit and more information about lunar geology. Quantitative analysis of landscape on lunar crater is an important approach in lunar geological unit dating which plays a key role in understanding and reconstruction of lunar geological evolution. In this paper, a new approach of automatic crater detection and classification is proposed based on the quantitative analysis of crater's landscape with different spatial resolution digital terrain models. The approach proposed in this paper includes the following key points: 1) A new crater detection method which selects profile similarity parameters as the distinguishing marks is presented. The new method overcomes the high error defect of former DTM based crater detection algorithm; 2) Craters are sorted by the morphological characteristics of profiles. The new quantitative classification method overcomes the subjectivity of the previously descriptive classification method. In order to verify the usefulness of the proposed method the pre-selected landing area of China's Chang'e-III lunar satellite-Sinus Iridum is chosen as the experimental zone. DTM with different resolutions from the Chang'e-I Laser Altimeter, the Chang'e-I Stereoscopic Camera and the Lunar Orbiter Laser Altimeter (LOLA) are used for crater detection and classification. Dating results of each geological unit are gotten using crater size-frequency distribution method (CSFD). By comparing the former dating and manual classification data, we found that the results obtained by our method and the former results have the strong consistency. With the combination of automatic crater detection and classification, this paper basically provided a quantitative approach which can analyze the lunar crater's landscape and get geological

  15. 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.

  16. 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

  17. Undercomplete learned dictionaries for land cover classification in multispectral imagery of Arctic landscapes using CoSA: clustering of sparse approximations

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Gangodagamage, Chandana

    2013-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a Hebbian learning rule to build undercomplete spectral-textural dictionaries that are adapted to the data. 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 our CoSA algorithm: unsupervised Clustering of Sparse Approximations. We demonstrate our method using multispectral Worldview-2 data from three Arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and geomorphic characteristics. To interpret and assign land cover categories to the clusters we both evaluate the spectral properties of the clusters and compare the clusters to both field- and remote sensing-derived classifications of landscape attributes. Our work suggests that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing.

  18. 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.

  19. Adaptive automatic sleep stage classification under covariate shift.

    PubMed

    Khalighi, Sirvan; Sousa, Teresa; Nunes, Urbano

    2012-01-01

    Current automatic sleep stage classification (ASSC) methods that rely on polysomnographic (PSG) signals suffer from inter-subject differences that make them unreliable in facing with new and different subjects. A novel adaptive sleep scoring method based on unsupervised domain adaptation, aiming to be robust to inter-subject variability, is proposed. We assume that the sleep quality variants follow a covariate shift model, where only the sleep features distribution change in the training and test phases. The maximum overlap discrete wavelet transform (MODWT) is applied to extract relevant features from EEG, EOG and EMG signals. A set of significant features are selected by minimum-redundancy maximum-relevance (mRMR) which is a powerful feature selection method. Finally, an instance-weighting method, namely the importance weighted kernel logistic regression (IWKLR) is applied for the purpose of obtaining adaptation in classification. The classification results using leave one out cross-validation (LOOCV), show that the proposed method performs at the state-of-the art in the field of ASSC.

  20. 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

  1. 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

  2. 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).

  3. Community participatory landscape classification and biodiversity assessment and monitoring of grazing lands in northern Kenya.

    PubMed

    Roba, Hassan G; Oba, Gufu

    2009-02-01

    In this study, we asked the Ariaal herders of northern Kenya to answer "why, what and how" they classified landscape, and assessed and monitored the biodiversity of 10 km(2) of grazing land. To answer the "why question" the herders classified grazing resources into 39 landscape patches grouped into six landscape types and classified soil as 'warm', 'intermediate' or 'cold' for the purpose of land use. For the "what question" the herders used soil conditions and vegetation characteristics to assess biodiversity. Plant species were described as 'increasers', 'decreasers' or 'stable'. The decreaser species were mostly grasses and forbs preferred for cattle and sheep grazing and the increasers were mostly woody species preferred by goats. The herders evaluated biodiversity in terms of key forage species and used absence or presence of the preferred species from individual landscapes for monitoring change in biodiversity. For the "how question" the herders used anthropogenic indicators concerned with livestock management for assessing landscape potential and suitability for grazing. The anthropogenic indicators were related to soils and biodiversity. The herders used plant species grazing preferences to determine the links between livestock production and biodiversity. By addressing these three questions, the study shows the value of incorporating the indigenous knowledge of herders into classification of landscape and assessment and monitoring of biodiversity in the grazing lands. We conclude that herder knowledge of biodiversity is related to the use as opposed to exclusive conservation practices. This type of knowledge is extremely valuable to conservation agencies for establishing a baseline for monitoring changes in biodiversity in the future.

  4. 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

  5. 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)...

  6. 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)...

  7. 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. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

  8. Adaptive Skin Classification Using Face and Body Detection.

    PubMed

    Bianco, Simone; Gasparini, Francesca; Schettini, Raimondo

    2015-12-01

    In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.

  9. Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection.

    PubMed

    Park, Juyoung; Kang, Mingon; Gao, Jean; Kim, Younghoon; Kang, Kyungtae

    2017-01-01

    Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.

  10. 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…

  11. 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…

  12. Achieving effective landscape conservation: evolving demands adaptive metrics

    USDA-ARS?s Scientific Manuscript database

    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...

  13. Fanning - A classification algorithm for mixture landscapes applied to Landsat data of Maine forests

    NASA Technical Reports Server (NTRS)

    Ungar, S. G.; Bryant, E.

    1981-01-01

    It is pointed out that typical landscapes include a relatively small number of 'pure' land cover types which combine in various proportions to form a myriad of mixture types. Most Landsat classifications algorithms used today require a separate user specification for each category, including mixture categories. Attention is given to a simpler approach, which would require the user to specify only the 'pure' types. Mixture pixels would be classified on the basis of the proportion of the area covered by each pure type within the pixel. The 'fanning' algorithm quantifies varying proportions of two 'pure' land cover types in selected mixture pixels. This algorithm was applied to 200,000 ha of forest land in Maine, taking into account a comparison with standard inventory information. Results compared well with a discrete categories classification of the same area.

  14. Fanning - A classification algorithm for mixture landscapes applied to Landsat data of Maine forests

    NASA Technical Reports Server (NTRS)

    Ungar, S. G.; Bryant, E.

    1981-01-01

    It is pointed out that typical landscapes include a relatively small number of 'pure' land cover types which combine in various proportions to form a myriad of mixture types. Most Landsat classifications algorithms used today require a separate user specification for each category, including mixture categories. Attention is given to a simpler approach, which would require the user to specify only the 'pure' types. Mixture pixels would be classified on the basis of the proportion of the area covered by each pure type within the pixel. The 'fanning' algorithm quantifies varying proportions of two 'pure' land cover types in selected mixture pixels. This algorithm was applied to 200,000 ha of forest land in Maine, taking into account a comparison with standard inventory information. Results compared well with a discrete categories classification of the same area.

  15. Visions of Restoration in Fire-Adapted Forest Landscapes: Lessons from the Collaborative Forest Landscape Restoration Program

    NASA Astrophysics Data System (ADS)

    Urgenson, Lauren S.; Ryan, Clare M.; Halpern, Charles B.; Bakker, Jonathan D.; Belote, R. Travis; Franklin, Jerry F.; Haugo, Ryan D.; Nelson, Cara R.; Waltz, Amy E. M.

    2017-02-01

    Collaborative approaches to natural resource management are becoming increasingly common on public lands. Negotiating a shared vision for desired conditions is a fundamental task of collaboration and serves as a foundation for developing management objectives and monitoring strategies. We explore the complex socio-ecological processes involved in developing a shared vision for collaborative restoration of fire-adapted forest landscapes. To understand participant perspectives and experiences, we analyzed interviews with 86 respondents from six collaboratives in the western U.S., part of the Collaborative Forest Landscape Restoration Program established to encourage collaborative, science-based restoration on U.S. Forest Service lands. Although forest landscapes and group characteristics vary considerably, collaboratives faced common challenges to developing a shared vision for desired conditions. Three broad categories of challenges emerged: meeting multiple objectives, collaborative capacity and trust, and integrating ecological science and social values in decision-making. Collaborative groups also used common strategies to address these challenges, including some that addressed multiple challenges. These included use of issue-based recommendations, field visits, and landscape-level analysis; obtaining support from local agency leadership, engaging facilitators, and working in smaller groups (sub-groups); and science engagement. Increased understanding of the challenges to, and strategies for, developing a shared vision of desired conditions is critical if other collaboratives are to learn from these efforts.

  16. Visions of Restoration in Fire-Adapted Forest Landscapes: Lessons from the Collaborative Forest Landscape Restoration Program.

    PubMed

    Urgenson, Lauren S; Ryan, Clare M; Halpern, Charles B; Bakker, Jonathan D; Belote, R Travis; Franklin, Jerry F; Haugo, Ryan D; Nelson, Cara R; Waltz, Amy E M

    2017-02-01

    Collaborative approaches to natural resource management are becoming increasingly common on public lands. Negotiating a shared vision for desired conditions is a fundamental task of collaboration and serves as a foundation for developing management objectives and monitoring strategies. We explore the complex socio-ecological processes involved in developing a shared vision for collaborative restoration of fire-adapted forest landscapes. To understand participant perspectives and experiences, we analyzed interviews with 86 respondents from six collaboratives in the western U.S., part of the Collaborative Forest Landscape Restoration Program established to encourage collaborative, science-based restoration on U.S. Forest Service lands. Although forest landscapes and group characteristics vary considerably, collaboratives faced common challenges to developing a shared vision for desired conditions. Three broad categories of challenges emerged: meeting multiple objectives, collaborative capacity and trust, and integrating ecological science and social values in decision-making. Collaborative groups also used common strategies to address these challenges, including some that addressed multiple challenges. These included use of issue-based recommendations, field visits, and landscape-level analysis; obtaining support from local agency leadership, engaging facilitators, and working in smaller groups (sub-groups); and science engagement. Increased understanding of the challenges to, and strategies for, developing a shared vision of desired conditions is critical if other collaboratives are to learn from these efforts.

  17. 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

  18. Transforming landscape ecological evaluations using sub-pixel remote sensing classifications: A study of invasive saltcedar (Tamarix spp.)

    NASA Astrophysics Data System (ADS)

    Frazier, Amy E.

    Invasive species disrupt landscape patterns and compromise the functionality of ecosystem processes. Non-native saltcedar (Tamarix spp.) poses significant threats to native vegetation and groundwater resources in the southwestern U.S. and Mexico, and quantifying spatial and temporal distribution patterns is essential for monitoring its spread. Advanced remote sensing classification techniques such as sub-pixel classifications are able to detect and discriminate saltcedar from native vegetation with high accuracy, but these types of classifications are not compatible with landscape metrics, which are the primary tool available for statistically assessing distribution patterns, because they do not have discrete class boundaries. The objective of this research is to develop new methods that allow sub-pixel classifications to be analyzed using landscape metrics. The research will be carried out through three specific aims: (1) develop and test a method to transform continuous sub-pixel classifications into categorical representations that are compatible with widely used landscape metric tools, (2) establish a gradient-based concept of landscape using sub-pixel classifications and the technique developed in the first objective to explore the relationships between pattern and process, and (3) generate a new super-resolution mapping technique method to predict the spatial locations of fractional land covers within a pixel. Results show that the threshold gradient method is appropriate for discretizing sub-pixel data, and can be used to generate increased information about the landscape compared to traditional single-value metrics. Additionally, the super-resolution classification technique was also able to provide detailed sub-pixel mapping information, but additional work will be needed to develop rigorous validation and accuracy assessment techniques.

  19. Autonomous underwater vehicle adaptive path planning for target classification

    NASA Astrophysics Data System (ADS)

    Edwards, Joseph R.; Schmidt, Henrik

    2002-11-01

    Autonomous underwater vehicles (AUVs) are being rapidly developed to carry sensors into the sea in ways that have previously not been possible. The full use of the vehicles, however, is still not near realization due to lack of the true vehicle autonomy that is promised in the label (AUV). AUVs today primarily attempt to follow as closely as possible a preplanned trajectory. The key to increasing the autonomy of the AUV is to provide the vehicle with a means to make decisions based on its sensor receptions. The current work examines the use of active sonar returns from mine-like objects (MLOs) as a basis for sensor-based adaptive path planning, where the path planning objective is to discriminate between real mines and rocks. Once a target is detected in the mine hunting phase, the mine classification phase is initialized with a derivative cost function to emphasize signal differences and enhance classification capability. The AUV moves adaptively to minimize the cost function. The algorithm is verified using at-sea data derived from the joint MIT/SACLANTCEN GOATS experiments and advanced acoustic simulation using SEALAB. The mission oriented operating system (MOOS) real-time simulator is then used to test the onboard implementation of the algorithm.

  20. 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

  1. Bayesian of inductive cognition algorithm for adaptive classification

    NASA Astrophysics Data System (ADS)

    Jin, Longcun; Wan, Wanggen; Cui, Bin; Wu, Yongliang

    2009-07-01

    In this paper, we proposed a Bayesian of inductive cognition algorithm using in virtual reality multimedia classification. We present a Bayesian of inductive cognition algorithm framework model for adaptively classifying scenes in virtual reality multimedia data. The Multimedia can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the scenes can be adaptively classified. The proposed algorithm consists of Bayesian inductive cognition part and Dirichlet process part. This algorithm has several advantages over traditional distance-based agglomerative adaptively classifying algorithms. Bayesian of inductive cognition algorithm based on Dirichlet process hypothesis testing is used to decide which merges are advantageous and to output the recommended depth of the scenes. The algorithm can be interpreted as a novel fast bottom-up approximate inference method for a Dirichlet process mixture model. We describe procedures for learning the model hyperparameters, computing the predictive distribution, and extensions to the Bayesian of inductive cognition algorithm. Experimental results on virtual reality multimedia data sets demonstrate useful properties of the Bayesian of inductive cognition algorithm.

  2. 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

  3. 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.

  4. Structured estimation - Sample size reduction for adaptive pattern classification

    NASA Technical Reports Server (NTRS)

    Morgera, S.; Cooper, D. B.

    1977-01-01

    The Gaussian two-category classification problem with known category mean value vectors and identical but unknown category covariance matrices is considered. The weight vector depends on the unknown common covariance matrix, so the procedure is to estimate the covariance matrix in order to obtain an estimate of the optimum weight vector. The measure of performance for the adapted classifier is the output signal-to-interference noise ratio (SIR). A simple approximation for the expected SIR is gained by using the general sample covariance matrix estimator; this performance is both signal and true covariance matrix independent. An approximation is also found for the expected SIR obtained by using a Toeplitz form covariance matrix estimator; this performance is found to be dependent on both the signal and the true covariance matrix.

  5. 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.

  6. 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…

  7. An Interactive Computer Program to Construct Adaptive Landscapes and to Simulate the Changes Expected with Selection

    ERIC Educational Resources Information Center

    Hull, Peter

    1978-01-01

    Describes an interactive computer program which can be used by students to construct adaptive landscapes of two types as an illustration of the expected effects of selection. Simulates effects of selection on populations of this type and changes of gene frequency can be plotted on the same contour map. (Author/MA)

  8. 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…

  9. Phase transition in random adaptive walks on correlated fitness landscapes

    NASA Astrophysics Data System (ADS)

    Park, Su-Chan; Szendro, Ivan G.; Neidhart, Johannes; Krug, Joachim

    2015-04-01

    We study biological evolution on a random fitness landscape where correlations are introduced through a linear fitness gradient of strength c . When selection is strong and mutations rare the dynamics is a directed uphill walk that terminates at a local fitness maximum. We analytically calculate the dependence of the walk length on the genome size L . When the distribution of the random fitness component has an exponential tail, we find a phase transition of the walk length D between a phase at small c , where walks are short (D ˜lnL ) , and a phase at large c , where walks are long (D ˜L ) . For all other distributions only a single phase exists for any c >0 . The considered process is equivalent to a zero temperature Metropolis dynamics for the random energy model in an external magnetic field, thus also providing insight into the aging dynamics of spin glasses.

  10. 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

  11. Application of Piedmont Landscape Ecosystem Classification as a Reference For A Vegetation and Herpetofaunal Survey on Lake Thurmond, SC

    Treesearch

    Victor B. Shelburne; Lawrence R. Gering; J. Drew Lanham; Gregory P. Smith; Thomas M. Floyd; Eran S. Kilpatrick

    2002-01-01

    Application of a Piedmont landscape ecosystem classification methodology was used as a basis for a survey of vegetation and herpetofaunal communities on a 343 hectare (846 acre) tract on Lake Thurmond near Plum Branch, SC. The site is located in the Carolina Slate Belt of the Midlands Plateau Region of the Piedmont province. A total of 160 plots were established and 30...

  12. Analysis of adaptive walks on NK fitness landscapes with different interaction schemes

    NASA Astrophysics Data System (ADS)

    Nowak, Stefan; Krug, Joachim

    2015-06-01

    Fitness landscapes are genotype to fitness mappings commonly used in evolutionary biology and computer science which are closely related to spin glass models. In this paper, we study the NK model for fitness landscapes where the interaction scheme between genes can be explicitly defined. The focus is on how this scheme influences the overall shape of the landscape. Our main tool for the analysis are adaptive walks, an idealized dynamics by which the population moves uphill in fitness and terminates at a local fitness maximum. We use three different types of walks and investigate how their length (the number of steps required to reach a local peak) and height (the fitness at the endpoint of the walk) depend on the dimensionality and structure of the landscape. We find that the distribution of local maxima over the landscape is particularly sensitive to the choice of interaction pattern. Most quantities that we measure are simply correlated to the rank of the scheme, which is equal to the number of nonzero coefficients in the expansion of the fitness landscape in terms of Walsh functions.

  13. [Ecological classification system of forest landscape in eastern mountainous region of Liaoning Province].

    PubMed

    Tang, Li-na; Wang, Qing-li; Dai, Li-min; Shao, Guo-fan

    2008-01-01

    Based on Digital Elevation Models (DEM) and satellite SPOT-5 data, and by using the spatial analysis function in Geographic Information System, a hierachical Ecological Classification System of forest landscape was developed for the eastern mountainous region of Liaoning Province, and the two lowest layers in the hierachical framework, Ecological Land Types (ELTs) and Ecological Land Type Phases (ELTPs), were mapped. The results indicated that there were 5 ELTs and 34 ELTPs. The boundaries of ELTs, which presented the potential vegetation distribution and potential forestry ecosystem productivity, were determined by environmental conditions quantified by DEM. ELTPs were classified by overlaying ELTs with forest vegetation data layers which were obtained from remotely sensed data, forest inventory data, and ground data. The ELTPs represented the divisions of land in terms of both natural and human-induced forest conditions, and therefore, were reliable units for forest inventories and management. ELTPs could function as conventional forest inventory sub-compartments. By this means, forestry departments could adjust forest management planning and forest management measures from the viewpoint of forest landscape scale to realize forest ecosystem management.

  14. 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.

  15. The adapting healer: pioneering through shifting epidemiological and sociocultural landscapes.

    PubMed

    McMillen, Heather

    2004-09-01

    While it is true that healers selectively adopt and/or refashion aspects of biomedicine, the influence is not unidirectional with information flowing exclusively from hospitals into the workplaces of healers. This article examines healers in Tanga, Tanzania to explore the reciprocal relations between practitioners of indigenous medicine and biomedicine. An abbreviated ethnography of one healer in coastal Tanzania is used to illustrate some of the relevant influences and possible adaptations of contemporary healers. His experiences illuminate how multiple factors, especially sociocultural changes, biomedicine, AIDS, and related research(ers) can influence healers' adaptations. In his case, biomedical health workers from a non-profit HIV organization call upon him not only to act as a liaison between their services and the community, but more importantly, to provide treatment for opportunistic infections and counseling for patients and to participate in biomedical and scientific projects. Reflecting on his experiences as a healer who has negotiated a position that straddles the world of biomedicine and the world of healers facilitates examination of important issues affecting healers today, including their relationship to biomedical health workers, bioprospectors, governments, non-profit organizations, and professional organizations of healers. Although the healer featured in this article is a pioneer in his own town, there are other examples in Africa where healers and biomedical practitioners are interacting. Therefore, he may represent a trend in healer adaptation.

  16. 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

  17. An assessment of streamflow vulnerability to climate using Hydrologic Landscape classification

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

    Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial distribution of climatic response in Oregon, Alaska, and the Pacific Northwest. HLs have also been applied to assess historic and projected climatic impacts across the Western U.S. In this project, we summarized (1) the HL classification methodology and (2) the utility of using HLs as a tool to classify the vulnerability of streams to climatic changes in the Western U.S. During the HL classification process, we analyzed 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. We derived the dominant hydrologic pathways (surface runoff or deep or shallow groundwater) from the HL classification of different catchments to test our hypotheses: 1) Changes in climate will have greater impacts on streamflow in catchments dominated by surface runoff. 2) Catchments historically fed by surface runoff from winter snowmelt in the spring will experience greater impact if precipitation falls as rain instead of snow. We calculated S* (precipitation surplus, which includes snowmelt runoff) as a proxy for streamflow for watersheds across the landscape under historic (1901-2000), modern (1971-2000), and future climate projections (2041-2070) to determine whether future projections of S* fall outside of the range of historic S*. Catchments with projections outside the historic range may be more vulnerable to changes in streamflow. Our results indicate that streams dominated by surface runoff and catchments with spring seasonality are more vulnerable to climatic changes. This

  18. 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.

  19. Adaptation of bird communities to farmland abandonment in a mountain landscape.

    PubMed

    Guilherme, João Lopes; Miguel Pereira, Henrique

    2013-01-01

    Widespread farmland abandonment has led to significant landscape transformations of many European mountain areas. These semi-natural multi-habitat landscapes are important reservoirs of biodiversity and their abandonment has important conservation implications. In multi-habitat landscapes the adaptation of communities depends on the differential affinity of the species to the available habitats. We use nested species-area relationships (SAR) to model species richness patterns of bird communities across scales in a mountain landscape, in NW Portugal. We compare the performance of the classic-SAR and the countryside-SAR (i.e. multi-habitat) models at the landscape scale, and compare species similarity decay (SSD) at the regional scale. We find a considerable overlap of bird communities in the different land-uses (farmland, shrubland and oak forest) at the landscape scale. Analysis of the classic and countryside SAR show that specialist species are strongly related to their favourite habitat. Farmland and shrubland have higher regional SSD compared to oak forests. However, this is due to the opportunistic use of farmlands by generalist birds. Forest specialists display significant regional turnover in oak forest. Overall, the countryside-SAR model had a better fit to the data showing that habitat composition determines species richness across scales. Finally, we use the countryside-SAR model to forecast bird diversity under four scenarios of land-use change. Farmland abandonment scenarios show little impact on bird diversity as the model predicts that the complete loss of farmland is less dramatic, in terms of species diversity loss, than the disappearance of native Galicio-Portuguese oak forest. The affinities of species to non-preferred habitats suggest that bird communities can adapt to land-use changes derived from farmland abandonment. Based on model predictions we argue that rewilding may be a suitable management option for many European mountain areas.

  20. Adaptation of Bird Communities to Farmland Abandonment in a Mountain Landscape

    PubMed Central

    Guilherme, João Lopes; Miguel Pereira, Henrique

    2013-01-01

    Widespread farmland abandonment has led to significant landscape transformations of many European mountain areas. These semi-natural multi-habitat landscapes are important reservoirs of biodiversity and their abandonment has important conservation implications. In multi-habitat landscapes the adaptation of communities depends on the differential affinity of the species to the available habitats. We use nested species-area relationships (SAR) to model species richness patterns of bird communities across scales in a mountain landscape, in NW Portugal. We compare the performance of the classic-SAR and the countryside-SAR (i.e. multi-habitat) models at the landscape scale, and compare species similarity decay (SSD) at the regional scale. We find a considerable overlap of bird communities in the different land-uses (farmland, shrubland and oak forest) at the landscape scale. Analysis of the classic and countryside SAR show that specialist species are strongly related to their favourite habitat. Farmland and shrubland have higher regional SSD compared to oak forests. However, this is due to the opportunistic use of farmlands by generalist birds. Forest specialists display significant regional turnover in oak forest. Overall, the countryside-SAR model had a better fit to the data showing that habitat composition determines species richness across scales. Finally, we use the countryside-SAR model to forecast bird diversity under four scenarios of land-use change. Farmland abandonment scenarios show little impact on bird diversity as the model predicts that the complete loss of farmland is less dramatic, in terms of species diversity loss, than the disappearance of native Galicio-Portuguese oak forest. The affinities of species to non-preferred habitats suggest that bird communities can adapt to land-use changes derived from farmland abandonment. Based on model predictions we argue that rewilding may be a suitable management option for many European mountain areas. PMID

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

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

    PubMed

    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

    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.

  8. Local adaptation in Trinidadian guppies alters stream ecosystem structure at landscape scales despite high environmental variability

    USGS Publications Warehouse

    Simon, Troy N.; Bassar, Ronald D.; Binderup, Andrew J.; Flecker, Alex S.; Freeman, Mary C.; Gilliam, James F.; Marshall, Michael C.; Thomas, Steve A.; Travis, Joseph; Reznick, David N.; Pringle, Catherine M.

    2017-01-01

    While previous studies have shown that evolutionary divergence alters ecological processes in small-scale experiments, a major challenge is to assess whether such evolutionary effects are important in natural ecosystems at larger spatial scales. At the landscape scale, across eight streams in the Caroni drainage, we found that the presence of locally adapted populations of guppies (Poecilia reticulata) is associated with reduced algal biomass and increased invertebrate biomass, while the opposite trends were true in streams with experimentally introduced populations of non-locally adapted guppies. Exclusion experiments conducted in two separate reaches of a single stream showed that guppies with locally adapted phenotypes significantly reduced algae with no effect on invertebrates, while non-adapted guppies had no effect on algae but significantly reduced invertebrates. These divergent effects of phenotype on stream ecosystems are comparable in strength to the effects of abiotic factors (e.g., light) known to be important drivers of ecosystem condition. They also corroborate the results of previous experiments conducted in artificial streams. Our results demonstrate that local adaptation can produce phenotypes with significantly different effects in natural ecosystems at a landscape scale, within a tropical watershed, despite high variability in abiotic factors: five of the seven physical and chemical parameters measured across the eight study streams varied by more than one order of magnitude. Our findings suggest that ecosystem structure is, in part, an evolutionary product and not simply an ecological pattern.

  9. Multiple-factor classification of a human-modified forest landscape in the Hsuehshan Mountain Range, Taiwan.

    PubMed

    Berg, Kevan J; Icyeh, Lahuy; Lin, Yih-Ren; Janz, Arnold; Newmaster, Steven G

    2016-12-01

    Human actions drive landscape heterogeneity, yet most ecosystem classifications omit the role of human influence. This study explores land use history to inform a classification of forestland of the Tayal Mrqwang indigenous people of Taiwan. Our objectives were to determine the extent to which human action drives landscape heterogeneity. We used interviews, field sampling, and multivariate analysis to relate vegetation patterns to environmental gradients and human modification across 76 sites. We identified eleven forest classes. In total, around 70 % of plots were at lower elevations and had a history of shifting cultivation, terrace farming, and settlement that resulted in alder, laurel, oak, pine, and bamboo stands. Higher elevation mixed conifer forests were least disturbed. Arboriculture and selective harvesting were drivers of other conspicuous forest patterns. The findings show that past land uses play a key role in shaping forests, which is important to consider when setting targets to guide forest management.

  10. Channel Classification across Arid West Landscapes in Support of OHW Delineation

    DTIC Science & Technology

    2013-01-01

    perspective at Agua Fria River, AZ ...................................................................... 24 Figure 12. Landscape perspective at...27 Figure 16. Active channel at Agua Fria River, AZ...33 Figure 24. Landscape perspective at Agua Fria River, AZ

  11. 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

    2016-08-08

    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.

  12. 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.

  13. 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…

  14. 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…

  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. 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…

  17. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation.

    PubMed

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-12-16

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency.

  18. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation

    PubMed Central

    Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi

    2016-01-01

    Automatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. PMID:27999261

  19. 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...

  20. [Hard and soft classification method of multi-spectral remote sensing image based on adaptive thresholds].

    PubMed

    Hu, Tan-Gao; Xu, Jun-Feng; Zhang, Deng-Rong; Wang, Jie; Zhang, Yu-Zhou

    2013-04-01

    Hard and soft classification techniques are the conventional methods of image classification for satellite data, but they have their own advantages and drawbacks. In order to obtain accurate classification results, we took advantages of both traditional hard classification methods (HCM) and soft classification models (SCM), and developed a new method called the hard and soft classification model (HSCM) based on adaptive threshold calculation. The authors tested the new method in land cover mapping applications. According to the results of confusion matrix, the overall accuracy of HCM, SCM, and HSCM is 71.06%, 67.86%, and 71.10%, respectively. And the kappa coefficient is 60.03%, 56.12%, and 60.07%, respectively. Therefore, the HSCM is better than HCM and SCM. Experimental results proved that the new method can obviously improve the land cover and land use classification accuracy.

  1. Land change in central Mexico: Landscape heterogeneity, natural variability, and classification uncertainty

    NASA Astrophysics Data System (ADS)

    Christman, Zachary John

    Landscapes across the world are experiencing unprecedented rates of change, and the quantification of change and identification of drivers is a grand challenge in land change science research. The Lerma-Chapala-Santiago watershed of central Mexico encompasses a diverse range of topographic conditions, vegetation types, and human activities, and exemplifies the conflation of discrete anthropogenic change with natural variability. The goal of detecting discrete, human-induced, land change is urgent and important, yet challenging for three major reasons: (1) The quantification of land change using coarse spatial resolution data over broad areas has high uncertainty and error. (2) The disaggregation of discrete land change from natural land cover variability is difficult. (3) The assessment of large spatial extents over time necessitates the integration data from multiple sources. New methods must be developed to evaluate uncertainty in the classification of coarse spatial resolution data, to identify and quantify the range of natural variability and its impact on land change, and to integrate land cover data for change analyses. This dissertation research addresses the challenge through the three research studies: The first study evaluated land cover and land cover change across the Lerma-Chapala-Santiago watershed from 2001 to 2007 using a Mahalanobis distance algorithm to classify imagery from Moderate Resolution Imaging Spectroradiometer (MODIS). In addition to the change assessment, this process generated soft-classified typicality images, a metric of classification uncertainty to identify locations of ambiguity or vagueness, and an index of confusion to quantify the potential for spurious change between the two maps. The second study utilized multiple linear regression to explain the variability of Enhanced Vegetation Index composites through independent variables of precipitation, temperature, and elevation from 2001 to 2007. Results of the interannual and

  2. A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax.

    PubMed

    Ogbunugafor, C Brandon; Hartl, Daniel

    2016-01-25

    adaptive landscape through epistatic interactions within a protein, leaving a population trapped on local fitness peaks in an adaptive landscape, unable to locate ancestral genotypes. This irreversibility suggests that the structure of an adaptive landscape for a resistance protein should be understood before considering resistance management strategies. This proposed mechanism for constraints on reverse evolution corroborates evidence from the field indicating that phenotypic reversal often occurs via compensatory mutation at sites independent of those associated with the forward evolution of resistance. Because of this, molecular methods that identify resistance patterns via single SNPs in resistance-associated markers might be missing signals for resistance and compensatory mutation throughout the genome. In these settings, whole genome sequencing efforts should be used to identify resistance patterns, and will likely reveal a more complicated genomic signature for resistance and susceptibility, especially in settings where anti-malarial medications have been used intermittently. Lastly, the findings suggest that, given their role in dictating the dynamics of evolution across the landscape, pivot mutations might serve as future targets for therapy.

  3. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Classification of chemical chaperones based on their effect on protein folding landscapes.

    PubMed

    Dandage, Rohan; Bandyopadhyay, Anannya; Jayaraj, Gopal Gunanathan; Saxena, Kanika; Dalal, Vijit; Das, Aritri; Chakraborty, Kausik

    2015-03-20

    Various small molecules present in biological systems can assist protein folding in vitro and are known as chemical chaperones. De novo design of chemical chaperones with higher activity than currently known examples is desirable to ameliorate protein misfolding and aggregation in multiple contexts. However, this development has been hindered by limited knowledge of their activities. It is thought that chemical chaperones are typically poor solvents for a protein backbone and hence facilitate native structure formation. However, it is unknown if different chemical chaperones can act differently to modulate folding energy landscapes. Using a model slow folding protein, double-mutant Maltose-binding protein (DM-MBP), we show that a canonical chemical chaperone, trimethylamine-N-oxide (TMAO), accelerates refolding by decreasing the flexibility of the refolding intermediate (RI). Among a number of small molecules that chaperone DM-MBP folding, proline and serine stabilize the transition state (TS) enthalpically, while trehalose behaves like TMAO and increases the rate of barrier crossing through nonenthalpic processes. We propose a two-group classification of chemical chaperones based upon their thermodynamic effect on RI and TS, which is also supported by single molecule Förster resonance energy transfer (smFRET) studies. Interestingly, for a different test protein, the molecular mechanisms of the two groups of chaperones are not conserved. This provides a glimpse into the complexity of chemical chaperoning activity of osmolytes. Future work would allow us to engineer synergism between the two classes to design more efficient chemical chaperones to ameliorate protein misfolding and aggregation problems.

  5. 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

  6. 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

  7. Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

    NASA Astrophysics Data System (ADS)

    Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

    2017-08-01

    This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

  8. Acoustic model adaptation for ortolan bunting (Emberiza hortulana L.) song-type classification.

    PubMed

    Tao, Jidong; Johnson, Michael T; Osiejuk, Tomasz S

    2008-03-01

    Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such methods, common in human speech recognition systems, create initial models trained on speaker independent data, then use small amounts of adaptation data to build individual-specific models. Since, as in human speech, individual vocal variability is a significant source of variation in bioacoustic data, acoustic model adaptation is naturally suited to classification in this domain as well. To demonstrate and evaluate the effectiveness of this approach, this paper presents the application of maximum likelihood linear regression adaptation to ortolan bunting (Emberiza hortulana L.) song-type classification. Classification accuracies for the adapted system are computed as a function of the amount of adaptation data and compared to caller-independent and caller-dependent systems. The experimental results indicate that given the same amount of data, supervised adaptation significantly outperforms both caller-independent and caller-dependent systems.

  9. An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.

    PubMed

    Liu, Ming; Zhang, Fan; Huang, He Helen

    2017-09-04

    Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.

  10. 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.

  11. Adaptation or Resistance: a classification of responses to sea-level rise

    NASA Astrophysics Data System (ADS)

    Cooper, J. A.

    2016-02-01

    Societal responses to sea level rise and associated coastal change are apparently diverse in nature and motivation. Most are commonly referred to as 'adaptation'. Based on a review of current practice, however, it is argued that many of these responses do not involve adaptation, but are rather resisting change. There are several instances where formerly adaptive initiatives involving human adaptability are being replaced by initiatives that resist change. A classification is presented that recognises a continuum of responses ranging from adaptation to resistance, depending upon the willingness to change human activities to accommodate environmental change. In many cases climate change adaptation resources are being used for projects that are purely resistant and which foreclose future adaptation options. It is argued that a more concise definition of adaptation is needed if coastal management is to move beyond the current position of holding the shoreline, other tah n in a few showcase examples.

  12. Noise-Tolerant Hyperspectral Signature Classification in Unresolved Object Detection with Adaptive Tabular Nearest Neighbor Encoding

    NASA Astrophysics Data System (ADS)

    Schmalz, M.; Key, G.

    Accurate spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications, especially where linear mixing models are employed, signature classification accuracy depends on accurate spectral endmember discrimination. In selected target recognition (ATR) applications, previous non-adaptive techniques for signature classification have yielded class separation and classifier refinement results that tend to be suboptimal. In practice, the number of signatures accurately classified often depends linearly on the number of inputs. This can lead to potentially severe classification errors in the presence of noise or densely interleaved signatures. In this paper, we present an enhancement of an emerging technology for nonimaging spectral signature classification based on a highly accurate, efficient search engine called Tabular Nearest Neighbor Encoding (TNE). Adaptive TNE can optimize its classifier performance to track input nonergodicities and yield measures of confidence or caution for evaluation of classification results. Unlike neural networks, TNE does not have a hidden intermediate data structure (e.g., a neural net weight matrix). Instead, TNE generates and exploits a user-accessible data structure called the agreement map (AM), which can be manipulated by Boolean logic operations to effect accurate classifier refinement through programmable algorithms. The open architecture and programmability of TNE's pattern-space (AM) processing allows a TNE developer to determine the qualitative and quantitative reasons for classification accuracy, as well as characterize in detail the signatures for which TNE does not obtain classification matches, and why such mis-matches occur. In this study AM-based classification has been modified to partially compensate for input statistical changes, in response to performance metrics such as probability of correct classification (Pd

  13. 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.

  14. 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. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. 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

  16. 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.

  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. Verification of hydrologic landscape derived basin-scale classifications in the Pacific Northwest

    Treesearch

    Keith Sawicz

    2016-01-01

    The interaction between the physical and climatic attributes of a basin (form) control how water is partitioned, stored, and conveyed through a catchment (function). Hydrologic Landscapes (HLs) were previously...

  19. 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...

  20. 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...

  1. Non-parametric transient classification using adaptive wavelets

    NASA Astrophysics Data System (ADS)

    Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.

    2015-11-01

    Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.

  2. Real-time dual-microphone noise classification for environment-adaptive pipelines of cochlear implants.

    PubMed

    Mirzahasanloo, Taher; Kehtarnavaz, Nasser

    2013-01-01

    This paper presents an improved noise classification in environment-adaptive speech processing pipelines of cochlear implants. This improvement is achieved by using a dual-microphone and by using a computationally efficient feature-level combination approach to achieve real-time operation. A new measure named Suppression Advantage is also defined in order to quantify the noise suppression improvement of an entire pipeline due to noise classification. The noise classification and suppression improvement results are presented for four commonly encountered noise environments.

  3. Space Object Classification and Characterization Via Multiple Model Adaptive Estimation

    DTIC Science & Technology

    2014-07-14

    mathematical models of the same physical process or of the same model but dependent upon different constants or model parameters . The classification approach...Rodrigues Parameters (GRPs) resulting in a minimum parameter representation for the attitude state error.8 To within first order, the state error...i)|| 2 f2 + ||χδpk (i)|| 2 (10b) δq−k (i) = [ δ̺−k (i) δq−4k(i) ] (10c) where a is a parameter from 0 to 1 and f is a scale factor, which is often

  4. Underwater target classification in changing environments using adaptive feature mapping schemes

    NASA Astrophysics Data System (ADS)

    Yao, De; Azimi-Sadjadi, Mahmood R.; Li, Donghui; Dobeck, Gerald J.

    2000-08-01

    A new adaptive feature mapping scheme is presented in this paper to cope with environmental and target signature changes in underwater target classification. A wavelet packet-based feature extraction scheme is used in conjunction with the linear prediction coding (LPC) scheme as the front-end processor. The core of the adaptive classification system is the adaptive feature mapping sub- system that minimizes the classification error of the classifier. The extracted feature vector is mapped by the resultant feature mapping matrix in such a way that the mapped version remains invariant to the environmental and sensory changes. The feedback to the adaptation mechanism is provided by a K-nearest neighbor (K-NN) classifier. In order to alleviate problems caused by poorly scaled features, a revised K-NN based on the scaled Euclidean distance was adopted. Two error criteria were used in the adaptive system, one is the least squares (LS) error criterion and the other is 2D sigmoid cost function. Those two criteria were combined together to offer a better performance. The test results on 40KHz sigmoid cost function. Those two criteria were combined together to offer a better performance. The test result on 40KHz linear FM acoustic backscattered data collected for six different objects are presented. The effectiveness of the adaptive system vs. non- adaptive system is demonstrated when the signal-to- reverberation ratio in the environment is varying.

  5. 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.

  6. Landscape genomics of Colorado potato beetle provides evidence of polygenic adaptation to insecticides.

    PubMed

    Crossley, Michael S; Chen, Yolanda H; Groves, Russell L; Schoville, Sean D

    2017-08-31

    The ability of insect pests to rapidly and repeatedly adapt to insecticides has long challenged entomologists and evolutionary biologists. Since Crow's seminal paper on insecticide resistance in 1957, new data and insights continue to emerge that are relevant to the old questions about how insecticide resistance evolves: such as whether it is predominantly mono- or polygenic, and evolving from standing vs. de novo genetic variation. Many studies support the monogenic hypothesis, and current management recommendations assume single- or two-locus models. But inferences could be improved by integrating data from a broader sample of pest populations and genomes. Here we generate evidence relevant to these questions by applying a landscape genomics framework to the study of insecticide resistance in a major agricultural pest, Colorado potato beetle, Leptinotarsa decemlineata (Say). Genome-environment association tests using genomic variation from 16 populations spanning gradients of landscape variables associated with insecticide exposure over time revealed 42 strong candidate insecticide resistance genes, with potentially overlapping roles in multiple resistance mechanisms. Measurements of resistance to a widely-used insecticide, imidacloprid, among 47 L. decemlineata populations revealed heterogeneity at a small (2 km) scale and no spatial signature of origin or spread throughout the landscape. Analysis of nucleotide diversity suggested candidate resistance loci have undergone varying degrees of selective sweeps, often maintaining similar levels of nucleotide diversity to neutral loci. This study suggests that many genes are involved in insecticide resistance in L. decemlineata and that resistance likely evolves from both de novo and standing genetic variation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  7. A just-in-time adaptive classification system based on the intersection of confidence intervals rule.

    PubMed

    Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

    2011-10-01

    Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.

  8. Designing Antibiotic Cycling Strategies by Determining and Understanding Local Adaptive Landscapes

    PubMed Central

    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 (blaTEM) varied greatly among 15 different β-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles blaTEM-50 and blaTEM-85, each of which differs from its ancestor blaTEM-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. PMID:23418506

  9. Adaptive on-line classification of multi-spectral scanner data

    NASA Technical Reports Server (NTRS)

    Fromm, F. R.; Northouse, R. A.

    1973-01-01

    A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

  10. Adaptive on-line classification of multi-spectral scanner data

    NASA Technical Reports Server (NTRS)

    Fromm, F. R.; Northouse, R. A.

    1973-01-01

    A possible solution to the analysis of the massive amounts of multi-spectral scanner data from the Earth Resource Technical Satellite (ERTS) program is proposed. This solution is offered as an adaptive on-line classification scheme. The classifier is described as well as its controller which is based on ground truth data. Cluster analysis is presented as an alternative approach to the ground truth data. Adaptive feature selection is discussed and possible mini-computer implementations are offered.

  11. 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

  12. Cultural adaptation, content validity and inter-rater reliability of the "STAR Skin Tear Classification System".

    PubMed

    Strazzieri-Pulido, Kelly Cristina; Santos, Vera Lúcia Conceição de Gouveia; Carville, Keryln

    2015-01-01

    to perform the cultural adaptation of the STAR Skin Tear Classification System into the Portuguese language and to test the content validity and inter-rater reliability of the adapted version. methodological study with a quantitative approach. The cultural adaptation was developed in three phases: translation, evaluation by a committee of judges and back-translation. The instrument was tested regarding content validity and inter-rater reliability. the adapted version obtained a regular level of concordance when it was applied by nurses using photographs of friction injuries. Regarding its application in clinical practice, the adapted version obtained a moderate and statistically significant level of concordance. the study tested the content validity and inter-rater reliability of the version adapted into the Portuguese language. Its inclusion in clinical practice will enable the correct identification of this type of injury, as well as the implementation of protocols for the prevention and treatment of friction injuries.

  13. 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…

  14. Underwater target classification in changing environments using an adaptive feature mapping.

    PubMed

    Azimi-Sadjadi, M R; Yao, D; Jamshidi, A A; Dobeck, G J

    2002-01-01

    A new adaptive underwater target classification system to cope with environmental changes in acoustic backscattered data from targets and nontargets is introduced. The core of the system is the adaptive feature mapping that minimizes the classification error rate of the classifier. The goal is to map the feature vector in such a way that the mapped version remains invariant to the environmental changes. A K-nearest neighbor (K-NN) system is used as a memory to provide the closest matches of an unknown pattern in the feature space. The classification decision is done by a backpropagation neural network (BPNN). Two different cost functions for adaptation are defined. These two cost functions are then combined together to improve the classification performance. The test results on a 40-kHz linear FM acoustic backscattered data set collected from six different objects are presented. These results demonstrate the effectiveness of the adaptive system versus nonadaptive system when the signal-to-reverberation ratio (SRR) in the environment is varying.

  15. Space-based RF signal classification using adaptive wavelet features

    SciTech Connect

    Caffrey, M.; Briles, S.

    1995-04-01

    RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.

  16. Rare ecomorphological convergence on a complex adaptive landscape: Body size and diet mediate evolution of jaw shape in squirrels (Sciuridae).

    PubMed

    Zelditch, Miriam Leah; Ye, Ji; Mitchell, Jonathan S; Swiderski, Donald L

    2017-03-01

    Convergence is widely regarded as compelling evidence for adaptation, often being portrayed as evidence that phenotypic outcomes are predictable from ecology, overriding contingencies of history. However, repeated outcomes may be very rare unless adaptive landscapes are simple, structured by strong ecological and functional constraints. One such constraint may be a limitation on body size because performance often scales with size, allowing species to adapt to challenging functions by modifying only size. When size is constrained, species might adapt by changing shape; convergent shapes may therefore be common when size is limiting and functions are challenging. We examine the roles of size and diet as determinants of jaw shape in Sciuridae. As expected, size and diet have significant interdependent effects on jaw shape and ecomorphological convergence is rare, typically involving demanding diets and limiting sizes. More surprising is morphological without ecological convergence, which is equally common between and within dietary classes. Those cases, like rare ecomorphological convergence, may be consequences of evolving on an adaptive landscape shaped by many-to-many relationships between ecology and function, many-to-one relationships between form and performance, and one-to-many relationships between functionally versatile morphologies and ecology. On complex adaptive landscapes, ecological selection can yield different outcomes. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

  17. 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.

  18. Using hydrologic landscape classification to assess streamflow vulnerability to changes in climate

    EPA Science Inventory

    Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

  19. An assessment of streamflow vulnerability to climate using Hydrologic Landscape classification

    EPA Science Inventory

    Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

  20. 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...

  1. 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...

  2. Regional landscape ecosystems of Michigan, Minnesota and Wisconsin: a working map and classification.

    Treesearch

    Dennis A. Albert

    1995-01-01

    Describes the landscape ecosystems (ecoregions) of Michigan, Minnesota, and Wisconsin and includes maps of all three states. Regional descriptions include climate, bedrock geology, landforms, lakes and streams, soils, presettlement vegetation, natural disturbance, present vegetation and land use, rare biota, natural areas, public land managers, and conservation...

  3. 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...

  4. Using hydrologic landscape classification to assess streamflow vulnerability to changes in climate

    EPA Science Inventory

    Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

  5. An assessment of streamflow vulnerability to climate using Hydrologic Landscape classification

    EPA Science Inventory

    Identifying regions with similar hydrology is useful for assessing water quality and quantity across the U.S., especially areas that are difficult or costly to monitor. For example, hydrologic landscapes (HLs) have been used to map streamflow variability and assess the spatial di...

  6. Water balances of two Piedmont headwater catchments: implications for regional hydrologic landscape classification

    Treesearch

    C. Dreps; G. Sun; J. Boggs

    2014-01-01

    In the Piedmont of North Carolina, a traditionally water-rich region, reservoirs that serve over 1 million people are under increasing pressure due to naturally occurring droughts and increasing land development. Innovative development approaches aim to maintain hydrologic conditions of the undisturbed landscape, but are based on insufficient target information. This...

  7. Classification of osteosarcoma T-ray responses using adaptive and rational wavelets for feature extraction

    NASA Astrophysics Data System (ADS)

    Ng, Desmond; Wong, Fu Tian; Withayachumnankul, Withawat; Findlay, David; Ferguson, Bradley; Abbott, Derek

    2007-12-01

    In this work we investigate new feature extraction algorithms on the T-ray response of normal human bone cells and human osteosarcoma cells. One of the most promising feature extraction methods is the Discrete Wavelet Transform (DWT). However, the classification accuracy is dependant on the specific wavelet base chosen. Adaptive wavelets circumvent this problem by gradually adapting to the signal to retain optimum discriminatory information, while removing redundant information. Using adaptive wavelets, classification accuracy, using a quadratic Bayesian classifier, of 96.88% is obtained based on 25 features. In addition, the potential of using rational wavelets rather than the standard dyadic wavelets in classification is explored. The advantage it has over dyadic wavelets is that it allows a better adaptation of the scale factor according to the signal. An accuracy of 91.15% is obtained through rational wavelets with 12 coefficients using a Support Vector Machine (SVM) as the classifier. These results highlight adaptive and rational wavelets as an efficient feature extraction method and the enormous potential of T-rays in cancer detection.

  8. An alternative to current psychiatric classifications: a psychological landscape hypothesis based on an integrative, dynamical and multidimensional approach

    PubMed Central

    2014-01-01

    Background Mental disorders as defined by current classifications are not fully supported by scientific evidence. It is unclear whether main disorders should be broken down into separate categories or disposed along a continuous spectrum. In the near future, new classes of mental disorders could be defined through associations of so-called abnormalities observed at the genetic, molecular and neuronal circuitry levels. Methods We propose an alternative hypothesis to these classifications based on an integrative, dynamical and multidimensional approach. Results We suggest that observed data collected in the general population can be used to build a psychological landscape. Innovative techniques issued from information processing and system dynamics can prove helpful in this task. Information preserving techniques can reduce the high dimensional data collected and provide an intrinsic map for psychological characteristics or behaviors. Dynamical patterns called attractors, which are linked to each other through continuous pathways, can be identified. Specific attractors can define mental disorders. Their causal structure can be investigated with causal networks. Conclusions Powerful and reliable tools are available so that an alternative to current psychiatric classifications can be built based on a genuine biopsychosocial model. The proposed model is ready to be tested on real data. PMID:25033795

  9. 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.

  10. 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.

  11. Fast Model Adaptation for Automated Section Classification in Electronic Medical Records.

    PubMed

    Ni, Jian; Delaney, Brian; Florian, Radu

    2015-01-01

    Medical information extraction is the automatic extraction of structured information from electronic medical records, where such information can be used for improving healthcare processes and medical decision making. In this paper, we study one important medical information extraction task called section classification. The objective of section classification is to automatically identify sections in a medical document and classify them into one of the pre-defined section types. Training section classification models typically requires large amounts of human labeled training data to achieve high accuracy. Annotating institution-specific data, however, can be both expensive and time-consuming; which poses a big hurdle for adapting a section classification model to new medical institutions. In this paper, we apply two advanced machine learning techniques, active learning and distant supervision, to reduce annotation cost and achieve fast model adaptation for automated section classification in electronic medical records. Our experiment results show that active learning reduces the annotation cost and time by more than 50%, and distant supervision can achieve good model accuracy using weakly labeled training data only.

  12. Adaptive road crack detection system by pavement classification.

    PubMed

    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.

  13. 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

  14. Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification

    NASA Astrophysics Data System (ADS)

    Dong, Weihua; Lan, Jianhang; Liang, Shunlin; Yao, Wei; Zhan, Zhicheng

    2017-08-01

    LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5-1.5 m), medium (1.5-6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification.

  15. 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.

  16. Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system.

    Treesearch

    D. McKenzie; C.L. Raymond; L.-K.B. Kellogg; R.A. Norheim; A.G. Andreu; A.C. Bayard; K.E. Kopper; E. Elman

    2007-01-01

    Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modeling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two...

  17. 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...

  18. 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...

  19. 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...

  20. 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...

  1. 2D/3D video content adaptation decision engine based on content classification and user assessment

    NASA Astrophysics Data System (ADS)

    Fernandes, Rui; Andrade, M. T.

    2017-07-01

    Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

  2. Domain adaptation problems: a DASVM classification technique and a circular validation strategy.

    PubMed

    Bruzzone, Lorenzo; Marconcini, Mattia

    2010-05-01

    This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.

  3. Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability.

    PubMed

    Santos, José; Monteagudo, Ángel

    2017-03-27

    The canonical code, although prevailing in complex genomes, is not universal. It was shown the canonical genetic code superior robustness compared to random codes, but it is not clearly determined how it evolved towards its current form. The error minimization theory considers the minimization of point mutation adverse effect as the main selection factor in the evolution of the code. We have used simulated evolution in a computer to search for optimized codes, which helps to obtain information about the optimization level of the canonical code in its evolution. A genetic algorithm searches for efficient codes in a fitness landscape that corresponds with the adaptability of possible hypothetical genetic codes. The lower the effects of errors or mutations in the codon bases of a hypothetical code, the more efficient or optimal is that code. The inclusion of the fitness sharing technique in the evolutionary algorithm allows the extent to which the canonical genetic code is in an area corresponding to a deep local minimum to be easily determined, even in the high dimensional spaces considered. The analyses show that the canonical code is not in a deep local minimum and that the fitness landscape is not a multimodal fitness landscape with deep and separated peaks. Moreover, the canonical code is clearly far away from the areas of higher fitness in the landscape. Given the non-presence of deep local minima in the landscape, although the code could evolve and different forces could shape its structure, the fitness landscape nature considered in the error minimization theory does not explain why the canonical code ended its evolution in a location which is not an area of a localized deep minimum of the huge fitness landscape.

  4. Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.

    PubMed

    Rodríguez-Bermúdez, Germán; García-Laencina, Pedro J

    2012-11-01

    Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

  5. Solving Local Minima Problem in Back Propagation Algorithm Using Adaptive Gain, Adaptive Momentum and Adaptive Learning Rate on Classification Problems

    NASA Astrophysics Data System (ADS)

    Hamid, Norhamreeza Abdul; Nawi, Nazri Mohd; Ghazali, Rozaida; Salleh, Mohd Najib Mohd

    This paper presents a new method to improve back propagation algorithm from getting stuck with local minima problem and slow convergence speeds which caused by neuron saturation in the hidden layer. In this proposed algorithm, each training pattern has its own activation functions of neurons in the hidden layer that are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. The efficiency of the proposed algorithm is compared with the conventional back propagation gradient descent and the current working back propagation gradient descent with adaptive gain by means of simulation on three benchmark problems namely iris, glass and thyroid.

  6. Landscape genomics and a common garden trial reveal adaptive differentiation to temperature across Europe in the tree species Alnus glutinosa.

    PubMed

    De Kort, Hanne; Vandepitte, Katrien; Bruun, Hans Henrik; Closset-Kopp, Déborah; Honnay, Olivier; Mergeay, Joachim

    2014-10-01

    The adaptive potential of tree species to cope with climate change has important ecological and economic implications. Many temperate tree species experience a wide range of environmental conditions, suggesting high adaptability to new environmental conditions. We investigated adaptation to regional climate in the drought-sensitive tree species Alnus glutinosa (Black alder), using a complementary approach that integrates genomic, phenotypic and landscape data. A total of 24 European populations were studied in a common garden and through landscape genomic approaches. Genotyping-by-sequencing was used to identify SNPs across the genome, resulting in 1990 SNPs. Although a relatively low percentage of putative adaptive SNPs was detected (2.86% outlier SNPs), we observed clear associations among outlier allele frequencies, temperature and plant traits. In line with the typical drought avoiding nature of A. glutinosa, leaf size varied according to a temperature gradient and significant associations with multiple outlier loci were observed, corroborating the ecological relevance of the observed outlier SNPs. Moreover, the lack of isolation by distance, the very low genetic differentiation among populations and the high intrapopulation genetic variation all support the notion that high gene exchange combined with strong environmental selection promotes adaptation to environmental cues.

  7. Adaptation approaches for conserving ecosystems services and biodiversity in dynamic landscapes caused by climate change

    Treesearch

    Oswald J. Schmitz; Anne M. Trainor

    2014-01-01

    Climate change stands to cause animal species to shift their geographic ranges. This will cause ecosystems to become reorganized across landscapes as species migrate into and out of specific locations with attendant impacts on values and services that ecosystems provide to humans. Conservation in an era of climate change needs to ensure that landscapes are resilient by...

  8. 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

  9. 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.

  10. Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.

    Automatic classification of non-stationary radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such signals are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. This thesis proposes an adaptive classification approach for poorly characterized targets and backgrounds based on sparse representations in non-analytical dictionaries learned from data. Conventional analytical orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of non-stationary signals, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They generally do not lead to sparse decompositions (i.e., with very few non-zero coefficients), and use in classification requires separate feature selection algorithms. Pursuit-type decompositions in analytical overcomplete (non-orthogonal) dictionaries yield sparse representations, by design, and work well for signals that are similar to the dictionary elements. The pursuit search, however, has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. One such overcomplete analytical dictionary method is also analyzed in this thesis for comparative purposes. The main thrust of the thesis is learning discriminative RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics. A pursuit search is used over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. Two state-of-the-art dictionary learning methods are compared, the K-SVD algorithm and Hebbian learning, in terms of their classification performance as a function of dictionary training parameters

  11. CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

    PubMed

    Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

    2016-12-07

    In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

  12. 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.

  13. 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.

  14. 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.

  15. Epithelium-stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images.

    PubMed

    Huang, Yue; Zheng, Han; Liu, Chi; Ding, Xinghao; Rohde, Gustavo

    2017-04-06

    Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our work assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.

  16. An Adaptive System for Home Monitoring Using a Multiagent Classification of Patterns

    PubMed Central

    Rammal, Ali; Trouilhet, Sylvie; Singer, Nicolas; Pécatte, Jean-Marie

    2008-01-01

    This research takes place in the S(MA)2D project which proposes software architecture to monitor elderly people in their own homes. We want to build patterns dynamically from data about activity, movements, and physiological information of the monitored people. To achieve that, we propose a multiagent method of classification: every agent has a simple know-how of classification. Data generated at this local level are communicated and adjusted between agents to obtain a set of patterns. The patterns are used at a personal level, for example to raise an alert, but also to evaluate global risks (epidemic, heat wave). These data are dynamic; the system has to maintain the built patterns and has to create new patterns. So, the system is adaptive and can be spread on a large scale. PMID:18437224

  17. 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.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  1. Domain adaptation based on deep denoising auto-encoders for classification of remote sensing images

    NASA Astrophysics Data System (ADS)

    Riz, Emanuele; Demir, Begüm; Bruzzone, Lorenzo

    2016-10-01

    This paper investigates the effectiveness of deep learning (DL) for domain adaptation (DA) problems in the classification of remote sensing images to generate land-cover maps. To this end, we introduce two different DL architectures: 1) single-stage domain adaptation (SS-DA) architecture; and 2) hierarchal domain adaptation (H-DA) architecture. Both architectures require that a reliable training set is available only for one of the images (i.e., the source domain) from a previous analysis, whereas it is not for another image to be classified (i.e., the target domain). To classify the target domain image, the proposed architectures aim to learn a shared feature representation that is invariant across the source and target domains in a completely unsupervised fashion. To this end, both architectures are defined based on the stacked denoising auto-encoders (SDAEs) due to their high capability to define high-level feature representations. The SS-DA architecture leads to a common feature space by: 1) initially unifying the samples in source and target domains; and 2) then feeding them simultaneously into the SDAE. To further increase the robustness of the shared representations, the H-DA employs: 1) two SDAEs for learning independently the high level representations of source and target domains; and 2) a consensus SDAE to learn the domain invariant high-level features. After obtaining the domain invariant features through proposed architectures, the classifier is trained by the domain invariant labeled samples of the source domain, and then the domain invariant samples of the target domain are classified to generate the related classification map. Experimental results obtained for the classification of very high resolution images confirm the effectiveness of the proposed DL architectures.

  2. 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.

  3. 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

  4. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification

    PubMed Central

    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. PMID:25945120

  5. 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

  6. 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…

  7. 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…

  8. Subject-adaptive real-time sleep stage classification based on conditional random field.

    PubMed

    Luo, Gang; Min, Wanli

    2007-10-11

    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 S(new) with limited training data D(new), we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from D(new) a regulated estimate of CRF's parameters. Using sleep recordings from human subjects, we show that even without any D(new), our sleep stager can achieve an average classification accuracy of 70% on S(new). This accuracy increases with the size of D(new) and eventually becomes close to the theoretical limit.

  9. 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%.

  10. Adapting Predictive Models for Cepheid Variable Star Classification Using Linear Regression and Maximum Likelihood

    NASA Astrophysics Data System (ADS)

    Gupta, Kinjal Dhar; Vilalta, Ricardo; Asadourian, Vicken; Macri, Lucas

    2014-05-01

    We describe an approach to automate the classification of Cepheid variable stars into two subtypes according to their pulsation mode. Automating such classification is relevant to obtain a precise determination of distances to nearby galaxies, which in addition helps reduce the uncertainty in the current expansion of the universe. One main difficulty lies in the compatibility of models trained using different galaxy datasets; a model trained using a training dataset may be ineffectual on a testing set. A solution to such difficulty is to adapt predictive models across domains; this is necessary when the training and testing sets do not follow the same distribution. The gist of our methodology is to train a predictive model on a nearby galaxy (e.g., Large Magellanic Cloud), followed by a model-adaptation step to make the model operable on other nearby galaxies. We follow a parametric approach to density estimation by modeling the training data (anchor galaxy) using a mixture of linear models. We then use maximum likelihood to compute the right amount of variable displacement, until the testing data closely overlaps the training data. At that point, the model can be directly used in the testing data (target galaxy).

  11. Effect of mutators on adaptability in time-varying fitness landscapes

    NASA Astrophysics Data System (ADS)

    Gorodetsky, Pavel; Tannenbaum, Emmanuel

    2008-04-01

    This Brief Report studies the quasispecies dynamics of a population capable of genetic repair evolving on a time-dependent fitness landscape. We develop a model that considers an asexual population of single-stranded, conservatively replicating genomes, whose only source of genetic variation is due to copying errors during replication. We consider a time-dependent, single-fitness-peak landscape where the master sequence changes by a single point mutation at every time τ . We are able to analytically solve for the evolutionary dynamics of the population in the point-mutation limit. In particular, our model provides an analytical expression for the fraction of mutators in the dynamic fitness landscape that agrees well with results from stochastic simulations.

  12. Using Pattern Classification to Measure Adaptation to the Orientation of High Order Aberrations

    PubMed Central

    Sawides, Lucie; Dorronsoro, Carlos; Haun, Andrew M.; Peli, Eli; Marcos, Susana

    2013-01-01

    Background The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA). We examined whether visual coding is also adapted to the orientation of the natural HOA of the eye. Methods and Findings Judgments of perceived blur were measured in 5 subjects in a psychophysical procedure inspired by the “Classification Images” technique. Subjects were presented 500 pairs of images, artificially blurred with HOA from 100 real eyes (i.e. different orientations), with total blur level adjusted to match the subject's natural blur. Subjects selected the image that appeared best focused in each random pair, in a 6-choice ranked response. Images were presented through Adaptive Optics correction of the subject's aberrations. The images selected as best focused were identified as positive, the other as negative responses. The highest classified positive responses correlated more with the subject's Point Spread Function, PSF, (r = 0.47 on average) than the negative (r = 0.34) and the difference was significant for all subjects (p<0.02). Using the orientation of the best fitting ellipse of angularly averaged integrated PSF intensities (weighted by the subject's responses) we found that in 4 subjects the positive PSF response was close to the subject's natural PSF orientation (within 21 degrees on average) whereas the negative PSF response was almost perpendicularly oriented to the natural PSF (at 76 degrees on average). Conclusions The Classification-Images inspired method is very powerful in identifying the internally coded blur of subjects. The consistent bias of the Positive PSFs towards the natural PSF in most subjects indicates that the internal code of blur appears rather specific to each subject's high order aberrations and reveals that the calibration mechanisms for normalizing blur also operate using orientation

  13. Automatic target classification of slow moving ground targets using space-time adaptive processing

    NASA Astrophysics Data System (ADS)

    Malas, John Alexander

    2002-04-01

    Air-to-ground surveillance radar technologies are increasingly being used by theater commanders to detect, track, and identify ground moving targets. New radar automatic target recognition (ATR) technologies are being developed to aid the pilot in assessing the ground combat picture. Most air-to-ground surveillance radars use Doppler filtering techniques to separate target returns from ground clutter. Unfortunately, Doppler filter techniques fall short on performance when target geometry and ground vehicle speed result in low line of sight velocities. New clutter filter techniques compatible with emerging advancements in wideband radar operation are needed to support surveillance modes of radar operation when targets enter this low velocity regime. In this context, space-time adaptive processing (STAP) in conjunction with other algorithms offers a class of signal processing that provide improved target detection, tracking, and classification in the presence of interference through the adaptive nulling of both ground clutter and/or jamming. Of particular interest is the ability of the radar to filter and process the complex target signature data needed to generate high range resolution (HRR) signature profiles on ground targets. A new approach is proposed which will allow air-to-ground target classification of slow moving vehicles in clutter. A wideband STAP approach for clutter suppression is developed which preserves the amplitude integrity of returns from multiple range bins consistent with the HRR ATR approach. The wideband STAP processor utilizes narrowband STAP principles to generate a series of adaptive sub-band filters. Each sub-band filter output is used to construct the complete filtered response of the ground target. The performance of this new approach is demonstrated and quantified through the implementation of a one dimensional template-based minimum mean squared error classifier. Successful minimum velocity identification is defined in terms of

  14. 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…

  15. Applying ecological site concepts to adaptive conservation management on an iconic Californian landscape

    USDA-ARS?s Scientific Manuscript database

    Tejon Ranch is a spectacular landscape valued for its biological diversity, livestock production, and cultural heritage. Situated at the convergence of three of California’s major biogeographic zones, it is the largest, contiguous private property in the state. Since 2008, the ranch has operated und...

  16. 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…

  17. 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

  18. 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

  19. Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions.

    PubMed

    Wojtas-Niziurski, Wojciech; Meng, Yilin; Roux, Benoit; Bernèche, Simon

    2013-04-09

    The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy.

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

    PubMed

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

    2015-01-01

    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.

  1. Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions

    PubMed Central

    Wojtas-Niziurski, Wojciech; Meng, Yilin; Roux, Benoit; Bernèche, Simon

    2013-01-01

    The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy. PMID:23814508

  2. 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.

  3. 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

  4. 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. Published by Elsevier Ltd.

  5. 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. © 2014 John Wiley & Sons Ltd/CNRS.

  6. 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.

  7. 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...

  8. 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...

  9. Learning self-adaptive color harmony model for aesthetic quality classification

    NASA Astrophysics Data System (ADS)

    Kuang, Zhijie; Lu, Peng; Wang, Xiaojie; Lu, Xiaofeng

    2015-03-01

    Color harmony is one of the key aspects in aesthetic quality classification for photos. The existing color harmony models either are in lack of quantization schemes or can assess simple color patterns only. Therefore, these models cannot be applied to assess color harmony of photos directly. To address this problem, we proposed a simple data-based self-adaptive color harmony model. In this model, the hue distribution of a photo is fitted by mean shift based method, then features are extracted according to this distribution and finally the Gaussian mixture model is applied for learning features extracted from all the photos. The experimental results on eight categories datasets show that the proposed method outperforms the classic rule-based methods and the state-of-the-art data-based model.

  10. Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.

    PubMed

    Gao, Zhong-Ke; Cai, Qing; Yang, Yu-Xuan; Dong, Na; Zhang, Shan-Shan

    2017-06-01

    Detecting epileptic seizure from EEG signals constitutes a challenging problem of significant importance. Combining adaptive optimal kernel time-frequency representation and visibility graph, we develop a novel method for detecting epileptic seizure from EEG signals. We construct complex networks from EEG signals recorded from healthy subjects and epilepsy patients. Then we employ clustering coefficient, clustering coefficient entropy and average degree to characterize the topological structure of the networks generated from different brain states. In addition, we combine energy deviation and network measures to recognize healthy subjects and epilepsy patients, and further distinguish brain states during seizure free interval and epileptic seizures. Three different experiments are designed to evaluate the performance of our method. The results suggest that our method allows a high-accurate classification of epileptiform EEG signals.

  11. 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

  12. Adaptive descriptor based on the geometric consistency of local image features: application to flower image classification

    NASA Astrophysics Data System (ADS)

    Najjar, Asma; Zagrouba, Ezzeddine

    2016-09-01

    Geometric consistency is, usually, considered as a postprocessing step to filter matched sets of local features in order to discard outliers. In this work, it is used to propose an adaptive feature that describes the geometric dispersion of keypoints. It is based on a distribution computed by a nonparametric estimator so that no assumption is made about the data. We investigated and discussed the invariance properties of our descriptor under the most common two- and three-dimensional transformations. Then, we applied it to flower recognition. The classification is performed using the precomputed kernel of support vector machines classifier. Indeed, a similarity computing framework that uses the Kullback-Leibler divergence is presented. Furthermore, a customized layout for each flower image is designed to describe and compare separately the boundary and the central area of flowers. Experimentations made on the Oxford flower-17 dataset prove the efficiency of our method in terms of classification accuracy and computational complexity. The limits of our descriptor are also discussed on a 10-class subset of the Oxford flower-102 dataset.

  13. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

    PubMed

    Kim, Jinkwon; Min, Se Dong; Lee, Myoungho

    2011-06-27

    Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

  14. An adaptive method with integration of multi-wavelet based features for unsupervised classification of SAR images

    NASA Astrophysics Data System (ADS)

    Chamundeeswari, V. V.; Singh, D.; Singh, K.

    2007-12-01

    In single band and single polarized synthetic aperture radar (SAR) images, the information is limited to intensity and texture only and it is very difficult to interpret such SAR images without any a priori information. For unsupervised classification of SAR images, M-band wavelet decomposition is performed on the SAR image and sub-band selection on the basis of energy levels is applied to improve the classification results since sparse representation of sub-bands degrades the performance of classification. Then, textural features are obtained from selected sub-bands and integrated with intensity features. An adaptive neuro-fuzzy algorithm is used to improve computational efficiency by extracting significant features. K-means classification is performed on the extracted features and land features are labeled. This classification algorithm involves user defined parameters. To remove the user dependency and to obtain maximum achievable classification accuracy, an algorithm is developed in this paper for classification accuracy in terms of the parameters involved in the segmentation process. This is very helpful to develop the automated land-cover monitoring system with SAR, where optimized parameters are to be identified only once and these parameters can be applied to SAR imagery of the same scene obtained year after year. A single band, single polarized SAR image is classified into water, urban and vegetation areas using this method and overall classification accuracy is obtained in the range of 85.92%-93.70% by comparing with ground truth data.

  15. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish.

    PubMed

    Reid, Noah M; Proestou, Dina A; Clark, Bryan W; Warren, Wesley C; Colbourne, John K; Shaw, Joseph R; Karchner, Sibel I; Hahn, Mark E; Nacci, Diane; Oleksiak, Marjorie F; Crawford, Douglas L; Whitehead, Andrew

    2016-12-09

    Atlantic killifish populations have rapidly adapted to normally lethal levels of pollution in four urban estuaries. Through analysis of 384 whole killifish genome sequences and comparative transcriptomics in four pairs of sensitive and tolerant populations, we identify the aryl hydrocarbon receptor-based signaling pathway as a shared target of selection. This suggests evolutionary constraint on adaptive solutions to complex toxicant mixtures at each site. However, distinct molecular variants apparently contribute to adaptive pathway modification among tolerant populations. Selection also targets other toxicity-mediating genes and genes of connected signaling pathways; this indicates complex tolerance phenotypes and potentially compensatory adaptations. Molecular changes are consistent with selection on standing genetic variation. In killifish, high nucleotide diversity has likely been a crucial substrate for selective sweeps to propel rapid adaptation. Copyright © 2016, American Association for the Advancement of Science.

  16. 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

  17. 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.

  18. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. 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.

  20. Cultural adaptation, content validity and inter-rater reliability of the "STAR Skin Tear Classification System"1

    PubMed Central

    Strazzieri-Pulido, Kelly Cristina; Santos, Vera Lúcia Conceição de Gouveia; Carville, Keryln

    2015-01-01

    AIMS: to perform the cultural adaptation of the STAR Skin Tear Classification System into the Portuguese language and to test the content validity and inter-rater reliability of the adapted version. METHODS: methodological study with a quantitative approach. The cultural adaptation was developed in three phases: translation, evaluation by a committee of judges and back-translation. The instrument was tested regarding content validity and inter-rater reliability. RESULTS: the adapted version obtained a regular level of concordance when it was applied by nurses using photographs of friction injuries. Regarding its application in clinical practice, the adapted version obtained a moderate and statistically significant level of concordance. CONCLUSION: the study tested the content validity and inter-rater reliability of the version adapted into the Portuguese language. Its inclusion in clinical practice will enable the correct identification of this type of injury, as well as the implementation of protocols for the prevention and treatment of friction injuries. PMID:25806644

  1. 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.

  2. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography

    PubMed Central

    Dubow, Michael; Pinhas, Alexander; Shah, Nishit; Cooper, Robert F.; Gan, Alexander; Gentile, Ronald C.; Hendrix, Vernon; Sulai, Yusufu N.; Carroll, Joseph; Chui, Toco Y. P.; Walsh, Joseph B.; Weitz, Rishard; Dubra, Alfredo; Rosen, Richard B.

    2014-01-01

    Purpose. Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Methods. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Results. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm2, respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm2, P = 0.0001). Conclusions. Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response. PMID:24425852

  3. Classification of human retinal microaneurysms using adaptive optics scanning light ophthalmoscope fluorescein angiography.

    PubMed

    Dubow, Michael; Pinhas, Alexander; Shah, Nishit; Cooper, Robert F; Gan, Alexander; Gentile, Ronald C; Hendrix, Vernon; Sulai, Yusufu N; Carroll, Joseph; Chui, Toco Y P; Walsh, Joseph B; Weitz, Rishard; Dubra, Alfredo; Rosen, Richard B

    2014-03-04

    Microaneurysms (MAs) are considered a hallmark of retinal vascular disease, yet what little is known about them is mostly based upon histology, not clinical observation. Here, we use the recently developed adaptive optics scanning light ophthalmoscope (AOSLO) fluorescein angiography (FA) to image human MAs in vivo and to expand on previously described MA morphologic classification schemes. Patients with vascular retinopathies (diabetic, hypertensive, and branch and central retinal vein occlusion) were imaged with reflectance AOSLO and AOSLO FA. Ninety-three MAs, from 14 eyes, were imaged and classified according to appearance into six morphologic groups: focal bulge, saccular, fusiform, mixed, pedunculated, and irregular. The MA perimeter, area, and feret maximum and minimum were correlated to morphology and retinal pathology. Select MAs were imaged longitudinally in two eyes. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging revealed microscopic features of MAs not appreciated on conventional images. Saccular MAs were most prevalent (47%). No association was found between the type of retinal pathology and MA morphology (P = 0.44). Pedunculated and irregular MAs were among the largest MAs with average areas of 4188 and 4116 μm(2), respectively. Focal hypofluorescent regions were noted in 30% of MAs and were more likely to be associated with larger MAs (3086 vs. 1448 μm(2), P = 0.0001). Retinal MAs can be classified in vivo into six different morphologic types, according to the geometry of their two-dimensional (2D) en face view. Adaptive optics scanning light ophthalmoscope fluorescein angiography imaging of MAs offers the possibility of studying microvascular change on a histologic scale, which may help our understanding of disease progression and treatment response.

  4. Virtual-system-coupled adaptive umbrella sampling to compute free-energy landscape for flexible molecular docking.

    PubMed

    Higo, Junichi; Dasgupta, Bhaskar; Mashimo, Tadaaki; Kasahara, Kota; Fukunishi, Yoshifumi; Nakamura, Haruki

    2015-07-30

    A novel enhanced conformational sampling method, virtual-system-coupled adaptive umbrella sampling (V-AUS), was proposed to compute 300-K free-energy landscape for flexible molecular docking, where a virtual degrees of freedom was introduced to control the sampling. This degree of freedom interacts with the biomolecular system. V-AUS was applied to complex formation of two disordered amyloid-β (Aβ30-35 ) peptides in a periodic box filled by an explicit solvent. An interpeptide distance was defined as the reaction coordinate, along which sampling was enhanced. A uniform conformational distribution was obtained covering a wide interpeptide distance ranging from the bound to unbound states. The 300-K free-energy landscape was characterized by thermodynamically stable basins of antiparallel and parallel β-sheet complexes and some other complex forms. Helices were frequently observed, when the two peptides contacted loosely or fluctuated freely without interpeptide contacts. We observed that V-AUS converged to uniform distribution more effectively than conventional AUS sampling did.

  5. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish

    EPA Science Inventory

    Atlantic killifish populations have rapidly adapted to normally lethal levels of pollution in four urban estuaries. Through analysis of 384 whole killifish genome sequences and comparative transcriptomics in four pairs of sensitive and tolerant populations, we identify the aryl h...

  6. The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish

    EPA Science Inventory

    Atlantic killifish populations have rapidly adapted to normally lethal levels of pollution in four urban estuaries. Through analysis of 384 whole killifish genome sequences and comparative transcriptomics in four pairs of sensitive and tolerant populations, we identify the aryl h...

  7. Gravitational self-organizing map-based seismic image classification with an adaptive spectral-textural descriptor

    NASA Astrophysics Data System (ADS)

    Hao, Yanling; Sun, Genyun

    2016-10-01

    Seismic image classification is of vital importance for extracting damage information and evaluating disaster losses. With the increasing availability of high resolution remote sensing images, automatic image classification offers a unique opportunity to accommodate the rapid damage mapping requirements. However, the diversity of disaster types and the lack of uniform statistical characteristics in seismic images increase the complexity of automated image classification. This paper presents a novel automatic seismic image classification approach by integrating an adaptive spectral-textural descriptor into gravitational self-organizing map (gSOM). In this approach, seismic image is first segmented into several objects based on mean shift (MS) method. These objects are then characterized explicitly by spectral and textural feature quantization histograms. To objectify the image object delineation adapt to various disaster types, an adaptive spectral-textural descriptor is developed by integrating the histograms automatically. Subsequently, these objects as classification units are represented by neurons in a self-organizing map and clustered by adjacency gravitation. By moving the neurons around the gravitational space and merging them according to the gravitation, the object-based gSOM is able to find arbitrary shape and determine the class number automatically. Taking advantage of the diversity of gSOM results, consensus function is then conducted to discover the most suitable classification result. To confirm the validity of the presented approach, three aerial seismic images in Wenchuan covering several disaster types are utilized. The obtained quantitative and qualitative experimental results demonstrated the feasibility and accuracy of the proposed seismic image classification method.

  8. Noise-Robust Spectral Signature Classification in Non-resolved Object Detection using Feedback Controlled Adaptive Learning

    NASA Astrophysics Data System (ADS)

    Schmalz, M.; Key, G.

    2012-09-01

    Accurate spectral signature classification is key to reliable nonresolved detection and recognition of spaceborne objects. In classical signature-based recognition applications, classification accuracy has been shown to depend on accurate spectral endmember discrimination. Unfortunately, signatures are corrupted by noise and clutter that can be nonergodic in astronomical imaging practice. In previous work, we have shown that object class separation and classifier refinement results can be severely corrupted by input noise, leading to suboptimal classification. We have also shown that computed pattern recognition, like its human counterpart, can benefit from processes such as learning or forgetting, which in spectral signature classification can support adaptive tracking of input nonergodicities. In this paper, we model learning as the acquisition or insertion of a new pattern into a classifier's knowledge base. For example, in neural nets (NNs), this insertion process could correspond to the superposition of a new pattern onto the NN weight matrix. Similarly, we model forgetting as the deletion of a pattern currently stored in the classifier knowledge base, for example, as a pattern deletion operation on the NN weight matrix, which is a difficult goal with classical neural nets (CNNs). In particular, this paper discusses the implementation of feedback control for pattern insertion and deletion in lattice associative memories (LAMs) and dynamically adaptive statistical data fusion (DASDAF) paradigms, in support of signature classification. It is shown that adaptive classifiers based on LNN or DASDAF technology can achieve accurate signature classification in the presence of nonergodic Gaussian and non-Gaussian noise, at low signal-to-noise ratio (SNR). Demonstration involves classification of multiple closely spaced, noise corrupted signatures from a NASA database of space material signatures at SNR > 0.1:1.

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

    PubMed Central

    Quirós, Elia; Felicísimo, Ángel 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

  10. 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

  11. 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

  12. Hydrological resiliency in the Western Boreal Plains: classification of hydrological responses using wavelet analysis to assess landscape resilience

    NASA Astrophysics Data System (ADS)

    Probert, Samantha; Kettridge, Nicholas; Devito, Kevin; Hannah, David; Parkin, Geoff

    2017-04-01

    The Boreal represents a system of substantial resilience to climate change, with minimal ecological change over the past 6000 years. However, unprecedented climatic warming, coupled with catchment disturbances could exceed thresholds of hydrological function in the Western Boreal Plains. Knowledge of ecohydrological and climatic feedbacks that shape the resilience of boreal forests has advanced significantly in recent years, but this knowledge is yet to be applied and understood at landscape scales. Hydrological modelling at the landscape scale is challenging in the WBP due to diverse, non-topographically driven hydrology across the mosaic of terrestrial and aquatic ecosystems. This study functionally divides the geologic and ecological components of the landscape into Hydrologic Response Areas (HRAs) and wetland, forestland, interface and pond Hydrologic Units (HUs) to accurately characterise water storage and infer transmission at multiple spatial and temporal scales. Wavelet analysis is applied to pond and groundwater levels to describe the patterns of water storage in response to climate signals; to isolate dominant controls on hydrological responses and to assess the relative importance of physical controls between wet and dry climates. This identifies which components of the landscape exhibit greater magnitude and frequency of variability to wetting and drying trends, further to testing the hierarchical framework for hydrological storage controls of: climate, bedrock geology, surficial geology, soil, vegetation, and topography. Classifying HRA and HU hydrological function is essential to understand and predict water storage and redistribution through drought cycles and wet periods. This work recognises which landscape components are most sensitive under climate change and disturbance and also creates scope for hydrological resiliency research in Boreal systems by recognising critical landscape components and their role in landscape collapse or catastrophic

  13. 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

  14. Classification.

    PubMed

    Tuxhorn, Ingrid; Kotagal, Prakash

    2008-07-01

    In this article, we review the practical approach and diagnostic relevance of current seizure and epilepsy classification concepts and principles as a basic framework for good management of patients with epileptic seizures and epilepsy. Inaccurate generalizations about terminology, diagnosis, and treatment may be the single most important factor, next to an inadequately obtained history, that determines the misdiagnosis and mismanagement of patients with epilepsy. A stepwise signs and symptoms approach for diagnosis, evaluation, and management along the guidelines of the International League Against Epilepsy and definitions of epileptic seizures and epilepsy syndromes offers a state-of-the-art clinical approach to managing patients with epilepsy.

  15. The Landscape of Extreme Genomic Variation in the Highly Adaptable Atlantic Killifish

    PubMed Central

    Reid, Noah M.; Jackson, Craig E.; Gilbert, Don; Minx, Patrick; Montague, Michael J.; Hampton, Thomas H.; Helfrich, Lily W.; King, Benjamin L.; Nacci, Diane E.; Aluru, Neel; Karchner, Sibel I.; Colbourne, John K.; Hahn, Mark E.; Shaw, Joseph R.; Oleksiak, Marjorie F.; Crawford, Douglas L.; Warren, Wesley C.

    2017-01-01

    Understanding and predicting the fate of populations in changing environments require knowledge about the mechanisms that support phenotypic plasticity and the adaptive value and evolutionary fate of genetic variation within populations. Atlantic killifish (Fundulus heteroclitus) exhibit extensive phenotypic plasticity that supports large population sizes in highly fluctuating estuarine environments. Populations have also evolved diverse local adaptations. To yield insights into the genomic variation that supports their adaptability, we sequenced a reference genome and 48 additional whole genomes from a wild population. Evolution of genes associated with cell cycle regulation and apoptosis is accelerated along the killifish lineage, which is likely tied to adaptations for life in highly variable estuarine environments. Genome-wide standing genetic variation, including nucleotide diversity and copy number variation, is extremely high. The highest diversity genes are those associated with immune function and olfaction, whereas genes under greatest evolutionary constraint are those associated with neurological, developmental, and cytoskeletal functions. Reduced genetic variation is detected for tight junction proteins, which in killifish regulate paracellular permeability that supports their extreme physiological flexibility. Low-diversity genes engage in more regulatory interactions than high-diversity genes, consistent with the influence of pleiotropic constraint on molecular evolution. High genetic variation is crucial for continued persistence of species given the pace of contemporary environmental change. Killifish populations harbor among the highest levels of nucleotide diversity yet reported for a vertebrate species, and thus may serve as a useful model system for studying evolutionary potential in variable and changing environments. PMID:28201664

  16. Quantitative classification of a historic northern Wisconsin (U.S.A.) landscape: mapping forests at regional scales

    Treesearch

    Lisa A. Schulte; David J. Mladenoff; Erik V. Nordheim

    2002-01-01

    We developed a quantitative and replicable classification system to improve understanding of historical composition and structure within northern Wisconsin's forests. The classification system was based on statistical cluster analysis and two forest metrics, relative dominance (% basal area) and relative importance (mean of relative dominance and relative density...

  17. 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.

  18. 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

  19. Landscape genetics of plants.

    PubMed

    Holderegger, Rolf; Buehler, Dominique; Gugerli, Felix; Manel, Stéphanie

    2010-12-01

    Landscape genetics is the amalgamation of landscape ecology and population genetics to help with understanding microevolutionary processes such as gene flow and adaptation. In this review, we examine why landscape genetics of plants lags behind that of animals, both in number of studies and consideration of landscape elements. The classical landscape distance/resistance approach to study gene flow is challenging in plants, whereas boundary detection and the assessment of contemporary gene flow are more feasible. By contrast, the new field of landscape genetics of adaptive genetic variation, establishing the relationship between adaptive genomic regions and environmental factors in natural populations, is prominent in plant studies. Landscape genetics is ideally suited to study processes such as migration and adaptation under global change.

  20. 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

  1. THE PEAKS AND GEOMETRY OF FITNESS LANDSCAPES

    PubMed Central

    CRONA, KRISTINA; GREENE, DEVIN; BARLOW, MIRIAM

    2012-01-01

    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 not contained in the geometric classification. We argue that a qualitative perspective may help relating theory of fitness landscapes and empirical observations. PMID:23036916

  2. Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit

    NASA Astrophysics Data System (ADS)

    Zou, Jinyi; Li, Wei; Huang, Xin; Du, Qian

    2014-01-01

    Simultaneous orthogonal matching pursuit (SOMP) has been recently developed for hyperspectral image classification. It utilizes a joint sparsity model with the assumption that each pixel can be represented by a linear combination of labeled samples. We present an approach to improve the performance of SOMP based on a priori segmentation map. According to the map, we first build a local region where within-segment pixels are preserved while between-segment pixels are excluded. Hyperspectral pixels in the preserved region around the test pixel are then simultaneously represented by a linear combination of training samples, whose weights are recovered by solving a sparsity-constrained optimization problem. Finally, the label of the test pixel is determined to be the class that yields the minimal total residuals between the test samples and the approximations. Experimental results demonstrate that the proposed adaptive SOMP (ASOMP) is superior to some existing classifiers, such as the original SOMP and the recently proposed weighted-SOMP (WSOMP). For example, the ASOMP performed with an accuracy of 95.53% for the ROSIS University of Pavia data with 120 training samples per class, while SOMP obtained an accuracy of 87.61%, an improvement of approximately 8%.

  3. 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.

  4. Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering

    NASA Astrophysics Data System (ADS)

    Berendes, Todd A.; Mecikalski, John R.; MacKenzie, Wayne M.; Bedka, Kristopher M.; Nair, U. S.

    2008-10-01

    This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., "fair weather, "towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value.

  5. Image classification with densely sampled image windows and generalized adaptive multiple kernel learning.

    PubMed

    Yan, Shengye; Xu, Xinxing; Xu, Dong; Lin, Stephen; Li, Xuelong

    2015-03-01

    We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive l(p)-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings.

  6. Dynamic transition of transcription and chromatin landscape during fission yeast adaptation to glucose starvation.

    PubMed

    Oda, Arisa; Takemata, Naomichi; Hirata, Yoshito; Miyoshi, Tomoichiro; Suzuki, Yutaka; Sugano, Sumio; Ohta, Kunihiro

    2015-05-01

    Shortage of glucose, the primary energy source for all organisms, is one of the most critical stresses influencing cell viability. Glucose starvation promptly induces changes in mRNA and noncoding RNA (ncRNA) transcription. We previously reported that glucose starvation induces long ncRNA (lncRNA) transcription in the 5' segment of a fission yeast gluconeogenesis gene (fbp1+), which leads to stepwise chromatin alteration around the fbp1+ promoter and to subsequent robust gene activation. Here, we analyzed genomewide transcription by strand-specific RNA sequencing, together with chromatin landscape by immunoprecipitation sequencing (ChIP-seq). Clustering analysis showed that distinct mRNAs and ncRNAs are induced at the early, middle and later stages of cellular response to glucose starvation. The starvation-induced transcription depends substantially on the stress-responsive transcription factor Atf1. Using a new computer program that examines dynamic changes in expression patterns, we identified ncRNAs with similar behavior to the fbp1+ lncRNA. We confirmed that there are continuous lncRNAs associated with local reduction of histone density. Overlapping with the regions for transcription of these lncRNAs, antisense RNAs are antagonistically transcribed under glucose-rich conditions. These results suggest that Atf1-dependent integrated networks of mRNA and lncRNA govern drastic changes in cell physiology in response to glucose starvation.

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

    PubMed Central

    Hether, Tyler D.; Hohenlohe, Paul A.

    2013-01-01

    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

  8. 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...

  9. 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 ...

  10. 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 ...

  11. 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...

  12. 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

  13. 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.

  14. 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.

  15. Landscape genomic analysis of candidate genes for climate adaptation in a California endemic oak, Quercus lobata.

    PubMed

    Sork, Victoria L; Squire, Kevin; Gugger, Paul F; Steele, Stephanie E; Levy, Eric D; Eckert, Andrew J

    2016-01-01

    The ability of California tree populations to survive anthropogenic climate change will be shaped by the geographic structure of adaptive genetic variation. Our goal is to test whether climate-associated candidate genes show evidence of spatially divergent selection in natural populations of valley oak, Quercus lobata, as preliminary indication of local adaptation. Using DNA from 45 individuals from 13 localities across the species' range, we sequenced portions of 40 candidate genes related to budburst/flowering, growth, osmotic stress, and temperature stress. Using 195 single nucleotide polymorphisms (SNPs), we estimated genetic differentiation across populations and correlated allele frequencies with climate gradients using single-locus and multivariate models. The top 5% of FST estimates ranged from 0.25 to 0.68, yielding loci potentially under spatially divergent selection. Environmental analyses of SNP frequencies with climate gradients revealed three significantly correlated SNPs within budburst/flowering genes and two SNPs within temperature stress genes with mean annual precipitation, after controlling for multiple testing. A redundancy model showed a significant association between SNPs and climate variables and revealed a similar set of SNPs with high loadings on the first axis. In the RDA, climate accounted for 67% of the explained variation, when holding climate constant, in contrast to a putatively neutral SSR data set where climate accounted for only 33%. Population differentiation and geographic gradients of allele frequencies in climate-associated functional genes in Q. lobata provide initial evidence of adaptive genetic variation and background for predicting population response to climate change. © 2016 Botanical Society of America.

  16. 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.

  17. Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification.

    PubMed

    Baldwin, Carryl L; Penaranda, B N

    2012-01-02

    Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms.

  18. 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

  19. Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts

    PubMed Central

    Sikosek, Tobias; Bornberg-Bauer, Erich; Chan, Hue Sun

    2012-01-01

    Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed. PMID:23028272

  20. Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch.

    PubMed

    Salojärvi, Jarkko; Smolander, Olli-Pekka; Nieminen, Kaisa; Rajaraman, Sitaram; Safronov, Omid; Safdari, Pezhman; Lamminmäki, Airi; Immanen, Juha; Lan, Tianying; Tanskanen, Jaakko; Rastas, Pasi; Amiryousefi, Ali; Jayaprakash, Balamuralikrishna; Kammonen, Juhana I; Hagqvist, Risto; Eswaran, Gugan; Ahonen, Viivi Helena; Serra, Juan Alonso; Asiegbu, Fred O; de Dios Barajas-Lopez, Juan; Blande, Daniel; Blokhina, Olga; Blomster, Tiina; Broholm, Suvi; Brosché, Mikael; Cui, Fuqiang; Dardick, Chris; Ehonen, Sanna E; Elomaa, Paula; Escamez, Sacha; Fagerstedt, Kurt V; Fujii, Hiroaki; Gauthier, Adrien; Gollan, Peter J; Halimaa, Pauliina; Heino, Pekka I; Himanen, Kristiina; Hollender, Courtney; Kangasjärvi, Saijaliisa; Kauppinen, Leila; Kelleher, Colin T; Kontunen-Soppela, Sari; Koskinen, J Patrik; Kovalchuk, Andriy; Kärenlampi, Sirpa O; Kärkönen, Anna K; Lim, Kean-Jin; Leppälä, Johanna; Macpherson, Lee; Mikola, Juha; Mouhu, Katriina; Mähönen, Ari Pekka; Niinemets, Ülo; Oksanen, Elina; Overmyer, Kirk; Palva, E Tapio; Pazouki, Leila; Pennanen, Ville; Puhakainen, Tuula; Poczai, Péter; Possen, Boy J H M; Punkkinen, Matleena; Rahikainen, Moona M; Rousi, Matti; Ruonala, Raili; van der Schoot, Christiaan; Shapiguzov, Alexey; Sierla, Maija; Sipilä, Timo P; Sutela, Suvi; Teeri, Teemu H; Tervahauta, Arja I; Vaattovaara, Aleksia; Vahala, Jorma; Vetchinnikova, Lidia; Welling, Annikki; Wrzaczek, Michael; Xu, Enjun; Paulin, Lars G; Schulman, Alan H; Lascoux, Martin; Albert, Victor A; Auvinen, Petri; Helariutta, Ykä; Kangasjärvi, Jaakko

    2017-06-01

    Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A.

  1. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. 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

  3. 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.

  4. Shifting adaptive landscapes: progress and challenges in reconstructing early hominid environments.

    PubMed

    Kingston, John D

    2007-01-01

    Since Darwin situated humans in an evolutionary framework, much discussion has focused on environmental factors that may have shaped or influenced the course of human evolution. Developing adaptive or causal perspectives on the morphological and behavioral variability documented in the human fossil record requires establishing a comprehensive paleoenvironmental context. Reconstructing environments in the past, however, is a complex undertaking, requiring assimilation of diverse datasets of varying quality, scale, and relevance. In response to these difficulties, human evolution has traditionally been interpreted in a somewhat generalized framework, characterized primarily by increasing aridity and seasonality periodically punctuated by pulses or intervals of environmental change, inferred largely from global climatic records. Although these broad paradigms provide useful heuristic approaches for interpreting human evolution, the spatiotemporal resolution remains far too coarse to develop unambiguous causal links. This challenge has become more acute as the emerging paleoenvironmental evidence from equatorial Africa is revealing a complex pattern of habitat heterogeneity and persistent ecological flux throughout the interval of human evolution. In addition, recent discoveries have revealed significant taxonomic diversity and substantially increased the geographic and temporal range of early hominids. These findings raise further questions regarding the role of the environment in mediating or directing the course of human evolution. As a consequence, it is imperative to critically assess the environmental criteria on which many theories and hypotheses of human evolution hinge. The goals here are to 1) compile, review, and evaluate relevant paleoecological datasets from equatorial Africa spanning the last 10 Ma, 2) develop a hierarchical perspective for developing and evaluating hypotheses linking paleoecology to patterns and processes in early hominid evolution, and

  5. Dynamics of a temperate deciduous forest under landscape-scale management: Implications for adaptability to climate change

    Treesearch

    Matthew G. Olson; Benjamin O. Knapp; John M. Kabrick

    2017-01-01

    Landscape forest management is an approach to meeting diverse objectives that collectively span multiple spatial scales. It is critical that we understand the long-term effects of landscape management on the structure and composition of forest tree communities to ensure that these practices are sustainable. Furthermore, it is increasingly important to also consider...

  6. 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

  7. 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.

  8. 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

  9. Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

    PubMed

    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.

  10. 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.

  11. 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

  12. Cultural adaptation and the Clavien-Dindo surgical complications classification translated to Brazilian Portuguese.

    PubMed

    Moreira, Luis Fernando; Pessôa, Marcelo Castro Marçal; Mattana, Diego Sachet; Schmitz, Fernando Fernandes; Volkweis, Bernardo Silveira; Antoniazzi, Jorge Luiz; Ribeiro, Liacyr

    2016-01-01

    to generate a translated and validated version of the Clavien-Dindo Classification of Surgical Complications (CDC) to Brazilian Portuguese (CDC-BR). the process of translation and adaptation followed the guideline of Beaton et al., 2000. We divided 76 participating surgeons, in different levels of experience, from the Department Surgery of the Hospital de Clínicas de Porto Alegre, into two groups: Group I applied the original version (CDC, n=36);r Group II used the modified version (CDC-BR, n=40). Each group classified 15 clinical cases of surgical complications. We compared performance between the groups (Mann-Whitney test) relating to the level of experience of the surgeon (Kruskal-Wallis test), considering p value <0.05 as significant. the performance of the Group II (CDC-BR) was higher, with 85% accuracy, compared with 79% of Group I (CDC), p-value =0.012. The performance of the groups as for surgeons experience displayed p=0.171 for Group I, p=0.528 for Group II, and p=0.135 for overall performance. we produced a translated and validated version of the CDC for Brazilian Portuguese. The instrument will be a useful tool in the production of evidence on surgical outcomes. gerar uma versão traduzida e validada da Classificação de Complicações Cirúrgicas de Clavien-Dindo (CCD) para o Português-Brasileiro (CCD-BR). o processo de tradução e adaptação seguiu a diretriz de Beaton et al., de 2000. Formaram-se dois grupos, Grupo I, que utilizou a versão original (CCD, n=36) testado em relação ao Grupo II, com a versão modificada (CCD-BR, n=40), com um total de 76 cirurgiões participantes em níveis de experiência distintos do Departamento de Cirurgia do Hospital de Clínicas de Porto Alegre. Quinze casos clínicos de complicações cirúrgicas foram classificados em cada grupo. Comparou-se o desempenho entre grupos (teste de Mann-Whitney) relacionando ao nível de experiência dos cirurgiões (teste de Kruskal-Wallis). Valor de p<0,05 como significativo

  13. 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

  14. An Automatic User-Adapted Physical Activity Classification Method Using Smartphones.

    PubMed

    Li, Pengfei; Wang, Yu; Tian, Yu; Zhou, Tian-Shu; Li, Jing-Song

    2017-03-01

    In recent years, an increasing number of people have become concerned about their health. Most chronic diseases are related to lifestyle, and daily activity records can be used as an important indicator of health. Specifically, using advanced technology to automatically monitor actual activities can effectively prevent and manage chronic diseases. The data used in this paper were obtained from acceleration sensors and gyroscopes integrated in smartphones. We designed an efficient Adaboost-Stump running on a smartphone to classify five common activities: cycling, running, sitting, standing, and walking and achieved a satisfactory classification accuracy of 98%. We designed an online learning method, and the classification model requires continuous training with actual data. The parameters in the model then become increasingly fitted to the specific user, which allows the classification accuracy to reach 95% under different use environments. In addition, this paper also utilized the OpenCL framework to design the program in parallel. This process can enhance the computing efficiency approximately ninefold.

  15. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms.

    PubMed

    Liu, Jiemeng; Wang, Haifeng; Yang, Hongxing; Zhang, Yizhe; Wang, Jinfeng; Zhao, Fangqing; Qi, Ji

    2013-01-07

    Compared with traditional algorithms for long metagenomic sequence classification, characterizing microorganisms' taxonomic and functional abundance based on tens of millions of very short reads are much more challenging. We describe an efficient composition and phylogeny-based algorithm [Metagenome Composition Vector (MetaCV)] to classify very short metagenomic reads (75-100 bp) into specific taxonomic and functional groups. We applied MetaCV to the Meta-HIT data (371-Gb 75-bp reads of 109 human gut metagenomes), and this single-read-based, instead of assembly-based, classification has a high resolution to characterize the composition and structure of human gut microbiota, especially for low abundance species. Most strikingly, it only took MetaCV 10 days to do all the computation work on a server with five 24-core nodes. To our knowledge, MetaCV, benefited from the strategy of composition comparison, is the first algorithm that can classify millions of very short reads within affordable time.

  16. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms

    PubMed Central

    Liu, Jiemeng; Wang, Haifeng; Yang, Hongxing; Zhang, Yizhe; Wang, Jinfeng; Zhao, Fangqing; Qi, Ji

    2013-01-01

    Compared with traditional algorithms for long metagenomic sequence classification, characterizing microorganisms’ taxonomic and functional abundance based on tens of millions of very short reads are much more challenging. We describe an efficient composition and phylogeny-based algorithm [Metagenome Composition Vector (MetaCV)] to classify very short metagenomic reads (75–100 bp) into specific taxonomic and functional groups. We applied MetaCV to the Meta-HIT data (371-Gb 75-bp reads of 109 human gut metagenomes), and this single-read-based, instead of assembly-based, classification has a high resolution to characterize the composition and structure of human gut microbiota, especially for low abundance species. Most strikingly, it only took MetaCV 10 days to do all the computation work on a server with five 24-core nodes. To our knowledge, MetaCV, benefited from the strategy of composition comparison, is the first algorithm that can classify millions of very short reads within affordable time. PMID:22941634

  17. Necessity to adapt land use and land cover classification systems to readily accept radar data

    NASA Technical Reports Server (NTRS)

    Drake, B.

    1977-01-01

    A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

  18. 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.

  19. 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.

  20. Evolution at ‘Sutures’ and ‘Centers’: Recombination Can Aid Adaptation of Spatially Structured Populations on Rugged Fitness Landscapes

    PubMed Central

    Cooper, Jacob D.; Kerr, Benjamin

    2016-01-01

    Epistatic interactions among genes can give rise to rugged fitness landscapes, in which multiple “peaks” of high-fitness allele combinations are separated by “valleys” of low-fitness genotypes. How populations traverse rugged fitness landscapes is a long-standing question in evolutionary biology. Sexual reproduction may affect how a population moves within a rugged fitness landscape. Sex may generate new high-fitness genotypes by recombination, but it may also destroy high-fitness genotypes by shuffling the genes of a fit parent with a genetically distinct mate, creating low-fitness offspring. Either of these opposing aspects of sex require genotypic diversity in the population. Spatially structured populations may harbor more diversity than well-mixed populations, potentially amplifying both positive and negative effects of sex. On the other hand, spatial structure leads to clumping in which mating is more likely to occur between like types, diminishing the effects of recombination. In this study, we use computer simulations to investigate the combined effects of recombination and spatial structure on adaptation in rugged fitness landscapes. We find that spatially restricted mating and offspring dispersal may allow multiple genotypes inhabiting suboptimal peaks to coexist, and recombination at the “sutures” between the clusters of these genotypes can create genetically novel offspring. Sometimes such an offspring genotype inhabits a new peak on the fitness landscape. In such a case, spatially restricted mating allows this fledgling subpopulation to avoid recombination with distinct genotypes, as mates are more likely to be the same genotype. Such population “centers” can allow nascent peaks to establish despite recombination. Spatial structure may therefore allow an evolving population to enjoy the creative side of sexual recombination while avoiding its destructive side. PMID:27973606

  1. Testing the hydrological landscape unit classification system and other terrain analysis measures for predicting low-flow nitrate and chloride in watersheds.

    PubMed

    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

  2. Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project

    PubMed Central

    Yüksel, Alaaddin; Akay, Abdullah E.; Gundogan, Recep

    2008-01-01

    The satellite imagery has been effectively utilized for classifying land cover types and detecting land cover conditions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor imagery has been widely used in classification process of land cover. However, atmospheric corrections have to be made by preprocessing satellite sensor imagery since the electromagnetic radiation signals received by the satellite sensors can be scattered and absorbed by the atmospheric gases and aerosols. In this study, an ASTER sensor imagery, which was converted into top-of-atmosphere reflectance (TOA), was used to classify the land use/cover types, according to COoRdination of INformation on the Environment (CORINE) land cover nomenclature, for an area representing the heterogonous characteristics of eastern Mediterranean regions in Kahramanmaras, Turkey. The results indicated that using the surface reflectance data of ASTER sensor imagery can provide accurate (i.e. overall accuracy and kappa values of 83.2% and 0.79, respectively) and low-cost cover mapping as a part of inventory for CORINE Land Cover Project. PMID:27879763

  3. Adaptive object recognition model using incremental feature representation and hierarchical classification.

    PubMed

    Jeong, Sungmoon; Lee, Minho

    2012-01-01

    This paper presents an adaptive object recognition model based on incremental feature representation and a hierarchical feature classifier that offers plasticity to accommodate additional input data and reduces the problem of forgetting previously learned information. The incremental feature representation method applies adaptive prototype generation with a cortex-like mechanism to conventional feature representation to enable an incremental reflection of various object characteristics, such as feature dimensions in the learning process. A feature classifier based on using a hierarchical generative model recognizes various objects with variant feature dimensions during the learning process. Experimental results show that the adaptive object recognition model successfully recognizes single and multiple-object classes with enhanced stability and flexibility.

  4. Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment

    ERIC Educational Resources Information Center

    Premlatha, K. R.; Dharani, B.; Geetha, T. V.

    2016-01-01

    E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…

  5. Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment

    ERIC Educational Resources Information Center

    Premlatha, K. R.; Dharani, B.; Geetha, T. V.

    2016-01-01

    E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…

  6. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    PubMed Central

    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. PMID:22163872

  7. 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.

  8. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

    SciTech Connect

    Petrick, N.; Chan, H.; Wei, D.; Sahiner, B.; Helvie, M.A.; Adler, D.D.

    1996-10-01

    This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90{percent} and 2.3 false positives per image at a true positive rate of 80{percent}. {copyright} {ital 1996 American Association of Physicists in Medicine.}

  9. 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.

  10. Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification.

    PubMed

    Liu, Hung-Ting; Sheu, Tony W H; Chang, Herng-Hua

    2013-10-01

    Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary. At the second stage, a contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of an adaptive balloon snake model. The model is designed to drive the contour in the inward normal direction to capture the brain boundary. The entire volume is segmented from the center slice toward both ends slice by slice. Our ABS algorithm was applied to numerous brain MR image data sets and compared with several state-of-the-art methods. Four similarity metrics were used to evaluate the performance of the proposed technique. Experimental results indicated that our method produced accurate segmentation results with higher conformity scores. The effectiveness of the ABS algorithm makes it a promising and potential tool in a wide variety of skull-stripping applications and studies.

  11. Chapter 3: Simulating fire hazard across landscapes through time: integrating state-and-transition models with the Fuel Characteristic Classification System

    Treesearch

    Jessica E. Halofsky; Stephanie K. Hart; Miles A. Hemstrom; Joshua S. Halofsky; Morris C. Johnson

    2014-01-01

    Information on the effects of management activities such as fuel reduction treatments and of processes such as vegetation growth and disturbance on fire hazard can help land managers prioritize treatments across a landscape to best meet management goals. State-and-transition models (STMs) allow landscape-scale simulations that incorporate effects of succession,...

  12. 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

  13. Classification of the hearing impaired for independent living using the Vineland Adaptive Behavior Scale.

    PubMed

    Dunlap, W R; Sands, D I

    1990-12-01

    Training hearing-impaired persons in independent living skills has become a focus of education and rehabilitation programs for the hearing impaired. Yet, few programs and assessment instruments are designed to evaluate a person's potential for acquiring independent living skills. In this study, the Vineland Adaptive Behavior Scale was used to classify 118 hearing-impaired persons in groups based on their ability to be trained in independent living skills. Cluster analysis was used to group the subjects according to four domains: communication, daily living, socialization, and maladaptive behavior. The results indicate that the behavior scale can be used to classify hearing-impaired persons according to their ability to acquire independent living skills. The cluster analysis resulted in three groups. The persons in the lowest group did not have the most severe hearing losses, but they were more likely to have additional handicaps. This suggests that additional handicaps may be more important than degree of hearing loss in determining whether hearing-impaired persons can acquire independent living skills.

  14. 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

  15. 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.

  16. Similarities and Differences in Barriers and Opportunities Affecting Climate Change Adaptation Action in Four North American Landscapes.

    PubMed

    Lonsdale, Whitney R; Kretser, Heidi E; Chetkiewicz, Cheryl-Lesley B; Cross, Molly S

    2017-09-07

    Climate change presents a complex set of challenges for natural resource managers across North America. Despite recognition that climate change poses serious threats to species, ecosystems, and human communities, implementation of adaptation measures is not yet happening on a broad scale. Among different regions, a range of climate change trajectories, varying political contexts, and diverse social and ecological systems generate a myriad of factors that can affect progress on climate change adaptation implementation. In order to understand the general versus site-specific nature of barriers and opportunities influencing implementation, we surveyed and interviewed practitioners, decision-makers, and scientists involved in natural resource management in four different North American regions, northern Ontario (Canada), the Adirondack State Park (US), Arctic Alaska (US), and the Transboundary Rocky Mountains (US and Canada). Common barriers among regions related to a lack of political support and financial resources, as well as challenges related to translating complex and interacting effects of climate change into management actions. Opportunities shared among regions related to collaboration, funding, and the presence of strong leadership. These commonalities indicate the importance of cross-site learning about ways to leverage opportunities and address adaptation barriers; however, regional variations also suggest that adaptation efforts will need to be tailored to fit specific ecological, political, social and economic contexts. Comparative findings on the similarities and differences in barriers and opportunities, as well as rankings of barriers and opportunities by region, offers important contextual insights into how to further refine efforts to advance adaptation actions in those regions.

  17. 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. © 2015 John Wiley & Sons Ltd.

  18. Research needs for our national landscapes

    Treesearch

    Elwood L. Shafer

    1979-01-01

    The prevailing research problem for our national landscapes is: How shall we organize, control, and coordinate public and private development so as to protect, maintain, improve, and manage those landscape features that we value most? Research questions discussed include: environmental/political conflicts, taxation and zoning, landscape classification, public...

  19. Adapting to the new radiology landscape: challenges and solutions discussed at the 2014 AMCLC open-microphone sessions.

    PubMed

    Hawkins, C Matthew; Flug, Jonathan A; Metter, Darlene; Strax, Richard; Lozano, Kay Denise Spong; Herrington, William; Applegate, Kimberly E

    2015-02-01

    Every year, multiple open-microphone sessions are hosted at the ACR AMCLC. These sessions allow members of the College to offer opinions, experiences, and questions regarding challenges facing radiologists and the future of the profession. At the 2014 AMCLC, 3 such sessions focused, respectively, on radiology's workforce, the obstacles slowing the shift from volume to value, and alternative reimbursement models and the shifting physician employment landscape. These open-microphone sessions framed contemporary obstacles and emerging challenges that professional radiology societies, such as the ACR, should target with new initiatives and use of resources; in addition, the sessions revealed opportunities for members, councilors, and state chapters to respond with meaningful resolutions and policy proposals.

  20. Landscape habitats [Chapter 2

    Treesearch

    C. L. Simmons

    1994-01-01

    This landscape habitat description is based on a ground reconnaissance of the Lost Lake, West Glacier Lake, and East Glacier Lake portions of GLEES conducted during 10 days in July-September 1986 and on subsequent photo interpretation of 1:6000 scale color-infrared photographs. A ground check was conducted in July-August 1987. The classification used is a physiognomic...

  1. 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.

  2. Do we know how to reconcile preservation of landscapes with adaptation of agriculture to climate change? A case-study in a hilly area in Southern Italy

    NASA Astrophysics Data System (ADS)

    Menenti, Massimo; Alfieri, Silvia; Basile, Angelo; Bonfante, Antonello; Monaco, Eugenia; Riccardi, Maria; De Lorenzi, Francesca

    2013-04-01

    Limited impacts of climate change on agricultural yields are unlikely to induce any significant changes in current landscapes. Larger impacts, unacceptable on economic or social ground, are likely to trigger interventions towards adaptation of agricultural production systems by reducing or removing vulnerabilities to climate variability and change. Such interventions may require a transition to a different production system, i.e. complete substitution of current crops, or displacement of current crops at their current location towards other locations, e.g. at higher elevations within the landscape. We have assessed the impacts of climate change and evaluated options for adaptation of a valley in Southern Italy, dominated by vine and olive orchards with a significant presence of wheat. We have first estimated the climatic requirements of several varieties for each dominant species. Next, to identify options for adaptation we have evaluated the compatibility of such requirements with indicators of a reference (current) climate and of future climate. This climate - compatibility assessment was done for each soil unit within the valley, leading to maps of locations where each crop is expected to be compatible with climate. This leads to identify both potential crop substitutions within the entire valley and crop displacements from one location to another within the valley. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics, and the latter from statistical downscaling of general circulation models (AOGCM). Climatic data consists of daily time series of maximum and minimum temperature, and daily rainfall on a grid with a spatial resolution of 35 km. We evaluated the adaptive capacity of the "Valle Telesina" (Campania Region, Southern Italy). A mechanistic model of water flow in the soil-plant-atmosphere system (SWAP) was used to describe the hydrological conditions in response to climate for each

  3. 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.

  4. [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.

  5. Phenotypic plasticity of nest timing in a post-glacial landscape: how do reptiles adapt to seasonal time constraints?

    PubMed

    Edge, Christopher B; Rollinson, Njal; Brooks, Ronald J; Congdon, Justin D; Iverson, John B; Janzen, Fredric J; Litzgus, Jacqueline D

    2017-02-01

    Life histories evolve in response to constraints on the time available for growth and development. Nesting date and its plasticity in response to spring temperature may therefore be important components of fitness in oviparous ectotherms near their northern range limit, as reproducing early provides more time for embryos to complete development before winter. We used data collected over several decades to compare air temperature and nest date plasticity in populations of painted turtles and snapping turtles from a relatively warm environment (southeastern Michigan) near the southern extent of the last glacial maximum to a relatively cool environment (central Ontario) near the northern extent of post-glacial recolonization. For painted turtles, population-level differences in reaction norm elevation for two phenological traits were consistent with adaptation to time constraints, but no differences in reaction norm slopes were observed. For snapping turtle populations, the difference in reaction norm elevation for a single phenological trait was in the opposite direction of what was expected under adaptation to time constraints, and no difference in reaction norm slope was observed. Finally, among-individual variation in individual plasticity for nesting date was detected only in the northern population of snapping turtles, suggesting that reaction norms are less canalized in this northern population. Overall, we observed evidence of phenological adaptation, and possibly maladaptation, to time constraints in long-lived reptiles. Where present, (mal)adaptation occurred by virtue of differences in reaction norm elevation, not reaction norm slope. Glacial history, generation time, and genetic constraint may all play an important role in the evolution of phenological timing and its plasticity in long-lived reptiles.

  6. Adaptive evolution and segregating load contribute to the genomic landscape of divergence in two tree species connected by episodic gene flow.

    PubMed

    Christe, Camille; Stölting, Kai N; Paris, Margot; Fraїsse, Christelle; Bierne, Nicolas; Lexer, Christian

    2017-01-01

    Speciation often involves repeated episodes of genetic contact between divergent populations before reproductive isolation (RI) is complete. Whole-genome sequencing (WGS) holds great promise for unravelling the genomic bases of speciation. We have studied two ecologically divergent, hybridizing species of the 'model tree' genus Populus (poplars, aspens, cottonwoods), Populus alba and P. tremula, using >8.6 million single nucleotide polymorphisms (SNPs) from WGS of population pools. We used the genomic data to (i) scan these species' genomes for regions of elevated and reduced divergence, (ii) assess key aspects of their joint demographic history based on genomewide site frequency spectra (SFS) and (iii) infer the potential roles of adaptive and deleterious coding mutations in shaping the genomic landscape of divergence. We identified numerous small, unevenly distributed genome regions without fixed polymorphisms despite high overall genomic differentiation. The joint SFS was best explained by ancient and repeated gene flow and allowed pinpointing candidate interspecific migrant tracts. The direction of selection (DoS) differed between genes in putative migrant tracts and the remainder of the genome, thus indicating the potential roles of adaptive divergence and segregating deleterious mutations on the evolution and breakdown of RI. Genes affected by positive selection during divergence were enriched for several functionally interesting groups, including well-known candidate 'speciation genes' involved in plant innate immunity. Our results suggest that adaptive divergence affects RI in these hybridizing species mainly through intrinsic and demographic processes. Integrating genomic with molecular data holds great promise for revealing the effects of particular genetic pathways on speciation. © 2016 John Wiley & Sons Ltd.

  7. 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

  8. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  9. 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 ...

  10. 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 ...

  11. Analysis of landscape character for visual resource management

    Treesearch

    Paul F. Anderson

    1979-01-01

    Description, classification and delineation of visual landscape character are initial steps in developing visual resource management plans. Landscape characteristics identified as key factors in visual landscape analysis include land cover/land use and landform. Landscape types, which are combinations of landform and surface features, were delineated for management...

  12. Landscape evolutionary genomics.

    PubMed

    Lowry, David B

    2010-08-23

    Tremendous advances in genetic and genomic techniques have resulted in the capacity to identify genes involved in adaptive evolution across numerous biological systems. One of the next major steps in evolutionary biology will be to determine how landscape-level geographical and environmental features are involved in the distribution of this functional adaptive genetic variation. Here, I outline how an emerging synthesis of multiple disciplines has and will continue to facilitate a deeper understanding of the ways in which heterogeneity of the natural landscapes mould the genomes of organisms.

  13. Assessment of landscape diversity and determination of landscape hotspots - a case of Slovenia

    NASA Astrophysics Data System (ADS)

    Perko, Drago; Ciglič, Rok; Hrvatin, Mauro

    2017-04-01

    Areas with high landscape diversity can be regarded as landscape hotspots, and vice versa areas with low landscape diversity can be marked as landscape coldspots. The main purpose of this paper is to use quantitative geoinformatical approach and identify parts of our test area (the country of Slovenia) that can be described as very diverse according to natural landscapes and natural elements. We used different digital raster data of natural elements and landscape classifications and defined landscape diversity and landscape hotspots. We defined diversity for each raster pixel by counting the number of different unique types of landscape elements and types of landscapes in its neighborhood. Namely, the method was used separately to define diversity according to natural elements (types of relief forms, rocks, and vegetation) and diversity according to existing geographical landscape classifications of Slovenia (types of landscapes). In both cases one-tenth of Slovenia's surface with the highest landscape diversity was defined as landscape hotspots. The same applies to the coldspots. Additionally we tested the same method of counting different types of landscapes in certain radius also for the area of Europe in order to find areas that are more diverse at continental level. By doing so we were able to find areas that have similar level of diversity as Slovenia according to different European landscape classifications. Areas with landscape diversity may have an advantage in economic development, especially in tourism. Such areas are also important for biodiversity, habitat, and species diversity. On the other hand, localities where various natural influences mix can also be areas where it is hard to transfer best practices from one place to another because of the varying responses of the landscapes to human intervention. Thus it is important to know where areas with high landscape diversity are.

  14. 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.

  15. 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

  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.

  17. The Evolutionary Landscape of Dbl-Like RhoGEF Families: Adapting Eukaryotic Cells to Environmental Signals

    PubMed Central

    Blangy, Anne

    2017-01-01

    Abstract The dynamics of cell morphology in eukaryotes is largely controlled by small GTPases of the Rho family. Rho GTPases are activated by guanine nucleotide exchange factors (RhoGEFs), of which diffuse B-cell lymphoma (Dbl)-like members form the largest family. Here, we surveyed Dbl-like sequences from 175 eukaryotic genomes and illuminate how the Dbl family evolved in all eukaryotic supergroups. By combining probabilistic phylogenetic approaches and functional domain analysis, we show that the human Dbl-like family is made of 71 members, structured into 20 subfamilies. The 71 members were already present in ancestral jawed vertebrates, but several members were subsequently lost in specific clades, up to 12% in birds. The jawed vertebrate repertoire was established from two rounds of duplications that occurred between tunicates, cyclostomes, and jawed vertebrates. Duplicated members showed distinct tissue distributions, conserved at least in Amniotes. All 20 subfamilies have members in Deuterostomes and Protostomes. Nineteen subfamilies are present in Porifera, the first phylum that diverged in Metazoa, 14 in Choanoflagellida and Filasterea, single-celled organisms closely related to Metazoa and three in Fungi, the sister clade to Metazoa. Other eukaryotic supergroups show an extraordinary variability of Dbl-like repertoires as a result of repeated and independent gain and loss events. Last, we observed that in Metazoa, the number of Dbl-like RhoGEFs varies in proportion of cell signaling complexity. Overall, our analysis supports the conclusion that Dbl-like RhoGEFs were present at the origin of eukaryotes and evolved as highly adaptive cell signaling mediators. PMID:28541439

  18. Complex Adaptive Systems, soil degradation and land sensitivity to desertification: A multivariate assessment of Italian agro-forest landscape.

    PubMed

    Salvati, Luca; Mavrakis, Anastasios; Colantoni, Andrea; Mancino, Giuseppe; Ferrara, Agostino

    2015-07-15

    Degradation of soils and sensitivity of land to desertification are intensified in last decades in the Mediterranean region producing heterogeneous spatial patterns determined by the interplay of factors such as climate, land-use changes, and human pressure. The present study hypothesizes that rising levels of soil degradation and land sensitivity to desertification are reflected into increasingly complex (and non-linear) relationships between environmental and socioeconomic variables. To verify this hypothesis, the Complex Adaptive Systems (CAS) framework was used to explore the spatiotemporal dynamics of eleven indicators derived from a standard assessment of soil degradation and land sensitivity to desertification in Italy. Indicators were made available on a detailed spatial scale (773 agricultural districts) for various years (1960, 1990, 2000 and 2010) and analyzed through a multi-dimensional exploratory data analysis. Our results indicate that the number of significant pair-wise correlations observed between indicators increased with the level of soil and land degradation, although with marked differences between northern and southern Italy. 'Fast' and 'slow' factors underlying soil and land degradation, and 'rapidly-evolving' or 'locked' agricultural districts were identified according to the rapidity of change estimated for each of the indicators studied. In southern Italy, 'rapidly-evolving' districts show a high level of soil degradation and land sensitivity to desertification during the whole period of investigation. On the contrary, those districts in northern Italy are those experiencing a moderate soil degradation and land sensitivity to desertification with the highest increase in the level of sensitivity over time. The study framework contributes to the assessment of complex local systems' dynamics in affluent but divided countries. Results may inform thematic strategies for the mitigation of land and soil degradation in the framework of action

  19. A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation.

    PubMed

    Joost, S; Bonin, A; Bruford, M W; Després, L; Conord, C; Erhardt, G; Taberlet, P

    2007-09-01

    The detection of adaptive loci in the genome is essential as it gives the possibility of understanding what proportion of a genome or which genes are being shaped by natural selection. Several statistical methods have been developed which make use of molecular data to reveal genomic regions under selection. In this paper, we propose an approach to address this issue from the environmental angle, in order to complement results obtained by population genetics. We introduce a new method to detect signatures of natural selection based on the application of spatial analysis, with the contribution of geographical information systems (GIS), environmental variables and molecular data. Multiple univariate logistic regressions were carried out to test for association between allelic frequencies at marker loci and environmental variables. This spatial analysis method (SAM) is similar to current population genomics approaches since it is designed to scan hundreds of markers to assess a putative association with hundreds of environmental variables. Here, by application to studies of pine weevils and breeds of sheep we demonstrate a strong correspondence between SAM results and those obtained using population genetics approaches. Statistical signals were found that associate loci with environmental parameters, and these loci behave atypically in comparison with the theoretical distribution for neutral loci. The contribution of this new tool is not only to permit the identification of loci under selection but also to establish hypotheses about ecological factors that could exert the selection pressure responsible. In the future, such an approach may accelerate the process of hunting for functional genes at the population level.

  20. Ecosystem services in changing landscapes: An introduction

    Treesearch

    Louis Iverson; Cristian Echeverria; Laura Nahuelhual; Sandra. Luque

    2014-01-01

    The concept of ecosystem services from landscapes is rapidly gaining momentum as a language to communicate values and benefits to scientists and lay alike. Landscape ecology has an enormous contribution to make to this field, and one could argue, uniquely so. Tools developed or adapted for landscape ecology are being increasingly used to assist with the quantification...

  1. The Communication Function Classification System: cultural adaptation, validity, and reliability of the Farsi version for patients with cerebral palsy.

    PubMed

    Soleymani, Zahra; Joveini, Ghodsiye; Baghestani, Ahmad Reza

    2015-03-01

    This study developed a Farsi language Communication Function Classification System and then tested its reliability and validity. Communication Function Classification System is designed to classify the communication functions of individuals with cerebral palsy. Up until now, there has been no instrument for assessment of this communication function in Iran. The English Communication Function Classification System was translated into Farsi and cross-culturally modified by a panel of experts. Professionals and parents then assessed the content validity of the modified version. A backtranslation of the Farsi version was confirmed by the developer of the English Communication Function Classification System. Face validity was assessed by therapists and parents of 10 patients. The Farsi Communication Function Classification System was administered to 152 individuals with cerebral palsy (age, 2 to 18 years; median age, 10 years; mean age, 9.9 years; standard deviation, 4.3 years). Inter-rater reliability was analyzed between parents, occupational therapists, and speech and language pathologists. The test-retest reliability was assessed for 75 patients with a 14 day interval between tests. The inter-rater reliability of the Communication Function Classification System was 0.81 between speech and language pathologists and occupational therapists, 0.74 between parents and occupational therapists, and 0.88 between parents and speech and language pathologists. The test-retest reliability was 0.96 for occupational therapists, 0.98 for speech and language pathologists, and 0.94 for parents. The findings suggest that the Farsi version of Communication Function Classification System is a reliable and valid measure that can be used in clinical settings to assess communication function in patients with cerebral palsy. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. 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...

  3. 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...

  4. 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.

  5. Dysregulation of innate and adaptive serum mediators precedes systemic lupus erythematosus classification and improves prognostic accuracy of autoantibodies.

    PubMed

    Lu, Rufei; Munroe, Melissa E; Guthridge, Joel M; Bean, Krista M; Fife, Dustin A; Chen, Hua; Slight-Webb, Samantha R; Keith, Michael P; Harley, John B; James, Judith A

    2016-11-01

    Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a poorly understood preclinical stage of immune dysregulation and symptom accrual. Accumulation of antinuclear autoantibody (ANA) specificities is a hallmark of impending clinical disease. Yet, many ANA-positive individuals remain healthy, suggesting that additional immune dysregulation underlies SLE pathogenesis. Indeed, we have recently demonstrated that interferon (IFN) pathways are dysregulated in preclinical SLE. To determine if other forms of immune dysregulation contribute to preclinical SLE pathogenesis, we measured SLE-associated autoantibodies and soluble mediators in samples from 84 individuals collected prior to SLE classification (average timespan = 5.98 years), compared to unaffected, healthy control samples matched by race, gender, age (±5 years), and time of sample procurement. We found that multiple soluble mediators, including interleukin (IL)-5, IL-6, and IFN-γ, were significantly elevated in cases compared to controls more than 3.5 years pre-classification, prior to or concurrent with autoantibody positivity. Additional mediators, including innate cytokines, IFN-associated chemokines, and soluble tumor necrosis factor (TNF) superfamily mediators increased longitudinally in cases approaching SLE classification, but not in controls. In particular, levels of B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL) were comparable in cases and controls until less than 10 months pre-classification. Over the entire pre-classification period, random forest models incorporating ANA and anti-Ro/SSA positivity with levels of IL-5, IL-6, and the IFN-γ-induced chemokine, MIG, distinguished future SLE patients with 92% (±1.8%) accuracy, compared to 78% accuracy utilizing ANA positivity alone. These data suggest that immune dysregulation involving multiple pathways contributes to SLE pathogenesis. Importantly, distinct immunological profiles are predictive for

  6. 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.

  7. Why some fitness landscapes are fractal.

    PubMed

    Weinberger, E D; Stadler, P F

    1993-07-21

    Many biological and biochemical measurements, for example the "fitness" of a particular genome, or the binding affinity to a particular substrate, can be treated as a "fitness landscape", an assignment of numerical values to points in sequence space (or some other configuration space). As an alternative to the enormous amount of data required to completely describe such a landscape, we propose a statistical characterization, based on the properties of a random walk through the landscape and, more specifically, its autocorrelation function. Under assumptions roughly satisfied by two classes of simple model landscapes (the N-k model and the p-spin model) and by the landscape of estimated free energies of RNA secondary structures, this autocorrelation function, along with the mean and variance of individual points and the size of the landscape, completely characterize it. Having noted that these and other landscapes of estimated replication and degradation rates all have a well-defined correlation length, we propose a classification of landscapes depending on how the correlation length scales with the diameter of the landscape. The landscapes of some of the kinetic parameters of RNA molecules scale similarly to the model landscapes introduced into evolutionary studies from other fields, such as quadratic spin glasses and the traveling salesman problem, but the correlation length of RNA landscapes are considerably smaller. Nevertheless, both the model and some of the RNA landscapes satisfy a test of self-similarity proposed by Sorkin (1988).

  8. 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.

  9. 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.

  10. 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

  11. [Cultural adaptation and content validation of the «Pain level» outcome of the Nursing Outcomes Classification].

    PubMed

    Bellido-Vallejo, José Carlos; Rodríguez-Torres, María Del Carmen; López-Medina, Isabel María; Pancorbo-Hidalgo, Pedro Luis

    2013-01-01

    To translate and culturally adapt the Pain Level outcome to the Spanish context to validate the contents of the Spanish version of the «Pain level» outcome. The original English version of the «Pain level» outcome was translated into Spanish (twice); then back-translated into English, and all the discrepancies were resolved after consulting with NOC authors. A panel consisting of 21 experts in pain care assessed this culturally adapted Spanish version, in order to score the content validity. In the first step, the experts scored the adequacy of each indicator to the concept «Pain level». In the second round, three new indicators were scored. The Statistical analysis included content validity index (CVI), probability of agreement by chance, and modified kappa statistic. A Spanish version was developed including label, definition, two groups of indicators, and two measurement scales. This version is fully adapted to the Spanish context and language. A set of 21 indicators (19 translated and two new) was selected, and 4 were deleted (three translated and one new). The CVI-average score was 0.83 and the CVI-universal agreement was 0.05. The Spanish-version of the outcome «Pain level» is semantically and culturally to adapted to a Spanish context and preserves equivalency with the original. Content validation has identified indicators useful for practice. The clinimetric properties (validity and reliability) of the adapted version could be tested in a clinical study with people suffering from acute pain. Copyright © 2013 Elsevier España, S.L. All rights reserved.

  12. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

    PubMed Central

    Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

    2016-01-01

    In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

  13. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

    PubMed

    Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

    2016-12-13

    In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

  14. Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation.

    PubMed

    Howsmon, Daniel P; Kruger, Uwe; Melnyk, Stepan; James, S Jill; Hahn, Juergen

    2017-03-01

    The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.

  15. Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation

    PubMed Central

    Melnyk, Stepan; James, S. Jill; Hahn, Juergen

    2017-01-01

    The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets. PMID:28301476

  16. 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…

  17. 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.

  18. Mars Landscapes

    NASA Image and Video Library

    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...

  19. Sequence landscapes.

    PubMed Central

    Clift, B; Haussler, D; McConnell, R; Schneider, T D; Stormo, G D

    1986-01-01

    We describe a method for representing the structure of repeating sequences in nucleic-acids, proteins and other texts. A portion of the sequence is presented at the bottom of a CRT screen. Above the sequence is its landscape, which looks like a mountain range. Each mountain corresponds to a subsequence of the sequence. At the peak of every mountain is written the number of times that the subsequence appears. A data structure called a DAWG, which can be built in time proportional to the length of the sequence, is used to construct the landscape. For the 40 thousand bases of bacteriophage T7, the DAWG can be built in 30 seconds. The time to display any portion of the landscape is less than a second. Using sequence landscapes, one can quickly locate significant repeats. PMID:3753762

  20. 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.

  1. Challenges and opportunities for large landscape-scale management in a shifting climate: The importance of nested adaptation responses across geospatial and temporal scales

    Treesearch

    Gary M. Tabor; Anne Carlson; Travis Belote

    2014-01-01

    The Yellowstone to Yukon Conservation Initiative (Y2Y) was established over 20 years ago as an experiment in large landscape conservation. Initially, Y2Y emerged as a response to large scale habitat fragmentation by advancing ecological connectivity. It also laid the foundation for large scale multi-stakeholder conservation collaboration with almost 200 non-...

  2. Public acceptance of disturbance-based forest management: a study of the Blue River Landscape Strategy in the Central Cascades Adaptive Management Area.

    Treesearch

    Bruce Shindler; Angela L. Mallon

    2009-01-01

    This report examines public perspectives on disturbance-based management conducted in the central Cascade Range in Oregon as part of the Blue River Landscape Strategy. A mail survey to local residents was used to describe the public’s understanding of this form of management, identify perceived associated risks and potential barriers to implementation, and the overall...

  3. Robust Detection and Classification of Regional Seismic Signals Using a Two Mode/Two Stage Cascaded Adaptive Arma (CAARMA) Model

    DTIC Science & Technology

    1985-03-01

    adaptive algorithms presented earlier, we will employ the SHARF algorithm. The analysis by * Ljuig [30-31] provides a convergence proof for the RLMS...algo- rithm. Since SHARF is not a gradient search algorithm, the convergence proof relies upon the concept of hyperstability [32-361. A direct form...realization of the transfer function -A A -N B bz + + bNZ A B(z) 0 1 N H(z) = = -_ -I a -N 3.45 a(z 1z a . N z is utilized by both the RLMS and SHARF

  4. Exploiting Elementary Landscapes for TSP, Vehicle Routing and Scheduling

    DTIC Science & Technology

    2015-09-03

    and part number, if applicable. On classified documents, enter the title classification in parentheses. 5a. CONTRACT NUMBER. Enter all contract numbers...security classification regulations, e.g. U, C, S, etc. If this form contains classified information, stamp classification level on the top and bottom of...Graph Partitioning and select simple satisfiability problems all have elementary landscapes. For all elementary landscapes one can compute

  5. 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.

  6. 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

  7. Landscape genetics: combining landscape ecology and population genetics

    Treesearch

    Stephanie Manel; Michael K. Schwartz; Gordon Luikart; Pierre Taberlet

    2003-01-01

    Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population...

  8. Epigenetic Inheritance across the Landscape

    PubMed Central

    Whipple, Amy V.; Holeski, Liza M.

    2016-01-01

    The study of epigenomic variation at the landscape-level in plants may add important insight to studies of adaptive variation. A major goal of landscape genomic studies is to identify genomic regions contributing to adaptive variation across the landscape. Heritable variation in epigenetic marks, resulting in transgenerational plasticity, can influence fitness-related traits. Epigenetic marks are influenced by the genome, the environment, and their interaction, and can be inherited independently of the genome. Thus, epigenomic variation likely influences the heritability of many adaptive traits, but the extent of this influence remains largely unknown. Here, we summarize the relevance of epigenetic inheritance to ecological and evolutionary processes, and review the literature on landscape-level patterns of epigenetic variation. Landscape-level patterns of epigenomic variation in plants generally show greater levels of isolation by distance and isolation by environment then is found for the genome, but the causes of these patterns are not yet clear. Linkage between the environment and epigenomic variation has been clearly shown within a single generation, but demonstrating transgenerational inheritance requires more complex breeding and/or experimental designs. Transgenerational epigenetic variation may alter the interpretation of landscape genomic studies that rely upon phenotypic analyses, but should have less influence on landscape genomic approaches that rely upon outlier analyses or genome–environment associations. We suggest that multi-generation common garden experiments conducted across multiple environments will allow researchers to understand which parts of the epigenome are inherited, as well as to parse out the relative contribution of heritable epigenetic variation to the phenotype. PMID:27826318

  9. Catchment classification by means of hydrological models

    NASA Astrophysics Data System (ADS)

    Hellebrand, Hugo; Ley, Rita; Casper, Markus

    2013-04-01

    An important hydrological objective is catchment classification that will serve as a basis for the regionalisation of discharge parameters or model parameters. The main task of this study is the development and assessment of two classification approaches with respect to their efficiency in catchment classification. The study area in western Germany comprises about 80 catchments that range in size from 8 km2 up to 1500 km2, covering a wide range of geological substrata, soils, landscapes and mean annual precipitation. In a first approach Self Organising Maps (SOMs) use discharge characteristics or catchment characteristics to classify the catchments of the study area. Next, a reference hydrological model calibrates the catchments of the study area and tests the possibilities of parameter transfer. Compared to the transfer of parameters outside a class, for most catchments the model performance improves when parameters within a class are transferred. Thus, it should be possible to distinguish catchment classes by means of a hydrological model. The classification results of the SOM are compared to the classification results of the reference hydrological model in order to determine the latter validity. The second approach builds on the first approach in such a way that it uses the Superflex Modelling Framework instead of only one reference model. Within this framework multiple conceptual model structures can be calibrated and adapted. Input data for each calibration of a catchment are hourly time series of runoff, precipitation and evaporation for at least eight years. The calibration of multiple models for each catchment and their comparison allows for the assessment of the influence of different model structures on model performance. Learning loops analyse model performance and adapt model structures accordingly with a view to performance improvement. The result of the modelling exercise is a best performing model structure for each catchment that serves as a basis

  10. Disposable Landscapes

    Treesearch

    Kevin T. Smith

    2008-01-01

    Whether we are a traditionalist or on the cutting edge of landscape care, we need to take a deep breath and think about what we are trying to achieve, before we select a specific treatment or practice for tree care. We should measure that treatment or practice against what we know about the tree system. I say "system" because the recent years of Modern...

  11. Assessing landscape vulnerability to wildfire in the USA

    Treesearch

    Nicole M. Vaillant; Crystal A. Kolden; Alistair M. S. Smith

    2016-01-01

    Wildfire is an ever present, natural process shaping landscapes. Having the ability to accurately measure and predict wildfire occurrence and impacts to ecosystem goods and services, both retrospectively and prospectively, is critical for adaptive management of landscapes. Landscape vulnerability is a concept widely utilized in the ecosystem management literature that...

  12. Eucalyptus as a landscape tree

    Treesearch

    W. Douglas Hamilton

    1983-01-01

    Ninety-two species of Eucalyptus were evaluated at the University of California re- search station in San Jose. The purpose: to find acceptable new street and park trees. Growth rates and horticultural characteristics were noted. Forty-three species were studied in locations statewide to evaluate site adaptation and landscape usefulness; flooded, cold, dry, saline....

  13. Planning Construction Research of Modern Urban Landscape

    NASA Astrophysics Data System (ADS)

    Xiao, Z. Q.; Chen, W.

    With the development and expansion of the city's traditional urban landscape planning methods have been difficult to adapt to the requirements of modern urban development, in the new urban construction, planning what kind of urban landscape is a new research topic. The article discusses the principles of modern urban landscape planning and development, promote the adoption of new concepts and theories, building more regional characteristics, more humane, more perfect, more emphasis on urban landscape pattern natural ecological protection and construction can sustainable development of urban living environment, and promote the development and construction of the city.

  14. Multi-scale forest landscape pattern characterization

    NASA Astrophysics Data System (ADS)

    Wang, Jialing

    The purpose of this dissertation is to examine several important issues in landscape pattern analysis, including the identification of important landscape metrics, the impact of the modifiable areal unit problem (MAUP) in landscape pattern analysis, the linkage between pattern and process, and the application of landscape pattern analysis. A theoretical framework of hierarchical patch dynamics paradigm and a technical framework of GIS and remote sensing integration are employed to address these questions. The Red Hills region of southwestern Georgia and northern Florida is chosen as the study area. Land use/cover (LULC) and longleaf pine distribution maps were generated through satellite image classification. Sub-watersheds were used as the main analysis units. Principal component analysis (PCA) was conducted on 43 sub-watersheds at three hierarchical LULC levels to identify important landscape metrics. At both landscape- and class-levels, the measurement of fragmentation was identified as the most important landscape dimension. Other dimensions and important metrics varied with different scales. Hexagons were used as an alternative zoning system to examine the MAUP impact in landscape pattern analysis. The results indicated that landscape pattern analyses at class level and at broader scales were more sensitive to MAUP than at landscape level and at finer scales. Local-scale pattern analysis based on moving window analysis greatly reduced the impact of MAUP at class level, but had little effects at landscape level. An examination of the relationship between landscape pattern variables and biophysical/socio-economic variables was undertaken by using statistical analysis. The biophysical variables of soil drainage and mean slope and the socio-economic variables of road density, population density, distance to Tallahassee, Florida, and plantation amount were found to be closely correlated to the landscape patterns in this region. However, a large amount of variation

  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. A spatially constrained ecological classification: rationale, methodology and implementation

    Treesearch

    Franz Mora; Louis Iverson; Louis Iverson

    2002-01-01

    The theory, methodology and implementation for an ecological and spatially constrained classification are presented. Ecological and spatial relationships among several landscape variables are analyzed in order to define a new approach for a landscape classification. Using ecological and geostatistical analyses, several ecological and spatial weights are derived to...

  17. The association forecasting of 13 variants within seven asthma susceptibility genes on 3 serum IgE groups in Taiwanese population by integrating of adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods.

    PubMed

    Wang, Cheng-Hang; Liu, Baw-Jhiune; Wu, Lawrence Shih-Hsin

    2012-02-01

    Asthma is one of the most common chronic diseases in children. It is caused by complicated coactions between various genetic factors and environmental allergens. The study aims to integrate the concept of implementing adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods for forecasting the association of asthma susceptibility genes on 3 serum IgE groups. The ANFIS model was trained and tested with data sets obtained from 425 asthmatic subjects and 483 non-asthma subjects from the Taiwanese population. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes; firstly, the proposed ANFIS model learned to reduce input features from the 13 SNPs. And secondly, the classification will be used to classify the serum IgE groups from the simulated SNPs results. The performance of the ANFIS model, classification accuracies and the results confirmed that the integration of ANFIS and classified analysis has potential in association discovery.

  18. 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. ...

  19. 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. ...

  20. A comparison of grazing behavior between desert adapted Mexican Criollo cattle and temperate british breeds using two diverse landscapes in New Mexico and Chihuahua.

    USDA-ARS?s Scientific Manuscript database

    This study was designed to test how grazing behaviors differ between desert adapted Mexican criollo cattle and temperate British beef breeds, to learn how each breed interacts with environments common to the southwestern US and northwestern Mexico. Additionally, criollo cattle may be a better breed ...

  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. [Eco-geographic landscapes of natural plague foci in China I. Eco-geographic landscapes of natural plague foci].

    PubMed

    Fang, Xi-ye; Xu, Lei; Liu, Qi-yong; Zhang, Rong-zu

    2011-12-01

    To study the eco-geographic landscapes of natural plague foci, in China. According to the surveillance records on plague epidemics and the eco-geographic landscapes of natural plague foci based on the county level, the criterion for classifying the ecological geographic zone of Chinese natural plague foci was established. 12 types and 19 subtypes of eco-geographic landscapes on Chinese natural plague foci were identified. Scientific basis for Chinese natural plague foci classification was provided.

  3. PESP Landscaping Initiative

    EPA Pesticide Factsheets

    Landscaping practices can positively or negatively affect local environments and human health. The Landscaping Initiative seeks to enhance benefits of landscaping while reducing need for pesticides, fertilizers, etc., by working with partners.

  4. [Segmentation-based multi-scale urban green space landscape].

    PubMed

    Sun, Xiaofang; Lu, Jian; Sun, Yibin

    2006-09-01

    In this study, three scales urban green space landscapes were generated by multi-resolution segmentation. With 50 and 300 pixels as the object segmentation thresholds, the small- and large-scale landscape object image layers were produced, and the two object image layers were obtained by the nearest neighbor classification method. The result of small-scale landscape classification image was segmented into middle-scale landscape image, and then classified. Green space information was extracted through vector form of object image layers of three scales landscape classification. The landscape indexes diversity, dominance, evenness, fractal dimension, fragmentation, and interior to edge ration were calculated, with the largest values of the former four indexes being 2.2, 0.681, 0.948, and 0.326, and the smallest values being 1.641, 0. 122, 0.707, and 0.113, respectively, indicating that the diversity, evenness and fragmentation decreased, while the dominance increased with increasing landscape scale. The method of multi-resolution segmentation to generate multi-scale landscape could meet the needs of urban green space landscape research.

  5. Assessing the habitat suitability of agricultural landscapes for characteristic breeding bird guilds using landscape metrics.

    PubMed

    Borges, Friederike; Glemnitz, Michael; Schultz, Alfred; Stachow, Ulrich

    2017-04-01

    Many of the processes behind the decline of farmland birds can be related to modifications in landscape structure (composition and configuration), which can partly be expressed quantitatively with measurable or computable indices, i.e. landscape metrics. This paper aims to identify statistical relationships between the occurrence of birds and the landscape structure. We present a method that combines two comprehensive procedures: the "landscape-centred approach" and "guild classification". Our study is based on more than 20,000 individual bird observations based on a 4-year bird monitoring approach in a typical agricultural area in the north-eastern German lowlands. Five characteristic bird guilds, each with three characteristic species, are defined for the typical habitat types of that area: farmland, grassland, hedgerow, forest and settlement. The suitability of each sample plot for each guild is indicated by the level of persistence (LOP) of occurrence of three respective species. Thus, the sample plots can be classified as "preferred" or "less preferred" depending on the lower and upper quartiles of the LOP values. The landscape structure is characterized by 16 different landscape metrics expressing various aspects of landscape composition and configuration. For each guild, the three landscape metrics with the strongest rank correlation with the LOP values and that are not mutually dependent were identified. For four of the bird guilds, the classification success was better than 80%, compared with only 66% for the grassland bird guild. A subset of six landscape metrics proved to be the most meaningful and sufficiently classified the sample areas with respect to bird guild suitability. In addition, derived logistic functions allowed the production of guild-specific habitat suitability maps for the whole landscape. The analytical results show that the proposed approach is appropriate to assess the habitat suitability of agricultural landscapes for characteristic

  6. Driving the Landscape

    NASA Astrophysics Data System (ADS)

    Haff, P. K.

    2012-12-01

    destination—whereas the natural evolution of landscape has no such goal. Goals will become an essential feature of landscape prediction. The presence of a goal potentially increases our ability to predict, provided it is possible to use feedback (i.e., management) to nudge the system back in the "right" direction when it starts to stray. Under a regime of accelerating technology the closest we can get to predicting the longer term future of landscape is adaptive management, which at large scale is really geoengineer the system. The goal presumably would be to maintain a condition conducive to human well-being, for example to maintain a suitable fraction of global arable land. A successful "prediction" would be to stay within an envelope of states consistent with that goal. We cannot say, however, in what specific state the landscape will be at any time beyond the near future; this will depend on the future sequence of management decisions, which are, like the system they are managing, unpredictable, except shortly before they are implemented. The landscape of the future will thus likely be the result of a series of quick fixes to previous trends in landscape change. Similar comments apply to the prediction, or management, of climate. There is of course no guarantee that it will be possible to stay within the desired envelope of well-being.

  7. Combined Use of SAR and Optical Satellite Images for Landscape Diversity Assessment

    NASA Astrophysics Data System (ADS)

    Kuchma, Tetyana

    2016-08-01

    Land cover change analysis is essential for effective land use management and biodiversity conservation. The advantages of Sentinel-1 and Landsat-8 image fusion for land cover classification and landscape diversity maps development were studied. The methodology of landscape metrics interpretation for sustainable land use planning is developed and tested on agricultural landscapes in Ukraine.

  8. PNW Hydrologic Landscape Class

    EPA Pesticide Factsheets

    Work has been done to expand the hydrologic landscapes (HLs) concept and to develop an approach for using it to address streamflow vulnerability from climate change. This work has included development of the HL classification framework and its application to Oregon, use of the HL classes to predict where a simple lumped hydrologic model accurately predicts daily streamflow, use of HL information to model the presence of cold-water patches at tributary confluences, and combining Oregon HL results with temperature and precipitation predictions to examine how HLs would vary as a result of climate change. As a part of the current work, the HL approach has been expanded to the Pacific Northwest (Oregon, Washington, and Idaho) based on a revision of the approach that makes it more broadly applicable. This revised approach has several advantages compared with the original approach: it is not limited to areas that have an aquifer permeability map; it uses a flexible approach to converting a nationally available geospatial dataset into assessment units; and it is more robust. These improvements should allow the revised HL approach to be applied more often in situations requiring hydrologic classification, and allow greater confidence in results. This effort paves the way for a climate change analysis for the Pacific Northwest that is currently underway, as well as expansion into the southwest (California, Arizona, and Nevada). This dataset contains a high resolutio

  9. Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

    PubMed Central

    Epps, Clinton W.; Landguth, Erin L.; Wehausen, John D.; Crowhurst, Rachel S.; Holton, Brandon; Monello, Ryan J.

    2017-01-01

    Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes. PMID:28464013

  10. Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep.

    PubMed

    Creech, Tyler G; Epps, Clinton W; Landguth, Erin L; Wehausen, John D; Crowhurst, Rachel S; Holton, Brandon; Monello, Ryan J

    2017-01-01

    Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

  11. 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…

  12. 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.

  13. Management for adaptation

    Treesearch

    John Innes; Linda A. Joyce; Seppo Kellomaki; Bastiaan Louman; Aynslie Ogden; John Parrotta; Ian Thompson; Matthew Ayres; Chin Ong; Heru Santoso; Brent Sohngen; Anita Wreford

    2009-01-01

    This chapter develops a framework to explore examples of adaptation options that could be used to ensure that the ecosystem services provided by forests are maintained under future climates. The services are divided into broad areas within which managers can identify specific management goals for individual forests or landscapes. Adaptation options exist for the major...

  14. Individual dispersal, landscape connectivity and ecological networks.

    PubMed

    Baguette, Michel; Blanchet, Simon; Legrand, Delphine; Stevens, Virginie M; Turlure, Camille

    2013-05-01

    Connectivity is classically considered an emergent property of landscapes encapsulating individuals' flows across space. However, its operational use requires a precise understanding of why and how organisms disperse. Such movements, and hence landscape connectivity, will obviously vary according to both organism properties and landscape features. We review whether landscape connectivity estimates could gain in both precision and generality by incorporating three fundamental outcomes of dispersal theory. Firstly, dispersal is a multi-causal process; its restriction to an 'escape reaction' to environmental unsuitability is an oversimplification, as dispersing individuals can leave excellent quality habitat patches or stay in poor-quality habitats according to the relative costs and benefits of dispersal and philopatry. Secondly, species, populations and individuals do not always react similarly to those cues that trigger dispersal, which sometimes results in contrasting dispersal strategies. Finally, dispersal is a major component of fitness and is thus under strong selective pressures, which could generate rapid adaptations of dispersal strategies. Such evolutionary responses will entail spatiotemporal variation in landscape connectivity. We thus strongly recommend the use of genetic tools to: (i) assess gene flow intensity and direction among populations in a given landscape; and (ii) accurately estimate landscape features impacting gene flow, and hence landscape connectivity. Such approaches will provide the basic data for planning corridors or stepping stones aiming at (re)connecting local populations of a given species in a given landscape. This strategy is clearly species- and landscape-specific. But we suggest that the ecological network in a given landscape could be designed by stacking up such linkages designed for several species living in different ecosystems. This procedure relies on the use of umbrella species that are representative of other species

  15. 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.

  16. Principles for ecological classification

    Treesearch

    Dennis H. Grossman; Patrick Bourgeron; Wolf-Dieter N. Busch; David T. Cleland; William Platts; G. Ray; C. Robins; Gary Roloff

    1999-01-01

    The principal purpose of any classification is to relate common properties among different entities to facilitate understanding of evolutionary and adaptive processes. In the context of this volume, it is to facilitate ecosystem stewardship, i.e., to help support ecosystem conservation and management objectives.

  17. 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.

  18. [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.

  19. Spectral classification

    NASA Astrophysics Data System (ADS)

    Jaschek, C.

    Taxonomic classification of astronomically observed stellar objects is described in terms of spectral properties. Stars receive a classification containing a letter, number, and a Roman numeral, which relates the star to other stars of higher or lower Roman numerals. The citation indicates the stellar chromatic emission in relation to the wavelengths of other stars. Standards are chosen from the available objects detected. Various classification schemes such as the MK, HD, and the Barbier-Chalonge-Divan systems are defined, including examples of indexing differences. Details delineating the separations between classifications are discussed with reference to the information content in spectral and in photometric classification schemes. The parameters usually used for classification include the temperature, luminosity, reddening, binarity, rotation, magnetic field, and elemental abundance or composition. The inclusion of recently discovered extended wavelength characteristics in nominal classifications is outlined, together with techniques involved in automated classification.

  20. 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.

  1. [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.

  2. 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.

  3. Computers and the landscape

    Treesearch

    Gary H. Elsner

    1979-01-01

    Computers can analyze and help to plan the visual aspects of large wildland landscapes. This paper categorizes and explains current computer methods available. It also contains a futuristic dialogue between a landscape architect and a computer.

  4. Landscape character assessment using region growing techniques in geographical information systems.

    PubMed

    Jellema, André; Stobbelaar, Derk-Jan; Groot, Jeroen C J; Rossing, Walter A H

    2009-05-01

    Landscape character can be defined as the presence, variety and arrangement of landscape features, which give a landscape a specific identity and make it stand out from surrounding landscapes. Landscape character contributes to the esthetical and perceptional value of an area, which is important for the development of non-production functions in the countryside as demanded by society. In this paper we present a new methodology for landscape character assessment using the pattern of landscape features as stored in a GIS to delineate, characterize and evaluate landscapes using a region growing algorithm. We have applied this methodology in a case study area in the north of The Netherlands and compared the results with a series of expert classifications of the study area. The results of the region growing algorithms were good and interpretable in relation to the underlying data. The resemblance between the expert classification and the classification based on the region growing results varied between 34% and 100% for the different landscape types. The differences between the two data sets can be explained in terms of input data and knowledge about the study area. The classification of the region growing algorithm was more consistent than the expert classification throughout the study area. The presented methodology for landscape character assessment is proposed as support for spatial planning processes and policy development for landscape conservation by providing a quantitative tool to analyze landscape patterns, to discriminate between the various landscapes in a study area and by elucidating features that are important for the identity of a region.

  5. Landscape Management: Field Supervisor.

    ERIC Educational Resources Information Center

    Newton, Deborah; Newton, Steve

    This module is the third volume in a series of instructional materials on landscape management. The materials are designed to help teachers train students in the job skills they will need in landscape occupations. The module contains six instructional units that cover the following topics: orientation; basic landscape design principles; irrigation…

  6. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Cold adaptations.

    PubMed

    Launay, Jean-Claude; Savourey, Gustave

    2009-07-01

    Nowdays, occupational and recreational activities in cold environments are common. Exposure to cold induces thermoregulatory responses like changes of behaviour and physiological adjustments to maintain thermal balance either by increasing metabolic heat production by shivering and/or by decreasing heat losses consecutive to peripheral cutaneous vasoconstriction. Those physiological responses present a great variability among individuals and depend mainly on biometrical characteristics, age, and general cold adaptation. During severe cold exposure, medical disorders may occur such as accidental hypothermia and/or freezing or non-freezing cold injuries. General cold adaptations have been qualitatively classified by Hammel and quantitatively by Savourey. This last classification takes into account the quantitative changes of the main cold reactions: higher or lower metabolic heat production, higher or lesser heat losses and finally the level of the core temperature observed at the end of a standardized exposure to cold. General cold adaptations observed previously in natives could also be developed in laboratory conditions by continuous or intermittent cold exposures. Beside general cold adaptation, local cold adaptation exists and is characterized by a lesser decrease of skin temperature, a more pronounced cold induced vasodilation, less pain and a higher manual dexterity. Adaptations to cold may reduce the occurrence of accidents and improve human performance as surviving in the cold. The present review describes both general and local cold adaptations in humans and how they are of interest for cold workers.

  8. Manifold alignment for classification of multitemporal hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Yang, Hsiu-Han

    Analyzing remotely sensed images to obtain land cover classification maps is an effective approach for acquiring information over landscapes that can be accomplished over extended areas with limited ground surveys. Further, with advances in remote sensing technology, spaceborne hyperspectral sensors provide the capability to acquire a set of images that have both high spectral and temporal resolution. These images are suitable for monitoring and analyzing environmental changes with subtle spectral characteristics. However, inherent characteristics of multitemporal hyperspectral images, including high dimensionality, nonlinearity, and nonstationarity phenomena over time and across large areas, pose several challenges for classification. This research addresses the issues of classification tasks in the presence of spectral shifts within multitemporal hyperspectral images by leveraging the concept of the data manifold. Although manifold learning has been applied successfully in single image hyperspectral data classification to address high dimensionality and nonlinear spectral responses, research related to manifold learning for multitemporal classification studies is limited. The proposed approaches utilize spectral signatures and spatial proximity to construct similar "local" geometries of temporal images. By aligning these underlying manifolds optimally, the impacts of nonstationary effects are mitigated and classification is accomplished in a representative temporal data manifold. "Global" manifolds learned from temporal hyperspectral images have a major advantage in faithful representation of the data in an image, such as retaining relationships between different classes. Local manifolds are favored in discriminating difficult classes and for computation efficiency. A new hybrid global-local manifold alignment method that combines the advantages of global and local manifolds for effective multitemporal image classification is also proposed. Results illustrate the

  9. Accuracy of Remotely Sensed Classifications For Stratification of Forest and Nonforest Lands

    Treesearch

    Raymond L. Czaplewski; Paul L. Patterson

    2001-01-01

    We specify accuracy standards for remotely sensed classifications used by FIA to stratify landscapes into two categories: forest and nonforest. Accuracy must be highest when forest area approaches 100 percent of the landscape. If forest area is rare in a landscape, then accuracy in the nonforest stratum must be very high, even at the expense of accuracy in the forest...

  10. 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.

  11. Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified 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 CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). 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 (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. 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.

  12. 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.

  13. 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.

  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. Adaptive reshaping of objects in (multiparameter) Hilbert space for enhanced detection and classification: an application of receiver operating curve statistics to laser-based mass spectroscopy.

    PubMed

    Romanov, Dmitri A; Healy, Dennis M; Brady, John J; Levis, Robert J

    2008-05-01

    We propose a new approach to the classical detection problem of discrimination of a true signal of interest from an interferent signal, which may be applied to the area of chemical sensing. We show that the detection performance, as quantified by the receiver operating curve (ROC), can be substantially improved when the signal is represented by a multicomponent data set that is actively manipulated by means of a shaped laser probe pulse. In this case, the signal sought (agent) and the interfering signal (interferent) are visualized by vectors in a multidimensional detection space. Separation of these vectors can be achieved by adaptive modification of a probing laser pulse to actively manipulate the Hamiltonian of the agent and interferent. We demonstrate one implementation of the concept of adaptive rotation of signal vectors to chemical agent detection by means of strong-field time-of-flight mass spectrometry.

  16. 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

  17. Simulating natural selection in landscape genetics.

    PubMed

    Landguth, E L; Cushman, S A; Johnson, N A

    2012-03-01

    Linking landscape effects to key evolutionary processes through individual organism movement and natural selection is essential to provide a foundation for evolutionary landscape genetics. Of particular importance is determining how spatially-explicit, individual-based models differ from classic population genetics and evolutionary ecology models based on ideal panmictic populations in an allopatric setting in their predictions of population structure and frequency of fixation of adaptive alleles. We explore initial applications of a spatially-explicit, individual-based evolutionary landscape genetics program that incorporates all factors--mutation, gene flow, genetic drift and selection--that affect the frequency of an allele in a population. We incorporate natural selection by imposing differential survival rates defined by local relative fitness values on a landscape. Selection coefficients thus can vary not only for genotypes, but also in space as functions of local environmental variability. This simulator enables coupling of gene flow (governed by resistance surfaces), with natural selection (governed by selection surfaces). We validate the individual-based simulations under Wright-Fisher assumptions. We show that under isolation-by-distance processes, there are deviations in the rate of change and equilibrium values of allele frequency. The program provides a valuable tool (cdpop v1.0; http://cel.dbs.umt.edu/software/CDPOP/) for the study of evolutionary landscape genetics that allows explicit evaluation of the interactions between gene flow and selection in complex landscapes.

  18. Spatial transferability of landscape-based hydrological models

    NASA Astrophysics Data System (ADS)

    Gao, Hongkai; Hrachowitz, Markus; Fenicia, Fabrizio; Gharari, Shervan; Sriwongsitanon, Nutchanart; Savenije, Hubert

    2015-04-01

    Landscapes, mainly distinguished by land surface topography and vegetation cover, are crucial in defining runoff generation mechanisms, interception capacity and transpiration processes. Landscapes information provides modelers with a way to take into account catchment heterogeneity, while simultaneously keeping model complexity low. A landscape-based hydrological modelling framework (FLEX-Topo), with parallel model structures, was developed and tested in various catchments with diverse climate, topography and land cover conditions. Landscape classification is the basic and most crucial procedure to create a tailor-made model for a certain catchment, as it explicitly relates hydrologic similarity to landscape similarity, which is the base of this type of models. Therefore, the study catchment is classified into different landscapes units that fulfil similar hydrological function, based on classification criteria such as the height above the nearest drainage, slope, aspect and land cover. At present, to suggested model includes four distinguishable landscapes: hillslopes, terraces/plateaus, riparian areas, and glacierized areas. Different parallel model structures are then associated with the different landscape units to describe their different dominant runoff generation mechanisms. These hydrological units are parallel and only connected by groundwater reservoir. The transferability of this landscape-based model can then be compared with the transferability of a lumped model. In this study, FLEX-Topo was developed and tested in three study sites: two cold-arid catchments in China (the upper Heihe River and the Urumqi Glacier No1 catchment), and one tropical catchment in Thailand (the upper Ping River). Stringent model tests indicate that FLEX-Topo, allowing for more process heterogeneity than lumped model formulations, exhibits higher capabilities to be spatially transferred. Furthermore, the simulated water balances, including internal fluxes, hydrograph

  19. 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.

  20. 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...

  1. 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...

  2. 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...

  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. 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)

  5. 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

  6. Cumulative effects of developed road network on woodland--a landscape approach.

    PubMed

    Hosseini Vardei, Mahla; Salmanmahiny, Abdolrasoul; Monavari, Seyed Masoud; Kheirkhah Zarkesh, Mir Masoud

    2014-11-01

    Population growth, during the twentieth century, has increased demand for new farmlands. Accordingly, road networks have rapidly been developed to facilitate and accelerate human access to the essential resources resulted in extensive land use changes. The present study aims at assessing cumulative effects of developed road network on tree cover of Golestan Province in northern Iran. In order to detect changes over the study period of 1987-2002, the LULC map of the study area was initially prepared from the satellite images of Landsat TM (1987) and ETM+ (2002) using maximum likelihood supervised classification method. Afterwards, a total number of seven landscape matrices were selected to detect cumulative effects of the developed road network on woodland cover. The obtained results indicated that the fragile patches are mainly located at a distance of 171-342 m from the roadside. Furthermore, the majority of the patches affected by cumulative effects of development activities are situated at a distance of 342-684 m from the roadside, over an approximate area of 55 ha. The analysis of landscape metrics revealed that the developed road network has increased the landscape metrics of "the number of patches" and "patches perimeter-area ratio". It has also followed by a decrease in metrics such as "patches area", "Euclidean nearest neighbor distance", "patches proximity", "shape index", "contiguity", and "mean patches fractal dimension". The road network has also increased the "number of patches" and decreased the "mean patches area" representing further fragmentation of the landscape. With identification of highly affected wooldland cover patches, it would be possible to apply adaptive environmental management strategies to preserve and rehabilitate high-priority patches.

  7. The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound.

    PubMed

    Moon, Woo Kyung; Chen, I-Ling; Chang, Jung Min; Shin, Sung Ui; Lo, Chung-Ming; Chang, Ruey-Feng

    2017-04-01

    Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.

  8. Patterns among the ashes: Exploring the relationship between landscape pattern and the emerald ash borer

    Treesearch

    Susan J. Crocker; Dacia M. Meneguzzo; Greg C. Liknes

    2010-01-01

    Landscape metrics, including host abundance and population density, were calculated using forest inventory and land cover data to assess the relationship between landscape pattern and the presence or absence of the emerald ash borer (EAB) (Agrilus planipennis Fairmaire). The Random Forests classification algorithm in the R statistical environment was...

  9. Risk-adapted management of papillary thyroid carcinoma according to our own risk group classification system: is thyroid lobectomy the treatment of choice for low-risk patients?

    PubMed

    Ebina, Aya; Sugitani, Iwao; Fujimoto, Yoshihide; Yamada, Keiko

    2014-12-01

    Our original system for risk group classification for predicting cause-specific death from papillary thyroid carcinoma (PTC) defined patients with distant metastasis and older patients (≥ 50 years) with either massive extrathyroidal extension or large (≥ 3 cm) lymph node metastasis as high risk; all others are low risk. For unilateral, low-risk PTC, the extent of thyroidectomy (less-than-total thyroidectomy vs total or near-total thyroidectomy) has been determined based on the choice of the patient since 2005. Of 1,187 patients who underwent initial thyroidectomy for PTC (tumor size [T] >1 cm) between 1993 and 2010, 967 (82%) were classified as low risk. Among low-risk patients, 791 (82%) underwent less than total thyroidectomy. The 10-year cause-specific survival and disease-free survival rates did not differ between patients who underwent total thyroidectomy versus less than total thyroidectomy (cause-specific survival, 99% vs 99% [P = .61]; disease-free survival, 91% vs 87% [P = .90]). Age ≥ 60 years, T ≥ 3 cm, and lymph node metastases >3 cm represented significant risk factors for distant recurrence. The favorable overall survival of low-risk patients, regardless of the extent of thyroidectomy, supports patient autonomy in treatment-related decision making. Low-risk patients possessing risk factors for distant recurrence would be likely to benefit from total thyroidectomy followed by radioactive iodine. Copyright © 2014 Elsevier Inc. All rights reserved.

  10. Vocal development in a Waddington landscape

    PubMed Central

    Teramoto, Yayoi; Takahashi, Daniel Y; Holmes, Philip; Ghazanfar, Asif A

    2017-01-01

    Vocal development is the adaptive coordination of the vocal apparatus, muscles, the nervous system, and social interaction. Here, we use a quantitative framework based on optimal control theory and Waddington’s landscape metaphor to provide an integrated view of this process. With a biomechanical model of the marmoset monkey vocal apparatus and behavioral developmental data, we show that only the combination of the developing vocal tract, vocal apparatus muscles and nervous system can fully account for the patterns of vocal development. Together, these elements influence the shape of the monkeys’ vocal developmental landscape, tilting, rotating or shifting it in different ways. We can thus use this framework to make quantitative predictions regarding how interfering factors or experimental perturbations can change the landscape within a species, or to explain comparative differences in vocal development across species DOI: http://dx.doi.org/10.7554/eLife.20782.001 PMID:28092262

  11. 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.

  12. Landscape assessment for tourism

    Treesearch

    Clare A. Gunn

    1979-01-01

    Increased development of landscapes for tourism now creates problems of integrating the many parts. Accomplishments at the site scale have not been matched with equal progress at the regional scale. This concept, and its example of application, shows promise of assisting regions in assessing their potential of landscapes before development. With such a concept, not...

  13. Properties of selected mutations and genotypic landscapes under Fisher’s Geometric Model

    PubMed Central

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

    2014-01-01

    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 co-selected 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. PMID:25311558

  14. 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.

  15. 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.

  16. 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

  17. 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. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  18. Automated classification of landforms on Mars

    NASA Astrophysics Data System (ADS)

    Bue, B. D.; Stepinski, T. F.

    2006-06-01

    We propose a numerical method for classification and characterization of landforms on Mars. The method provides an alternative to manual geomorphic mapping of the Martian surface. Digital elevation data is used to calculate several topographic attributes for each pixel in a landscape. Unsupervised classification, based on the self-organizing map technique, divides all pixels into mutually exclusive and exhaustive landform classes on the basis of similarity between attribute vectors. The results are displayed as a thematic map of landforms and statistics of attributes are used to assign semantic meaning to the classes. This method is used to produce a geomorphic map of the Terra Cimmeria region on Mars. We assess the quality of the automated classification and discuss differences between results of automated and manual mappings. Potential applications of our method, including crater counting, landscape feature search, and large scale quantitative comparisons of Martian surface morphology, are identified and evaluated.

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

    DOE PAGES

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

    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

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

    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. Our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.

  2. 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...

  3. Developing an ecosystem diversity framework for landscape assessment

    Treesearch

    Robert D. Pfister; Michael D. Sweet

    2000-01-01

    Ecological diversity is being addressed in various research and management efforts, but a common foundation is not explicitly defined or displayed. A formal Ecosystem Diversity Framework (EDF) would improve landscape analysis and communication across multiple scales. The EDF represents a multiple-component vegetation classification system with inherent flexibility for...

  4. 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...

  5. Predicting plant species diversity in a longleaf pine landscape

    Treesearch

    L. Katherine Kirkman; P. Charles Goebel; Brian J. Palik; Larry T. West

    2004-01-01

    In this study, we used a hierarchical, multifactor ecological classification system to examine how spatial patterns of biodiversity develop in one of the most species-rich ecosystems in North America, the fire-maintained longleaf pine-wiregrass ecosystem and associated depressional wetlands and riparian forests. Our goal was to determine which landscape features are...

  6. 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

  7. Classifications in Routine Use

    PubMed Central

    Stausberg, Jürgen; Lang, Hauke; Obertacke, Udo; Rauhut, Friedhelm

    2001-01-01

    Objective: Classifications of diagnoses and procedures are very important for the economical as well as the quality assessment of surgical departments. They should reflect the morbidity of the patients treated and the work done. The authors investigated the fulfillment of these requirements by ICD-9 (International Classification of Diseases: 9th Revision) and OPS-301, a German adaptation of the ICPM (International Classification of Procedures in Medicine), in clinical practice. Design: A retrospective study was conducted using the data warehouse of the Surgical Center II at the Medical Faculty in Essen, Germany. The sample included 28,293 operations from the departments of general surgery, neurosurgery, and trauma surgery. Distribution of cases per ICD-9 and OPS-301 codes, aggregation through the digits of the codes, and concordance between the classifications were used as measurements. Median and range were calculated as distribution parameters. The concentration of cases per code was graphed using Lorenz curves. The most frequent codes of diagnoses were compared with the most frequent codes of surgical procedures concerning their medical information. Results: The total number of codes used from ICD-9 and OPS-301 went up to 14 percent, depending on the surgical field. The median number of cases per code was between 2 and 4. The concentration of codes was enormous: 10 percent of the codes were used for about 70 percent of the surgical procedures. The distribution after an aggregation by digit was better with OPS-301 than with ICD-9. The views with OPS-301 and ICD-9 were quite different. Conclusion: Statistics based on ICD-9 or OPS-301 will not properly reflect the morbidity in different surgical departments. Neither classification adequately represents the work done by surgical staff. This is because of an uneven granularity in the classifications. The results demand a replacement of the ICD-9 by an improved terminological system in surgery. The OPS-301 should be

  8. 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

  9. Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA

    Treesearch

    Thomas A. Spies; Eric White; Alan Ager; Jeffrey D. Kline; John P. Bolte; Emily K. Platt; Keith A. Olsen; Robert J. Pabst; Ana M. G. Barros; John D. Bailey; Susan Charnley; Anita T. Morzillo; Jennifer Koch; Michelle M. Steen-Adams; Peter H. Singleton; James Sulzman; Cynthia Schwartz; Blair Csuti

    2017-01-01

    Fire-prone landscapes present many challenges for both managers and policy makers in developing adaptive behaviors and institutions. We used a coupled human and natural systems framework and an agent-based landscape model to examine how alternative management scenarios affect fire and ecosystem services metrics in a fire-prone multiownership landscape in the eastern...

  10. 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.

  11. Applicability of Hydrologic Landscapes for Model Calibration ...

    EPA Pesticide Factsheets

    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 techniques were applied to the HL assessment unit composition in watersheds across the Pacific Northwest to aggregate the hydrologic behavior of the Hydrologic Landscapes from the assessment unit scale to the watershed scale. This non-trivial solution both emphasizes HL classifications within the watershed that provide that majority of moisture surplus/deficit and considers the relative position (upstream vs. downstream) of these HL classifications. A clustering algorithm was applied to the HL-based characterization of assessment units within 185 watersheds to help organize watersheds into nine classes hypothesized to have similar hydrologic behavior. The HL-based classes were used to organize and describe hydrologic behavior information about watershed classes and both predictions and validations were independently performed with regard to the general magnitude of six hydroclimatic signature values. A second cluster analysis was then performed using the independently calculated signature values as similarity metrics, and it was found that the six signature clusters showed substantial overlap in watershed class membership to those in the HL-based classes. One hypothesis set forward from thi

  12. Applicability of Hydrologic Landscapes for Model Calibration ...

    EPA Pesticide Factsheets

    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 techniques were applied to the HL assessment unit composition in watersheds across the Pacific Northwest to aggregate the hydrologic behavior of the Hydrologic Landscapes from the assessment unit scale to the watershed scale. This non-trivial solution both emphasizes HL classifications within the watershed that provide that majority of moisture surplus/deficit and considers the relative position (upstream vs. downstream) of these HL classifications. A clustering algorithm was applied to the HL-based characterization of assessment units within 185 watersheds to help organize watersheds into nine classes hypothesized to have similar hydrologic behavior. The HL-based classes were used to organize and describe hydrologic behavior information about watershed classes and both predictions and validations were independently performed with regard to the general magnitude of six hydroclimatic signature values. A second cluster analysis was then performed using the independently calculated signature values as similarity metrics, and it was found that the six signature clusters showed substantial overlap in watershed class membership to those in the HL-based classes. One hypothesis set forward from thi

  13. Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software

    USGS Publications Warehouse

    West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Kumar, Sunil; Swallow, Aaron; Luizza, Matthew; Chignell, Steve

    2017-01-01

    Among the most pressing concerns of land managers in post-wildfire landscapes are the establishment and spread of invasive species. Land managers need accurate maps of invasive species cover for targeted management post-disturbance that are easily transferable across space and time. In this study, we sought to develop an iterative, replicable methodology based on limited invasive species occurrence data, freely available remotely sensed data, and open source software to predict the distribution of Bromus tectorum (cheatgrass) in a post-wildfire landscape. We developed four species distribution models using eight spectral indices derived from five months of Landsat 8 Operational Land Imager (OLI) data in 2014. These months corresponded to both cheatgrass growing period and time of field data collection in the study area. The four models were improved using an iterative approach in which a threshold for cover was established, and all models had high sensitivity values when tested on an independent dataset. We also quantified the area at highest risk for invasion in future seasons given 2014 distribution, topographic covariates, and seed dispersal limitations. These models demonstrate the effectiveness of using derived multi-date spectral indices as proxies for species occurrence on the landscape, the importance of selecting thresholds for invasive species cover to evaluate ecological risk in species distribution models, and the applicability of Landsat 8 OLI and the Software for Assisted Habitat Modeling for targeted invasive species management.

  14. Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software

    NASA Astrophysics Data System (ADS)

    West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Kumar, Sunil; Swallow, Aaron; Luizza, Matthew W.; Chignell, Stephen M.

    2017-07-01

    Among the most pressing concerns of land managers in post-wildfire landscapes are the establishment and spread of invasive species. Land managers need accurate maps of invasive species cover for targeted management post-disturbance that are easily transferable across space and time. In this study, we sought to develop an iterative, replicable methodology based on limited invasive species occurrence data, freely available remotely sensed data, and open source software to predict the distribution of Bromus tectorum (cheatgrass) in a post-wildfire landscape. We developed four species distribution models using eight spectral indices derived from five months of Landsat 8 Operational Land Imager (OLI) data in 2014. These months corresponded to both cheatgrass growing period and time of field data collection in the study area. The four models were improved using an iterative approach in which a threshold for cover was established, and all models had high sensitivity values when tested on an independent dataset. We also quantified the area at highest risk for invasion in future seasons given 2014 distribution, topographic covariates, and seed dispersal limitations. These models demonstrate the effectiveness of using derived multi-date spectral indices as proxies for species occurrence on the landscape, the importance of selecting thresholds for invasive species cover to evaluate ecological risk in species distribution models, and the applicability of Landsat 8 OLI and the Software for Assisted Habitat Modeling for targeted invasive species management.

  15. 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,…

  16. 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,…

  17. 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.

  18. An evaluation of unsupervised and supervised learning algorithms for clustering landscape types in the United States

    USGS Publications Warehouse

    Wendel, Jochen; Buttenfield, Barbara P.; Stanislawski, Larry V.

    2016-01-01

    Knowledge of landscape type can inform cartographic generalization of hydrographic features, because landscape characteristics provide an important geographic context that affects variation in channel geometry, flow pattern, and network configuration. Landscape types are characterized by expansive spatial gradients, lacking abrupt changes between adjacent classes; and as having a limited number of outliers that might confound classification. The US Geological Survey (USGS) is exploring methods to automate generalization of features in the National Hydrography Data set (NHD), to associate specific sequences of processing operations and parameters with specific landscape characteristics, thus obviating manual selection of a unique processing strategy for every NHD watershed unit. A chronology of methods to delineate physiographic regions for the United States is described, including a recent maximum likelihood classification based on seven input variables. This research compares unsupervised and supervised algorithms applied to these seven input variables, to evaluate and possibly refine the recent classification. Evaluation metrics for unsupervised methods include the Davies–Bouldin index, the Silhouette index, and the Dunn index as well as quantization and topographic error metrics. Cross validation and misclassification rate analysis are used to evaluate supervised classification methods. The paper reports the comparative analysis and its impact on the selection of landscape regions. The compared solutions show problems in areas of high landscape diversity. There is some indication that additional input variables, additional classes, or more sophisticated methods can refine the existing classification.

  19. 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

  20. 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…

  1. Landscape genetics and limiting factors

    Treesearch

    Samuel A. Cushman; Andrew J. Shirk; Erin L. Landguth

    2013-01-01

    Population connectivity is mediated by the movement of organisms or propagules through landscapes. However, little is known about how variation in the pattern of landscape mosaics affects the detectability of landscape genetic relationships. The goal of this paper is to explore the impacts of limiting factors on landscape genetic processes using simulation...

  2. Landscape metrics, scales of resolution

    Treesearch

    Samuel A. Cushman; Kevin McGarigal

    2008-01-01

    Effective implementation of the "multiple path" approach to managing green landscapes depends fundamentally on rigorous quantification of the composition and structure of the landscapes of concern at present, modelling landscape structure trajectories under alternative management paths, and monitoring landscape structure into the future to confirm...

  3. Land Use and Landscape Pattern In Mesoscale Watersheds: Analysis and Assessment of Their Interactions and Impacts On Processes With Regard To The Eu Water Framework Directive

    NASA Astrophysics Data System (ADS)

    Volk, M.; Steinhardt, U.; Lausch, A.

    The implementation of the EU water framework directive requires the comprehensive consideration of environmental impacts within whole watersheds during the next 9 years in order to ensure a sound quality and availability of both surface and groundwater. Especially the change of landscape pattern caused by land use alterations has strong impacts on the water balance and the waterbound fluxes within landscapes. Thus, land use systems and cultivation practices unadapted to the natural conditions of the landscapes can lead to environmental impairments of water and soil affecting the regulation functions. In order to develop concepts for adapted land use systems with respect to the goals of the EU water framework directive, integrated investigation and assessment approaches on different spatio-temporal scales have to be developed. The authors suggest a hierachical nested approach applied on the example of the Saale watershed (ca. 23.000 km2) and some of its subbasins of different sizes (8 km2, 360km2, 5.000 km2). The approach is structured into four main steps: · Investigation and assessment of the effects of actual and future trends of regional, national and european land use changes for the study areas (probability according to the landscape conditions) · Investigation of the effects of these land use changes on the landscape structure · Investigation of the impact respectively the interactions between the (resulted) changes of the landscape structure and fluxes of water and material (retention capability, intensity, duration and range of processes, etc.) · Development of a multiscalar parameter system in order to assess the impact of land use changes on the regulation functions (with particular view on water quantity and quality) and to derive adapted land use systems for their protection (suitability and sensitivity of landscapes for land use types). The approach requires the development and coupling of "classic" methods of landscape analysis with innovative GIS

  4. 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.

  5. 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.

  6. Populating the whole landscape.

    PubMed

    Brown, Adam R; Dahlen, Alex

    2011-10-21

    Every de Sitter vacuum can transition to every other de Sitter vacuum despite any obstacle, despite intervening anti-de Sitter sinks, despite not being connected by an instanton. Eternal inflation populates the whole landscape. © 2011 American Physical Society

  7. Landscape Water Budget Tool

    EPA Pesticide Factsheets

    WaterSense created the Water Budget Tool as one option to help builders, landscape professionals, and irrigation professionals certified by a WaterSense labeled program meet the criteria specified in the WaterSense New Home Specification.

  8. 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

  9. 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…

  10. Classification Analysis.

    ERIC Educational Resources Information Center

    Ball, Geoffrey H.

    Sorting things into groups is a basic intellectual task that allows people to simplify with minimal reduction in information. Classification techniques, which include both clustering and discrimination, provide step-by-step computer-based procedures for sorting things based on notions of generalized similarity and on the "class description"…

  11. Xenolog classification.

    PubMed

    Darby, Charlotte A; Stolzer, Maureen; Ropp, Patrick J; Barker, Daniel; Durand, Dannie

    2017-03-01

    Orthology analysis is a fundamental tool in comparative genomics. Sophisticated methods have been developed to distinguish between orthologs and paralogs and to classify paralogs into subtypes depending on the duplication mechanism and timing, relative to speciation. However, no comparable framework exists for xenologs: gene pairs whose history, since their divergence, includes a horizontal transfer. Further, the diversity of gene pairs that meet this broad definition calls for classification of xenologs with similar properties into subtypes. We present a xenolog classification that uses phylogenetic reconciliation to assign each pair of genes to a class based on the event responsible for their divergence and the historical association between genes and species. Our classes distinguish between genes related through transfer alone and genes related through duplication and transfer. Further, they separate closely-related genes in distantly-related species from distantly-related genes in closely-related species. We present formal rules that assign gene pairs to specific xenolog classes, given a reconciled gene tree with an arbitrary number of duplications and transfers. These xenology classification rules have been implemented in software and tested on a collection of ∼13 000 prokaryotic gene families. In addition, we present a case study demonstrating the connection between xenolog classification and gene function prediction. The xenolog classification rules have been implemented in N otung 2.9, a freely available phylogenetic reconciliation software package. http://www.cs.cmu.edu/~durand/Notung . Gene trees are available at http://dx.doi.org/10.7488/ds/1503 . durand@cmu.edu. Supplementary data are available at Bioinformatics online.

  12. 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

  13. The Adaptive Kernel Neural Network

    DTIC Science & Technology

    1989-10-01

    A neural network architecture for clustering and classification is described. The Adaptive Kernel Neural Network (AKNN) is a density estimation...classif