WorldWideScience

Sample records for predicting species interactions

  1. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

    Science.gov (United States)

    Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

    2017-06-01

    Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  2. The multidimensional behavioural hypervolumes of two interacting species predict their space use and survival.

    Science.gov (United States)

    Lichtenstein, James L L; Wright, Colin M; McEwen, Brendan; Pinter-Wollman, Noa; Pruitt, Jonathan N

    2017-10-01

    Individual animals differ consistently in their behaviour, thus impacting a wide variety of ecological outcomes. Recent advances in animal personality research have established the ecological importance of the multidimensional behavioural volume occupied by individuals and by multispecies communities. Here, we examine the degree to which the multidimensional behavioural volume of a group predicts the outcome of both intra- and interspecific interactions. In particular, we test the hypothesis that a population of conspecifics will experience low intraspecific competition when the population occupies a large volume in behavioural space. We further hypothesize that populations of interacting species will exhibit greater interspecific competition when one or both species occupy large volumes in behavioural space. We evaluate these hypotheses by studying groups of katydids ( Scudderia nymphs) and froghoppers ( Philaenus spumarius ), which compete for food and space on their shared host plant, Solidago canadensis . We found that individuals in single-species groups of katydids positioned themselves closer to one another, suggesting reduced competition, when groups occupied a large behavioural volume. When both species were placed together, we found that the survival of froghoppers was greatest when both froghoppers and katydids occupied a small volume in behavioural space, particularly at high froghopper densities. These results suggest that groups that occupy large behavioural volumes can have low intraspecific competition but high interspecific competition. Thus, behavioural hypervolumes appear to have ecological consequences at both the level of the population and the community and may help to predict the intensity of competition both within and across species.

  3. Interaction of species traits and environmental disturbance predicts invasion success of aquatic microorganisms.

    Directory of Open Access Journals (Sweden)

    Elvira Mächler

    Full Text Available Factors such as increased mobility of humans, global trade and climate change are affecting the range of many species, and cause large-scale translocations of species beyond their native range. Many introduced species have a strong negative influence on the new local environment and lead to high economic costs. There is a strong interest to understand why some species are successful in invading new environments and others not. Most of our understanding and generalizations thereof, however, are based on studies of plants and animals, and little is known on invasion processes of microorganisms. We conducted a microcosm experiment to understand factors promoting the success of biological invasions of aquatic microorganisms. In a controlled lab experiment, protist and rotifer species originally isolated in North America invaded into a natural, field-collected community of microorganisms of European origin. To identify the importance of environmental disturbances on invasion success, we either repeatedly disturbed the local patches, or kept them as undisturbed controls. We measured both short-term establishment and long-term invasion success, and correlated it with species-specific life-history traits. We found that environmental disturbances significantly affected invasion success. Depending on the invading species' identity, disturbances were either promoting or decreasing invasion success. The interaction between habitat disturbance and species identity was especially pronounced for long-term invasion success. Growth rate was the most important trait promoting invasion success, especially when the species invaded into a disturbed local community. We conclude that neither species traits nor environmental factors alone conclusively predict invasion success, but an integration of both of them is necessary.

  4. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels.

    Science.gov (United States)

    Van der Putten, Wim H; Macel, Mirka; Visser, Marcel E

    2010-07-12

    Current predictions on species responses to climate change strongly rely on projecting altered environmental conditions on species distributions. However, it is increasingly acknowledged that climate change also influences species interactions. We review and synthesize literature information on biotic interactions and use it to argue that the abundance of species and the direction of selection during climate change vary depending on how their trophic interactions become disrupted. Plant abundance can be controlled by aboveground and belowground multitrophic level interactions with herbivores, pathogens, symbionts and their enemies. We discuss how these interactions may alter during climate change and the resulting species range shifts. We suggest conceptual analogies between species responses to climate warming and exotic species introduced in new ranges. There are also important differences: the herbivores, pathogens and mutualistic symbionts of range-expanding species and their enemies may co-migrate, and the continuous gene flow under climate warming can make adaptation in the expansion zone of range expanders different from that of cross-continental exotic species. We conclude that under climate change, results of altered species interactions may vary, ranging from species becoming rare to disproportionately abundant. Taking these possibilities into account will provide a new perspective on predicting species distribution under climate change.

  5. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  6. Species interactions and plant polyploidy.

    Science.gov (United States)

    Segraves, Kari A; Anneberg, Thomas J

    2016-07-01

    Polyploidy is a common mode of speciation that can have far-reaching consequences for plant ecology and evolution. Because polyploidy can induce an array of phenotypic changes, there can be cascading effects on interactions with other species. These interactions, in turn, can have reciprocal effects on polyploid plants, potentially impacting their establishment and persistence. Although there is a wealth of information on the genetic and phenotypic effects of polyploidy, the study of species interactions in polyploid plants remains a comparatively young field. Here we reviewed the available evidence for how polyploidy may impact many types of species interactions that range from mutualism to antagonism. Specifically, we focused on three main questions: (1) Does polyploidy directly cause the formation of novel interactions not experienced by diploids, or does it create an opportunity for natural selection to then form novel interactions? (2) Does polyploidy cause consistent, predictable changes in species interactions vs. the evolution of idiosyncratic differences? (3) Does polyploidy lead to greater evolvability in species interactions? From the scarce evidence available, we found that novel interactions are rare but that polyploidy can induce changes in pollinator, herbivore, and pathogen interactions. Although further tests are needed, it is likely that selection following whole-genome duplication is important in all types of species interaction and that there are circumstances in which polyploidy can enhance the evolvability of interactions with other species. © 2016 Botanical Society of America.

  7. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  8. Climate change and species interactions: ways forward.

    Science.gov (United States)

    Angert, Amy L; LaDeau, Shannon L; Ostfeld, Richard S

    2013-09-01

    With ongoing and rapid climate change, ecologists are being challenged to predict how individual species will change in abundance and distribution, how biotic communities will change in structure and function, and the consequences of these climate-induced changes for ecosystem functioning. It is now well documented that indirect effects of climate change on species abundances and distributions, via climatic effects on interspecific interactions, can outweigh and even reverse the direct effects of climate. However, a clear framework for incorporating species interactions into projections of biological change remains elusive. To move forward, we suggest three priorities for the research community: (1) utilize tractable study systems as case studies to illustrate possible outcomes, test processes highlighted by theory, and feed back into modeling efforts; (2) develop a robust analytical framework that allows for better cross-scale linkages; and (3) determine over what time scales and for which systems prediction of biological responses to climate change is a useful and feasible goal. We end with a list of research questions that can guide future research to help understand, and hopefully mitigate, the negative effects of climate change on biota and the ecosystem services they provide. © 2013 New York Academy of Sciences.

  9. Estimating Effects of Species Interactions on Populations of Endangered Species.

    Science.gov (United States)

    Roth, Tobias; Bühler, Christoph; Amrhein, Valentin

    2016-04-01

    Global change causes community composition to change considerably through time, with ever-new combinations of interacting species. To study the consequences of newly established species interactions, one available source of data could be observational surveys from biodiversity monitoring. However, approaches using observational data would need to account for niche differences between species and for imperfect detection of individuals. To estimate population sizes of interacting species, we extended N-mixture models that were developed to estimate true population sizes in single species. Simulations revealed that our model is able to disentangle direct effects of dominant on subordinate species from indirect effects of dominant species on detection probability of subordinate species. For illustration, we applied our model to data from a Swiss amphibian monitoring program and showed that sizes of expanding water frog populations were negatively related to population sizes of endangered yellow-bellied toads and common midwife toads and partly of natterjack toads. Unlike other studies that analyzed presence and absence of species, our model suggests that the spread of water frogs in Central Europe is one of the reasons for the decline of endangered toad species. Thus, studying population impacts of dominant species on population sizes of endangered species using data from biodiversity monitoring programs should help to inform conservation policy and to decide whether competing species should be subject to population management.

  10. Confronting species distribution model predictions with species functional traits.

    Science.gov (United States)

    Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M

    2016-02-01

    Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.

  11. Incorporating Context Dependency of Species Interactions in Species Distribution Models.

    Science.gov (United States)

    Lany, Nina K; Zarnetske, Phoebe L; Gouhier, Tarik C; Menge, Bruce A

    2017-07-01

    Species distribution models typically use correlative approaches that characterize the species-environment relationship using occurrence or abundance data for a single species. However, species distributions are determined by both abiotic conditions and biotic interactions with other species in the community. Therefore, climate change is expected to impact species through direct effects on their physiology and indirect effects propagated through their resources, predators, competitors, or mutualists. Furthermore, the sign and strength of species interactions can change according to abiotic conditions, resulting in context-dependent species interactions that may change across space or with climate change. Here, we incorporated the context dependency of species interactions into a dynamic species distribution model. We developed a multi-species model that uses a time-series of observational survey data to evaluate how abiotic conditions and species interactions affect the dynamics of three rocky intertidal species. The model further distinguishes between the direct effects of abiotic conditions on abundance and the indirect effects propagated through interactions with other species. We apply the model to keystone predation by the sea star Pisaster ochraceus on the mussel Mytilus californianus and the barnacle Balanus glandula in the rocky intertidal zone of the Pacific coast, USA. Our method indicated that biotic interactions between P. ochraceus and B. glandula affected B. glandula dynamics across >1000 km of coastline. Consistent with patterns from keystone predation, the growth rate of B. glandula varied according to the abundance of P. ochraceus in the previous year. The data and the model did not indicate that the strength of keystone predation by P. ochraceus varied with a mean annual upwelling index. Balanus glandula cover increased following years with high phytoplankton abundance measured as mean annual chlorophyll-a. M. californianus exhibited the same

  12. Species interactions reverse grassland responses to changing climate.

    Science.gov (United States)

    Suttle, K B; Thomsen, Meredith A; Power, Mary E

    2007-02-02

    Predictions of ecological response to climate change are based largely on direct climatic effects on species. We show that, in a California grassland, species interactions strongly influence responses to changing climate, overturning direct climatic effects within 5 years. We manipulated the seasonality and intensity of rainfall over large, replicate plots in accordance with projections of leading climate models and examined responses across several trophic levels. Changes in seasonal water availability had pronounced effects on individual species, but as precipitation regimes were sustained across years, feedbacks and species interactions overrode autecological responses to water and reversed community trajectories. Conditions that sharply increased production and diversity through 2 years caused simplification of the food web and deep reductions in consumer abundance after 5 years. Changes in these natural grassland communities suggest a prominent role for species interactions in ecosystem response to climate change.

  13. Phylogeny and species traits predict bird detectability

    Science.gov (United States)

    Solymos, Peter; Matsuoka, Steven M.; Stralberg, Diana; Barker, Nicole K. S.; Bayne, Erin M.

    2018-01-01

    Avian acoustic communication has resulted from evolutionary pressures and ecological constraints. We therefore expect that auditory detectability in birds might be predictable by species traits and phylogenetic relatedness. We evaluated the relationship between phylogeny, species traits, and field‐based estimates of the two processes that determine species detectability (singing rate and detection distance) for 141 bird species breeding in boreal North America. We used phylogenetic mixed models and cross‐validation to compare the relative merits of using trait data only, phylogeny only, or the combination of both to predict detectability. We found a strong phylogenetic signal in both singing rates and detection distances; however the strength of phylogenetic effects was less than expected under Brownian motion evolution. The evolution of behavioural traits that determine singing rates was found to be more labile, leaving more room for species to evolve independently, whereas detection distance was mostly determined by anatomy (i.e. body size) and thus the laws of physics. Our findings can help in disentangling how complex ecological and evolutionary mechanisms have shaped different aspects of detectability in boreal birds. Such information can greatly inform single‐ and multi‐species models but more work is required to better understand how to best correct possible biases in phylogenetic diversity and other community metrics.

  14. Predicting rates of interspecific interaction from phylogenetic trees.

    Science.gov (United States)

    Nuismer, Scott L; Harmon, Luke J

    2015-01-01

    Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

  15. Predictions For New, Exotic Actinide Species

    International Nuclear Information System (INIS)

    Pyykko, P.

    2002-01-01

    The approach. New, simple chemical species can be predicted by studying isoelectronic series using ab initio quantum chemistry. We currently use in most cases relativistic pseudopotentials and handle the electron correlation using density functional theory (DFT) or wave-function-based methods, from MP2 to CCSD(T). Typical codes are Gaussian 98, Turbomole or MolCas. For full four-component Dirac-Fock calculations, the DREAMS code of K. G. Dyall has been utilized. For mapping out the possible new species, complete maps of all possibilities are made, whenever possible, and the new species typically occur along the coast-line of the 'island of stability' of already known species

  16. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

    Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

  17. Measured and Predicted Vapor Liquid Equilibrium of Ethanol-Gasoline Fuels with Insight on the Influence of Azeotrope Interactions on Aromatic Species Enrichment and Particulate Matter Formation in Spark Ignition Engines

    Energy Technology Data Exchange (ETDEWEB)

    Ratcliff, Matthew A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); McCormick, Robert L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Burke, Stephen [Colorado State University; Rhoads, Robert [University of Colorado; Windom, Bret [Colorado State University

    2018-04-03

    A relationship has been observed between increasing ethanol content in gasoline and increased particulate matter (PM) emissions from direct injection spark ignition (DISI) vehicles. The fundamental cause of this observation is not well understood. One potential explanation is that increased evaporative cooling as a result of ethanol's high HOV may slow evaporation and prevent sufficient reactant mixing resulting in the combustion of localized fuel rich regions within the cylinder. In addition, it is well known that ethanol when blended in gasoline forms positive azeotropes which can alter the liquid/vapor composition during the vaporization process. In fact, it was shown recently through a numerical study that these interactions can retain the aromatic species within the liquid phase impeding the in-cylinder mixing of these compounds, which would accentuate PM formation upon combustion. To better understand the role of the azeotrope interactions on the vapor/liquid composition evolution of the fuel, distillations were performed using the Advanced Distillation Curve apparatus on carefully selected samples consisting of gasoline blended with ethanol and heavy aromatic and oxygenated compounds with varying vapor pressures, including cumene, p-cymene, 4-tertbutyl toluene, anisole, and 4-methyl anisole. Samples collected during the distillation indicate an enrichment of the heavy aromatic or oxygenated additive with an increase in initial ethanol concentration from E0 to E30. A recently developed distillation and droplet evaporation model is used to explore the influence of dilution effects versus azeotrope interactions on the aromatic species enrichment. The results suggest that HOV-cooling effects as well as aromatic species enrichment behaviors should be considered in future development of predictive indices to forecast the PM potential of fuels containing oxygenated compounds with comparatively high HOV.

  18. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

    Science.gov (United States)

    Wisz, Mary Susanne; Pottier, Julien; Kissling, W Daniel; Pellissier, Loïc; Lenoir, Jonathan; Damgaard, Christian F; Dormann, Carsten F; Forchhammer, Mads C; Grytnes, John-Arvid; Guisan, Antoine; Heikkinen, Risto K; Høye, Toke T; Kühn, Ingolf; Luoto, Miska; Maiorano, Luigi; Nilsson, Marie-Charlotte; Normand, Signe; Öckinger, Erik; Schmidt, Niels M; Termansen, Mette; Timmermann, Allan; Wardle, David A; Aastrup, Peter; Svenning, Jens-Christian

    2013-02-01

    Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally

  19. Thematic and spatial resolutions affect model-based predictions of tree species distribution.

    Science.gov (United States)

    Liang, Yu; He, Hong S; Fraser, Jacob S; Wu, ZhiWei

    2013-01-01

    Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution.

  20. Inferring species interactions through joint mark–recapture analysis

    Science.gov (United States)

    Yackulic, Charles B.; Korman, Josh; Yard, Michael D.; Dzul, Maria C.

    2018-01-01

    Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub (Gila cypha) and introduced rainbow trout (Oncorhynchus mykiss) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly

  1. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  2. Predictive Systems for Customer Interactions

    Science.gov (United States)

    Vijayaraghavan, Ravi; Albert, Sam; Singh, Vinod Kumar; Kannan, Pallipuram V.

    With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company, not only through the traditional phone channel but also through chat, email, SMS or web self-service. Each of these channels is best suited for some services and ill-matched for others. Customer service organizations today struggle with the challenge of delivering seamlessly integrated services through these different channels. This paper will evaluate some of the key challenges in multi-channel customer service. It will address the challenge of creating the right channel mix i.e. providing the right choice of channels for a given customer/behavior/issue profile. It will also provide strategies for optimizing the performance of a given channel in creating the right customer experience.

  3. Gaussian interaction profile kernels for predicting drug-target interaction.

    Science.gov (United States)

    van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena

    2011-11-01

    The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.

  4. Predicting community composition from pairwise interactions

    Science.gov (United States)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

    The ability to predict the structure of complex, multispecies communities is crucial for understanding the impact of species extinction and invasion on natural communities, as well as for engineering novel, synthetic communities. Communities are often modeled using phenomenological models, such as the classical generalized Lotka-Volterra (gLV) model. While a lot of our intuition comes from such models, their predictive power has rarely been tested experimentally. To directly assess the predictive power of this approach, we constructed synthetic communities comprised of up to 8 soil bacteria. We measured the outcome of competition between all species pairs, and used these measurements to predict the composition of communities composed of more than 2 species. The pairwise competitions resulted in a diverse set of outcomes, including coexistence, exclusion, and bistability, and displayed evidence for both interference and facilitation. Most pair outcomes could be captured by the gLV framework, and the composition of multispecies communities could be predicted for communities composed solely of such pairs. Our results demonstrate the predictive ability and utility of simple phenomenology, which enables accurate predictions in the absence of mechanistic details.

  5. Geographical patterns of adaptation within a species' range : Interactions between drift and gene flow

    NARCIS (Netherlands)

    Alleaume-Benharira, M; Pen, IR; Ronce, O

    We use individual-based stochastic simulations and analytical deterministic predictions to investigate the interaction between drift, natural selection and gene flow on the patterns of local adaptation across a fragmented species' range under clinally varying selection. Migration between populations

  6. Simulated tri-trophic networks reveal complex relationships between species diversity and interaction diversity.

    Science.gov (United States)

    Pardikes, Nicholas A; Lumpkin, Will; Hurtado, Paul J; Dyer, Lee A

    2018-01-01

    Most of earth's biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships

  7. PREDICTING RELEVANT EMPTY SPOTS IN SOCIAL INTERACTION

    Institute of Scientific and Technical Information of China (English)

    Yoshiharu MAENO; Yukio OHSAWA

    2008-01-01

    An empty spot refers to an empty hard-to-fill space which can be found in the records of the social interaction, and is the clue to the persons in the underlying social network who do not appear in the records. This contribution addresses a problem to predict relevant empty spots in social interaction. Homogeneous and inhomogeneous networks are studied as a model underlying the social interaction. A heuristic predictor function method is presented as a new method to address the problem. Simulation experiment is demonstrated over a homogeneous network. A test data set in the form of market baskets is generated from the simulated communication. Precision to predict the empty spots is calculated to demonstrate the performance of the presented method.

  8. Depletion of heterogeneous source species pools predicts future invasion rates

    Science.gov (United States)

    Andrew M. Liebhold; Eckehard G. Brockerhoff; Mark Kimberley; Jacqueline Beggs

    2017-01-01

    Predicting how increasing rates of global trade will result in new establishments of potentially damaging invasive species is a question of critical importance to the development of national and international policies aimed at minimizing future invasions. Centuries of historical movement and establishment of invading species may have depleted the supply of species...

  9. Diversity-interaction modeling: estimating contributions of species identities and interactions to ecosystem function

    DEFF Research Database (Denmark)

    Kirwan, L; Connolly, J; Finn, J A

    2009-01-01

    to the roles of evenness, functional groups, and functional redundancy. These more parsimonious descriptions can be especially useful in identifying general diversity-function relationships in communities with large numbers of species. We provide an example of the application of the modeling framework......We develop a modeling framework that estimates the effects of species identity and diversity on ecosystem function and permits prediction of the diversity-function relationship across different types of community composition. Rather than just measure an overall effect of diversity, we separately....... These models describe community-level performance and thus do not require separate measurement of the performance of individual species. This flexible modeling approach can be tailored to test many hypotheses in biodiversity research and can suggest the interaction mechanisms that may be acting....

  10. Improving LMA predictions with non standard interactions

    CERN Document Server

    Das, C R

    2010-01-01

    It has been known for some time that the well established LMA solution to the observed solar neutrino deficit fails to predict a flat energy spectrum for SuperKamiokande as opposed to what the data indicates. It also leads to a Chlorine rate which appears to be too high as compared to the data. We investigate the possible solution to these inconsistencies with non standard neutrino interactions, assuming that they come as extra contributions to the $\

  11. Effects of biotic interactions and dispersal on the presence-absence of multiple species

    International Nuclear Information System (INIS)

    Mohd, Mohd Hafiz; Murray, Rua; Plank, Michael J.; Godsoe, William

    2017-01-01

    One of the important issues in ecology is to predict which species will be present (or absent) across a geographical region. Dispersal is thought to have an important influence on the range limits of species, and understanding this problem in a multi-species community with priority effects (i.e. initial abundances determine species presence-absence) is a challenging task because dispersal also interacts with biotic and abiotic factors. Here, we propose a simple multi-species model to investigate the joint effects of biotic interactions and dispersal on species presence-absence. Our results show that dispersal can substantially expand species ranges when biotic and abiotic forces are present; consequently, coexistence of multiple species is possible. The model also exhibits ecologically interesting priority effects, mediated by intense biotic interactions. In the absence of dispersal, competitive exclusion of all but one species occurs. We find that dispersal reduces competitive exclusion effects that occur in no-dispersal case and promotes coexistence of multiple species. These results also show that priority effects are still prevalent in multi-species communities in the presence of dispersal process. We also illustrate the existence of threshold values of competitive strength (i.e. transcritical bifurcations), which results in different species presence-absence in multi-species communities with and without dispersal.

  12. Interaction features for prediction of perceptual segmentation

    DEFF Research Database (Denmark)

    Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri

    2017-01-01

    As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise...... and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners...... was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians...

  13. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  14. Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication.

    Science.gov (United States)

    Song, Kun; Cui, Yichong; Zhang, Xijin; Pan, Yingji; Xu, Junli; Xu, Kaiqin; Da, Liangjun

    2017-10-01

    Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian-based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co-occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes' distribution under eutrophication stress.

  15. Seasonal species interactions minimize the impact of species turnover on the likelihood of community persistence.

    Science.gov (United States)

    Saavedra, Serguei; Rohr, Rudolf P; Fortuna, Miguel A; Selva, Nuria; Bascompte, Jordi

    2016-04-01

    Many of the observed species interactions embedded in ecological communities are not permanent, but are characterized by temporal changes that are observed along with abiotic and biotic variations. While work has been done describing and quantifying these changes, little is known about their consequences for species coexistence. Here, we investigate the extent to which changes of species composition impact the likelihood of persistence of the predator-prey community in the highly seasonal Białowieza Primeval Forest (northeast Poland), and the extent to which seasonal changes of species interactions (predator diet) modulate the expected impact. This likelihood is estimated extending recent developments on the study of structural stability in ecological communities. We find that the observed species turnover strongly varies the likelihood of community persistence between summer and winter. Importantly, we demonstrate that the observed seasonal interaction changes minimize the variation in the likelihood of persistence associated with species turnover across the year. We find that these community dynamics can be explained as the coupling of individual species to their environment by minimizing both the variation in persistence conditions and the interaction changes between seasons. Our results provide a homeostatic explanation for seasonal species interactions and suggest that monitoring the association of interactions changes with the level of variation in community dynamics can provide a good indicator of the response of species to environmental pressures.

  16. Species-Specific Effects on Ecosystem Functioning Can Be Altered by Interspecific Interactions.

    Science.gov (United States)

    Clare, David S; Spencer, Matthew; Robinson, Leonie A; Frid, Christopher L J

    2016-01-01

    Biological assemblages are constantly undergoing change, with species being introduced, extirpated and experiencing shifts in their densities. Theory and experimentation suggest that the impacts of such change on ecosystem functioning should be predictable based on the biological traits of the species involved. However, interspecific interactions could alter how species affect functioning, with the strength and sign of interactions potentially depending on environmental context (e.g. homogenous vs. heterogeneous conditions) and the function considered. Here, we assessed how concurrent changes to the densities of two common marine benthic invertebrates, Corophium volutator and Hediste diversicolor, affected the ecological functions of organic matter consumption and benthic-pelagic nutrient flux. Complementary experiments were conducted within homogenous laboratory microcosms and naturally heterogeneous field plots. When the densities of the species were increased within microcosms, interspecific interactions enhanced effects on organic matter consumption and reduced effects on nutrient flux. Trait-based predictions of how each species would affect functioning were only consistently supported when the density of the other species was low. In field plots, increasing the density of either species had a positive effect on organic matter consumption (with no significant interspecific interactions) but no effect on nutrient flux. Our results indicate that species-specific effects on ecosystem functioning can be altered by interspecific interactions, which can be either facilitative (positive) or antagonistic (negative) depending on the function considered. The impacts of biodiversity change may therefore not be predictable based solely on the biological traits of the species involved. Possible explanations for why interactions were detected in microcosms but not in the field are discussed.

  17. Species-Specific Effects on Ecosystem Functioning Can Be Altered by Interspecific Interactions.

    Directory of Open Access Journals (Sweden)

    David S Clare

    Full Text Available Biological assemblages are constantly undergoing change, with species being introduced, extirpated and experiencing shifts in their densities. Theory and experimentation suggest that the impacts of such change on ecosystem functioning should be predictable based on the biological traits of the species involved. However, interspecific interactions could alter how species affect functioning, with the strength and sign of interactions potentially depending on environmental context (e.g. homogenous vs. heterogeneous conditions and the function considered. Here, we assessed how concurrent changes to the densities of two common marine benthic invertebrates, Corophium volutator and Hediste diversicolor, affected the ecological functions of organic matter consumption and benthic-pelagic nutrient flux. Complementary experiments were conducted within homogenous laboratory microcosms and naturally heterogeneous field plots. When the densities of the species were increased within microcosms, interspecific interactions enhanced effects on organic matter consumption and reduced effects on nutrient flux. Trait-based predictions of how each species would affect functioning were only consistently supported when the density of the other species was low. In field plots, increasing the density of either species had a positive effect on organic matter consumption (with no significant interspecific interactions but no effect on nutrient flux. Our results indicate that species-specific effects on ecosystem functioning can be altered by interspecific interactions, which can be either facilitative (positive or antagonistic (negative depending on the function considered. The impacts of biodiversity change may therefore not be predictable based solely on the biological traits of the species involved. Possible explanations for why interactions were detected in microcosms but not in the field are discussed.

  18. Interactions between arsenic species and marine algae

    Energy Technology Data Exchange (ETDEWEB)

    Sanders, J.G.

    1978-01-01

    The arsenic concentration and speciation of marine algae varies widely, from 0.4 to 23 ng.mg/sup -1/, with significant differences in both total arsenic content and arsenic speciation occurring between algal classes. The Phaeophyceae contain more arsenic than other algal classes, and a greater proportion of the arsenic is organic. The concentration of inorganic arsenic is fairly constant in macro-algae, and may indicate a maximum level, with the excess being reduced and methylated. Phytoplankton take up As(V) readily, and incorporate a small percentage of it into the cell. The majority of the As(V) is reduced, methylated, and released to the surrounding media. The arsenic speciation in phytoplankton and Valonia also changes when As(V) is added to cultures. Arsenate and phosphate compete for uptake by algal cells. Arsenate inhibits primary production at concentrations as low as 5 ..mu..g.1/sup -1/ when the phosphate concentration is low. The inhibition is competitive. A phosphate enrichment of > 0.3 ..mu..M alleviates this inhibition; however, the As(V) stress causes an increase in the cell's phosphorus requirement. Arsenite is also toxic to phytoplankton at similar concentrations. Methylated arsenic species did not affect cell productivity, even at concentrations of 25 ..mu..g.1/sup -1/. Thus, the methylation of As(V) by the cell produces a stable, non-reactive compound which is nontoxic. The uptake and subsequent reduction and methylation of As(V) is a significant factor in determining the arsenic biogeochemistry of productive systems, and also the effect that the arsenic may have on algal productivity. Therefore, the role of marine algae in determining the arsenic speciation of marine systems cannot be ignored. (ERB)

  19. Generalist Bee Species on Brazilian Bee-Plant Interaction Networks

    Directory of Open Access Journals (Sweden)

    Astrid de Matos Peixoto Kleinert

    2012-01-01

    Full Text Available Determining bee and plant interactions has an important role on understanding general biology of bee species as well as the potential pollinating relationship between them. Bee surveys have been conducted in Brazil since the end of the 1960s. Most of them applied standardized methods and had identified the plant species where the bees were collected. To analyze the most generalist bees on Brazilian surveys, we built a matrix of bee-plant interactions. We estimated the most generalist bees determining the three bee species of each surveyed locality that presented the highest number of interactions. We found 47 localities and 39 species of bees. Most of them belong to Apidae (31 species and Halictidae (6 families and to Meliponini (14 and Xylocopini (6 tribes. However, most of the surveys presented Apis mellifera and/or Trigona spinipes as the most generalist species. Apis mellifera is an exotic bee species and Trigona spinipes, a native species, is also widespread and presents broad diet breath and high number of individuals per colony.

  20. Neutral Community Dynamics and the Evolution of Species Interactions.

    Science.gov (United States)

    Coelho, Marco Túlio P; Rangel, Thiago F

    2018-04-01

    A contemporary goal in ecology is to determine the ecological and evolutionary processes that generate recurring structural patterns in mutualistic networks. One of the great challenges is testing the capacity of neutral processes to replicate observed patterns in ecological networks, since the original formulation of the neutral theory lacks trophic interactions. Here, we develop a stochastic-simulation neutral model adding trophic interactions to the neutral theory of biodiversity. Without invoking ecological differences among individuals of different species, and assuming that ecological interactions emerge randomly, we demonstrate that a spatially explicit multitrophic neutral model is able to capture the recurrent structural patterns of mutualistic networks (i.e., degree distribution, connectance, nestedness, and phylogenetic signal of species interactions). Nonrandom species distribution, caused by probabilistic events of migration and speciation, create nonrandom network patterns. These findings have broad implications for the interpretation of niche-based processes as drivers of ecological networks, as well as for the integration of network structures with demographic stochasticity.

  1. Do predictions from Species Sensitivity Distributions match with field data?

    International Nuclear Information System (INIS)

    Smetanová, S.; Bláha, L.; Liess, M.; Schäfer, R.B.; Beketov, M.A.

    2014-01-01

    Species Sensitivity Distribution (SSD) is a statistical model that can be used to predict effects of contaminants on biological communities, but only few comparisons of this model with field studies have been conducted so far. In the present study we used measured pesticides concentrations from streams in Germany, France, and Finland, and we used SSD to calculate msPAF (multiple substance potentially affected fraction) values based on maximum toxic stress at localities. We compared these SSD-based predictions with the actual effects on stream invertebrates quantified by the SPEAR pesticides bioindicator. The results show that the msPAFs correlated well with the bioindicator, however, the generally accepted SSD threshold msPAF of 0.05 (5% of species are predicted to be affected) severely underestimated the observed effects (msPAF values causing significant effects are 2–1000-times lower). These results demonstrate that validation with field data is required to define the appropriate thresholds for SSD predictions. - Highlights: • We validated the statistical model Species Sensitivity Distribution with field data. • Good correlation was found between the model predictions and observed effects. • But, the generally accepted threshold msPAF 0.05 severely underestimated the effects. - Comparison of the SSD-based prediction with the field data evaluated with the SPEAR pesticides index shows that SSD threshold msPAF of 0.05 severely underestimates the effects observed in the field

  2. Predicting weed problems in maize cropping by species distribution modelling

    Directory of Open Access Journals (Sweden)

    Bürger, Jana

    2014-02-01

    Full Text Available Increasing maize cultivation and changed cropping practices promote the selection of typical maize weeds that may also profit strongly from climate change. Predicting potential weed problems is of high interest for plant production. Within the project KLIFF, experiments were combined with species distribution modelling for this task in the region of Lower Saxony, Germany. For our study, we modelled ecological and damage niches of nine weed species that are significant and wide spread in maize cropping in a number of European countries. Species distribution models describe the ecological niche of a species, these are the environmental conditions under which a species can maintain a vital population. It is also possible to estimate a damage niche, i.e. the conditions under which a species causes damage in agricultural crops. For this, we combined occurrence data of European national data bases with high resolution climate, soil and land use data. Models were also projected to simulated climate conditions for the time horizon 2070 - 2100 in order to estimate climate change effects. Modelling results indicate favourable conditions for typical maize weed occurrence virtually all over the study region, but only a few species are important in maize cropping. This is in good accordance with the findings of an earlier maize weed monitoring. Reaction to changing climate conditions is species-specific, for some species neutral (E. crus-galli, other species may gain (Polygonum persicaria or loose (Viola arvensis large areas of suitable habitats. All species with damage potential under present conditions will remain important in maize cropping, some more species will gain regional importance (Calystegia sepium, Setara viridis.

  3. Do competitive interactions in dry heathlands explain plant abundance patterns and species coexistence?

    DEFF Research Database (Denmark)

    Ransijn, Johannes; Damgaard, Christian; Schmidt, Inger K

    2015-01-01

    Plant community patterns in space and time may be explained by the interactions between competing plant species. The presented study investigates this in a nutrient and species poor ecosystem. The study presents a methodology for inferring competitive interactions from yearly vegetation inventories...... to predict the community dynamics of C. vulgaris and D. flexuosa. This was compared with the observed plant community structure at 198 Danish dry heathland sites. Interspecific competition will most likely lead to competitive exclusion of D. flexuosa at the observed temporal and spatial scale...... and uses this to assess the outcome of competitive interactions and to predict community patterns and dynamics in a Northwest-European dry heathland. Inferred competitive interactions from five consecutive years of measurements in permanent vegetation frames at a single dry heathland site were used...

  4. Multi-scale approach for predicting fish species distributions across coral reef seascapes.

    Directory of Open Access Journals (Sweden)

    Simon J Pittman

    Full Text Available Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT and Maximum Entropy Species Distribution Modelling (MaxEnt. The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9 for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9. In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy than BRT (68% map accuracy. We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support

  5. Development of an Integrated Moisture Index for predicting species composition

    Science.gov (United States)

    Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

    1996-01-01

    A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

  6. Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change

    Science.gov (United States)

    McCluney, Kevin E.; Belnap, Jayne; Collins, Scott L.; González, Angélica L.; Hagen, Elizabeth M.; Holland, J. Nathaniel; Kotler, Burt P.; Maestre, Fernando T.; Smith, Stanley D.; Wolf, Blair O.

    2012-01-01

    Species interactions play key roles in linking the responses of populations, communities, and ecosystems to environmental change. For instance, species interactions are an important determinant of the complexity of changes in trophic biomass with variation in resources. Water resources are a major driver of terrestrial ecology and climate change is expected to greatly alter the distribution of this critical resource. While previous studies have documented strong effects of global environmental change on species interactions in general, responses can vary from region to region. Dryland ecosystems occupy more than one-third of the Earth's land mass, are greatly affected by changes in water availability, and are predicted to be hotspots of climate change. Thus, it is imperative to understand the effects of environmental change on these globally significant ecosystems. Here, we review studies of the responses of population-level plant-plant, plant-herbivore, and predator-prey interactions to changes in water availability in dryland environments in order to develop new hypotheses and predictions to guide future research. To help explain patterns of interaction outcomes, we developed a conceptual model that views interaction outcomes as shifting between (1) competition and facilitation (plant-plant), (2) herbivory, neutralism, or mutualism (plant-herbivore), or (3) neutralism and predation (predator-prey), as water availability crosses physiological, behavioural, or population-density thresholds. We link our conceptual model to hypothetical scenarios of current and future water availability to make testable predictions about the influence of changes in water availability on species interactions. We also examine potential implications of our conceptual model for the relative importance of top-down effects and the linearity of patterns of change in trophic biomass with changes in water availability. Finally, we highlight key research needs and some possible broader impacts

  7. Setting realistic recovery targets for two interacting endangered species, sea otter and northern abalone.

    Science.gov (United States)

    Chadès, Iadine; Curtis, Janelle M R; Martin, Tara G

    2012-12-01

    Failure to account for interactions between endangered species may lead to unexpected population dynamics, inefficient management strategies, waste of scarce resources, and, at worst, increased extinction risk. The importance of species interactions is undisputed, yet recovery targets generally do not account for such interactions. This shortcoming is a consequence of species-centered legislation, but also of uncertainty surrounding the dynamics of species interactions and the complexity of modeling such interactions. The northern sea otter (Enhydra lutris kenyoni) and one of its preferred prey, northern abalone (Haliotis kamtschatkana), are endangered species for which recovery strategies have been developed without consideration of their strong predator-prey interactions. Using simulation-based optimization procedures from artificial intelligence, namely reinforcement learning and stochastic dynamic programming, we combined sea otter and northern abalone population models with functional-response models and examined how different management actions affect population dynamics and the likelihood of achieving recovery targets for each species through time. Recovery targets for these interacting species were difficult to achieve simultaneously in the absence of management. Although sea otters were predicted to recover, achieving abalone recovery targets failed even when threats to abalone such as predation and poaching were reduced. A management strategy entailing a 50% reduction in the poaching of northern abalone was a minimum requirement to reach short-term recovery goals for northern abalone when sea otters were present. Removing sea otters had a marginally positive effect on the abalone population but only when we assumed a functional response with strong predation pressure. Our optimization method could be applied more generally to any interacting threatened or invasive species for which there are multiple conservation objectives. © 2012 Society for

  8. SESAM – a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

    DEFF Research Database (Denmark)

    Guisan, Antoine; Rahbek, Carsten

    2011-01-01

    Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realized properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts...

  9. Biotic Interactions Shape the Ecological Distributions of Staphylococcus Species.

    Science.gov (United States)

    Kastman, Erik K; Kamelamela, Noelani; Norville, Josh W; Cosetta, Casey M; Dutton, Rachel J; Wolfe, Benjamin E

    2016-10-18

    Many metagenomic sequencing studies have observed the presence of closely related bacterial species or genotypes in the same microbiome. Previous attempts to explain these patterns of microdiversity have focused on the abiotic environment, but few have considered how biotic interactions could drive patterns of microbiome diversity. We dissected the patterns, processes, and mechanisms shaping the ecological distributions of three closely related Staphylococcus species in cheese rind biofilms. Paradoxically, the most abundant species (S. equorum) is the slowest colonizer and weakest competitor based on growth and competition assays in the laboratory. Through in vitro community reconstructions, we determined that biotic interactions with neighboring fungi help resolve this paradox. Species-specific stimulation of the poor competitor by fungi of the genus Scopulariopsis allows S. equorum to dominate communities in vitro as it does in situ Results of comparative genomic and transcriptomic experiments indicate that iron utilization pathways, including a homolog of the S. aureus staphyloferrin B siderophore operon pathway, are potential molecular mechanisms underlying Staphylococcus-Scopulariopsis interactions. Our integrated approach demonstrates that fungi can structure the ecological distributions of closely related bacterial species, and the data highlight the importance of bacterium-fungus interactions in attempts to design and manipulate microbiomes. Decades of culture-based studies and more recent metagenomic studies have demonstrated that bacterial species in agriculture, medicine, industry, and nature are unevenly distributed across time and space. The ecological processes and molecular mechanisms that shape these distributions are not well understood because it is challenging to connect in situ patterns of diversity with mechanistic in vitro studies in the laboratory. Using tractable cheese rind biofilms and a focus on coagulase-negative staphylococcus (CNS

  10. Macroecological signals of species interactions in the Danish avifauna

    DEFF Research Database (Denmark)

    Gotelli, N.J.; Graves, Christopher R.; Rahbek, C.

    2010-01-01

    that community-wide patterns of spatial segregation could not be attributed to the patchy distribution of habitat or to gross differences in habitat utilization among ecologically similar species. We hypothesize that, when habitat patch size is limited, conspecific attraction in concert with interspecific...... territoriality may result in spatially segregated distributions of ecologically similar species at larger spatial scales. In the Danish avifauna, the effects of species interactions on community assembly appear pervasive and can be discerned at grain sizes up to four orders of magnitude larger than those...

  11. Species coexistence: macroevolutionary relationships and the contingency of historical interactions.

    Science.gov (United States)

    Germain, Rachel M; Weir, Jason T; Gilbert, Benjamin

    2016-03-30

    Evolutionary biologists since Darwin have hypothesized that closely related species compete more intensely and are therefore less likely to coexist. However, recent theory posits that species diverge in two ways: either through the evolution of 'stabilizing differences' that promote coexistence by causing individuals to compete more strongly with conspecifics than individuals of other species, or through the evolution of 'fitness differences' that cause species to differ in competitive ability and lead to exclusion of the weaker competitor. We tested macroevolutionary patterns of divergence by competing pairs of annual plant species that differ in their phylogenetic relationships, and in whether they have historically occurred in the same region or different regions (sympatric versus allopatric occurrence). For sympatrically occurring species pairs, stabilizing differences rapidly increased with phylogenetic distance. However, fitness differences also increased with phylogenetic distance, resulting in coexistence outcomes that were unpredictable based on phylogenetic relationships. For allopatric species, stabilizing differences showed no trend with phylogenetic distance, whereas fitness differences increased, causing coexistence to become less likely among distant relatives. Our results illustrate the role of species' historical interactions in shaping how phylogenetic relationships structure competitive dynamics, and offer an explanation for the evolution of invasion potential of non-native species. © 2016 The Author(s).

  12. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  13. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  14. Climatic extremes improve predictions of spatial patterns of tree species

    Science.gov (United States)

    Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

    2009-01-01

    Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

  15. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  16. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  17. Yakima River species interactions studies annual report, 2000; ANNUAL

    International Nuclear Information System (INIS)

    Pearsons, Todd N.

    2001-01-01

    Species interactions research and monitoring was initiated in 1989 to investigate ecological interactions among fish in response to proposed supplementation of salmon and steelhead in the upper Yakima River basin. This is the ninth of a series of progress reports that address species interactions research and supplementation monitoring of fishes in the Yakima River basin. Data have been collected prior to supplementation to characterize the ecology and demographics of non-target taxa (NTT) and target taxon, and develop methods to monitor interactions and supplementation success. Major topics of this report are associated with the chronology of ecological interactions that occur throughout a supplementation program, implementing NTT monitoring prescriptions for detecting potential impacts of hatchery supplementation, hatchery fish interactions, and monitoring fish predation indices. This report is organized into four chapters, with a general introduction preceding the first chapter. This annual report summarizes data collected primarily by the Washington Department of Fish and Wildlife (WDFW) between January 1, 2000 and December 31, 2000 in the Yakima basin, however these data were compared to data from previous years to identify preliminary trends and patterns. Summaries of each of the chapters included in this report are described

  18. Species Coexistence in Nitrifying Chemostats: A Model of Microbial Interactions

    Directory of Open Access Journals (Sweden)

    Maxime Dumont

    2016-12-01

    Full Text Available In a previous study, the two nitrifying functions (ammonia oxidizing bacteria (AOB or nitrite-oxidizing bacteria (NOB of a nitrification reactor—operated continuously over 525 days with varying inputs—were assigned using a mathematical modeling approach together with the monitoring of bacterial phylotypes. Based on these theoretical identifications, we develop here a chemostat model that does not explicitly include only the resources’ dynamics (different forms of soluble nitrogen but also explicitly takes into account microbial inter- and intra-species interactions for the four dominant phylotypes detected in the chemostat. A comparison of the models obtained with and without interactions has shown that such interactions permit the coexistence of two competing ammonium-oxidizing bacteria and two competing nitrite-oxidizing bacteria in competition for ammonium and nitrite, respectively. These interactions are analyzed and discussed.

  19. Does scale matter? A systematic review of incorporating biological realism when predicting changes in species distributions.

    Science.gov (United States)

    Record, Sydne; Strecker, Angela; Tuanmu, Mao-Ning; Beaudrot, Lydia; Zarnetske, Phoebe; Belmaker, Jonathan; Gerstner, Beth

    2018-01-01

    There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

  20. Dynamic Seascapes Predict the Marine Occurrence of an Endangered Species

    Science.gov (United States)

    Breece, M.; Fox, D. A.; Dunton, K. J.; Frisk, M. G.; Jordaan, A.; Oliver, M. J.

    2016-12-01

    Landscapes are powerful environmental partitions that index complex biogeochemical processes that drive terrestrial species distributions. However, translating landscapes into seascapes requires that the dynamic nature of the fluid environment be reflected in spatial and temporal boundaries such that seascapes can be used in marine species distribution models and conservation decisions. A seascape product derived from satellite ocean color and sea surface temperature partitioned mid-Atlantic coastal waters on scales commensurate with the Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus coastal migration. The seascapes were then matched with acoustic telemetry records of Atlantic Sturgeon to determine seascape selectivity. To test our model, we used real-time satellite seascape maps to normalize the sampling of an autonomous underwater vehicle that resampled similar geographic regions with time varying seascape classifications. We found that Atlantic Sturgeon exhibited preference for one seascape class over those available in the coastal ocean, indicating selection for environmental properties that co-varied with the dynamic seascape class rather than geographical location. The recent listing of Atlantic Sturgeon as Endangered throughout much of their United States range has highlighted the need for improved understanding of their occurrence in marine waters to reduce interactions with various anthropogenic stressors. Narrow dynamic migration corridors may enable seascapes to be used as a daily decision tool by industry and managers to reduce interactions with this Endangered Species during coastal migrations.

  1. Construction of analytically solvable models for interacting species. [biological species competition

    Science.gov (United States)

    Rosen, G.

    1976-01-01

    The basic form of a model representation for systems of n interacting biological species is a set of essentially nonlinear autonomous ordinary differential equations. A generic canonical expression for the rate functions in the equations is reported which permits the analytical general solution to be obtained by elementary computation. It is shown that a general analytical solution is directly obtainable for models where the rate functions are prescribed by the generic canonical expression from the outset. Some illustrative examples are given which demonstrate that the generic canonical expression can be used to construct analytically solvable models for two interacting species with limit-cycle dynamics as well as for a three-species interdependence.

  2. Competitive interactions between native and invasive exotic plant species are altered under elevated carbon dioxide.

    Science.gov (United States)

    Manea, Anthony; Leishman, Michelle R

    2011-03-01

    We hypothesized that the greater competitive ability of invasive exotic plants relative to native plants would increase under elevated CO(2) because they typically have traits that confer the ability for fast growth when resources are not limiting and thus are likely to be more responsive to elevated CO(2). A series of competition experiments under ambient and elevated CO(2) glasshouse conditions were conducted to determine an index of relative competition intensity for 14 native-invasive exotic species-pairs. Traits including specific leaf area, leaf mass ratio, leaf area ratio, relative growth rate, net assimilation rate and root weight ratio were measured. Competitive rankings within species-pairs were not affected by CO(2) concentration: invasive exotic species were more competitive in 9 of the 14 species-pairs and native species were more competitive in the remaining 5 species-pairs, regardless of CO(2) concentration. However, there was a significant interaction between plant type and CO(2) treatment due to reduced competitive response of native species under elevated compared with ambient CO(2) conditions. Native species had significantly lower specific leaf area and leaf area ratio under elevated compared with ambient CO(2). We also compared traits of more-competitive with less-competitive species, regardless of plant type, under both CO(2) treatments. More-competitive species had smaller leaf weight ratio and leaf area ratio, and larger relative growth rate and net assimilation rate under both ambient and elevated CO(2) conditions. These results suggest that growth and allocation traits can be useful predictors of the outcome of competitive interactions under both ambient and elevated CO(2) conditions. Under predicted future atmospheric CO(2) conditions, competitive rankings among species may not change substantially, but the relative success of invasive exotic species may be increased. Thus, under future atmospheric CO(2) conditions, the ecological and

  3. Charge-transfer interactions of Cr species with DNA.

    Science.gov (United States)

    Nowicka, Anna M; Matysiak-Brynda, Edyta; Hepel, Maria

    2017-10-01

    Interactions of Cr species with nucleic acids in living organisms depend strongly on Cr oxidation state and the environmental conditions. As the effects of these interactions range from benign to pre-mutagenic to carcinogenic, careful assessment of the hazard they pose to human health is necessary. We have investigated methods that would enable quantifying the DNA damage caused by Cr species under varying environmental conditions, including UV, O 2 , and redox potential, using simple instrumental techniques which could be in future combined into a field-deployable instrumentation. We have employed electrochemical quartz crystal nanogravimetry (EQCN), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) to evaluate the extent of DNA damage expressed in terms of guanine oxidation yield (η) and changes in specific characteristics provided by these techniques. The effects of the interactions of Cr species with DNA were analyzed using a model calf thymus DNA (ctDNA) film on a gold electrode (Au@ctDNA) in different media, including: (i) Cr(VI), (ii) Cr(VI) reduced at -0.2V, (iii) Cr(III)+UV radiation+O 2 , and Cr(III), obtaining the η values: 7.4±1.4, 1.5±0.4, 1.1±0.31%, and 0%, respectively, thus quantifying the hazard posed. The EIS measurements have enabled utilizing the decrease in charge-transfer resistance (R ct ) for ferri/ferrocyanide redox probe at an Au@ctDNA electrode to assess the oxidative ctDNA damage by Cr(VI) species. In this case, circular dichroism indicates an extensive damage to the ctDNA hydrogen bonding. On the other hand, Cr(III) species have not induced any damage to ctDNA, although the EQCN measurements show an electrostatic binding to DNA. Copyright © 2017 Elsevier Inc. All rights reserved.

  4. Species as Stressors: Heterospecific Interactions and the Cellular Stress Response under Global Change.

    Science.gov (United States)

    Gunderson, Alex R; King, Emily E; Boyer, Kirsten; Tsukimura, Brian; Stillman, Jonathon H

    2017-07-01

    Anthropogenic global change is predicted to increase the physiological stress of organisms through changes in abiotic conditions such as temperature, pH, and pollution. However, organisms can also experience physiological stress through interactions with other species, especially parasites, predators, and competitors. The stress of species interactions could be an important driver of species' responses to global change as the composition of biological communities change through factors such as distributional and phenological shifts. Interactions between biotic and abiotic stressors could also induce non-linear physiological stress responses under global change. One of the primary means by which organisms deal with physiological stress is through the cellular stress response (CSR), which is broadly the upregulation of a conserved set of genes that facilitate the removal and repair of damaged macromolecules. Here, we present data on behavioral interactions and CSR gene expression for two competing species of intertidal zone porcelain crab (Petrolisthes cinctipes and Petrolisthes manimaculis). We found that P. cinctipes and P. manimaculis engage in more agonistic behaviors when interacting with heterospecifics than conspecifics; however, we found no evidence that heterospecific interactions induced a CSR in these species. In addition to our new data, we review the literature with respect to CSR induction via species interactions, focusing on predator-prey systems and heterospecific competition. We find extensive evidence for predators to induce cellular stress and aspects of the CSR in prey, even in the absence of direct physical contact between species. Effects of heterospecific competition on the CSR have been studied far less, but we do find evidence that agonistic interactions with heterospecifics can induce components of the CSR. Across all published studies, there is clear evidence that species interactions can lead to cellular stress and induction of the CSR

  5. Ecological interactions in Aedes species on Reunion Island.

    Science.gov (United States)

    Bagny Beilhe, L; Delatte, H; Juliano, S A; Fontenille, D; Quilici, S

    2013-12-01

    Two invasive, container-breeding mosquito species, Aedes aegypti (Stegomyia aegypti) and Aedes albopictus (Stegomyia albopicta) (Diptera: Culicidae), have different distribution patterns on Reunion Island. Aedes albopictus occurs in all areas and Ae. aegypti colonizes only some restricted areas already occupied by Ae. albopictus. This study investigates the abiotic and biotic ecological mechanisms that determine the distribution of Aedes species on Reunion Island. Life history traits (duration of immature stages, survivorship, fecundity, estimated finite rate of increase) in Ae. aegypti and Ae. albopictus were compared at different temperatures. These fitness measures were characterized in both species in response to competitive interactions among larvae. Aedes aegypti was drastically affected by temperature, performing well only at around 25 °C, at which it achieved its highest survivorship and greatest estimated rate of increase. The narrow distribution of this species in the field on Reunion Island may thus relate to its poor ability to cope with unfavourable temperatures. Aedes aegypti was also more negatively affected by high population densities and to some extent by interactions with Ae. albopictus, particularly in the context of limited food supplies. Aedes albopictus exhibited better population performance across a range of environmental conditions. Its ecological plasticity and its superior competitive ability relative to its congener may further enhance its invasion success on Reunion Island. © 2012 The Royal Entomological Society.

  6. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  7. Yakima River Species Interactions Studies, Annual Report 1998

    International Nuclear Information System (INIS)

    Pearsons, Todd N.; Ham, Kenneth D.; McMichael, Geoffrey A.

    1999-01-01

    Species interactions research and monitoring was initiated in 1989 to investigate ecological interactions among fish in response to proposed supplementation of salmon and steelhead in the upper Yakima River basin. This is the seventh of a series of progress reports that address species interactions research and pre-supplementation monitoring of fishes in the Yakima River basin. Data have been collected prior to supplementation to characterize the ecology and demographics of non-target taxa (NTT) and target taxon, and develop methods to monitor interactions and supplementation success. Major topics of this report are associated with monitoring potential impacts to support adaptive management of NTT and baseline monitoring of fish predation indices on spring chinook salmon smolts. This report is organized into three chapters, with a general introduction preceding the first chapter. This annual report summarizes data collected primarily by the Washington Department of Fish and Wildlife (WDFW) between January 1, 1998 and December 31, 1998 in the Yakima basin, however these data were compared to data from previous years to identify preliminary trends and patterns

  8. Using citizen science butterfly counts to predict species population trends.

    Science.gov (United States)

    Dennis, Emily B; Morgan, Byron J T; Brereton, Tom M; Roy, David B; Fox, Richard

    2017-12-01

    Citizen scientists are increasingly engaged in gathering biodiversity information, but trade-offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011-2014) of a short-duration citizen science project (Big Butterfly Count [BBC]) with those from long-running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3-week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3-week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short-duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species' flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass-participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass-participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land-use types), as well as serving to reconnect an increasingly urban human population with nature. © 2017 The Authors. Conservation Biology published

  9. Hydraulic lift and tolerance to salinity of semiarid species: consequences for species interactions.

    Science.gov (United States)

    Armas, Cristina; Padilla, Francisco M; Pugnaire, Francisco I; Jackson, Robert B

    2010-01-01

    The different abilities of plant species to use ephemeral or permanent water sources strongly affect physiological performance and species coexistence in water-limited ecosystems. In addition to withstanding drought, plants in coastal habitats often have to withstand highly saline soils, an additional ecological stress. Here we tested whether observed competitive abilities and C-water relations of two interacting shrub species from an arid coastal system were more related to differences in root architecture or salinity tolerance. We explored water sources of interacting Juniperus phoenicea Guss. and Pistacia lentiscus L. plants by conducting physiology measurements, including water relations, CO2 exchange, photochemical efficiency, sap osmolality, and water and C isotopes. We also conducted parallel soil analyses that included electrical conductivity, humidity, and water isotopes. During drought, Pistacia shrubs relied primarily on permanent salty groundwater, while isolated Juniperus plants took up the scarce and relatively fresh water stored in upper soil layers. As drought progressed further, the physiological activity of Juniperus plants nearly stopped while Pistacia plants were only slightly affected. Juniperus plants growing with Pistacia had stem-water isotopes that matched Pistacia, unlike values for isolated Juniperus plants. This result suggests that Pistacia shrubs supplied water to nearby Juniperus plants through hydraulic lift. This lifted water, however, did not appear to benefit Juniperus plants, as their physiological performance with co-occurring Pistacia plants was poor, including lower water potentials and rates of photosynthesis than isolated plants. Juniperus was more salt sensitive than Pistacia, which withstood salinity levels similar to that of groundwater. Overall, the different abilities of the two species to use salty water appear to drive the outcome of their interaction, resulting in asymmetric competition where Juniperus is negatively

  10. Predicting Achievable Fundamental Frequency Ranges in Vocalization Across Species.

    Directory of Open Access Journals (Sweden)

    Ingo Titze

    2016-06-01

    Full Text Available Vocal folds are used as sound sources in various species, but it is unknown how vocal fold morphologies are optimized for different acoustic objectives. Here we identify two main variables affecting range of vocal fold vibration frequency, namely vocal fold elongation and tissue fiber stress. A simple vibrating string model is used to predict fundamental frequency ranges across species of different vocal fold sizes. While average fundamental frequency is predominantly determined by vocal fold length (larynx size, range of fundamental frequency is facilitated by (1 laryngeal muscles that control elongation and by (2 nonlinearity in tissue fiber tension. One adaptation that would increase fundamental frequency range is greater freedom in joint rotation or gliding of two cartilages (thyroid and cricoid, so that vocal fold length change is maximized. Alternatively, tissue layers can develop to bear a disproportionate fiber tension (i.e., a ligament with high density collagen fibers, increasing the fundamental frequency range and thereby vocal versatility. The range of fundamental frequency across species is thus not simply one-dimensional, but can be conceptualized as the dependent variable in a multi-dimensional morphospace. In humans, this could allow for variations that could be clinically important for voice therapy and vocal fold repair. Alternative solutions could also have importance in vocal training for singing and other highly-skilled vocalizations.

  11. Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs.

    Science.gov (United States)

    Huo, Tong; Liu, Wei; Guo, Yu; Yang, Cheng; Lin, Jianping; Rao, Zihe

    2015-03-26

    Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease. We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by 'interolog' method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins. This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

  12. Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change.

    Science.gov (United States)

    McCluney, Kevin E; Belnap, Jayne; Collins, Scott L; González, Angélica L; Hagen, Elizabeth M; Nathaniel Holland, J; Kotler, Burt P; Maestre, Fernando T; Smith, Stanley D; Wolf, Blair O

    2012-08-01

    Species interactions play key roles in linking the responses of populations, communities, and ecosystems to environmental change. For instance, species interactions are an important determinant of the complexity of changes in trophic biomass with variation in resources. Water resources are a major driver of terrestrial ecology and climate change is expected to greatly alter the distribution of this critical resource. While previous studies have documented strong effects of global environmental change on species interactions in general, responses can vary from region to region. Dryland ecosystems occupy more than one-third of the Earth's land mass, are greatly affected by changes in water availability, and are predicted to be hotspots of climate change. Thus, it is imperative to understand the effects of environmental change on these globally significant ecosystems. Here, we review studies of the responses of population-level plant-plant, plant-herbivore, and predator-prey interactions to changes in water availability in dryland environments in order to develop new hypotheses and predictions to guide future research. To help explain patterns of interaction outcomes, we developed a conceptual model that views interaction outcomes as shifting between (1) competition and facilitation (plant-plant), (2) herbivory, neutralism, or mutualism (plant-herbivore), or (3) neutralism and predation (predator-prey), as water availability crosses physiological, behavioural, or population-density thresholds. We link our conceptual model to hypothetical scenarios of current and future water availability to make testable predictions about the influence of changes in water availability on species interactions. We also examine potential implications of our conceptual model for the relative importance of top-down effects and the linearity of patterns of change in trophic biomass with changes in water availability. Finally, we highlight key research needs and some possible broader impacts

  13. Predicting drug-target interactions using restricted Boltzmann machines.

    Science.gov (United States)

    Wang, Yuhao; Zeng, Jianyang

    2013-07-01

    In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are

  14. Drug-Target Interactions: Prediction Methods and Applications.

    Science.gov (United States)

    Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

    2018-01-01

    Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  16. Drug-target interaction prediction from PSSM based evolutionary information.

    Science.gov (United States)

    Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

    2016-01-01

    The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization.

    Science.gov (United States)

    Ezzat, Ali; Zhao, Peilin; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2017-01-01

    Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

  18. What Predicts Use of Learning-Centered, Interactive Engagement Methods?

    Science.gov (United States)

    Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

    2014-01-01

    What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

  19. Modeling plant interspecific interactions from experiments with perennial crop mixtures to predict optimal combinations.

    Science.gov (United States)

    Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

    2017-12-01

    The contribution of plant species richness to productivity and ecosystem functioning is a longstanding issue in ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modeling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modeled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e., a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficients- from, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modeling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. © 2017 by the Ecological Society of America.

  20. Biological interactions and cooperative management of multiple species.

    Science.gov (United States)

    Jiang, Jinwei; Min, Yong; Chang, Jie; Ge, Ying

    2017-01-01

    Coordinated decision making and actions have become the primary solution for the overexploitation of interacting resources within ecosystems. However, the success of coordinated management is highly sensitive to biological, economic, and social conditions. Here, using a game theoretic framework and a 2-species model that considers various biological relationships (competition, predation, and mutualism), we compute cooperative (or joint) and non-cooperative (or separate) management equilibrium outcomes of the model and investigate the effects of the type and strength of the relationships. We find that cooperation does not always show superiority to non-cooperation in all biological interactions: (1) if and only if resources are involved in high-intensity predation relationships, cooperation can achieve a win-win scenario for ecosystem services and resource diversity; (2) for competitive resources, cooperation realizes higher ecosystem services by sacrificing resource diversity; and (3) for mutual resources, cooperation has no obvious advantage for either ecosystem services or resource evenness but can slightly improve resource abundance. Furthermore, by using a fishery model of the North California Current Marine Ecosystem with 63 species and seven fleets, we demonstrate that the theoretical results can be reproduced in real ecosystems. Therefore, effective ecosystem management should consider the interconnection between stakeholders' social relationship and resources' biological relationships.

  1. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    Directory of Open Access Journals (Sweden)

    Ji-Wei Chang

    2016-11-01

    Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

  2. Predator-prey interactions as macro-scale drivers of species diversity in mammals

    DEFF Research Database (Denmark)

    Sandom, Christopher James; Sandel, Brody Steven; Dalby, Lars

    Background/Question/Methods Understanding the importance of predator-prey interactions for species diversity is a central theme in ecology, with fundamental consequences for predicting the responses of ecosystems to land use and climate change. We assessed the relative support for different...... mechanistic drivers of mammal species richness at macro-scales for two trophic levels: predators and prey. To disentangle biotic (i.e. functional predator-prey interactions) from abiotic (i.e. environmental) and bottom-up from top-down determinants we considered three hypotheses: 1) environmental factors...... that determine ecosystem productivity drive prey and predator richness (the productivity hypothesis, abiotic, bottom-up), 2) consumer richness is driven by resource diversity (the resource diversity hypothesis, biotic, bottom-up) and 3) consumers drive richness of their prey (the top-down hypothesis, biotic, top...

  3. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  4. Species interactions and the effects of climate variability on a wetland amphibian metacommunity

    Science.gov (United States)

    Davis, Courtney L.; Miller, David A.W.; Walls, Susan C.; Barichivich, William J.; Riley, Jeffrey W.; Brown, Mary E.

    2017-01-01

    Disentangling the role that multiple interacting factors have on species responses to shifting climate poses a significant challenge. However, our ability to do so is of utmost importance to predict the effects of climate change on species distributions. We examined how populations of three species of wetland-breeding amphibians, which varied in life history requirements, responded to a six-year period of extremely variable precipitation. This interval was punctuated by both extensive drought and heavy precipitation and flooding, providing a natural experiment to measure community responses to environmental perturbations. We estimated occurrence dynamics using a discrete hidden Markov modeling approach that incorporated information regarding habitat state and predator–prey interactions. This approach allowed us to measure how metapopulation dynamics of each amphibian species was affected by interactions among weather, wetland hydroperiod, and co-occurrence with fish predators. The pig frog, a generalist, proved most resistant to perturbations, with both colonization and persistence being unaffected by seasonal variation in precipitation or co-occurrence with fishes. The ornate chorus frog, an ephemeral wetland specialist, responded positively to periods of drought owing to increased persistence and colonization rates during periods of low-rainfall. Low probabilities of occurrence of the ornate chorus frog in long-duration wetlands were driven by interactions with predators due to low colonization rates when fishes were present. The mole salamander was most sensitive to shifts in water availability. In our study area, this species never occurred in short-duration wetlands and persistence probabilities decreased during periods of drought. At the same time, negative effects occurred with extreme precipitation because flooding facilitated colonization of fishes to isolated wetlands and mole salamanders did not colonize wetlands once fishes were present. We

  5. Predicting behavior during interracial interactions: a stress and coping approach.

    Science.gov (United States)

    Trawalter, Sophie; Richeson, Jennifer A; Shelton, J Nicole

    2009-11-01

    The social psychological literature maintains unequivocally that interracial contact is stressful. Yet research and theory have rarely considered how stress may shape behavior during interracial interactions. To address this empirical and theoretical gap, the authors propose a framework for understanding and predicting behavior during interracial interactions rooted in the stress and coping literature. Specifically, they propose that individuals often appraise interracial interactions as a threat, experience stress, and therefore cope-they antagonize, avoid, freeze, or engage. In other words, the behavioral dynamics of interracial interactions can be understood as initial stress reactions and subsequent coping responses. After articulating the framework and its predictions for behavior during interracial interactions, the authors examine its ability to organize the extant literature on behavioral dynamics during interracial compared with same-race contact. They conclude with a discussion of the implications of the stress and coping framework for improving research and fostering more positive interracial contact.

  6. An experimental test of fitness variation across a hydrologic gradient predicts willow and poplar species distributions.

    Science.gov (United States)

    Wei, Xiaojing; Savage, Jessica A; Riggs, Charlotte E; Cavender-Bares, Jeannine

    2017-05-01

    Environmental filtering is an important community assembly process influencing species distributions. Contrasting species abundance patterns along environmental gradients are commonly used to provide evidence for environmental filtering. However, the same abundance patterns may result from alternative or concurrent assembly processes. Experimental tests are an important means to decipher whether species fitness varies with environment, in the absence of dispersal constraints and biotic interactions, and to draw conclusions about the importance of environmental filtering in community assembly. We performed an experimental test of environmental filtering in 14 closely related willow and poplar species (family Salicaceae) by transplanting cuttings of each species into 40 common gardens established along a natural hydrologic gradient in the field, where competition was minimized and herbivory was controlled. We analyzed species fitness responses to the hydrologic environment based on cumulative growth and survival over two years using aster fitness models. We also examined variation in nine drought and flooding tolerance traits expected to contribute to performance based on a priori understanding of plant function in relation to water availability and stress. We found substantial evidence that environmental filtering along the hydrologic gradient played a critical role in determining species distributions. Fitness variation of each species in the field experiment was used to model their water table depth optima. These optima predicted 68% of the variation in species realized hydrologic niches based on peak abundance in naturally assembled communities in the surrounding region. Multiple traits associated with water transport efficiency and water stress tolerance were correlated with species hydrologic niches, but they did not necessarily covary with each other. As a consequence, species occupying similar hydrologic niches had different combinations of trait values

  7. Deep-Learning-Based Drug-Target Interaction Prediction.

    Science.gov (United States)

    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-04-07

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  8. Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models

    Science.gov (United States)

    Chunco, Amanda J.; Phimmachak, Somphouthone; Sivongxay, Niane; Stuart, Bryan L.

    2013-01-01

    The Lao newt (Laotriton laoensis) is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC) values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation) in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed. PMID:23555808

  9. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  10. Species Interactions Drive Fish Biodiversity Loss in a High-CO2 World.

    Science.gov (United States)

    Nagelkerken, Ivan; Goldenberg, Silvan U; Ferreira, Camilo M; Russell, Bayden D; Connell, Sean D

    2017-07-24

    Accelerating climate change is eroding the functioning and stability of ecosystems by weakening the interactions among species that stabilize biological communities against change [1]. A key challenge to forecasting the future of ecosystems centers on how to extrapolate results from short-term, single-species studies to community-level responses that are mediated by key mechanisms such as competition, resource availability (bottom-up control), and predation (top-down control) [2]. We used CO 2 vents as potential analogs of ocean acidification combined with in situ experiments to test current predictions of fish biodiversity loss and community change due to elevated CO 2 [3] and to elucidate the potential mechanisms that drive such change. We show that high risk-taking behavior and competitive strength, combined with resource enrichment and collapse of predator populations, fostered already common species, enabling them to double their populations under acidified conditions. However, the release of these competitive dominants from predator control led to suppression of less common and subordinate competitors that did not benefit from resource enrichment and reduced predation. As a result, local biodiversity was lost and novel fish community compositions were created under elevated CO 2 . Our study identifies the species interactions most affected by ocean acidification, revealing potential sources of natural selection. We also reveal how diminished predator abundances can have cascading effects on local species diversity, mediated by complex species interactions. Reduced overfishing of predators could therefore act as a key action to stall diversity loss and ecosystem change in a high-CO 2 world. VIDEO ABSTRACT. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Plant species distribution along environmental gradient: do belowground interactions with fungi matter?

    Directory of Open Access Journals (Sweden)

    Loïc ePellissier

    2013-12-01

    Full Text Available The distribution of plants along environmental gradients is constrained by abiotic and biotic factors. Cumulative evidence attests of the impact of abiotic factors on plant distributions, but only few studies discuss the role of belowground communities. Soil fungi, in particular, are thought to play an important role in how plant species assemble locally into communities. We first review existing evidence, and then test the effect of the number of soil fungal operational taxonomic units (OTUs on plant species distributions using a recently collected dataset of plant and metagenomic information on soil fungi in the Western Swiss Alps. Using species distribution models, we investigated whether the distribution of individual plant species is correlated to the number of OTUs of two important soil fungal classes known to interact with plants: the Glomeromycetes, that are obligatory symbionts of plants, and the Agaricomycetes, that may be facultative plant symbionts, pathogens, or wood decayers. We show that including the fungal richness information in the models of plant species distributions improves predictive accuracy. Number of fungal OTUs is especially correlated to the distribution of high elevation plant species. We suggest that high elevation soil show greater variation in fungal assemblages that may in turn impact plant turnover among communities. We finally discuss how to move beyond correlative analyses, through the design of field experiments manipulating plant and fungal communities along environmental gradients.

  12. Temperature-Dependent Species Interactions Shape Priority Effects and the Persistence of Unequal Competitors.

    Science.gov (United States)

    Grainger, Tess Nahanni; Rego, Adam Ivan; Gilbert, Benjamin

    2018-02-01

    The order of species arrival at a site can determine the outcome of competitive interactions when early arrivers alter the environment or deplete shared resources. These priority effects are predicted to be stronger at high temperatures, as higher vital rates caused by warming allow early arrivers to more rapidly impact a shared environment. We tested this prediction using a pair of congeneric aphid species that specialize on milkweed plants. We manipulated temperature and arrival order of the two aphid species and measured aphid population dynamics and milkweed survival and defensive traits. We found that warming increased the impact of aphids on the quantity and quality of milkweed, which amplified the importance of priority effects by increasing the competitive exclusion of the inferior competitor when it arrived late. Warming also enhanced interspecific differences in dispersal, which could alter relative arrival times at a regional scale. Our experiment provides a first link between temperature-dependent trophic interactions, priority effects, and dispersal. This study suggests that the indirect and cascading effects of temperature observed here may be important determinants of diversity in the temporally and spatially complex landscapes that characterize ecological communities.

  13. Reactive oxygen species, essential molecules, during plant-pathogen interactions.

    Science.gov (United States)

    Camejo, Daymi; Guzmán-Cedeño, Ángel; Moreno, Alexander

    2016-06-01

    Reactive oxygen species (ROS) are continually generated as a consequence of the normal metabolism in aerobic organisms. Accumulation and release of ROS into cell take place in response to a wide variety of adverse environmental conditions including salt, temperature, cold stresses and pathogen attack, among others. In plants, peroxidases class III, NADPH oxidase (NOX) locates in cell wall and plasma membrane, respectively, may be mainly enzymatic systems involving ROS generation. It is well documented that ROS play a dual role into cells, acting as important signal transduction molecules and as toxic molecules with strong oxidant power, however some aspects related to its function during plant-pathogen interactions remain unclear. This review focuses on the principal enzymatic systems involving ROS generation addressing the role of ROS as signal molecules during plant-pathogen interactions. We described how the chloroplasts, mitochondria and peroxisomes perceive the external stimuli as pathogen invasion, and trigger resistance response using ROS as signal molecule. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  14. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

  15. Predicting and detecting reciprocity between indirect ecological interactions and evolution.

    Science.gov (United States)

    Estes, James A; Brashares, Justin S; Power, Mary E

    2013-05-01

    Living nature can be thought of as a tapestry, defined not only by its constituent parts but also by how these parts are woven together. The weaving of this tapestry is a metaphor for species interactions, which can be divided into three broad classes: competitive, mutualistic, and consumptive. Direct interactions link together as more complex networks, for example, the joining of consumptive interactions into food webs. Food web dynamics are driven, in turn, by changes in the abundances of web members, whose numbers or biomass respond to bottom-up (resource limitation) and top-down (consumer limitation) forcing. The relative strengths of top-down and bottom-up forcing on the abundance of a given web member depend on its ecological context, including its topological position within the food web. Top-down effects by diverse consumers are nearly ubiquitous, in many cases influencing the structure and operation of ecosystems. While the ecological effects of such interactions are well known, far less is known of their evolutionary consequences. In this essay, we describe sundry consequences of these interaction chains on species and ecosystem processes, explain several known or suspected evolutionary effects of consumer-induced interaction chains, and identify areas where reciprocity between ecology and evolution involving the indirect effects of consumer-prey interaction chains might be further explored.

  16. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-03-16

    Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

  17. PREDICTING FIELD PERFORMANCE OF HERBACEOUS SPECIES FOR PHYTOREMEDIATION OF PERCHLORATE

    Science.gov (United States)

    Results of these short-term experiments coupled with ecological knowledge of the nine herbaceous plant species tested suggest that several species may by successful in on-site remediation of perchlorate. The two wetland species which appear to be most suitable for field experimen...

  18. Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

    Directory of Open Access Journals (Sweden)

    Xia Jiang

    Full Text Available The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS datasets, which involve millions of single nucleotide polymorphism (SNPs, where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly

  19. Assessment of CFD capability for prediction of hypersonic shock interactions

    Science.gov (United States)

    Knight, Doyle; Longo, José; Drikakis, Dimitris; Gaitonde, Datta; Lani, Andrea; Nompelis, Ioannis; Reimann, Bodo; Walpot, Louis

    2012-01-01

    The aerothermodynamic loadings associated with shock wave boundary layer interactions (shock interactions) must be carefully considered in the design of hypersonic air vehicles. The capability of Computational Fluid Dynamics (CFD) software to accurately predict hypersonic shock wave laminar boundary layer interactions is examined. A series of independent computations performed by researchers in the US and Europe are presented for two generic configurations (double cone and cylinder) and compared with experimental data. The results illustrate the current capabilities and limitations of modern CFD methods for these flows.

  20. Species richness alone does not predict cultural ecosystem service value

    Science.gov (United States)

    Graves, Rose A.; Pearson, Scott M.; Turner, Monica G.

    2017-01-01

    Many biodiversity-ecosystem services studies omit cultural ecosystem services (CES) or use species richness as a proxy and assume that more species confer greater CES value. We studied wildflower viewing, a key biodiversity-based CES in amenity-based landscapes, in Southern Appalachian Mountain forests and asked (i) How do aesthetic preferences for wildflower communities vary with components of biodiversity, including species richness?; (ii) How do aesthetic preferences for wildflower communities vary across psychographic groups?; and (iii) How well does species richness perform as an indicator of CES value compared with revealed social preferences for wildflower communities? Public forest visitors (n = 293) were surveyed during the summer of 2015 and asked to choose among images of wildflower communities in which flower species richness, flower abundance, species evenness, color diversity, and presence of charismatic species had been digitally manipulated. Aesthetic preferences among images were unrelated to species richness but increased with more abundant flowers, greater species evenness, and greater color diversity. Aesthetic preferences were consistent across psychographic groups and unaffected by knowledge of local flora or value placed on wildflower viewing. When actual wildflower communities (n = 54) were ranked based on empirically measured flower species richness or wildflower viewing utility based on multinomial logit models of revealed preferences, rankings were broadly similar. However, designation of hotspots (CES values above the median) based on species richness alone missed 27% of wildflower viewing utility hotspots. Thus, conservation priorities for sustaining CES should incorporate social preferences and consider multiple dimensions of biodiversity that underpin CES supply. PMID:28320953

  1. Antagonistic interactions between an invasive alien and a native coccinellid species may promote coexistence.

    Science.gov (United States)

    Hentley, William T; Vanbergen, Adam J; Beckerman, Andrew P; Brien, Melanie N; Hails, Rosemary S; Jones, T Hefin; Johnson, Scott N

    2016-07-01

    Despite the capacity of invasive alien species to alter ecosystems, the mechanisms underlying their impact remain only partly understood. Invasive alien predators, for example, can significantly disrupt recipient communities by consuming prey species or acting as an intraguild predator (IGP). Behavioural interactions are key components of interspecific competition between predators, yet these are often overlooked invasion processes. Here, we show how behavioural, non-lethal IGP interactions might facilitate the establishment success of an invading alien species. We experimentally assessed changes in feeding behaviour (prey preference and consumption rate) of native UK coccinellid species (Adalia bipunctata and Coccinella septempunctata), whose populations are, respectively, declining and stable, when exposed to the invasive intraguild predator, Harmonia axyridis. Using a population dynamics model parameterized with these experimental data, we predicted how intraguild predation, accommodating interspecific behavioural interactions, might impact the abundance of the native and invasive alien species over time. When competing for the same aphid resource, the feeding rate of A. bipunctata significantly increased compared to the feeding in isolation, while the feeding rate of H. axyridis significantly decreased. This suggests that despite significant declines in the UK, A. bipunctata is a superior competitor to the intraguild predator H. axyridis. In contrast, the behaviour of non-declining C. septempunctata was unaltered by the presence of H. axyridis. Our experimental data show the differential behavioural plasticity of competing native and invasive alien predators, but do not explain A. bipunctata declines observed in the UK. Using behavioural plasticity as a parameter in a population dynamic model for A. bipunctata and H. axyridis, coexistence is predicted between the native and invasive alien following an initial period of decline in the native species. We

  2. Interactive Translation Prediction versus Conventional Post-editing in Practice

    DEFF Research Database (Denmark)

    Sanchis-Trilles, German; Alabau, Vicent; Buck, Christian

    2014-01-01

    We conducted a field trial in computer-assisted professional translation to compare Interactive Translation Prediction (ITP) against conventional post- editing (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translatio...

  3. Predicting the establishment success of introduced target species in grassland restoration by functional traits.

    Science.gov (United States)

    Engst, Karina; Baasch, Annett; Bruelheide, Helge

    2017-09-01

    Species-rich semi-natural grasslands are highly endangered habitats in Central Europe and numerous restoration efforts have been made to compensate for the losses in the last decades. However, some plant species could become more easily established than others. The establishment success of 37 species was analyzed over 6 years at two study sites of a restoration project in Germany where hay transfer and sowing of threshing material in combination with additional sowing were applied. The effects of the restoration method applied, time since the restoration took place, traits related to germination, dispersal, and reproduction, and combinations of these traits on the establishment were analyzed. While the specific restoration method of how seeds were transferred played a subordinate role, the establishment success depended in particular on traits such as flower season or the lifeform. Species flowering in autumn, such as Pastinaca sativa and Serratula tinctoria , became established better than species flowering in other seasons, probably because they could complete their life cycle, resulting in increasingly stronger seed pressure with time. Geophytes, like Allium angulosum and Galium boreale , became established very poorly, but showed an increase with study duration. For various traits, we found significant trait by method and trait by year interactions, indicating that different traits promoted establishment under different conditions. Using a multi-model approach, we tested whether traits acted in combination. For the first years and the last year, we found that models with three traits explained establishment success better than models with a single trait or two traits. While traits had only an additive effect on the establishment success in the first years, trait interactions became important thereafter. The most important trait was the season of flowering, which occurred in all best models from the third year onwards. Overall, our approach revealed the

  4. Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

    2013-01-01

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some

  5. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    Directory of Open Access Journals (Sweden)

    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  6. Vaporization of chemical species and the production of aerosols during a core debris/concrete interaction

    International Nuclear Information System (INIS)

    Butland, A.T.D.; Mignanelli, M.A.; Potter, P.E.; Smith, P.N.

    1987-01-01

    The equilibrium chemical composition within gas bubbles sparging through isothermal molten corium-concrete mixtures has been evaluated theoretically. A series of sensitivity calculations gives some insight into a number of factors which are of importance in determining the radionuclide and non-radioactive releases during core-concrete interaction. The degree of mixing or layering of the pool has turned out to be of paramount importance in determining the magnitudes of the releases. The presence of unoxidized zirconium in the melt tends to enhance the release of a number of species and the type of concrete used for the base mat can have a significant effect. The predictions can be sensitive to the thermodynamic data used in the calculations. The vaporization of various species into the gas bubbles can require large amounts of heat; the loss of this heat from the melt can have an effect on the extent of the vaporization

  7. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

    Science.gov (United States)

    The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

  8. ROUNDTABLE SESSION 2B: NATIONAL INTERACTIONS BETWEEN NON-INDIGENOUS AND INDIGENOUS CRAYFISH SPECIES

    Directory of Open Access Journals (Sweden)

    GHERARDI F.

    2002-07-01

    Full Text Available The main object of the present essay is to summarise some aspects underlying the interactions between non-indigenous (NICS and indigenous (ICS crayfish species. The discussion has been also extended to the effects exercised by NICS on the natural habitats they occupy. While doing research on the dyads NICS/ICS, one starting point is to extrapolate common traits that make NICS good invaders from the analysis of their biology, ecology and ethology and the comparison with indigenous species. A subsequent step is to switch attention to the understanding of the characteristics that make ecosystems less vulnerable to invasions and then to analyse both the complex interactions of invaders and target communities and the negative and positive impacts exerted by NICS on the occupied habitats. Examples from Sweden, Britain, and Italy have shown that NICS can replace the native species by a combination of several interacting mechanisms. Besides the transmission of the crayfish plague fungus, mechanisms into action include mostly competitive interference, but also diverse life history traits, recruitment failure, differential susceptibility to predation, and reproductive interference. It has been claimed that invasion theory is full of rules of thumb that, having no precise predictive powers, are thus useless to guide reliable public policy. The solution of the prediction problem requires an in-depth study of every potential invader and target community, trespassing the boundaries among disciplines and having a look at crayfish as a whole and not a single entity. The expectation is thus the return to precise and clear empirical generalisations that can be most useful to develop management strategies.

  9. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  10. Predicting the dynamics of local adaptation in invasive species

    Science.gov (United States)

    An invasive plant species may restrict its spread to only one habitat, or, after some time, may continue to spread into a different, secondary, habitat. The question of whether evolution is required for an invasive species to spread from one habitat to another is currently hotly debated. In order fo...

  11. Moving Species Redundancy Toward a More Predictive Framework

    Science.gov (United States)

    Human activities are driving rapid changes in species diversity in a wide range of habitats globally. These changes in species diversity raise questions about the ability of altered systems to continue to offer valuable ecosystem services. Maintenance of ecosystem services unde...

  12. Experiments on growth interactions between two invasive macrophyte species

    NARCIS (Netherlands)

    Barrat-Segretain, M-H.; Elger, A.F.

    2004-01-01

    The success of invasive species has been attributed to the ability to displace other species by direct competition. We studied growth and possible competition between the two macrophyte species Elodea nuttallii and E. canadensis, because the former has been observed to replace the latter in the

  13. Predictions for an invaded world: A strategy to predict the distribution of native and non-indigenous species at multiple scales

    Science.gov (United States)

    Reusser, D.A.; Lee, H.

    2008-01-01

    Habitat models can be used to predict the distributions of marine and estuarine non-indigenous species (NIS) over several spatial scales. At an estuary scale, our goal is to predict the estuaries most likely to be invaded, but at a habitat scale, the goal is to predict the specific locations within an estuary that are most vulnerable to invasion. As an initial step in evaluating several habitat models, model performance for a suite of benthic species with reasonably well-known distributions on the Pacific coast of the US needs to be compared. We discuss the utility of non-parametric multiplicative regression (NPMR) for predicting habitat- and estuary-scale distributions of native and NIS. NPMR incorporates interactions among variables, allows qualitative and categorical variables, and utilizes data on absence as well as presence. Preliminary results indicate that NPMR generally performs well at both spatial scales and that distributions of NIS are predicted as well as those of native species. For most species, latitude was the single best predictor, although similar model performance could be obtained at both spatial scales with combinations of other habitat variables. Errors of commission were more frequent at a habitat scale, with omission and commission errors approximately equal at an estuary scale. ?? 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved.

  14. Plant interactions alter the predictions of metabolic scaling theory.

    Directory of Open Access Journals (Sweden)

    Yue Lin

    Full Text Available Metabolic scaling theory (MST is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning. Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric, and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

  15. Toward a community ecology of landscapes: predicting multiple predator-prey interactions across geographic space.

    Science.gov (United States)

    Schmitz, Oswald J; Miller, Jennifer R B; Trainor, Anne M; Abrahms, Briana

    2017-09-01

    Community ecology was traditionally an integrative science devoted to studying interactions between species and their abiotic environments in order to predict species' geographic distributions and abundances. Yet for philosophical and methodological reasons, it has become divided into two enterprises: one devoted to local experimentation on species interactions to predict community dynamics; the other devoted to statistical analyses of abiotic and biotic information to describe geographic distribution. Our goal here is to instigate thinking about ways to reconnect the two enterprises and thereby return to a tradition to do integrative science. We focus specifically on the community ecology of predators and prey, which is ripe for integration. This is because there is active, simultaneous interest in experimentally resolving the nature and strength of predator-prey interactions as well as explaining patterns across landscapes and seascapes. We begin by describing a conceptual theory rooted in classical analyses of non-spatial food web modules used to predict species interactions. We show how such modules can be extended to consideration of spatial context using the concept of habitat domain. Habitat domain describes the spatial extent of habitat space that predators and prey use while foraging, which differs from home range, the spatial extent used by an animal to meet all of its daily needs. This conceptual theory can be used to predict how different spatial relations of predators and prey could lead to different emergent multiple predator-prey interactions such as whether predator consumptive or non-consumptive effects should dominate, and whether intraguild predation, predator interference or predator complementarity are expected. We then review the literature on studies of large predator-prey interactions that make conclusions about the nature of multiple predator-prey interactions. This analysis reveals that while many studies provide sufficient information

  16. Role of the noise on the transient dynamics of an ecosystem of interacting species

    Science.gov (United States)

    Spagnolo, B.; La Barbera, A.

    2002-11-01

    We analyze the transient dynamics of an ecosystem described by generalized Lotka-Volterra equations in the presence of a multiplicative noise and a random interaction parameter between the species. We consider specifically three cases: (i) two competing species, (ii) three interacting species (one predator-two preys), (iii) n-interacting species. The interaction parameter in case (i) is a stochastic process which obeys a stochastic differential equation. We find noise delayed extinction of one of two species, which is akin to the noise-enhanced stability phenomenon. Other two noise-induced effects found are temporal oscillations and spatial patterns of the two competing species. In case (ii) the noise induces correlated spatial patterns of the predator and of the two preys concentrations. Finally, in case (iii) we find the asymptotic behavior of the time average of the ith population when the ecosystem is composed of a great number of interacting species.

  17. Big Five aspects of personality interact to predict depression.

    Science.gov (United States)

    Allen, Timothy A; Carey, Bridget E; McBride, Carolina; Bagby, R Michael; DeYoung, Colin G; Quilty, Lena C

    2017-09-16

    Research has shown that three personality traits-Neuroticism, Extraversion, and Conscientiousness-moderate one another in a three-way interaction that predicts depressive symptoms in healthy populations. We test the hypothesis that this effect is driven by three lower-order traits: withdrawal, industriousness, and enthusiasm. We then replicate this interaction within a clinical population for the first time. Sample 1 included 376 healthy adults. Sample 2 included 354 patients diagnosed with current major depressive disorder. Personality and depressive tendencies were assessed via the Big Five Aspect Scales and Personality Inventory for DSM-5 in Sample 1, respectively, and by the NEO-PI-R and Beck Depression Inventory-II in Sample 2. Withdrawal, industriousness, and enthusiasm interacted to predict depressive tendencies in both samples. The pattern of the interaction supported a "best two out of three" principle, in which low risk scores on two trait dimensions protects against a high risk score on the third trait. Evidence was also present for a "worst two out of three" principle, in which high risk scores on two traits are associated with equivalent depressive severity as high risk scores on all three traits. These results highlight the importance of examining interactive effects of personality traits on psychopathology. © 2017 Wiley Periodicals, Inc.

  18. Competitive interactions between co-occurring invaders: identifying asymmetries between two invasive crayfish species

    NARCIS (Netherlands)

    Hudina, S.; Galic, N.G.; Roessink, I.; Hock, K.

    2011-01-01

    Ecosystems today increasingly suffer invasions by multiple invasive species. Complex interactions between invasive species can have different fitness implications for each invader, which can in turn determine the future progression of their invasions and result in differential impacts on native

  19. Herb-drug interactions: challenges and opportunities for improved predictions.

    Science.gov (United States)

    Brantley, Scott J; Argikar, Aneesh A; Lin, Yvonne S; Nagar, Swati; Paine, Mary F

    2014-03-01

    Supported by a usage history that predates written records and the perception that "natural" ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb-drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb-drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb-drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb-drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens.

  20. Herb–Drug Interactions: Challenges and Opportunities for Improved Predictions

    Science.gov (United States)

    Brantley, Scott J.; Argikar, Aneesh A.; Lin, Yvonne S.; Nagar, Swati

    2014-01-01

    Supported by a usage history that predates written records and the perception that “natural” ensures safety, herbal products have increasingly been incorporated into Western health care. Consumers often self-administer these products concomitantly with conventional medications without informing their health care provider(s). Such herb–drug combinations can produce untoward effects when the herbal product perturbs the activity of drug metabolizing enzymes and/or transporters. Despite increasing recognition of these types of herb–drug interactions, a standard system for interaction prediction and evaluation is nonexistent. Consequently, the mechanisms underlying herb–drug interactions remain an understudied area of pharmacotherapy. Evaluation of herbal product interaction liability is challenging due to variability in herbal product composition, uncertainty of the causative constituents, and often scant knowledge of causative constituent pharmacokinetics. These limitations are confounded further by the varying perspectives concerning herbal product regulation. Systematic evaluation of herbal product drug interaction liability, as is routine for new drugs under development, necessitates identifying individual constituents from herbal products and characterizing the interaction potential of such constituents. Integration of this information into in silico models that estimate the pharmacokinetics of individual constituents should facilitate prospective identification of herb–drug interactions. These concepts are highlighted with the exemplar herbal products milk thistle and resveratrol. Implementation of this methodology should help provide definitive information to both consumers and clinicians about the risk of adding herbal products to conventional pharmacotherapeutic regimens. PMID:24335390

  1. Sexual selection predicts species richness across the animal kingdom.

    Science.gov (United States)

    Janicke, Tim; Ritchie, Michael G; Morrow, Edward H; Marie-Orleach, Lucas

    2018-05-16

    Our improving knowledge of the animal tree of life consistently demonstrates that some taxa diversify more rapidly than others, but what contributes to this variation remains poorly understood. An influential hypothesis proposes that selection arising from competition for mating partners plays a key role in promoting speciation. However, empirical evidence showing a link between proxies of this sexual selection and species richness is equivocal. Here, we collected standardized metrics of sexual selection for a broad range of animal taxa, and found that taxonomic families characterized by stronger sexual selection on males show relatively higher species richness. Thus, our data support the hypothesis that sexual selection elevates species richness. This could occur either by promoting speciation and/or by protecting species against extinction. © 2018 The Author(s).

  2. SeqAPASS: Predicting chemical susceptibility to threatened/endangered species

    Science.gov (United States)

    Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS; https://seqapass.epa.gov/seqapass/) application was devel...

  3. Attachment predicts cortisol response and closeness in dyadic social interaction.

    Science.gov (United States)

    Ketay, Sarah; Beck, Lindsey A

    2017-06-01

    The present study examined how the interplay of partners' attachment styles influences cortisol response, actual closeness, and desired closeness during friendship initiation. Participants provided salivary cortisol samples at four timepoints throughout either a high or low closeness task that facilitated high or low levels of self-disclosure with a potential friend (i.e., another same-sex participant). Levels of actual closeness and desired closeness following the task were measured via inclusion of other in the self. Results from multi-level modeling indicated that the interaction of both participants' attachment avoidance predicted cortisol response patterns, with participants showing the highest cortisol response when there was a mismatch between their own and their partners' attachment avoidance. Further, the interaction between both participants' attachment anxiety predicted actual closeness and desired closeness, with participants both feeling and wanting the most closeness with partners when both they and their partners were low in attachment anxiety. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Boosting compound-protein interaction prediction by deep learning.

    Science.gov (United States)

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Are trade-offs among species' ecological interactions scale dependent? A test using pitcher-plant inquiline species.

    Science.gov (United States)

    Kneitel, Jamie M

    2012-01-01

    Trade-offs among species' ecological interactions is a pervasive explanation for species coexistence. The traits associated with trade-offs are typically measured to mechanistically explain species coexistence at a single spatial scale. However, species potentially interact at multiple scales and this may be reflected in the traits among coexisting species. I quantified species' ecological traits associated with the trade-offs expected at both local (competitive ability and predator tolerance) and regional (competitive ability and colonization rate) community scales. The most common species (four protozoa and a rotifer) from the middle trophic level of a pitcher plant (Sarracenia purpurea) inquiline community were used to link species traits to previously observed patterns of species diversity and abundance. Traits associated with trade-offs (competitive ability, predator tolerance, and colonization rate) and other ecological traits (size, growth rate, and carrying capacity) were measured for each of the focal species. Traits were correlated with one another with a negative relationship indicative of a trade-off. Protozoan and rotifer species exhibited a negative relationship between competitive ability and predator tolerance, indicative of coexistence at the local community scale. There was no relationship between competitive ability and colonization rate. Size, growth rate, and carrying capacity were correlated with each other and the trade-off traits: Size was related to both competitive ability and predator tolerance, but growth rate and carrying capacity were correlated with predator tolerance. When partial correlations were conducted controlling for size, growth rate and carrying capacity, the trade-offs largely disappeared. These results imply that body size is the trait that provides the basis for ecological interactions and trade-offs. Altogether, this study showed that the examination of species' traits in the context of coexistence at different scales

  6. Prediction of rotor blade-vortex interaction using Volterra integrals

    Energy Technology Data Exchange (ETDEWEB)

    Wong, A.; Nitzsche, F. [Carleton Univ., Dept. of Mechanical and Aerospace Engineering, Ottawa, Ontario (Canada)]. E-mail: Fred_Nitzsche@carleton.ca; Khalid, M. [National Research Council Canada, Inst. for Aerospace Research, Ottawa, Ontario (Canada)

    2004-07-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  7. DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

    2016-01-01

    DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://​www.​cbrc.​kaust.​edu.​sa/​daspfind.

  8. Prediction of rotor blade-vortex interaction using Volterra integrals

    International Nuclear Information System (INIS)

    Wong, A.; Nitzsche, F.; Khalid, M.

    2004-01-01

    The theory of Volterra integral equations for nonlinear system is applied to the prediction of the nonlinear aerodynamic response of an NACA 0012 airfoil experiencing blade-vortex interaction. The phenomenon is first modeled in two-dimensions using an Euler/Navier-Stoke code, and the resulting unsteady aerodynamic flow field sequences are appropriately combined to form a training dataset. The Volterra kernels are identified in the time-domain characteristics of the selected data, which is in turn used to predict the nonlinear aerodynamic response of the airfoil. The Volterra kernel based data is then compared against a standard airfoil response. The predicted lift time histories of the airfoil are shown to be in good agreement with the aerodynamic data. (author)

  9. Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation.

    Science.gov (United States)

    Angelieri, Cintia Camila Silva; Adams-Hosking, Christine; Ferraz, Katia Maria Paschoaletto Micchi de Barros; de Souza, Marcelo Pereira; McAlpine, Clive Alexander

    2016-01-01

    A mosaic of intact native and human-modified vegetation use can provide important habitat for top predators such as the puma (Puma concolor), avoiding negative effects on other species and ecological processes due to cascade trophic interactions. This study investigates the effects of restoration scenarios on the puma's habitat suitability in the most developed Brazilian region (São Paulo State). Species Distribution Models incorporating restoration scenarios were developed using the species' occurrence information to (1) map habitat suitability of pumas in São Paulo State, Southeast, Brazil; (2) test the relative contribution of environmental variables ecologically relevant to the species habitat suitability and (3) project the predicted habitat suitability to future native vegetation restoration scenarios. The Maximum Entropy algorithm was used (Test AUC of 0.84 ± 0.0228) based on seven environmental non-correlated variables and non-autocorrelated presence-only records (n = 342). The percentage of native vegetation (positive influence), elevation (positive influence) and density of roads (negative influence) were considered the most important environmental variables to the model. Model projections to restoration scenarios reflected the high positive relationship between pumas and native vegetation. These projections identified new high suitability areas for pumas (probability of presence >0.5) in highly deforested regions. High suitability areas were increased from 5.3% to 8.5% of the total State extension when the landscapes were restored for ≥ the minimum native vegetation cover rule (20%) established by the Brazilian Forest Code in private lands. This study highlights the importance of a landscape planning approach to improve the conservation outlook for pumas and other species, including not only the establishment and management of protected areas, but also the habitat restoration on private lands. Importantly, the results may inform environmental

  10. Fast prediction of RNA-RNA interaction using heuristic algorithm.

    Science.gov (United States)

    Montaseri, Soheila

    2015-01-01

    Interaction between two RNA molecules plays a crucial role in many medical and biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. Some algorithms have been formed to predict the structure of the RNA-RNA interaction. High computational time is a common challenge in most of the presented algorithms. In this context, a heuristic method is introduced to accurately predict the interaction between two RNAs based on minimum free energy (MFE). This algorithm uses a few dot matrices for finding the secondary structure of each RNA and binding sites between two RNAs. Furthermore, a parallel version of this method is presented. We describe the algorithm's concurrency and parallelism for a multicore chip. The proposed algorithm has been performed on some datasets including CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS, and IncRNA54-RepZ in Escherichia coli bacteria. The method has high validity and efficiency, and it is run in low computational time in comparison to other approaches.

  11. Theoretical predictions of arsenic and selenium species under atmospheric conditions

    Energy Technology Data Exchange (ETDEWEB)

    Monahan-Pendergast, M.T.; Przybylek, M.; Lindblad, M.; Wilcox, J. [Worcester Polytechnic Institute, Worcester, MA (United States). Dept. of Chemical Engineering

    2008-03-15

    Thermochemical properties of arsenic and selenium species thought to be released into the atmosphere during the coal combustion were examined using ab initio methods. At various levels of theory, calculated geometries and vibrational frequencies of the species were compared with experimental data, where available. Through a comparison of equilibrium constants for a series of gaseous arsenic and selenium oxidation reactions involving OH and HO{sub 2}, five thermodynamically favored reactions were found. In addition, it was determined that all favored reactions were more likely to go to completion tinder tropospheric, rather than stratospheric, conditions.

  12. Net root growth and nutrient acquisition in response to predicted climate change in two contrasting heathland species

    DEFF Research Database (Denmark)

    Arndal, M.F.; Merrild, M.P.; Michelsen, A.

    2013-01-01

    Accurate predictions of nutrient acquisition by plant roots and mycorrhizas are critical in modelling plant responses to climate change.We conducted a field experiment with the aim to investigate root nutrient uptake in a future climate and studied root production by ingrowth cores, mycorrhizal...... to elevated CO2. The species-specific response to the treatments suggests different sensitivity to global change factors, which could result in changed plant competitive interactions and belowground nutrient pool sizes in response to future climate change....

  13. Predicting animal production on sourveld: a species-based approach

    African Journals Online (AJOL)

    The model was based upon measured ingestive and digestive characteristics of different grass species and incorporates an explicit digestive constraint based upon rumen mass and turnover rate. Illustrates with graphs, diagrams and tables. Keywords: ADG; Andropogon appendiculatus; Average daily gain; Cattle; Cynodon ...

  14. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  15. Establishing endangered species recovery criteria using predictive simulation modeling

    Science.gov (United States)

    McGowan, Conor P.; Catlin, Daniel H.; Shaffer, Terry L.; Gratto-Trevor, Cheri L.; Aron, Carol

    2014-01-01

    Listing a species under the Endangered Species Act (ESA) and developing a recovery plan requires U.S. Fish and Wildlife Service to establish specific and measurable criteria for delisting. Generally, species are listed because they face (or are perceived to face) elevated risk of extinction due to issues such as habitat loss, invasive species, or other factors. Recovery plans identify recovery criteria that reduce extinction risk to an acceptable level. It logically follows that the recovery criteria, the defined conditions for removing a species from ESA protections, need to be closely related to extinction risk. Extinction probability is a population parameter estimated with a model that uses current demographic information to project the population into the future over a number of replicates, calculating the proportion of replicated populations that go extinct. We simulated extinction probabilities of piping plovers in the Great Plains and estimated the relationship between extinction probability and various demographic parameters. We tested the fit of regression models linking initial abundance, productivity, or population growth rate to extinction risk, and then, using the regression parameter estimates, determined the conditions required to reduce extinction probability to some pre-defined acceptable threshold. Binomial regression models with mean population growth rate and the natural log of initial abundance were the best predictors of extinction probability 50 years into the future. For example, based on our regression models, an initial abundance of approximately 2400 females with an expected mean population growth rate of 1.0 will limit extinction risk for piping plovers in the Great Plains to less than 0.048. Our method provides a straightforward way of developing specific and measurable recovery criteria linked directly to the core issue of extinction risk. Published by Elsevier Ltd.

  16. Species interactions can maintain resistance of subtidal algal habitats to an increasingly modified world

    Directory of Open Access Journals (Sweden)

    Laura J. Falkenberg

    2015-07-01

    Full Text Available Current trends in habitat loss have been forecast to accelerate under anticipated global change, thereby focusing conservation attention on identifying the circumstances under which key species interactions retard habitat loss. Urbanised coastlines are associated with broad-scale loss of kelp canopies and their replacement by less productive mats of algal turf, a trend predicted to accelerate under ocean acidification and warming (i.e. enhanced CO2 and temperature. Here we use kelp forests as a model system to test whether efforts to maintain key species interactions can maintain habitat integrity under forecasted conditions. First, we assessed whether increasing intensity of local human activity is associated with more extensive turf mats and sparser canopies via structured field observations. Second, we experimentally tested the hypothesis that intact canopies can resist turf expansion under enhanced CO2 and temperature in large mesocosms. In the field, there was a greater proportion of turf patches on urbanised coasts of South Australia than in agricultural and urban catchments in which there was a greater proportion of canopy-forming algae. Mesocosm experiments revealed this expansion of turfs is likely to accelerate under increases in CO2 and temperature, but may be limited by the presence of intact canopies. We note that even in the presence of canopy, increases in CO2 and temperature facilitate greater turf covers than occurs under contemporary conditions. The influence of canopy would likely be due to shading of the understorey turfs which, in turn, can modify their photosynthetic activity. These results suggest that resistance of habitat to change under human-dominated conditions may be managed via the retention of key species and their interactions. Management that directly reduces the disturbance of habitat-forming organisms (e.g. harvesting or reverses loss through restoration may, therefore, reinforce habitat resistance in an

  17. Comparative Genomics and Disorder Prediction Identify Biologically Relevant SH3 Protein Interactions.

    Directory of Open Access Journals (Sweden)

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  18. Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

    Directory of Open Access Journals (Sweden)

    Pedro Beltrao

    2005-08-01

    Full Text Available Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus, or a long time ago (Neurospora crassa and Schizosaccharomyces pombe, contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

  19. QSAR Modeling and Prediction of Drug-Drug Interactions.

    Science.gov (United States)

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  20. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

    2016-01-01

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970

  1. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

    Directory of Open Access Journals (Sweden)

    Jaime Cuevas

    2017-01-01

    Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u   and   f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .

  2. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

    Science.gov (United States)

    Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

    2017-01-05

    The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.

  3. SPECIES INTERACTIONS BETWEEN ESTUARINE DETRITIVORES: INHIBITION OR FACILITATION?

    Science.gov (United States)

    Native Hawaiian estuarine detritivores; the prawn Macrobrachium grandimanus, and the neritid gastropod Neritina vespertina, were maintained in flow-through microcosms with conditioned leaves from two riparian tree species, Hau (Hibiscus tiliaceus) and guava (Psidium guajava). Th...

  4. The Effects of Species Interaction and Pond Stocking Density on ...

    African Journals Online (AJOL)

    Burchell) and Heterobranchus bidorsalis (Geoffrey Saint-Hilaire) and pond stocking density on the culture of tilapia species which display different parental care strategies. In the presence of catfishes, the maternal mouth-brooder O. niloticus ...

  5. Evolutionary relationships can be more important than abiotic conditions in predicting the outcome of plant-plant interactions

    Science.gov (United States)

    Soliveres, Santiago; Torices, Rubén; Maestre, Fernando T.

    2015-01-01

    Positive and negative plant-plant interactions are major processes shaping plant communities. They are affected by environmental conditions and evolutionary relationships among the interacting plants. However, the generality of these factors as drivers of pairwise plant interactions and their combined effects remain virtually unknown. We conducted an observational study to assess how environmental conditions (altitude, temperature, irradiance and rainfall), the dispersal mechanism of beneficiary species and evolutionary relationships affected the co-occurrence of pairwise interactions in 11 Stipa tenacissima steppes located along an environmental gradient in Spain. We studied 197 pairwise plant-plant interactions involving the two major nurse plants (the resprouting shrub Quercus coccifera and the tussock grass S. tenacissima) found in these communities. The relative importance of the studied factors varied with the nurse species considered. None of the factors studied were good predictors of the co-ocurrence between S. tenacissima and its neighbours. However, both the dispersal mechanism of the beneficiary species and the phylogenetic distance between interacting species were crucial factors affecting the co-occurrence between Q. coccifera and its neighbours, while climatic conditions (irradiance) played a secondary role. Values of phylogenetic distance between 207-272.8 Myr led to competition, while values outside this range or fleshy-fruitness in the beneficiary species led to positive interactions. The low importance of environmental conditions as a general driver of pairwise interactions was caused by the species-specific response to changes in either rainfall or radiation. This result suggests that factors other than climatic conditions must be included in theoretical models aimed to generally predict the outcome of plant-plant interactions. Our study helps to improve current theory on plant-plant interactions and to understand how these interactions can

  6. Social interactions predict genetic diversification: an experimental manipulation in shorebirds.

    Science.gov (United States)

    Cunningham, Charles; Parra, Jorge E; Coals, Lucy; Beltrán, Marcela; Zefania, Sama; Székely, Tamás

    2018-01-01

    Mating strategy and social behavior influence gene flow and hence affect levels of genetic differentiation and potentially speciation. Previous genetic analyses of closely related plovers Charadrius spp. found strikingly different population genetic structure in Madagascar: Kittlitz's plovers are spatially homogenous whereas white-fronted plovers have well segregated and geographically distinct populations. Here, we test the hypotheses that Kittlitz's plovers are spatially interconnected and have extensive social interactions that facilitate gene flow, whereas white-fronted plovers are spatially discrete and have limited social interactions. By experimentally removing mates from breeding pairs and observing the movements of mate-searching plovers in both species, we compare the spatial behavior of Kittlitz's and white-fronted plovers within a breeding season. The behavior of experimental birds was largely consistent with expectations: Kittlitz's plovers travelled further, sought new mates in larger areas, and interacted with more individuals than white-fronted plovers, however there was no difference in breeding dispersal. These results suggest that mating strategies, through spatial behavior and social interactions, are predictors of gene flow and thus genetic differentiation and speciation. Our study highlights the importance of using social behavior to understand gene flow. However, further work is needed to investigate the relative importance of social structure, as well as intra- and inter-season dispersal, in influencing the genetic structures of populations.

  7. Context-dependent interactions and the regulation of species richness in freshwater fish

    Science.gov (United States)

    MacDougall, Andrew S.; Harvey, Eric; McCune, Jenny L.; Nilsson, Karin A.; Bennett, Joseph; Firn, Jennifer; Bartley, Timothy; Grace, James B.; Kelly, Jocelyn; Tunney, Tyler D.; McMeans, Bailey; Matsuzaki, Shin-Ichiro S.; Kadoya, Taku; Esch, Ellen; Cazelles, Kevin; Lester, Nigel; McCann, Kevin S.

    2018-01-01

    Species richness is regulated by a complex network of scale-dependent processes. This complexity can obscure the influence of limiting species interactions, making it difficult to determine if abiotic or biotic drivers are more predominant regulators of richness. Using integrative modeling of freshwater fish richness from 721 lakes along an 11olatitudinal gradient, we find negative interactions to be a relatively minor independent predictor of species richness in lakes despite the widespread presence of predators. Instead, interaction effects, when detectable among major functional groups and 231 species pairs, were strong, often positive, but contextually dependent on environment. These results are consistent with the idea that negative interactions internally structure lake communities but do not consistently ‘scale-up’ to regulate richness independently of the environment. The importance of environment for interaction outcomes and its role in the regulation of species richness highlights the potential sensitivity of fish communities to the environmental changes affecting lakes globally.

  8. Regional climate model downscaling may improve the prediction of alien plant species distributions

    Science.gov (United States)

    Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

    2014-12-01

    Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

  9. Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

    Science.gov (United States)

    Hsing, Michael; Byler, Kendall; Cherkasov, Artem

    2009-01-01

    Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

  10. Plant interactions alter the predictions of metabolic scaling theory

    DEFF Research Database (Denmark)

    Lin, Yue; Berger, Uta; Grimm, Volker

    2013-01-01

    Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of 24/3 between mean individual biomass and density during densitydependent mortality (self-thinning). Empirical tests have...... processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive....... of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories...

  11. Explicit and Implicit Approach Motivation Interact to Predict Interpersonal Arrogance

    Science.gov (United States)

    Robinson, Michael D.; Ode, Scott; Spencer L., Palder; Fetterman, Adam K.

    2012-01-01

    Self-reports of approach motivation are unlikely to be sufficient in understanding the extent to which the individual reacts to appetitive cues in an approach-related manner. A novel implicit probe of approach tendencies was thus developed, one that assessed the extent to which positive affective (versus neutral) stimuli primed larger size estimates, as larger perceptual sizes co-occur with locomotion toward objects in the environment. In two studies (total N = 150), self-reports of approach motivation interacted with this implicit probe of approach motivation to predict individual differences in arrogance, a broad interpersonal dimension previously linked to narcissism, antisocial personality tendencies, and aggression. The results of the two studies were highly parallel in that self-reported levels of approach motivation predicted interpersonal arrogance in the particular context of high, but not low, levels of implicit approach motivation. Implications for understanding approach motivation, implicit probes of it, and problematic approach-related outcomes are discussed. PMID:22399360

  12. Explicit and implicit approach motivation interact to predict interpersonal arrogance.

    Science.gov (United States)

    Robinson, Michael D; Ode, Scott; Palder, Spencer L; Fetterman, Adam K

    2012-07-01

    Self-reports of approach motivation are unlikely to be sufficient in understanding the extent to which the individual reacts to appetitive cues in an approach-related manner. A novel implicit probe of approach tendencies was thus developed, one that assessed the extent to which positive affective (versus neutral) stimuli primed larger size estimates, as larger perceptual sizes co-occur with locomotion toward objects in the environment. In two studies (total N = 150), self-reports of approach motivation interacted with this implicit probe of approach motivation to predict individual differences in arrogance, a broad interpersonal dimension previously linked to narcissism, antisocial personality tendencies, and aggression. The results of the two studies were highly parallel in that self-reported levels of approach motivation predicted interpersonal arrogance in the particular context of high, but not low, levels of implicit approach motivation. Implications for understanding approach motivation, implicit probes of it, and problematic approach-related outcomes are discussed.

  13. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  14. Prediction of localization and interactions of apoptotic proteins

    Directory of Open Access Journals (Sweden)

    Matula Pavel

    2009-07-01

    Full Text Available Abstract During apoptosis several mitochondrial proteins are released. Some of them participate in caspase-independent nuclear DNA degradation, especially apoptosis-inducing factor (AIF and endonuclease G (endoG. Another interesting protein, which was expected to act similarly as AIF due to the high sequence homology with AIF is AIF-homologous mitochondrion-associated inducer of death (AMID. We studied the structure, cellular localization, and interactions of several proteins in silico and also in cells using fluorescent microscopy. We found the AMID protein to be cytoplasmic, most probably incorporated into the cytoplasmic side of the lipid membranes. Bioinformatic predictions were conducted to analyze the interactions of the studied proteins with each other and with other possible partners. We conducted molecular modeling of proteins with unknown 3D structures. These models were then refined by MolProbity server and employed in molecular docking simulations of interactions. Our results show data acquired using a combination of modern in silico methods and image analysis to understand the localization, interactions and functions of proteins AMID, AIF, endonuclease G, and other apoptosis-related proteins.

  15. Species biogeography predicts drought responses in a seasonally dry tropical forest

    Science.gov (United States)

    Schwartz, N.; Powers, J. S.; Vargas, G.; Xu, X.; Smith, C. M.; Brodribb, T.; Werden, L. K.; Becknell, J.; Medvigy, D.

    2017-12-01

    The timing, distribution, and amount of rainfall in the seasonal tropics have shifted in recent years, with consequences for seasonally dry tropical forests (SDTF). SDTF are sensitive to changing rainfall regimes and drought conditions, but sensitivity to drought varies substantially across species. One potential explanation of species differences is that species that experience dry conditions more frequently throughout their range will be better able to cope with drought than species from wetter climates, because species from drier climates will be better adapted to drought. An El-Niño induced drought in 2015 presented an opportunity to assess species-level differences in mortality in SDTF, and to ask whether the ranges of rainfall conditions species experience and the average rainfall regimes in species' ranges predict differences in mortality rates in Costa Rican SDTF. We used field plot data from northwest Costa Rica to determine species' level mortality rates. Mortality rates ranged substantially across species, with some species having no dead individuals to as high as 50% mortality. To quantify rainfall conditions across species' ranges, we used species occurrence data from the Global Biodiversity Information Facility, and rainfall data from the Chelsa climate dataset. We found that while the average and range of mean annual rainfall across species ranges did not predict drought-induced mortality in the field plots, across-range averages of the seasonality index, a measure of rainfall seasonality, was strongly correlated with species-level drought mortality (r = -0.62, p < 0.05), with species from more strongly seasonal climates experiencing less severe drought mortality. Furthermore, we found that the seasonality index was a stronger predictor of mortality than any individual functional trait we considered. This result shows that species' biogeography may be an important factor for how species will respond to future drought, and may be a more integrative

  16. [Effects of sampling plot number on tree species distribution prediction under climate change].

    Science.gov (United States)

    Liang, Yu; He, Hong-Shi; Wu, Zhi-Wei; Li, Xiao-Na; Luo, Xu

    2013-05-01

    Based on the neutral landscapes under different degrees of landscape fragmentation, this paper studied the effects of sampling plot number on the prediction of tree species distribution at landscape scale under climate change. The tree species distribution was predicted by the coupled modeling approach which linked an ecosystem process model with a forest landscape model, and three contingent scenarios and one reference scenario of sampling plot numbers were assumed. The differences between the three scenarios and the reference scenario under different degrees of landscape fragmentation were tested. The results indicated that the effects of sampling plot number on the prediction of tree species distribution depended on the tree species life history attributes. For the generalist species, the prediction of their distribution at landscape scale needed more plots. Except for the extreme specialist, landscape fragmentation degree also affected the effects of sampling plot number on the prediction. With the increase of simulation period, the effects of sampling plot number on the prediction of tree species distribution at landscape scale could be changed. For generalist species, more plots are needed for the long-term simulation.

  17. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

    2014-04-08

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

  18. Predicting the presence and cover of management relevant invasive plant species on protected areas.

    Science.gov (United States)

    Iacona, Gwenllian; Price, Franklin D; Armsworth, Paul R

    2016-01-15

    Invasive species are a management concern on protected areas worldwide. Conservation managers need to predict infestations of invasive plants they aim to treat if they want to plan for long term management. Many studies predict the presence of invasive species, but predictions of cover are more relevant for management. Here we examined how predictors of invasive plant presence and cover differ across species that vary in their management priority. To do so, we used data on management effort and cover of invasive plant species on central Florida protected areas. Using a zero-inflated multiple regression framework, we showed that protected area features can predict the presence and cover of the focal species but the same features rarely explain both. There were several predictors of either presence or cover that were important across multiple species. Protected areas with three days of frost per year or fewer were more likely to have occurrences of four of the six focal species. When invasive plants were present, their proportional cover was greater on small preserves for all species, and varied with surrounding household density for three species. None of the predictive features were clearly related to whether species were prioritized for management or not. Our results suggest that predictors of cover and presence can differ both within and across species but do not covary with management priority. We conclude that conservation managers need to select predictors of invasion with care as species identity can determine the relationship between predictors of presence and the more management relevant predictors of cover. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Intraspecific morphological and genetic variation of common species predicts ranges of threatened ones

    Science.gov (United States)

    Fuller, Trevon L.; Thomassen, Henri A.; Peralvo, Manuel; Buermann, Wolfgang; Milá, Borja; Kieswetter, Charles M.; Jarrín-V, Pablo; Devitt, Susan E. Cameron; Mason, Eliza; Schweizer, Rena M.; Schlunegger, Jasmin; Chan, Janice; Wang, Ophelia; Schneider, Christopher J.; Pollinger, John P.; Saatchi, Sassan; Graham, Catherine H.; Wayne, Robert K.; Smith, Thomas B.

    2013-01-01

    Predicting where threatened species occur is useful for making informed conservation decisions. However, because they are usually rare, surveying threatened species is often expensive and time intensive. Here, we show how regions where common species exhibit high genetic and morphological divergence among populations can be used to predict the occurrence of species of conservation concern. Intraspecific variation of common species of birds, bats and frogs from Ecuador were found to be a significantly better predictor for the occurrence of threatened species than suites of environmental variables or the occurrence of amphibians and birds. Fully 93 per cent of the threatened species analysed had their range adequately represented by the geographical distribution of the morphological and genetic variation found in seven common species. Both higher numbers of threatened species and greater genetic and morphological variation of common species occurred along elevation gradients. Higher levels of intraspecific divergence may be the result of disruptive selection and/or introgression along gradients. We suggest that collecting data on genetic and morphological variation in common species can be a cost effective tool for conservation planning, and that future biodiversity inventories include surveying genetic and morphological data of common species whenever feasible. PMID:23595273

  20. Models of alien species richness show moderate predictive accuracy and poor transferability

    Directory of Open Access Journals (Sweden)

    César Capinha

    2018-06-01

    Full Text Available Robust predictions of alien species richness are useful to assess global biodiversity change. Nevertheless, the capacity to predict spatial patterns of alien species richness remains largely unassessed. Using 22 data sets of alien species richness from diverse taxonomic groups and covering various parts of the world, we evaluated whether different statistical models were able to provide useful predictions of absolute and relative alien species richness, as a function of explanatory variables representing geographical, environmental and socio-economic factors. Five state-of-the-art count data modelling techniques were used and compared: Poisson and negative binomial generalised linear models (GLMs, multivariate adaptive regression splines (MARS, random forests (RF and boosted regression trees (BRT. We found that predictions of absolute alien species richness had a low to moderate accuracy in the region where the models were developed and a consistently poor accuracy in new regions. Predictions of relative richness performed in a superior manner in both geographical settings, but still were not good. Flexible tree ensembles-type techniques (RF and BRT were shown to be significantly better in modelling alien species richness than parametric linear models (such as GLM, despite the latter being more commonly applied for this purpose. Importantly, the poor spatial transferability of models also warrants caution in assuming the generality of the relationships they identify, e.g. by applying projections under future scenario conditions. Ultimately, our results strongly suggest that predictability of spatial variation in richness of alien species richness is limited. The somewhat more robust ability to rank regions according to the number of aliens they have (i.e. relative richness, suggests that models of aliens species richness may be useful for prioritising and comparing regions, but not for predicting exact species numbers.

  1. Gear-based species selectivity and potential interactions between ...

    African Journals Online (AJOL)

    ... and competition between different co-occurring fisheries is therefore important for the implementation of ecosystem based fisheries management interventions. In this study, we used multivariate and ecological approaches to evaluate gear competition and interactions between artisanal and aquarium fishers using a case ...

  2. Asymmetric biotic interactions and abiotic niche differences revealed by a dynamic joint species distribution model.

    Science.gov (United States)

    Lany, Nina K; Zarnetske, Phoebe L; Schliep, Erin M; Schaeffer, Robert N; Orians, Colin M; Orwig, David A; Preisser, Evan L

    2018-05-01

    A species' distribution and abundance are determined by abiotic conditions and biotic interactions with other species in the community. Most species distribution models correlate the occurrence of a single species with environmental variables only, and leave out biotic interactions. To test the importance of biotic interactions on occurrence and abundance, we compared a multivariate spatiotemporal model of the joint abundance of two invasive insects that share a host plant, hemlock woolly adelgid (HWA; Adelges tsugae) and elongate hemlock scale (EHS; Fiorina externa), to independent models that do not account for dependence among co-occurring species. The joint model revealed that HWA responded more strongly to abiotic conditions than EHS. Additionally, HWA appeared to predispose stands to subsequent increase of EHS, but HWA abundance was not strongly dependent on EHS abundance. This study demonstrates how incorporating spatial and temporal dependence into a species distribution model can reveal the dependence of a species' abundance on other species in the community. Accounting for dependence among co-occurring species with a joint distribution model can also improve estimation of the abiotic niche for species affected by interspecific interactions. © 2018 by the Ecological Society of America.

  3. General two-species interacting Lotka-Volterra system: Population dynamics and wave propagation

    Science.gov (United States)

    Zhu, Haoqi; Wang, Mao-Xiang; Lai, Pik-Yin

    2018-05-01

    The population dynamics of two interacting species modeled by the Lotka-Volterra (LV) model with general parameters that can promote or suppress the other species is studied. It is found that the properties of the two species' isoclines determine the interaction of species, leading to six regimes in the phase diagram of interspecies interaction; i.e., there are six different interspecific relationships described by the LV model. Four regimes allow for nontrivial species coexistence, among which it is found that three of them are stable, namely, weak competition, mutualism, and predator-prey scenarios can lead to win-win coexistence situations. The Lyapunov function for general nontrivial two-species coexistence is also constructed. Furthermore, in the presence of spatial diffusion of the species, the dynamics can lead to steady wavefront propagation and can alter the population map. Propagating wavefront solutions in one dimension are investigated analytically and by numerical solutions. The steady wavefront speeds are obtained analytically via nonlinear dynamics analysis and verified by numerical solutions. In addition to the inter- and intraspecific interaction parameters, the intrinsic speed parameters of each species play a decisive role in species populations and wave properties. In some regimes, both species can copropagate with the same wave speeds in a finite range of parameters. Our results are further discussed in the light of possible biological relevance and ecological implications.

  4. The predictive skill of species distribution models for plankton in a changing climate

    DEFF Research Database (Denmark)

    Brun, Philipp Georg; Kiørboe, Thomas; Licandro, Priscilla

    2016-01-01

    Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real-world are rare. Here, we use data from the Continuous Plankton Recorder program, one...... null models, is essential to assess the robustness of projections of marine planktonic species under climate change...

  5. Can nutrient status of four woody plant species be predicted using field spectrometry?

    NARCIS (Netherlands)

    Ferwerda, J.G.; Skidmore, A.K.

    2007-01-01

    This paper demonstrates the potential of hyperspectral remote sensing to predict the chemical composition (i.e., nitrogen, phosphorous, calcium, potassium, sodium, and magnesium) of three tree species (i.e., willow, mopane and olive) and one shrub species (i.e., heather). Reflectance spectra,

  6. Novel view on predicting acute toxicity: Decomposing toxicity data in species vulnerability and chemical potency.

    NARCIS (Netherlands)

    Jager, D.T.; Posthuma, L.; Zwart, D.D.; van de Meent, D.

    2007-01-01

    Chemical risk assessment usually applies empirical methods to predict toxicant effects on different species. We propose a more mechanism-oriented approach, and introduce a method to decompose toxicity data in a contribution from the chemical (potency) and from the exposed species (vulnerability). We

  7. Species co-occurrence affects the trophic interactions of two juvenile reef shark species in tropical lagoon nurseries in Moorea (French Polynesia).

    Science.gov (United States)

    Matich, Philip; Kiszka, Jeremy J; Mourier, Johann; Planes, Serge; Heithaus, Michael R

    2017-06-01

    Food web structure is shaped by interactions within and across trophic levels. As such, understanding how the presence and absence of predators, prey, and competitors affect species foraging patterns is important for predicting the consequences of changes in species abundances, distributions, and behaviors. Here, we used plasma δ 13 C and δ 15 N values from juvenile blacktip reef sharks (Carcharhinus melanopterus) and juvenile sicklefin lemon sharks (Negaprion acutidens) to investigate how species co-occurrence affects their trophic interactions in littoral waters of Moorea, French Polynesia. Co-occurrence led to isotopic niche partitioning among sharks within nurseries, with significant increases in δ 15 N values among sicklefin lemon sharks, and significant decreases in δ 15 N among blacktip reef sharks. Niche segregation likely promotes coexistence of these two predators during early years of growth and development, but data do not suggest coexistence affects life history traits, such as body size, body condition, and ontogenetic niche shifts. Plasticity in trophic niches among juvenile blacktip reef sharks and sicklefin lemon sharks also suggests these predators are able to account for changes in community structure, resource availability, and intra-guild competition, and may fill similar functional roles in the absence of the other species, which is important as environmental change and human impacts persist in coral reef ecosystems. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Environmental variability uncovers disruptive effects of species' interactions on population dynamics.

    Science.gov (United States)

    Gudmundson, Sara; Eklöf, Anna; Wennergren, Uno

    2015-08-07

    How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species-species interactions to come into play. Our results give new insights into species' response to environmental variation. They especially highlight the importance of considering both species' interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment. © 2015 The Author(s).

  9. Predicting plant invasions under climate change: are species distribution models validated by field trials?

    Science.gov (United States)

    Sheppard, Christine S; Burns, Bruce R; Stanley, Margaret C

    2014-09-01

    Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced

  10. [Novel Hyphenated Techniques of Atomic Spectrometry for Metal Species Interaction with Biomolecules].

    Science.gov (United States)

    Li, Yan; Yan, Xiu-ping

    2015-09-01

    Trace metals may be adopted by biological systems to assist in the syntheses and metabolic functions of genes (DNA and RNA) and proteins in the environment. These metals may be beneficial or may pose a risk to humans and other life forms. Novel hybrid techniques are required for studies on the interaction between different metal species and biomolecules, which is significant for biology, biochemistry, nutrition, agriculture, medicine, pharmacy, and environmental science. In recent years, our group dwells on new hyphenated techniques based on capillary electrophoresis (CE), electrothermal atomic absorption spectrometry (ETAAS), and inductively coupled plasma mass spectroscopy (ICP-MS), and their application for different metal species interaction with biomolecules such as DNA, HSA, and GSH. The CE-ETAAS assay and CE-ICP-MS assay allow sensitively probing the level of biomolecules such as DNA damage by different metal species and extracting the kinetic and thermodynamic information on the interactions of different metal species with biomolecules, provides direct evidences for the formation of different metal species--biomolecule adducts. In addition, the consequent structural information were extracted from circular dichroism (CD) and X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. The present works represent the most complete and extensive study to date on the interactions between different metal species with biomolecules, and also provide new evidences for and insights into the interactions of different metal species with biomolecules for further understanding of the toxicological effects of metal species.

  11. Consideration on thermodynamic data for predicting solubility and chemical species of elements in groundwater. Part 2: Np, Pu

    International Nuclear Information System (INIS)

    Yamaguchi, Tetsuji

    2000-11-01

    The solubility determines the release of a radionuclide from waste form and is used as a source term in radionuclide migration analysis in performance assessment of radioactive waste repository. Complexations of the radionuclide by ligands in groundwater affect the interaction between radionuclides and geologic media, thus affect their migration behavior. It is essential to estimate the solubility and to predict the chemical species for the radionuclide based on thermodynamic data. The thermodynamic data of aqueous species and compounds were reviewed and compiled for Np and Pu. Thermodynamic data were reviewed with emphasis on the hydrolysis and carbonate complexation that can dominate the speciation in groundwater. Thermodynamic data for other species were selected based on existing databases. Thermodynamic data for other important elements are under investigation, thus shown in an appendix for temporary use. (author)

  12. Leapfrogging of tree species provenances? Interaction of microclimate and genetics on upward shifts in tree species' range limits

    Science.gov (United States)

    Reinhardt, K.; Castanha, C.; Germino, M. J.; Kueppers, L. M.

    2011-12-01

    The elevation limit of tree growth (alpine treeline) is considered to be constrained by environmental (i.e., thermal) and genetic (i.e., inability to adapt to climatic conditions) limitations to growth. Warming conditions due to climate change are predicted to cause upward shifts in the elevation of alpine treelines, through relief of cold-induced physiological limitations on seedling recruitment beyond current treeline boundaries. To determine how genetics and climate may interact to affect seedling establishment, we transplanted recently germinated seedlings from high- and low-elevation provenances (HI and LO, respectively) of Pinus flexilis in common gardens arrayed along an elevation and canopy gradient from subalpine forest into the alpine zone at Niwot Ridge, CO. We compared differences in microclimate and seedling ecophysiology among sites and between provenances. During the first summer of growth, frequently cloudy skies resulted in similar solar radiation incidence and air and soil temperatures among sites, despite nearly a 500 m-span in elevation across all sites. Preliminary findings suggest that survival of seedlings was similar between the lowest and highest elevations, with greater survival of LO (60%) compared to HI (40%) seedlings at each of these sites. Photosynthesis, carbon balance (photosynthesis/respiration), and conductance increased more than 2X with elevation for both provenances, and were 35-77% greater in LO seedlings compared to HI seedlings. There were no differences in dark-adapted chlorophyll fluorescence (Fv/Fm) among sites or between provenances. However, in a common-garden study at low elevation, we observed no differences in carbon or water relations between two naturally-germinated mitochondrial haplotypes of P. flexilis (of narrow and wide-ranging distributions). We did observe water-related thresholds on seedling carbon balance and survival that occurred when soil volumetric water content dropped below 10% and seedling water

  13. Abundance of introduced species at home predicts abundance away in herbaceous communities

    Science.gov (United States)

    Firn, Jennifer; Moore, Joslin L.; MacDougall, Andrew S.; Borer, Elizabeth T.; Seabloom, Eric W.; HilleRisLambers, Janneke; Harpole, W. Stanley; Cleland, Elsa E.; Brown, Cynthia S.; Knops, Johannes M.H.; Prober, Suzanne M.; Pyke, David A.; Farrell, Kelly A.; Bakker, John D.; O'Halloran, Lydia R.; Adler, Peter B.; Collins, Scott L.; D'Antonio, Carla M.; Crawley, Michael J.; Wolkovich, Elizabeth M.; La Pierre, Kimberly J.; Melbourne, Brett A.; Hautier, Yann; Morgan, John W.; Leakey, Andrew D.B.; Kay, Adam; McCulley, Rebecca; Davies, Kendi F.; Stevens, Carly J.; Chu, Cheng-Jin; Holl, Karen D.; Klein, Julia A.; Fay, Phillip A.; Hagenah, Nicole; Kirkman, Kevin P.; Buckley, Yvonne M.

    2011-01-01

    Many ecosystems worldwide are dominated by introduced plant species, leading to loss of biodiversity and ecosystem function. A common but rarely tested assumption is that these plants are more abundant in introduced vs. native communities, because ecological or evolutionary-based shifts in populations underlie invasion success. Here, data for 26 herbaceous species at 39 sites, within eight countries, revealed that species abundances were similar at native (home) and introduced (away) sites - grass species were generally abundant home and away, while forbs were low in abundance, but more abundant at home. Sites with six or more of these species had similar community abundance hierarchies, suggesting that suites of introduced species are assembling similarly on different continents. Overall, we found that substantial changes to populations are not necessarily a pre-condition for invasion success and that increases in species abundance are unusual. Instead, abundance at home predicts abundance away, a potentially useful additional criterion for biosecurity programmes.

  14. Comparison of Ablation Predictions for Carbonaceous Materials Using CEA and JANAF-Based Species Thermodynamics

    Science.gov (United States)

    Milos, Frank S.

    2011-01-01

    In most previous work at NASA Ames Research Center, ablation predictions for carbonaceous materials were obtained using a species thermodynamics database developed by Aerotherm Corporation. This database is derived mostly from the JANAF thermochemical tables. However, the CEA thermodynamics database, also used by NASA, is considered more up to date. In this work, the FIAT code was modified to use CEA-based curve fits for species thermodynamics, then analyses using both the JANAF and CEA thermodynamics were performed for carbon and carbon phenolic materials over a range of test conditions. The ablation predictions are comparable at lower heat fluxes where the dominant mechanism is carbon oxidation. However, the predictions begin to diverge in the sublimation regime, with the CEA model predicting lower recession. The disagreement is more significant for carbon phenolic than for carbon, and this difference is attributed to hydrocarbon species that may contribute to the ablation rate.

  15. Interactions among species in a tri-trophic system: the influence of ...

    African Journals Online (AJOL)

    BioMAP

    persistence/abundance are affected by more than two interacting species (Begon ... importance of methodology in revealing why an endangered population is ..... closely related butterfly which also oviposits on thyme buds) failed because the ...

  16. Species interactions within a fouling diatom community: Roles of nutrients, initial inoculum and competitive strategies

    Digital Repository Service at National Institute of Oceanography (India)

    Mitbavkar, S.; Anil, A

    Diatoms constitute an important component of the fouling community. Although a lot of work has dealt with the fouling diatom community structure, work on the species interactions within the community is still meager. In this regard, a study...

  17. Do abundance distributions and species aggregation correctly predict macroecological biodiversity patterns in tropical forests?

    Science.gov (United States)

    Wiegand, Thorsten; Lehmann, Sebastian; Huth, Andreas; Fortin, Marie‐Josée

    2016-01-01

    Abstract Aim It has been recently suggested that different ‘unified theories of biodiversity and biogeography’ can be characterized by three common ‘minimal sufficient rules’: (1) species abundance distributions follow a hollow curve, (2) species show intraspecific aggregation, and (3) species are independently placed with respect to other species. Here, we translate these qualitative rules into a quantitative framework and assess if these minimal rules are indeed sufficient to predict multiple macroecological biodiversity patterns simultaneously. Location Tropical forest plots in Barro Colorado Island (BCI), Panama, and in Sinharaja, Sri Lanka. Methods We assess the predictive power of the three rules using dynamic and spatial simulation models in combination with census data from the two forest plots. We use two different versions of the model: (1) a neutral model and (2) an extended model that allowed for species differences in dispersal distances. In a first step we derive model parameterizations that correctly represent the three minimal rules (i.e. the model quantitatively matches the observed species abundance distribution and the distribution of intraspecific aggregation). In a second step we applied the parameterized models to predict four additional spatial biodiversity patterns. Results Species‐specific dispersal was needed to quantitatively fulfil the three minimal rules. The model with species‐specific dispersal correctly predicted the species–area relationship, but failed to predict the distance decay, the relationship between species abundances and aggregations, and the distribution of a spatial co‐occurrence index of all abundant species pairs. These results were consistent over the two forest plots. Main conclusions The three ‘minimal sufficient’ rules only provide an incomplete approximation of the stochastic spatial geometry of biodiversity in tropical forests. The assumption of independent interspecific placements is most

  18. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  19. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2013-01-01

    Full Text Available Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1 chemical interaction between drugs, (2 protein interactions between drugs’ targets, and (3 target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.

  20. Species traits and their non-additive interactions control the water economy of bryophyte cushions.

    NARCIS (Netherlands)

    Michel, P.; Lee, W.G.; During, H.J.; Cornelissen, J.H.C.; van der Putten, W.H.

    2012-01-01

    1. Ecological processes in mixed-species assemblages are not always an additive function of those in monocultures. In areas with high ground cover of bryophytes, renowned for their considerable water retention capacity, non-additive interactions in mixed-species cushions could play a key role in the

  1. Historical and projected interactions between climate change and insect voltinism in a multivoltine species

    Science.gov (United States)

    Patrick C. Tobin; Sudha Nagarkatti; Greg Loeb; Michael C. Saunders

    2008-01-01

    Climate change can cause major changes to the dynamics of individual species and to those communities in which they interact. One effect of increasing temperatures is on insect voltinism, with the logical assumption that increases in surface temperatures would permit multivoltine species to increase the number of generations per year. Though insect development is...

  2. Interactive influences of wildfire and nonnative species on plant community succession in Hawaii Volcanoes National Park.

    Science.gov (United States)

    Alison Ainsworth

    2007-01-01

    The role of fire as a natural disturbance, its interactions with nonnative species and effects of repeated fires in the Hawaiian Islands have received little investigation. We are unsure of the role fire played in shaping forest structure and composition as well as affecting evolutionary processes of the native biota. Yet, many species do have adaptations that...

  3. Metabolomics Reveals Cryptic Interactive Effects of Species Interactions and Environmental Stress on Nitrogen and Sulfur Metabolism in Seagrass

    DEFF Research Database (Denmark)

    Hasler-Sheetal, Harald; Castorani, Max C. N.; Glud, Ronnie N.

    2016-01-01

    Eutrophication of estuaries and coastal seas is accelerating, increasing light stress on subtidal marine plants and changing their interactions with other species. To date, we have limited understanding of how such variations in environmental and biological stress modify the impact of interactions...... among foundational species and eventually affect ecosystem health. Here, we used metabolomics to assess the impact of light reductions on interactions between the seagrass Zostera marina, an important habitat-forming marine plant, and the abundant and commercially important blue mussel Mytilus edulis....... Plant performance varied with light availability but was unaffected by the presence of mussels. Metabolomic analysis, on the other hand, revealed an interaction between light availability and presence of M. edulis on seagrass metabolism. Under high light, mussels stimulated seagrass nitrogen and energy...

  4. Predicting continental-scale patterns of bird species richness with spatially explicit models

    DEFF Research Database (Denmark)

    Rahbek, Carsten; Gotelli, Nicholas J; Colwell, Robert K

    2007-01-01

    the extraordinary diversity of avian species in the montane tropics, the most species-rich region on Earth. Our findings imply that correlative climatic models substantially underestimate the importance of historical factors and small-scale niche-driven assembly processes in shaping contemporary species-richness......The causes of global variation in species richness have been debated for nearly two centuries with no clear resolution in sight. Competing hypotheses have typically been evaluated with correlative models that do not explicitly incorporate the mechanisms responsible for biotic diversity gradients....... Here, we employ a fundamentally different approach that uses spatially explicit Monte Carlo models of the placement of cohesive geographical ranges in an environmentally heterogeneous landscape. These models predict species richness of endemic South American birds (2248 species) measured...

  5. Pre-damage biomass allocation and not invasiveness predicts tolerance to damage in seedlings of woody species in Hawaii.

    Science.gov (United States)

    Lurie, Matthew H; Barton, Kasey E; Daehler, Curtis C

    2017-12-01

    Plant-herbivore interactions have been predicted to play a fundamental role in plant invasions, although support for this assertion from previous research is mixed. While plants may escape from specialist herbivores in their introduced ranges, herbivory from generalists is common. Tolerance traits may allow non-native plants to mitigate the negative consequences of generalist herbivory that they cannot avoid in their introduced range. Here we address whether tolerance to herbivory, quantified as survival and compensatory growth, is associated with plant invasion success in Hawaii and investigate traits that may enhance tolerance in seedlings, the life stage most susceptible to herbivory. In a greenhouse experiment, we measured seedling tolerance to simulated herbivory through mechanical damage (50% leaf removal) of 16 non-native woody plant species differing in invasion status (invasive vs. non-invasive). Seedlings were grown for 2 weeks following damage and analyzed for biomass to determine whether damaged plants could fully compensate for the lost leaf tissue. Over 99% of all seedlings survived defoliation. Although species varied significantly in their levels of compensation, there was no consistent difference between invasive and non-invasive species. Seedlings of 11 species undercompensated and remained substantially smaller than control seedlings 2 weeks after damage; four species were close to compensating, while one species overcompensated. Across species, compensation was positively associated with an increased investment in potential storage reserves, specifically cotyledons and roots, suggesting that these organs provide resources that help seedlings re-grow following damage. Our results add to a growing consensus that pre-damage growth patterns determine tolerance to damage, even in young seedlings which have relatively low biomass. The lack of higher tolerance in highly invasive species may suggest that invaders overcome herbivory barriers to invasion

  6. Estimating the effect of the reorganization of interactions on the adaptability of species to changing environments.

    Science.gov (United States)

    Cenci, Simone; Montero-Castaño, Ana; Saavedra, Serguei

    2018-01-21

    A major challenge in community ecology is to understand how species respond to environmental changes. Previous studies have shown that the reorganization of interactions among co-occurring species can modulate their chances to adapt to novel environmental conditions. Moreover, empirical evidence has shown that these ecological dynamics typically facilitate the persistence of groups of species rather than entire communities. However, so far, we have no systematic methodology to identify those groups of species with the highest or lowest chances to adapt to new environments through a reorganization of their interactions. Yet, this could prove extremely valuable for developing new conservation strategies. Here, we introduce a theoretical framework to estimate the effect of the reorganization of interactions on the adaptability of a group of species, within a community, to novel environmental conditions. We introduce the concept of the adaptation space of a group of species based on a feasibility analysis of a population dynamics model. We define the adaptation space of a group as the set of environmental conditions that can be made compatible with its persistence thorough the reorganization of interactions among species within the group. The larger the adaptation space of a group, the larger its likelihood to adapt to a novel environment. We show that the interactions in the community outside a group can act as structural constraints and be used to quantitatively compare the size of the adaptation space among different groups of species within a community. To test our theoretical framework, we perform a data analysis on several pairs of natural and artificially perturbed ecological communities. Overall, we find that the groups of species present in both control and perturbed communities are among the ones with the largest adaptation space. We believe that the results derived from our framework point out towards new directions to understand and estimate the

  7. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  8. Cross-species mapping of bidirectional promoters enables prediction of unannotated 5' UTRs and identification of species-specific transcripts

    Directory of Open Access Journals (Sweden)

    Lewin Harris A

    2009-04-01

    Full Text Available Abstract Background Bidirectional promoters are shared regulatory regions that influence the expression of two oppositely oriented genes. This type of regulatory architecture is found more frequently than expected by chance in the human genome, yet many specifics underlying the regulatory design are unknown. Given that the function of most orthologous genes is similar across species, we hypothesized that the architecture and regulation of bidirectional promoters might also be similar across species, representing a core regulatory structure and enabling annotation of these regions in additional mammalian genomes. Results By mapping the intergenic distances of genes in human, chimpanzee, bovine, murine, and rat, we show an enrichment for pairs of genes equal to or less than 1,000 bp between their adjacent 5' ends ("head-to-head" compared to pairs of genes that fall in the same orientation ("head-to-tail" or whose 3' ends are side-by-side ("tail-to-tail". A representative set of 1,369 human bidirectional promoters was mapped to orthologous sequences in other mammals. We confirmed predictions for 5' UTRs in nine of ten manual picks in bovine based on comparison to the orthologous human promoter set and in six of seven predictions in human based on comparison to the bovine dataset. The two predictions that did not have orthology as bidirectional promoters in the other species resulted from unique events that initiated transcription in the opposite direction in only those species. We found evidence supporting the independent emergence of bidirectional promoters from the family of five RecQ helicase genes, which gained their bidirectional promoters and partner genes independently rather than through a duplication process. Furthermore, by expanding our comparisons from pairwise to multispecies analyses we developed a map representing a core set of bidirectional promoters in mammals. Conclusion We show that the orthologous positions of bidirectional

  9. Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

    Directory of Open Access Journals (Sweden)

    Elizabeth A. Becker

    2016-02-01

    Full Text Available Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”, but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE, observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

  10. Biomechanical warfare in ecology; negative interactions between species by habitat modification

    NARCIS (Netherlands)

    van Wesenbeeck, B. K.; van de Koppel, J.; Herman, P. M. J.; Bakker, J. P.; Bouma, T. J.

    Since the introduction of the term ecosystem engineering by Jones et al. many studies have focused on positive, facilitative interactions caused by ecosystem engineering. Much less emphasis has been placed on the role of ecosystem engineering in causing negative interactions between species. Here,

  11. Biomechanical warfare in ecology; negative interactions between species by habitat modification

    NARCIS (Netherlands)

    Van Wesenbeeck, B.K.; Van de Koppel, J.; Herman, P.M.J.; Bakker, J.P.; Bouma, T.J.

    2007-01-01

    Since the introduction of the term ecosystem engineering by Jones et al. many studies have focused on positive, facilitative interactions caused by ecosystem engineering. Much less emphasis has been placed on the role of ecosystem engineering in causing negative interactions between species. Here,

  12. Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function.

    Science.gov (United States)

    Burkle, Laura A; Marlin, John C; Knight, Tiffany M

    2013-03-29

    Using historic data sets, we quantified the degree to which global change over 120 years disrupted plant-pollinator interactions in a temperate forest understory community in Illinois, USA. We found degradation of interaction network structure and function and extirpation of 50% of bee species. Network changes can be attributed to shifts in forb and bee phenologies resulting in temporal mismatches, nonrandom species extinctions, and loss of spatial co-occurrences between extant species in modified landscapes. Quantity and quality of pollination services have declined through time. The historic network showed flexibility in response to disturbance; however, our data suggest that networks will be less resilient to future changes.

  13. Association with humans and seasonality interact to reverse predictions for animal space use.

    Science.gov (United States)

    Laver, Peter N; Alexander, Kathleen A

    2018-01-01

    Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association

  14. Predicting the impact of climate change on threatened species in UK waters.

    Directory of Open Access Journals (Sweden)

    Miranda C Jones

    Full Text Available Global climate change is affecting the distribution of marine species and is thought to represent a threat to biodiversity. Previous studies project expansion of species range for some species and local extinction elsewhere under climate change. Such range shifts raise concern for species whose long-term persistence is already threatened by other human disturbances such as fishing. However, few studies have attempted to assess the effects of future climate change on threatened vertebrate marine species using a multi-model approach. There has also been a recent surge of interest in climate change impacts on protected areas. This study applies three species distribution models and two sets of climate model projections to explore the potential impacts of climate change on marine species by 2050. A set of species in the North Sea, including seven threatened and ten major commercial species were used as a case study. Changes in habitat suitability in selected candidate protected areas around the UK under future climatic scenarios were assessed for these species. Moreover, change in the degree of overlap between commercial and threatened species ranges was calculated as a proxy of the potential threat posed by overfishing through bycatch. The ensemble projections suggest northward shifts in species at an average rate of 27 km per decade, resulting in small average changes in range overlap between threatened and commercially exploited species. Furthermore, the adverse consequences of climate change on the habitat suitability of protected areas were projected to be small. Although the models show large variation in the predicted consequences of climate change, the multi-model approach helps identify the potential risk of increased exposure to human stressors of critically endangered species such as common skate (Dipturus batis and angelshark (Squatina squatina.

  15. Positive indirect interactions between neighboring plant species via a lizard pollinator.

    OpenAIRE

    Hansen, D M; Kiesbüy, H C; Jones, C G; Müller, C B

    2007-01-01

    In natural communities, species are embedded in networks of direct and indirect interactions. Most studies on indirect interactions have focused on how they affect predator-prey or competitive relationships. However, it is equally likely that indirect interactions play an important structuring role in mutualistic relationships in a natural community. We demonstrate experimentally that on a small spatial scale, dense thickets of endemic Pandanus plants have a strong positive trait-mediated ind...

  16. Natural enemy-mediated indirect interactions among prey species: potential for enhancing biocontrol services in agroecosystems

    NARCIS (Netherlands)

    Chailleux, A.; Mohl, E.K.; Teixeira Alves, M.; Messelink, G.J.; Desneux, N.

    2014-01-01

    Understanding how arthropod pests and their natural enemies interact in complex agroecosystems is essential for pest management programmes. Theory predicts that prey sharing a predator, such as a biological control agent, can indirectly reduce each other's density at equilibrium (apparent

  17. Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.

    Science.gov (United States)

    Fricker, Geoffrey A; Wolf, Jeffrey A; Saatchi, Sassan S; Gillespie, Thomas W

    2015-10-01

    There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r² = 0.35) and trees with dbh > 10 cm (adjusted r² = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r² = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high

  18. Land-use change interacts with climate to determine elevational species redistribution.

    Science.gov (United States)

    Guo, Fengyi; Lenoir, Jonathan; Bonebrake, Timothy C

    2018-04-03

    Climate change is driving global species redistribution with profound social and economic impacts. However, species movement is largely constrained by habitat availability and connectivity, of which the interaction effects with climate change remain largely unknown. Here we examine published data on 2798 elevational range shifts from 43 study sites to assess the confounding effect of land-use change on climate-driven species redistribution. We show that baseline forest cover and recent forest cover change are critical predictors in determining the magnitude of elevational range shifts. Forest loss positively interacts with baseline temperature conditions, such that forest loss in warmer regions tends to accelerate species' upslope movement. Consequently, not only climate but also habitat loss stressors and, importantly, their synergistic effects matter in forecasting species elevational redistribution, especially in the tropics where both stressors will increase the risk of net lowland biotic attrition.

  19. Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

    Science.gov (United States)

    Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

    2018-01-01

    Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

  20. The effects of model and data complexity on predictions from species distributions models

    DEFF Research Database (Denmark)

    García-Callejas, David; Bastos, Miguel

    2016-01-01

    How complex does a model need to be to provide useful predictions is a matter of continuous debate across environmental sciences. In the species distributions modelling literature, studies have demonstrated that more complex models tend to provide better fits. However, studies have also shown...... that predictive performance does not always increase with complexity. Testing of species distributions models is challenging because independent data for testing are often lacking, but a more general problem is that model complexity has never been formally described in such studies. Here, we systematically...

  1. Predicting interactions from mechanistic information: Can omic data validate theories?

    International Nuclear Information System (INIS)

    Borgert, Christopher J.

    2007-01-01

    To address the most pressing and relevant issues for improving mixture risk assessment, researchers must first recognize that risk assessment is driven by both regulatory requirements and scientific research, and that regulatory concerns may expand beyond the purely scientific interests of researchers. Concepts of 'mode of action' and 'mechanism of action' are used in particular ways within the regulatory arena, depending on the specific assessment goals. The data requirements for delineating a mode of action and predicting interactive toxicity in mixtures are not well defined from a scientific standpoint due largely to inherent difficulties in testing certain underlying assumptions. Understanding the regulatory perspective on mechanistic concepts will be important for designing experiments that can be interpreted clearly and applied in risk assessments without undue reliance on extrapolation and assumption. In like fashion, regulators and risk assessors can be better equipped to apply mechanistic data if the concepts underlying mechanistic research and the limitations that must be placed on interpretation of mechanistic data are understood. This will be critically important for applying new technologies to risk assessment, such as functional genomics, proteomics, and metabolomics. It will be essential not only for risk assessors to become conversant with the language and concepts of mechanistic research, including new omic technologies, but also, for researchers to become more intimately familiar with the challenges and needs of risk assessment

  2. Predicting the geographical distribution of two invasive termite species from occurrence data.

    Science.gov (United States)

    Tonini, Francesco; Divino, Fabio; Lasinio, Giovanna Jona; Hochmair, Hartwig H; Scheffrahn, Rudolf H

    2014-10-01

    Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.

  3. Floral scent composition predicts bee pollination system in five butterfly bush (Buddleja, Scrophulariaceae) species.

    OpenAIRE

    Gong, W-C; Chen, G; Vereecken, Nicolas; Dunn, B L; Ma, Y-P; Sun, W-B

    2014-01-01

    Traditionally, plant-pollinator interactions have been interpreted as pollination syndrome. However, the validity of pollination syndrome has been widely doubted in modern studies of pollination ecology. The pollination ecology of five Asian Buddleja species, B. asiatica, B. crispa, B. forrestii, B. macrostachya and B. myriantha, in the Sino-Himalayan region in Asia, flowering in different local seasons, with scented inflorescences were investigated during 2011 and 2012. These five species ex...

  4. Modeling invasive alien plant species in river systems: Interaction with native ecosystem engineers and effects on hydro-morphodynamic processes

    Science.gov (United States)

    van Oorschot, M.; Kleinhans, M. G.; Geerling, G. W.; Egger, G.; Leuven, R. S. E. W.; Middelkoop, H.

    2017-08-01

    Invasive alien plant species negatively impact native plant communities by out-competing species or changing abiotic and biotic conditions in their introduced range. River systems are especially vulnerable to biological invasions, because waterways can function as invasion corridors. Understanding interactions of invasive and native species and their combined effects on river dynamics is essential for developing cost-effective management strategies. However, numerical models for simulating long-term effects of these processes are lacking. This paper investigates how an invasive alien plant species affects native riparian vegetation and hydro-morphodynamics. A morphodynamic model has been coupled to a dynamic vegetation model that predicts establishment, growth and mortality of riparian trees. We introduced an invasive alien species with life-history traits based on Japanese Knotweed (Fallopia japonica), and investigated effects of low- and high propagule pressure on invasion speed, native vegetation and hydro-morphodynamic processes. Results show that high propagule pressure leads to a decline in native species cover due to competition and the creation of unfavorable native colonization sites. With low propagule pressure the invader facilitates native seedling survival by creating favorable hydro-morphodynamic conditions at colonization sites. With high invader abundance, water levels are raised and sediment transport is reduced during the growing season. In winter, when the above-ground invader biomass is gone, results are reversed and the floodplain is more prone to erosion. Invasion effects thus depend on seasonal above- and below ground dynamic vegetation properties and persistence of the invader, on the characteristics of native species it replaces, and the combined interactions with hydro-morphodynamics.

  5. Landscape-scale evaluation of asymmetric interactions between Brown Trout and Brook Trout using two-species occupancy models

    Science.gov (United States)

    Wagner, Tyler; Jefferson T. Deweber,; Jason Detar,; John A. Sweka,

    2013-01-01

    Predicting the distribution of native stream fishes is fundamental to the management and conservation of many species. Modeling species distributions often consists of quantifying relationships between species occurrence and abundance data at known locations with environmental data at those locations. However, it is well documented that native stream fish distributions can be altered as a result of asymmetric interactions between dominant exotic and subordinate native species. For example, the naturalized exotic Brown Trout Salmo trutta has been identified as a threat to native Brook Trout Salvelinus fontinalis in the eastern United States. To evaluate large-scale patterns of co-occurrence and to quantify the potential effects of Brown Trout presence on Brook Trout occupancy, we used data from 624 stream sites to fit two-species occupancy models. These models assumed that asymmetric interactions occurred between the two species. In addition, we examined natural and anthropogenic landscape characteristics we hypothesized would be important predictors of occurrence of both species. Estimated occupancy for Brook Trout, from a co-occurrence model with no landscape covariates, at sites with Brown Trout present was substantially lower than sites where Brown Trout were absent. We also observed opposing patterns for Brook and Brown Trout occurrence in relation to percentage forest, impervious surface, and agriculture within the network catchment. Our results are consistent with other studies and suggest that alterations to the landscape, and specifically the transition from a forested catchment to one that contains impervious surface or agriculture, reduces the occurrence probability of wild Brook Trout. Our results, however, also suggest that the presence of Brown Trout results in lower occurrence probability of Brook Trout over a range of anthropogenic landscape characteristics, compared with streams where Brown Trout were absent.

  6. Predicting the consequences of species loss using size-structured biodiversity approaches

    DEFF Research Database (Denmark)

    Brose, Ulrich; Blanchard, Julia L.; Eklöf, Anna

    2017-01-01

    Understanding the consequences of species loss in complex ecological communities is one of the great challenges in current biodiversity research. For a long time, this topic has been addressed by traditional biodiversity experiments. Most of these approaches treat species as trait-free, taxonomic...... stability, and (iii) ecosystem functioning. Contrasting current expectations, size-structured approaches suggest that the loss of large species, that typically exploit most resource species, may lead to future food webs that are less interwoven and more structured by chains of interactions and compartments...... trait when analysing the consequences of biodiversity loss for natural ecosystems. Applying size-structured approaches provides an integrative ecological concept that enables a better understanding of each species' unique role across communities and the causes and consequences of biodiversity loss....

  7. Consideration on thermodynamic data for predicting solubility and chemical species of elements in groundwater. Part 1: Tc, U, Am

    Energy Technology Data Exchange (ETDEWEB)

    Yamaguchi, Tetsuji; Takeda, Seiji [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    1999-01-01

    The solubility determines the release of radionuclides from waste form and is used as a source term in radionuclide migration analysis in performance assessment of radioactive waste repository. Complexations of radionuclides by ligands in groundwater affect the interaction between radionuclides and geologic media, thus affect their migration behavior. Thermodynamic data for Tc, Am and U were reviewed and compiled to be used for predicting the solubility and chemical species in groundwater. Thermodynamic data were reviewed with emphasis on the hydrolysis and carbonate complexation that can dominate the speciation in typical groundwater. Thermodynamic data for other species were selected based on existing database. Thermodynamic data for other important elements are under investigation, thus shown in an appendix for temporary use. (author)

  8. Using phylogenetic and ionomic relationships to predict the uptake of radionuclides by any plant species

    Energy Technology Data Exchange (ETDEWEB)

    Willey, Neil J.; Siasou, Eleni [Centre for Research In Biosciences, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY (United Kingdom)

    2014-07-01

    It is not practical to empirically derive soil-to-plant TFs for all soil-plant combinations that are important in radiological assessments, so predictions for a range of species on different soils types are frequently impossible because TFs are unknown. This severely hampers predictions of both doses to biota and of the contamination of a variety of food chains with radioisotopes. Compilations of TFs in themselves provide no fundamental understanding of the plant factors that control the soil-to-plant transfer of radionuclides and thus no method of prediction. We have developed methods for the meta-analyses of radionuclide transfer data that can be used to make predictions of the transfer of radionuclides into any plants species for which TFs do not exist based on an understand of the plant factors that control radionuclide uptake. There is no reason a priori to think that variation in TF should be constrained by species. The species is, essentially, a reproductive unit and variation in many plant traits, some of which might control radionuclide uptake, occurs at taxonomic levels above the species. In the last 15 years genomic information has transformed the understanding of the evolutionary relationships of the living world so that new 'trees of life' (phylogenies) are now available. Using a Residual Maximum Likelihood modeling procedure to compile a significant proportion of all existing TF data onto a single scale, we here present a synthesis of the influence of phylogeny on variation in soil-to-plant TFs for radioisotopes of Cs, Sr, Co, I, Tc, and S. We show that a significant proportion of variation in TF is associated with major branches of the phylogeny of angiosperms (flowering plants) so that knowledge of a species' position on the phylogeny can be used to make predictions of transfer relative to other species. These phylogenetically-based predictions of relative transfer to any species can be used to make absolute predictions to any species

  9. The role of biotic interactions in plant community assembly: What is the community species pool?

    Science.gov (United States)

    Švamberková, Eva; Vítová, Alena; Lepš, Jan

    2017-11-01

    Differences in plant species composition between a community and its species pool are considered to reflect the effect of community filters. If we define the species pool as a set of species able to reach a site and form a viable population in a given abiotic environment (i.e. to pass the dispersal and abiotic filter), the difference in species composition should correspond to the effect of biotic interactions. However, most of the operational definitions of the species pool are based on co-occurrence patterns and thus also reflect the effect of biotic relationships, including definitions based on functional plant traits, Ellenberg indicator values or Beals index. We conducted two seed introduction experiments in an oligotrophic wet meadow with the aim of demonstrating that many species excluded, according to the above definitions, from a species pool are in fact able to establish there successfully if competition is removed. In sowing experiments, we studied the establishment and survival of species after the removal of competition (i.e. in artificial gaps) and in intact vegetation. We also investigated inter-annual variability of seed germination and seedling establishment and competitive exclusion of sown species. The investigated species also included those from very different habitats (i.e. species with very low corresponding Beals index or Ellenberg indicator values that were different from the target community weighted mean). Many of these species were able to grow in the focal wet meadow if competition was removed, but they did not establish and survive in the intact community. These species are thus not limited by abiotic conditions, but by the biotic filter. We also recorded a great inter-annual variability in seed germination and seedling establishment. Competitive exclusion of species with different ecological requirements could be quite fast (one and half seasons) in some species, but some non-resident species were able to survive several seasons; the

  10. Predictive models of threatened plant species distribution in the Iberian arid south-east

    OpenAIRE

    Benito, Blas M.

    2013-01-01

    Poster on the distribution of three rare, endemic and endangered annual plants of arid zones in the south-eastern Iberian peninsula. Presented in the workshop "Predictive Modelling of Species Distribution: New Tools for the XXI Century (Baeza, Spain, november 2005).

  11. Equations for predicting biomass of six introduced tree species, island of Hawaii

    Science.gov (United States)

    Thomas H. Schukrt; Robert F. Strand; Thomas G. Cole; Katharine E. McDuffie

    1988-01-01

    Regression equations to predict total and stem-only above-ground dry biomass for six species (Acacia melanoxylon, Albizio falcataria, Eucalyptus globulus, E. grandis, E. robusta, and E. urophylla) were developed by felling and measuring 2- to 6-year-old...

  12. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  13. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

    Directory of Open Access Journals (Sweden)

    Neto Armando

    2012-11-01

    Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

  14. Separation of methyltin species from inorganic tin, and their interactions with humates in natural waters

    International Nuclear Information System (INIS)

    Omar, M.; Bowen, H.J.M.

    1982-01-01

    Tin(II) and tin(IV) are absorbed from aqueous solutions by Sephadex G-25 gel, from which they can be eluted by humates or fulvates, with which they interact more strongly. Methyltin species are not absorbed by Sephadex G-25, and so can be separated from inorganic tin. Both inorganic tin and methyltin species in natural waters at pH 7.4 can be quantitatively retained by passing through small columns of Chelex-100 resin: the methyltin species can then be washed off the resin with 4M nitric acid. Trimethyltin chloride 113 Sn in water scarcely interacts with fulvates, humates, kaolinite or montmorillonite but is absorbed by Sphagnum peat. Dimethyltin dichloride- 113 Sn reacts significantly with all the above materials after 2 hours equilibration. Methyltin trichloride- 113 Sn interacts weakly in alkaline solutions. (author)

  15. Mechanisms of Disease: Host-Pathogen Interactions between Burkholderia Species and Lung Epithelial Cells

    Science.gov (United States)

    David, Jonathan; Bell, Rachel E.; Clark, Graeme C.

    2015-01-01

    Members of the Burkholderia species can cause a range of severe, often fatal, respiratory diseases. A variety of in vitro models of infection have been developed in an attempt to elucidate the mechanism by which Burkholderia spp. gain entry to and interact with the body. The majority of studies have tended to focus on the interaction of bacteria with phagocytic cells with a paucity of information available with regard to the lung epithelium. However, the lung epithelium is becoming more widely recognized as an important player in innate immunity and the early response to infections. Here we review the complex relationship between Burkholderia species and epithelial cells with an emphasis on the most pathogenic species, Burkholderia pseudomallei and Burkholderia mallei. The current gaps in knowledge in our understanding are highlighted along with the epithelial host-pathogen interactions that offer potential opportunities for therapeutic intervention. PMID:26636042

  16. Reproductive interference and fecundity affect competitive interactions of sibling species with low mating barriers: experimental and theoretical evidence.

    Science.gov (United States)

    Gebiola, M; Kelly, S E; Velten, L; Zug, R; Hammerstein, P; Giorgini, M; Hunter, M S

    2017-12-01

    When allopatric species with incomplete prezygotic isolation come into secondary contact, the outcome of their interaction is not easily predicted. The parasitoid wasp Encarsia suzannae (iES), infected by Cardinium inducing cytoplasmic incompatibility (CI), and its sibling species E. gennaroi (EG), not infected by bacterial endosymbionts, may have diverged because of the complementary action of CI and asymmetric hybrid incompatibilities. Whereas postzygotic isolation is now complete because of sterility of F1 hybrid progeny, prezygotic isolation is still incipient. We set up laboratory population cage experiments to evaluate the outcome of the interaction between ES and EG in two pairwise combinations: iES vs EG and cured ES (cES, where Cardinium was removed with antibiotics) vs EG. We also built a theoretical model aimed at exploring the role of life-history differences and asymmetric mating on competitive outcomes. In three of four cages in each treatment, ES dominated the interaction. We found evidence for reproductive interference, driven by asymmetric mating preferences, that gave a competitive edge to ES, the species that better discriminated against heterospecifics. However, we did not find the fecundity cost previously shown to be associated with Cardinium infection in iES. The model largely supported the experimental results. The finding of only a slight competitive edge of ES over EG in population cages suggests that in a more heterogeneous environment the species could coexist. This is supported by evidence that the two species coexist in sympatry, where preliminary data suggest reproductive character displacement may have reinforced postzygotic isolation.

  17. Are species' responses to global change predicted by past niche evolution?

    Science.gov (United States)

    Lavergne, Sébastien; Evans, Margaret E. K.; Burfield, Ian J.; Jiguet, Frederic; Thuiller, Wilfried

    2013-01-01

    Predicting how and when adaptive evolution might rescue species from global change, and integrating this process into tools of biodiversity forecasting, has now become an urgent task. Here, we explored whether recent population trends of species can be explained by their past rate of niche evolution, which can be inferred from increasingly available phylogenetic and niche data. We examined the assemblage of 409 European bird species for which estimates of demographic trends between 1970 and 2000 are available, along with a species-level phylogeny and data on climatic, habitat and trophic niches. We found that species' proneness to demographic decline is associated with slow evolution of the habitat niche in the past, in addition to certain current-day life-history and ecological traits. A similar result was found at a higher taxonomic level, where families prone to decline have had a history of slower evolution of climatic and habitat niches. Our results support the view that niche conservatism can prevent some species from coping with environmental change. Thus, linking patterns of past niche evolution and contemporary species dynamics for large species samples may provide insights into how niche evolution may rescue certain lineages in the face of global change. PMID:23209172

  18. Can temporal and spatial NDVI predict regional bird-species richness?

    Directory of Open Access Journals (Sweden)

    Sebastián Nieto

    2015-01-01

    Full Text Available Understanding the distribution of the species and its controls over biogeographic scales is still a major challenge in ecology. National Park Networks provide an opportunity to assess the relationship between ecosystem functioning and biodiversity in areas with low human impacts. We tested the productivity–biodiversity hypothesis which states that the number of species increases with the available energy, and the ​variability–biodiversity hypothesis which states that the number of species increases with the diversity of habitats. The available energy and habitat heterogeneity estimated by the normalized difference vegetation index (NDVI was shown as a good predictor of bird-species richness for a diverse set of biomes in previously published studies. However, there is not a universal relationship between NDVI and bird-species richness. Here we tested if the NDVI can predict bird species richness in areas with low human impact in Argentina. Using a dataset from the National Park Network of Argentina we found that the best predictor of bird species richness was the minimum value of NDVI per year which explained 75% of total variability. The inclusion of the spatial heterogeneity of NDVI improved the explanation power to 80%. Minimum NDVI was highly correlated with precipitation and winter temperature. Our analysis provides a tool for assessing bird-species richness at scales on which land-use planning practitioners make their decisions for Southern South America.

  19. Antagonistic interactions between an invasive alien and a native coccinellid species may promote coexistence.

    OpenAIRE

    Hentley, W.T.; Vanbergen, A.J.; Beckerman, A.P.; Brien, M.N.; Hails, R.S.; Jones, T.H.; Johnson, S.N.

    2016-01-01

    1. Despite the capacity of invasive alien species to alter ecosystems, the mechanisms underlying their impact remain only partly understood. Invasive alien predators, for example, can significantly disrupt recipient communities by consuming prey species or acting as an intraguild predator (IGP). 2. Behavioural interactions are key components of interspecific competition between predators,yet these are often overlooked invasion processes. Here, we show how behavioural, nonlethal IGP intera...

  20. Effects of plant diversity on primary production and species interactions in brackish water angiosperm communities

    DEFF Research Database (Denmark)

    Salo, Tiina; Gustafsson, Camilla; Boström, Christoffer

    2009-01-01

    Research on plant biodiversity and ecosystem functioning has mainly focused on terrestrial ecosystems, and our understanding of how plant species diversity and interactions affect processes in marine ecosystems is still limited. To investigate if plant species richness and composition influence...... plant productivity in brackish water angiosperm communities, a 14 wk field experiment was conducted. Using a replacement design with a standardized initial aboveground biomass, shoots of Zostera marina, Potamogeton filiformis and P. perfoliatus were planted on a shallow, sandy bottom in replicated...

  1. Consumer-resource theory predicts dynamic transitions between outcomes of interspecific interactions

    Science.gov (United States)

    Holland, J. Nathaniel; DeAngelis, Donald L.

    2009-01-01

    Interactions between two populations are often defined by their interaction outcomes; that is, the positive, neutral, or negative effects of species on one another. Yet, signs of outcomes are not absolute, but vary with the biotic and abiotic contexts of interactions. Here, we develop a general theory for transitions between outcomes based on consumer-resource (C-R) interactions in which one or both species exploit the other as a resource. Simple models of C-R interactions revealed multiple equilibria, including one for species coexistence and others for extinction of one or both species, indicating that species densities alone could determine the fate of interactions. All possible outcomes (+ +), (+ -), (- -), (+ 0), (- 0), (0 0) of species coexistence emerged merely through changes in parameter values of C-R interactions, indicating that variation in C-R interactions resulting from biotic and abiotic conditions could determine shifts in outcomes. These results suggest that C-R interactions can provide a broad mechanism for understanding context- and density-dependent transitions between interaction outcomes.

  2. Landscape genomic prediction for restoration of a Eucalyptus foundation species under climate change.

    Science.gov (United States)

    Supple, Megan Ann; Bragg, Jason G; Broadhurst, Linda M; Nicotra, Adrienne B; Byrne, Margaret; Andrew, Rose L; Widdup, Abigail; Aitken, Nicola C; Borevitz, Justin O

    2018-04-24

    As species face rapid environmental change, we can build resilient populations through restoration projects that incorporate predicted future climates into seed sourcing decisions. Eucalyptus melliodora is a foundation species of a critically endangered community in Australia that is a target for restoration. We examined genomic and phenotypic variation to make empirical based recommendations for seed sourcing. We examined isolation by distance and isolation by environment, determining high levels of gene flow extending for 500 km and correlations with climate and soil variables. Growth experiments revealed extensive phenotypic variation both within and among sampling sites, but no site-specific differentiation in phenotypic plasticity. Model predictions suggest that seed can be sourced broadly across the landscape, providing ample diversity for adaptation to environmental change. Application of our landscape genomic model to E. melliodora restoration projects can identify genomic variation suitable for predicted future climates, thereby increasing the long term probability of successful restoration. © 2018, Supple et al.

  3. Hydrological Conditions Affect the Interspecific Interaction between Two Emergent Wetland Species

    Directory of Open Access Journals (Sweden)

    Jian Zhou

    2018-01-01

    Full Text Available Hydrological conditions determine the distribution of plant species in wetlands, where conditions such as water depth and hydrological fluctuations are expected to affect the interspecific interactions among emergent wetland species. To test such effects, we conducted a greenhouse experiment with three treatment categories, interspecific interaction (mixed culture or monoculture, water depth (10 or 30 cm depth, and hydrological fluctuation (static or fluctuating water level, and two common emergent wetland plant species, Scirpus planiculumis Fr. (Cyperaceae and Phragmites australis var. baiyangdiansis (Gramineae. An increase in the water depth significantly restrained the growth of both S. planiculumis and P. australis, while hydrological fluctuations did not obviously alter the growth of either species. In addition, both water depth and hydrological fluctuations significantly affected the interspecific interaction between these two wetland species. P. australis benefited from interspecific interaction under increasing water depth and hydrological fluctuations, and the RII values were clearly positive for plants grown at a water depth that fluctuated around 30 cm. The results may have some implications for understanding how S. planiculumis and P. australis, as well as wetland communities, respond to the natural variation or human modification of hydrological conditions.

  4. A rapid method for selecting suitable animal species for studying pathogen interactions with plasma protein ligands in vivo.

    Science.gov (United States)

    Naudin, Clément; Schumski, Ariane; Salo-Ahen, Outi M H; Herwald, Heiko; Smeds, Emanuel

    2017-05-01

    Species tropism constitutes a serious problem for developing relevant animal models of infection. Human pathogens can express virulence factors that show specific selectivity to human proteins, while their affinity for orthologs from other species can vary significantly. Suitable animal species must be used to analyse whether virulence factors are potential targets for drug development. We developed an assay that rapidly predicts applicable animal species for studying virulence factors binding plasma proteins. We used two well-characterized Staphylococcus aureus proteins, SSL7 and Efb, to develop an ELISA-based inhibition assay using plasma from different animal species. The interaction between SSL7 and human C5 and the binding of Efb to human fibrinogen and human C3 was studied. Affinity experiments and Western blot analyses were used to validate the assay. Human, monkey and cat plasma interfered with binding of SSL7 to human C5. Binding of Efb to human fibrinogen was blocked in human, monkey, gerbil and pig plasma, while human, monkey, gerbil, rabbit, cat and guinea pig plasma inhibited the binding of Efb to human C3. These results emphasize the importance of choosing correct animal models, and thus, our approach is a rapid and cost-effective method that can be used to prevent unnecessary animal experiments. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

  5. Quantitative thermodynamic predication of interactions between nucleic acid and non-nucleic acid species using Microsoft excel.

    Science.gov (United States)

    Zou, Jiaqi; Li, Na

    2013-09-01

    Proper design of nucleic acid sequences is crucial for many applications. We have previously established a thermodynamics-based quantitative model to help design aptamer-based nucleic acid probes by predicting equilibrium concentrations of all interacting species. To facilitate customization of this thermodynamic model for different applications, here we present a generic and easy-to-use platform to implement the algorithm of the model with Microsoft(®) Excel formulas and VBA (Visual Basic for Applications) macros. Two Excel spreadsheets have been developed: one for the applications involving only nucleic acid species, the other for the applications involving both nucleic acid and non-nucleic acid species. The spreadsheets take the nucleic acid sequences and the initial concentrations of all species as input, guide the user to retrieve the necessary thermodynamic constants, and finally calculate equilibrium concentrations for all species in various bound and unbound conformations. The validity of both spreadsheets has been verified by comparing the modeling results with the experimental results on nucleic acid sequences reported in the literature. This Excel-based platform described here will allow biomedical researchers to rationalize the sequence design of nucleic acid probes using the thermodynamics-based modeling even without relevant theoretical and computational skills. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

    Directory of Open Access Journals (Sweden)

    Sarah J. Graves

    2016-02-01

    Full Text Available Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation

  7. Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

    Science.gov (United States)

    Wuchty, Stefan

    2006-05-23

    While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

  8. Topology and weights in a protein domain interaction network – a novel way to predict protein interactions

    Directory of Open Access Journals (Sweden)

    Wuchty Stefan

    2006-05-01

    Full Text Available Abstract Background While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. Results We consider a web of interactions between protein domains of the Protein Family database (PFAM, which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Conclusion Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we

  9. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  10. Inter and intra-guild interactions in egg parasitoid species of the soybean stink bug complex

    Directory of Open Access Journals (Sweden)

    Sujii Edison Ryoiti

    2002-01-01

    Full Text Available The objective of this research was to evaluate the parasitism behavior of Telenomus podisi Ashmead, Trissolcus basalis (Wollaston e Trissolcus urichi Crawford (Hymenoptera: Scelionidae on eggs of Nezara viridula L., Euschistus heros F., Piezodorus guildinii Westwood and Acrosternum aseadum Rolston (Heteroptera: Pentatomidae, in no choice and multiple choice experiments. For all parasitoid species, the results demonstrated the existence of a main host species that maximizes the reproductive success. The competitive interactions among the parasitoid species were investigated in experiments of sequential and simultaneous release of different combinations of parasitoid pairs on the hosts N. viridula, E. heros and A. aseadum. Exploitative competition was observed for egg batches at the genus level (Telenomus vs. Trissolcus and interference competition at the species level (T. basalis vs. T. urichi. Trissolcus urichi was the most aggressive species, interfering with the parasitism of T. basalis. Generally, T. basalis showed an opportunistic behavior trying to parasitise eggs after T. urichi had abandoned the egg batch. The selection of parasitoid species for use in augmentative biological control programs should take into account the diversity of pentatomids present in soybean in addition to the interactions among the different species of parasitoids.

  11. Predicting diet and consumption rate differences between and within species using gut ecomorphology.

    Science.gov (United States)

    Griffen, Blaine D; Mosblack, Hallie

    2011-07-01

    1. Rapid environmental changes and pressing human needs to forecast the consequences of environmental change are increasingly driving ecology to become a predictive science. The need for effective prediction requires both the development of new tools and the refocusing of existing tools that may have previously been used primarily for purposes other than prediction. One such tool that historically has been more descriptive in nature is ecomorphology (the study of relationships between ecological roles and morphological adaptations of species and individuals). 2. Here, we examine relationships between diet and gut morphology for 15 species of brachyuran crabs, a group of pervasive and highly successful consumers for which trophic predictions would be highly valuable. 3. We show that patterns in crab stomach volume closely match some predictions of metabolic theory and demonstrate that individual diet differences and associated morphological variation reflect, at least in some instances, individual choice or diet specialization. 4. We then present examples of how stomach volume can be used to predict both the per cent herbivory of brachyuran crabs and the relative consumption rates of individual crabs. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

  12. Floral scent composition predicts bee pollination system in five butterfly bush (Buddleja, Scrophulariaceae) species.

    Science.gov (United States)

    Gong, W-C; Chen, G; Vereecken, N J; Dunn, B L; Ma, Y-P; Sun, W-B

    2015-01-01

    Traditionally, plant-pollinator interactions have been interpreted as pollination syndrome. However, the validity of pollination syndrome has been widely doubted in modern studies of pollination ecology. The pollination ecology of five Asian Buddleja species, B. asiatica, B. crispa, B. forrestii, B. macrostachya and B. myriantha, in the Sino-Himalayan region in Asia, flowering in different local seasons, with scented inflorescences were investigated during 2011 and 2012. These five species exhibited diverse floral traits, with narrow and long corolla tubes and concealed nectar. According to their floral morphology, larger bees and Lepidoptera were expected to be the major pollinators. However, field observations showed that only larger bees (honeybee/bumblebee) were the primary pollinators, ranging from 77.95% to 97.90% of total visits. In this study, floral scents of each species were also analysed using coupled gas chromatography and mass spectrometry (GC-MS). Although the five Buddleja species emitted differentiated floral scent compositions, our results showed that floral scents of the five species are dominated by substances that can serve as attractive signals to bees, including species-specific scent compounds and principal compounds with larger relative amounts. This suggests that floral scent compositions are closely associated with the principal pollinator assemblages in these five species. Therefore, we conclude that floral scent compositions rather than floral morphology traits should be used to interpret plant-pollinator interactions in these Asian Buddleja species. © 2014 German Botanical Society and The Royal Botanical Society of the Netherlands.

  13. Synergistic Interactions within a Multispecies Biofilm Enhance Individual Species Protection against Grazing by a Pelagic Protozoan

    Directory of Open Access Journals (Sweden)

    Prem K. Raghupathi

    2018-01-01

    Full Text Available Biofilm formation has been shown to confer protection against grazing, but little information is available on the effect of grazing on biofilm formation and protection in multispecies consortia. With most biofilms in nature being composed of multiple bacterial species, the interactions and dynamics of a multispecies bacterial biofilm subject to grazing by a pelagic protozoan predator were investigated. To this end, a mono and multispecies biofilms of four bacterial soil isolates, namely Xanthomonas retroflexus, Stenotrophomonas rhizophila, Microbacterium oxydans and Paenibacillus amylolyticus, were constructed and subjected to grazing by the ciliate Tetrahymena pyriformis. In monocultures, grazing strongly reduced planktonic cell numbers in P. amylolyticus and S. rhizophila and also X. retroflexus. At the same time, cell numbers in the underlying biofilms increased in S. rhizophila and X. retroflexus, but not in P. amylolyticus. This may be due to the fact that while grazing enhanced biofilm formation in the former two species, no biofilm was formed by P. amylolyticus in monoculture, either with or without grazing. In four-species biofilms, biofilm formation was higher than in the best monoculture, a strong biodiversity effect that was even more pronounced in the presence of grazing. While cell numbers of X. retroflexus, S. rhizophila, and P. amylolyticus in the planktonic fraction were greatly reduced in the presence of grazers, cell numbers of all three species strongly increased in the biofilm. Our results show that synergistic interactions between the four-species were important to induce biofilm formation, and suggest that bacterial members that produce more biofilm when exposed to the grazer not only protect themselves but also supported other members which are sensitive to grazing, thereby providing a “shared grazing protection” within the four-species biofilm model. Hence, complex interactions shape the dynamics of the biofilm and

  14. Biotic interactions overrule plant responses to climate, depending on the species' biogeography.

    Directory of Open Access Journals (Sweden)

    Astrid Welk

    Full Text Available This study presents an experimental approach to assess the relative importance of climatic and biotic factors as determinants of species' geographical distributions. We asked to what extent responses of grassland plant species to biotic interactions vary with climate, and to what degree this variation depends on the species' biogeography. Using a gradient from oceanic to continental climate represented by nine common garden transplant sites in Germany, we experimentally tested whether congeneric grassland species of different geographic distribution (oceanic vs. continental plant range type responded differently to combinations of climate, competition and mollusc herbivory. We found the relative importance of biotic interactions and climate to vary between the different components of plant performance. While survival and plant height increased with precipitation, temperature had no effect on plant performance. Additionally, species with continental plant range type increased their growth in more benign climatic conditions, while those with oceanic range type were largely unable to take a similar advantage of better climatic conditions. Competition generally caused strong reductions of aboveground biomass and growth. In contrast, herbivory had minor effects on survival and growth. Against expectation, these negative effects of competition and herbivory were not mitigated under more stressful continental climate conditions. In conclusion we suggest variation in relative importance of climate and biotic interactions on broader scales, mediated via species-specific sensitivities and factor-specific response patterns. Our results have important implications for species distribution models, as they emphasize the large-scale impact of biotic interactions on plant distribution patterns and the necessity to take plant range types into account.

  15. Interactions between terrestrial mammals and the fruits of two neotropical rainforest tree species

    Science.gov (United States)

    Camargo-Sanabria, Angela A.; Mendoza, Eduardo

    2016-05-01

    Mammalian frugivory is a distinctive biotic interaction of tropical forests; however, most efforts in the Neotropics have focused on cases of animals foraging in the forest canopy, in particular primates and bats. In contrast much less is known about this interaction when it involves fruits deposited on the forest floor and terrestrial mammals. We conducted a camera-trapping survey to analyze the characteristics of the mammalian ensembles visiting fruits of Licania platypus and Pouteria sapota deposited on the forest floor in a well preserved tropical rainforest of Mexico. Both tree species produce large fruits but contrast in their population densities and fruit chemical composition. In particular, we expected that more species of terrestrial mammals would consume P. sapota fruits due to its higher pulp:seed ratio, lower availability and greater carbohydrate content. We monitored fruits at the base of 13 trees (P. sapota, n = 4 and L. platypus, n = 9) using camera-traps. We recorded 13 mammal species from which we had evidence of 8 consuming or removing fruits. These eight species accounted for 70% of the species of mammalian frugivores active in the forest floor of our study area. The ensemble of frugivores associated with L. platypus (6 spp.) was a subset of that associated with P. sapota (8 spp). Large body-sized species such as Tapirus bairdii, Pecari tajacu and Cuniculus paca were the mammals more frequently interacting with fruits of the focal species. Our results further our understanding of the characteristics of the interaction between terrestrial mammalian frugivores and large-sized fruits, helping to gain a more balanced view of its importance across different tropical forests and providing a baseline to compare against defaunated forests.

  16. Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape

    Directory of Open Access Journals (Sweden)

    Sebastián Block

    2016-10-01

    Full Text Available Google Earth provides a freely available, global mosaic of high-resolution imagery from different sensors that has become popular in environmental and ecological studies. However, such imagery lacks the near-infrared band often used in studying vegetation, thus its potential for estimating vegetation properties remains unclear. In this study, we assess the potential of Google Earth imagery to describe and predict vegetation attributes. Further, we compare it to the potential of SPOT imagery, which has additional spectral information. We measured basal area, vegetation height, crown cover, density of individuals, and species richness in 60 plots in the oak forests of a complex volcanic landscape in central Mexico. We modelled each vegetation attribute as a function of surface metrics derived from Google Earth and SPOT images, and selected the best-supported linear models from each source. Total species richness was the best-described and predicted variable: the best Google Earth-based model explained nearly as much variation in species richness as its SPOT counterpart (R2 = 0.44 and 0.51, respectively. However, Google Earth metrics emerged as poor predictors of all remaining vegetation attributes, whilst SPOT metrics showed potential for predicting vegetation height. We conclude that Google Earth imagery can be used to estimate species richness in complex landscapes. As it is freely available, Google Earth can broaden the use of remote sensing by researchers and managers in low-income tropical countries where most biodiversity hotspots are found.

  17. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  18. A Source Area Approach Demonstrates Moderate Predictive Ability but Pronounced Variability of Invasive Species Traits.

    Directory of Open Access Journals (Sweden)

    Günther Klonner

    Full Text Available The search for traits that make alien species invasive has mostly concentrated on comparing successful invaders and different comparison groups with respect to average trait values. By contrast, little attention has been paid to trait variability among invaders. Here, we combine an analysis of trait differences between invasive and non-invasive species with a comparison of multidimensional trait variability within these two species groups. We collected data on biological and distributional traits for 1402 species of the native, non-woody vascular plant flora of Austria. We then compared the subsets of species recorded and not recorded as invasive aliens anywhere in the world, respectively, first, with respect to the sampled traits using univariate and multiple regression models; and, second, with respect to their multidimensional trait diversity by calculating functional richness and dispersion metrics. Attributes related to competitiveness (strategy type, nitrogen indicator value, habitat use (agricultural and ruderal habitats, occurrence under the montane belt, and propagule pressure (frequency were most closely associated with invasiveness. However, even the best multiple model, including interactions, only explained a moderate fraction of the differences in invasive success. In addition, multidimensional variability in trait space was even larger among invasive than among non-invasive species. This pronounced variability suggests that invasive success has a considerable idiosyncratic component and is probably highly context specific. We conclude that basing risk assessment protocols on species trait profiles will probably face hardly reducible uncertainties.

  19. What are the most crucial soil factors for predicting the distribution of alpine plant species?

    Science.gov (United States)

    Buri, A.; Pinto-Figueroa, E.; Yashiro, E.; Guisan, A.

    2017-12-01

    Nowadays the use of species distribution models (SDM) is common to predict in space and time the distribution of organisms living in the critical zone. The realized environmental niche concept behind the development of SDM imply that many environmental factors must be accounted for simultaneously to predict species distributions. Climatic and topographic factors are often primary included, whereas soil factors are frequently neglected, mainly due to the paucity of soil information available spatially and temporally. Furthermore, among existing studies, most included soil pH only, or few other soil parameters. In this study we aimed at identifying what are the most crucial soil factors for explaining alpine plant distributions and, among those identified, which ones further improve the predictive power of plant SDMs. To test the relative importance of the soil factors, we performed plant SDMs using as predictors 52 measured soil properties of various types such as organic/inorganic compounds, chemical/physical properties, water related variables, mineral composition or grain size distribution. We added them separately to a standard set of topo-climatic predictors (temperature, slope, solar radiation and topographic position). We used ensemble forecasting techniques combining together several predictive algorithms to model the distribution of 116 plant species over 250 sites in the Swiss Alps. We recorded the variable importance for each model and compared the quality of the models including different soil proprieties (one at a time) as predictors to models having only topo-climatic variables as predictors. Results show that 46% of the soil proprieties tested become the second most important variable, after air temperature, to explain spatial distribution of alpine plants species. Moreover, we also assessed that addition of certain soil factors, such as bulk soil water density, could improve over 80% the quality of some plant species models. We confirm that soil p

  20. Aggregation Behaviors of a Two-Species System with Lose-Lose Interactions

    International Nuclear Information System (INIS)

    Song Meixia; Lin Zhenquan; Li Xiaodong; Ke Jianhong

    2010-01-01

    We propose an aggregation evolution model of two-species (A- and B-species) aggregates to study the prevalent aggregation phenomena in social and economic systems. In this model, A- and B-species aggregates perform self-exchange-driven growths with the exchange rate kernels K (k,l) = Kkl and L(k,l) = Lkl, respectively, and the two species aggregates perform self-birth processes with the rate kernels J 1 (k) = J 1 k and J 2 (k) = J 2 k, and meanwhile the interaction between the aggregates of different species A and B causes a lose-lose scheme with the rate kernel H(k,l) = Hkl. Based on the mean-field theory, we investigated the evolution behaviors of the two species aggregates to study the competitions among above three aggregate evolution schemes on the distinct initial monomer concentrations A 0 and B 0 of the two species. The results show that the evolution behaviors of A- and B-species are crucially dominated by the competition between the two self-birth processes, and the initial monomer concentrations A 0 and B 0 play important roles, while the lose-lose scheme play important roles in some special cases. (interdisciplinary physics and related areas of science and technology)

  1. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  2. Predicting habitat suitability for rare plants at local spatial scales using a species distribution model.

    Science.gov (United States)

    Gogol-Prokurat, Melanie

    2011-01-01

    If species distribution models (SDMs) can rank habitat suitability at a local scale, they may be a valuable conservation planning tool for rare, patchily distributed species. This study assessed the ability of Maxent, an SDM reported to be appropriate for modeling rare species, to rank habitat suitability at a local scale for four edaphic endemic rare plants of gabbroic soils in El Dorado County, California, and examined the effects of grain size, spatial extent, and fine-grain environmental predictors on local-scale model accuracy. Models were developed using species occurrence data mapped on public lands and were evaluated using an independent data set of presence and absence locations on surrounding lands, mimicking a typical conservation-planning scenario that prioritizes potential habitat on unsurveyed lands surrounding known occurrences. Maxent produced models that were successful at discriminating between suitable and unsuitable habitat at the local scale for all four species, and predicted habitat suitability values were proportional to likelihood of occurrence or population abundance for three of four species. Unfortunately, models with the best discrimination (i.e., AUC) were not always the most useful for ranking habitat suitability. The use of independent test data showed metrics that were valuable for evaluating which variables and model choices (e.g., grain, extent) to use in guiding habitat prioritization for conservation of these species. A goodness-of-fit test was used to determine whether habitat suitability values ranked habitat suitability on a continuous scale. If they did not, a minimum acceptable error predicted area criterion was used to determine the threshold for classifying habitat as suitable or unsuitable. I found a trade-off between model extent and the use of fine-grain environmental variables: goodness of fit was improved at larger extents, and fine-grain environmental variables improved local-scale accuracy, but fine-grain variables

  3. Cross-Species Extrapolation of Models for Predicting Lead Transfer from Soil to Wheat Grain.

    Directory of Open Access Journals (Sweden)

    Ke Liu

    Full Text Available The transfer of Pb from the soil to crops is a serious food hygiene security problem in China because of industrial, agricultural, and historical contamination. In this study, the characteristics of exogenous Pb transfer from 17 Chinese soils to a popular wheat variety (Xiaoyan 22 were investigated. In addition, bioaccumulation prediction models of Pb in grain were obtained based on soil properties. The results of the analysis showed that pH and OC were the most important factors contributing to Pb uptake by wheat grain. Using a cross-species extrapolation approach, the Pb uptake prediction models for cultivar Xiaoyan 22 in different soil Pb levels were satisfactorily applied to six additional non-modeled wheat varieties to develop a prediction model for each variety. Normalization of the bioaccumulation factor (BAF to specific soil physico-chemistry is essential, because doing so could significantly reduce the intra-species variation of different wheat cultivars in predicted Pb transfer and eliminate the influence of soil properties on ecotoxicity parameters for organisms of interest. Finally, the prediction models were successfully verified against published data (including other wheat varieties and crops and used to evaluate the ecological risk of Pb for wheat in contaminated agricultural soils.

  4. Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.

    Directory of Open Access Journals (Sweden)

    Jens Christian Claussen

    2017-06-01

    Full Text Available The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.

  5. Deciphering microbial interactions and detecting keystone species with co-occurrence networks

    Directory of Open Access Journals (Sweden)

    David eBerry

    2014-05-01

    Full Text Available Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics, construct co-occurrence networks, and evaluate how well networks reveal the underlying interactions, and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  6. Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

    Science.gov (United States)

    Berry, David; Widder, Stefanie

    2014-01-01

    Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

  7. Drug-target interaction prediction via class imbalance-aware ensemble learning.

    Science.gov (United States)

    Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

    2016-12-22

    Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was

  8. Spontaneous cross-species imitation in interactions between chimpanzees and zoo visitors.

    Science.gov (United States)

    Persson, Tomas; Sauciuc, Gabriela-Alina; Madsen, Elainie Alenkær

    2018-01-01

    Imitation is a cornerstone of human development, serving both a cognitive function (e.g. in the acquisition and transmission of skills and knowledge) and a social-communicative function, whereby the imitation of familiar actions serves to maintain social interaction and promote prosociality. In nonhuman primates, this latter function is poorly understood, or even claimed to be absent. In this observational study, we documented interactions between chimpanzees and zoo visitors and found that the two species imitated each other at a similar rate, corresponding to almost 10% of all produced actions. Imitation appeared to accomplish a social-communicative function, as cross-species interactions that contained imitative actions lasted significantly longer than interactions without imitation. In both species, physical proximity promoted cross-species imitation. Overall, imitative precision was higher among visitors than among chimpanzees, but this difference vanished in proximity contexts, i.e. in the indoor environment. Four of five chimpanzees produced imitations; three of them exhibited comparable imitation rates, despite large individual differences in level of cross-species interactivity. We also found that chimpanzees evidenced imitation recognition, yet only when visitors imitated their actions (as opposed to postures). Imitation recognition was expressed by returned imitation in 36% of the cases, and all four imitating chimpanzees engaged in so-called imitative games. Previously regarded as unique to early human socialization, such games serve to maintain social engagement. The results presented here indicate that nonhuman apes exhibit spontaneous imitation that can accomplish a communicative function. The study raises a number of novel questions for imitation research and highlights the imitation of familiar behaviours as a relevant-yet thus far understudied-research topic.

  9. Conditions Promoting Mycorrhizal Parasitism Are of Minor Importance for Competitive Interactions in Two Differentially Mycotrophic Species

    Science.gov (United States)

    Friede, Martina; Unger, Stephan; Hellmann, Christine; Beyschlag, Wolfram

    2016-01-01

    Interactions of plants with arbuscular mycorrhizal fungi (AMF) may range along a broad continuum from strong mutualism to parasitism, with mycorrhizal benefits received by the plant being determined by climatic and edaphic conditions affecting the balance between carbon costs vs. nutritional benefits. Thus, environmental conditions promoting either parasitism or mutualism can influence the mycorrhizal growth dependency (MGD) of a plant and in consequence may play an important role in plant-plant interactions. In a multifactorial field experiment we aimed at disentangling the effects of environmental and edaphic conditions, namely the availability of light, phosphorus and nitrogen, and the implications for competitive interactions between Hieracium pilosella and Corynephorus canescens for the outcome of the AMF symbiosis. Both species were planted in single, intraspecific and interspecific combinations using a target-neighbor approach with six treatments distributed along a gradient simulating conditions for the interaction between plants and AMF ranking from mutualistic to parasitic. Across all treatments we found mycorrhizal association of H. pilosella being consistently mutualistic, while pronounced parasitism was observed in C. canescens, indicating that environmental and edaphic conditions did not markedly affect the cost:benefit ratio of the mycorrhizal symbiosis in both species. Competitive interactions between both species were strongly affected by AMF, with the impact of AMF on competition being modulated by colonization. Biomass in both species was lowest when grown in interspecific competition, with colonization being increased in the less mycotrophic C. canescens, while decreased in the obligate mycotrophic H. pilosella. Although parasitism-promoting conditions negatively affected MGD in C. canescens, these effects were small as compared to growth decreases related to increased colonization levels in this species. Thus, the lack of plant control over

  10. Conditions Promoting Mycorrhizal Parasitism are of Minor Importance for Competitive Interactions in Two Differentially Mycotrophic Species

    Directory of Open Access Journals (Sweden)

    Martina Friede

    2016-09-01

    Full Text Available Interactions of plants with arbuscular mycorrhizal fungi (AMF may range along a broad continuum from strong mutualism to parasitism, with mycorrhizal benefits received by the plant being determined by climatic and edaphic conditions affecting the balance between carbon costs vs. nutritional benefits. Thus, environmental conditions promoting either parasitism or mutualism can influence the mycorrhizal growth dependency (MGD of a plant and in consequence may play an important role in plant-plant interactions.In a multifactorial field experiment we aimed at disentangling the effects of environmental and edaphic conditions, namely the availability of light, phosphorus and nitrogen, and the implications for competitive interactions between Hieracium pilosella and Corynephorus canescens for the outcome of the AMF symbiosis. Both species were planted in single, intraspecific and interspecific combinations using a target-neighbor approach with six treatments distributed along a gradient simulating conditions for the interaction between plants and AMF ranking from mutualistic to parasitic.Across all treatments we found mycorrhizal association of H. pilosella being consistently mutualistic, while pronounced parasitism was observed in C. canescens, indicating that environmental and edaphic conditions did not markedly affect the cost:benefit ratio of the mycorrhizal symbiosis in both species. Competitive interactions between both species were strongly affected by AMF, with the impact of AMF on competition being modulated by colonization. Biomass in both species was lowest when grown in interspecific competition, with colonization being increased in the less mycotrophic C. canescens, while decreased in the obligate mycotrophic H. pilosella. Although parasitism-promoting conditions negatively affected MGD in C. canescens, these effects were small as compared to growth decreases related to increased colonization levels in this species. Thus, the lack of plant

  11. PDEAR model prediction of Protea species in years 2070-2100

    Science.gov (United States)

    Guo, Danni; Guo, Renkuan; Midgley, Guy F.; Rebelo, A. G.

    2009-10-01

    Global warming and climate changes are changing the environment and therefore changing the distribution and behaviour of the plant species. Plant species often move and change their distributions as they find their original habitats are no longer suitable to their needs. It is therefore important to establish a statistical model to catch up the movement and patterns of the endangered species in order to effectively manage environmental protection under the inevitable biodiversity changes that are taking place. In this paper, we are focusing on the population category of rare Proteas that has an estimated population size from 1 to 10 per sample site, which is very small. We used the partial differential equation associated regression (PDEAR) model, which merges the partial differential equation theory, (statistical) linear model theory and random fuzzy variable theory together into a efficient small-sample oriented model, for the spatial pattern changing analysis. The regression component in a PDEAR model is in nature a special random fuzzy multivariate regression model. We developed a bivariate model for investigating the impacts from rainfall and temperature on the Protea species in average sense in the population size of 1 to 10, in the Cape Floristic Region, from 1992 to 2002, South Africa. Under same the average biodiversity structure assumptions, we explore the future spatial change patterns of Protea species in the population size of 1 to 10 with future (average) predicted rainfall and temperature. The spatial distribution and patterns are clearly will help us to explore global climate changing impacts on endangered species.

  12. Species interactions in an Andean bird–flowering plant network: phenology is more important than abundance or morphology

    Directory of Open Access Journals (Sweden)

    Oscar Gonzalez

    2016-12-01

    Full Text Available Biological constraints and neutral processes have been proposed to explain the properties of plant–pollinator networks. Using interactions between nectarivorous birds (hummingbirds and flowerpiercers and flowering plants in high elevation forests (i.e., “elfin” forests of the Andes, we explore the importance of biological constraints and neutral processes (random interactions to explain the observed species interactions and network metrics, such as connectance, specialization, nestedness and asymmetry. In cold environments of elfin forests, which are located at the top of the tropical montane forest zone, many plants are adapted for pollination by birds, making this an ideal system to study plant–pollinator networks. To build the network of interactions between birds and plants, we used direct field observations. We measured abundance of birds using mist-nets and flower abundance using transects, and phenology by scoring presence of birds and flowers over time. We compared the length of birds’ bills to flower length to identify “forbidden interactions”—those interactions that could not result in legitimate floral visits based on mis-match in morphology. Diglossa flowerpiercers, which are characterized as “illegitimate” flower visitors, were relatively abundant. We found that the elfin forest network was nested with phenology being the factor that best explained interaction frequencies and nestedness, providing support for biological constraints hypothesis. We did not find morphological constraints to be important in explaining observed interaction frequencies and network metrics. Other network metrics (connectance, evenness and asymmetry, however, were better predicted by abundance (neutral process models. Flowerpiercers, which cut holes and access flowers at their base and, consequently, facilitate nectar access for other hummingbirds, explain why morphological mis-matches were relatively unimportant in this system. Future

  13. Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

    Science.gov (United States)

    Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

    2018-01-01

    To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

  14. A cost minimisation and Bayesian inference model predicts startle reflex modulation across species

    OpenAIRE

    Bach, Dominik R

    2015-01-01

    In many species, rapid defensive reflexes are paramount to escaping acute danger. These reflexes are modulated by the state of the environment. This is exemplified in fear-potentiated startle, a more vigorous startle response during conditioned anticipation of an unrelated threatening event. Extant explanations of this phenomenon build on descriptive models of underlying psychological states, or neural processes. Yet, they fail to predict invigorated startle during reward anticipation and ins...

  15. Getting the Right Traits: Reproductive and Dispersal Characteristics Predict the Invasiveness of Herbaceous Plant Species

    Czech Academy of Sciences Publication Activity Database

    Moravcová, Lenka; Pyšek, Petr; Jarošík, Vojtěch; Pergl, Jan

    2015-01-01

    Roč. 10, č. 4 (2015), s. 1-16,no.e0123634 E-ISSN 1932-6203 R&D Projects: GA ČR GB14-36079G; GA ČR GA206/05/0323 Grant - others:AV ČR(CZ) AP1002 Program:Akademická prémie - Praemium Academiae Institutional support: RVO:67985939 Keywords : species traits * prediction * invasiveness Subject RIV: EF - Botanics Impact factor: 3.057, year: 2015

  16. An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species.

    Science.gov (United States)

    Zhang, Keda; Abraham, Michael H; Liu, Xiangli

    2017-04-15

    Experimental values of permeability coefficients, as log K p , of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37°C for the effect of temperature. The values of log K p for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log K p values have been regressed against Abraham solute descriptors to yield a correlation equation with R 2 =0.866 and SD=0.432 log units. The equation can provide valid predictions for log K p of neutral molecules, ions and ionic species, with predictive R 2 =0.858 and predictive SD=0.445 log units calculated by the leave-one-out statistics. The predicted log K p values for Na + and Et 4 N + are in good agreement with the observed values. We calculated the values of log K p of ketoprofen as a function of the pH of the donor solution, and found that log K p markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log K p was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log K p . Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Biodiversity and the Lotka-Volterra theory of species interactions: open systems and the distribution of logarithmic densities.

    Science.gov (United States)

    Wilson, William G; Lundberg, Per

    2004-09-22

    Theoretical interest in the distributions of species abundances observed in ecological communities has focused recently on the results of models that assume all species are identical in their interactions with one another, and rely upon immigration and speciation to promote coexistence. Here we examine a one-trophic level system with generalized species interactions, including species-specific intraspecific and interspecific interaction strengths, and density-independent immigration from a regional species pool. Comparisons between results from numerical integrations and an approximate analytic calculation for random communities demonstrate good agreement, and both approaches yield abundance distributions of nearly arbitrary shape, including bimodality for intermediate immigration rates.

  18. Leaf Area Prediction Using Three Alternative Sampling Methods for Seven Sierra Nevada Conifer Species

    Directory of Open Access Journals (Sweden)

    Dryw A. Jones

    2015-07-01

    Full Text Available Prediction of projected tree leaf area using allometric relationships with sapwood cross-sectional area is common in tree- and stand-level production studies. Measuring sapwood is difficult and often requires destructive sampling. This study tested multiple leaf area prediction models across seven diverse conifer species in the Sierra Nevada of California. The best-fit whole tree leaf area prediction model for overall simplicity, accuracy, and utility for all seven species was a nonlinear model with basal area as the primary covariate. A new non-destructive procedure was introduced to extend the branch summation approach to leaf area data collection on trees that cannot be destructively sampled. There were no significant differences between fixed effects assigned to sampling procedures, indicating that data from the tested sampling procedures can be combined for whole tree leaf area modeling purposes. These results indicate that, for the species sampled, accurate leaf area estimates can be obtained through partially-destructive sampling and using common forest inventory data.

  19. Interaction between Digestive Strategy and Niche Specialization Predicts Speciation Rates across Herbivorous Mammals.

    Science.gov (United States)

    Tran, Lucy A P

    2016-04-01

    Biotic and abiotic factors often are treated as mutually exclusive drivers of diversification processes. In this framework, ecological specialists are expected to have higher speciation rates than generalists if abiotic factors are the primary controls on species diversity but lower rates if biotic interactions are more important. Speciation rate is therefore predicted to positively correlate with ecological specialization in the purely abiotic model but negatively correlate in the biotic model. In this study, I show that the positive relationship between ecological specialization and speciation expected from the purely abiotic model is recovered only when a species-specific trait, digestive strategy, is modeled in the terrestrial, herbivorous mammals (Mammalia). This result suggests a more nuanced model in which the response of specialized lineages to abiotic factors is dependent on a biological trait. I also demonstrate that the effect of digestive strategy on the ecological specialization-speciation rate relationship is not due to a difference in either the degree of ecological specialization or the speciation rate between foregut- and hindgut-fermenting mammals. Together, these findings suggest that a biological trait, alongside historical abiotic events, played an important role in shaping mammal speciation at long temporal and large geographic scales.

  20. Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

    Directory of Open Access Journals (Sweden)

    Azadeh Abdollahnejad

    2017-02-01

    Full Text Available Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM. Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF, support vector machine (SVM and k-nearest neighbor (k-NN algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

  1. Working and Playing Together: Prediction of Preschool Social-Emotional Competence from Mother-Child Interaction.

    Science.gov (United States)

    Denham, Susanne A.; And Others

    1991-01-01

    Examined mother-child interaction in play and teaching tasks. Mother-child interaction aggregates represented task orientation, positive emotion, and allowance of autonomy. Maternal interaction aggregates predicted teachers' ratings of children's positive social behavior, assertiveness, and sadness in the preschool setting. (BC)

  2. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    ABSTRACT: BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  3. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  4. Arabidopsis thaliana polyamine content is modified by the interaction with different Trichoderma species.

    Science.gov (United States)

    Salazar-Badillo, Fatima Berenice; Sánchez-Rangel, Diana; Becerra-Flora, Alicia; López-Gómez, Miguel; Nieto-Jacobo, Fernanda; Mendoza-Mendoza, Artemio; Jiménez-Bremont, Juan Francisco

    2015-10-01

    Plants are associated with a wide range of microorganisms throughout their life cycle, and some interactions result on plant benefits. Trichoderma species are plant beneficial fungi that enhance plant growth and development, contribute to plant nutrition and induce defense responses. Nevertheless, the molecules involved in these beneficial effects still need to be identify. Polyamines are ubiquitous molecules implicated in plant growth and development, and in the establishment of plant microbe interactions. In this study, we assessed the polyamine profile in Arabidopsis plants during the interaction with Trichoderma virens and Trichoderma atroviride, using a system that allows direct plant-fungal contact or avoids their physical interaction (split system). The plantlets that grew in the split system exhibited higher biomass than the ones in direct contact with Trichoderma species. After 3 days of interaction, a significant decrease in Arabidopsis polyamine levels was observed in both systems (direct contact and split). After 5 days of interaction polyamine levels were increased. The highest levels were observed with T. atroviride (split system), and with T. virens (direct contact). The expression levels of Arabidopsis ADC1 and ADC2 genes during the interaction with the fungi were also assessed. We observed a time dependent regulation of ADC1 and ADC2 genes, which correlates with polyamine levels. Our data show an evident change in polyamine profile during Arabidopsis - Trichoderma interaction, accompanied by evident alterations in plant root architecture. Polyamines could be involved in the changes undergone by plant during the interaction with this beneficial fungus. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  5. A cost minimisation and Bayesian inference model predicts startle reflex modulation across species.

    Science.gov (United States)

    Bach, Dominik R

    2015-04-07

    In many species, rapid defensive reflexes are paramount to escaping acute danger. These reflexes are modulated by the state of the environment. This is exemplified in fear-potentiated startle, a more vigorous startle response during conditioned anticipation of an unrelated threatening event. Extant explanations of this phenomenon build on descriptive models of underlying psychological states, or neural processes. Yet, they fail to predict invigorated startle during reward anticipation and instructed attention, and do not explain why startle reflex modulation evolved. Here, we fill this lacuna by developing a normative cost minimisation model based on Bayesian optimality principles. This model predicts the observed pattern of startle modification by rewards, punishments, instructed attention, and several other states. Moreover, the mathematical formalism furnishes predictions that can be tested experimentally. Comparing the model with existing data suggests a specific neural implementation of the underlying computations which yields close approximations to the optimal solution under most circumstances. This analysis puts startle modification into the framework of Bayesian decision theory and predictive coding, and illustrates the importance of an adaptive perspective to interpret defensive behaviour across species. Copyright © 2015 The Author. Published by Elsevier Ltd.. All rights reserved.

  6. Multifunctionality is affected by interactions between green roof plant species, substrate depth, and substrate type.

    Science.gov (United States)

    Dusza, Yann; Barot, Sébastien; Kraepiel, Yvan; Lata, Jean-Christophe; Abbadie, Luc; Raynaud, Xavier

    2017-04-01

    Green roofs provide ecosystem services through evapotranspiration and nutrient cycling that depend, among others, on plant species, substrate type, and substrate depth. However, no study has assessed thoroughly how interactions between these factors alter ecosystem functions and multifunctionality of green roofs. We simulated some green roof conditions in a pot experiment. We planted 20 plant species from 10 genera and five families (Asteraceae, Caryophyllaceae, Crassulaceae, Fabaceae, and Poaceae) on two substrate types (natural vs. artificial) and two substrate depths (10 cm vs. 30 cm). As indicators of major ecosystem functions, we measured aboveground and belowground biomasses, foliar nitrogen and carbon content, foliar transpiration, substrate water retention, and dissolved organic carbon and nitrates in leachates. Interactions between substrate type and depth strongly affected ecosystem functions. Biomass production was increased in the artificial substrate and deeper substrates, as was water retention in most cases. In contrast, dissolved organic carbon leaching was higher in the artificial substrates. Except for the Fabaceae species, nitrate leaching was reduced in deep, natural soils. The highest transpiration rates were associated with natural soils. All functions were modulated by plant families or species. Plant effects differed according to the observed function and the type and depth of the substrate. Fabaceae species grown on natural soils had the most noticeable patterns, allowing high biomass production and high water retention but also high nitrate leaching from deep pots. No single combination of factors enhanced simultaneously all studied ecosystem functions, highlighting that soil-plant interactions induce trade-offs between ecosystem functions. Substrate type and depth interactions are major drivers for green roof multifunctionality.

  7. Protein-protein interaction network-based detection of functionally similar proteins within species.

    Science.gov (United States)

    Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

    2012-07-01

    Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

  8. Integrating effects of species composition and soil properties to predict shifts in montane forest carbon-water relations.

    Science.gov (United States)

    Maxwell, Toby M; Silva, Lucas C R; Horwath, William R

    2018-05-01

    This study was designed to address a major source of uncertainty pertaining to coupled carbon-water cycles in montane forest ecosystems. The Sierra Nevada of California was used as a model system to investigate connections between the physiological performance of trees and landscape patterns of forest carbon and water use. The intrinsic water-use efficiency (iWUE)-an index of CO 2 fixed per unit of potential water lost via transpiration-of nine dominant species was determined in replicated transects along an ∼1,500-m elevation gradient, spanning a broad range of climatic conditions and soils derived from three different parent materials. Stable isotope ratios of carbon and oxygen measured at the leaf level were combined with field-based and remotely sensed metrics of stand productivity, revealing that variation in iWUE depends primarily on leaf traits (∼24% of the variability), followed by stand productivity (∼16% of the variability), climatic regime (∼13% of the variability), and soil development (∼12% of the variability). Significant interactions between species composition and soil properties proved useful to predict changes in forest carbon-water relations. On the basis of observed shifts in tree species composition, ongoing since the 1950s and intensified in recent years, an increase in water loss through transpiration (ranging from 10 to 60% depending on parent material) is now expected in mixed conifer forests throughout the region. Copyright © 2018 the Author(s). Published by PNAS.

  9. Predicting how altering propagule pressure changes establishment rates of biological invaders across species pools.

    Science.gov (United States)

    Brockerhoff, Eckehard G; Kimberley, Mark; Liebhold, Andrew M; Haack, Robert A; Cavey, Joseph F

    2014-03-01

    Biological invasions resulting from international trade can cause major environmental and economic impacts. Propagule pressure is perhaps the most important factor influencing establishment, although actual arrival rates of species are rarely recorded. Furthermore, the pool of potential invaders includes many species that vary in their arrival rate and establishment potential. Therefore, we stress that it is essential to consider the size and composition of species pools arriving from source regions when estimating probabilities of establishment and effects of pathway infestation rates. To address this, we developed a novel framework and modeling approach to enable prediction of future establishments in relation to changes in arrival rate across entire species pools. We utilized 13 828 border interception records from the United States and New Zealand for 444 true bark beetle (Scolytinae) and longhorned beetle (Cerambycidae) species detected between 1949 and 2008 as proxies for arrival rates to model the relationship between arrival and establishment rates. Nonlinearity in this relationship implies that measures intended to reduce the unintended transport of potential invaders (such as phytosanitary treatments) must be highly effective in order to substantially reduce the rate of future invasions, particularly if trade volumes continue to increase.

  10. Chasing a changing climate: Reproductive and dispersal traits predict how sessile species respond to global warming

    Science.gov (United States)

    Archambault, Jennifer M.; Cope, W. Gregory; Kwak, Thomas J.

    2018-01-01

    AimStudies of species' range shifts have become increasingly relevant for understanding ecology and biogeography in the face of accelerated global change. The combination of limited mobility and imperilled status places some species at a potentially greater risk of range loss, extirpation or extinction due to climate change. To assess the ability of organisms with limited movement and dispersal capabilities to track shifts associated with climate change, we evaluated reproductive and dispersal traits of freshwater mussels (Unionida), sessile invertebrates that require species‐specific fish for larval dispersal.LocationNorth American Atlantic Slope rivers.MethodsTo understand how unionid mussels may cope with and adapt to current and future warming trends, we identified mechanisms that facilitated their colonization of the northern Atlantic Slope river basins in North America after the Last Glacial Maximum. We compiled species occurrence and life history trait information for each of 55 species, and then selected life history traits for which ample data were available (larval brooding duration, host fish specificity, host infection strategy, and body size) and analysed whether the trait state for each was related to mussel distribution in Atlantic Slope rivers.ResultsBrooding duration (p  .10).Main conclusionsOur results are potentially applicable to many species for which life history traits have not been well‐documented, because reproductive and dispersal traits in unionid mussels typically follow phylogenetic relationships. These findings may help resource managers prioritize species according to climate change vulnerability and predict which species might become further imperilled with climate warming. Finally, we suggest that similar trait‐based decision support frameworks may be applicable for other movement limited taxa.

  11. Novel species interactions: American black bears respond to Pacific herring spawn.

    Science.gov (United States)

    Fox, Caroline Hazel; Paquet, Paul Charles; Reimchen, Thomas Edward

    2015-05-26

    In addition to the decline and extinction of the world's species, the decline and eventual loss of species interactions is one of the major consequences of the biodiversity crisis. On the Pacific coast of North America, diminished runs of salmon (Oncorhynchus spp.) drive numerous marine-terrestrial interactions, many of which have been intensively studied, but marine-terrestrial interactions driven by other species remain relatively unknown. Bears (Ursus spp.) are major vectors of salmon into terrestrial ecosystems, but their participation in other cross-ecosystem interactions is similarly poorly described. Pacific herring (Clupea pallasii), a migratory forage fish in coastal marine ecosystems of the North Pacific Ocean and the dominant forage fish in British Columbia (BC), spawn in nearshore subtidal and intertidal zones. Spawn resources (eggs, milt, and spawning adults) at these events are available to coastal predators and scavengers, including terrestrial species. In this study, we investigated the interaction between American black bears (Ursus americanus) and Pacific herring at spawn events in Quatsino Sound, BC, Canada. Using remote cameras to monitor bear activity (1,467 camera days, 29 sites, years 2010-2012) in supratidal and intertidal zones and a machine learning approach, we determined that the quantity of Pacific herring eggs in supratidal and intertidal zones was a leading predictor of black bear activity, with bears positively responding to increasing herring egg masses. Other important predictors included day of the year and Talitrid amphipod (Traskorchestia spp.) mass. A complementary analysis of black bear scats indicated that Pacific herring egg mass was the highest ranked predictor of egg consumption by bears. Pacific herring eggs constituted a substantial yet variable component of the early springtime diet of black bears in Quatsino Sound (frequency of occurrence 0-34%; estimated dietary content 0-63%). Other major dietary items included

  12. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Directory of Open Access Journals (Sweden)

    Mile Sikić

    2009-01-01

    Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

  13. Measure solutions for non-local interaction PDEs with two species

    Energy Technology Data Exchange (ETDEWEB)

    Francesco, Marco Di [Department of Mathematical and Statistical Sciences, University of Bath, Claverton Down, Bath, BA2 7AY (United Kingdom); Fagioli, Simone [DISIM—Department of Information Engineering, Computer Science and Mathematics, University of L' Aquila, Via Vetoio 1 (Coppito) 67100 L' Aquila (AQ) (Italy)

    2013-10-01

    This paper presents a systematic existence and uniqueness theory of weak measure solutions for systems of non-local interaction PDEs with two species, which are the PDE counterpart of systems of deterministic interacting particles with two species. The main motivations behind those models arise in cell biology, pedestrian movements, and opinion formation. In case of symmetrizable systems (i.e. with cross-interaction potentials one multiple of the other), we provide a complete existence and uniqueness theory within (a suitable generalization of) the Wasserstein gradient flow theory in Ambrosio et al (2008 Gradient Flows in Metric Spaces and in the Space of Probability Measures (Lectures in Mathematics ETH Zürich) 2nd edn (Basel: Birkhäuser)) and Carrillo et al (2011 Duke Math. J. 156 229–71), which allows the consideration of interaction potentials with a discontinuous gradient at the origin. In the general case of non-symmetrizable systems, we provide an existence result for measure solutions which uses a semi-implicit version of the Jordan–Kinderlehrer–Otto (JKO) scheme (Jordan et al 1998 SIAM J. Math. Anal. 29 1–17), which holds in a reasonable non-smooth setting for the interaction potentials. Uniqueness in the non-symmetrizable case is proven for C{sup 2} potentials using a variant of the method of characteristics. (paper)

  14. Measure solutions for non-local interaction PDEs with two species

    International Nuclear Information System (INIS)

    Francesco, Marco Di; Fagioli, Simone

    2013-01-01

    This paper presents a systematic existence and uniqueness theory of weak measure solutions for systems of non-local interaction PDEs with two species, which are the PDE counterpart of systems of deterministic interacting particles with two species. The main motivations behind those models arise in cell biology, pedestrian movements, and opinion formation. In case of symmetrizable systems (i.e. with cross-interaction potentials one multiple of the other), we provide a complete existence and uniqueness theory within (a suitable generalization of) the Wasserstein gradient flow theory in Ambrosio et al (2008 Gradient Flows in Metric Spaces and in the Space of Probability Measures (Lectures in Mathematics ETH Zürich) 2nd edn (Basel: Birkhäuser)) and Carrillo et al (2011 Duke Math. J. 156 229–71), which allows the consideration of interaction potentials with a discontinuous gradient at the origin. In the general case of non-symmetrizable systems, we provide an existence result for measure solutions which uses a semi-implicit version of the Jordan–Kinderlehrer–Otto (JKO) scheme (Jordan et al 1998 SIAM J. Math. Anal. 29 1–17), which holds in a reasonable non-smooth setting for the interaction potentials. Uniqueness in the non-symmetrizable case is proven for C 2 potentials using a variant of the method of characteristics. (paper)

  15. Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

    Science.gov (United States)

    Zhang, Wen; Chen, Yanlin; Li, Dingfang

    2017-11-25

    Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

  16. Simplified method to predict mutual interactions of human transcription factors based on their primary structure

    KAUST Repository

    Schmeier, Sebastian

    2011-07-05

    Background: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account. © 2011 Schmeier et al.

  17. Simplified method to predict mutual interactions of human transcription factors based on their primary structure.

    Directory of Open Access Journals (Sweden)

    Sebastian Schmeier

    Full Text Available BACKGROUND: Physical interactions between transcription factors (TFs are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. METHODOLOGY: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus and in the same type of molecular process (transcription initiation. Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. CONCLUSIONS: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.

  18. Predicting Nanocrystal Shape through Consideration of Surface-Ligand Interactions

    KAUST Repository

    Bealing, Clive R.

    2012-03-27

    Density functional calculations for the binding energy of oleic acid-based ligands on Pb-rich {100} and {111} facets of PbSe nanocrystals determine the surface energies as a function of ligand coverage. Oleic acid is expected to bind to the nanocrystal surface in the form of lead oleate. The Wulff construction predicts the thermodynamic equilibrium shape of the PbSe nanocrystals. The equilibrium shape is a function of the ligand surface coverage, which can be controlled by changing the concentration of oleic acid during synthesis. The different binding energy of the ligand on the {100} and {111} facets results in different equilibrium ligand coverages on the facets, and a transition in the equilibrium shape from octahedral to cubic is predicted when increasing the ligand concentration during synthesis. © 2012 American Chemical Society.

  19. Trophic interactions between native and introduced fish species in a littoral fish community.

    Science.gov (United States)

    Monroy, M; Maceda-Veiga, A; Caiola, N; De Sostoa, A

    2014-11-01

    The trophic interactions between 15 native and two introduced fish species, silverside Odontesthes bonariensis and rainbow trout Oncorhynchus mykiss, collected in a major fishery area at Lake Titicaca were explored by integrating traditional ecological knowledge and stable-isotope analyses (SIA). SIA suggested the existence of six trophic groups in this fish community based on δ(13)C and δ(15)N signatures. This was supported by ecological evidence illustrating marked spatial segregation between groups, but a similar trophic level for most of the native groups. Based on Bayesian ellipse analyses, niche overlap appeared to occur between small O. bonariensis (<90 mm) and benthopelagic native species (31.6%), and between the native pelagic killifish Orestias ispi and large O. bonariensis (39%) or O. mykiss (19.7%). In addition, Bayesian mixing models suggested that O. ispi and epipelagic species are likely to be the main prey items for the two introduced fish species. This study reveals a trophic link between native and introduced fish species, and demonstrates the utility of combining both SIA and traditional ecological knowledge to understand trophic relationships between fish species with similar feeding habits. © 2014 The Fisheries Society of the British Isles.

  20. Genetic variation in foundation species governs the dynamics of trophic interactions

    Science.gov (United States)

    Valencia-Cuevas, Leticia; Mussali-Galante, Patricia; Cano-Santana, Zenón; Pujade-Villar, Juli; Equihua-Martínez, Armando

    2018-01-01

    Abstract Various studies have demonstrated that the foundation species genetic diversity can have direct effects that extend beyond the individual or population level, affecting the dependent communities. Additionally, these effects may be indirectly extended to higher trophic levels throughout the entire community. Quercus castanea is an oak species with characteristics of foundation species beyond presenting a wide geographical distribution and being a dominant element of Mexican temperate forests. In this study, we analyzed the influence of population (He) and individual (HL) genetic diversity of Q. castanea on its canopy endophagous insect community and associated parasitoids. Specifically, we studied the composition, richness (S) and density of leaf-mining moths (Lepidoptera: Tischeridae, Citheraniidae), gall-forming wasps (Hymenoptera: Cynipidae), and canopy parasitoids of Q. castanea. We sampled 120 trees belonging to six populations (20/site) through the previously recognized gradient of genetic diversity. In total, 22 endophagous insect species belonging to three orders (Hymenoptera, Lepidoptera, and Diptera) and 20 parasitoid species belonging to 13 families were identified. In general, we observed that the individual genetic diversity of the host plant (HL) has a significant positive effect on the S and density of the canopy endophagous insect communities. In contrast, He has a significant negative effect on the S of endophagous insects. Additionally, indirect effects of HL were observed, affecting the S and density of parasitoid insects. Our results suggest that genetic variation in foundation species can be one of the most important factors governing the dynamics of tritrophic interactions that involve oaks, herbivores, and parasitoids. PMID:29492034

  1. Ulysses - an application for the projection of molecular interactions across species.

    Science.gov (United States)

    Kemmer, Danielle; Huang, Yong; Shah, Sohrab P; Lim, Jonathan; Brumm, Jochen; Yuen, Macaire M S; Ling, John; Xu, Tao; Wasserman, Wyeth W; Ouellette, B F Francis

    2005-01-01

    We developed Ulysses as a user-oriented system that uses a process called Interolog Analysis for the parallel analysis and display of protein interactions detected in various species. Ulysses was designed to perform such Interolog Analysis by the projection of model organism interaction data onto homologous human proteins, and thus serves as an accelerator for the analysis of uncharacterized human proteins. The relevance of projections was assessed and validated against published reference collections. All source code is freely available, and the Ulysses system can be accessed via a web interface http://www.cisreg.ca/ulysses.

  2. Species interactions in the western Baltic Sea: With focus on the ecological role of whiting

    DEFF Research Database (Denmark)

    Ross, Stine Dalmann

    , which potentially prey on and compete for food with whiting. Here, the growth dynamics and feeding ecology of whiting in the western Baltic Sea is investigated and discussed in an ecosystem context. Furthermore, the diet of the harbour porpoise is examined and the interactions between whiting, cod......, implementation of the models in strategic management advice for commercially important fish stocks and protected marine mammals is not common practice. This is due to the lack of sufficient information about species interactions including knowledge about the diet, food intake and growth dynamics. This thesis...

  3. Integrated Predictive Models for ICRF-Edge Plasma Interactions

    International Nuclear Information System (INIS)

    Daniel A. D'Ippolito

    2005-01-01

    The coupling of radiofrequency waves to the edge plasma of a fusion device produces strong nonlinear interactions with the plasma and surrounding material walls which must be controlled in order to protect the antenna and to obtain efficient heating of the core plasma. The goal of the STTR project was to develop the first quantitative numerical simulation of this problem. This report describes the results of the Phase I work by Lodestar and ORNL on this project

  4. Reranking candidate gene models with cross-species comparison for improved gene prediction

    Directory of Open Access Journals (Sweden)

    Pereira Fernando CN

    2008-10-01

    Full Text Available Abstract Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc. Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models.

  5. Early establishment of trees at the alpine treeline: idiosyncratic species responses to temperature-moisture interactions.

    Science.gov (United States)

    Loranger, Hannah; Zotz, Gerhard; Bader, Maaike Y

    2016-01-01

    On a global scale, temperature is the main determinant of arctic and alpine treeline position. However on a local scale, treeline form and position vary considerably due to other climatic factors, tree species ecology and life-stage-dependent responses. For treelines to advance poleward or uphill, the first steps are germination and seedling establishment. These earliest life stages may be major bottlenecks for treeline tree populations and will depend differently on climatic conditions than adult trees. We investigated the effect of soil temperature and moisture on germination and early seedling survival in a field experiment in the French Alps near the local treeline (2100 m a.s.l.) using passive temperature manipulations and two watering regimes. Five European treeline tree species were studied: Larix decidua, Picea abies, Pinus cembra, Pinus uncinata and Sorbus aucuparia In addition, we monitored the germination response of three of these species to low temperatures under controlled conditions in growth chambers. The early establishment of these trees at the alpine treeline was limited either by temperature or by moisture, the sensitivity to one factor often depending on the intensity of the other. The results showed that the relative importance of the two factors and the direction of the effects are highly species-specific, while both factors tend to have consistent effects on both germination and early seedling survival within each species. We show that temperature and water availability are both important contributors to establishment patterns of treeline trees and hence to species-specific forms and positions of alpine treelines. The observed idiosyncratic species responses highlight the need for studies including several species and life-stages to create predictive power concerning future treeline dynamics. © The Authors 2016. Published by Oxford University Press on behalf of the Annals of Botany Company.

  6. Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

    DEFF Research Database (Denmark)

    Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

    2012-01-01

    Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

  7. Feeding behavior and trophic interaction of three shark species in the Galapagos Marine Reserve

    Directory of Open Access Journals (Sweden)

    Diego Páez-Rosas

    2018-05-01

    Full Text Available There is great concern about the future of sharks in Ecuador because of the lack of biological knowledge of most species that inhabit the region. This paper analyzes the feeding behavior of the pelagic thresher shark (Alopias pelagicus, the blue shark (Prionace glauca and the silky shark (Carcharhinus falciformis through the use of stable isotopes of carbon and nitrogen (δ13C and δ15N, with the aim of determining the degree of interaction between these species in the Galapagos Marine Reserve. No interspecific differences were found in use of oceanic vs. inshore feeding areas (δ13C: Kruskal–Wallis test, p = 0.09. The position in the hierarchy of the food web where A. pelagicus feeds differed from that of the other species (δ15N: Kruskal–Wallis test, p = 0.01. There were no significant differences in δ13C and δ15N values between males and females of the three species (Student’s t-test, p > 0.05, which suggests that both sexes have a similar feeding behavior. A specialist strategy was observed in P. glauca (trophic niche breadth TNB = 0.69, while the other species were found to be generalist (A. pelagicus TNB = 1.50 and C. falciformis TNB = 1.09. The estimated trophic level (TL varied between the three species. C. falciformis occupied the highest trophic level (TL = 4.4, making it a quaternary predator in the region. The results of this study coincide with the identified behavior in these predators in other areas of the tropical Pacific (Colombia and Mexico, and suggest a pelagic foraging strategy with differential consumption of prey between the three species. These ecological aspects can provide timely information when implementing in conservation measures for these shark species in the Tropical Pacific and Galapagos Marine Reserve.

  8. Feeding behavior and trophic interaction of three shark species in the Galapagos Marine Reserve.

    Science.gov (United States)

    Páez-Rosas, Diego; Insuasti-Zarate, Paul; Riofrío-Lazo, Marjorie; Galván-Magaña, Felipe

    2018-01-01

    There is great concern about the future of sharks in Ecuador because of the lack of biological knowledge of most species that inhabit the region. This paper analyzes the feeding behavior of the pelagic thresher shark ( Alopias pelagicus ), the blue shark ( Prionace glauca ) and the silky shark ( Carcharhinus falciformis ) through the use of stable isotopes of carbon and nitrogen ( δ 13 C and δ 15 N), with the aim of determining the degree of interaction between these species in the Galapagos Marine Reserve. No interspecific differences were found in use of oceanic vs. inshore feeding areas ( δ 13 C: Kruskal-Wallis test, p = 0.09). The position in the hierarchy of the food web where A. pelagicus feeds differed from that of the other species ( δ 15 N: Kruskal-Wallis test, p = 0.01). There were no significant differences in δ 13 C and δ 15 N values between males and females of the three species (Student's t -test, p  > 0.05), which suggests that both sexes have a similar feeding behavior. A specialist strategy was observed in P. glauca (trophic niche breadth TNB = 0.69), while the other species were found to be generalist ( A. pelagicus TNB = 1.50 and C. falciformis TNB = 1.09). The estimated trophic level (TL) varied between the three species. C. falciformis occupied the highest trophic level (TL = 4.4), making it a quaternary predator in the region. The results of this study coincide with the identified behavior in these predators in other areas of the tropical Pacific (Colombia and Mexico), and suggest a pelagic foraging strategy with differential consumption of prey between the three species. These ecological aspects can provide timely information when implementing in conservation measures for these shark species in the Tropical Pacific and Galapagos Marine Reserve.

  9. From inter-specific behavioural interactions to species distribution patterns along gradients of habitat heterogeneity.

    Science.gov (United States)

    Laiolo, Paola

    2013-01-01

    The strength of the behavioural processes associated with competitor coexistence may vary when different physical environments, and their biotic communities, come into contact, although empirical evidence of how interference varies across gradients of environmental complexity is still scarce in vertebrates. Here, I analyse how behavioural interactions and habitat selection regulate the local distribution of steppeland larks (Alaudidae) in a gradient from simple to heterogeneous agricultural landscapes in Spain, using crested lark Galerida cristata and Thekla lark G. theklae as study models. Galerida larks significantly partitioned by habitat but frequently co-occurred in heterogeneous environments. Irrespective of habitat divergence, however, the local densities of the two larks were negatively correlated, and the mechanisms beyond this pattern were investigated by means of playback experiments. When simulating the intrusion of the congener by broadcasting the species territorial calls, both larks responded with an aggressive response as intense with respect to warning and approach behaviour as when responding to the intrusion of a conspecific. However, birds promptly responded to playbacks only when congener territories were nearby, a phenomenon that points to learning as the mechanisms through which individuals finely tune their aggressive responses to the local competition levels. Heterospecifics occurred in closer proximity in diverse agro-ecosystems, possibly because of more abundant or diverse resources, and here engage in antagonistic interactions. The drop of species diversity associated with agricultural homogenisation is therefore likely to also bring about the disappearance of the behavioural repertoires associated with species interactions.

  10. Predicting Emergence of the Most Important Weed Species in Soybean (Glycine max L. under Different Management Operation

    Directory of Open Access Journals (Sweden)

    R. Khakzad

    2017-12-01

    Full Text Available Introduction: Summer annual weeds typically germinate in spring and early summer, grow throughout the summer, and set seeds by fall. Summer annual weeds are a persistent problem in summer annual row crops, competing directly for water, light, and nutrients, causing yield losses in quantity and quality. Although agriculture is increasingly relying on modern technology, knowledge of the biological systems in which these technologies are used is still critical for implementation of management strategies. Biological information about weeds is valuable and necessary for developing management strategies to minimize their impact. Scouting fields for pest problems are essential in any cropping system and knowledge of the timing and sequence of weed species emergence could increase the effectiveness of weed scouting trips and subsequent management practices. The success of any annual plant is directly correlated to its time of seedling emergence because it determines the ability of a plant to compete with its neighbors, survive biotic and abiotic stresses, and reproduce. The period and pattern of emergence of the weed community depend on the species present in the seed bank and their interaction with the environment. Therefore, knowledge of the weed species present in the soil seed bank and when these species are most likely to emerge is important in planning effective weed control programs. Temperature has been reported to be the main environmental factor regulating germination and emergence of weed species. Scientists have developed TT models to predict the emergence of weed species based on a daily accumulation of heat units or growing degree days (GDD above a minimum base threshold value (Tbase. The predictive models for weed emergence based on the accumulation of TT appear to be accurate enough for projections of weed emergence time (Grundy 2003. Moreover, soil temperature data are easily accessible, making this type of model practical and useful to

  11. Large-scale biotic interaction effects - tree cover interacts with shade toler-ance to affect distribution patterns of herb and shrub species across the Alps

    DEFF Research Database (Denmark)

    Nieto-Lugilde, Diego; Lenoir, Jonathan; Abdulhak, Sylvain

    2012-01-01

    on the occurrence on light-demanding species via size-asymmetric competition for light, but a facilitative effect on shade-tolerant species. In order to compare the relative importance of tree cover, four models with different combinations of variables (climate, soil and tree cover) were run for each species. Then...... role. Results indicated that high tree cover causes range contraction, especially at the upper limit, for light-demanding species, whereas it causes shade-tolerant species to extend their range upwards and downwards. Tree cover thus drives plant-plant interactions to shape plant species distribution...

  12. Method of predicting Splice Sites based on signal interactions

    Directory of Open Access Journals (Sweden)

    Deogun Jitender S

    2006-04-01

    Full Text Available Abstract Background Predicting and proper ranking of canonical splice sites (SSs is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. Results According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE and Intronic (ISE Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. Conclusion Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. Reviewers This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand.

  13. Stability of strong species interactions resist the synergistic effects of local and global pollution in kelp forests.

    Directory of Open Access Journals (Sweden)

    Laura J Falkenberg

    Full Text Available Foundation species, such as kelp, exert disproportionately strong community effects and persist, in part, by dominating taxa that inhibit their regeneration. Human activities which benefit their competitors, however, may reduce stability of communities, increasing the probability of phase-shifts. We tested whether a foundation species (kelp would continue to inhibit a key competitor (turf-forming algae under moderately increased local (nutrient and near-future forecasted global pollution (CO(2. Our results reveal that in the absence of kelp, local and global pollutants combined to cause the greatest cover and mass of turfs, a synergistic response whereby turfs increased more than would be predicted by adding the independent effects of treatments (kelp absence, elevated nutrients, forecasted CO(2. The positive effects of nutrient and CO(2 enrichment on turfs were, however, inhibited by the presence of kelp, indicating the competitive effect of kelp was stronger than synergistic effects of moderate enrichment of local and global pollutants. Quantification of physicochemical parameters within experimental mesocosms suggests turf inhibition was likely due to an effect of kelp on physical (i.e. shading rather than chemical conditions. Such results indicate that while forecasted climates may increase the probability of phase-shifts, maintenance of intact populations of foundation species could enable the continued strength of interactions and persistence of communities.

  14. Interactions of fire and nonnative species across an elevation/plant community gradient in Hawaii volcanoes national park

    Science.gov (United States)

    Alison Ainsworth; J. Boone Kauffman

    2010-01-01

    Invasive species interacting with fires pose a relatively unknown, but potentially serious, threat to the tropical forests of Hawaii. Fires may create conditions that facilitate species invasions, but the degree to which this occurs in different tropical plant communities has not been quantified. We documented the survival and establishment of plant species for 2 yr...

  15. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    Science.gov (United States)

    Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

  16. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

    Directory of Open Access Journals (Sweden)

    Ching-Tai Chen

    Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

  17. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  18. Genetic interactions underlying hybrid male sterility in the Drosophila bipectinata species complex.

    Science.gov (United States)

    Mishra, Paras Kumar; Singh, Bashisth Narayan

    2006-06-01

    Understanding genetic mechanisms underlying hybrid male sterility is one of the most challenging problems in evolutionary biology especially speciation. By using the interspecific hybridization method roles of Y chromosome, Major Hybrid Sterility (MHS) genes and cytoplasm in sterility of hybrid males have been investigated in a promising group, the Drosophila bipectinata species complex that consists of four closely related species: D. pseudoananassae, D. bipectinata, D. parabipectinata and D. malerkotliana. The interspecific introgression analyses show that neither cytoplasm nor MHS genes are involved but X-Y interactions may be playing major role in hybrid male sterility between D. pseudoananassae and the other three species. The results of interspecific introgression analyses also show considerable decrease in the number of males in the backcross offspring and all males have atrophied testes. There is a significant positive correlation between sex - ratio distortion and severity of sterility in backcross males. These findings provide evidence that D. pseudoananassae is remotely related with other three species of the D. bipectinata species complex.

  19. Structural prediction and analysis of VIH-related peptides from selected crustacean species.

    Science.gov (United States)

    Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

    2009-08-17

    The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family.

  20. Interaction between the genomes of Lactococcus lactis and phages of the P335 species

    Science.gov (United States)

    Kelly, William J.; Altermann, Eric; Lambie, Suzanne C.; Leahy, Sinead C.

    2013-01-01

    Phages of the P335 species infect Lactococcus lactis and have been particularly studied because of their association with strains of L. lactis subsp. cremoris used as dairy starter cultures. Unlike other lactococcal phages, those of the P335 species may have a temperate or lytic lifestyle, and are believed to originate from the starter cultures themselves. We have sequenced the genome of L. lactis subsp. cremoris KW2 isolated from fermented corn and found that it contains an integrated P335 species prophage. This 41 kb prophage (Φ KW2) has a mosaic structure with functional modules that are highly similar to several other phages of the P335 species associated with dairy starter cultures. Comparison of the genomes of 26 phages of the P335 species, with either a lytic or temperate lifestyle, shows that they can be divided into three groups and that the morphogenesis gene region is the most conserved. Analysis of these phage genomes in conjunction with the genomes of several L. lactis strains shows that prophage insertion is site specific and occurs at seven different chromosomal locations. Exactly how induced or lytic phages of the P335 species interact with carbohydrate cell surface receptors in the host cell envelope remains to be determined. Genes for the biosynthesis of a variable cell surface polysaccharide and for lipoteichoic acids (LTAs) are found in L. lactis and are the main candidates for phage receptors, as the genes for other cell surface carbohydrates have been lost from dairy starter strains. Overall, phages of the P335 species appear to have had only a minor role in the adaptation of L. lactis subsp. cremoris strains to the dairy environment, and instead they appear to be an integral part of the L. lactis chromosome. There remains a great deal to be discovered about their role, and their contribution to the evolution of the bacterial genome. PMID:24009606

  1. Plant Water Stress Affects Interactions Between an Invasive and a Naturalized Aphid Species on Cereal Crops.

    Science.gov (United States)

    Foote, N E; Davis, T S; Crowder, D W; Bosque-Pérez, N A; Eigenbrode, S D

    2017-06-01

    In cereal cropping systems of the Pacific Northwestern United States (PNW), climate change is projected to increase the frequency of drought during summer months, which could increase water stress for crop plants. Yet, it remains uncertain how interactions between herbivore species are affected by drought stress. Here, interactions between two cereal aphids present in PNW cereal systems, Metopolophium festucae (Theobald) subsp. cerealium (a newly invasive species) and Rhopalosiphum padi L. (a naturalized species), were tested relative to wheat water stress. When aphids were confined in leaf cages on wheat, asymmetrical facilitation occurred; per capita fecundity of R. padi was increased by 46% when M. festucae cerealium was also present, compared to when only R. padi was present. Imposed water stress did not influence this interaction. When aphids were confined on whole wheat plants, asymmetrical competition occurred; cocolonization inhibited M. festucae cerealium population growth but did not affect R. padi population growth. Under conditions of plant water stress, however, the inhibitory effect of R. padi on M. festucae cerealium was not observed. We conclude that beneficial effects of cocolonization on R. padi are due to a localized plant response to M. festucae cerealium feeding, and that cocolonization of plants is likely to suppress M. festucae cerealium populations under ample water conditions, but not when plants are water stressed. This suggests that plant responses to water stress alter the outcome of competition between herbivore species, with implications for the structure of pest communities on wheat during periods of drought. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America.

  2. Development of migration prediction system (MIGSTEM) for cationic species of radionuclides through soil layers

    International Nuclear Information System (INIS)

    Ohnuki, Toshihiko; Takebe, Shinichi; Yamamoto, Tadatoshi

    1989-01-01

    The migration prediction system (MIGSTEM) has been developed for estimating the migration of cationic species of radionuclides through soil layers systematically. The MIGSTEM consists of the migration experiments, the one-dimensional fitting code (inverse analysis code) for determining retardation factor and dispersivity (migration factors) and the three-dimensional differential code (prediction code) for estimating the migration of the radionuclides. The migration experiments are carried out for obtaining the concentration profiles of the radionuclides in unsaturated and saturated soil layers. Using the inverse analysis code, the migration factors are obtained at one time by fitting the concentration profiles calculated to those observed. The prediction code can give the contours of concentration and the one-dimensional concentration profiles at selected time, as well as the changing in the concentration at a selected position with time. The validity of the MIGSTEM was obtained by the benchmark test on the prediction and inverse analysis codes. The MIGSTEM was applied to estimate the migration of Sr 2+ through the sandy soil. (author)

  3. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  4. Climatic associations of British species distributions show good transferability in time but low predictive accuracy for range change.

    Directory of Open Access Journals (Sweden)

    Giovanni Rapacciuolo

    Full Text Available Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records

  5. Balance of Interactions Determines Optimal Survival in Multi-Species Communities.

    Directory of Open Access Journals (Sweden)

    Anshul Choudhary

    Full Text Available We consider a multi-species community modelled as a complex network of populations, where the links are given by a random asymmetric connectivity matrix J, with fraction 1 - C of zero entries, where C reflects the over-all connectivity of the system. The non-zero elements of J are drawn from a Gaussian distribution with mean μ and standard deviation σ. The signs of the elements Jij reflect the nature of density-dependent interactions, such as predatory-prey, mutualism or competition, and their magnitudes reflect the strength of the interaction. In this study we try to uncover the broad features of the inter-species interactions that determine the global robustness of this network, as indicated by the average number of active nodes (i.e. non-extinct species in the network, and the total population, reflecting the biomass yield. We find that the network transitions from a completely extinct system to one where all nodes are active, as the mean interaction strength goes from negative to positive, with the transition getting sharper for increasing C and decreasing σ. We also find that the total population, displays distinct non-monotonic scaling behaviour with respect to the product μC, implying that survival is dependent not merely on the number of links, but rather on the combination of the sparseness of the connectivity matrix and the net interaction strength. Interestingly, in an intermediate window of positive μC, the total population is maximal, indicating that too little or too much positive interactions is detrimental to survival. Rather, the total population levels are optimal when the network has intermediate net positive connection strengths. At the local level we observe marked qualitative changes in dynamical patterns, ranging from anti-phase clusters of period 2 cycles and chaotic bands, to fixed points, under the variation of mean μ of the interaction strengths. We also study the correlation between synchronization and survival

  6. Monte Carlo prediction of neutron interactions in sonofusion experiment

    International Nuclear Information System (INIS)

    Walter, J.; Gert, G.; Bougaev, A.; Bertodano, B.; Tsoukalas, I.H.; Jevremovic, T. . E-mail address of corresponding author: tatjanaj@ecn.purdue.edu

    2005-01-01

    Evidence of neutron induced sonofusion has been reported by Taleyarkhan, et. al, (Science, 8 March 2002). This involves the creation and collapse of cavities with acoustic waves and neutrons in deuterated acetone. The collapse of these bubbles creates conditions sufficient for D-D fusion to occur. As part of a bigger effort to reproduce these results, the neutral condition (without the acoustic waves) case was considered. This limits the neutron interactions to scattering and attenuation. MCNP5 was used to simulate the experiment for this neutral case. The set-up consisted of a cylindrical glass vessel that contained 500 mL of 99.9% D-acetone that was exposed to a 9.70 Ci Americium Beryllium neutron source. MCNP5 gave a production rate of 4.99E-11 (Relative Error: +/- 0.0005) tritons per source neutron for neutron absorption in deuterium. The resulting simulation's tritium activity was corrected for decay and detector efficiency, then compared to the actual experimental results. (author)

  7. Study on the interaction of U(VI) species with natural organic matters in KURT groundwater

    Energy Technology Data Exchange (ETDEWEB)

    Jung, Euo Chang; Baik, Min Hoon; Cho, Hye Ryun; Kim, Hee Kyung; Cha, Wansik [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2017-06-15

    The interaction of U(VI) (hexavalent uranium) species with natural organic matter (NOM) in KURT (KAERI Underground Research Tunnel) groundwater is investigated using a laser spectroscopic technique. The luminescence spectra of the NOM are observed in the ultraviolet and blue wavelength regions by irradiating a laser beam at 266 nm in groundwater. The luminescence spectra of U(VI) species in groundwater containing uranium concentrations of 0.034-0.788 mg·L-1 are measured in the green-colored wavelength region. The luminescence characteristics (peak wavelengths and lifetime) of U(VI) in the groundwater agree well with those of Ca{sub 2}UO{sub 2}(CO{sub 3}){sub 3}(aq) in a standard solution prepared in a laboratory. The luminescence intensities of U(VI) in the groundwater are weaker than those of Ca{sub 2}UO{sub 2}(CO{sub 3}){sub 3}(aq) in the standard solution at the same uranium concentrations. The luminescence intensities of Ca{sub 2}UO{sub 2}(CO{sub 3}){sub 3}(aq) in the standard solution mixed with the groundwater are also weaker than those of Ca{sub 2}UO{sub 2}(CO{sub 3}){sub 3}(aq) in the standard solution at the same uranium concentrations. These results can be ascribed to calcium-U(VI)-carbonate species interacting with NOM and forming non-radiative U(VI) complexes in groundwater.

  8. Interaction of selenite with reduced Fe and/or S species: An XRD and XAS study.

    Science.gov (United States)

    Finck, Nicolas; Dardenne, Kathy

    2016-05-01

    In this study, we investigated the interaction between selenite and either Fe((II))aq or S((-II))aq in solution, and the results were used to investigate the interaction between Se((IV))aq and FeS in suspension. The reaction products were characterized by a combination of methods (SEM, XRD and XAS) and the reaction mechanisms were identified. In a first experiment, Se((IV))aq was reduced to Se((0)) by interaction with Fe((II))aq which was oxidized to Fe((III)), but the reaction was only partial. Subsequently, some Fe((III)) produced akaganeite (β-FeOOH) and the release of proton during that reaction decreased the pH. The pH decrease changed the Se speciation in solution which hindered further Se((IV)) reduction by Fe((II))aq. In a second experiment, Se((IV))aq was quantitatively reduced to Se((0)) by S((-II))aq and the reaction was fast. Two sulfide species were needed to reduce one Se((IV)), and the observed pH increase was due to a proton consumption. For both experiments, experimental results are consistent with expectations based on the oxidation reduction potential of the various species. Upon interaction with FeS, Se((IV))aq was reduced to Se((0)) and minute amounts of pyrite were detected, a consequence of partial mackinawite oxidation at surface sulfur sites. These results are of prime importance with respect to safe deep disposal of nuclear waste which contains the long-lived radionuclide (79)Se. This study shows that after release of (79)Se((IV)) upon nuclear waste matrix corrosion, selenite can be reduced in the near field to low soluble Se((0)) by interaction with Fe((II))aq and/or S((-II))aq species. Because the solubility of Se((0)) species is significantly lower than that of Se((IV)), selenium will become much less (bio)available and its migration out of deep HLW repositories may be drastically hindered. Copyright © 2016. Published by Elsevier B.V.

  9. Prediction efficiency of the hydrographical parameters as related to distribution patterns of the Pleuromamma species in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Saraswathy, M.

    . Multiple regression model of P. indica abundance on the parameters: temperature, salinity, dissolved oxygen and phosphate-phosphorus could explain more than 85% of the variation in the predicted abundance, while those of 8 species obtained from...

  10. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices.

    Science.gov (United States)

    Gagic, Vesna; Bartomeus, Ignasi; Jonsson, Tomas; Taylor, Astrid; Winqvist, Camilla; Fischer, Christina; Slade, Eleanor M; Steffan-Dewenter, Ingolf; Emmerson, Mark; Potts, Simon G; Tscharntke, Teja; Weisser, Wolfgang; Bommarco, Riccardo

    2015-02-22

    Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  11. Corruption, development and governance indicators predict invasive species risk from trade.

    Science.gov (United States)

    Brenton-Rule, Evan C; Barbieri, Rafael F; Lester, Philip J

    2016-06-15

    Invasive species have an enormous global impact, with international trade being the leading pathway for their introduction. Current multinational trade deals under negotiation will dramatically change trading partnerships and pathways. These changes have considerable potential to influence biological invasions and global biodiversity. Using a database of 47 328 interceptions spanning 10 years, we demonstrate how development and governance socio-economic indicators of trading partners can predict exotic species interceptions. For import pathways associated with vegetable material, a significantly higher risk of exotic species interceptions was associated with countries that are poorly regulated, have more forest cover and have surprisingly low corruption. Corruption and indicators such as political stability or adherence to rule of law were important in vehicle or timber import pathways. These results will be of considerable value to policy makers, primarily by shifting quarantine procedures to focus on countries of high risk based on their socio-economic status. Further, using New Zealand as an example, we demonstrate how a ninefold reduction in incursions could be achieved if socio-economic indicators were used to select trade partners. International trade deals that ignore governance and development indicators may facilitate introductions and biodiversity loss. Development and governance within countries clearly have biodiversity implications beyond borders. © 2016 The Author(s).

  12. Habitat and species identity, not diversity, predict the extent of refuse consumption by urban arthropods.

    Science.gov (United States)

    Youngsteadt, Elsa; Henderson, Ryanna C; Savage, Amy M; Ernst, Andrew F; Dunn, Robert R; Frank, Steven D

    2015-03-01

    Urban green spaces provide ecosystem services to city residents, but their management is hindered by a poor understanding of their ecology. We examined a novel ecosystem service relevant to urban public health and esthetics: the consumption of littered food waste by arthropods. Theory and data from natural systems suggest that the magnitude and resilience of this service should increase with biological diversity. We measured food removal by presenting known quantities of cookies, potato chips, and hot dogs in street medians (24 sites) and parks (21 sites) in New York City, USA. At the same sites, we assessed ground-arthropod diversity and abiotic conditions, including history of flooding during Hurricane Sandy 7 months prior to the study. Arthropod diversity was greater in parks (on average 11 hexapod families and 4.7 ant species per site), than in medians (nine hexapod families and 2.7 ant species per site). However, counter to our diversity-based prediction, arthropods in medians removed 2-3 times more food per day than did those in parks. We detected no effect of flooding (at 19 sites) on this service. Instead, greater food removal was associated with the presence of the introduced pavement ant (Tetramorium sp. E) and with hotter, drier conditions that may have increased arthropod metabolism. When vertebrates also had access to food, more was removed, indicating that arthropods and vertebrates compete for littered food. We estimate that arthropods alone could remove 4-6.5 kg of food per year in a single street median, reducing its availability to less desirable fauna such as rats. Our results suggest that species identity and habitat may be more relevant than diversity for predicting urban ecosystem services. Even small green spaces such as street medians provide ecosystem services that may complement those of larger habitat patches across the urban landscape. © 2014 John Wiley & Sons Ltd.

  13. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men

    Science.gov (United States)

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2013-01-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies. PMID:23584507

  14. Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

    Science.gov (United States)

    Liu, Zhi-Ping; Chen, Luonan

    2016-01-01

    Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

  15. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  16. A human protein interaction network shows conservation of aging processes between human and invertebrate species.

    Directory of Open Access Journals (Sweden)

    Russell Bell

    2009-03-01

    Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

  17. Genes, communities & invasive species: understanding the ecological and evolutionary dynamics of host-pathogen interactions.

    Science.gov (United States)

    Burdon, J J; Thrall, P H; Ericson, L

    2013-08-01

    Reciprocal interactions between hosts and pathogens drive ecological, epidemiological and co-evolutionary trajectories, resulting in complex patterns of diversity at population, species and community levels. Recent results confirm the importance of negative frequency-dependent rather than 'arms-race' processes in the evolution of individual host-pathogen associations. At the community level, complex relationships between species abundance and diversity dampen or alter pathogen impacts. Invasive pathogens challenge these controls reflecting the earliest stages of evolutionary associations (akin to arms-race) where disease effects may be so great that they overwhelm the host's and community's ability to respond. Viewing these different stabilization/destabilization phases as a continuum provides a valuable perspective to assessment of the role of genetics and ecology in the dynamics of both natural and invasive host-pathogen associations. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Coulombic interactions and multicomponent ionic dispersion during transport of charged species in heterogeneous porous media

    DEFF Research Database (Denmark)

    Muniruzzaman, Muhammad; Rolle, Massimo

    Electrochemical cross-coupling plays a significant role for transport of charged species in porous media [1, 2]. In this study we performed flow-through experiments in a quasi two-dimensional setup using dilute solutions of strong electrolytes to study the influence of charge interactions on mass...... occurred. To quantitatively interpret the outcomes of our laboratory experiments in the spatially variable flow fields we developed a two dimensional numerical model based on a multicomponent formulation, on charge conservation and on the accurate description of transverse dispersion. The results...... of the multicomponent transport simulations were compared with the high-resolution (5 mm spacing) concentration measurements of the ionic species at the outlet of the flow-through domain. The excellent agreement between the measured concentrations and the results of purely forward numerical simulations demonstrates...

  19. IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions.

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R; Backofen, Rolf

    2017-07-03

    The IntaRNA algorithm enables fast and accurate prediction of RNA-RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA-RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  20. IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

    Science.gov (United States)

    Mann, Martin; Wright, Patrick R.

    2017-01-01

    Abstract The IntaRNA algorithm enables fast and accurate prediction of RNA–RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA–RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. PMID:28472523

  1. Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

    Science.gov (United States)

    Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

    2005-01-01

    We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

  2. Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

    Science.gov (United States)

    Germaine, Stephen S.; Ignizio, Drew; Keinath, Doug; Copeland, Holly

    2014-01-01

    Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an

  3. A new species of Desmopachria Babington (Coleoptera: Dytiscidae) from Cuba with a prediction of its geographic distribution and notes on other Cuban species of the genus.

    Science.gov (United States)

    Megna, Yoandri S; Sánchez-Fernández, David

    2014-01-10

    A new species, Desmopachria andreae sp. n. is described from Cuba. Diagnostic characters including illustrations of male genitalia are provided and illustrated for the five species of the genus occurring on the island. For these five species both a simple key to adults and maps of their known distribution in Cuba are also provided. Using a Maximun Entropy method (MaxEnt), a distribution model was developed for D. andreae sp.n. Based on the model's predictions, this species has a higher probability of occurring in high altitude forests (above 1000 m a.s.l.), characterised by relatively low temperatures especially during the hottest and wettest seasons, specifically, the mountainous areas of the Macizo de Guamuhaya (Central Cuba), Sierra Maestra (S Cuba) and Nipe-Sagua-Baracoa (NE Cuba). In some of these areas the species has not yet been recorded, and should be searched for in future field surveys.

  4. Species-environment interactions changed by introduced herbivores in an oceanic high-mountain ecosystem.

    Science.gov (United States)

    Seguí, Jaume; López-Darias, Marta; Pérez, Antonio J; Nogales, Manuel; Traveset, Anna

    2017-01-05

    Summit areas of oceanic islands constitute some of the most isolated ecosystems on earth, highly vulnerable to climate change and introduced species. Within the unique high-elevation communities of Tenerife (Canary Islands), reproductive success and thus long-term survival of species may depend on environmental suitability as well as threat by introduced herbivores. By experimentally modifying the endemic and vulnerable species Viola cheiranthifolia along its entire altitudinal occurrence range, we studied plant performance, autofertility, pollen limitation and visitation rate and the interactive effect of grazing by non-native rabbits on them. We assessed the grazing effects by recording (1) the proportion of consumed plants and flowers along the gradient, (2) comparing fitness traits of herbivore-excluded plants along the gradient, and (3) comparing fitness traits, autofertility and pollen limitation between plants excluded from herbivores with unexcluded plants at the same locality. Our results showed that V. cheiranthifolia performance is mainly affected by inter-annual and microhabitat variability along the gradient, especially in the lowest edge. Despite the increasingly adverse environmental conditions, the plant showed no pollen limitation with elevation, which is attributed to the increase in autofertility levels (≥ 50% of reproductive output) and decrease in competition for pollinators at higher elevations. Plant fitness is, however, extremely reduced owing to the presence of non-native rabbits in the area (consuming more than 75% of the individuals in some localities), which in turn change plant trait-environment interactions along the gradient. Taken together, these findings indicate that the elevational variation found on plant performance results from the combined action of non-native rabbits with the microhabitat variability, exerting intricate ecological influences that threaten the survival of this violet species. Published by Oxford University

  5. Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

    Science.gov (United States)

    Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

    2010-09-13

    Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

  6. Species distribution modeling for the invasive raccoon dog (Nyctereutes procyonoides) in Austria and first range predictions for alpine environments

    OpenAIRE

    Duscher Tanja; Nopp-Mayr Ursula

    2017-01-01

    Species distribution models are important tools for wildlife management planning, particularly in the case of invasive species. We employed a recent framework for niche-based invasive species distribution modeling to predict the probability of presence for the invasive raccoon dog (Nyctereutes procyonoides) in Austria. The raccoon dog is an adaptive, mobile and highly reproductive Asiatic canid that has successfully invaded many parts of Europe. It is known...

  7. Species Diversity and Functional Prediction of Surface Bacterial Communities on Aging Flue-Cured Tobaccos.

    Science.gov (United States)

    Wang, Fan; Zhao, Hongwei; Xiang, Haiying; Wu, Lijun; Men, Xiao; Qi, Chang; Chen, Guoqiang; Zhang, Haibo; Wang, Yi; Xian, Mo

    2018-06-05

    Microbes on aging flue-cured tobaccos (ATFs) improve the aroma and other qualities desirable in products. Understanding the relevant organisms would picture microbial community diversity, metabolic potential, and their applications. However, limited efforts have been made on characterizing the microbial quality and functional profiling. Herein, we present our investigation of the bacterial diversity and predicted potential genetic capability of the bacteria from two AFTs using 16S rRNA gene sequences and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) software. The results show that dominant bacteria from AFT surfaces were classified into 48 genera, 36 families, and 7 phyla. In addition, Bacillus spp. was found prevalent on both ATFs. Furthermore, PICRUSt predictions of bacterial community functions revealed many attractive metabolic capacities in the AFT microbiota, including several involved in the biosynthesis of flavors and fragrances and the degradation of harmful compounds, such as nicotine and nitrite. These results provide insights into the importance of AFT bacteria in determining product qualities and indicate specific microbial species with predicted enzymatic capabilities for the production of high-efficiency flavors, the degradation of undesirable compounds, and the provision of nicotine and nitrite tolerance which suggest fruitful areas of investigation into the manipulation of AFT microbiota for AFT and other product improvements.

  8. Data-driven prediction of adverse drug reactions induced by drug-drug interactions.

    Science.gov (United States)

    Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Kumar, Kamal; Yu, Xueping; Wallqvist, Anders; Reifman, Jaques

    2017-06-08

    The expanded use of multiple drugs has increased the occurrence of adverse drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions are typically not observed in clinical drug-development studies because most of them focus on single-drug therapies. ADR reporting systems collect information on adverse health effects caused by both single drugs and DDIs. A major challenge is to unambiguously identify the effects caused by DDIs and to attribute them to specific drug interactions. A computational method that provides prospective predictions of potential DDI-induced ADRs will help to identify and mitigate these adverse health effects. We hypothesize that drug-protein interactions can be used as independent variables in predicting ADRs. We constructed drug pair-protein interaction profiles for ~800 drugs using drug-protein interaction information in the public domain. We then constructed statistical models to score drug pairs for their potential to induce ADRs based on drug pair-protein interaction profiles. We used extensive clinical database information to construct categorical prediction models for drug pairs that are likely to induce ADRs via synergistic DDIs and showed that model performance deteriorated only slightly, with a moderate amount of false positives and false negatives in the training samples, as evaluated by our cross-validation analysis. The cross validation calculations showed an average prediction accuracy of 89% across 1,096 ADR models that captured the deleterious effects of synergistic DDIs. Because the models rely on drug-protein interactions, we made predictions for pairwise combinations of 764 drugs that are currently on the market and for which drug-protein interaction information is available. These predictions are publicly accessible at http://avoid-db.bhsai.org . We used the predictive models to analyze broader aspects of DDI-induced ADRs, showing that ~10% of all combinations have the potential to induce ADRs

  9. Enhancing the prediction of protein pairings between interacting families using orthology information

    Directory of Open Access Journals (Sweden)

    Pazos Florencio

    2008-01-01

    Full Text Available Abstract Background It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other. The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. Results For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. Conclusion Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.

  10. Cross-Species Virus-Host Protein-Protein Interactions Inhibiting Innate Immunity

    Science.gov (United States)

    2016-07-01

    diseases are a regular occurrence globally (Figure 1). The Zika virus is the latest example gaining widespread attention. Many of the (re-)emerging...for establishing infection and/or modulating pathogenesis (Figures 2 and 3). 3 Figure 2. Schematic of several virus -host protein interactions within...8725 John J. Kingman Road, MS 6201 Fort Belvoir, VA 22060-6201 T E C H N IC A L R E P O R T DTRA-TR-16-79 Cross-species virus -host

  11. Helminth community structure and diet of three Afrotropical anuran species: a test of the interactive-versus-isolationist parasite communities hypothesis

    Directory of Open Access Journals (Sweden)

    G. C. Akani

    2011-09-01

    Full Text Available The interactive-versus-isolationist hypothesis predicts that parasite communities should be depauperated and weakly structured by interspecific competition in amphibians. A parasitological survey was carried out to test this hypothesis using three anuran species from Nigeria, tropical Africa (one Bufonidae; two Ranidae. High values of parasite infection parameters were found in all three species, which were infected by nematodes, cestodes and trematodes. Nonetheless, the parasite communities of the three anurans were very depauperated in terms of number of species (4 to 6. Interspecific competition was irrelevant in all species, as revealed by null models and Monte Carlo permutations. Cluster analyses revealed that, in terms of parasite community composition, the two Ranidae were similar, whereas the Bufonidae was more different. However, when prevalence, intensity, and abundance of parasites are combined into a multivariate analysis, each anuran species was clearly spaced apart from the others, thus revealing considerable species-specific differences in terms of their parasite communities. All anurans were generalists and probably opportunistic in terms of dietary habits, and showed no evidence of interspecific competition for food. Overall, our data are widely consistent with expectations driven from the interactive-versus-isolationist parasite communities hypothesis.

  12. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio

    2013-10-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  13. Exploitation of genetic interaction network topology for the prediction of epistatic behavior

    KAUST Repository

    Alanis Lobato, Gregorio; Cannistraci, Carlo; Ravasi, Timothy

    2013-01-01

    Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks.We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks.Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab. © 2013 Elsevier Inc.

  14. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Ecological multiplex interactions determine the role of species for parasite spread amplification.

    Science.gov (United States)

    Stella, Massimo; Selakovic, Sanja; Antonioni, Alberto; Andreazzi, Cecilia

    2018-04-23

    Despite their potential interplay, multiple routes of many disease transmissions are often investigated separately. As an unifying framework for understanding parasite spread through interdependent transmission paths, we present the 'ecomultiplex' model, where the multiple transmission paths among a diverse community of interacting hosts are represented as a spatially explicit multiplex network. We adopt this framework for designing and testing potential control strategies for T. cruzi spread in two empirical host communities. We show that the ecomultiplex model is an efficient and low data-demanding method to identify which species enhances parasite spread and should thus be a target for control strategies. We also find that the interplay between predator-prey and host-parasite interactions leads to a phenomenon of parasite amplification, in which top predators facilitate T. cruzi spread, offering a mechanistic interpretation of previous empirical findings. Our approach can provide novel insights in understanding and controlling parasite spreading in real-world complex systems. © 2018, Stella et al.

  16. Empathy and Prosocial Behaviours. Insights from Intra- and Inter-species Interactions

    Directory of Open Access Journals (Sweden)

    Maria elide Vanutelli

    2015-04-01

    Full Text Available It has been suggested that “sharing the same body” between the observer and the observed subject allows for a direct form of understanding and emotional attuning by a process of simulation. Then, what happens when we don’t share the same body? The aim of the present paper is to review available evidence of intra- and inter-species empathic and prosocial behaviours, with respect to within-human, within-animals and cross-specifies interactions. Similarities and differences will be evaluated using a comparative perspective, and some possible moral and ethical implications for human-animal interactions will be discussed. According to Charles Darwin’s work, the perceived differences between human and animal empathy could be more quantitative than qualitative, suggesting a common affective core which allows both categories to mirror and tune to conspecifics’ feelings, where in the case of humans it can be integrated with more complex cognitive processes.

  17. Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

    Science.gov (United States)

    Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

    2016-10-01

    Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

  18. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

    Directory of Open Access Journals (Sweden)

    Gomez Shawn M

    2010-04-01

    Full Text Available Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

  19. Sexual conflict predicts morphology and behavior in two species of penduline tits

    Directory of Open Access Journals (Sweden)

    Komdeur Jan

    2010-04-01

    Full Text Available Abstract Background The evolutionary interests of males and females rarely coincide (sexual conflict, and these conflicting interests influence morphology, behavior and speciation in various organisms. We examined consequences of variation in sexual conflict in two closely-related passerine birds with contrasting breeding systems: the Eurasian penduline tit Remiz pendulinus (EPT exhibiting a highly polygamous breeding system with sexually antagonistic interests over parental care, and the socially monogamous Cape penduline tit Anthoscopus minutus (CPT. We derived four a priori predictions from sexual conflict theory and tested these using data collected in Central Europe (EPT and South Africa (CPT. Firstly, we predicted that EPTs exhibit more sexually dimorphic plumage than CPTs due to more intense sexual selection. Secondly, we expected brighter EPT males to provide less care than duller males. Thirdly, since song is a sexually selected trait in many birds, male EPTs were expected to exhibit more complex songs than CPT males. Finally, intense sexual conflict in EPT was expected to lead to low nest attendance as an indication of sexually antagonistic interests, whereas we expected more cooperation between parents in CPT consistent with their socially monogamous breeding system. Results Consistent with our predictions EPTs exhibited greater sexual dimorphism in plumage and more complex song than CPTs, and brighter EPT males provided less care than duller ones. EPT parents attended the nest less frequently and less simultaneously than CPT parents. Conclusions These results are consistent with sexual conflict theory: species in which sexual conflict is more manifested (EPT exhibited a stronger sexual dimorphism and more elaborated sexually selected traits than species with less intense sexual conflict (CPT. Our results are also consistent with the notion that EPTs attempt to force their partner to work harder as expected under sexual conflict: each

  20. Sexual conflict predicts morphology and behavior in two species of penduline tits.

    Science.gov (United States)

    van Dijk, René E; Pogány, Akos; Komdeur, Jan; Lloyd, Penn; Székely, Tamás

    2010-04-23

    The evolutionary interests of males and females rarely coincide (sexual conflict), and these conflicting interests influence morphology, behavior and speciation in various organisms. We examined consequences of variation in sexual conflict in two closely-related passerine birds with contrasting breeding systems: the Eurasian penduline tit Remiz pendulinus (EPT) exhibiting a highly polygamous breeding system with sexually antagonistic interests over parental care, and the socially monogamous Cape penduline tit Anthoscopus minutus (CPT). We derived four a priori predictions from sexual conflict theory and tested these using data collected in Central Europe (EPT) and South Africa (CPT). Firstly, we predicted that EPTs exhibit more sexually dimorphic plumage than CPTs due to more intense sexual selection. Secondly, we expected brighter EPT males to provide less care than duller males. Thirdly, since song is a sexually selected trait in many birds, male EPTs were expected to exhibit more complex songs than CPT males. Finally, intense sexual conflict in EPT was expected to lead to low nest attendance as an indication of sexually antagonistic interests, whereas we expected more cooperation between parents in CPT consistent with their socially monogamous breeding system. Consistent with our predictions EPTs exhibited greater sexual dimorphism in plumage and more complex song than CPTs, and brighter EPT males provided less care than duller ones. EPT parents attended the nest less frequently and less simultaneously than CPT parents. These results are consistent with sexual conflict theory: species in which sexual conflict is more manifested (EPT) exhibited a stronger sexual dimorphism and more elaborated sexually selected traits than species with less intense sexual conflict (CPT). Our results are also consistent with the notion that EPTs attempt to force their partner to work harder as expected under sexual conflict: each member of the breeding pair attempts to shift the

  1. Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type.

    Science.gov (United States)

    Marshall, Leon; Carvalheiro, Luísa G; Aguirre-Gutiérrez, Jesús; Bos, Merijn; de Groot, G Arjen; Kleijn, David; Potts, Simon G; Reemer, Menno; Roberts, Stuart; Scheper, Jeroen; Biesmeijer, Jacobus C

    2015-10-01

    Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs' usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and

  2. Predicting climate change impacts on native and invasive tree species using radial growth and twenty-first century climate scenarios

    NARCIS (Netherlands)

    González-Muñoz, N.; Linares, J.C.; Castro-Díez, P.; Sass-Klaassen, U.G.W.

    2014-01-01

    The climatic conditions predicted for the twenty-first century may aggravate the extent and impacts of plant invasions, by favouring those invaders more adapted to altered conditions or by hampering the native flora. We aim to predict the fate of native and invasive tree species in the oak forests

  3. The role of gene-gene interaction in the prediction of criminal behavior.

    Science.gov (United States)

    Boutwell, Brian B; Menard, Scott; Barnes, J C; Beaver, Kevin M; Armstrong, Todd A; Boisvert, Danielle

    2014-04-01

    A host of research has examined the possibility that environmental risk factors might condition the influence of genes on various outcomes. Less research, however, has been aimed at exploring the possibility that genetic factors might interact to impact the emergence of human traits. Even fewer studies exist examining the interaction of genes in the prediction of behavioral outcomes. The current study expands this body of research by testing the interaction between genes involved in neural transmission. Our findings suggest that certain dopamine genes interact to increase the odds of criminogenic outcomes in a national sample of Americans. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

    Science.gov (United States)

    Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

    2016-01-01

    Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

  5. Kinetic model for predicting the concentrations of active halogen species in chlorinated saline cooling waters

    International Nuclear Information System (INIS)

    Lietzke, M.H.; Haag, W.R.

    1979-01-01

    A kinetic model for predicting the composition of chlorinated water discharged from power plants using fresh water for cooling was previously reported. The model has now been extended to be applicable to power plants located on estuaries or on the seacoast where saline water is used for cooling purposes. When chloride is added to seawater to prevent biofouling in cooling systems, bromine is liberated. Since this reaction proceeds at a finite rate there is a competition between the bromine (i.e., hypobromous acid) and the added chlorine (i.e., hypochlorous acid) for halogenation of any amine species present in the water. Hence not only chloramines but also bromamines and bromochloramines will be formed, with the relative concentrations a function of the pH, temperature, and salinity of the water. The kinetic model takes into account the chemical reactions leading to the formation and disappearance of the more important halamines and hypohalous acids likely to be encountered in chlorinated saline water

  6. Magnetic interactions as a stabilizing factor of semiquinone species of lawsone by metal complexation

    International Nuclear Information System (INIS)

    Valle-Bourrouet, Grettel; Ugalde-Saldivar, Victor M.; Gomez, Martin; Ortiz-Frade, Luis A.; Gonzalez, Ignacio; Frontana, Carlos

    2010-01-01

    Changes in electrochemical reactivity for lawsone anions (lawsone, 2-hydroxy-1,4-naphthoquinone, HLw) being coordinated to a series of metallic ions in dimethylsulfoxide solution were evaluated. Upon performing cyclic voltammetry experiments for metal complexes of this quinone with pyridine (Py) - structural formula M(II)(Lw - ) 2 (Py) 2 ; M: Co(II), Ni(II), Zn(II) - it was found that the reduction of coordinated Lw - units occurs during the first and second electron uptake in the analyzed compounds. The stability of the electrogenerated intermediates for each complex depends on the d electron configuration in each metal center and is determined by magnetic interactions with the available spins considering an octahedral conformation for all the compounds. This was evidenced by in situ spectroelectrochemical-ESR measurements in the Zn(II) complex in which due to the lack of magnetic interaction owing to its electron configuration, the structure of the coordinated anion radical species was determined. Successive reduction of the associated Lw - units leads to partial dissociation of the complex, determined by the identification of free radical dianion structures in solution. These results show some insights on how metal-lawsone complexation can modify the solution reactivity and stability of the electrogenerated radical species.

  7. Magnetic interactions as a stabilizing factor of semiquinone species of lawsone by metal complexation

    Energy Technology Data Exchange (ETDEWEB)

    Valle-Bourrouet, Grettel [Universidad de Costa Rica, Escuela de Quimica, San Jose (Costa Rica); Ugalde-Saldivar, Victor M. [Facultad de Quimica, Universidad Nacional Autonoma de Mexico, Ciudad Universitaria, C.P. 04510, Mexico, D.F. (Mexico); Gomez, Martin [Departamento de Sistemas Biologicos, Universidad Autonoma Metropolitana-Xochimilco, C.P. 04960, Mexico, D.F. (Mexico); Ortiz-Frade, Luis A. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica, Parque Tecnologico Queretaro, Sanfandila, 76703, Pedro Escobedo, Queretaro (Mexico); Gonzalez, Ignacio [Universidad Autonoma Metropolitana - Iztapalapa, Departamento de Quimica, Area de Electroquimica, Apartado postal 55-534, 09340, Mexico, D.F. (Mexico); Frontana, Carlos, E-mail: ultrabuho@yahoo.com.m [Departamento de Quimica, Centro de Investigacion y Estudios Avanzados, Av. Instituto Politecnico Nacional No. 2508 Col. San Pedro Zacatenco, C.P. 07360, Mexico, D.F. (Mexico)

    2010-12-01

    Changes in electrochemical reactivity for lawsone anions (lawsone, 2-hydroxy-1,4-naphthoquinone, HLw) being coordinated to a series of metallic ions in dimethylsulfoxide solution were evaluated. Upon performing cyclic voltammetry experiments for metal complexes of this quinone with pyridine (Py) - structural formula M(II)(Lw{sup -}){sub 2}(Py){sub 2}; M: Co(II), Ni(II), Zn(II) - it was found that the reduction of coordinated Lw{sup -} units occurs during the first and second electron uptake in the analyzed compounds. The stability of the electrogenerated intermediates for each complex depends on the d electron configuration in each metal center and is determined by magnetic interactions with the available spins considering an octahedral conformation for all the compounds. This was evidenced by in situ spectroelectrochemical-ESR measurements in the Zn(II) complex in which due to the lack of magnetic interaction owing to its electron configuration, the structure of the coordinated anion radical species was determined. Successive reduction of the associated Lw{sup -} units leads to partial dissociation of the complex, determined by the identification of free radical dianion structures in solution. These results show some insights on how metal-lawsone complexation can modify the solution reactivity and stability of the electrogenerated radical species.

  8. Changes in hyphal morphology and activity of phenoloxidases during interactions between selected ectomycorrhizal fungi and two species of Trichoderma.

    Science.gov (United States)

    Mucha, Joanna

    2011-06-01

    Patterns of phenoloxidase activity can be used to characterize fungi of different life styles, and changes in phenoloxidase synthesis were suspected to play a role in the interaction between ectomycorrhizal and two species of Trichoderma. Confrontation between the ectomycorrhizal fungi Amanita muscaria and Laccaria laccata with species of Trichoderma resulted in induction of laccase synthesis, and the laccase enzyme was bound to mycelia of ectomycorrhizal fungi. Tyrosinase release was noted only during interaction of L. laccata strains with Trichoderma harzianum and T. virens. Ectomycorrhizal fungi, especially strains of Suillus bovinus and S. luteus, inhibited growth of Trichoderma species and caused morphological changes in its colonies in the zone of interaction. In contrast, hyphal changes occurred less often in the ectomycorrhizal fungi tested. Species of Suillus are suggested to present a different mechanism in their interaction with other fungi than A. muscaria and L. laccata.

  9. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    International Nuclear Information System (INIS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-01-01

    The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

  10. Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

    KAUST Repository

    Cannistraci, Carlo

    2013-06-21

    Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

  11. Studies on interaction of colloidal silver nanoparticles (SNPs) with five different bacterial species.

    Science.gov (United States)

    Khan, S Sudheer; Mukherjee, Amitava; Chandrasekaran, N

    2011-10-01

    Silver nanoparticles (SNPs) are being increasingly used in many consumer products like textile fabrics, cosmetics, washing machines, food and drug products owing to its excellent antimicrobial properties. Here we have studied the adsorption and toxicity of SNPs on bacterial species such as Pseudomonas aeruginosa, Micrococcus luteus, Bacillus subtilis, Bacillus barbaricus and Klebsiella pneumoniae. The influence of zeta potential on the adsorption of SNPs on bacterial cell surface was investigated at acidic, neutral and alkaline pH and with varying salt (NaCl) concentrations (0.05, 0.1, 0.5, 1 and 1.5 M). The survival rate of bacterial species decreased with increase in adsorption of SNPs. Maximum adsorption and toxicity was observed at pH 5, and NaCl concentration of 0.5 M, there by resulting in less toxicity. The zeta potential study suggests that, the adsorption of SNPs on the cell surface was related to electrostatic force of attraction. The equilibrium and kinetics of the adsorption process were also studied. The adsorption equilibrium isotherms fitted well to the Langmuir model. The kinetics of adsorption fitted best to pseudo-first-order. These findings form a basis for interpreting the interaction of nanoparticles with environmental bacterial species. Copyright © 2011 Elsevier B.V. All rights reserved.

  12. Predicted Changes in Climatic Niche and Climate Refugia of Conservation Priority Salamander Species in the Northeastern United States

    Directory of Open Access Journals (Sweden)

    William B. Sutton

    2014-12-01

    Full Text Available Global climate change represents one of the most extensive and pervasive threats to wildlife populations. Amphibians, specifically salamanders, are particularly susceptible to the effects of changing climates due to their restrictive physiological requirements and low vagility; however, little is known about which landscapes and species are vulnerable to climate change. Our study objectives included, (1 evaluating species-specific predictions (based on 2050 climate projections and vulnerabilities to climate change and (2 using collective species responses to identify areas of climate refugia for conservation priority salamanders in the northeastern United States. All evaluated salamander species were projected to lose a portion of their climatic niche. Averaged projected losses ranged from 3%–100% for individual species, with the Cow Knob Salamander (Plethodon punctatus, Cheat Mountain Salamander (Plethodon nettingi, Shenandoah Mountain Salamander (Plethodon virginia, Mabee’s Salamander (Ambystoma mabeei, and Streamside Salamander (Ambystoma barbouri predicted to lose at least 97% of their landscape-scale climatic niche. The Western Allegheny Plateau was predicted to lose the greatest salamander climate refugia richness (i.e., number of species with a climatically-suitable niche in a landscape patch, whereas the Central Appalachians provided refugia for the greatest number of species during current and projected climate scenarios. Our results can be used to identify species and landscapes that are likely to be further affected by climate change and potentially resilient habitats that will provide consistent climatic conditions in the face of environmental change.

  13. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  14. Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

    Science.gov (United States)

    van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A

    2018-02-01

    This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.

    Science.gov (United States)

    Liu, Yong; Wu, Min; Miao, Chunyan; Zhao, Peilin; Li, Xiao-Li

    2016-02-01

    In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

  16. Periodic and chaotic events in a discrete model of logistic type for the competitive interaction of two species

    International Nuclear Information System (INIS)

    Lopez-Ruiz, Ricardo; Fournier-Prunaret, Daniele

    2009-01-01

    Two symmetrically coupled logistic equations are proposed to mimic the competitive interaction between two species. The phenomena of coexistence, oscillations and chaos are present in this cubic discrete system. This work, together with two other similar ones recently published by the authors, completes a triptych dedicated to the two species relationships present in Nature, namely the symbiosis, the predator-prey and the competition. These models can be used as basic ingredients to build up more complex interactions in the ecological networks.

  17. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change.

    Science.gov (United States)

    Zhang, Keliang; Yao, Linjun; Meng, Jiasong; Tao, Jun

    2018-09-01

    Paeonia (Paeoniaceae), an economically important plant genus, includes many popular ornamentals and medicinal plant species used in traditional Chinese medicine. Little is known about the properties of the habitat distribution and the important eco-environmental factors shaping the suitability. Based on high-resolution environmental data for current and future climate scenarios, we modeled the present and future suitable habitat for P. delavayi and P. rockii by Maxent, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. The results showed that the moderate and high suitable areas for P. delavayi and P. rockii encompassed ca. 4.46×10 5 km 2 and 1.89×10 5 km 2 , respectively. Temperature seasonality and isothermality were identified as the most critical factors shaping P. delavayi distribution, and UVB-4 and annual precipitation were identified as the most critical for shaping P. rockii distribution. Under the scenario with a low concentration of greenhouse gas emissions (RCP2.6), the range of both species increased as global warming intensified; however, under the scenario with higher concentrations of emissions (RCP8.5), the suitable habitat range of P. delavayi decreased while P. rockii increased. Overall, our prediction showed that a shift in distribution of suitable habitat to higher elevations would gradually become more significant. The information gained from this study should provide a useful reference for implementing long-term conservation and management strategies for these species. Copyright © 2018. Published by Elsevier B.V.

  18. Prediction of interface residue based on the features of residue interaction network.

    Science.gov (United States)

    Jiao, Xiong; Ranganathan, Shoba

    2017-11-07

    Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. InterProSurf: a web server for predicting interacting sites on protein surfaces

    Science.gov (United States)

    Negi, Surendra S.; Schein, Catherine H.; Oezguen, Numan; Power, Trevor D.; Braun, Werner

    2009-01-01

    Summary A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. PMID:17933856

  20. Process-based species pools reveal the hidden signature of biotic interactions amid the influence of temperature filtering

    DEFF Research Database (Denmark)

    Lessard, Jean-Philippe; Weinstein, Ben G.; Borregaard, Michael Krabbe

    2016-01-01

    A persistent challenge in ecology is to tease apart the in-fluence of multiple processes acting simultaneously and interacting in complex ways to shape the structure of species assemblages. We implement a heuristic approach that relies on explicitly defining spe-cies pools and permits assessment ...

  1. Interplay between subsurface structural heterogeneity and multi-species reactive transport in human health risk predictions

    Science.gov (United States)

    Henri, C.; Fernandez-Garcia, D.; de Barros, F.

    2013-12-01

    The increasing presence of toxic chemicals released in the subsurface has led to a rapid growth of social concerns and to the need to develop and employ models that can predict the impact of groundwater contamination in human health under uncertainty. Monitored natural attenuation is a common remediation action in many contamination cases and represents an attractive decontamination method. However, natural attenuation can lead to the production of subspecies of distinct toxicity that may pose challenges in pollution management strategies. The actual threat that these contaminants pose to human health and ecosystems greatly depends on the interplay between the complexity of the geological system and the toxicity of the pollutants and their byproducts. In this work, we examine the interplay between multispecies reactive transport and the heterogeneous structure of the contaminated aquifer on human health risk predictions. The structure and organization of hydraulic properties of the aquifer can lead to preferential flow channels and fast contamination pathways. Early travel times, associated to channeling effects, are intuitively perceived as an indicator for high risk. However, in the case of multi-species systems, early travel times may also lead a limited production of daughter species that may contain higher toxicity as in the case of chlorinated compounds. In this work, we model a Perchloroethylene (PCE) contamination problem followed by the sequential first-order production/biodegradation of its daughter species Trichloroethylene (TCE), Dichloroethylene (DCE) and Vinyl Chlorine (VC). For this specific case, VC is known to be a highly toxic contaminant. By performing numerical experiments, we evaluate transport for two distinct three-dimensional aquifer structures. First, a multi-Gaussian hydraulic conductivity field and secondly, a geostatistically equivalent connected field. These two heterogeneity structures will provide two distinct ranges of mean travel

  2. DomPep--a general method for predicting modular domain-mediated protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Lei Li

    Full Text Available Protein-protein interactions (PPIs are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

  3. Interfacial Interaction of Titania Nanoparticles and Ligated Uranyl Species: A Relativistic DFT Investigation.

    Science.gov (United States)

    Zhao, Hong-Bo; Zheng, Ming; Schreckenbach, Georg; Pan, Qing-Jiang

    2017-03-06

    To understand interfacial behavior of actinides adsorbed onto mineral surfaces and unravel their structure-property relationship, the structures, electronic properties, and energetics of various ligated uranyl species adsorbed onto TiO 2 surface nanoparticle clusters (SNCs) were examined using relativistic density functional theory. Rutile (110) and anatase (101) titania surfaces, experimentally known to be stable, were fully optimized. For the former, models studied include clean and water-free Ti 27 O 64 H 20 (dry), partially hydrated (Ti 27 O 64 H 20 )(H 2 O) 8 (sol) and proton-saturated [(Ti 27 O 64 H 20 )(H 2 O) 8 (H) 2 ] 2+ (sat), while defect-free and defected anatase SNCs involving more than 38 TiO 2 units were considered. The aquouranyl sorption onto rutile SNCs is energetically preferred, with interaction energies of -8.54, -10.36, and -2.39 eV, respectively. Energy decomposition demonstrates that the sorption is dominated by orbital attractive interactions and modified by steric effects. Greater hydrogen-bonding involvement leads to increased orbital interactions (i.e., more negative energy) from dry to sol/sat complexes, while much larger steric interaction in the sat complex significantly reduces the sorption interaction (i.e., more positive energy). For dry SNC, adsorbates were varied from aquo to aquo-carbonato, to carbonato, to hydroxo uranyl species. Longer U-O surf /U-Ti distances and more positive sorption energies were calculated upon introducing carbonato and hydroxo ligands, indicative of weaker uranyl sorption onto the substrate. This is consistent with experimental observations that the uranyl sorption rate decreases upon raising solution pH value or adding carbon dioxide. Anatase SNCs adsorbing aquouranyl are even more exothermic, because more bonds are formed than in the case of rutile. Moreover, the anatase sorption can be tuned by surface defects as well as its Ti and O stoichiometry. All the aquouranyl-SNC complexes show similar

  4. Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

    Directory of Open Access Journals (Sweden)

    Julian Zubek

    2015-07-01

    Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

  5. Species interactions slow warming-induced upward shifts of treelines on the Tibetan Plateau.

    Science.gov (United States)

    Liang, Eryuan; Wang, Yafeng; Piao, Shilong; Lu, Xiaoming; Camarero, Jesús Julio; Zhu, Haifeng; Zhu, Liping; Ellison, Aaron M; Ciais, Philippe; Peñuelas, Josep

    2016-04-19

    The alpine treeline is commonly regarded as being sensitive to climatic warming because regeneration and growth of trees at treeline generally are limited by low temperature. The alpine treelines of the Tibetan Plateau (TP) occur at the highest elevations (4,900 m above sea level) in the Northern Hemisphere. Ongoing climatic warming is expected to shift treelines upward. Studies of treeline dynamics at regional and local scales, however, have yielded conflicting results, indicating either unchanging treeline elevations or upward shifts. To reconcile this conflict, we reconstructed in detail a century of treeline structure and tree recruitment at sites along a climatic gradient of 4 °C and mean annual rainfall of 650 mm on the eastern TP. Species interactions interacted with effects of warming on treeline and could outweigh them. Densification of shrubs just above treeline inhibited tree establishment, and slowed upward movement of treelines on a time scale of decades. Interspecific interactions are major processes controlling treeline dynamics that may account for the absence of an upward shift at some TP treelines despite continued climatic warming.

  6. Species interactions slow warming-induced upward shifts of treelines on the Tibetan Plateau

    Science.gov (United States)

    Liang, Eryuan; Wang, Yafeng; Piao, Shilong; Lu, Xiaoming; Camarero, Jesús Julio; Zhu, Haifeng; Zhu, Liping; Ciais, Philippe; Peñuelas, Josep

    2016-01-01

    The alpine treeline is commonly regarded as being sensitive to climatic warming because regeneration and growth of trees at treeline generally are limited by low temperature. The alpine treelines of the Tibetan Plateau (TP) occur at the highest elevations (4,900 m above sea level) in the Northern Hemisphere. Ongoing climatic warming is expected to shift treelines upward. Studies of treeline dynamics at regional and local scales, however, have yielded conflicting results, indicating either unchanging treeline elevations or upward shifts. To reconcile this conflict, we reconstructed in detail a century of treeline structure and tree recruitment at sites along a climatic gradient of 4 °C and mean annual rainfall of 650 mm on the eastern TP. Species interactions interacted with effects of warming on treeline and could outweigh them. Densification of shrubs just above treeline inhibited tree establishment, and slowed upward movement of treelines on a time scale of decades. Interspecific interactions are major processes controlling treeline dynamics that may account for the absence of an upward shift at some TP treelines despite continued climatic warming. PMID:27044083

  7. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    Energy Technology Data Exchange (ETDEWEB)

    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    2016-02-25

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  8. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

    International Nuclear Information System (INIS)

    Hao, Ming; Wang, Yanli; Bryant, Stephen H.

    2016-01-01

    Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision–recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  9. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    Science.gov (United States)

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions

    DEFF Research Database (Denmark)

    Seemann, Ernst Stefan; Richter, Andreas S.; Gorodkin, Jan

    2010-01-01

    of that used for individual multiple alignments. Results: We derived a rather extensive algorithm. One of the advantages of our approach (in contrast to other RNARNA interaction prediction methods) is the application of covariance detection and prediction of pseudoknots between intra- and inter-molecular base...... pairs. As a proof of concept, we show an example and discuss the strengths and weaknesses of the approach....

  11. BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

    Science.gov (United States)

    Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

    2012-07-01

    Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

  12. Is racial bias malleable? Whites' lay theories of racial bias predict divergent strategies for interracial interactions.

    Science.gov (United States)

    Neel, Rebecca; Shapiro, Jenessa R

    2012-07-01

    How do Whites approach interracial interactions? We argue that a previously unexamined factor-beliefs about the malleability of racial bias-guides Whites' strategies for difficult interracial interactions. We predicted and found that those who believe racial bias is malleable favor learning-oriented strategies such as taking the other person's perspective and trying to learn why an interaction is challenging, whereas those who believe racial bias is fixed favor performance-oriented strategies such as overcompensating in the interaction and trying to end the interaction as quickly as possible. Four studies support these predictions. Whether measured (Studies 1, 3, and 4) or manipulated (Study 2), beliefs that racial bias is fixed versus malleable yielded these divergent strategies for difficult interracial interactions. Furthermore, beliefs about the malleability of racial bias are distinct from related constructs (e.g., prejudice and motivations to respond without prejudice; Studies 1, 3, and 4) and influence self-reported (Studies 1-3) and actual (Study 4) strategies in imagined (Studies 1-2) and real (Studies 3-4) interracial interactions. Together, these findings demonstrate that beliefs about the malleability of racial bias influence Whites' approaches to and strategies within interracial interactions. PsycINFO Database Record (c) 2012 APA, all rights reserved

  13. Predicting the sensitivity of populations from individual exposure to chemicals: the role of ecological interactions.

    Science.gov (United States)

    Gabsi, Faten; Schäffer, Andreas; Preuss, Thomas G

    2014-07-01

    Population responses to chemical stress exposure are influenced by nonchemical, environmental processes such as species interactions. A realistic quantification of chemical toxicity to populations calls for the use of methodologies that integrate these multiple stress effects. The authors used an individual-based model for Daphnia magna as a virtual laboratory to determine the influence of ecological interactions on population sensitivity to chemicals with different modes of action on individuals. In the model, hypothetical chemical toxicity targeted different vital individual-level processes: reproduction, survival, feeding rate, or somatic growth rate. As for species interactions, predatory and competition effects on daphnid populations were implemented following a worst-case approach. The population abundance was simulated at different food levels and exposure scenarios, assuming exposure to chemical stress solely or in combination with either competition or predation. The chemical always targeted one vital endpoint. Equal toxicity-inhibition levels differently affected the population abundance with and without species interactions. In addition, population responses to chemicals were highly sensitive to the environmental stressor (predator or competitor) and to the food level. Results show that population resilience cannot be attributed to chemical stress only. Accounting for the relevant ecological interactions would reduce uncertainties when extrapolating effects of chemicals from individuals to the population level. Validated population models should be used for a more realistic risk assessment of chemicals. © 2014 SETAC.

  14. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Directory of Open Access Journals (Sweden)

    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  15. Tools for Trustworthy Autonomy: Robust Predictions, Intuitive Control, and Optimized Interaction

    OpenAIRE

    Driggs Campbell, Katherine Rose

    2017-01-01

    In the near future, robotics will impact nearly every aspect of life. Yet for technology to smoothly integrate into society, we need interactive systems to be well modeled and predictable; have robust decision making and control; and be trustworthy to improve cooperation and interaction. To achieve these goals, we propose taking a human-centered approach to ease the transition into human-dominated fields. In this work, our modeling methods and control schemes are validated through user stu...

  16. Species distribution models contribute to determine the effect of climate and interspecific interactions in moving hybrid zones.

    Science.gov (United States)

    Engler, J O; Rödder, D; Elle, O; Hochkirch, A; Secondi, J

    2013-11-01

    Climate is a major factor delimiting species' distributions. However, biotic interactions may also be prominent in shaping geographical ranges, especially for parapatric species forming hybrid zones. Determining the relative effect of each factor and their interaction of the contact zone location has been difficult due to the lack of broad scale environmental data. Recent developments in species distribution modelling (SDM) now allow disentangling the relative contributions of climate and species' interactions in hybrid zones and their responses to future climate change. We investigated the moving hybrid zone between the breeding ranges of two parapatric passerines in Europe. We conducted SDMs representing the climatic conditions during the breeding season. Our results show a large mismatch between the realized and potential distributions of the two species, suggesting that interspecific interactions, not climate, account for the present location of the contact zone. The SDM scenarios show that the southerly distributed species, Hippolais polyglotta, might lose large parts of its southern distribution under climate change, but a similar gain of novel habitat along the hybrid zone seems unlikely, because interactions with the other species (H. icterina) constrain its range expansion. Thus, whenever biotic interactions limit range expansion, species may become 'trapped' if range loss due to climate change is faster than the movement of the contact zone. An increasing number of moving hybrid zones are being reported, but the proximate causes of movement often remain unclear. In a global context of climate change, we call for more interest in their interactions with climate change. © 2013 The Authors. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

  17. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  18. Childhood and adult socioeconomic position interact to predict health in mid life in a cohort of British women

    Directory of Open Access Journals (Sweden)

    Daniel Nettle

    2017-06-01

    Full Text Available Background Low childhood socioeconomic position (cSEP is associated with poorer adult health, even after adult socioeconomic position (aSEP is adjusted for. However, whether cSEP and aSEP combine additively or non-additively in predicting adult health is less well studied. Some evidence suggests that the combination of low cSEP and low aSEP is associated with worse health than would be predicted from the sum of their individual effects. Methods Using data from female members of the British National Child Development Study cohort, we developed continuous quantitative measures of aSEP and cSEP, and used these to predict self-rated health at ages 23, 33, and 42. Results Lower aSEP predicted poorer heath at all ages. Lower cSEP predicted poorer health at all ages, even after adjustment for aSEP, but the direct effects of cSEP were substantially weaker than those of aSEP. At age 23, the effects of cSEP and aSEP were additive. At ages 33 and 42, cSEP and aSEP interacted, such that the effects of low aSEP on health were more negative if cSEP had also been low. Conclusions As women age, aSEP and cSEP may affect their health interactively. High cSEP, by providing a good start in life, may be partially protective against later negative impacts of low aSEP. We relate this to the extended ‘silver spoon’ principle recently documented in a non-human species.

  19. Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

    Science.gov (United States)

    Poovathingal, Savio

    An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of

  20. Analysis of clinical drug-drug interaction data to predict uncharacterized interaction magnitudes between antiretroviral drugs and co-medications.

    Science.gov (United States)

    Stader, Felix; Kinvig, Hannah; Battegay, Manuel; Khoo, Saye; Owen, Andrew; Siccardi, Marco; Marzolini, Catia

    2018-04-23

    Despite their high potential for drug-drug-interactions (DDI), clinical DDI studies of antiretroviral drugs (ARVs) are often lacking, because the full range of potential interactions cannot feasibly or pragmatically be studied, with some high-risk DDI studies also ethically difficult to undertake. Thus, a robust method to screen and to predict the likelihood of DDIs is required.We developed a method to predict DDIs based on two parameters: the degree of metabolism by specific enzymes such as CYP3A and the strength of an inhibitor or inducer. These parameters were derived from existing studies utilizing paradigm substrates, inducers and inhibitors of CYP3A, to assess the predictive performance of this method by verifying predicted magnitudes of changes in drug exposure against clinical DDI studies involving ARVs.The derived parameters were consistent with the FDA classification of sensitive CYP3A substrates and the strength of CYP3A inhibitors and inducers. Characterized DDI magnitudes (n = 68) between ARVs and co-medications were successfully quantified meaning 53%, 85% and 98% of the predictions were within 1.25-fold (0.80 - 1.25), 1.5-fold (0.66 - 1.48) and 2-fold (0.66 - 1.94) of the observed clinical data. In addition, the method identifies CYP3A substrates likely to be highly or conversely minimally impacted by CYP3A inhibitors or inducers, thus categorizing the magnitude of DDIs.The developed effective and robust method has the potential to support a more rational identification of dose adjustment to overcome DDIs being particularly relevant in a HIV-setting giving the treatments complexity, high DDI risk and limited guidance on the management of DDIs. Copyright © 2018 American Society for Microbiology.

  1. Efficient prediction of human protein-protein interactions at a global scale.

    Science.gov (United States)

    Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

    2014-12-10

    Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  2. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat

    2017-09-27

    Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

  3. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

    Science.gov (United States)

    Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

    2018-02-15

    A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  4. LCF life prediction for waspaloy in the creep-fatigue interaction regime

    International Nuclear Information System (INIS)

    Yeom, Jong Taek; Park, Nho Kwang

    2001-01-01

    This paper describes the empirical rule of strain rate modified linear accumulation of creep damage(SRM rule) for Low-Cycle Fatigue(LCF) life prediction of Waspaloy in the creep-fatigue interaction regime and Chaboche type unified viscoplastic model predicting the stress-strain response in various cyclic loading conditions. The comparison of the experimental data and the predictions for strain controlled LCF tests carried out for various strain ranges at 600 .deg. C and 650 .deg. C was made. Chaboche type unified viscoplastic model described efficiently the inelastic deformation behavior during LCF tests. Crack-initiation lifting method to predict the material life was investigated with Strain Rate Modification(SRM) rule. The application of SRM rule to LCF tests on Waspaloy indicated a good agreement between measured and predicted cycles to failure

  5. Interactions between polystyrene microplastics and marine phytoplankton lead to species-specific hetero-aggregation.

    Science.gov (United States)

    Long, Marc; Paul-Pont, Ika; Hégaret, Hélène; Moriceau, Brivaela; Lambert, Christophe; Huvet, Arnaud; Soudant, Philippe

    2017-09-01

    To understand the fate and impacts of microplastics (MP) in the marine ecosystems, it is essential to investigate their interactions with phytoplankton as these may affect MP bioavailability to marine organisms as well as their fate in the water column. However, the behaviour of MP with marine phytoplanktonic cells remains little studied and thus unpredictable. The present study assessed the potential for phytoplankton cells to form hetero-aggregates with small micro-polystyrene (micro-PS) particles depending on microalgal species and physiological status. A prymnesiophycea, Tisochrysis lutea, a dinoflagellate, Heterocapsa triquetra, and a diatom, Chaetoceros neogracile, were exposed to micro-PS (2 μm diameter; 3.96 μg L -1 ) during their growth culture cycles. Micro-PS were quantified using an innovative flow-cytometry approach, which allowed the monitoring of the micro-PS repartition in microalgal cultures and the distinction between free suspended micro-PS and hetero-aggregates of micro-PS and microalgae. Hetero-aggregation was observed for C. neogracile during the stationary growth phase. The highest levels of micro-PS were "lost" from solution, sticking to flasks, with T. lutea and H. triquetra cultures. This loss of micro-PS sticking to the flask walls increased with the age of the culture for both species. No effects of micro-PS were observed on microalgal physiology in terms of growth and chlorophyll fluorescence. Overall, these results highlight the potential for single phytoplankton cells and residual organic matter to interact with microplastics, and thus potentially influence their distribution and bioavailability in experimental systems and the water column. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. XenDB: Full length cDNA prediction and cross species mapping in Xenopus laevis

    Directory of Open Access Journals (Sweden)

    Giegerich Robert

    2005-09-01

    Full Text Available Abstract Background Research using the model system Xenopus laevis has provided critical insights into the mechanisms of early vertebrate development and cell biology. Large scale sequencing efforts have provided an increasingly important resource for researchers. To provide full advantage of the available sequence, we have analyzed 350,468 Xenopus laevis Expressed Sequence Tags (ESTs both to identify full length protein encoding sequences and to develop a unique database system to support comparative approaches between X. laevis and other model systems. Description Using a suffix array based clustering approach, we have identified 25,971 clusters and 40,877 singleton sequences. Generation of a consensus sequence for each cluster resulted in 31,353 tentative contig and 4,801 singleton sequences. Using both BLASTX and FASTY comparison to five model organisms and the NR protein database, more than 15,000 sequences are predicted to encode full length proteins and these have been matched to publicly available IMAGE clones when available. Each sequence has been compared to the KOG database and ~67% of the sequences have been assigned a putative functional category. Based on sequence homology to mouse and human, putative GO annotations have been determined. Conclusion The results of the analysis have been stored in a publicly available database XenDB http://bibiserv.techfak.uni-bielefeld.de/xendb/. A unique capability of the database is the ability to batch upload cross species queries to identify potential Xenopus homologues and their associated full length clones. Examples are provided including mapping of microarray results and application of 'in silico' analysis. The ability to quickly translate the results of various species into 'Xenopus-centric' information should greatly enhance comparative embryological approaches. Supplementary material can be found at http://bibiserv.techfak.uni-bielefeld.de/xendb/.

  7. Allelopathic interactions between the opportunistic species Ulva prolifera and the native macroalga Gracilaria lichvoides.

    Directory of Open Access Journals (Sweden)

    Dong Xu

    Full Text Available Allelopathy, one type of direct plant competition, can be a potent mechanism through which plant communities are structured. The aim of this study was to determine whether allelopathic interactions occur between the opportunistic green tide-forming species Ulva prolifera and the native macroalga Gracilaria lichvoides, both of which were collected from the coastline of East China sea. In laboratory experiments, the presence of G. lichvoides at 1.25 g wet weight L(-1 significantly inhibited growth and photosynthesis of U. prolifera at concentrations of 1.25, 2.50, and 3.75 g wet weight L(-1 (p0.05. Culture medium experiments further confirmed that some allelochemicals may be released by both of the tested macroalgae, and these could account for the observed physiological inhibition of growth and photosynthesis. Moreover, the native macroalgae G. lichvoides was a stronger competitor than the opportunistic species U. prolifera. Collectively, the results of the present study represent a significant advance in exploring ecological questions about the effects of green tide blooms on the macroalgal community.

  8. Plant species' origin predicts dominance and response to nutrient enrichment and herbivores in global grasslands

    Science.gov (United States)

    Seabloom, Eric W.; Borer, Elizabeth T.; Buckley, Yvonne M.; Cleland, Elsa E.; Davies, Kendi F.; Firn, Jennifer; Harpole, W. Stanley; Hautier, Yann; Lind, Eric M.; MacDougall, Andrew S.; Orrock, John L.; Prober, Suzanne M.; Adler, Peter B.; Anderson, T. Michael; Bakker, Jonathan D.; Biederman, Lori A.; Blumenthal, Dana M.; Brown, Cynthia S.; Brudvig, Lars A.; Cadotte, Marc; Chu, Chengjin; Cottingham, Kathryn L.; Crawley, Michael J.; Damschen, Ellen I.; Dantonio, Carla M.; DeCrappeo, Nicole M.; Du, Guozhen; Fay, Philip A.; Frater, Paul; Gruner, Daniel S.; Hagenah, Nicole; Hector, Andy; Hillebrand, Helmut; Hofmockel, Kirsten S.; Humphries, Hope C.; Jin, Virginia L.; Kay, Adam; Kirkman, Kevin P.; Klein, Julia A.; Knops, Johannes M. H.; La Pierre, Kimberly J.; Ladwig, Laura; Lambrinos, John G.; Li, Qi; Li, Wei; Marushia, Robin; McCulley, Rebecca L.; Melbourne, Brett A.; Mitchell, Charles E.; Moore, Joslin L.; Morgan, John; Mortensen, Brent; O'Halloran, Lydia R.; Pyke, David A.; Risch, Anita C.; Sankaran, Mahesh; Schuetz, Martin; Simonsen, Anna; Smith, Melinda D.; Stevens, Carly J.; Sullivan, Lauren; Wolkovich, Elizabeth; Wragg, Peter D.; Wright, Justin; Yang, Louie

    2015-01-01

    Exotic species dominate many communities; however the functional significance of species' biogeographic origin remains highly contentious. This debate is fuelled in part by the lack of globally replicated, systematic data assessing the relationship between species provenance, function and response to perturbations. We examined the abundance of native and exotic plant species at 64 grasslands in 13 countries, and at a subset of the sites we experimentally tested native and exotic species responses to two fundamental drivers of invasion, mineral nutrient supplies and vertebrate herbivory. Exotic species are six times more likely to dominate communities than native species. Furthermore, while experimental nutrient addition increases the cover and richness of exotic species, nutrients decrease native diversity and cover. Native and exotic species also differ in their response to vertebrate consumer exclusion. These results suggest that species origin has functional significance, and that eutrophication will lead to increased exotic dominance in grasslands. PMID:26173623

  9. Interactive effects of plant-available soil silicon and herbivory on competition between two grass species

    Science.gov (United States)

    Garbuzov, Mihail; Reidinger, Stefan; Hartley, Susan E.

    2011-01-01

    Background and Aims The herbivore defence system of true grasses (Poaceae) is predominantly based on silicon that is taken up from the soil and deposited in the leaves in the form of abrasive phytoliths. Silicon uptake mechanisms can be both passive and active, with the latter suggesting that there is an energetic cost to silicon uptake. This study assessed the effects of plant-available soil silicon and herbivory on the competitive interactions between the grasses Poa annua, a species that has previously been reported to accumulate only small amounts of silicon, and Lolium perenne, a high silicon accumulator. Methods Plants were grown in mono- and mixed cultures under greenhouse conditions. Plant-available soil silicon levels were manipulated by adding silicon to the soil in the form of sodium silicate. Subsets of mixed culture pots were exposed to above-ground herbivory by desert locusts (Schistocerca gregaria). Key Results In the absence of herbivory, silicon addition increased biomass of P. annua but decreased biomass of L. perenne. Silicon addition increased foliar silicon concentrations of both grass species >4-fold. Under low soil-silicon availability the herbivores removed more leaf biomass from L. perenne than from P. annua, whereas under high silicon availability the reverse was true. Consequently, herbivory shifted the competitive balance between the two grass species, with the outcome depending on the availability of soil silicon. Conclusions It is concluded that a complex interplay between herbivore abundance, growth–defence trade-offs and the availability of soil silicon in the grasses' local environment affects the outcome of inter-specific competition, and so has the potential to impact on plant community structure. PMID:21868406

  10. Competitive Interactions in Mixed-Species Biofilms Containing the Marine Bacterium Pseudoalteromonas tunicata

    Science.gov (United States)

    Rao, Dhana; Webb, Jeremy S.; Kjelleberg, Staffan

    2005-01-01

    Pseudoalteromonas tunicata is a biofilm-forming marine bacterium that is often found in association with the surface of eukaryotic organisms. It produces a range of extracellular inhibitory compounds, including an antibacterial protein (AlpP) thought to be beneficial for P. tunicata during competition for space and nutrients on surfaces. As part of our studies on the interactions between P. tunicata and the epiphytic bacterial community on the marine plant Ulva lactuca, we investigated the hypothesis that P. tunicata is a superior competitor compared with other bacteria isolated from the plant. A number of U. lactuca bacterial isolates were (i) identified by 16S rRNA gene sequencing, (ii) characterized for the production of or sensitivity to extracellular antibacterial proteins, and (iii) labeled with a fluorescent color tag (either the red fluorescent protein DsRed or green fluorescent protein). We then grew single- and mixed-species bacterial biofilms containing P. tunicata in glass flow cell reactors. In pure culture, all the marine isolates formed biofilms containing microcolony structures within 72 h. However, in mixed-species biofilms, P. tunicata removed the competing strain unless its competitor was relatively insensitive to AlpP (Pseudoalteromonas gracilis) or produced strong inhibitory activity against P. tunicata (Roseobacter gallaeciensis). Moreover, biofilm studies conducted with an AlpP− mutant of P. tunicata indicated that the mutant was less competitive when it was introduced into preestablished biofilms, suggesting that AlpP has a role during competitive biofilm formation. When single-species biofilms were allowed to form microcolonies before the introduction of a competitor, these microcolonies coexisted with P. tunicata for extended periods of time before they were removed. Two marine bacteria (R. gallaeciensis and P. tunicata) were superior competitors in this study. Our data suggest that this dominance can be attributed to the ability of

  11. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    Science.gov (United States)

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  12. Contextual Predictive Factors of Child Sexual Abuse: The Role of Parent-Child Interaction

    Science.gov (United States)

    Ramirez, Clemencia; Pinzon-Rondon, Angela Maria; Botero, Juan Carlos

    2011-01-01

    Objectives: To determine the prevalence of child sexual abuse in the Colombian coasts, as well as to assess the role of parent-child interactions on its occurrence and to identify factors from different environmental levels that predict it. Methods: This cross-sectional study explores the results of 1,089 household interviews responded by mothers.…

  13. PREDICTION OF THE MIXING ENTHALPIES OF BINARY LIQUID ALLOYS BY MOLECULAR INTERACTION VOLUME MODEL

    Institute of Scientific and Technical Information of China (English)

    H.W.Yang; D.P.Tao; Z.H.Zhou

    2008-01-01

    The mixing enthalpies of 23 binary liquid alloys are calculated by molecular interaction volume model (MIVM), which is a two-parameter model with the partial molar infinite dilute mixing enthalpies. The predicted values are in agreement with the experimental data and then indicate that the model is reliable and convenient.

  14. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Science.gov (United States)

    Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

    2018-01-01

    The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

  15. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Directory of Open Access Journals (Sweden)

    Hanna Domin

    2018-04-01

    Full Text Available The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.

  16. Observation of shadowing of neutrino- and antineutrino-nucleus interactions and comparison with PCAC predictions

    International Nuclear Information System (INIS)

    Allport, P.P.; Radojicic, D.; Guy, J.; Venus, W.; Cooper-Sarkar, A.M.; Aderholz, M.; Schmitz, N.; Wittek, W.; Berggren, M.; Jones, G.T.; O'Neale, S.; Varvell, K.; Marage, P.; Mobayyen, M.; Morrison, D.R.O.; Parker, M.A.; Wachsmuth, H.; Neveu, M.; Simopoulou, E.; Vayaki, A.; Apeldoorn, G. van

    1989-01-01

    Comparing the kinematical distributions of events obtained on neon and deuterium targets in similar experimental conditions reveals a reduction of the neutrino and antineutrino charged current cross section per nucleon in neon at low Q 2 . The effect, interpreted as due to geometric shadowing of the weak propagator in interactions of neutrinos and antineutrinos with nuclei, agrees well with predictions derived from PCAC. (orig.)

  17. Observation of shadowing of neutrino- and antineutrino-nucleus interactions and comparison with PCAC predictions

    Science.gov (United States)

    Allport, P. P.; Erriquez, O.; Guy, J.; Venus, W.; Aderholz, M.; Berggren, M.; Bullock, F. W.; Calicchio, M.; Coghen, T.; Cooper-Sarkar, A. M.; Jones, G. T.; Marage, P.; Mobayyen, M.; Morrison, D. R. O.; Neveu, M.; Parker, M. A.; Radojicic, D.; Sansum, R. A.; Saitta, B.; Schmitz, N.; Simopoulou, E.; O'neale, S.; Van Apeldoorn, G.; Varvell, K.; Vayaki, A.; Wachsmuth, H.; Wittek, W.; BEBC WA59 Collaboration

    1989-12-01

    Comparing the kinematical distributions of events obtained on neon and deuterium targets in similar experimental conditions reveals a reduction of the neutrino and antineutrino charged current cross section per nucleon in neon at low Q2. The effect, interpreted as due to geometric shadowing of the weak propagator in interactions of neutrinos and antineutrinos with nuclei, agrees well with predictions derived from PCAC.

  18. Family Conflict Interacts with Genetic Liability in Predicting Childhood and Adolescent Depression

    Science.gov (United States)

    Rice, Frances; Harold, Gordon T.; Shelton, Katherine H.; Thapar, Anita

    2006-01-01

    Objective: To test for gene-environment interaction with depressive symptoms and family conflict. Specifically, to first examine whether the influence of family conflict in predicting depressive symptoms is increased in individuals at genetic risk of depression. Second, to test whether the genetic component of variance in depressive symptoms…

  19. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

    Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

  20. Predicting stimulation-dependent enhancer-promoter interactions from ChIP-Seq time course data

    Directory of Open Access Journals (Sweden)

    Tomasz Dzida

    2017-09-01

    Full Text Available We have developed a machine learning approach to predict stimulation-dependent enhancer-promoter interactions using evidence from changes in genomic protein occupancy over time. The occupancy of estrogen receptor alpha (ERα, RNA polymerase (Pol II and histone marks H2AZ and H3K4me3 were measured over time using ChIP-Seq experiments in MCF7 cells stimulated with estrogen. A Bayesian classifier was developed which uses the correlation of temporal binding patterns at enhancers and promoters and genomic proximity as features to predict interactions. This method was trained using experimentally determined interactions from the same system and was shown to achieve much higher precision than predictions based on the genomic proximity of nearest ERα binding. We use the method to identify a genome-wide confident set of ERα target genes and their regulatory enhancers genome-wide. Validation with publicly available GRO-Seq data demonstrates that our predicted targets are much more likely to show early nascent transcription than predictions based on genomic ERα binding proximity alone.

  1. Clustering gene expression data based on predicted differential effects of GV interaction.

    Science.gov (United States)

    Pan, Hai-Yan; Zhu, Jun; Han, Dan-Fu

    2005-02-01

    Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent "noise" within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

  2. Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Donald Adjeroh

    2018-03-01

    Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

  3. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence

    Directory of Open Access Journals (Sweden)

    Chunrong Mi

    2017-01-01

    Full Text Available Species distribution models (SDMs have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33, White-naped Crane (Grus vipio, n = 40, and Black-necked Crane (Grus nigricollis, n = 75 in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model, Random Forest, CART (Classification and Regression Tree and Maxent (Maximum Entropy Models. In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC and true skill statistic (TSS were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid

  4. Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

    Science.gov (United States)

    Mi, Chunrong; Huettmann, Falk; Guo, Yumin; Han, Xuesong; Wen, Lijia

    2017-01-01

    Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane ( Grus monacha , n  = 33), White-naped Crane ( Grus vipio , n  = 40), and Black-necked Crane ( Grus nigricollis , n  = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid

  5. Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

    KAUST Repository

    Ba Alawi, Wail

    2016-01-01

    Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

  6. Biotic and abiotic factors investigated in two Drosophila species – evidence of both negative and positive fitness effects of interactions on performance

    DEFF Research Database (Denmark)

    Ørsted, Michael; Schou, Mads Fristrup; Kristensen, Torsten Nygård

    2017-01-01

    more informative descriptions of complex interactions we implemented re-conceptualised definitions of synergism and antagonism. We found approximately equal proportions of synergistic and antagonistic interactions in both species, however the effects of interactions on performance differed between...

  7. How and when predictability interacts with accentuation in temporally selective attention during speech comprehension.

    Science.gov (United States)

    Li, Xiaoqing; Lu, Yong; Zhao, Haiyan

    2014-11-01

    The present study used EEG to investigate how and when top-down prediction interacts with bottom-up acoustic signals in temporally selective attention during speech comprehension. Mandarin Chinese spoken sentences were used as stimuli. We systematically manipulated the predictability and de/accentuation of the critical words in the sentence context. Meanwhile, a linguistic attention probe 'ba' was presented concurrently with the critical words or not. The results showed that, first, words with a linguistic attention probe elicited a larger N1 than those without a probe. The latency of this N1 effect was shortened for accented or lowly predictable words, indicating more attentional resources allocated to these words. Importantly, prediction and accentuation showed a complementary interplay on the latency of this N1 effect, demonstrating that when the words had already attracted attention due to low predictability or due to the presence of pitch accent, the other factor did not modulate attention allocation anymore. Second, relative to the lowly predictable words, the highly predictable words elicited a reduced N400 and enhanced gamma-band power increases, especially under the accented conditions; moreover, under the accented conditions, shorter N1 peak-latency was found to correlate with larger gamma-band power enhancement, which indicates that a close relationship might exist between early selective attention and later semantic integration. Finally, the interaction between top-down selective attention (driven by prediction) and bottom-up selective attention (driven by accentuation) occurred before lexical-semantic processing, namely before the N400 effect evoked by predictability, which was discussed with regard to the language comprehension models. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Social interaction anxiety and personality traits predicting engagement in health risk sexual behaviors.

    Science.gov (United States)

    Rahm-Knigge, Ryan L; Prince, Mark A; Conner, Bradley T

    2018-06-01

    Individuals with social interaction anxiety, a facet of social anxiety disorder, withdraw from or avoid social encounters and generally avoid risks. However, a subset engages in health risk sexual behavior (HRSB). Because sensation seeking, emotion dysregulation, and impulsivity predict engagement in HRSB among adolescents and young adults, the present study hypothesized that latent classes of social interaction anxiety and these personality traits would differentially predict likelihood of engagement in HRSB. Finite mixture modeling was used to discern four classes: two low social interaction anxiety classes distinguished by facets of emotion dysregulation, positive urgency, and negative urgency (Low SIAS High Urgency and Low SIAS Low Urgency) and two high social interaction anxiety classes distinguished by positive urgency, negative urgency, risk seeking, and facets of emotion dysregulation (High SIAS High Urgency and High SIAS Low Urgency). HRSB were entered into the model as auxiliary distal outcomes. Of importance to this study were findings that the High SIAS High Urgency class was more likely to engage in most identified HRSB than the High SIAS Low Urgency class. This study extends previous findings on the heterogeneity of social interaction anxiety by identifying the effects of social interaction anxiety and personality on engagement in HRSB. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Phenology of species interactions in response to climate change: two case studies of plant-pollinator interactions using long-term data

    Science.gov (United States)

    McKinney, A. M.; Inouye, D. W.

    2012-12-01

    Climate change may alter the temporal overlap among interacting taxa with potential demographic consequences. Evidence of mistimed interactions in response to climate change, especially between plants and pollinators, is mixed, and few long-term datasets exist to test for changes in synchrony. Furthermore, advancements in flowering driven by climate change are especially pronounced at higher latitudes, so that migratory pollinators from lower latitudes may increasingly arrive at breeding grounds after the appearance of floral resources. We explored long-term shifts in phenological synchrony in two plant-pollinator systems:1) syrphid fly and flowering phenology in the Colorado Rocky Mountains, USA (1992-2011) and 2) hummingbird arrival relative to onset of early-season nectar resources in the Colorado Rocky Mountains (1975-2011) and the Santa Catalina Mountains, Arizona, USA (1984-2010). We investigated the abiotic cues associated with the phenology of the activity period of syrphid flies and their floral resources, including degree days above freezing, precipitation, and timing of snowmelt as potential explanatory variables. Timing of snowmelt was the best predictor of the onset of flowering and syrphid emergence. Snowmelt was also the best predictor of the end of flowering, while temperature and precipitation best predicted the end of the syrphid period. Both the onset and end of flowering advanced more rapidly than syrphids in response to earlier snowmelt. These different rates of phenological advancement resulted in increased temporal overlap between the flower and syrphid community in years of early snowmelt, because of longer flowering and fly activity periods during these years. If snowmelt continues to advance, temporal overlap between syrphids and their floral resources is therefore likely to increase. This case study shows that the phenology of interacting taxa may respond differently to climate cues, but that this does not necessarily lead to phenological

  10. Multi-species interactions impact the accumulation of weathered 2,2-bis (p-chlorophenyl)-1,1-dichloroethylene (p,p'-DDE) from soil

    International Nuclear Information System (INIS)

    Kelsey, Jason W.; White, Jason C.

    2005-01-01

    The impact of interactions between the earthworms Eisenia foetida and Lumbricus terrestris and the plants Cucurbita pepo and Cucurbita maxima on the uptake of weathered p,p'-DDE from soil was determined. Although some combinations of earthworm and plant species caused significant changes in the p,p'-DDE burden in both organisms, the effects were species specific. Contaminant bioconcentration in C. pepo was increased slightly by E. foetida and by 3-fold when the plant was grown with L. terrestris. E. foetida had no effect on the contaminant BCF by C. maxima, but L. terrestris caused a 2-fold reduction in p,p'-DDE uptake by the plant. Contaminant levels in E. foetida and L. terrestris were unaffected by C. pepo. When grown with C. maxima, the concentration of p,p'-DDE decreased by approximately 4-fold and 7-fold in E. foetida and L. terrestris, respectively. The data suggest that the prediction of contaminant bioavailability should consider interactions among species. - Interactions between earthworms and plants affect both the phytoextraction and bioaccumulation of p,p'-DDE in soil

  11. Regression Trees Identify Relevant Interactions: Can This Improve the Predictive Performance of Risk Adjustment?

    Science.gov (United States)

    Buchner, Florian; Wasem, Jürgen; Schillo, Sonja

    2017-01-01

    Risk equalization formulas have been refined since their introduction about two decades ago. Because of the complexity and the abundance of possible interactions between the variables used, hardly any interactions are considered. A regression tree is used to systematically search for interactions, a methodologically new approach in risk equalization. Analyses are based on a data set of nearly 2.9 million individuals from a major German social health insurer. A two-step approach is applied: In the first step a regression tree is built on the basis of the learning data set. Terminal nodes characterized by more than one morbidity-group-split represent interaction effects of different morbidity groups. In the second step the 'traditional' weighted least squares regression equation is expanded by adding interaction terms for all interactions detected by the tree, and regression coefficients are recalculated. The resulting risk adjustment formula shows an improvement in the adjusted R 2 from 25.43% to 25.81% on the evaluation data set. Predictive ratios are calculated for subgroups affected by the interactions. The R 2 improvement detected is only marginal. According to the sample level performance measures used, not involving a considerable number of morbidity interactions forms no relevant loss in accuracy. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  12. A domain-based approach to predict protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Resat Haluk

    2007-06-01

    Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

  13. Shock loading predictions from application of indicial theory to shock-turbulence interactions

    Science.gov (United States)

    Keefe, Laurence R.; Nixon, David

    1991-01-01

    A sequence of steps that permits prediction of some of the characteristics of the pressure field beneath a fluctuating shock wave from knowledge of the oncoming turbulent boundary layer is presented. The theory first predicts the power spectrum and pdf of the position and velocity of the shock wave, which are then used to obtain the shock frequency distribution, and the pdf of the pressure field, as a function of position within the interaction region. To test the validity of the crucial assumption of linearity, the indicial response of a normal shock is calculated from numerical simulation. This indicial response, after being fit by a simple relaxation model, is used to predict the shock position and velocity spectra, along with the shock passage frequency distribution. The low frequency portion of the shock spectra, where most of the energy is concentrated, is satisfactorily predicted by this method.

  14. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  15. Changes in mangrove species assemblages and future prediction of the Bangladesh Sundarbans using Markov chain model and cellular automata.

    Science.gov (United States)

    Mukhopadhyay, Anirban; Mondal, Parimal; Barik, Jyotiskona; Chowdhury, S M; Ghosh, Tuhin; Hazra, Sugata

    2015-06-01

    The composition and assemblage of mangroves in the Bangladesh Sundarbans are changing systematically in response to several environmental factors. In order to understand the impact of the changing environmental conditions on the mangrove forest, species composition maps for the years 1985, 1995 and 2005 were studied. In the present study, 1985 and 1995 species zonation maps were considered as base data and the cellular automata-Markov chain model was run to predict the species zonation for the year 2005. The model output was validated against the actual dataset for 2005 and calibrated. Finally, using the model, mangrove species zonation maps for the years 2025, 2055 and 2105 have been prepared. The model was run with the assumption that the continuation of the current tempo and mode of drivers of environmental factors (temperature, rainfall, salinity change) of the last two decades will remain the same in the next few decades. Present findings show that the area distribution of the following species assemblages like Goran (Ceriops), Sundari (Heritiera), Passur (Xylocarpus), and Baen (Avicennia) would decrease in the descending order, whereas the area distribution of Gewa (Excoecaria), Keora (Sonneratia) and Kankra (Bruguiera) dominated assemblages would increase. The spatial distribution of projected mangrove species assemblages shows that more salt tolerant species will dominate in the future; which may be used as a proxy to predict the increase of salinity and its spatial variation in Sundarbans. Considering the present rate of loss of forest land, 17% of the total mangrove cover is predicted to be lost by the year 2105 with a significant loss of fresh water loving mangroves and related ecosystem services. This paper describes a unique approach to assess future changes in species composition and future forest zonation in mangroves under the 'business as usual' scenario of climate change.

  16. Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.

    Directory of Open Access Journals (Sweden)

    Suyu Mei

    Full Text Available Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM, where support vector machine (SVM is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

  17. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    Science.gov (United States)

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  18. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  19. An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

    Science.gov (United States)

    Kundu, Kousik; Backofen, Rolf

    2017-01-01

    Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.

  20. Species abundance distributions : moving beyond single prediction theories to integration within an ecological framework

    NARCIS (Netherlands)

    McGill, Brian J.; Etienne, Rampal S.; Gray, John S.; Alonso, David; Anderson, Marti J.; Benecha, Habtamu Kassa; Dornelas, Maria; Enquist, Brian J.; Green, Jessica L.; He, Fangliang; Hurlbert, Allen H.; Magurran, Anne E.; Marquet, Pablo A.; Maurer, Brian A.; Ostling, Annette; Soykan, Candan U.; Ugland, Karl I.; White, Ethan P.

    2007-01-01

    Species abundance distributions (SADs) follow one of ecology's oldest and most universal laws - every community shows a hollow curve or hyperbolic shape on a histogram with many rare species and just a few common species. Here, we review theoretical, empirical and statistical developments in the

  1. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework

    NARCIS (Netherlands)

    McGill, B.J.; Etienne, R.S.; Gray, J.S.; Alonso, D.; Anderson, M.J.; Benecha, H.K.

    2007-01-01

    Species abundance distributions (SADs) follow one of ecology's oldest and most universal laws ¿ every community shows a hollow curve or hyperbolic shape on a histogram with many rare species and just a few common species. Here, we review theoretical, empirical and statistical developments in the

  2. Predicting abundance of 80 tree species following climate change in the Eastern United States

    Science.gov (United States)

    Louis R. Iverson; Anantha M. Prasad; Anantha M. Prasad

    1998-01-01

    Projected climate warming will potentially have profound effects on the earth?s biota, including a large redistribution of tree species. We developed models to evaluate potential shifts for 80 individual tree species in the eastern United States. First, environmental factors associated with current ranges of tree species were assessed using geographic information...

  3. GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

    Science.gov (United States)

    Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

    2018-03-01

    MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

  4. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour.

    Science.gov (United States)

    Kaiser-Bunbury, Christopher N; Muff, Stefanie; Memmott, Jane; Müller, Christine B; Caflisch, Amedeo

    2010-04-01

    Species extinctions pose serious threats to the functioning of ecological communities worldwide. We used two qualitative and quantitative pollination networks to simulate extinction patterns following three removal scenarios: random removal and systematic removal of the strongest and weakest interactors. We accounted for pollinator behaviour by including potential links into temporal snapshots (12 consecutive 2-week networks) to reflect mutualists' ability to 'switch' interaction partners (re-wiring). Qualitative data suggested a linear or slower than linear secondary extinction while quantitative data showed sigmoidal decline of plant interaction strength upon removal of the strongest interactor. Temporal snapshots indicated greater stability of re-wired networks over static systems. Tolerance of generalized networks to species extinctions was high in the random removal scenario, with an increase in network stability if species formed new interactions. Anthropogenic disturbance, however, that promote the extinction of the strongest interactors might induce a sudden collapse of pollination networks.

  5. Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

    OpenAIRE

    Bustamante, Javier; Seoane, Javier

    2004-01-01

    Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short-toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southe...

  6. The ecological niche and reciprocal prediction of the disjunct distribution of an invasive species: the example of Ailanthus altissima

    Science.gov (United States)

    Thomas P. Albright; Hao Chen; Lijun Chen; Qinfeng Guo

    2010-01-01

    Knowledge of the ecological niches of invasive species in native and introduced ranges can inform management as well as ecological and evolutionary theory. Here, we identified and compared factors associated with the distribution of an invasive tree, Ailanthus altissima, in both its native Chinese and introduced US ranges and predicted potential US...

  7. Sensitivity of Seven Diverse Species to Blue and Green Light: Interactions with Photon Flux.

    Directory of Open Access Journals (Sweden)

    M Chase Snowden

    Full Text Available Despite decades of research, the effects of spectral quality on plant growth, and development are not well understood. Much of our current understanding comes from studies with daily integrated light levels that are less than 10% of summer sunlight thus making it difficult to characterize interactions between light quality and quantity. Several studies have reported that growth is increased under fluorescent lamps compared to mixtures of wavelengths from LEDs. Conclusions regarding the effect of green light fraction range from detrimental to beneficial. Here we report the effects of eight blue and green light fractions at two photosynthetic photon fluxes (PPF; 200 and 500 μmol m-2 s-1; with a daily light integral of 11.5 and 29 mol m-2 d-1 on growth (dry mass, leaf expansion, stem and petiole elongation, and whole-plant net assimilation of seven diverse plant species. The treatments included cool, neutral, and warm white LEDs, and combinations of blue, green and/or red LEDs. At the higher PPF (500, increasing blue light in increments from 11 to 28% reduced growth in tomato, cucumber, and pepper by 22, 26, and 14% respectively, but there was no statistically significant effect on radish, soybean, lettuce or wheat. At the lower PPF (200, increasing blue light reduced growth only in tomato (41%. The effects of blue light on growth were mediated by changes in leaf area and radiation capture, with minimal effects on whole-plant net-assimilation. In contrast to the significant effects of blue light, increasing green light in increments from 0 to 30% had a relatively small effect on growth, leaf area and net assimilation at either low or high PPF. Surprisingly, growth of three of the seven species was not reduced by a treatment with 93% green light compared to the broad spectrum treatments. Collectively, these results are consistent with a shade avoidance response associated with either low blue or high green light fractions.

  8. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Science.gov (United States)

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. The Density Functional Theory of Flies: Predicting distributions of interacting active organisms

    Science.gov (United States)

    Kinkhabwala, Yunus; Valderrama, Juan; Cohen, Itai; Arias, Tomas

    On October 2nd, 2016, 52 people were crushed in a stampede when a crowd panicked at a religious gathering in Ethiopia. The ability to predict the state of a crowd and whether it is susceptible to such transitions could help prevent such catastrophes. While current techniques such as agent based models can predict transitions in emergent behaviors of crowds, the assumptions used to describe the agents are often ad hoc and the simulations are computationally expensive making their application to real-time crowd prediction challenging. Here, we pursue an orthogonal approach and ask whether a reduced set of variables, such as the local densities, are sufficient to describe the state of a crowd. Inspired by the theoretical framework of Density Functional Theory, we have developed a system that uses only measurements of local densities to extract two independent crowd behavior functions: (1) preferences for locations and (2) interactions between individuals. With these two functions, we have accurately predicted how a model system of walking Drosophila melanogaster distributes itself in an arbitrary 2D environment. In addition, this density-based approach measures properties of the crowd from only observations of the crowd itself without any knowledge of the detailed interactions and thus it can make predictions about the resulting distributions of these flies in arbitrary environments, in real-time. This research was supported in part by ARO W911NF-16-1-0433.

  10. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  11. Young adults' internet addiction: Prediction by the interaction of parental marital conflict and respiratory sinus arrhythmia.

    Science.gov (United States)

    Zhang, Hui; Spinrad, Tracy L; Eisenberg, Nancy; Luo, Yun; Wang, Zhenhong

    2017-10-01

    The aim of the current study was to address the potential moderating roles of respiratory sinus arrhythmia (RSA; baseline and suppression) and participant sex in the relation between parents' marital conflict and young adults' internet addiction. Participants included 105 (65 men) Chinese young adults who reported on their internet addiction and their parents' marital conflict. Marital conflict interacted with RSA suppression to predict internet addiction. Specifically, high RSA suppression was associated with low internet addiction, regardless of parental marital conflict; however, for participants with low RSA suppression, a positive relation between marital conflict and internet addiction was found. Internet addiction also was predicted by a significant three-way interaction among baseline RSA, marital conflict, and participant sex. Specifically, for men, marital conflict positively predicted internet addiction under conditions of low (but not high) baseline RSA. For women, marital conflict positively predicted internet addiction under conditions of high (but not low) baseline RSA. Findings highlight the importance of simultaneous consideration of physiological factors, in conjunction with family factors, in the prediction of young adults' internet addiction. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

    Science.gov (United States)

    Panwar, Bharat; Gupta, Sudheer; Raghava, Gajendra P S

    2013-02-07

    The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0

  13. Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

    Science.gov (United States)

    Freilich, Mara A; Wieters, Evie; Broitman, Bernardo R; Marquet, Pablo A; Navarrete, Sergio A

    2018-03-01

    Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction

  14. Computational Approaches for Prediction of Pathogen-Host Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Esmaeil eNourani

    2015-02-01

    Full Text Available Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key part of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. This has motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multi task learning methods are preferred. Here, we present an overview of these computational approaches for PHI prediction, discussing their weakness and abilities, with future directions.

  15. Rotor Wake/Stator Interaction Noise Prediction Code Technical Documentation and User's Manual

    Science.gov (United States)

    Topol, David A.; Mathews, Douglas C.

    2010-01-01

    This report documents the improvements and enhancements made by Pratt & Whitney to two NASA programs which together will calculate noise from a rotor wake/stator interaction. The code is a combination of subroutines from two NASA programs with many new features added by Pratt & Whitney. To do a calculation V072 first uses a semi-empirical wake prediction to calculate the rotor wake characteristics at the stator leading edge. Results from the wake model are then automatically input into a rotor wake/stator interaction analytical noise prediction routine which calculates inlet aft sound power levels for the blade-passage-frequency tones and their harmonics, along with the complex radial mode amplitudes. The code allows for a noise calculation to be performed for a compressor rotor wake/stator interaction, a fan wake/FEGV interaction, or a fan wake/core stator interaction. This report is split into two parts, the first part discusses the technical documentation of the program as improved by Pratt & Whitney. The second part is a user's manual which describes how input files are created and how the code is run.

  16. Prediction of surgical view of neurovascular decompression using interactive computer graphics.

    Science.gov (United States)

    Kin, Taichi; Oyama, Hiroshi; Kamada, Kyousuke; Aoki, Shigeki; Ohtomo, Kuni; Saito, Nobuhito

    2009-07-01

    To assess the value of an interactive visualization method for detecting the offending vessels in neurovascular compression syndrome in patients with facial spasm and trigeminal neuralgia. Computer graphics models are created by fusion of fast imaging employing steady-state acquisition and magnetic resonance angiography. High-resolution magnetic resonance angiography and fast imaging employing steady-state acquisition were performed preoperatively in 17 patients with neurovascular compression syndromes (facial spasm, n = 10; trigeminal neuralgia, n = 7) using a 3.0-T magnetic resonance imaging scanner. Computer graphics models were created with computer software and observed interactively for detection of offending vessels by rotation, enlargement, reduction, and retraction on a graphic workstation. Two-dimensional images were reviewed by 2 radiologists blinded to the clinical details, and 2 neurosurgeons predicted the offending vessel with the interactive visualization method before surgery. Predictions from the 2 imaging approaches were compared with surgical findings. The vessels identified during surgery were assumed to be the true offending vessels. Offending vessels were identified correctly in 16 of 17 patients (94%) using the interactive visualization method and in 10 of 17 patients using 2-dimensional images. These data demonstrated a significant difference (P = 0.015 by Fisher's exact method). The interactive visualization method data corresponded well with surgical findings (surgical field, offending vessels, and nerves). Virtual reality 3-dimensional computer graphics using fusion magnetic resonance angiography and fast imaging employing steady-state acquisition may be helpful for preoperative simulation.

  17. Multiple genetic interaction experiments provide complementary information useful for gene function prediction.

    Directory of Open Access Journals (Sweden)

    Magali Michaut

    Full Text Available Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

  18. Age and area predict patterns of species richness in pumice rafts contingent on oceanic climatic zone encountered.

    Science.gov (United States)

    Velasquez, Eleanor; Bryan, Scott E; Ekins, Merrick; Cook, Alex G; Hurrey, Lucy; Firn, Jennifer

    2018-05-01

    The theory of island biogeography predicts that area and age explain species richness patterns (or alpha diversity) in insular habitats. Using a unique natural phenomenon, pumice rafting, we measured the influence of area, age, and oceanic climate on patterns of species richness. Pumice rafts are formed simultaneously when submarine volcanoes erupt, the pumice clasts breakup irregularly, forming irregularly shaped pumice stones which while floating through the ocean are colonized by marine biota. We analyze two eruption events and more than 5,000 pumice clasts collected from 29 sites and three climatic zones. Overall, the older and larger pumice clasts held more species. Pumice clasts arriving in tropical and subtropical climates showed this same trend, where in temperate locations species richness (alpha diversity) increased with area but decreased with age. Beta diversity analysis of the communities forming on pumice clasts that arrived in different climatic zones showed that tropical and subtropical clasts transported similar communities, while species composition on temperate clasts differed significantly from both tropical and subtropical arrivals. Using these thousands of insular habitats, we find strong evidence that area and age but also climatic conditions predict the fundamental dynamics of species richness colonizing pumice clasts.

  19. Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics

    Science.gov (United States)

    Nijland, Wiebe; Nielsen, Scott E.; Coops, Nicholas C.; Wulder, Michael A.; Stenhouse, Gordon B.

    2014-01-01

    Food and habitat resources are critical components of wildlife management and conservation efforts. The grizzly bear (Ursus arctos) has diverse diets and habitat requirements particularly for understory plant species, which are impacted by human developments and forest management activities. We use light detection and ranging (LiDAR) data to predict the occurrence of 14 understory plant species relevant to bear forage and compare our predictions with more conventional climate- and land cover-based models. We use boosted regression trees to model each of the 14 understory species across 4435 km2 using occurrence (presence-absence) data from 1941 field plots. Three sets of models were fitted: climate only, climate and basic land and forest covers from Landsat 30-m imagery, and a climate- and LiDAR-derived model describing both the terrain and forest canopy. Resulting model accuracies varied widely among species. Overall, 8 of 14 species models were improved by including the LiDAR-derived variables. For climate-only models, mean annual precipitation and frost-free periods were the most important variables. With inclusion of LiDAR-derived attributes, depth-to-water table, terrain-intercepted annual radiation, and elevation were most often selected. This suggests that fine-scale terrain conditions affect the distribution of the studied species more than canopy conditions.

  20. Combining many interaction networks to predict gene function and analyze gene lists.

    Science.gov (United States)

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Nucleon-nucleon interaction with quark exchanges and prediction to colour van der Waals potential

    International Nuclear Information System (INIS)

    Osman, A.

    1985-11-01

    The nucleon-nucleon interaction is considered by including the colour nucleon clusters. The nucleon-nucleon system is treated as a six-quark system. The obtained local potentials reduce the short-range repulsion. The resulted nucleon-nucleon potential by using a quark-quark potential well agrees with the central-force potentials. The phase shifts calculated by using these local potentials are in good agreement with those obtained from other methods. Introducing the quark-quark potential in the nucleon-nucleon interaction, leads to a colour van der Waals potential very strong compared with that predicted by experiments. (author)

  2. NOAA ESRI Grid - predictions of seabird species richness in the New York offshore planning area made by the NOAA Biogeography Branch

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset represents seabird species richness, or number of species, predictions from spatial models developed for the New York offshore spatial planning area....

  3. Spatial patterns of primary productivity derived from the Dynamic Habitat Indices predict patterns of species richness and distributions in the tropics

    Science.gov (United States)

    Suttidate, Naparat

    Humans are changing the Earth's ecosystems, which has profound consequences for biodiversity. To understand how species respond to these changes, biodiversity science requires accurate assessments of biodiversity. However, biodiversity assessments are still limited in tropical regions. The Dynamic Habitat Indices (DHIs), derived from satellite data, summarize dynamic patterns of annual primary productivity: (a) cumulative annual productivity, (b) minimum annual productivity, and (c) seasonal variation in productivity. The DHIs have been successfully used in temperate regions, but not yet in the tropics. My goal was to evaluate the importance of primary productivity measured via the DHIs for assessing patterns of species richness and distributions in Thailand. First, I assessed the relationships between the DHIs and tropical bird species richness. I also evaluated the complementarity of the DHIs and topography, climate, latitudinal gradients, habitat heterogeneity, and habitat area in explaining bird species richness. I found that among three DHIs, cumulative annual productivity was the most important factor in explaining bird species richness and that the DHIs outperformed other environmental variables. Second, I developed texture measures derive from DHI cumulative annual productivity, and compared them to habitat composition and fragmentation as predictors of tropical forest bird distributions. I found that adding texture measures to habitat composition and fragmentation models improved the prediction of tropical bird distributions, especially area- and edge-sensitive tropical forest bird species. Third, I predicted the effects of trophic interactions between primary productivity, prey, and predators in relation to habitat connectivity for Indochinese tigers (Panthera tigris). I found that including trophic interactions improved habitat suitability models for tigers. However, tiger habitat is highly fragmented with few dispersal corridors. I also identified

  4. Identification of residue pairing in interacting β-strands from a predicted residue contact map.

    Science.gov (United States)

    Mao, Wenzhi; Wang, Tong; Zhang, Wenxuan; Gong, Haipeng

    2018-04-19

    Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Our algorithm RDb 2 C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb 2 C. Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C .

  5. IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.

    Science.gov (United States)

    Wong, Aaron K; Krishnan, Arjun; Yao, Victoria; Tadych, Alicja; Troyanskaya, Olga G

    2015-07-01

    IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  6. Infant attachment security and early childhood behavioral inhibition interact to predict adolescent social anxiety symptoms.

    Science.gov (United States)

    Lewis-Morrarty, Erin; Degnan, Kathryn A; Chronis-Tuscano, Andrea; Pine, Daniel S; Henderson, Heather A; Fox, Nathan A

    2015-01-01

    Insecure attachment and behavioral inhibition (BI) increase risk for internalizing problems, but few longitudinal studies have examined their interaction in predicting adolescent anxiety. This study included 165 adolescents (ages 14-17 years) selected based on their reactivity to novelty at 4 months. Infant attachment was assessed with the Strange Situation. Multimethod BI assessments were conducted across childhood. Adolescents and their parents independently reported on anxiety. The interaction of attachment and BI significantly predicted adolescent anxiety symptoms, such that BI and anxiety were only associated among adolescents with histories of insecure attachment. Exploratory analyses revealed that this effect was driven by insecure-resistant attachment and that the association between BI and social anxiety was significant only for insecure males. Clinical implications are discussed. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

  7. Different Endosymbiotic Interactions in Two Hydra Species Reflect the Evolutionary History of Endosymbiosis

    KAUST Repository

    Ishika