Science.gov

Sample records for change predict functional

  1. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism.

    PubMed

    Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L; Kenworthy, Lauren; Martin, Alex

    2015-12-01

    Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome--adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement. PMID:26627261

  2. Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism

    PubMed Central

    Plitt, Mark; Barnes, Kelly Anne; Wallace, Gregory L.; Kenworthy, Lauren; Martin, Alex

    2015-01-01

    Although typically identified in early childhood, the social communication symptoms and adaptive behavior deficits that are characteristic of autism spectrum disorder (ASD) persist throughout the lifespan. Despite this persistence, even individuals without cooccurring intellectual disability show substantial heterogeneity in outcomes. Previous studies have found various behavioral assessments [such as intelligence quotient (IQ), early language ability, and baseline autistic traits and adaptive behavior scores] to be predictive of outcome, but most of the variance in functioning remains unexplained by such factors. In this study, we investigated to what extent functional brain connectivity measures obtained from resting-state functional connectivity MRI (rs-fcMRI) could predict the variance left unexplained by age and behavior (follow-up latency and baseline autistic traits and adaptive behavior scores) in two measures of outcome—adaptive behaviors and autistic traits at least 1 y postscan (mean follow-up latency = 2 y, 10 mo). We found that connectivity involving the so-called salience network (SN), default-mode network (DMN), and frontoparietal task control network (FPTCN) was highly predictive of future autistic traits and the change in autistic traits and adaptive behavior over the same time period. Furthermore, functional connectivity involving the SN, which is predominantly composed of the anterior insula and the dorsal anterior cingulate, predicted reliable improvement in adaptive behaviors with 100% sensitivity and 70.59% precision. From rs-fcMRI data, our study successfully predicted heterogeneity in outcomes for individuals with ASD that was unaccounted for by simple behavioral metrics and provides unique evidence for networks underlying long-term symptom abatement. PMID:26627261

  3. A global remote sensing mission to detect and predict plant functional biodiversity change

    NASA Astrophysics Data System (ADS)

    Cavender-Bares, J.; Jetz, W.; Pavlick, R.; Schimel, D.; Gamon, J. A.; Hobbie, S. E.; Townsend, P. A.

    2015-12-01

    Global biodiversity is one of the most crucial and least-observed dimensions of the earth system and increasingly important for anticipating changes to both the climate system and ecosystem services. Parallel developments in biodiversity science and remote sensing show that new satellite observations could directly provide global monitoring of one key dimension of global biodiversity, plant functional trait diversity. Remote sensing has already proven a pivotal aid to address the biodiversity data gap. Data on plant productivity, phenology, land-cover and other environmental parameters from MODIS and Landsat satellites currently serve as highly effective covariates for spatiotemporal biodiversity models. The growing functional trait paradigm in ecology, supported by the development of a global plant trait database that includes information for more than one-third of the global flora, highlights the importance of detecting functional diversity globally. Functional traits such as nutrient concentrations, characteristic growth forms and wood density drive both, how organisms respond to environmental change and the effects of organisms on ecosystems. Additionally, the ever more complete tree of life for plants, which presents a link to the shared evolutionary history of plant traits within lineages, coupled with advances in macroevolutionary models and data gap filling techniques, allows predictions of traits that cannot be directly observed. Using experimental manipulations of plant functional and phylogenetic diversity, our team is testing the extent to which we can link above and belowground measurements of biodiversity to remotely sensed optical diversity using hyperspectral data. These efforts will provide the means to fruitfully harness functional diversity data from space from the envisioned Global Biodiversity Observatory (GBO) mission. In turn, remotely sensed hyperspectral data from GBO will allow fundamental breakthroughs and resolve one of the most

  4. Can structural or functional changes following traumatic brain injury in the rat predict the epileptic outcome?

    PubMed Central

    Shultz, Sandy R; Cardamone, Lisa; Liu, Ying R; Hogan, R. Edward; Maccotta, Luigi; Wright, David K; Zheng, Ping; Koe, Amelia; Gregoire, Marie-Claude; Williams, John P; Hicks, Rodney J; Jones, Nigel C; Myers, Damian E; O’Brien, Terence J; Bouilleret, Viviane

    2014-01-01

    Summary Purpose Post-traumatic epilepsy (PTE) occurs in a proportion of traumatic brain injury (TBI) cases, significantly compounding the disability, risk of injury, and death for sufferers. To date, predictive biomarkers for PTE have not been identified. This study used the lateral fluid percussion injury (LFPI) rat model of TBI to investigate whether structural, functional, and behavioral changes post-TBI relate to the later development of PTE. Methods Adult male Wistar rats underwent LFPI or sham-injury. Serial MR and PET imaging, and behavioral analyses were performed over six months post-injury. Rats were then implanted with recording electrodes and monitored for two consecutive weeks using video-EEG to assess for PTE. Of the LFPI rats, 52% (n=12) displayed spontaneous recurring seizures and/or epileptic discharges on the video-EEG recordings. Key findings MRI volumetric and signal analysis of changes in cortex, hippocampus, thalamus, and amygdala, 18F-FDG PET analysis of metabolic function, and behavioral analysis of cognitive and emotional changes, at one week, one month, three months, and six months post-LFPI, all failed to identify significant differences on univariate analysis between the epileptic and non-epileptic groups. However, hippocampal surface shape analysis using high dimensional mapping-large deformation identified significant changes in the ipsilateral hippocampus at one week post-injury relative to baseline that differed between rats that would go onto become epileptic versus those who did not. Furthermore, a multivariate logistic regression model that incorporated the one week, one month, and three month 18F-FDG PET parameters from the ipsilateral hippocampus was able to correctly predict the epileptic outcome in all of the LFPI cases. As such, these subtle changes in the ipsilateral hippocampus at acute phases after LFPI may be related to PTE and require further examination. Significance These findings suggest PTE may be independent of

  5. Using Prediction Markets to Generate Probability Density Functions for Climate Change Risk Assessment

    NASA Astrophysics Data System (ADS)

    Boslough, M.

    2011-12-01

    Climate-related uncertainty is traditionally presented as an error bar, but it is becoming increasingly common to express it in terms of a probability density function (PDF). PDFs are a necessary component of probabilistic risk assessments, for which simple "best estimate" values are insufficient. Many groups have generated PDFs for climate sensitivity using a variety of methods. These PDFs are broadly consistent, but vary significantly in their details. One axiom of the verification and validation community is, "codes don't make predictions, people make predictions." This is a statement of the fact that subject domain experts generate results using assumptions within a range of epistemic uncertainty and interpret them according to their expert opinion. Different experts with different methods will arrive at different PDFs. For effective decision support, a single consensus PDF would be useful. We suggest that market methods can be used to aggregate an ensemble of opinions into a single distribution that expresses the consensus. Prediction markets have been shown to be highly successful at forecasting the outcome of events ranging from elections to box office returns. In prediction markets, traders can take a position on whether some future event will or will not occur. These positions are expressed as contracts that are traded in a double-action market that aggregates price, which can be interpreted as a consensus probability that the event will take place. Since climate sensitivity cannot directly be measured, it cannot be predicted. However, the changes in global mean surface temperature are a direct consequence of climate sensitivity, changes in forcing, and internal variability. Viable prediction markets require an undisputed event outcome on a specific date. Climate-related markets exist on Intrade.com, an online trading exchange. One such contract is titled "Global Temperature Anomaly for Dec 2011 to be greater than 0.65 Degrees C." Settlement is based

  6. Predictive Models for Pulmonary Function Changes After Radiotherapy for Breast Cancer and Lymphoma

    SciTech Connect

    Sanchez-Nieto, Beatriz; Goset, Karen C.; Caviedes, Ivan; Delgado, Iris O.; Cordova, Andres

    2012-02-01

    Purpose: To propose multivariate predictive models for changes in pulmonary function tests ({Delta}PFTs) with respect to preradiotherapy (pre-RT) values in patients undergoing RT for breast cancer and lymphoma. Methods and Materials: A prospective study was designed to measure {Delta}PFTs of patients undergoing RT. Sixty-six patients were included. Spirometry, lung capacity (measured by helium dilution), and diffusing capacity of carbon monoxide tests were used to measure lung function. Two lung definitions were considered: paired lung vs. irradiated lung (IL). Correlation analysis of dosimetric parameters (mean lung dose and the percentage of lung volume receiving more than a threshold dose) and {Delta}PFTs was carried out to find the best dosimetric predictor. Chemotherapy, age, smoking, and the selected dose-volume parameter were considered as single and interaction terms in a multivariate analysis. Stability of results was checked by bootstrapping. Results: Both lung definitions proved to be similar. Modeling was carried out for IL. Acute and late damage showed the highest correlations with volumes irradiated above {approx}20 Gy (maximum R{sup 2} = 0.28) and {approx}40 Gy (maximum R{sup 2} = 0.21), respectively. RT alone induced a minor and transitory restrictive defect (p = 0.013). Doxorubicin-cyclophosphamide-paclitaxel (Taxol), when administered pre-RT, induced a late, large restrictive effect, independent of RT (p = 0.031). Bootstrap values confirmed the results. Conclusions: None of the dose-volume parameters was a perfect predictor of outcome. Thus, different predictor models for {Delta}PFTs were derived for the IL, which incorporated other nondosimetric parameters mainly through interaction terms. Late {Delta}PFTs seem to behave more serially than early ones. Large restrictive defects were demonstrated in patients pretreated with doxorubicin-cyclophosphamide-paclitaxel.

  7. Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

    PubMed

    Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

    2015-10-01

    Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large

  8. Final technical report. Can microbial functional traits predict the response and resilience of decomposition to global change?

    SciTech Connect

    Allison, Steven D.

    2015-09-24

    The role of specific micro-organisms in the carbon cycle, and their responses to environmental change, are unknown in most ecosystems. This knowledge gap limits scientists’ ability to predict how important ecosystem processes, like soil carbon storage and loss, will change with climate and other environmental factors. The investigators addressed this knowledge gap by transplanting microbial communities from different environments into new environments and measuring the response of community composition and carbon cycling over time. Using state-of-the-art sequencing techniques, computational tools, and nanotechnology, the investigators showed that microbial communities on decomposing plant material shift dramatically with natural and experimentally-imposed drought. Microbial communities also shifted in response to added nitrogen, but the effects were smaller. These changes had implications for carbon cycling, with lower rates of carbon loss under drought conditions, and changes in the efficiency of decomposition with nitrogen addition. Even when transplanted into the same conditions, microbial communities from different environments remained distinct in composition and functioning for up to one year. Changes in functioning were related to differences in enzyme gene content across different microbial groups. Computational approaches developed for this project allowed the conclusions to be tested more broadly in other ecosystems, and new computer models will facilitate the prediction of microbial traits and functioning across environments. The data and models resulting from this project benefit the public by improving the ability to predict how microbial communities and carbon cycling functions respond to climate change, nutrient enrichment, and other large-scale environmental changes.

  9. Complexity in relational processing predicts changes in functional brain network dynamics.

    PubMed

    Cocchi, Luca; Halford, Graeme S; Zalesky, Andrew; Harding, Ian H; Ramm, Brentyn J; Cutmore, Tim; Shum, David H K; Mattingley, Jason B

    2014-09-01

    The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks. PMID:23563963

  10. A trait-based framework for predicting when and where microbial adaptation to climate change will affect ecosystem functioning

    USGS Publications Warehouse

    Wallenstein, Matthew D.; Hall, Edward K.

    2012-01-01

    As the earth system changes in response to human activities, a critical objective is to predict how biogeochemical process rates (e.g. nitrification, decomposition) and ecosystem function (e.g. net ecosystem productivity) will change under future conditions. A particular challenge is that the microbial communities that drive many of these processes are capable of adapting to environmental change in ways that alter ecosystem functioning. Despite evidence that microbes can adapt to temperature, precipitation regimes, and redox fluctuations, microbial communities are typically not optimally adapted to their local environment. For example, temperature optima for growth and enzyme activity are often greater than in situ temperatures in their environment. Here we discuss fundamental constraints on microbial adaptation and suggest specific environments where microbial adaptation to climate change (or lack thereof) is most likely to alter ecosystem functioning. Our framework is based on two principal assumptions. First, there are fundamental ecological trade-offs in microbial community traits that occur across environmental gradients (in time and space). These trade-offs result in shifting of microbial function (e.g. ability to take up resources at low temperature) in response to adaptation of another trait (e.g. limiting maintenance respiration at high temperature). Second, the mechanism and level of microbial community adaptation to changing environmental parameters is a function of the potential rate of change in community composition relative to the rate of environmental change. Together, this framework provides a basis for developing testable predictions about how the rate and degree of microbial adaptation to climate change will alter biogeochemical processes in aquatic and terrestrial ecosystems across the planet.

  11. Motor cortex excitability changes within 8 hours after ischaemic stroke may predict the functional outcome.

    PubMed

    Di Lazzaro, V; Oliviero, A; Profice, P; Saturno, E; Pilato, F; Tonali, P

    1999-06-01

    Motor evoked potentials after magnetic transcranial stimulation and the excitability of the motor cortex to increasing magnetic stimulus intensities were evaluated in six patients with hemiparesis after ischaemic stroke within 8 hours after stroke. The latencies of motor evoked potentials were normal in all patients. After stimulation of the ischaemic hemisphere we obtained responses comparable with the contralateral ones in two patients (mean NIH score 2 (SD 0)) and this group was completely asymptomatic after 15 days (NIH score 0). In four patients the excitability of the motor cortex involved by the ischaemia was reduced and magnetic motor threshold was higher than that of the spared motor cortex. This finding was associated with a poor motor recovery and the NIH score after 15 days was unchanged (NIH score 1.75 (SD 1.5)). The present data suggest that the evaluation of the excitability of motor cortex may offer a mean of predicting functional outcome following stroke. PMID:10461555

  12. Detecting and Predicting Changes

    ERIC Educational Resources Information Center

    Brown, Scott D.; Steyvers, Mark

    2009-01-01

    When required to predict sequential events, such as random coin tosses or basketball free throws, people reliably use inappropriate strategies, such as inferring temporal structure when none is present. We investigate the ability of observers to predict sequential events in dynamically changing environments, where there is an opportunity to detect…

  13. A computer model of lens structure and function predicts experimental changes to steady state properties and circulating currents

    PubMed Central

    2013-01-01

    Background In a previous study (Vaghefi et al. 2012) we described a 3D computer model that used finite element modeling to capture the structure and function of the ocular lens. This model accurately predicted the steady state properties of the lens including the circulating ionic and fluid fluxes that are believed to underpin the lens internal microcirculation system. In the absence of a blood supply, this system brings nutrients to the core of the lens and removes waste products faster than would be achieved by passive diffusion alone. Here we test the predictive properties of our model by investigating whether it can accurately mimic the experimentally measured changes to lens steady-state properties induced by either depolarising the lens potential or reducing Na+ pump rate. Methods To mimic experimental manipulations reported in the literature, the boundary conditions of the model were progressively altered and the model resolved for each new set of conditions. Depolarisation of lens potential was implemented by increasing the extracellular [K+], while inhibition of the Na+ pump was stimulated by utilising the inherent temperature sensitivity of the pump and changing the temperature at which the model was solved. Results Our model correctly predicted that increasing extracellular [K+] depolarizes the lens potential, reducing and then reversing the magnitude of net current densities around the lens. While lowering the temperature reduced Na+ pump activity and caused a reduction in circulating current, it had a minimal effect on the lens potential, a result consistent with published experimental data. Conclusion We have shown that our model is capable of accurately simulating the effects of two known experimental manipulations on lens steady-state properties. Our results suggest that the model will be a valuable predictive tool to support ongoing studies of lens structure and function. PMID:23988187

  14. Longitudinal Changes in Functional Brain Connectivity Predicts Conversion to Alzheimer's Disease.

    PubMed

    Serra, Laura; Cercignani, Mara; Mastropasqua, Chiara; Torso, Mario; Spanò, Barbara; Makovac, Elena; Viola, Vanda; Giulietti, Giovanni; Marra, Camillo; Caltagirone, Carlo; Bozzali, Marco

    2016-01-01

    This longitudinal study investigates the modifications in structure and function occurring to typical Alzheimer's disease (AD) brains over a 2-year follow-up, from pre-dementia stages of disease, with the aim of identifying biomarkers of prognostic value. Thirty-one patients with amnestic mild cognitive impairment were recruited and followed-up with clinical, neuropsychological, and MRI assessments. Patients were retrospectively classified as AD Converters or Non-Converters, and the data compared between groups. Cross-sectional MRI data at baseline, assessing volume and functional connectivity abnormalities, confirmed previous findings, showing a more severe pattern of regional grey matter atrophy and default-mode network disconnection in Converters than in Non-Converters. Longitudinally, Converters showed more grey matter atrophy in the frontotemporal areas, accompanied by increased connectivity in the precuneus. Discriminant analysis revealed that functional connectivity of the precuneus within the default mode network at baseline is the parameter able to correctly classify patients in Converters and Non-Converters with high sensitivity, specificity, and accuracy. PMID:26890769

  15. Changing identity: predicting adjustment to organizational restructure as a function of subgroup and superordinate identification.

    PubMed

    Jetten, Jolanda; O'Brien, Anne; Trindall, Nicole

    2002-06-01

    We investigated a work-team restructure within an organization obtaining measures before and after the change occurred. Pre-restructure analyses revealed that, in addition to informational variables, subgroup identification (work-team) and superordinate identification (organization) were important predictors of negative feelings towards the restructure. The more that employees identified with the subgroup, the more negative feelings they reported about the upcoming change. In contrast, the higher the identification with the superordinate group, the less negative employees felt. Longitudinal analysis revealed that compared with the pre-restructure, post-restructure levels of work-team identification, organizational identification, job satisfaction and perceived work-team performance were significantly lower. Pre-restructure work-team identification was a stronger predictor of post-restructure job satisfaction than pre-restructure organizational identification. In addition, it was found that pre-restructure work-team identification and organizational identification had opposing effects on post-restructure organizational identification. There was some evidence that high initial organizational identification protected long-term organizational commitment. PMID:12133229

  16. Individual differences in executive function and central coherence predict developmental changes in theory of mind in autism.

    PubMed

    Pellicano, Elizabeth

    2010-03-01

    There is strong evidence to suggest that individuals with autism show atypicalities in multiple cognitive domains, including theory of mind (ToM), executive function (EF), and central coherence (CC). In this study, the longitudinal relationships among these 3 aspects of cognition in autism were investigated. Thirty-seven cognitively able children with an autism spectrum condition were assessed on tests targeting ToM (false-belief prediction), EF (planning ability, cognitive flexibility, and inhibitory control), and CC (local processing) at intake and again 3 years later. Time 1 EF and CC skills were longitudinally predictive of change in children's ToM test performance, independent of age, language, nonverbal intelligence, and early ToM skills. Predictive relations in the opposite direction were not significant, and there were no developmental links between EF and CC. Rather than showing problems in ToM, EF and CC as co-occurring and independent atypicalities in autism, these findings suggest that early domain-general skills play a critical role in shaping the developmental trajectory of children's ToM. PMID:20210511

  17. Assessment of trait anxiety and prediction of changes in state anxiety using functional brain imaging: A test-retest study.

    PubMed

    Tian, Xue; Wei, Dongtao; Du, Xue; Wang, Kangcheng; Yang, Junyi; Liu, Wei; Meng, Jie; Liu, Huijuan; Liu, Guangyuan; Qiu, Jiang

    2016-06-01

    Anxiety is a multidimensional construct that includes stable trait anxiety and momentary state anxiety, which have a combined effect on our mental and physical well-being. However, the relationship between intrinsic brain activity and the feeling of anxiety, particularly trait and state anxiety, remain unclear. In this study, we used resting-state functional magnetic resonance imaging (fMRI) (amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo)) to determine the effects of intrinsic brain activity on stable inter-individual trait anxiety and intra-individual state anxiety variability in a cross-sectional and test-retest study. We found that at both time points, the trait anxiety score was significantly associated with intrinsic brain activity (both the ALFF and ReHo) in the right ventral medial prefrontal cortex (vmPFC) and ALFF of the dorsal anterior cingulate cortex/anterior midcingulate cortex (dACC/aMCC). More importantly, the change in intrinsic brain activity in the right insula was predictive of intra-individual state anxiety variability over a 9-month interval. The test-retest nature of this study's design could provide an opportunity to distinguish between the intrinsic brain activity associated with state and trait anxiety. These results could deepen our understanding of anxiety from a neuroscientific perspective. PMID:27001499

  18. Predicting Changes in Macrophyte Community Structure from Functional Traits in a Freshwater Lake: A Test of Maximum Entropy Model

    PubMed Central

    Fu, Hui; Zhong, Jiayou; Yuan, Guixiang; Guo, Chunjing; Lou, Qian; Zhang, Wei; Xu, Jun; Ni, Leyi; Xie, Ping; Cao, Te

    2015-01-01

    Trait-based approaches have been widely applied to investigate how community dynamics respond to environmental gradients. In this study, we applied a series of maximum entropy (maxent) models incorporating functional traits to unravel the processes governing macrophyte community structure along water depth gradient in a freshwater lake. We sampled 42 plots and 1513 individual plants, and measured 16 functional traits and abundance of 17 macrophyte species. Study results showed that maxent model can be highly robust (99.8%) in predicting the species relative abundance of macrophytes with observed community-weighted mean (CWM) traits as the constraints, while relative low (about 30%) with CWM traits fitted from water depth gradient as the constraints. The measured traits showed notably distinct importance in predicting species abundances, with lowest for perennial growth form and highest for leaf dry mass content. For tuber and leaf nitrogen content, there were significant shifts in their effects on species relative abundance from positive in shallow water to negative in deep water. This result suggests that macrophyte species with tuber organ and greater leaf nitrogen content would become more abundant in shallow water, but would become less abundant in deep water. Our study highlights how functional traits distributed across gradients provide a robust path towards predictive community ecology. PMID:26167856

  19. Predicting earth's dynamic changes

    NASA Technical Reports Server (NTRS)

    Rasool, S. I.

    1986-01-01

    Given a suitable strategy for conducting measurements, satellite-based remote sensing of the earth can furnish valuable information on the dynamic changes of such planetary characteristics as ocean surface temperatures and atmospheric CO2. Observations must be global and synoptic, quantitatively validated, and consistent over the long term. A program spanning 20 years will study such critical variables as solar flux, stratospheric temperature, aerosols and ozone, cloud cover, tropospheric gases and aerosols, radiation balance, surface temperature, albedo, precipitation, vegetation cover, moisture, snow and ice, as well as oceanic color, topography, and wind stress.

  20. Predicting communities from functional traits.

    PubMed

    Cadotte, Marc W; Arnillas, Carlos A; Livingstone, Stuart W; Yasui, Simone-Louise E

    2015-09-01

    Species traits influence where species live and how they interact. While there have been many advances in describing the functional composition and diversity of communities, only recently do researchers have the ability to predict community composition and diversity. This predictive ability can offer fundamental insights into ecosystem resilience and restoration. PMID:26190136

  1. Predicting cognitive change within domains

    PubMed Central

    Duff, Kevin; Beglinger, Leigh J.; Moser, David J.; Paulsen, Jane S.

    2010-01-01

    Standardized regression based (SRB) formulas, a method for predicting cognitive change across time, traditionally use baseline performance on a neuropsychological measure to predict future performance on that same measure. However, there are instances in which the same tests may not be given at follow-up assessments (e.g., lack of continuity of provider, avoiding practice effects). The current study sought to expand this methodology by developing SRBs to predict performance on different tests within the same cognitive domain. Using a sample of 127 non-demented community-dwelling older adults assessed at baseline and after one year, two sets of SRBs were developed: 1. those predicting performance on the same test, and 2. those predicting performance on a different test within the same cognitive domain. The domains examined were learning and memory, processing speed, and language. Across both sets of SRBs, one year scores were significantly predicted by baseline scores, especially for the learning and memory and processing speed measures. Although SRBs developed for the same test were comparable to those developed for different tests within the same domain, less variance was accounted for as tests became less similar. The current results lend preliminary support for additional development of SRBs, both for same- and different-tests, as well as beginning to examine domain-based SRBs. PMID:20358479

  2. Sea Level Affecting Marshes Model (SLAMM) ‐ New functionality for predicting changes in distribution of submerged aquatic vegetation in response to sea level rise

    USGS Publications Warehouse

    Lee II, Henry; Reusser, Deborah A.; Frazier, Melanie R; McCoy, Lee M; Clinton, Patrick J.; Clough, Jonathan S.

    2014-01-01

    The “Sea‐Level Affecting Marshes Model” (SLAMM) is a moderate resolution model used to predict the effects of sea level rise on marsh habitats (Craft et al. 2009). SLAMM has been used extensively on both the west coast (e.g., Glick et al., 2007) and east coast (e.g., Geselbracht et al., 2011) of the United States to evaluate potential changes in the distribution and extent of tidal marsh habitats. However, a limitation of the current version of SLAMM, (Version 6.2) is that it lacks the ability to model distribution changes in seagrass habitat resulting from sea level rise. Because of the ecological importance of SAV habitats, U.S. EPA, USGS, and USDA partnered with Warren Pinnacle Consulting to enhance the SLAMM modeling software to include new functionality in order to predict changes in Zostera marina distribution within Pacific Northwest estuaries in response to sea level rise. Specifically, the objective was to develop a SAV model that used generally available GIS data and parameters that were predictive and that could be customized for other estuaries that have GIS layers of existing SAV distribution. This report describes the procedure used to develop the SAV model for the Yaquina Bay Estuary, Oregon, appends a statistical script based on the open source R software to generate a similar SAV model for other estuaries that have data layers of existing SAV, and describes how to incorporate the model coefficients from the site‐specific SAV model into SLAMM to predict the effects of sea level rise on Zostera marina distributions. To demonstrate the applicability of the R tools, we utilize them to develop model coefficients for Willapa Bay, Washington using site‐specific SAV data.

  3. Individual Differences in Executive Function and Central Coherence Predict Developmental Changes in Theory of Mind in Autism

    ERIC Educational Resources Information Center

    Pellicano, Elizabeth

    2010-01-01

    There is strong evidence to suggest that individuals with autism show atypicalities in multiple cognitive domains, including theory of mind (ToM), executive function (EF), and central coherence (CC). In this study, the longitudinal relationships among these 3 aspects of cognition in autism were investigated. Thirty-seven cognitively able children…

  4. Smoking Abstinence-Induced Changes in Resting State Functional Connectivity with Ventral Striatum Predict Lapse During a Quit Attempt.

    PubMed

    Sweitzer, Maggie M; Geier, Charles F; Addicott, Merideth A; Denlinger, Rachel; Raiff, Bethany R; Dallery, Jesse; McClernon, F Joseph; Donny, Eric C

    2016-09-01

    The ventral and dorsal striatum are critical substrates of reward processing and motivation and have been repeatedly linked to addictive disorders, including nicotine dependence. However, little is known about how functional connectivity between these and other brain regions is modulated by smoking withdrawal and may contribute to relapse vulnerability. In the present study, 37 smokers completed resting state fMRI scans during both satiated and 24-h abstinent conditions, prior to engaging in a 3-week quit attempt supported by contingency management. We examined the effects of abstinence condition and smoking outcome (lapse vs non-lapse) on whole-brain connectivity with ventral and dorsal striatum seed regions. Results indicated a significant condition by lapse outcome interaction for both right and left ventral striatum seeds. Robust abstinence-induced increases in connectivity with bilateral ventral striatum were observed across a network of regions implicated in addictive disorders, including insula, superior temporal gyrus, and anterior/mid-cingulate cortex among non-lapsers; the opposite pattern was observed for those who later lapsed. For dorsal striatum seeds, 24-h abstinence decreased connectivity across both groups with several regions, including medial prefrontal cortex, posterior cingulate cortex, hippocampus, and supplemental motor area. These findings suggest that modulation of striatal connectivity with the cingulo-insular network during early withdrawal may be associated with smoking cessation outcomes. PMID:27091382

  5. Prenatal Arsenic Exposure and the Epigenome: Identifying Sites of 5-methylcytosine Alterations that Predict Functional Changes in Gene Expression in Newborn Cord Blood and Subsequent Birth Outcomes

    PubMed Central

    Rojas, Daniel; Rager, Julia E.; Smeester, Lisa; Bailey, Kathryn A.; Drobná, Zuzana; Rubio-Andrade, Marisela; Stýblo, Miroslav; García-Vargas, Gonzalo; Fry, Rebecca C.

    2015-01-01

    Prenatal exposure to inorganic arsenic (iAs) is detrimental to the health of newborns and increases the risk of disease development later in life. Here we examined a subset of newborn cord blood leukocyte samples collected from subjects enrolled in the Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico, who were exposed to a range of drinking water arsenic concentrations (0.456–236 µg/l). Changes in iAs-associated DNA 5-methylcytosine methylation were assessed across 424 935 CpG sites representing 18 761 genes and compared with corresponding mRNA expression levels and birth outcomes. In the context of arsenic exposure, a total of 2919 genes were identified with iAs-associated differences in DNA methylation. Site-specific analyses identified DNA methylation changes that were most predictive of gene expression levels where CpG methylation within CpG islands positioned within the first exon, the 5′ untranslated region and 200 bp upstream of the transcription start site yielded the most significant association with gene expression levels. A set of 16 genes was identified with correlated iAs-associated changes in DNA methylation and mRNA expression and all were highly enriched for binding sites of the early growth response (EGR) and CCCTC-binding factor (CTCF) transcription factors. Furthermore, DNA methylation levels of 7 of these genes were associated with differences in birth outcomes including gestational age and head circumference.These data highlight the complex interplay between DNA methylation, functional changes in gene expression and health outcomes and underscore the need for functional analyses coupled to epigenetic assessments. PMID:25304211

  6. Predicting hand function after hemidisconnection.

    PubMed

    Küpper, Hanna; Kudernatsch, Manfred; Pieper, Tom; Groeschel, Samuel; Tournier, Jacques-Donald; Raffelt, David; Winkler, Peter; Holthausen, Hans; Staudt, Martin

    2016-09-01

    Hemidisconnections (i.e. hemispherectomies or hemispherotomies) invariably lead to contralateral hemiparesis. Many patients with a pre-existing hemiparesis, however, experience no deterioration in motor functions, and some can still grasp with their paretic hand after hemidisconnection. The scope of our study was to predict this phenomenon. Hypothesizing that preserved contralateral grasping ability after hemidisconnection can only occur in patients controlling their paretic hands via ipsilateral corticospinal projections already in the preoperative situation, we analysed the asymmetries of the brainstem (by manual magnetic resonance imaging volumetry) and of the structural connectivity of the corticospinal tracts within the brainstem (by magnetic resonance imaging diffusion tractography), assuming that marked hypoplasia or Wallerian degeneration on the lesioned side in patients who can grasp with their paretic hands indicate ipsilateral control. One hundred and two patients who underwent hemidisconnections between 0.8 and 36 years of age were included. Before the operation, contralateral hand function was normal in 3/102 patients, 47/102 patients showed hemiparetic grasping ability and 52/102 patients could not grasp with their paretic hands. After hemidisconnection, 20/102 patients showed a preserved grasping ability, and 5/102 patients began to grasp with their paretic hands only after the operation. All these 25 patients suffered from pre- or perinatal brain lesions. Thirty of 102 patients lost their grasping ability. This group included all seven patients with a post-neonatally acquired or progressive brain lesion who could grasp before the operation, and also all three patients with a preoperatively normal hand function. The remaining 52/102 patients were unable to grasp pre- and postoperatively. On magnetic resonance imaging, the patients with preserved grasping showed significantly more asymmetric brainstem volumes than the patients who lost their grasping

  7. [Time course of changes in gustatory function test results and subjective symptoms, and predictive factors for response in patients with taste disorder receiving 24-week zinc replacement treatment].

    PubMed

    Sakagami, Masafumi; Kurono, Yuichi; Inokuchi, Akira; Takeda, Noriaki; Aiba, Tsunemasa; Nin, Tomomi; Ikeda, Minoru

    2014-08-01

    In a taste disorder, an agreement between patients' complaints and gustatory function test results is not necessarily found both at the initial hospital visit and during the course of treatment; therefore, it is difficult to assess treatment responses and review treatment strategies based on the assessed treatment responses. The present study investigated the time course of changes in disc gustometry results and subjective symptom scores measured at 4-week intervals in 44 patients with a taste disorder who were considered eligible for zinc replacement treatment and who received polaprezinc at a dose of 150 mg/day (equivalent to a 34 mg/day dose of zinc) for up to 24 weeks. The study also examined the potential differences in treatment outcomes according to the predictive factors for response such as patient background and assessed disc gustometry results during the course of treatment. Results indicated that disc gustometry results and subjective symptom scores showed different time courses of changes. The response rate as measured by disc gustometry was 47.7% at week 12 of treatment, and showed a subsequent slow increase to 56.8% at week 24. On the other hand, subjective symptom scores showed a time-proportional improvement up to week 24. Among the patients included in the present study, a clear difference was found according to the presence or absence of an improving trend as determined by disc gustometry at week 12 of treatment, although there were no differences in ultimate treatment responses, including categories of taste disorder, according to patient background. Patients showing a trend toward improvement had significantly better treatment responses in terms of both ultimate response rates and subjective symptom scores, whereas patients showing no trend toward improvement were less likely to respond to the subsequent 12-week continued treatment. PMID:25255648

  8. Predicting Persuasion-Induced Behavior Change from the Brain

    PubMed Central

    Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

    2011-01-01

    Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

  9. Contextual Factors Predict Patterns of Change in Functioning over 10 Years among Adolescents and Adults with Autism Spectrum Disorders

    ERIC Educational Resources Information Center

    Woodman, Ashley C.; Smith, Leann E.; Greenberg, Jan S.; Mailick, Marsha R.

    2016-01-01

    In the present study, we jointly employ and integrate variable- and person-centered approaches to identify groups of individuals with autism spectrum disorders (ASD) who have similar profiles of change over a period of 10 years across three critical domains of functioning: maladaptive behaviors, autism symptoms, and daily living skills. Two…

  10. PREDICTION OF NONLINEAR SPATIAL FUNCTIONALS. (R827257)

    EPA Science Inventory

    Spatial statistical methodology can be useful in the arena of environmental regulation. Some regulatory questions may be addressed by predicting linear functionals of the underlying signal, but other questions may require the prediction of nonlinear functionals of the signal. ...

  11. Quality changes and predictive models of radial basis function neural networks for brined common carp (Cyprinus carpio) fillets during frozen storage.

    PubMed

    Kong, Chunli; Wang, Huiyi; Li, Dapeng; Zhang, Yuemei; Pan, Jinfeng; Zhu, Beiwei; Luo, Yongkang

    2016-06-15

    To investigate and predict quality of 2% brined common carp (Cyprinus carpio) fillets during frozen storage, free fatty acids (FFA), salt extractable protein (SEP), total sulfhydryl (SH) content, and Ca(2+)-ATPase activity were determined at 261 K, 253 K, and 245 K, respectively. There was a dramatic increase (P<0.05) in FFA and a sharp decrease (P<0.05) in SH at 261 K during the first 3 weeks. SEP decreased to 67.31% after 17 weeks at 245 K, whereas it took about 7 weeks and 13 weeks to decrease to the same extent at 261 K and 253 K, respectively. Ca(2+)-ATPase activity kept decreasing to 18.28% after 7 weeks at 261 K. Furthermore, radial basis function neural networks (RBFNNs) were developed to predict quality (FFA, SEP, SH, and Ca(2+)-ATPase activity) of brined carp fillets during frozen storage with relative errors all within ±5%. Thus, RBFNN is a promising method to predict quality of carp fillets during storage at 245-261 K. PMID:26868584

  12. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  13. Rumination prospectively predicts executive functioning impairments in adolescents

    PubMed Central

    Connolly, Samantha L.; Wagner, Clara A.; Shapero, Benjamin G.; Pendergast, Laura L.; Abramson, Lyn Y.; Alloy, Lauren B.

    2014-01-01

    Background and objectives The current study tested the resource allocation hypothesis, examining whether baseline rumination or depressive symptom levels prospectively predicted deficits in executive functioning in an adolescent sample. The alternative to this hypothesis was also evaluated by testing whether lower initial levels of executive functioning predicted increases in rumination or depressive symptoms at follow-up. Methods A community sample of 200 adolescents (ages 12–13) completed measures of depressive symptoms, rumination, and executive functioning at baseline and at a follow-up session approximately 15 months later. Results Adolescents with higher levels of baseline rumination displayed decreases in selective attention and attentional switching at follow-up. Rumination did not predict changes in working memory or sustained and divided attention. Depressive symptoms were not found to predict significant changes in executive functioning scores at follow-up. Baseline executive functioning was not associated with change in rumination or depression over time. Conclusions Findings partially support the resource allocation hypothesis that engaging in ruminative thoughts consumes cognitive resources that would otherwise be allocated towards difficult tests of executive functioning. Support was not found for the alternative hypothesis that lower levels of initial executive functioning would predict increased rumination or depressive symptoms at follow-up. Our study is the first to find support for the resource allocation hypothesis using a longitudinal design and an adolescent sample. Findings highlight the potentially detrimental effects of rumination on executive functioning during early adolescence. PMID:23978629

  14. Year 2 Report: Protein Function Prediction Platform

    SciTech Connect

    Zhou, C E

    2012-04-27

    Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fully automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.

  15. Quantitative assessment of protein function prediction programs.

    PubMed

    Rodrigues, B N; Steffens, M B R; Raittz, R T; Santos-Weiss, I C R; Marchaukoski, J N

    2015-01-01

    Fast prediction of protein function is essential for high-throughput sequencing analysis. Bioinformatic resources provide cheaper and faster techniques for function prediction and have helped to accelerate the process of protein sequence characterization. In this study, we assessed protein function prediction programs that accept amino acid sequences as input. We analyzed the classification, equality, and similarity between programs, and, additionally, compared program performance. The following programs were selected for our assessment: Blast2GO, InterProScan, PANTHER, Pfam, and ScanProsite. This selection was based on the high number of citations (over 500), fully automatic analysis, and the possibility of returning a single best classification per sequence. We tested these programs using 12 gold standard datasets from four different sources. The gold standard classification of the databases was based on expert analysis, the Protein Data Bank, or the Structure-Function Linkage Database. We found that the miss rate among the programs is globally over 50%. Furthermore, we observed little overlap in the correct predictions from each program. Therefore, a combination of multiple types of sources and methods, including experimental data, protein-protein interaction, and data mining, may be the best way to generate more reliable predictions and decrease the miss rate. PMID:26782400

  16. Stock price change rate prediction by utilizing social network activities.

    PubMed

    Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

    2014-01-01

    Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

  17. Stock Price Change Rate Prediction by Utilizing Social Network Activities

    PubMed Central

    Mitsubuchi, Takashi; Sakurai, Akito

    2014-01-01

    Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

  18. Protein function prediction based on data fusion and functional interrelationship.

    PubMed

    Meng, Jun; Wekesa, Jael-Sanyanda; Shi, Guan-Li; Luan, Yu-Shi

    2016-04-01

    One of the challenging tasks of bioinformatics is to predict more accurate and confident protein functions from genomics and proteomics datasets. Computational approaches use a variety of high throughput experimental data, such as protein-protein interaction (PPI), protein sequences and phylogenetic profiles, to predict protein functions. This paper presents a method that uses transductive multi-label learning algorithm by integrating multiple data sources for classification. Multiple proteomics datasets are integrated to make inferences about functions of unknown proteins and use a directed bi-relational graph to assign labels to unannotated proteins. Our method, bi-relational graph based transductive multi-label function annotation (Bi-TMF) uses functional correlation and topological PPI network properties on both the training and testing datasets to predict protein functions through data fusion of the individual kernel result. The main purpose of our proposed method is to enhance the performance of classifier integration for protein function prediction algorithms. Experimental results demonstrate the effectiveness and efficiency of Bi-TMF on multi-sources datasets in yeast, human and mouse benchmarks. Bi-TMF outperforms other recently proposed methods. PMID:26869536

  19. Behavioral Changes Predicting Temporal Changes in Perceived Popular Status

    ERIC Educational Resources Information Center

    Bowker, Julie C.; Rubin, Kenneth H.; Buskirk-Cohen, Allison; Rose-Krasnor, Linda; Booth-LaForce, Cathryn

    2010-01-01

    The primary objectives of this investigation were to determine the extent to which young adolescents are stable in high perceived popular status across the middle school transition and to examine whether changes in social behaviors predict the stability, gain, and loss of perceived popular status after the transition. The sample included 672 young…

  20. Biological cluster evaluation for gene function prediction.

    PubMed

    Klie, Sebastian; Nikoloski, Zoran; Selbig, Joachim

    2014-06-01

    Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set. PMID:20059365

  1. CAPE: Automatically Predicting Changes in Group Behavior

    NASA Astrophysics Data System (ADS)

    Sliva, Amy; Subrahmanian, V. S.; Martinez, Vanina; Simari, Gerardo

    There is now intense interest in the problem of forecasting what a group will do in the future. Past work [1, 2, 3] has built complex models of a group’s behavior and used this to predict what the group might do in the future. However, almost all past work assumes that the group will not change its past behavior. Whether the group is a group of investors, or a political party, or a terror group, there is much interest in when and how the group will change its behavior. In this paper, we develop an architecture and algorithms called CAPE to forecast the conditions under which a group will change its behavior. We have tested CAPE on social science data about the behaviors of seven terrorist groups and show that CAPE is highly accurate in its predictions—at least in this limited setting.

  2. Optimizing nondecomposable loss functions in structured prediction.

    PubMed

    Ranjbar, Mani; Lan, Tian; Wang, Yang; Robinovitch, Steven N; Li, Ze-Nian; Mori, Greg

    2013-04-01

    We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as Fβ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines), and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function. The loss augmented inference forms a Quadratic Program (QP), which we solve using LP relaxation. We apply this approach to two tasks: object class-specific segmentation and human action retrieval from videos. We show significant improvement over baseline approaches that either use simple loss functions or simple scoring functions on the PASCAL VOC and H3D Segmentation datasets, and a nursing home action recognition dataset. PMID:22868650

  3. A Prospective Longitudinal Study of Shyness from Infancy to Adolescence: Stability, Age-Related Changes, and Prediction of Socio-Emotional Functioning

    ERIC Educational Resources Information Center

    Karevold, Evalill; Ystrom, Eivind; Coplan, Robert J.; Sanson, Ann V.; Mathiesen, Kristin S.

    2012-01-01

    This longitudinal, population-based and prospective study investigated the stability, age-related changes, and socio-emotional outcomes of shyness from infancy to early adolescence. A sample of 921 children was followed from ages 1.5 to 12.5 years. Parent-reported shyness was assessed at five time points and maternal- and self-reported social…

  4. Graph pyramids for protein function prediction

    PubMed Central

    2015-01-01

    Background Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy. Methods Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels. Results Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data. PMID:26044522

  5. Permafrost, climate, and change: predictive modelling approach.

    NASA Astrophysics Data System (ADS)

    Anisimov, O.

    2003-04-01

    Predicted by GCMs enhanced warming of the Arctic will lead to discernible impacts on permafrost and northern environment. Mathematical models of different complexity forced by scenarios of climate change may be used to predict such changes. Permafrost models that are currently in use may be divided into four groups: index-based models (e.g. frost index model, N-factor model); models of intermediate complexity based on equilibrium simplified solution of the Stephan problem ("Koudriavtcev's" model and its modifications), and full-scale comprehensive dynamical models. New approach of stochastic modelling came into existence recently and has good prospects for the future. Important task is to compare the ability of the models that are different in complexity, concept, and input data requirements to capture the major impacts of changing climate on permafrost. A progressive increase in the depth of seasonal thawing (often referred to as the active-layer thickness, ALT) could be a relatively short-term reaction to climatic warming. At regional and local scales, it may produce substantial effects on vegetation, soil hydrology and runoff, as the water storage capacity of near-surface permafrost will be changed. Growing public concerns are associated with the impacts that warming of permafrost may have on engineered infrastructure built upon it. At the global scale, increase of ALT could facilitate further climatic change if more greenhouse gases are released when the upper layer of the permafrost thaws. Since dynamic permafrost models require complete set of forcing data that is not readily available on the circumpolar scale, they could be used most effectively in regional studies, while models of intermediate complexity are currently best tools for the circumpolar assessments. Set of five transient scenarios of climate change for the period 1980 - 2100 has been constructed using outputs from GFDL, NCAR, CCC, HadCM, and ECHAM-4 models. These GCMs were selected in the course

  6. Behavioral Changes Predicting Temporal Changes in Perceived Popular Status

    PubMed Central

    Bowker, Julie C.; Rubin, Kenneth H.; Buskirk-Cohen, Alison; Rose-Krasnor, Linda; Booth-LaForce, Cathryn

    2009-01-01

    The primary objectives of this investigation were to determine the extent to which young adolescents are stable in high perceived popular status across the middle school transition and to examine whether changes in social behaviors predict the stability, gain, and loss of perceived popular status after the transition. The sample included 672 young adolescents (323 boys) who completed peer-nomination assessments of social behavior and perceived popularity at the end of elementary school (5th grade) and the beginning of middle school (6th grade). Findings indicated that 62 percent of perceived popular adolescents remained stable in their high popular status across the middle school transition. Multinomial logistic regression analyses revealed that a combination of aggression and arrogance/conceit was associated with stable and newly-gained perceived popular status after the middle school transition. Taken together, findings highlight the significance of contextual and temporal changes in adolescents’ perceived popular status. PMID:20209113

  7. Global perceived stress predicts cognitive change among older adults.

    PubMed

    Munoz, Elizabeth; Sliwinski, Martin J; Scott, Stacey B; Hofer, Scott

    2015-09-01

    Research on stress and cognitive aging has primarily focused on examining the effects of biological and psychosocial indicators of stress, with little attention provided to examining the association between perceived stress and cognitive aging. We examined the longitudinal association between global perceived stress (GPS) and cognitive change among 116 older adults (M(age) = 80, SD = 6.40, range = 67-96) in a repeated measurement burst design. Bursts of 6 daily cognitive assessments were repeated every 6 months over a 2-year period, with self-reported GPS assessed at the start of every burst. Using a double-exponential learning model, 2 parameters were estimated: (a) asymptotic level (peak performance), and (b) asymptotic change (the rate at which peak performance changed across bursts). We hypothesized that greater GPS would predict slowed performance in tasks of attention, working memory, and speed of processing and that increases in GPS across time would predict cognitive slowing. Results from latent growth curve analyses were consistent with our first hypothesis and indicated that level of GPS predicted cognitive slowing across time. Changes in GPS did not predict cognitive slowing. This study extends previous findings by demonstrating a prospective association between level of GPS and cognitive slowing across a 2-year period, highlighting the role of psychological stress as a risk factor for poor cognitive function. PMID:26121285

  8. Advection and diffusion in shoreline change prediction

    NASA Astrophysics Data System (ADS)

    Anderson, T. R.; Frazer, L. N.

    2010-12-01

    We added longshore advection and diffusion to the simple cross-shore rate calculation method, as used widely by the USGS and others, to model historic shorelines and to predict future shoreline positions; and applied this to Hawaiian Island beach data. Aerial photographs, sporadically taken throughout the past century, yield usable, albeit limited, historic shoreline data. These photographs provide excellent spatial coverage, but poor temporal resolution, of the shoreline. Due to the sparse historic shoreline data, and the many natural and anthropogenic events influencing coastlines, we constructed a simplistic shoreline change model that can identify long-term behavior of a beach. Our new, two-dimensional model combines the simple rate method to accommodate for cross-shore sediment transport with the classic Pelnard-Considère model for diffusion, as well as a longshore advection speed term. Inverse methods identify cross-shore rate, longshore advection speed, and longshore diffusivity down a sandy coastline. A spatial averaging technique then identifies shoreline segments where one parameter can reasonably account for the cross-shore and longshore transport rates in that area. This produces model results with spatial resolution more appropriate to the temporal spacing of the data. Because changes in historic data can be accounted for by varying degrees of cross-shore and longshore sediment transport - for example, beach erosion can equally be explained by sand moving either off-shore or laterally - we tested several different model scenarios on the data: allowing only cross-shore sediment movement, only longshore movement, and a combination of the two. We used statistical information criteria to determine both the optimal spatial resolution and best-fitting scenario. Finally, we employed a voting method predicting the relaxed shoreline position over time.

  9. Executive functions predict conceptual learning of science.

    PubMed

    Rhodes, Sinéad M; Booth, Josephine N; Palmer, Lorna Elise; Blythe, Richard A; Delibegovic, Mirela; Wheate, Nial J

    2016-06-01

    We examined the relationship between executive functions and both factual and conceptual learning of science, specifically chemistry, in early adolescence. Sixty-three pupils in their second year of secondary school (aged 12-13 years) participated. Pupils completed tasks of working memory (Spatial Working Memory), inhibition (Stop-Signal), attention set-shifting (ID/ED), and planning (Stockings of Cambridge), from the CANTAB. They also participated in a chemistry teaching session, practical, and assessment on the topic of acids and alkalis designed specifically for this study. Executive function data were related to (1) the chemistry assessment which included aspects of factual and conceptual learning and (2) a recent school science exam. Correlational analyses between executive functions and both the chemistry assessment and science grades revealed that science achievements were significantly correlated with working memory. Linear regression analysis revealed that visuospatial working memory ability was predictive of chemistry performance. Interestingly, this relationship was observed solely in relation to the conceptual learning condition of the assessment highlighting the role of executive functions in understanding and applying knowledge about what is learned within science teaching. PMID:26751597

  10. Probabilistic climate change predictions applying Bayesian model averaging.

    PubMed

    Min, Seung-Ki; Simonis, Daniel; Hense, Andreas

    2007-08-15

    This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation-maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required. PMID:17569647

  11. Predicting folding free energy changes upon single point mutations

    PubMed Central

    Zhang, Zhe; Wang, Lin; Gao, Yang; Zhang, Jie; Zhenirovskyy, Maxim; Alexov, Emil

    2012-01-01

    Motivation: The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies. Results: We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters. Availability: All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar Contact: ealexov@clemson.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22238268

  12. Children in Psychodynamic Psychotherapy: Changes in Global Functioning

    ERIC Educational Resources Information Center

    Odhammar, Fredrik; Sundin, Eva C.; Jonson, Mattias; Carlberg, Gunnar

    2011-01-01

    This study was part of the Erica Process and Outcome Study. The aim was to investigate if children's global functioning improves after psychodynamic psychotherapy. Variables that may predict changes in global functioning were examined both statistically and qualitatively, for example, the child's age and gender; diagnosis and comorbidity;…

  13. Developmental Changes in Executive Functioning

    ERIC Educational Resources Information Center

    Lee, Kerry; Bull, Rebecca; Ho, Ringo M. H.

    2013-01-01

    Although early studies of executive functioning in children supported Miyake et al.'s (2000) three-factor model, more recent findings supported a variety of undifferentiated or two-factor structures. Using a cohort-sequential design, this study examined whether there were age-related differences in the structure of executive functioning among…

  14. Spinal meningiomas: clinicoradiological factors predicting recurrence and functional outcome.

    PubMed

    Maiti, Tanmoy K; Bir, Shyamal C; Patra, Devi Prasad; Kalakoti, Piyush; Guthikonda, Bharat; Nanda, Anil

    2016-08-01

    OBJECTIVE Spinal meningiomas are benign tumors with a wide spectrum of clinical and radiological features at presentation. The authors analyzed multiple clinicoradiological factors to predict recurrence and functional outcome in a cohort with a mean follow-up of more than 4 years. The authors also discuss the results of clinical studies regarding spinal meningiomas in the last 15 years. METHODS The authors retrospectively reviewed the clinical and radiological details of patients who underwent surgery for spinal tumors between 2001 and 2015 that were histopathologically confirmed as meningiomas. Demographic parameters, such as age, sex, race, and association with neurofibromatosis Type 2, were considered. Radiological parameters, such as tumor size, signal changes of spinal cord, spinal level, number of levels, location of tumor attachment, shape of tumor, and presence of dural tail/calcification, were noted. These factors were analyzed to predict recurrence and functional outcome. Furthermore, a pooled analysis was performed from 13 reports of spinal meningiomas in the last 15 years. RESULTS A total of 38 patients were included in this study. Male sex and tumors with radiological evidence of a dural tail were associated with an increased risk of recurrence at a mean follow-up of 51.2 months. Ventral or ventrolateral location, large tumors, T2 cord signal changes, and poor preoperative functional status were associated with poor functional outcome at 1-year follow-up. CONCLUSIONS Spine surgeons must be aware of the natural history and risk factors of spinal meningiomas to establish a prognosis for their patients. PMID:27476848

  15. Text Mining Improves Prediction of Protein Functional Sites

    PubMed Central

    Cohn, Judith D.; Ravikumar, Komandur E.

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

  16. A Prediction Model of the Capillary Pressure J-Function.

    PubMed

    Xu, W S; Luo, P Y; Sun, L; Lin, N

    2016-01-01

    The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701

  17. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

    Xie, Shang-Ping; Deser, Clara; Vecchi, Gabriel A.; Collins, Matthew; Delworth, Thomas L.; Hall, Alex; Hawkins, Ed; Johnson, Nathaniel C.; Cassou, Christophe; Giannini, Alessandra; Watanabe, Masahiro

    2015-10-01

    Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

  18. Ongoing dynamics in large-scale functional connectivity predict perception

    PubMed Central

    Sadaghiani, Sepideh; Poline, Jean-Baptiste; Kleinschmidt, Andreas; D’Esposito, Mark

    2015-01-01

    Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22–40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency. PMID:26106164

  19. Platelet Serotonin Transporter Function Predicts Default-Mode Network Activity

    PubMed Central

    Kasess, Christian H.; Meyer, Bernhard M.; Hofmaier, Tina; Diers, Kersten; Bartova, Lucie; Pail, Gerald; Huf, Wolfgang; Uzelac, Zeljko; Hartinger, Beate; Kalcher, Klaudius; Perkmann, Thomas; Haslacher, Helmuth; Meyer-Lindenberg, Andreas; Kasper, Siegfried; Freissmuth, Michael; Windischberger, Christian; Willeit, Matthäus; Lanzenberger, Rupert; Esterbauer, Harald; Brocke, Burkhard; Moser, Ewald; Sitte, Harald H.; Pezawas, Lukas

    2014-01-01

    Background The serotonin transporter (5-HTT) is abundantly expressed in humans by the serotonin transporter gene SLC6A4 and removes serotonin (5-HT) from extracellular space. A blood-brain relationship between platelet and synaptosomal 5-HT reuptake has been suggested, but it is unknown today, if platelet 5-HT uptake can predict neural activation of human brain networks that are known to be under serotonergic influence. Methods A functional magnetic resonance study was performed in 48 healthy subjects and maximal 5-HT uptake velocity (Vmax) was assessed in blood platelets. We used a mixed-effects multilevel analysis technique (MEMA) to test for linear relationships between whole-brain, blood-oxygen-level dependent (BOLD) activity and platelet Vmax. Results The present study demonstrates that increases in platelet Vmax significantly predict default-mode network (DMN) suppression in healthy subjects independent of genetic variation within SLC6A4. Furthermore, functional connectivity analyses indicate that platelet Vmax is related to global DMN activation and not intrinsic DMN connectivity. Conclusion This study provides evidence that platelet Vmax predicts global DMN activation changes in healthy subjects. Given previous reports on platelet-synaptosomal Vmax coupling, results further suggest an important role of neuronal 5-HT reuptake in DMN regulation. PMID:24667541

  20. "Reverse Genomics" Predicts Function of Human Conserved Noncoding Elements.

    PubMed

    Marcovitz, Amir; Jia, Robin; Bejerano, Gill

    2016-05-01

    Evolutionary changes in cis-regulatory elements are thought to play a key role in morphological and physiological diversity across animals. Many conserved noncoding elements (CNEs) function as cis-regulatory elements, controlling gene expression levels in different biological contexts. However, determining specific associations between CNEs and related phenotypes is a challenging task. Here, we present a computational "reverse genomics" approach that predicts the phenotypic functions of human CNEs. We identify thousands of human CNEs that were lost in at least two independent mammalian lineages (IL-CNEs), and match their evolutionary profiles against a diverse set of phenotypes recently annotated across multiple mammalian species. We identify 2,759 compelling associations between human CNEs and a diverse set of mammalian phenotypes. We discuss multiple CNEs, including a predicted ear element near BMP7, a pelvic CNE in FBN1, a brain morphology element in UBE4B, and an aquatic adaptation forelimb CNE near EGR2, and provide a full list of our predictions. As more genomes are sequenced and more traits are annotated across species, we expect our method to facilitate the interpretation of noncoding mutations in human disease and expedite the discovery of individual CNEs that play key roles in human evolution and development. PMID:26744417

  1. Prediction uncertainty of environmental change effects on temperate European biodiversity.

    PubMed

    Dormann, Carsten F; Schweiger, Oliver; Arens, P; Augenstein, I; Aviron, St; Bailey, Debra; Baudry, J; Billeter, R; Bugter, R; Bukácek, R; Burel, F; Cerny, M; Cock, Raphaël De; De Blust, Geert; DeFilippi, R; Diekötter, Tim; Dirksen, J; Durka, W; Edwards, P J; Frenzel, M; Hamersky, R; Hendrickx, Frederik; Herzog, F; Klotz, St; Koolstra, B; Lausch, A; Le Coeur, D; Liira, J; Maelfait, J P; Opdam, P; Roubalova, M; Schermann-Legionnet, Agnes; Schermann, N; Schmidt, T; Smulders, M J M; Speelmans, M; Simova, P; Verboom, J; van Wingerden, Walter; Zobel, M

    2008-03-01

    Observed patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land-use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best-model predictions, which we show, using cross-validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land-use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections. PMID:18070098

  2. Relationship between efficiency and predictability in stock price change

    NASA Astrophysics Data System (ADS)

    Eom, Cheoljun; Oh, Gabjin; Jung, Woo-Sung

    2008-09-01

    In this study, we evaluate the relationship between efficiency and predictability in the stock market. The efficiency, which is the issue addressed by the weak-form efficient market hypothesis, is calculated using the Hurst exponent and the approximate entropy (ApEn). The predictability corresponds to the hit-rate; this is the rate of consistency between the direction of the actual price change and that of the predicted price change, as calculated via the nearest neighbor prediction method. We determine that the Hurst exponent and the ApEn value are negatively correlated. However, predictability is positively correlated with the Hurst exponent.

  3. LSD-induced entropic brain activity predicts subsequent personality change.

    PubMed

    Lebedev, A V; Kaelen, M; Lövdén, M; Nilsson, J; Feilding, A; Nutt, D J; Carhart-Harris, R L

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. PMID:27151536

  4. Predicted vibrational spectra from anharmonic potential functions

    SciTech Connect

    Dunn, K.M.

    1986-01-01

    The dissertation develops a procedure for predicting vibrational spectra of polyatomic molecules from a combination of theoretical and experimental information. Ab initio quantum chemical calculations provide anharmonic force constants including cubics and diagonal quartics. A variational procedure analogous to configuration interaction is then used to compute eigenvalues of the pure vibrational Hamiltonian. The diagonal quadratic force constants are then adjusted until the calculated fundamental frequencies agree with experiment. The resulting theoretical-experimental force field may then be used to predict the energies of vibrationally excited states. The method is applied to three molecules: hydrogen cyanide, ammonia, and methyl fluoride. For hydrogen cyanide, the dissertation presents predicted energies for all of the vibrationally excited states with up to four quanta of excitation distributed among the four modes. The root-mean-square error is 8.7 cm{sup {minus}1} for the states below 11,000 cm{sup {minus}1}. The force constants for ammonia are adjusted to reproduce the fundamental frequencies of ND{sub 3}. The force constants then predict the energies of states below 7000 cm{sup {minus}1} with an rms error of 5.8 cm{sup {minus}1} for ND{sub 3} and 16.7 cm{sup {minus}1} for NH{sub 3}. Finally, the adjusted force constants for methyl fluoride predict the energies of states below 4100 cm{sup {minus}1} with an rms error of 4.3 cm{sup {minus}1}. These force constants are also used to predict the CH stretching overtone region of CH{sub 3}F and the first, second and third overtone regions of CD{sub 2}FH for which experimental information is not available.

  5. Predicting Stages of Change in Battered Women

    ERIC Educational Resources Information Center

    Alexander, Pamela C.; Tracy, Allison; Radek, Megan; Koverola, Catherine

    2009-01-01

    Battered women's stages of change (SOCs) are examined in this study. First, confirmatory factor analysis and latent profile analysis were conducted on 754 battered women's responses on the Problems in Relationship Scale (Brown, 1998). Factor loadings were strong, and latent variable mixture modeling produces a two-class solution. Second,…

  6. Past paleoclimatic changes, origin and prediction

    NASA Astrophysics Data System (ADS)

    Mörner, N.-A.; Nevanlinna, H.; Shumilov, O.

    2003-04-01

    In north-western to western Europe, short periods of cold climate are recorded in the decades of 1440-1460, 1687-1703 and 1808-1821. This fits reasonably well with periods of sunspot minima; viz. the Spörer (1420-1500), Maunder (1645-1705) and Dalton (1800-1820) Minima, and a causal connection has been advocated. During these minima, Earth’s rotation experienced a speeding-up leading to a changed ocean circulation pulling down cold Arctic water along the European coasts and concentrating the hot Gulf Stream to the south European region (Mörner, 1995). Via the recording of past changes in aurora frequency and the relation between sunspot activity and aurora frequency, the sunspot activity can be approximated for 1500-2000 years. Even for this period, there seems to be a reasonable correlation between sunspot activity and recorded changes in climate. The combined Schwabe-Gleisberg sunspot cycles provide good correlation with observed changes in climate for the last 300-400 years (Shumilov). The phase of the sunspot cycles and global climate for the period 1860 to 1985 fit exceptionally well (Friis-Christensen &Lassen, 1991). The aa-index and climate fit well for the last 150 years all the way up to 1985 (Pulkkinen et al., 2001). If we extrapolate the combined Schwabe-Gleisberg cycles or the aa-index curve, a new period of cold climate is to be expected in AD 2050-2100. This fact has not yet been included in the IPCC scenarios on future changes in climate, and is quite contrary to their main conclusions.

  7. Chromatin changes predict recurrence after radical prostatectomy

    PubMed Central

    Hveem, Tarjei S; Kleppe, Andreas; Vlatkovic, Ljiljana; Ersvær, Elin; Wæhre, Håkon; Nielsen, Birgitte; Kjær, Marte Avranden; Pradhan, Manohar; Syvertsen, Rolf Anders; Nesheim, John Arne; Liestøl, Knut; Albregtsen, Fritz; Danielsen, Håvard E

    2016-01-01

    Background: Pathological evaluations give the best prognostic markers for prostate cancer patients after radical prostatectomy, but the observer variance is substantial. These risk assessments should be supported and supplemented by objective methods for identifying patients at increased risk of recurrence. Markers of epigenetic aberrations have shown promising results in several cancer types and can be assessed by automatic analysis of chromatin organisation in tumour cell nuclei. Methods: A consecutive series of 317 prostate cancer patients treated with radical prostatectomy at a national hospital between 1987 and 2005 were followed for a median of 10 years (interquartile range, 7–14). On average three tumour block samples from each patient were included to account for tumour heterogeneity. We developed a novel marker, termed Nucleotyping, based on automatic assessment of disordered chromatin organisation, and validated its ability to predict recurrence after radical prostatectomy. Results: Nucleotyping predicted recurrence with a hazard ratio (HR) of 3.3 (95% confidence interval (CI), 2.1–5.1). With adjustment for clinical and pathological characteristics, the HR was 2.5 (95% CI, 1.5–4.1). An updated stratification into three risk groups significantly improved the concordance with patient outcome compared with a state-of-the-art risk-stratification tool (P<0.001). The prognostic impact was most evident for the patients who were high-risk by clinical and pathological characteristics and for patients with Gleason score 7. Conclusion: A novel assessment of epigenetic aberrations was capable of improving risk stratification after radical prostatectomy. PMID:27124335

  8. Changes in intentions, planning, and self-efficacy predict changes in behaviors: an application of latent true change modeling.

    PubMed

    Reuter, Tabea; Ziegelmann, Jochen P; Wiedemann, Amelie U; Geiser, Christian; Lippke, Sonia; Schüz, Benjamin; Schwarzer, Ralf

    2010-09-01

    Can latent true changes in intention, planning, and self-efficacy account for latent change in two health behaviors (physical activity as well as fruit and vegetable intake)? Baseline data on predictors and behaviors and corresponding follow-up data four weeks later were collected from 853 participants. Interindividual differences in change and change-change associations were analyzed using structural equation modeling. For both behaviors, similar prediction patterns were found: changes in intention and self-efficacy predicted changes in planning, which in turn corresponded to changes in behavior. This evidence confirms that change predicts change, which is an inherent precondition in behavior change theories. PMID:20453049

  9. Network-based prediction of protein function

    PubMed Central

    Sharan, Roded; Ulitsky, Igor; Shamir, Ron

    2007-01-01

    Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community. PMID:17353930

  10. Maintaining Change: The Maintenance Function and the Change Process.

    ERIC Educational Resources Information Center

    Cooke, Fang Lee

    2003-01-01

    Explores the organization of the maintenance function of five manufacturing and utility companies and the involvement of maintenance workers in plant improvement. Highlights the role of tacit skills of maintenance workers and of the maintenance function on technological change and organizational performance. (Contains 41 references.) (JOW)

  11. MASS FUNCTION PREDICTIONS BEYOND {Lambda}CDM

    SciTech Connect

    Bhattacharya, Suman; Lukic, Zarija; Habib, Salman; Heitmann, Katrin; White, Martin; Wagner, Christian

    2011-05-10

    The statistics of dark matter halos is an essential component of precision cosmology. The mass distribution of halos, as specified by the halo mass function, is a key input for several cosmological probes. The sizes of N-body simulations are now such that, for the most part, results need no longer be statistics-limited, but are still subject to various systematic uncertainties. Discrepancies in the results of simulation campaigns for the halo mass function remain in excess of statistical uncertainties and of roughly the same size as the error limits set by near-future observations; we investigate and discuss some of the reasons for these differences. Quantifying error sources and compensating for them as appropriate, we carry out a high-statistics study of dark matter halos from 67 N-body simulations to investigate the mass function and its evolution for a reference {Lambda}CDM cosmology and for a set of wCDM cosmologies. For the reference {Lambda}CDM cosmology (close to WMAP5), we quantify the breaking of universality in the form of the mass function as a function of redshift, finding an evolution of as much as 10% away from the universal form between redshifts z = 0 and z = 2. For cosmologies very close to this reference we provide a fitting formula to our results for the (evolving) {Lambda}CDM mass function over a mass range of 6 x 10{sup 11}-3 x 10{sup 15} M{sub sun} to an estimated accuracy of about 2%. The set of wCDM cosmologies is taken from the Coyote Universe simulation suite. The mass functions from this suite (which includes a {Lambda}CDM cosmology and others with w {approx_equal} -1) are described by the fitting formula for the reference {Lambda}CDM case at an accuracy level of 10%, but with clear systematic deviations. We argue that, as a consequence, fitting formulae based on a universal form for the mass function may have limited utility in high-precision cosmological applications.

  12. Mass Function Predictions Beyond ΛCDM

    NASA Astrophysics Data System (ADS)

    Bhattacharya, Suman; Heitmann, Katrin; White, Martin; Lukić, Zarija; Wagner, Christian; Habib, Salman

    2011-05-01

    The statistics of dark matter halos is an essential component of precision cosmology. The mass distribution of halos, as specified by the halo mass function, is a key input for several cosmological probes. The sizes of N-body simulations are now such that, for the most part, results need no longer be statistics-limited, but are still subject to various systematic uncertainties. Discrepancies in the results of simulation campaigns for the halo mass function remain in excess of statistical uncertainties and of roughly the same size as the error limits set by near-future observations; we investigate and discuss some of the reasons for these differences. Quantifying error sources and compensating for them as appropriate, we carry out a high-statistics study of dark matter halos from 67 N-body simulations to investigate the mass function and its evolution for a reference ΛCDM cosmology and for a set of wCDM cosmologies. For the reference ΛCDM cosmology (close to WMAP5), we quantify the breaking of universality in the form of the mass function as a function of redshift, finding an evolution of as much as 10% away from the universal form between redshifts z = 0 and z = 2. For cosmologies very close to this reference we provide a fitting formula to our results for the (evolving) ΛCDM mass function over a mass range of 6 × 1011-3 × 1015 M sun to an estimated accuracy of about 2%. The set of wCDM cosmologies is taken from the Coyote Universe simulation suite. The mass functions from this suite (which includes a ΛCDM cosmology and others with w ~= -1) are described by the fitting formula for the reference ΛCDM case at an accuracy level of 10%, but with clear systematic deviations. We argue that, as a consequence, fitting formulae based on a universal form for the mass function may have limited utility in high-precision cosmological applications.

  13. Changes in gastrointestinal tract function and structure in functional dyspepsia.

    PubMed

    Vanheel, Hanne; Farré, Ricard

    2013-03-01

    Functional dyspepsia is an extremely common disorder of gastrointestinal function. The disorder is thought to be heterogeneous, with different pathophysiological mechanisms underlying varied symptom patterns. A diversity of changes in gastrointestinal tract function and structure has been described in functional dyspepsia. These involve alterations in the stomach, such as impaired accommodation, delayed gastric emptying and hypersensitivity, and alterations in the duodenum, such as increased sensitivity to duodenal acid and/or lipids and low-grade inflammation. In this Review, we summarize all these abnormalities in an attempt to provide an integrated overview of the pathophysiological mechanisms in functional dyspepsia. PMID:23318268

  14. Early executive function predicts reasoning development.

    PubMed

    Richland, Lindsey E; Burchinal, Margaret R

    2013-01-01

    Analogical reasoning is a core cognitive skill that distinguishes humans from all other species and contributes to general fluid intelligence, creativity, and adaptive learning capacities. Yet its origins are not well understood. In the study reported here, we analyzed large-scale longitudinal data from the Study of Early Child Care and Youth Development to test predictors of growth in analogical-reasoning skill from third grade to adolescence. Our results suggest an integrative resolution to the theoretical debate regarding contributory factors arising from smaller-scale, cross-sectional experiments on analogy development. Children with greater executive-function skills (both composite and inhibitory control) and vocabulary knowledge in early elementary school displayed higher scores on a verbal analogies task at age 15 years, even after adjusting for key covariates. We posit that knowledge is a prerequisite to analogy performance, but strong executive-functioning resources during early childhood are related to long-term gains in fundamental reasoning skills. PMID:23184588

  15. Free light fields can change the predictions of hybrid inflation

    SciTech Connect

    Matsuda, Tomohiro

    2012-04-01

    We show that the free light scalar fields that may exist in the inflationary Universe can change the predictions of the hybrid inflation model. Possible signatures are discussed, which can be used to discriminate the sources of the spectrum.

  16. Probabilistic Protein Function Prediction from Heterogeneous Genome-Wide Data

    PubMed Central

    Nariai, Naoki; Kolaczyk, Eric D.; Kasif, Simon

    2007-01-01

    Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function. PMID:17396164

  17. Metabolic Syndrome Biomarkers Predict Lung Function Impairment

    PubMed Central

    Naveed, Bushra; Weiden, Michael D.; Kwon, Sophia; Gracely, Edward J.; Comfort, Ashley L.; Ferrier, Natalia; Kasturiarachchi, Kusali J.; Cohen, Hillel W.; Aldrich, Thomas K.; Rom, William N.; Kelly, Kerry; Prezant, David J.

    2012-01-01

    Rationale: Cross-sectional studies demonstrate an association between metabolic syndrome and impaired lung function. Objectives: To define if metabolic syndrome biomarkers are risk factors for loss of lung function after irritant exposure. Methods: A nested case-control study of Fire Department of New York personnel with normal pre–September 11th FEV1 and who presented for subspecialty pulmonary evaluation before March 10, 2008. We correlated metabolic syndrome biomarkers obtained within 6 months of World Trade Center dust exposure with subsequent FEV1. FEV1 at subspecialty pulmonary evaluation within 6.5 years defined disease status; cases had FEV1 less than lower limit of normal, whereas control subjects had FEV1 greater than or equal to lower limit of normal. Measurements and Main Results: Clinical data and serum sampled at the first monitoring examination within 6 months of September 11, 2001, assessed body mass index, heart rate, serum glucose, triglycerides and high-density lipoprotein (HDL), leptin, pancreatic polypeptide, and amylin. Cases and control subjects had significant differences in HDL less than 40 mg/dl with triglycerides greater than or equal to 150 mg/dl, heart rate greater than or equal to 66 bpm, and leptin greater than or equal to 10,300 pg/ml. Each increased the odds of abnormal FEV1 at pulmonary evaluation by more than twofold, whereas amylin greater than or equal to 116 pg/ml decreased the odds by 84%, in a multibiomarker model adjusting for age, race, body mass index, and World Trade Center arrival time. This model had a sensitivity of 41%, a specificity of 86%, and a receiver operating characteristic area under the curve of 0.77. Conclusions: Abnormal triglycerides and HDL and elevated heart rate and leptin are independent risk factors of greater susceptibility to lung function impairment after September 11, 2001, whereas elevated amylin is protective. Metabolic biomarkers are predictors of lung disease, and may be useful for assessing

  18. Changes in Memory Prediction Accuracy: Age and Performance Effects

    ERIC Educational Resources Information Center

    Pearman, Ann; Trujillo, Amanda

    2013-01-01

    Memory performance predictions are subjective estimates of possible memory task performance. The purpose of this study was to examine possible factors related to changes in word list performance predictions made by younger and older adults. Factors included memory self-efficacy, actual performance, and perceptions of performance. The current study…

  19. Functional prediction of hypothetical proteins in human adenoviruses.

    PubMed

    Dorden, Shane; Mahadevan, Padmanabhan

    2015-01-01

    Assigning functional information to hypothetical proteins in virus genomes is crucial for gaining insight into their proteomes. Human adenoviruses are medium sized viruses that cause a range of diseases. Their genomes possess proteins with uncharacterized function known as hypothetical proteins. Using a wide range of protein function prediction servers, functional information was obtained about these hypothetical proteins. A comparison of functional information obtained from these servers revealed that some of them produced functional information, while others provided little functional information about these human adenovirus hypothetical proteins. The PFP, ESG, PSIPRED, 3d2GO, and ProtFun servers produced the most functional information regarding these hypothetical proteins. PMID:26664031

  20. A Simple Model Predicting Individual Weight Change in Humans

    PubMed Central

    Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

    2010-01-01

    Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

  1. A Simple Model Predicting Individual Weight Change in Humans.

    PubMed

    Thomas, Diana M; Martin, Corby K; Heymsfield, Steven; Redman, Leanne M; Schoeller, Dale A; Levine, James A

    2011-11-01

    Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants' weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

  2. Using search engine technology for protein function prediction.

    PubMed

    Chen, Ziyang; Cai, Zhao; Li, Min; Liu, Binbin

    2011-01-01

    Prediction of protein function is one of the most challenging problems in the post-genomic era. In this paper, we propose a novel algorithm Improved ProteinRank (IPR) for protein function prediction, which is based on the search engine technology and the preferential attachment criteria. In addition, an improved algorithm IPRW is developed from IPR to be used in the weighted protein?protein interaction (PPI) network. The proposed algorithms IPR and IPRW are applied to the PPI network of S.cerevisiae. The experimental results show that both IPR and IPRW outweigh the previous methods for the prediction of protein functions. PMID:21441099

  3. Predicting individual brain maturity using dynamic functional connectivity

    PubMed Central

    Qin, Jian; Chen, Shan-Guang; Hu, Dewen; Zeng, Ling-Li; Fan, Yi-Ming; Chen, Xiao-Ping; Shen, Hui

    2015-01-01

    Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7–30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains. PMID:26236224

  4. Dopamine neurons share common response function for reward prediction error

    PubMed Central

    Eshel, Neir; Tian, Ju; Bukwich, Michael; Uchida, Naoshige

    2016-01-01

    Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically-identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found striking homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we could describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal. PMID:26854803

  5. Roommate Changes in Residence Halls: Can They Be Predicted?

    ERIC Educational Resources Information Center

    Hallisey, Jacqueline N.; Harren, Vincent A.; Caple, Richard B.

    2015-01-01

    The purpose of this study was to examine the demographic and academic variables of students involved in roommate changes to determine which variables predict who will move from a room and who will stay in a room and what alternatives to current housing arrangements are selected by those who initiate the roommate changes. [This article was…

  6. A new protein structure representation for efficient protein function prediction.

    PubMed

    Maghawry, Huda A; Mostafa, Mostafa G M; Gharib, Tarek F

    2014-12-01

    One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average. PMID:25343279

  7. Gene function prediction with knowledge from gene ontology.

    PubMed

    Shen, Ying; Zhang, Lin

    2015-01-01

    Gene function prediction is an important problem in bioinformatics. Due to the inherent noise existing in the gene expression data, the attempt to improve the prediction accuracy resorting to new classification techniques is limited. With the emergence of Gene Ontology (GO), extra knowledge about the gene products can be extracted from GO and facilitates solving the gene function prediction problem. In this paper, we propose a new method which utilises GO information to improve the classifiers' performance in gene function prediction. Specifically, our method learns a distance metric under the supervision of the GO knowledge using the distance learning technique. Compared with the traditional distance metrics, the learned one produces a better performance and consequently classification accuracy can be improved. The effectiveness of our proposed method has been corroborated by the extensive experimental results. PMID:26529907

  8. A Survey of Computational Intelligence Techniques in Protein Function Prediction

    PubMed Central

    Tiwari, Arvind Kumar; Srivastava, Rajeev

    2014-01-01

    During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

  9. Empirical sediment transport function predicting seepage erosion undercutting for cohesive bank failure prediction

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Seepage erosion is an important factor in hillslope instability and failure. However, predicting erosion by subsurface flow or seepage and incorporating its effects into stability models remains a challenge. Limitations exist with all existing seepage erosion sediment transport functions, including ...

  10. Can future land use change be usefully predicted?

    NASA Astrophysics Data System (ADS)

    Ramankutty, N.; Coomes, O.

    2011-12-01

    There has been increasing recognition over the last decade that land use and land cover change is an important driver of global environmental change. Consequently, there have been growing efforts to understanding processes of land change from local-to-global scales, and to develop models to predict future changes in the land. However, we believe that such efforts are hampered by limited attention being paid to the critical points of land change. Here, we present a framework for understanding land use change by distinguishing within-regime land-use dynamics from land-use regime shifts. Illustrative historical examples reveal the significance of land-use regime shifts. We further argue that the land-use literature predominantly demonstrates a good understanding (with predictive power) of within-regime dynamics, while understanding of land-use regime shifts is limited to ex post facto explanations with limited predictive capability. The focus of land use change science needs to be redirected toward studying land-use regime shifts if we are to have any hope of making useful future projections. We present a preliminary framework for understanding land-use regime-shifts, using two case studies in Latin America as examples. We finally discuss the implications of our proposal for land change science.

  11. Preschool Executive Functioning Abilities Predict Early Mathematics Achievement

    ERIC Educational Resources Information Center

    Clark, Caron A. C.; Pritchard, Verena E.; Woodward, Lianne J.

    2010-01-01

    Impairments in executive function have been documented in school-age children with mathematical learning difficulties. However, the utility and specificity of preschool executive function abilities in predicting later mathematical achievement are poorly understood. This study examined linkages between children's developing executive function…

  12. A Unitary Executive Function Predicts Intelligence in Children

    ERIC Educational Resources Information Center

    Brydges, Christopher R.; Reid, Corinne L.; Fox, Allison M.; Anderson, Mike

    2012-01-01

    Executive functions (EF) and intelligence are of critical importance to success in many everyday tasks. Working memory, or updating, which is one latent variable identified in confirmatory factor analytic models of executive functions, predicts intelligence (both fluid and crystallised) in adults, but inhibition and shifting do not (Friedman et…

  13. INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES

    PubMed Central

    Grant, Marianne A.

    2014-01-01

    Pharmaceutical researchers must evaluate vast numbers of protein sequences and formulate innovative strategies for identifying valid targets and discovering leads against them as a way of accelerating drug discovery. The ever increasing number and diversity of novel protein sequences identified by genomic sequencing projects and the success of worldwide structural genomics initiatives have spurred great interest and impetus in the development of methods for accurate, computationally empowered protein function prediction and active site identification. Previously, in the absence of direct experimental evidence, homology-based protein function annotation remained the gold-standard for in silico analysis and prediction of protein function. However, with the continued exponential expansion of sequence databases, this approach is not always applicable, as fewer query protein sequences demonstrate significant homology to protein gene products of known function. As a result, several non-homology based methods for protein function prediction that are based on sequence features, structure, evolution, biochemical and genetic knowledge have emerged. Herein, we review current bioinformatic programs and approaches for protein function prediction/annotation and discuss their integration into drug discovery initiatives. The development of such methods to annotate protein functional sites and their application to large protein functional families is crucial to successfully utilizing the vast amounts of genomic sequence information available to drug discovery and development processes. PMID:25530654

  14. Beyond predictions: biodiversity conservation in a changing climate.

    PubMed

    Dawson, Terence P; Jackson, Stephen T; House, Joanna I; Prentice, Iain Colin; Mace, Georgina M

    2011-04-01

    Climate change is predicted to become a major threat to biodiversity in the 21st century, but accurate predictions and effective solutions have proved difficult to formulate. Alarming predictions have come from a rather narrow methodological base, but a new, integrated science of climate-change biodiversity assessment is emerging, based on multiple sources and approaches. Drawing on evidence from paleoecological observations, recent phenological and microevolutionary responses, experiments, and computational models, we review the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change, including the extent of species' natural resilience. We introduce a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses. Although much of the information reviewed is on species, our framework and conclusions are also applicable to ecosystems, habitats, ecological communities, and genetic diversity, whether terrestrial, marine, or fresh water. PMID:21454781

  15. Changes in Pilot Behavior with Predictive System Status Information

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.

    1998-01-01

    Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

  16. A large-scale evaluation of computational protein function prediction

    PubMed Central

    Radivojac, Predrag; Clark, Wyatt T; Ronnen Oron, Tal; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kassner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Böhm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

    2013-01-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools. PMID:23353650

  17. A large-scale evaluation of computational protein function prediction.

    PubMed

    Radivojac, Predrag; Clark, Wyatt T; Oron, Tal Ronnen; Schnoes, Alexandra M; Wittkop, Tobias; Sokolov, Artem; Graim, Kiley; Funk, Christopher; Verspoor, Karin; Ben-Hur, Asa; Pandey, Gaurav; Yunes, Jeffrey M; Talwalkar, Ameet S; Repo, Susanna; Souza, Michael L; Piovesan, Damiano; Casadio, Rita; Wang, Zheng; Cheng, Jianlin; Fang, Hai; Gough, Julian; Koskinen, Patrik; Törönen, Petri; Nokso-Koivisto, Jussi; Holm, Liisa; Cozzetto, Domenico; Buchan, Daniel W A; Bryson, Kevin; Jones, David T; Limaye, Bhakti; Inamdar, Harshal; Datta, Avik; Manjari, Sunitha K; Joshi, Rajendra; Chitale, Meghana; Kihara, Daisuke; Lisewski, Andreas M; Erdin, Serkan; Venner, Eric; Lichtarge, Olivier; Rentzsch, Robert; Yang, Haixuan; Romero, Alfonso E; Bhat, Prajwal; Paccanaro, Alberto; Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A; Kaufmann, Stefanie; Kiening, Michael; Krompass, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Björne, Jari; Salakoski, Tapio; Wong, Andrew; Shatkay, Hagit; Gatzmann, Fanny; Sommer, Ingolf; Wass, Mark N; Sternberg, Michael J E; Škunca, Nives; Supek, Fran; Bošnjak, Matko; Panov, Panče; Džeroski, Sašo; Šmuc, Tomislav; Kourmpetis, Yiannis A I; van Dijk, Aalt D J; ter Braak, Cajo J F; Zhou, Yuanpeng; Gong, Qingtian; Dong, Xinran; Tian, Weidong; Falda, Marco; Fontana, Paolo; Lavezzo, Enrico; Di Camillo, Barbara; Toppo, Stefano; Lan, Liang; Djuric, Nemanja; Guo, Yuhong; Vucetic, Slobodan; Bairoch, Amos; Linial, Michal; Babbitt, Patricia C; Brenner, Steven E; Orengo, Christine; Rost, Burkhard; Mooney, Sean D; Friedberg, Iddo

    2013-03-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools. PMID:23353650

  18. Protein Structure and Function Prediction Using I-TASSER

    PubMed Central

    Yang, Jianyi; Zhang, Yang

    2016-01-01

    I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets. PMID:26678386

  19. Predicting effects of environmental change on a migratory herbivore

    USGS Publications Warehouse

    Stillman, R A; Wood, K A; Gilkerson, Whelan; Elkinton, E; Black, J. M.; Ward, David H.; Petrie, M.

    2015-01-01

    Changes in climate, food abundance and disturbance from humans threaten the ability of species to successfully use stopover sites and migrate between non-breeding and breeding areas. To devise successful conservation strategies for migratory species we need to be able to predict how such changes will affect both individuals and populations. Such predictions should ideally be process-based, focusing on the mechanisms through which changes alter individual physiological state and behavior. In this study we use a process-based model to evaluate how Black Brant (Branta bernicla nigricans) foraging on common eelgrass (Zostera marina) at a stopover site (Humboldt Bay, USA), may be affected by changes in sea level, food abundance and disturbance. The model is individual-based, with empirically based parameters, and incorporates the immigration of birds into the site, tidal changes in availability of eelgrass, seasonal and depth-related changes in eelgrass biomass, foraging behavior and energetics of the birds, and their mass-dependent decisions to emigrate. The model is validated by comparing predictions to observations across a range of system properties including the time birds spent foraging, probability of birds emigrating, mean stopover duration, peak bird numbers, rates of mass gain and distribution of birds within the site: all 11 predictions were within 35% of the observed value, and 8 within 20%. The model predicted that the eelgrass within the site could potentially support up to five times as many birds as currently use the site. Future predictions indicated that the rate of mass gain and mean stopover duration were relatively insensitive to sea level rise over the next 100 years, primarily because eelgrass habitat could redistribute shoreward into intertidal mudflats within the site to compensate for higher sea levels. In contrast, the rate of mass gain and mean stopover duration were sensitive to changes in total eelgrass biomass and the percentage of time

  20. The Mutational Spectrum of Holoprosencephaly-Associated Changes within the SHH Gene in Humans Predicts Loss-of-Function Through Either Key Structural Alterations of the Ligand or Its Altered Synthesis

    PubMed Central

    Roessler, Erich; El-Jaick, Kenia B.; Dubourg, Christèle; Vélez, Jorge I.; Solomon, Benjamin D.; Pineda-Álvarez, Daniel E.; Lacbawan, Felicitas; Zhou, Nan; Ouspenskaia, Maia; Paulussen, Aimée; Smeets, Hubert J.; Hehr, Ute; Bendavid, Claude; Bale, Sherri; Odent, Sylvie; David, Véronique; Muenke, Maximilian

    2009-01-01

    Mutations within either the SHH gene or its related pathway components are the most common, and best understood, pathogenetic changes observed in holoprosencephaly patients; this fact is consistent with the essential functions of this gene during forebrain development and patterning. Here we summarize the nature and types of deleterious sequence alterations among over one hundred distinct mutations in the SHH gene (64 novel mutations) and compare these to over a dozen mutations in disease-related Hedgehog family members IHH and DHH. This combined structural analysis suggests that dysfunction of Hedgehog signaling in human forebrain development can occur through truncations or major structural changes to the signaling domain, SHH-N, as well as due to defects in the processing of the mature ligand from its pre-pro-precursor or defective post-translation bi-lipid modifications with palmitate and cholesterol PMID:19603532

  1. Revisiting the prediction of protein function at CASP6.

    PubMed

    Pellegrini-Calace, Marialuisa; Soro, Simonetta; Tramontano, Anna

    2006-07-01

    The ability to predict the function of a protein, given its sequence and/or 3D structure, is an essential requirement for exploiting the wealth of data made available by genomics and structural genomics projects and is therefore raising increasing interest in the computational biology community. To foster developments in the area as well as to establish the state of the art of present methods, a function prediction category was tentatively introduced in the 6th edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP) worldwide experiment. The assessment of the performance of the methods was made difficult by at least two factors: (a) the experimentally determined function of the targets was not available at the time of assessment; (b) the experiment is run blindly, preventing verification of whether the convergence of different predictions towards the same functional annotation was due to the similarity of the methods or to a genuine signal detectable by different methodologies. In this work, we collected information about the methods used by the various predictors and revisited the results of the experiment by verifying how often and in which cases a convergent prediction was obtained by methods based on different rationale. We propose a method for classifying the type and redundancy of the methods. We also analyzed the cases in which a function for the target protein has become available. Our results show that predictions derived from a consensus of different methods can reach an accuracy as high as 80%. It follows that some of the predictions submitted to CASP6, once reanalyzed taking into account the type of converging methods, can provide very useful information to researchers interested in the function of the target proteins. PMID:16759228

  2. Predicting vulnerabilities of North American shorebirds to climate change.

    PubMed

    Galbraith, Hector; DesRochers, David W; Brown, Stephen; Reed, J Michael

    2014-01-01

    Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at-risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners-in-Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower-risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change. PMID:25268907

  3. Predicting Vulnerabilities of North American Shorebirds to Climate Change

    PubMed Central

    Galbraith, Hector; DesRochers, David W.; Brown, Stephen; Reed, J. Michael

    2014-01-01

    Despite an increase in conservation efforts for shorebirds, there are widespread declines of many species of North American shorebirds. We wanted to know whether these declines would be exacerbated by climate change, and whether relatively secure species might become at–risk species. Virtually all of the shorebird species breeding in the USA and Canada are migratory, which means climate change could affect extinction risk via changes on the breeding, wintering, and/or migratory refueling grounds, and that ecological synchronicities could be disrupted at multiple sites. To predict the effects of climate change on shorebird extinction risks, we created a categorical risk model complementary to that used by Partners–in–Flight and the U.S. Shorebird Conservation Plan. The model is based on anticipated changes in breeding, migration, and wintering habitat, degree of dependence on ecological synchronicities, migration distance, and degree of specialization on breeding, migration, or wintering habitat. We evaluated 49 species, and for 3 species we evaluated 2 distinct populations each, and found that 47 (90%) taxa are predicted to experience an increase in risk of extinction. No species was reclassified into a lower–risk category, although 6 species had at least one risk factor decrease in association with climate change. The number of species that changed risk categories in our assessment is sensitive to how much of an effect of climate change is required to cause the shift, but even at its least sensitive, 20 species were at the highest risk category for extinction. Based on our results it appears that shorebirds are likely to be highly vulnerable to climate change. Finally, we discuss both how our approach can be integrated with existing risk assessments and potential future directions for predicting change in extinction risk due to climate change. PMID:25268907

  4. Changes in Monocyte Functions of Astronauts

    NASA Technical Reports Server (NTRS)

    Kaur, I.; Simons, E.; Castro, V.; Ott, C. Mark; Pierson, Duane L.

    2004-01-01

    Monocyte cell numbers and functions, including phagocytosis, oxidative burst capacity, and degranulation and expression of related surface molecules, were studied in blood specimens from 25 astronauts and 9 healthy control subjects. Blood samples were obtained 10 days before a space flight, 3 hours after landing and 3 days after landing. The number of monocytes in astronauts did not change significantly among the three sample collection periods. Following space flight, the monocytes ability to phagocytize Escherichia coli, to exhibit an oxidative burst, and to degranulate was reduced as compared to monocytes from control subjects. These alterations in monocyte functions after space flight correlated with alterations in the expression of CD32 and CD64.

  5. Climate-Induced Boreal Forest Change: Predictions versus Current Observations

    NASA Technical Reports Server (NTRS)

    Soja, Amber J.; Tchebakova, Nadezda M.; French, Nancy H. F.; Flannigan, Michael D.; Shugart, Herman H.; Stocks, Brian J.; Sukhinin, Anatoly I.; Parfenova, E. I.; Chapin, F. Stuart, III; Stackhouse, Paul W., Jr.

    2007-01-01

    For about three decades, there have been many predictions of the potential ecological response in boreal regions to the currently warmer conditions. In essence, a widespread, naturally occurring experiment has been conducted over time. In this paper, we describe previously modeled predictions of ecological change in boreal Alaska, Canada and Russia, and then we investigate potential evidence of current climate-induced change. For instance, ecological models have suggested that warming will induce the northern and upslope migration of the treeline and an alteration in the current mosaic structure of boreal forests. We present evidence of the migration of keystone ecosystems in the upland and lowland treeline of mountainous regions across southern Siberia. Ecological models have also predicted a moisture-stress-related dieback in white spruce trees in Alaska, and current investigations show that as temperatures increase, white spruce tree growth is declining. Additionally, it was suggested that increases in infestation and wildfire disturbance would be catalysts that precipitate the alteration of the current mosaic forest composition. In Siberia, five of the last seven years have resulted in extreme fire seasons, and extreme fire years have also been more frequent in both Alaska and Canada. In addition, Alaska has experienced extreme and geographically expansive multi-year outbreaks of the spruce beetle, which had been previously limited by the cold, moist environment. We suggest that there is substantial evidence throughout the circumboreal region to conclude that the biosphere within the boreal terrestrial environment has already responded to the transient effects of climate change. Additionally, temperature increases and warming-induced change are progressing faster than had been predicted in some regions, suggesting a potential non-linear rapid response to changes in climate, as opposed to the predicted slow linear response to climate change.

  6. Predicting the response of populations to environmental change

    SciTech Connect

    Ives, A.R.

    1995-04-01

    When subject to long-term directional environmental perturbations, changes in population densities depend on the positive and negative feedbacks operating through interactions within and among species in a community. This paper develops techniques to predict the long-term responses of population densities to environmental changes using data on short-term population fluctuations driven by short-term environmental variability. In addition to giving quantitative predictions, the techniques also reveal how different qualitative patterns of species interactions either buffer or accentuate population responses to environmental trends. All of the predictions are based on regression coefficients extracted from time series data, and they can therefore be applied with a minimum of mathematical and statistical gymnastics. 48 refs., 10 figs., 4 tabs.

  7. Adherence indicators predict changes in health outcomes: HUB City Steps

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Participant adherence is a major threat to intervention effectiveness. Most researchers have reported effects of a single adherence measure on health outcomes. The objective of this analysis was to evaluate two adherence measures, separately and in combination, for predicting changes in health out...

  8. Decision support system for predicting color change after tooth whitening.

    PubMed

    Thanathornwong, Bhornsawan; Suebnukarn, Siriwan; Ouivirach, Kan

    2016-03-01

    Tooth whitening is becoming increasingly popular among patients and dentists since it is a relatively noninvasive approach. However, the degree of color change after tooth whitening is known to vary substantially between studies. The present study aims to develop a clinical decision support system for predicting color change after in-office tooth whitening. We used the information from patients' data sets, and applied the multiple regression equation of CIELAB color coordinates including L*, a*, and b* of the original tooth color and the color difference (ΔE) that expresses the color change after tooth whitening. To evaluate the system performance, the patient's post-treatment color was used as "gold standard" to compare with the post-treatment color predicted by the system. There was a high degree of agreement between the patient's post-treatment color and the post-treatment color predicted by the system (kappa value=0.894). The results obtained have demonstrated that the decision support system is possible to predict the color change obtained using an in-office whitening system using colorimetric values. PMID:26657921

  9. Predicting Change in Eelgrass Distribution Due to Sea Level Rise

    EPA Science Inventory

    The eelgrass species Zostera marina is the dominant estuarine seagrass on the Pacific Northwest coast of North America and provides important ecosystem services and functions. The loss of eelgrass bed acreage due to environmental pressures is of world-wide concern, yet predicted ...

  10. Changes in Neutrophil Functions in Astronauts

    NASA Technical Reports Server (NTRS)

    Kaur, Indreshpal; Simons, Elizabeth R.; Castro, Victoria; Pierson, Duane L.

    2002-01-01

    Neutrophil functions (phagocytosis, oxidative burst, degranulation) and expression of surface markers involved in these functions were studied in 25 astronauts before and after 4 space shuttle missions. Space flight duration ranged from 5 to 11 days. Blood specimens were obtained 10 days before launch (preflight or L-10), immediately after landing (landing or R+0), and again at 3 days after landing (postflight or R+3). Blood samples were also collected from 9 healthy low-stressed subjects at 3 time points simulating a 10-day shuttle mission. The number of neutrophils increased at landing by 85 percent when compared to the preflight numbers. Neutrophil functions were studied in whole blood using flow cytometric methods. Phagocytosis of E.coli-FITC and oxidative burst capacity of the neutrophils following the 9 to 11 day missions were lower at all three sampling points than the mean values for control subjects. Phagocytosis and oxidative burst capacity of the astronauts was decreased even 10-days before space flight. Mission duration appears to be a factor in phagocytic and oxidative functions. In contrast, following the short-duration (5-days) mission, these functions were unchanged from control values. No consistent changes in degranulation were observed following either short or medium length space missions. The expression of CD16, CD32, CD11a, CD11b, CD11c, L-selectin and CD36 was measured and found to be variable. Specifically, CD16 and CD32 did not correlate with the changes in oxidative burst and phagocytosis. We can conclude from this study that the stresses associated with space flight can alter the important functions of neutrophils.

  11. Predicting plasticity: acute context-dependent changes to vocal performance predict long-term age-dependent changes.

    PubMed

    James, Logan S; Sakata, Jon T

    2015-10-01

    Understanding the factors that predict and guide variation in behavioral change can lend insight into mechanisms of motor plasticity and individual differences in behavior. The performance of adult birdsong changes with age in a manner that is similar to rapid context-dependent changes to song. To reveal mechanisms of vocal plasticity, we analyzed the degree to which variation in the direction and magnitude of age-dependent changes to Bengalese finch song could be predicted by variation in context-dependent changes. Using a repeated-measures design, we found that variation in age-dependent changes to the timing, sequencing, and structure of vocal elements ("syllables") was significantly predicted by variation in context-dependent changes. In particular, the degree to which the duration of intersyllable gaps, syllable sequencing at branch points, and fundamental frequency of syllables within spontaneous [undirected (UD)] songs changed over time was correlated with the degree to which these features changed from UD song to female-directed (FD) song in young-adult finches (FDyoung). As such, the structure of some temporal features of UD songs converged over time onto the structure of FDyoung songs. This convergence suggested that the FDyoung song could serve as a stable target for vocal motor plasticity. Consequently, we analyzed the stability of FD song and found that the temporal structure of FD song changed significantly over time in a manner similar to UD song. Because FD song is considered a state of heightened performance, these data suggest that age-dependent changes could reflect practice-related improvements in vocal motor performance. PMID:26311186

  12. PredictProtein—an open resource for online prediction of protein structural and functional features

    PubMed Central

    Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

    2014-01-01

    PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

  13. Prediction of Postchemotherapy Ovarian Function Using Markers of Ovarian Reserve

    PubMed Central

    Xia, Rong; Schott, Anne F.; McConnell, Daniel; Banerjee, Mousumi; Hayes, Daniel F.

    2014-01-01

    Background. Reproductive-aged women frequently receive both chemotherapy and endocrine therapy as part of their treatment regimen for early stage hormone receptor-positive breast cancer. Chemotherapy results in transient or permanent ovarian failure in the majority of women. The difficulty in determining which patients will recover ovarian function has implications for adjuvant endocrine therapy decision making. We hypothesized that pretreatment serum anti-Müllerian hormone (AMH) and inhibin B concentrations would predict for ovarian function following chemotherapy. Methods. Pre- and perimenopausal women aged 25–50 years with newly diagnosed breast cancer were enrolled. Subjects underwent phlebotomy for assessment of serum AMH, inhibin B, follicle-stimulating hormone, and estradiol prior to chemotherapy and 1 month and 1 year following completion of treatment. Associations among hormone concentrations, clinical factors, and biochemically assessed ovarian function were assessed. Results. Twenty-seven subjects were evaluable for the primary endpoint. Median age was 41. Twenty subjects (74.1%) experienced recovery of ovarian function within 18 months. Of the 26 evaluable subjects assessed prior to chemotherapy, 19 (73.1%) had detectable serum concentrations of AMH. The positive predictive value of a detectable baseline serum AMH concentration for recovery of ovarian function was 94.7%, and the negative predictive value was 85.7%. On univariate analysis, younger age and detectable serum AMH concentration at chemotherapy initiation were predictive of increased likelihood of recovery of ovarian function. Conclusion. Prechemotherapy assessment of serum AMH may be useful for predicting postchemotherapy ovarian function. This finding has implications for decision making about adjuvant endocrine therapy in premenopausal women treated with chemotherapy. PMID:24319018

  14. Predictions of avian Plasmodium expansion under climate change

    PubMed Central

    Loiseau, Claire; Harrigan, Ryan J.; Bichet, Coraline; Julliard, Romain; Garnier, Stéphane; Lendvai, Ádám Z.; Chastel, Olivier; Sorci, Gabriele

    2013-01-01

    Vector-borne diseases are particularly responsive to changing environmental conditions. Diurnal temperature variation has been identified as a particularly important factor for the development of malaria parasites within vectors. Here, we conducted a survey across France, screening populations of the house sparrow (Passer domesticus) for malaria (Plasmodium relictum). We investigated whether variation in remotely-sensed environmental variables accounted for the spatial variation observed in prevalence and parasitemia. While prevalence was highly correlated to diurnal temperature range and other measures of temperature variation, environmental conditions could not predict spatial variation in parasitemia. Based on our empirical data, we mapped malaria distribution under climate change scenarios and predicted that Plasmodium occurrence will spread to regions in northern France, and that prevalence levels are likely to increase in locations where transmission already occurs. Our findings, based on remote sensing tools coupled with empirical data suggest that climatic change will significantly alter transmission of malaria parasites. PMID:23350033

  15. Biochemical functional predictions for protein structures of unknown or uncertain function

    PubMed Central

    Mills, Caitlyn L.; Beuning, Penny J.; Ondrechen, Mary Jo

    2015-01-01

    With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations. PMID:25848497

  16. Residential property values predict prevalent obesity but do not predict 1 year weight change

    PubMed Central

    Drewnowski, Adam; Aggarwal, Anju; Tang, Wesley; Moudon, Anne Vernez

    2014-01-01

    Objective Lower socioeconomic status (SES) has been linked with higher obesity rates but not with weight gain. This study examined whether SES can predict short-term weight change. Design and Methods The Seattle Obesity Study II was based on an observational cohort of 440 adults. Weights and heights were measured at baseline and at 1 y. Self-reported education and incomes were obtained by questionnaire. Home addresses were linked to tax parcel property values from the King Co. tax assessor. Associations among SES variables, prevalent obesity, and 1 y weight change were examined using multivariable linear regressions. Results Low residential property values at tax parcel predicted prevalent obesity at baseline and at 1 y. Living in the top quartile of house prices reduced obesity risk by 80% at both time points. At 1 year, about 38% of the sample lost >1kg body weight; 32% maintained (± 1kg), and 30% gained >1kg. In adjusted models, none of the baseline SES measures had any impact on 1 y weight change. Conclusions SES variables, including tax parcel property values predicted prevalent obesity but did not predict short-term weight change. These findings, based on longitudinal cohort data, suggest other mechanisms are involved in short-term weight change. PMID:25684713

  17. Drought Predictability and Prediction in a Changing Climate: Assessing Current Predictive Knowledge and Capabilities, User Requirements and Research Priorities

    NASA Technical Reports Server (NTRS)

    Schubert, Siegfried

    2011-01-01

    Drought is fundamentally the result of an extended period of reduced precipitation lasting anywhere from a few weeks to decades and even longer. As such, addressing drought predictability and prediction in a changing climate requires foremost that we make progress on the ability to predict precipitation anomalies on subseasonal and longer time scales. From the perspective of the users of drought forecasts and information, drought is however most directly viewed through its impacts (e.g., on soil moisture, streamflow, crop yields). As such, the question of the predictability of drought must extend to those quantities as well. In order to make progress on these issues, the WCRP drought information group (DIG), with the support of WCRP, the Catalan Institute of Climate Sciences, the La Caixa Foundation, the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation, has organized a workshop to focus on: 1. User requirements for drought prediction information on sub-seasonal to centennial time scales 2. Current understanding of the mechanisms and predictability of drought on sub-seasonal to centennial time scales 3. Current drought prediction/projection capabilities on sub-seasonal to centennial time scales 4. Advancing regional drought prediction capabilities for variables and scales most relevant to user needs on sub-seasonal to centennial time scales. This introductory talk provides an overview of these goals, and outlines the occurrence and mechanisms of drought world-wide.

  18. Predicting real-world functional milestones in schizophrenia.

    PubMed

    Olsson, Anna-Karin; Hjärthag, Fredrik; Helldin, Lars

    2016-08-30

    Schizophrenia is a severe disorder that often causes impairments in major areas of functioning, and most patients do not achieve expected real-world functional milestones. The aim of this study was to identify which variables of demography, illness activity, and functional capacity predict patients' ability to attain real-world functional milestones. Participants were 235 outpatients, 149 men and 86 women, diagnosed with schizophrenia spectrum disorder. Our results showed that younger patients managed to achieve a higher level of functioning in educational level, marital status, and social contacts. Patients' functional capacity was primarily associated with educational level and housing situation. We also found that women needed less support regarding housing and obtained a higher level of marital status as compared with men. Our findings demonstrate the importance of considering current symptoms, especially negative symptoms, and remission stability over time, together with age, duration of illness, gender, educational level, and current functional capacity, when predicting patients' future real-world functioning. We also conclude that there is an advantage in exploring symptoms divided into positive, negative, and general domains considering their probable impact on functional achievements. PMID:27235985

  19. Changes in Reflective Functioning during Psychoanalytic Psychotherapies.

    PubMed

    Hörz-Sagstetter, Susanne; Mertens, Wolfgang; Isphording, Sybille; Buchheim, Anna; Taubner, Svenja

    2015-06-01

    This study examines how reflective functioning (RF) can be assessed in analytic sessions and throughout psychoanalytic psychotherapy. The goals are to replicate in part a study by Josephs and colleagues (2004) by applying the RF Scale to analytic sessions and to study fluctuations of RF within each session. Additionally, RF based on sessions was compared with the RF ratings based on the Adult Attachment Interview (AAI) during the course of two psychoanalytic psychotherapies with a duration of 240 hours. RF changes based on 10 sessions per patient, assessed at baseline and after 80, 160, and 240 hours of therapy, and RF changes based on AAI ratings measured at baseline and after 240 hours of therapy, and in one case at follow-up, were related to changes of symptoms and attachment classifications over time. Results showed that in both cases RF fluctuated within sessions. The average RF rating per session increased over the course of treatment, while the AAI-based RF rating needed longer to increase. Rather good correspondence was found between session-based RF ratings and independent AAI-based RF ratings. In both cases, changes in RF over time were compared to changes in attachment classification based on the AAI and to symptomatic change. Better correspondence between symptomatic and attachment changes was found with the AAI-based RF rating. It was tentatively interpreted that session-based RF ratings may represent a state of RF that is strongly influenced by the therapist-patient interaction, whereas AAI-based RF can be considered to have more trait characteristics. PMID:26185290

  20. Beyond Genotype: Serotonin Transporter Epigenetic Modification Predicts Human Brain Function

    PubMed Central

    Nikolova, Yuliya S.; Koenen, Karestan C.; Galea, Sandro; Wang, Chiou-Miin; Seney, Marianne L.; Sibille, Etienne; Williamson, Douglas E.; Hariri, Ahmad R.

    2014-01-01

    We examined epigenetic regulation in regards to behaviorally and clinically relevant human brain function. Specifically, we found that increased promoter methylation of the serotonin transporter gene predicted increased threat-related amygdala reactivity and decreased mRNA expression in postmortem amygdala tissue. These patterns were independent of functional genetic variation in the same region. Furthermore, the association with amygdala reactivity was replicated in a second cohort and was robust to both sampling methods and age. PMID:25086606

  1. Change in avian abundance predicted from regional forest inventory data

    USGS Publications Warehouse

    Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R., III; Uihlein, William B.; Fitzgerald, Jane A.

    2010-01-01

    An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will

  2. Predicting Changes in the Radio Emission Fluxes of Extragalactic Sources

    NASA Astrophysics Data System (ADS)

    Sukharev, A. L.; Ryabov, M. I.; Donskikh, G. I.

    2016-06-01

    Data from long-term monitoring with the 26-m University of Michigan radio telescope at a frequency of 14.5 GHz (1974-2011) is used to predict changes in the radio emission fluxes from the extragalactic sources 3C273, 3C120, 3C345, 3C446, 3C454.3, OJ287, OT081, and BLLac. The predictions are based on data on the major periods of variability and their durations obtained by wavelet analysis. The radio emission fluxes from the sources 3C345, 3C446, and 3C454.3, which have complicated variabilities, are predicted using an autoregression linear prediction method. This yields a forecast of the flux variations extending up to 5 years. Harmonic prediction is used for another group of sources, BLLac, OJ287, and OT081, with rapid variability. This approach yielded forecasts extending 4-9 years. For the sources 3C273 and 3C120, which have stable long periods, the harmonic method was also used and yielded a forecast extending up to 16 years. The reliability of the prediction was confirmed by independent observational data from the MOJAVE program for 2011-2015.

  3. Predicting changes in the distribution and abundance of species under environmental change

    PubMed Central

    Ehrlén, Johan; Morris, William F

    2015-01-01

    Environmental changes are expected to alter both the distribution and the abundance of organisms. A disproportionate amount of past work has focused on distribution only, either documenting historical range shifts or predicting future occurrence patterns. However, simultaneous predictions of abundance and distribution across landscapes would be far more useful. To critically assess which approaches represent advances towards the goal of joint predictions of abundance and distribution, we review recent work on changing distributions and on effects of environmental drivers on single populations. Several methods have been used to predict changing distributions. Some of these can be easily modified to also predict abundance, but others cannot. In parallel, demographers have developed a much better understanding of how changing abiotic and biotic drivers will influence growth rate and abundance in single populations. However, this demographic work has rarely taken a landscape perspective and has largely ignored the effects of intraspecific density. We advocate a synthetic approach in which population models accounting for both density dependence and effects of environmental drivers are used to make integrated predictions of equilibrium abundance and distribution across entire landscapes. Such predictions would constitute an important step forward in assessing the ecological consequences of environmental changes. PMID:25611188

  4. Predicting changes in the distribution and abundance of species under environmental change.

    PubMed

    Ehrlén, Johan; Morris, William F

    2015-03-01

    Environmental changes are expected to alter both the distribution and the abundance of organisms. A disproportionate amount of past work has focused on distribution only, either documenting historical range shifts or predicting future occurrence patterns. However, simultaneous predictions of abundance and distribution across landscapes would be far more useful. To critically assess which approaches represent advances towards the goal of joint predictions of abundance and distribution, we review recent work on changing distributions and on effects of environmental drivers on single populations. Several methods have been used to predict changing distributions. Some of these can be easily modified to also predict abundance, but others cannot. In parallel, demographers have developed a much better understanding of how changing abiotic and biotic drivers will influence growth rate and abundance in single populations. However, this demographic work has rarely taken a landscape perspective and has largely ignored the effects of intraspecific density. We advocate a synthetic approach in which population models accounting for both density dependence and effects of environmental drivers are used to make integrated predictions of equilibrium abundance and distribution across entire landscapes. Such predictions would constitute an important step forward in assessing the ecological consequences of environmental changes. PMID:25611188

  5. Caregiver Confidence: Does It Predict Changes in Disability among Elderly Home Care Recipients?

    ERIC Educational Resources Information Center

    Li, Lydia W.; McLaughlin, Sara J.

    2012-01-01

    Purpose of the study: The primary aim of this investigation was to determine whether caregiver confidence in their care recipients' functional capabilities predicts changes in the performance of activities of daily living (ADL) among elderly home care recipients. A secondary aim was to explore how caregiver confidence and care recipient functional…

  6. Predicting IQ change from brain structure: A cross-validation study

    PubMed Central

    Price, C.J.; Ramsden, S.; Hope, T.M.H.; Friston, K.J.; Seghier, M.L.

    2013-01-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers – each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however – unlike the Leave-One-Out procedure – regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature. PMID:23567505

  7. Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

    PubMed Central

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. PMID:24972109

  8. The Prediction of Ego Functioning in Adolescence. Final Report.

    ERIC Educational Resources Information Center

    Taube, Irvin; Vreeland, Rebecca

    The object of this study was to predict ego functioning in college among a group of successful high school graduates. Two hundred and seventy-one graduates of Phillips Exeter Academy who had been admitted to Harvard University during 4 consecutive years were studied. Three types of previously collected data were used: (1) teacher reports on the…

  9. Human transfer functions used to predict system performance parameters

    NASA Technical Reports Server (NTRS)

    1966-01-01

    Automatic, parameter-tracking, model-matching technique compares the responses of a human operator with those of an analog computer model of a human operator to predict and analyze the performance of mechanical or electromechanical systems prior to construction. Transfer functions represent the input-output relation of an operator controlling a closed-loop system.

  10. Protein side chain conformation predictions with an MMGBSA energy function.

    PubMed

    Gaillard, Thomas; Panel, Nicolas; Simonson, Thomas

    2016-06-01

    The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc. PMID:26948696

  11. firestar--advances in the prediction of functionally important residues.

    PubMed

    Lopez, Gonzalo; Maietta, Paolo; Rodriguez, Jose Manuel; Valencia, Alfonso; Tress, Michael L

    2011-07-01

    firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio.es/Php/FireStar.php. PMID:21672959

  12. Perinatal medical variables predict executive function within a sample of preschoolers born very low birth weight.

    PubMed

    Duvall, Susanne W; Erickson, Sarah J; MacLean, Peggy; Lowe, Jean R

    2015-05-01

    The goal was to identify perinatal predictors of early executive dysfunction in preschoolers born very low birth weight. Fifty-seven preschoolers completed 3 executive function tasks: Dimensional Change Card Sort-Separated (inhibition, working memory, and cognitive flexibility), Bear Dragon (inhibition and working memory), and Gift Delay Open (inhibition). Relationships between executive function and perinatal medical severity factors (gestational age, days on ventilation, size for gestational age, maternal steroids, and number of surgeries) and chronological age were investigated by multiple linear regression and logistic regression. Different perinatal medical severity factors were predictive of executive function tasks, with gestational age predicting Bear Dragon and Gift Open; and number of surgeries and maternal steroids predicting performance on Dimensional Change Card Sort-Separated. By understanding the relationship between perinatal medical severity factors and preschool executive outcomes, we can identify children at highest risk for future executive dysfunction, thereby focusing targeted early intervention services. PMID:25117418

  13. Functional consequences of realistic biodiversity changes in a marine ecosystem

    PubMed Central

    Bracken, Matthew E. S.; Friberg, Sara E.; Gonzalez-Dorantes, Cirse A.; Williams, Susan L.

    2008-01-01

    Declines in biodiversity have prompted concern over the consequences of species loss for the goods and services provided by natural ecosystems. However, relatively few studies have evaluated the functional consequences of realistic, nonrandom changes in biodiversity. Instead, most designs have used randomly selected assemblages from a local species pool to construct diversity gradients. It is therefore difficult, based on current evidence, to predict the functional consequences of realistic declines in biodiversity. In this study, we used tide pool microcosms to demonstrate that the effects of real-world changes in biodiversity may be very different from those of random diversity changes. Specifically, we measured the relationship between the diversity of a seaweed assemblage and its ability to use nitrogen, a key limiting nutrient in nearshore marine systems. We quantified nitrogen uptake using both experimental and model seaweed assemblages and found that natural increases in diversity resulted in enhanced rates of nitrogen use, whereas random diversity changes had no effect on nitrogen uptake. Our results suggest that understanding the real-world consequences of declining biodiversity will require addressing changes in species performance along natural diversity gradients and understanding the relationships between species' susceptibility to loss and their contributions to ecosystem functioning. PMID:18195375

  14. Predicting Protein Function via Semantic Integration of Multiple Networks.

    PubMed

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically i ntegrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet. PMID:26800544

  15. Analysis and Functional Prediction of Reactive Cysteine Residues*

    PubMed Central

    Marino, Stefano M.; Gladyshev, Vadim N.

    2012-01-01

    Cys is much different from other common amino acids in proteins. Being one of the least abundant residues, Cys is often observed in functional sites in proteins. This residue is reactive, polarizable, and redox-active; has high affinity for metals; and is particularly responsive to the local environment. A better understanding of the basic properties of Cys is essential for interpretation of high-throughput data sets and for prediction and classification of functional Cys residues. We provide an overview of approaches used to study Cys residues, from methods for investigation of their basic properties, such as exposure and pKa, to algorithms for functional prediction of different types of Cys in proteins. PMID:22157013

  16. Pattern recognition methods for protein functional site prediction.

    PubMed

    Yang, Zheng Rong; Wang, Lipo; Young, Natasha; Trudgian, Dave; Chou, Kuo-Chen

    2005-10-01

    Protein functional site prediction is closely related to drug design, hence to public health. In order to save the cost and the time spent on identifying the functional sites in sequenced proteins in biology laboratory, computer programs have been widely used for decades. Many of them are implemented using the state-of-the-art pattern recognition algorithms, including decision trees, neural networks and support vector machines. Although the success of this effort has been obvious, advanced and new algorithms are still under development for addressing some difficult issues. This review will go through the major stages in developing pattern recognition algorithms for protein functional site prediction and outline the future research directions in this important area. PMID:16248799

  17. Predicting Climate Change: Lessons From Reductionism, Emergence, and the Past

    NASA Astrophysics Data System (ADS)

    Harrison, Stephan; Stainforth, Dave

    2009-03-01

    Climate and Earth system models are the only tools used to make predictions of future climate change. Such predictions are subject to considerable uncertainties, and understanding these uncertainties has clear and important policy implications. This Forum highlights the concepts of reductionism and emergence, and past climate variability, to illuminate some of the uncertainties faced by those wishing to model the future evolution of global climate. General circulation models (GCMs) of the atmosphere-ocean system are scientists' principal tools for providing information about future climate. GCMs consequently have considerable influence on climate change-related policy questions. Over the past decade, there have been significant attempts, mainly by statisticians and mathematicians, to explore the uncertainties in model simulations of possible futures, accompanied by growing debate about the interpretation of these simulations as aids in societal decisions. In this Forum, we discuss atmosphere-ocean GCMs in the context of reductionist and emergent approaches to scientific study.

  18. Predicting biotic interactions and their variability in a changing environment.

    PubMed

    Kadowaki, Kohmei; Barbera, Claire G; Godsoe, William; Delsuc, Frédéric; Mouquet, Nicolas

    2016-05-01

    Global environmental change is altering the patterns of biodiversity worldwide. Observation and theory suggest that species' distributions and abundances depend on a suite of processes, notably abiotic filtering and biotic interactions, both of which are constrained by species' phylogenetic history. Models predicting species distribution have historically mostly considered abiotic filtering and are only starting to integrate biotic interaction. However, using information on present interactions to forecast the future of biodiversity supposes that biotic interactions will not change when species are confronted with new environments. Using bacterial microcosms, we illustrate how biotic interactions can vary along an environmental gradient and how this variability can depend on the phylogenetic distance between interacting species. PMID:27220858

  19. Predicting plants -modeling traits as a function of environment

    NASA Astrophysics Data System (ADS)

    Franklin, Oskar

    2016-04-01

    A central problem in understanding and modeling vegetation dynamics is how to represent the variation in plant properties and function across different environments. Addressing this problem there is a strong trend towards trait-based approaches, where vegetation properties are functions of the distributions of functional traits rather than of species. Recently there has been enormous progress in in quantifying trait variability and its drivers and effects (Van Bodegom et al. 2012; Adier et al. 2014; Kunstler et al. 2015) based on wide ranging datasets on a small number of easily measured traits, such as specific leaf area (SLA), wood density and maximum plant height. However, plant function depends on many other traits and while the commonly measured trait data are valuable, they are not sufficient for driving predictive and mechanistic models of vegetation dynamics -especially under novel climate or management conditions. For this purpose we need a model to predict functional traits, also those not easily measured, and how they depend on the plants' environment. Here I present such a mechanistic model based on fitness concepts and focused on traits related to water and light limitation of trees, including: wood density, drought response, allocation to defense, and leaf traits. The model is able to predict observed patterns of variability in these traits in relation to growth and mortality, and their responses to a gradient of water limitation. The results demonstrate that it is possible to mechanistically predict plant traits as a function of the environment based on an eco-physiological model of plant fitness. References Adier, P.B., Salguero-Gómez, R., Compagnoni, A., Hsu, J.S., Ray-Mukherjee, J., Mbeau-Ache, C. et al. (2014). Functional traits explain variation in plant lifehistory strategies. Proc. Natl. Acad. Sci. U. S. A., 111, 740-745. Kunstler, G., Falster, D., Coomes, D.A., Hui, F., Kooyman, R.M., Laughlin, D.C. et al. (2015). Plant functional traits

  20. Concurrent and Predictive Relations between Hormone Levels and Social-Emotional Functioning in Early Adolescence.

    ERIC Educational Resources Information Center

    Nottelmann, Editha D.; And Others

    Hormone levels and changes in hormone levels were evaluated three times across a 1-year period as concurrent and predictive correlates of the socio-emotional functioning of 56 boys 10- to 14-years-old and 52 girls 9- to 14-years-old who represented the five stages of Tanner's criteria of pubertal development. The hormone measures were serum levels…

  1. Scoring functions for prediction of protein-ligand interactions.

    PubMed

    Wang, Jui-Chih; Lin, Jung-Hsin

    2013-01-01

    The scoring functions for protein-ligand interactions plays central roles in computational drug design, virtual screening of chemical libraries for new lead identification, and prediction of possible binding targets of small chemical molecules. An ideal scoring function for protein-ligand interactions is expected to be able to recognize the native binding pose of a ligand on the protein surface among decoy poses, and to accurately predict the binding affinity (or binding free energy) so that the active molecules can be discriminated from the non-active ones. Due to the empirical nature of most, if not all, scoring functions for protein-ligand interactions, the general applicability of empirical scoring functions, especially to domains far outside training sets, is a major concern. In this review article, we will explore the foundations of different classes of scoring functions, their possible limitations, and their suitable application domains. We also provide assessments of several scoring functions on weakly-interacting protein-ligand complexes, which will be useful information in computational fragment-based drug design or virtual screening. PMID:23016847

  2. Predicting Species-environment Relationships with Functional Traits for the Understory Flora of Wisconsin

    NASA Astrophysics Data System (ADS)

    Ash, J.; Li, D.; Johnson, S.; Rogers, D. A.; Waller, D. M.

    2015-12-01

    Understanding the processes that structure species' abundance patterns is a central problem in ecology, both for explaining current species' distributions and predicting future changes. Environmental gradients affect species' distribution patterns with these responses likely depending on species' functional traits. Thus, tracking shifts in species' traits can provide insight into the mechanisms by which species respond to dynamic environmental conditions. We examined how functional traits are associated with long-term changes in the distribution and abundance of understory plants in Wisconsin forests over the last 50+ years. We relied on detailed surveys and resurveys of the same Wisconsin forest plots, data on 12 functional traits, and site-level environmental variables including soil and climate conditions. We then related changes in the abundance of 293 species across a network of 249 sites to these environmental variables and explored whether functional traits served to predict these relationships using multilevel models. Species abundance patterns were strongly related to variation in environmental conditions among sites, but species appear to be responding to distinct sets of environmental variables. Functional traits only weakly predicted these species-environment relationships, limiting our ability to generalize these results to other systems. Nonetheless, understanding how traits interact with environmental gradients to structure species distribution patterns helps us to disentangle the drivers of ecological change across diverse landscapes.

  3. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function.

    PubMed

    Warde-Farley, David; Donaldson, Sylva L; Comes, Ovi; Zuberi, Khalid; Badrawi, Rashad; Chao, Pauline; Franz, Max; Grouios, Chris; Kazi, Farzana; Lopes, Christian Tannus; Maitland, Anson; Mostafavi, Sara; Montojo, Jason; Shao, Quentin; Wright, George; Bader, Gary D; Morris, Quaid

    2010-07-01

    GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist. PMID:20576703

  4. Predicting Key Change Events in Emotionally Focused Couple Therapy.

    PubMed

    Dalgleish, Tracy L; Johnson, Susan M; Burgess Moser, Melissa; Wiebe, Stephanie A; Tasca, Giorgio A

    2015-07-01

    In emotionally focused couple therapy (EFT), the blamer-softening event helps individuals express and respond to partners' unmet attachment needs. This study examined the impact of this event in relation to attachment at intake and changes in marital satisfaction from pre- to posttherapy. Thirty-two couples were provided an average of 21 sessions of EFT. Hierarchical linear modeling revealed that the occurrence of a softening event significantly predicted increased marital satisfaction. Furthermore, the occurrence of a softening event significantly moderated the relationship between attachment avoidance at intake and change in marital satisfaction from pre- to posttherapy. For couples who had a softening event, partners with higher levels of attachment avoidance were less likely to have positive changes in marital satisfaction. PMID:25329234

  5. Systematic prediction of gene function in Arabidopsis thaliana using a probabilistic functional gene network

    PubMed Central

    Hwang, Sohyun; Rhee, Seung Y; Marcotte, Edward M; Lee, Insuk

    2012-01-01

    AraNet is a functional gene network for the reference plant Arabidopsis and has been constructed in order to identify new genes associated with plant traits. It is highly predictive for diverse biological pathways and can be used to prioritize genes for functional screens. Moreover, AraNet provides a web-based tool with which plant biologists can efficiently discover novel functions of Arabidopsis genes (http://www.functionalnet.org/aranet/). This protocol explains how to conduct network-based prediction of gene functions using AraNet and how to interpret the prediction results. Functional discovery in plant biology is facilitated by combining candidate prioritization by AraNet with focused experimental tests. PMID:21886106

  6. Computational predictions of energy materials using density functional theory

    NASA Astrophysics Data System (ADS)

    Jain, Anubhav; Shin, Yongwoo; Persson, Kristin A.

    2016-01-01

    In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery.

  7. Plant functional traits predict green roof ecosystem services.

    PubMed

    Lundholm, Jeremy; Tran, Stephanie; Gebert, Luke

    2015-02-17

    Plants make important contributions to the services provided by engineered ecosystems such as green roofs. Ecologists use plant species traits as generic predictors of geographical distribution, interactions with other species, and ecosystem functioning, but this approach has been little used to optimize engineered ecosystems. Four plant species traits (height, individual leaf area, specific leaf area, and leaf dry matter content) were evaluated as predictors of ecosystem properties and services in a modular green roof system planted with 21 species. Six indicators of ecosystem services, incorporating thermal, hydrological, water quality, and carbon sequestration functions, were predicted by the four plant traits directly or indirectly via their effects on aggregate ecosystem properties, including canopy density and albedo. Species average height and specific leaf area were the most useful traits, predicting several services via effects on canopy density or growth rate. This study demonstrates that easily measured plant traits can be used to select species to optimize green roof performance across multiple key services. PMID:25599106

  8. Optimizing Non-Decomposable Loss Functions in Structured Prediction

    PubMed Central

    Ranjbar, Mani; Lan, Tian; Wang, Yang; Robinovitch, Steven N.; Li, Ze-Nian; Mori, Greg

    2012-01-01

    We develop an algorithm for structured prediction with non-decomposable performance measures. The algorithm learns parameters of Markov random fields and can be applied to multivariate performance measures. Examples include performance measures such as Fβ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines) and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function. The loss augmented inference forms a quadratic program (QP), which we solve using LP relaxation. We apply this approach to two tasks: object class-specific segmentation and human action retrieval from videos. We show significant improvement over baseline approaches that either use simple loss functions or simple scoring functions on the PASCAL VOC and H3D Segmentation datasets, and a nursing home action recognition dataset. PMID:22868650

  9. Rapid D-Affine Biventricular Cardiac Function with Polar Prediction

    PubMed Central

    Gilbert, Kathleen; Cowan, Brett; Suinesiaputra, Avan; Occleshaw, Christopher; Young, Alistair

    2014-01-01

    Although many solutions have been proposed for left ventricular functional analysis of the heart, right and left (bi-) ventricular function has been problematic due to the complex geometry and large motions. Biventricular function is particularly important in congenital heart disease, the most common type of birth defects. We describe a rapid interactive analysis tool for biventricular function which incorporates 1) a 3D+ time finite element model of biventricular geometry, 2) a fast prediction step which estimates an initial geometry in a polar coordinate system, and 3) a Cartesian update which penalizes deviations from affine transformations (D-Affine) from a prior. Solution times were very rapid, enabling interaction in real time using guide point modeling. The method was applied to 13 patients with congenital heart disease and compared with the clinical gold standard of manual tracing. Results between the methods showed good correlation (R2 > 0.9) and good precision (volume<17ml; mass<11g) for both chambers. PMID:25485422

  10. High Precision Prediction of Functional Sites in Protein Structures

    PubMed Central

    Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

    2014-01-01

    We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

  11. Challenges in predicting climate change impacts on pome fruit phenology

    NASA Astrophysics Data System (ADS)

    Darbyshire, Rebecca; Webb, Leanne; Goodwin, Ian; Barlow, E. W. R.

    2014-08-01

    Climate projection data were applied to two commonly used pome fruit flowering models to investigate potential differences in predicted full bloom timing. The two methods, fixed thermal time and sequential chill-growth, produced different results for seven apple and pear varieties at two Australian locations. The fixed thermal time model predicted incremental advancement of full bloom, while results were mixed from the sequential chill-growth model. To further investigate how the sequential chill-growth model reacts under climate perturbed conditions, four simulations were created to represent a wider range of species physiological requirements. These were applied to five Australian locations covering varied climates. Lengthening of the chill period and contraction of the growth period was common to most results. The relative dominance of the chill or growth component tended to predict whether full bloom advanced, remained similar or was delayed with climate warming. The simplistic structure of the fixed thermal time model and the exclusion of winter chill conditions in this method indicate it is unlikely to be suitable for projection analyses. The sequential chill-growth model includes greater complexity; however, reservations in using this model for impact analyses remain. The results demonstrate that appropriate representation of physiological processes is essential to adequately predict changes to full bloom under climate perturbed conditions with greater model development needed.

  12. Predictions of Geospace Drivers By the Probability Distribution Function Model

    NASA Astrophysics Data System (ADS)

    Bussy-Virat, C.; Ridley, A. J.

    2014-12-01

    Geospace drivers like the solar wind speed, interplanetary magnetic field (IMF), and solar irradiance have a strong influence on the density of the thermosphere and the near-Earth space environment. This has important consequences on the drag on satellites that are in low orbit and therefore on their position. One of the basic problems with space weather prediction is that these drivers can only be measured about one hour before they affect the environment. In order to allow for adequate planning for some members of the commercial, military, or civilian communities, reliable long-term space weather forecasts are needed. The study presents a model for predicting geospace drivers up to five days in advance. This model uses the same general technique to predict the solar wind speed, the three components of the IMF, and the solar irradiance F10.7. For instance, it uses Probability distribution functions (PDFs) to relate the current solar wind speed and slope to the future solar wind speed, as well as the solar wind speed to the solar wind speed one solar rotation in the future. The PDF Model has been compared to other models for predictions of the speed. It has been found that it is better than using the current solar wind speed (i.e., persistence), and better than the Wang-Sheeley-Arge Model for prediction horizons of 24 hours. Once the drivers are predicted, and the uncertainty on the drivers are specified, the density in the thermosphere can be derived using various models of the thermosphere, such as the Global Ionosphere Thermosphere Model. In addition, uncertainties on the densities can be estimated, based on ensembles of simulations. From the density and uncertainty predictions, satellite positions, as well as the uncertainty in those positions can be estimated. These can assist operators in determining the probability of collisions between objects in low Earth orbit.

  13. White matter morphometric changes uniquely predict children's reading acquisition.

    PubMed

    Myers, Chelsea A; Vandermosten, Maaike; Farris, Emily A; Hancock, Roeland; Gimenez, Paul; Black, Jessica M; Casto, Brandi; Drahos, Miroslav; Tumber, Mandeep; Hendren, Robert L; Hulme, Charles; Hoeft, Fumiko

    2014-10-01

    This study examined whether variations in brain development between kindergarten and Grade 3 predicted individual differences in reading ability at Grade 3. Structural MRI measurements indicated that increases in the volume of two left temporo-parietal white matter clusters are unique predictors of reading outcomes above and beyond family history, socioeconomic status, and cognitive and preliteracy measures at baseline. Using diffusion MRI, we identified the left arcuate fasciculus and superior corona radiata as key fibers within the two clusters. Bias-free regression analyses using regions of interest from prior literature revealed that volume changes in temporo-parietal white matter, together with preliteracy measures, predicted 56% of the variance in reading outcomes. Our findings demonstrate the important contribution of developmental differences in areas of left dorsal white matter, often implicated in phonological processing, as a sensitive early biomarker for later reading abilities, and by extension, reading difficulties. PMID:25212581

  14. Predicting when climate-driven phenotypic change affects population dynamics.

    PubMed

    McLean, Nina; Lawson, Callum R; Leech, Dave I; van de Pol, Martijn

    2016-06-01

    Species' responses to climate change are variable and diverse, yet our understanding of how different responses (e.g. physiological, behavioural, demographic) relate and how they affect the parameters most relevant for conservation (e.g. population persistence) is lacking. Despite this, studies that observe changes in one type of response typically assume that effects on population dynamics will occur, perhaps fallaciously. We use a hierarchical framework to explain and test when impacts of climate on traits (e.g. phenology) affect demographic rates (e.g. reproduction) and in turn population dynamics. Using this conceptual framework, we distinguish four mechanisms that can prevent lower-level responses from impacting population dynamics. Testable hypotheses were identified from the literature that suggest life-history and ecological characteristics which could predict when these mechanisms are likely to be important. A quantitative example on birds illustrates how, even with limited data and without fully-parameterized population models, new insights can be gained; differences among species in the impacts of climate-driven phenological changes on population growth were not explained by the number of broods or density dependence. Our approach helps to predict the types of species in which climate sensitivities of phenotypic traits have strong demographic and population consequences, which is crucial for conservation prioritization of data-deficient species. PMID:27062059

  15. Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression

    PubMed Central

    Lehtinen, Sonja; Lees, Jon; Bähler, Jürg; Shawe-Taylor, John; Orengo, Christine

    2015-01-01

    With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction. PMID:26288239

  16. Prediction of subsurface water level change from satellite data

    NASA Astrophysics Data System (ADS)

    Saykawlard, Suphan; Honda, Kiyoshi; Das Gupta, Ashim; Eiumnoh, Apisit; Chen, Xiaoyong

    2005-03-01

    This study explores the potential for predicting the spatial variation in subsurface water level change with crop growth stage from satellite data in Thabua Irrigation Project, situated in the northern central region of Thailand. The relationship between subsurface water level change from pumping water to irrigate rice in the dry season and the age of the rice was analysed. The spatial model of subsurface water level change was developed from the classification using greenness or (normalized difference vegetation index NDVI) derived from Landsat 5 Thematic Mapper data. The NDVI of 52 rice fields was employed to assess its relationship to the age of the rice. It was found that NDVI and rice age have a good correlation (R2 = 0.73). The low NDVI values (-0.059 to 0.082) in these fields were related to the young rice stage (0-30 days). NDVI and subsurface water level change were also correlated in this study and found to have a high correlation (Water level change (m day-1) = 0.3442 × NDVI - 0.0372; R2 = 0.96). From this model, the water level change caused by rice at different growth stages was derived. This was used to show the spatial variation of water level change in the project during the 1998-99 dry-season cropping. This simple method of using NDVI relationships with water level change and crop growth stages proves to be useful in determining the areas prone to excessive lowering of the subsurface water level during the dry season. This could assist in the appropriate planning of the use of subsurface water resources in dry-season cropping.

  17. Multitrophic functional diversity predicts ecosystem functioning in experimental assemblages of estuarine consumers.

    PubMed

    Lefcheck, Jonathan S; Duffy, J Emmett

    2015-11-01

    The use of functional traits to explain how biodiversity affects ecosystem functioning has attracted intense interest, yet few studies have a priori altered functional diversity, especially in multitrophic communities. Here, we manipulated multivariate functional diversity of estuarine grazers and predators within multiple levels of species richness to test how species richness and functional diversity predicted ecosystem functioning in a multitrophic food web. Community functional diversity was a better predictor than species richness for the majority of ecosystem properties, based on generalized linear mixed-effects models. Combining inferences from eight traits into a single multivariate index increased prediction accuracy of these models relative to any individual trait. Structural equation modeling revealed that functional diversity of both grazers and predators was important in driving final biomass within trophic levels, with stronger effects observed for predators. We also show that different species drove different ecosystem responses, with evidence for both sampling effects and complementarity. Our study extends experimental investigations of functional trait diversity to a multilevel food web, and demonstrates that functional diversity can be more accurate and effective than species richness in predicting community biomass in a food web context. PMID:27070016

  18. Cognitive Functioning Predicts Driver Safety On Road-Tests 1 and 2 Years Later

    PubMed Central

    Aksan, Nazan; Anderson, Steven W.; Dawson, Jeffrey D.; Johnson, Amy M.; Uc, Ergun Y.; Rizzo, Matthew

    2011-01-01

    BACKGROUND Our ability to predict aging related declines in driving performance from off-road assessments have clinical practice and social policy implications. OBJECTIVES 1) To describe longitudinal changes in mean-level and evaluate rank-order stability in potential predictors of driving safety (visual sensory, motor, visual attention, and cognitive functioning) and safety errors during an 18-mile on-road-drive-test among older adults. 2) To evaluate the relative predictive power of earlier visual sensory, motor, visual attention, and cognitive functioning on future safety errors controlling for earlier driving capacity. DESIGN A three-year longitudinal observational study; SETTING A large teaching hospital in the Mid-West; PARTICIPANTS 111 neurologically normal older adults (60 to 89 years at baseline); MEASUREMENTS Safety errors based on video review of a standard 18-mile on-road driving test served as the outcome measure. Comprehensive battery of tests on the predictor side included visual sensory functioning, motor functioning, cognitive functioning, and a measure of Useful Field of View. RESULTS Longitudinal changes in mean-levels of safety errors and cognitive functioning were small from year-to-year. Relative rank-order stability between consecutive assessments was moderate in overall safety errors, it was moderate to strong in visual attention and cognitive functioning. While prospective bivariate correlations ranged from fair to moderate between safety errors and predictors, only functioning in the cognitive domain predicted future driver performance one and two-years later in multivariate analyses. CONCLUSION Normative aging related declines in driver performance as assessed by on-road tests emerge slowly. The findings clearly demonstrated that even in the presence conservative controls, such as previous driving ability, age, visual sensory and motor functioning, cognitive functioning predicted future driving performance on-road one and two-years later

  19. Catchment Prediction In Changing Environments (CAPICHE): A collaborative experiment in an open water science laboratory

    NASA Astrophysics Data System (ADS)

    Hutton, Christopher; Wagener, Thorsten; Freer, Jim; Han, Dawei

    2015-04-01

    Predicting the function of hydrological systems under near-stationary conditions faces a number of challenges due to incomplete system understanding, and uncertainty in models and measurements. However, due to changes in climate, land use/land cover, and water demand, the hydrological function of many catchments cannot be considered as stationary. Such changes make modelling catchment systems more difficult, as models need to account for non-stationary forcing and boundary conditions, which in turn can change internal catchment function, and the states and processes that dominate hydrological response. In addition, such models may need to be used to make predictions beyond a range of conditions for which they were originally calibrated. Despite these problems, deriving accurate hydrological predictions under changing conditions is increasingly important for future water resource and flood hazard assessment. Simulating catchments under changing conditions may require more complex distributed models in order to adequately represent spatial changes in boundary conditions (e.g. land cover change). However, the potential for complex models to address these issues cannot be realised in many places because of data problems, which may result from a lack of data, data access issues, and time-consuming problems in bringing diverse sources of data together and into a useable format. A greater understanding of the link between model complexity and data is required to make appropriate modelling choices. Virtual water science laboratories offer the ideal opportunity to explore the issues of model complexity and data availability in the context of predictions under changing environments because they: (1) provide an opportunity to share open data; (2) provide a platform to compare different models; (3) facilitate collaboration between different modelling research groups. This paper introduces a new collaborative experiment, conducted in an open virtual water science laboratory as

  20. Remote sensing of vegetation ecophysiological function for improved hydrologic prediction

    NASA Astrophysics Data System (ADS)

    Drewry, D.; Ruddell, B. L.

    2014-12-01

    Land surface hydrology in vegetated landscapes is strongly controlled by ecophysiological function. The coupling between photosynthesis, stomatal dynamics and leaf energy balance fundamentally links the hydrologic and carbon cycles, and provides a basis for examining the utility of observations of functional plant traits for hydrologic prediction. Here we explore the potential of solar induced fluorescence (SIF) and thermal infrared (TIR) remote sensing observations to improve the accuracy and reduce the uncertainty in hydrologic prediction. While SIF represents an emission of radiation associated with photosynthesis, TIR provides information on foliage temperature and is related to stomatal function and water stress. A set of remote observing system simulation experiments are conducted to quantify the value of remotely sensed observations of SIF and TIR when assimilated into a detailed vegetation biophysical model. The MLCan model discretizes a dense plant canopy to resolve vertical variation in photosynthesis, water vapor and energy exchange. Here we present extensions to MLCan that allow for direct computation of the canopy emission of both SIF and TIR. The detailed representation of the physical environment and biological functioning of structurally complex canopies makes MLCan an ideal simulation tool for exploring the impact of these two unique, and potentially synergistic observables. This work specifically addresses remote sensing capabilities on both recently launched (OCO-2) and near-term (ECOSTRESS) satellite platforms. We contrast the information gained through the assimilation of SIF and TIR observations to that of the assimilation of data related to physical states such as soil moisture and leaf area index.

  1. Resting amygdala and medial prefrontal metabolism predicts functional activation of the fear extinction circuit

    PubMed Central

    Linnman, Clas; Zeidan, Mohamed A.; Furtak, Sharon C.; Pitman, Roger K.; Quirk, Gregory J.; Milad, Mohammed R.

    2014-01-01

    Objective Individual differences in ability to control fear have been linked to activation of dorsal anterior cingulate cortex, ventromedial prefrontal cortex, and amygdala. This study investigated whether functional variance in this network can be predicted by resting metabolism in these same regions. Methods Healthy subject volunteers were studied with positron emission tomography using [18F]-deoxyglucose to measure resting brain metabolism. This was followed by a two-day fear conditioning and extinction training paradigm in a functional magnetic resonance imaging scanner to measure brain activation during fear extinction and its recall. Skin conductance response was used to index conditioned responding. Resting metabolism in amygdala, dorsal anterior cingulate cortex and ventromedial prefrontal cortex were used to predict responses during fear extinction and extinction recall. Results During extinction training, resting amygdala metabolism positively predicted ventromedial prefrontal cortex, and negatively predicted dorsal anterior cingulate cortex, activation. In contrast, during extinction recall, resting amygdala metabolism negatively predicted ventromedial prefrontal cortex, and positively predicted dorsal anterior cingulate cortex, activation. Resting dorsal anterior cingulate cortex metabolism predicted fear expression (skin conductance response) during extinction recall. Conclusions Brain metabolism at rest predicts neuronal reactivity and skin conductance changes associated with recall of the fear extinction memory. PMID:22318762

  2. Change in BMI Accurately Predicted by Social Exposure to Acquaintances

    PubMed Central

    Oloritun, Rahman O.; Ouarda, Taha B. M. J.; Moturu, Sai; Madan, Anmol; Pentland, Alex (Sandy); Khayal, Inas

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R2. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. PMID

  3. Predicting Change in Marital Satisfaction Throughout Emotionally Focused Couple Therapy.

    PubMed

    Dalgleish, Tracy L; Johnson, Susan M; Burgess Moser, Melissa; Lafontaine, Marie-France; Wiebe, Stephanie A; Tasca, Giorgio A

    2015-07-01

    Emotionally focused couple therapy (EFT) is an empirically validated approach to couple therapy that uses attachment theory to understand the needs and emotions of romantic partners. EFT is recognized as one of the most effective approaches to couple therapy, but to guide therapists in their use of EFT, a theoretically based model to predict change is needed. This study tested such a model by recruiting 32 couples, and 14 therapists who provided approximately 21 sessions of EFT. Couples completed self-report measures of marital satisfaction, attachment security, relationship trust, and emotional control at pre- and posttherapy and after each therapy session. Results of hierarchical linear modeling suggested that individuals higher on self-report attachment anxiety and higher levels of emotional control had greater change in marital satisfaction across EFT sessions. Assessing attachment security at the start of therapy will inform therapists of the emotion regulating strategies used by couples and may help couples achieve positive outcomes from EFT. PMID:24910261

  4. Models for predicting objective function weights in prostate cancer IMRT

    SciTech Connect

    Boutilier, Justin J. Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.

    2015-04-15

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  5. Simple topological properties predict functional misannotations in a metabolic network

    PubMed Central

    Liberal, Rodrigo; Pinney, John W.

    2013-01-01

    Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation. Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes). Contact: j.pinney@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID

  6. Functional traits predict relationship between plant abundance dynamic and long-term climate warming.

    PubMed

    Soudzilovskaia, Nadejda A; Elumeeva, Tatiana G; Onipchenko, Vladimir G; Shidakov, Islam I; Salpagarova, Fatima S; Khubiev, Anzor B; Tekeev, Dzhamal K; Cornelissen, Johannes H C

    2013-11-01

    Predicting climate change impact on ecosystem structure and services is one of the most important challenges in ecology. Until now, plant species response to climate change has been described at the level of fixed plant functional types, an approach limited by its inflexibility as there is much interspecific functional variation within plant functional types. Considering a plant species as a set of functional traits greatly increases our possibilities for analysis of ecosystem functioning and carbon and nutrient fluxes associated therewith. Moreover, recently assembled large-scale databases hold comprehensive per-species data on plant functional traits, allowing a detailed functional description of many plant communities on Earth. Here, we show that plant functional traits can be used as predictors of vegetation response to climate warming, accounting in our test ecosystem (the species-rich alpine belt of Caucasus mountains, Russia) for 59% of variability in the per-species abundance relation to temperature. In this mountain belt, traits that promote conservative leaf water economy (higher leaf mass per area, thicker leaves) and large investments in belowground reserves to support next year's shoot buds (root carbon content) were the best predictors of the species increase in abundance along with temperature increase. This finding demonstrates that plant functional traits constitute a highly useful concept for forecasting changes in plant communities, and their associated ecosystem services, in response to climate change. PMID:24145400

  7. Electrocortical indices of selective attention predict adolescent executive functioning.

    PubMed

    Lackner, Christine L; Santesso, Diane L; Dywan, Jane; Wade, Terrance J; Segalowitz, Sidney J

    2013-05-01

    Executive functioning is considered a powerful predictor of behavioral and mental health outcomes during adolescence. Our question was whether executive functioning skills, normally considered "top-down" processes, are related to automatic aspects of selective attention. Event-related potentials (ERPs) were recorded from typically-developing 12-14-year-old adolescents as they responded to tones presented in attended and unattended channels in an auditory selective attention task. Examining these ERPs in relation to parental reports on the Behavior Rating Inventory of Executive Function (BRIEF) revealed that an early frontal positivity (EFP) elicited by to-be-ignored/unattended tones was larger in those with poorer executive functions, driven by scores on the BRIEF Metacognition Index. As is traditionally found, N1 amplitudes were more negative for the to-be-attended rather than unattended tones. Additionally, N1 latencies to unattended tones correlated with parent-ratings on the BRIEF Behavior Regulation Index, where shorter latencies predicted better executive functions. Results suggest that the ability to disengage attention from distractor information in the early stages of stimulus processing is associated with adolescent executive functioning skills. PMID:23528784

  8. Traumatic Brain Injury and Neuronal Functionality Changes in Sensory Cortex

    PubMed Central

    Carron, Simone F.; Alwis, Dasuni S.; Rajan, Ramesh

    2016-01-01

    Traumatic brain injury (TBI), caused by direct blows to the head or inertial forces during relative head-brain movement, can result in long-lasting cognitive and motor deficits which can be particularly consequential when they occur in young people with a long life ahead. Much is known of the molecular and anatomical changes produced in TBI but much less is known of the consequences of these changes to neuronal functionality, especially in the cortex. Given that much of our interior and exterior lives are dependent on responsiveness to information from and about the world around us, we have hypothesized that a significant contributor to the cognitive and motor deficits seen after TBI could be changes in sensory processing. To explore this hypothesis, and to develop a model test system of the changes in neuronal functionality caused by TBI, we have examined neuronal encoding of simple and complex sensory input in the rat’s exploratory and discriminative tactile system, the large face macrovibrissae, which feeds to the so-called “barrel cortex” of somatosensory cortex. In this review we describe the short-term and long-term changes in the barrel cortex encoding of whisker motion modeling naturalistic whisker movement undertaken by rats engaged in a variety of tasks. We demonstrate that the most common form of TBI results in persistent neuronal hyperexcitation specifically in the upper cortical layers, likely due to changes in inhibition. We describe the types of cortical inhibitory neurons and their roles and how selective effects on some of these could produce the particular forms of neuronal encoding changes described in TBI, and then generalize to compare the effects on inhibition seen in other forms of brain injury. From these findings we make specific predictions as to how non-invasive extra-cranial electrophysiology can be used to provide the high-precision information needed to monitor and understand the temporal evolution of changes in neuronal

  9. Predictions of a Global Climate Change and Cycle on Jupiter

    NASA Astrophysics Data System (ADS)

    Marcus, P. S.

    2003-12-01

    We predict that most of Jupiter's large vortices, similar to (but not including) the Great Red Spot, will soon disappear due to vortex mergers. This will cause global temperature changes of ˜10oK. Within a decade, several of Jupiter's westward jet streams (there are 12) will form waves. They will grow, break, roll-up and re-populate Jupiter with new vortices. These dynamics should be visible from earth as the break-up of a circumferential band of clouds into ``spots''. The new vortices will be similar to those that were observed during most of the 20th century. For ˜60 years they will change only slowly, then abruptly bunch together. Shortly afterward, most will disappear by merging with other vortices. The cycle described above will repeat with a ˜70-year time scale, with many of the events detectable from earth or by satellite. The formation of the White Oval ``spots'' in 1939 began the current global climate cycle, and their mergers in 1997--2000 signaled the beginning of its end. Our predictions are based on fundamental vortex dynamics rather than global circulation models.

  10. Predicting the Impacts of Climate Change on Central American Agriculture

    NASA Astrophysics Data System (ADS)

    Winter, J. M.; Ruane, A. C.; Rosenzweig, C.

    2011-12-01

    Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.

  11. Contrast sensitivity function calibration based on image quality prediction

    NASA Astrophysics Data System (ADS)

    Han, Yu; Cai, Yunze

    2014-11-01

    Contrast sensitivity functions (CSFs) describe visual stimuli based on their spatial frequency. However, CSF calibration is limited by the size of the sample collection and this remains an open issue. In this study, we propose an approach for calibrating CSFs that is based on the hypothesis that a precise CSF model can accurately predict image quality. Thus, CSF calibration is regarded as the inverse problem of image quality prediction according to our hypothesis. A CSF could be calibrated by optimizing the performance of a CSF-based image quality metric using a database containing images with known quality. Compared with the traditional method, this would reduce the work involved in sample collection dramatically. In the present study, we employed three image databases to optimize some existing CSF models. The experimental results showed that the performance of a three-parameter CSF model was better than that of other models. The results of this study may be helpful in CSF and image quality research.

  12. Does Change on the MOLEST and RAPE Scales Predict Sexual Recidivism?

    PubMed

    Nunes, Kevin L; Pettersen, Cathrine; Hermann, Chantal A; Looman, Jan; Spape, Jessica

    2016-08-01

    The purpose of the current study was to examine whether the MOLEST and RAPE scales and change on these measures predicted sexual recidivism in a sample of 146 adult male sexual offenders who participated in a high-intensity treatment program while incarcerated. The majority of subjects had functional scores on the MOLEST and RAPE scales prior to treatment. Of those who had dysfunctional pre-treatment scores, the majority made significant gains. However, the MOLEST and RAPE scales did not significantly predict sexual recidivism. This was the case for pre-treatment scores, post-treatment scores, and change scores. Our findings are generally not consistent with the view that these measures assess dynamic risk factors for sexual recidivism. However, this is the first published study to examine the predictive validity of these scales and more rigorous research is needed before firm conclusions can be drawn. PMID:24996579

  13. Prediction of functional residues in water channels and related proteins.

    PubMed Central

    Froger, A.; Tallur, B.; Thomas, D.; Delamarche, C.

    1998-01-01

    In this paper, we present an updated classification of the ubiquitous MIP (Major Intrinsic Protein) family proteins, including 153 fully or partially sequenced members available in public databases. Presently, about 30 of these proteins have been functionally characterized, exhibiting essentially two distinct types of channel properties: (1) specific water transport by the aquaporins, and (2) small neutral solutes transport, such as glycerol by the glycerol facilitators. Sequence alignments were used to predict amino acids and motifs discriminant in channel specificity. The protein sequences were also analyzed using statistical tools (comparisons of means and correspondence analysis). Five key positions were clearly identified where the residues are specific for each functional subgroup and exhibit high dissimilar physico-chemical properties. Moreover, we have found that the putative channels for small neutral solutes clearly differ from the aquaporins by the amino acid content and the length of predicted loop regions, suggesting a substrate filter function for these loops. From these results, we propose a signature pattern for water transport. PMID:9655351

  14. Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

    PubMed

    Kuang, Jialiang; Huang, Linan; He, Zhili; Chen, Linxing; Hua, Zhengshuang; Jia, Pu; Li, Shengjin; Liu, Jun; Li, Jintian; Zhou, Jizhong; Shu, Wensheng

    2016-06-01

    Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural

  15. Predicting implementation from organizational readiness for change: a study protocol

    PubMed Central

    2011-01-01

    Background There is widespread interest in measuring organizational readiness to implement evidence-based practices in clinical care. However, there are a number of challenges to validating organizational measures, including inferential bias arising from the halo effect and method bias - two threats to validity that, while well-documented by organizational scholars, are often ignored in health services research. We describe a protocol to comprehensively assess the psychometric properties of a previously developed survey, the Organizational Readiness to Change Assessment. Objectives Our objective is to conduct a comprehensive assessment of the psychometric properties of the Organizational Readiness to Change Assessment incorporating methods specifically to address threats from halo effect and method bias. Methods and Design We will conduct three sets of analyses using longitudinal, secondary data from four partner projects, each testing interventions to improve the implementation of an evidence-based clinical practice. Partner projects field the Organizational Readiness to Change Assessment at baseline (n = 208 respondents; 53 facilities), and prospectively assesses the degree to which the evidence-based practice is implemented. We will conduct predictive and concurrent validities using hierarchical linear modeling and multivariate regression, respectively. For predictive validity, the outcome is the change from baseline to follow-up in the use of the evidence-based practice. We will use intra-class correlations derived from hierarchical linear models to assess inter-rater reliability. Two partner projects will also field measures of job satisfaction for convergent and discriminant validity analyses, and will field Organizational Readiness to Change Assessment measures at follow-up for concurrent validity (n = 158 respondents; 33 facilities). Convergent and discriminant validities will test associations between organizational readiness and different aspects of job

  16. Predicting impacts of climate change on Fasciola hepatica risk.

    PubMed

    Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

    2011-01-01

    Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits. PMID:21249228

  17. Australian Tropical Cyclone Activity: Interannual Prediction and Climate Change

    NASA Astrophysics Data System (ADS)

    Nicholls, N.

    2014-12-01

    It is 35 years since it was first demonstrated that interannual variations in seasonal Australian region tropical cyclone (TC) activity could be predicted using simple indices of the El Niño - Southern Oscillation (ENSO). That demonstration (Nicholls, 1979), which was surprising and unexpected at the time, relied on only 25 years of data (1950-1975), but its later confirmation eventually led to the introduction of operational seasonal tropical cyclone activity. It is worth examining how well the ENSO-TC relationship has performed, over the period since 1975. Changes in observational technology, and even how a tropical cyclone is defined, have affected the empirical relationships between ENSO and seasonal activity, and ways to overcome this in forecasting seasonal activity will be discussed. Such changes also complicate the investigation of long-term trends in cyclone activity. The early work linked cyclone activity to local sea surface temperature thereby leading to the expectation that global warming would result in an increase in cyclone activity. But studies in the 1990s (eg., Nicholls et al., 1998) suggested that such an increase in activity was not occurring, neither in the Australian region nor elsewhere. Trends in Australian tropical cyclone activity will be discussed, and the confounding influence of factors such as changes in observational technologies will be examined. Nicholls, N. 1979. A possible method for predicting seasonal tropical cyclone activity in the Australian region. Mon. Weath. Rev., 107, 1221-1224 Nicholls, N., Landsea, C., and Gill, J., 1998. Recent trends in Australian region tropical cyclone activity. Meteorology and Atmospheric Physics, 65, 197-205.

  18. Predictions of the solar wind speed by the probability distribution function model

    NASA Astrophysics Data System (ADS)

    Bussy-Virat, C. D.; Ridley, A. J.

    2014-06-01

    The near-Earth space environment is strongly driven by the solar wind and interplanetary magnetic field. This study presents a model for predicting the solar wind speed up to 5 days in advance. Probability distribution functions (PDFs) were created that relate the current solar wind speed and slope to the future solar wind speed, as well as the solar wind speed to the solar wind speed one solar rotation in the future. It was found that a major limitation of this type of technique is that the solar wind periodicity is close to 27 days but can be from about 22 to 32 days. Further, the optimum lag between two solar rotations can change from day to day, making a prediction of the future solar wind speed based solely on the solar wind speed approximately 27 days ago quite difficult. It was found that using a linear combination of the solar wind speed one solar rotation ago and a prediction of the solar wind speed based on the current speed and slope is optimal. The linear weights change as a function of the prediction horizon, with shorter prediction times putting more weight on the prediction based on the current solar wind speed and the longer prediction times based on an even spread between the two. For all prediction horizons from 8 h up to 120 h, the PDF Model is shown to be better than using the current solar wind speed (i.e., persistence), and better than the Wang-Sheeley-Arge Model for prediction horizons of 24 h.

  19. The Evolutionary Legacy of Diversification Predicts Ecosystem Function.

    PubMed

    Yguel, Benjamin; Jactel, Hervé; Pearse, Ian S; Moen, Daniel; Winter, Marten; Hortal, Joaquin; Helmus, Matthew R; Kühn, Ingolf; Pavoine, Sandrine; Purschke, Oliver; Weiher, Evan; Violle, Cyrille; Ozinga, Wim; Brändle, Martin; Bartish, Igor; Prinzing, Andreas

    2016-10-01

    Theory suggests that the structure of evolutionary history represented in a species community may affect its functioning, but phylogenetic diversity metrics do not allow for the identification of major differences in this structure. Here we propose a new metric, ELDERness (for Evolutionary Legacy of DivERsity) to estimate evolutionary branching patterns within communities by fitting a polynomial function to lineage-through-time (LTT) plots. We illustrate how real and simulated community branching patterns can be more correctly described by ELDERness and can successfully predict ecosystem functioning. In particular, the evolutionary history of branching patterns can be encapsulated by the parameters of third-order polynomial functions and further measured through only two parameters, the "ELDERness surfaces." These parameters captured variation in productivity of a grassland community better than existing phylogenetic diversity or diversification metrics and independent of species richness or presence of nitrogen fixers. Specifically, communities with small ELDERness surfaces (constant accumulation of lineages through time in LTT plots) were more productive, consistent with increased productivity resulting from complementary lineages combined with niche filling within lineages. Overall, while existing phylogenetic diversity metrics remain useful in many contexts, we suggest that our ELDERness approach better enables testing hypotheses that relate complex patterns of macroevolutionary history represented in local communities to ecosystem functioning. PMID:27622874

  20. Testable Predictions for Large-Scale Coastline-Shape Change in Response to Changing Storm Climate

    NASA Astrophysics Data System (ADS)

    Murray, A. B.; Moore, L. J.; McNamara, D.; Brenner, O.; Slott, J.

    2008-12-01

    Recent modeling (Ashton et al. 2001; Ashton and Murray, 2006a) and observations (Ashton and Murray 2006b) suggest that sandy coastlines self-organize into large-scale, plan-view shapes that depend sensitively on the regional wave climate-the distribution of influences on alongshore sediment transport from different deep-water wave-approach angles. Subsequent modeling (Slott et al., 2007) shows that even moderate changes in wave climate, as are likely to arise as storm behaviors shift in the coming century, will cause coastlines to change shape rapidly, compared to a steady-wave-climate scenario. Such large-scale shape changes involve greatly accentuated rates of local erosion, and highly variable erosion/accretion rates. A recent analysis of wave records from the Southeastern US (Komar and Allen, 2007) indicates that wave climates have already been changing over the past three decades; the heights of waves attributable to tropical storms have been increasing, changing the angular distribution of wave influences. Modeling based on these observations leads to predictions of how coastlines in this region should already be changing shape (McNamara et al., in prep.). As a case study, we are examining historical shorelines for the Carolina coastline, to test whether the predicted alongshore patterns of shoreline change can already be detected.

  1. Utility functions predict variance and skewness risk preferences in monkeys

    PubMed Central

    Genest, Wilfried; Stauffer, William R.; Schultz, Wolfram

    2016-01-01

    Utility is the fundamental variable thought to underlie economic choices. In particular, utility functions are believed to reflect preferences toward risk, a key decision variable in many real-life situations. To assess the validity of utility representations, it is therefore important to examine risk preferences. In turn, this approach requires formal definitions of risk. A standard approach is to focus on the variance of reward distributions (variance-risk). In this study, we also examined a form of risk related to the skewness of reward distributions (skewness-risk). Thus, we tested the extent to which empirically derived utility functions predicted preferences for variance-risk and skewness-risk in macaques. The expected utilities calculated for various symmetrical and skewed gambles served to define formally the direction of stochastic dominance between gambles. In direct choices, the animals’ preferences followed both second-order (variance) and third-order (skewness) stochastic dominance. Specifically, for gambles with different variance but identical expected values (EVs), the monkeys preferred high-variance gambles at low EVs and low-variance gambles at high EVs; in gambles with different skewness but identical EVs and variances, the animals preferred positively over symmetrical and negatively skewed gambles in a strongly transitive fashion. Thus, the utility functions predicted the animals’ preferences for variance-risk and skewness-risk. Using these well-defined forms of risk, this study shows that monkeys’ choices conform to the internal reward valuations suggested by their utility functions. This result implies a representation of utility in monkeys that accounts for both variance-risk and skewness-risk preferences. PMID:27402743

  2. Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests

    PubMed Central

    Nagao, Chioko; Nagano, Nozomi; Mizuguchi, Kenji

    2014-01-01

    Determining enzyme functions is essential for a thorough understanding of cellular processes. Although many prediction methods have been developed, it remains a significant challenge to predict enzyme functions at the fourth-digit level of the Enzyme Commission numbers. Functional specificity of enzymes often changes drastically by mutations of a small number of residues and therefore, information about these critical residues can potentially help discriminate detailed functions. However, because these residues must be identified by mutagenesis experiments, the available information is limited, and the lack of experimentally verified specificity determining residues (SDRs) has hindered the development of detailed function prediction methods and computational identification of SDRs. Here we present a novel method for predicting enzyme functions by random forests, EFPrf, along with a set of putative SDRs, the random forests derived SDRs (rf-SDRs). EFPrf consists of a set of binary predictors for enzymes in each CATH superfamily and the rf-SDRs are the residue positions corresponding to the most highly contributing attributes obtained from each predictor. EFPrf showed a precision of 0.98 and a recall of 0.89 in a cross-validated benchmark assessment. The rf-SDRs included many residues, whose importance for specificity had been validated experimentally. The analysis of the rf-SDRs revealed both a general tendency that functionally diverged superfamilies tend to include more active site residues in their rf-SDRs than in less diverged superfamilies, and superfamily-specific conservation patterns of each functional residue. EFPrf and the rf-SDRs will be an effective tool for annotating enzyme functions and for understanding how enzyme functions have diverged within each superfamily. PMID:24416252

  3. Predicting activity approach based on new atoms similarity kernel function.

    PubMed

    Abu El-Atta, Ahmed H; Moussa, M I; Hassanien, Aboul Ella

    2015-07-01

    Drug design is a high cost and long term process. To reduce time and costs for drugs discoveries, new techniques are needed. Chemoinformatics field implements the informational techniques and computer science like machine learning and graph theory to discover the chemical compounds properties, such as toxicity or biological activity. This is done through analyzing their molecular structure (molecular graph). To overcome this problem there is an increasing need for algorithms to analyze and classify graph data to predict the activity of molecules. Kernels methods provide a powerful framework which combines machine learning with graph theory techniques. These kernels methods have led to impressive performance results in many several chemoinformatics problems like biological activity prediction. This paper presents a new approach based on kernel functions to solve activity prediction problem for chemical compounds. First we encode all atoms depending on their neighbors then we use these codes to find a relationship between those atoms each other. Then we use relation between different atoms to find similarity between chemical compounds. The proposed approach was compared with many other classification methods and the results show competitive accuracy with these methods. PMID:26117822

  4. Predicting clinical responses in major depression using intrinsic functional connectivity.

    PubMed

    Qin, Jian; Shen, Hui; Zeng, Ling-Li; Jiang, Weixiong; Liu, Li; Hu, Dewen

    2015-08-19

    There has been increasing interest in multivariate pattern analysis (MVPA) as a means of distinguishing psychiatric patients from healthy controls using brain imaging. However, it remains unclear whether MVPA methods can accurately estimate the medication status of psychiatric patients. This study aims to develop an MVPA approach to accurately predict the antidepressant medication status of individuals with major depression on the basis of whole-brain resting-state functional connectivity MRI (rs-fcMRI). We investigated data from rs-fcMRI of 24 medication-naive depressed patients, 16 out of whom subsequently underwent antidepressant treatment and achieved clinical recovery, and 29 demographically similar controls. By training a linear support vector machine classifier and combining it with principal component analysis, the medication-naive patients were identified from the healthy controls with 100% accuracy. In addition, we found reliable correlations between MVPA prediction scores and clinical symptom severity. Moreover, the most discriminative functional connections were located within or across the cerebellum and default mode, affective, and sensorimotor networks, indicating that these networks may play important roles in major depression. Most importantly, only ∼30% of these discriminative connections were normalized in clinically recovered patients after antidepressant treatment. The current study may not only show the feasibility of estimating medication status by MVPA of whole-brain rs-fcMRI data in major depression but also shed new light on the pathological mechanism of this disorder. PMID:26164454

  5. Predictive Equations Using Regression Analysis of Pulmonary Function for Healthy Children in Northeast China

    PubMed Central

    Ma, Ya-Nan; Wang, Jing; Dong, Guang-Hui; Liu, Miao-Miao; Wang, Da; Liu, Yu-Qin; Zhao, Yang; Ren, Wan-Hui; Lee, Yungling Leo; Zhao, Ya-Dong; He, Qin-Cheng

    2013-01-01

    Background There have been few published studies on spirometric reference values for healthy children in China. We hypothesize that there would have been changes in lung function that would not have been precisely predicted by the existing spirometric reference equations. The objective of the study was to develop more accurate predictive equations for spirometric reference values for children aged 9 to 15 years in Northeast China. Methodology/Principal Findings Spirometric measurements were obtained from 3,922 children, including 1,974 boys and 1,948 girls, who were randomly selected from five cities of Liaoning province, Northeast China, using the ATS (American Thoracic Society) and ERS (European Respiratory Society) standards. The data was then randomly split into a training subset containing 2078 cases and a validation subset containing 1844 cases. Predictive equations used multiple linear regression techniques with three predictor variables: height, age and weight. Model goodness of fit was examined using the coefficient of determination or the R2 and adjusted R2. The predicted values were compared with those obtained from the existing spirometric reference equations. The results showed the prediction equations using linear regression analysis performed well for most spirometric parameters. Paired t-tests were used to compare the predicted values obtained from the developed and existing spirometric reference equations based on the validation subset. The t-test for males was not statistically significant (p>0.01). The predictive accuracy of the developed equations was higher than the existing equations and the predictive ability of the model was also validated. Conclusion/Significance We developed prediction equations using linear regression analysis of spirometric parameters for children aged 9–15 years in Northeast China. These equations represent the first attempt at predicting lung function for Chinese children following the ATS/ERS Task Force 2005

  6. Better prediction of functional effects for sequence variants

    PubMed Central

    2015-01-01

    Elucidating the effects of naturally occurring genetic variation is one of the major challenges for personalized health and personalized medicine. Here, we introduce SNAP2, a novel neural network based classifier that improves over the state-of-the-art in distinguishing between effect and neutral variants. Our method's improved performance results from screening many potentially relevant protein features and from refining our development data sets. Cross-validated on >100k experimentally annotated variants, SNAP2 significantly outperformed other methods, attaining a two-state accuracy (effect/neutral) of 83%. SNAP2 also outperformed combinations of other methods. Performance increased for human variants but much more so for other organisms. Our method's carefully calibrated reliability index informs selection of variants for experimental follow up, with the most strongly predicted half of all effect variants predicted at over 96% accuracy. As expected, the evolutionary information from automatically generated multiple sequence alignments gave the strongest signal for the prediction. However, we also optimized our new method to perform surprisingly well even without alignments. This feature reduces prediction runtime by over two orders of magnitude, enables cross-genome comparisons, and renders our new method as the best solution for the 10-20% of sequence orphans. SNAP2 is available at: https://rostlab.org/services/snap2web Definitions used Delta, input feature that results from computing the difference feature scores for native amino acid and feature scores for variant amino acid; nsSNP, non-synoymous SNP; PMD, Protein Mutant Database; SNAP, Screening for non-acceptable polymorphisms; SNP, single nucleotide polymorphism; variant, any amino acid changing sequence variant. PMID:26110438

  7. Prediction of functional aerobic capacity without exercise testing

    NASA Technical Reports Server (NTRS)

    Jackson, A. S.; Blair, S. N.; Mahar, M. T.; Wier, L. T.; Ross, R. M.; Stuteville, J. E.

    1990-01-01

    The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS).

  8. Habitual fat intake predicts memory function in younger women

    PubMed Central

    Gibson, E. Leigh; Barr, Suzanne; Jeanes, Yvonne M.

    2013-01-01

    High intakes of fat have been linked to greater cognitive decline in old age, but such associations may already occur in younger adults. We tested memory and learning in 38 women (25 to 45 years old), recruited for a larger observational study in women with polycystic ovary syndrome. These women varied in health status, though not significantly between cases (n = 23) and controls (n = 15). Performance on tests sensitive to medial temporal lobe function (CANTABeclipse, Cambridge Cognition Ltd, Cambridge, UK), i.e., verbal memory, visuo-spatial learning, and delayed pattern matching (DMS), were compared with intakes of macronutrients from 7-day diet diaries and physiological indices of metabolic syndrome. Partial correlations were adjusted for age, activity, and verbal IQ (National Adult Reading Test). Greater intakes of saturated and trans fats, and higher saturated to unsaturated fat ratio (Sat:UFA), were associated with more errors on the visuo-spatial task and with poorer word recall and recognition. Unexpectedly, higher UFA intake predicted poorer performance on the word recall and recognition measures. Fasting insulin was positively correlated with poorer word recognition only, whereas higher blood total cholesterol was associated only with visuo-spatial learning errors. None of these variables predicted performance on a DMS test. The significant nutrient–cognition relationships were tested for mediation by total energy intake: saturated and trans fat intakes, and Sat:UFA, remained significant predictors specifically of visuo-spatial learning errors, whereas total fat and UFA intakes now predicted only poorer word recall. Examination of associations separately for monounsaturated (MUFA) and polyunsaturated fats suggested that only MUFA intake was predictive of poorer word recall. Saturated and trans fats, and fasting insulin, may already be associated with cognitive deficits in younger women. The findings need extending but may have important implications for

  9. Soda Consumption During Ad Libitum Food Intake Predicts Weight Change

    PubMed Central

    Bundrick, Sarah C.; Thearle, Marie S.; Venti, Colleen A.; Krakoff, Jonathan; Votruba, Susanne B.

    2013-01-01

    Soda consumption may contribute to weight gain over time. Objective data were used to determine whether soda consumption predicts weight gain or changes in glucose regulation over time. Subjects without diabetes (128 men, 75 women; mean age 34.3±8.9 years; mean body mass index [BMI] 32.5±7.4; mean percentage body fat 31.6%±8.6%) self-selected their food from an ad libitum vending machine system for 3 days. Mean daily energy intake was calculated from food weight. Energy consumed from soda was recorded as were food choices that were low in fat (<20%) or high in simple sugars (>30%). Food choices were expressed as percentage of daily energy intake. A subset of 85 subjects had measurement of follow-up weights and oral glucose tolerance (57 men, 28 women; mean follow-up time=2.5±2.1 years, range 6 months to 9.9 years). Energy consumed from soda was negatively related to age (r=–0.27, P=0.0001), and choosing low-fat foods (r=−0.35, P<0.0001), but positively associated with choosing solid foods high in simple sugars (r=0.45, P<0.0001) and overall average daily energy intake (r=0.46, P<0.0001). Energy intake from food alone did not differ between individuals who did and did not consume beverage calories (P=0.11). Total daily energy intake had no relationship with change in weight (P=0.29) or change in glucose regulation (P=0.38) over time. However, energy consumed from soda correlated with change in weight (r=0.21, P=0.04). This relationship was unchanged after adjusting for follow-up time and initial weight. Soda consumption is a marker for excess energy consumption and is associated with weight gain. PMID:24321742

  10. How and when should interactome-derived clusters be used to predict functional modules and protein function?

    PubMed Central

    Song, Jimin; Singh, Mona

    2009-01-01

    Motivation: Clustering of protein–protein interaction networks is one of the most common approaches for predicting functional modules, protein complexes and protein functions. But, how well does clustering perform at these tasks? Results: We develop a general framework to assess how well computationally derived clusters in physical interactomes overlap functional modules derived via the Gene Ontology (GO). Using this framework, we evaluate six diverse network clustering algorithms using Saccharomyces cerevisiae and show that (i) the performances of these algorithms can differ substantially when run on the same network and (ii) their relative performances change depending upon the topological characteristics of the network under consideration. For the specific task of function prediction in S.cerevisiae, we demonstrate that, surprisingly, a simple non-clustering guilt-by-association approach outperforms widely used clustering-based approaches that annotate a protein with the overrepresented biological process and cellular component terms in its cluster; this is true over the range of clustering algorithms considered. Further analysis parameterizes performance based on the number of annotated proteins, and suggests when clustering approaches should be used for interactome functional analyses. Overall our results suggest a re-examination of when and how clustering approaches should be applied to physical interactomes, and establishes guidelines by which novel clustering approaches for biological networks should be justified and evaluated with respect to functional analysis. Contact: msingh@cs.princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19770263

  11. Transition length prediction for flows with rapidly changing pressure gradients

    SciTech Connect

    Solomon, W.J.; Walker, G.J.; Gostelow, J.P.

    1996-10-01

    A new method for calculating intermittency in transitional boundary layers with changing pressure gradients is proposed and tested against standard turbomachinery flow cases. It is based on recent experimental studies, which show the local pressure gradient parameter to have a significant effect on turbulent spot spreading angles and propagation velocities (and hence transition length). This can be very important for some turbomachinery flows. On a turbine blade suction surface, for example, it is possible for transition to start in a region of favorable pressure gradient and finish in a region of adverse pressure gradient. Calculation methods that estimate the transition length from the local pressure gradient parameter at the start of transition will seriously overestimate the transition length under these conditions. Conventional methods based on correlations of zero pressure gradient transition date are similarly inaccurate. The new calculation method continuously adjusts the spot growth parameters in response to changes in the local pressure gradient through transition using correlations based on data given in the companion paper by Gostelow et al. (1996). Recent experiment correlations of Gostelow et al. (1994a) are used to estimate the turbulent spot generation rate at the start of transition. The method has been incorporated in a linear combination integral computation and tested with good results on cases that report both the intermittency and surface pressure distribution data. It has resulted in a much reduced sensitivity to errors in predicting the start of the transition zone, and can be recommended for engineering use in calculating boundary layer development on axial turbomachine blades.

  12. Predicting Gene-Regulation Functions: Lessons from Temperate Bacteriophages

    PubMed Central

    Teif, Vladimir B.

    2010-01-01

    Gene-regulation functions (GRF) provide a unique characteristic of a cis-regulatory module (CRM), relating the concentrations of transcription factors (input) to the promoter activities (output). The challenge is to predict GRFs from the sequence. Here we systematically consider the lysogeny-lysis CRMs of different temperate bacteriophages such as the Lactobacillus casei phage A2, Escherichia coli phages λ, and 186 and Lactococcal phage TP901-1. This study allowed explaining a recent experimental puzzle on the role of Cro protein in the lambda switch. Several general conclusions have been drawn: 1), long-range interactions, multilayer assembly and DNA looping may lead to complex GRFs that cannot be described by linear functions of binding site occupancies; 2), in general, GRFs cannot be described by the Boolean logic, whereas a three-state non-Boolean logic suffices for the studied examples; 3), studied CRMs of the intact phages seemed to have a similar GRF topology (the number of plateaus and peaks corresponding to different expression regimes); we hypothesize that functionally equivalent CRMs might have topologically equivalent GRFs for a larger class of genetic systems; and 4) within a given GRF class, a set of mechanistic-to-mathematical transformations has been identified, which allows shaping the GRF before carrying out a system-level analysis. PMID:20371324

  13. Dynamic Predictions of Semi-Arid Land Cover Change

    NASA Astrophysics Data System (ADS)

    Foster-Wittig, T. A.

    2011-12-01

    Savannas make up about 18% of the global landmass and contain about 22% of the world's population (Falkenmark and Rockstrom, 2008). They are unique ecosystems in that they consist of both grass and trees. Depending on the land use, amount of precipitation, herbivory, and fire frequency, either trees or grasses can be more prevalent than the other (Sankaran et al., 2005). Savannas in sub-Saharan Africa are usually considered water-limited ecosystems due to the seasonal rainfall. It has been shown that the vegetation responds on a short timescale to the rainfall (Scanlon et al, 2002). Therefore, savannas are foreseen as vulnerable ecosystems to future changes in the land use and climate change (Sankaran et al, 2005). The goal of this research is to quantify the vulnerability of this ecosystem by projecting future changes in the savanna structure due to land use and climate change through the use of a dynamic vegetation model. This research will provide a better understanding of the relationship between precipitation and vegetation in savannas through the use of a Vegetation Dynamics Model developed to predict surface water fluxes and vegetation dynamics in water-limited ecosystems (Williams and Albertson, 2005). In this project, it will be used to model leaf area index (LAI) for point locations within sub-Saharan Africa between Kenya and Botswana with a range of annual rainfall and savanna type. With this model, future projections are developed for what can be anticipated in the future for the savanna structure based on three climate change scenarios; (1) decreased depth, (2) decreased frequency, and (3) decreased wet season length. The effect of the climate change scenarios on the plant water stress and plant water uptake will be analyzed in order to understand the dynamic effects of precipitation on vegetation. Therefore, this will allow conclusions to be drawn about how mean precipitation and a changing climate effect the sensitivity of savanna vegetation. It is

  14. Functional innovation from changes in protein domains and their combinations.

    PubMed

    Lees, Jonathan G; Dawson, Natalie L; Sillitoe, Ian; Orengo, Christine A

    2016-06-01

    Domains are the functional building blocks of proteins. In this work we discuss how domains can contribute to the evolution of new functions. Domains themselves can evolve through various mechanisms, altering their intrinsic function. Domains can also facilitate functional innovations by combining with other domains to make novel proteins. We discuss the mechanisms by which domain and domain combinations support functional innovations. We highlight interesting examples where changes in domain combination promote changes at the domain level. PMID:27309309

  15. Sexual abuse predicts functional somatic symptoms: an adolescent population study.

    PubMed

    Bonvanie, Irma J; van Gils, Anne; Janssens, Karin A M; Rosmalen, Judith G M

    2015-08-01

    The main aim of this study was to investigate the effect of childhood sexual abuse on medically not well explained or functional somatic symptoms (FSSs) in adolescents. We hypothesized that sexual abuse predicts higher levels of FSSs and that anxiety and depression contribute to this relationship. In addition, we hypothesized that more severe abuse is associated with higher levels of FSSs and that sexual abuse is related to gastrointestinal FSSs in particular. This study was part of the Tracking Adolescents' Individual Lives Survey (TRAILS): a general population cohort which started in 2001 (N=2,230; 50.8% girls, mean age 11.1 years). The current study uses data of 1,680 participants over four assessment waves (75% of baseline, mean duration of follow-up: 8 years). FSSs were measured by the Somatic Complaints subscale of the Youth Self-Report at all waves. Sexual abuse before the age of sixteen was assessed retrospectively with a questionnaire at T4. To test the hypotheses linear mixed models were used adjusted for age, sex, socioeconomic status, anxiety and depression. Sexual abuse predicted higher levels of FSSs after adjustment for age sex and socioeconomic status (B=.06) and after additional adjustment for anxiety and depression (B=.03). While sexual abuse involving physical contact significantly predicted the level of FSSs (assault; B=.08, rape; B=.05), non-contact sexual abuse was not significantly associated with FSSs (B=.04). Sexual abuse was not a stronger predictor of gastrointestinal FSSs (B=.06) than of all FSSs. Further research is needed to clarify possible mechanisms underlying relationship between sexual abuse and FSSs. PMID:26142915

  16. Predicting Stability Constants for Uranyl Complexes Using Density Functional Theory

    SciTech Connect

    Vukovic, Sinisa; Hay, Benjamin P.; Bryantsev, Vyacheslav S.

    2015-04-02

    The ability to predict the equilibrium constants for the formation of 1:1 uranyl:ligand complexes (log K1 values) provides the essential foundation for the rational design of ligands with enhanced uranyl affinity and selectivity. We also use density functional theory (B3LYP) and the IEFPCM continuum solvation model to compute aqueous stability constants for UO22+ complexes with 18 donor ligands. Theoretical calculations permit reasonably good estimates of relative binding strengths, while the absolute log K1 values are significantly overestimated. Accurate predictions of the absolute log K1 values (root mean square deviation from experiment < 1.0 for log K1 values ranging from 0 to 16.8) can be obtained by fitting the experimental data for two groups of mono and divalent negative oxygen donor ligands. The utility of correlations is demonstrated for amidoxime and imide dioxime ligands, providing a useful means of screening for new ligands with strong chelate capability to uranyl.

  17. Predicting Stability Constants for Uranyl Complexes Using Density Functional Theory

    DOE PAGESBeta

    Vukovic, Sinisa; Hay, Benjamin P.; Bryantsev, Vyacheslav S.

    2015-04-02

    The ability to predict the equilibrium constants for the formation of 1:1 uranyl:ligand complexes (log K1 values) provides the essential foundation for the rational design of ligands with enhanced uranyl affinity and selectivity. We also use density functional theory (B3LYP) and the IEFPCM continuum solvation model to compute aqueous stability constants for UO22+ complexes with 18 donor ligands. Theoretical calculations permit reasonably good estimates of relative binding strengths, while the absolute log K1 values are significantly overestimated. Accurate predictions of the absolute log K1 values (root mean square deviation from experiment < 1.0 for log K1 values ranging from 0more » to 16.8) can be obtained by fitting the experimental data for two groups of mono and divalent negative oxygen donor ligands. The utility of correlations is demonstrated for amidoxime and imide dioxime ligands, providing a useful means of screening for new ligands with strong chelate capability to uranyl.« less

  18. Predicting stability constants for uranyl complexes using density functional theory.

    PubMed

    Vukovic, Sinisa; Hay, Benjamin P; Bryantsev, Vyacheslav S

    2015-04-20

    The ability to predict the equilibrium constants for the formation of 1:1 uranyl/ligand complexes (log K1 values) provides the essential foundation for the rational design of ligands with enhanced uranyl affinity and selectivity. We use density functional theory (B3LYP) and the integral equation formalism polarizable continuum model (IEF-PCM) to compute aqueous stability constants for UO2(2+) complexes with 18 donor ligands. Theoretical calculations permit reasonably good estimates of relative binding strengths, while the absolute log K1 values are significantly overestimated. Accurate predictions of the absolute log K1 values (root-mean-square deviation from experiment <1.0 for log K1 values ranging from 0 to 16.8) can be obtained by fitting the experimental data for two groups of mono- and divalent negative oxygen donor ligands. The utility of correlations is demonstrated for amidoxime and imide dioxime ligands, providing a useful means of screening for new ligands with strong chelating capability to uranyl. PMID:25835578

  19. Pacific Walrus and climate change: observations and predictions.

    PubMed

    Maccracken, James G

    2012-08-01

    The extent and duration of sea-ice habitats used by Pacific walrus (Odobenus rosmarus divergens) are diminishing resulting in altered walrus behavior, mortality, and distribution. I document changes that have occurred over the past several decades and make predictions to the end of the 21st century. Climate models project that sea ice will monotonically decline resulting in more ice-free summers of longer duration. Several stressors that may impact walruses are directly influenced by sea ice. How these stressors materialize were modeled as most likely-case, worst-case, and best-case scenarios for the mid- and late-21st century, resulting in four comprehensive working hypotheses that can help identify and prioritize management and research projects, identify comprehensive mitigation actions, and guide monitoring programs to track future developments and adjust programs as needed. In the short term, the most plausible hypotheses predict a continuing northward shift in walrus distribution, increasing use of coastal haulouts in summer and fall, and a population reduction set by the carrying capacity of the near shore environment and subsistence hunting. Alternatively, under worst-case conditions, the population will decline to a level where the probability of extinction is high. In the long term, walrus may seasonally abandon the Bering and Chukchi Seas for sea-ice refugia to the northwest and northeast, ocean warming and pH decline alter walrus food resources, and subsistence hunting exacerbates a large population decline. However, conditions that reverse current trends in sea ice loss cannot be ruled out. Which hypothesis comes to fruition depends on how the stressors develop and the success of mitigation measures. Best-case scenarios indicate that successful mitigation of unsustainable harvests and terrestrial haulout-related mortalities can be effective. Management and research should focus on monitoring, elucidating effects, and mitigation, while ultimately

  20. Extreme hydrometeorological events and climate change predictions in Europe

    NASA Astrophysics Data System (ADS)

    Millán, Millán M.

    2014-10-01

    Field meteorological data collected in several European Commission projects (from 1974 to 2011) were re-analysed in the context of a perceived reduction in summer storms around the Western Mediterranean Basin (WMB). The findings reveal some hitherto overlooked processes that raise questions about direct impacts on European hydrological cycles, e.g., extreme hydrometeorological events, and about the role of feedbacks on climate models and climate predictions. For instance, the summer storms are affected by land-use changes along the coasts and mountain slopes. Their loss triggers a chain of events that leads to an Accumulation Mode (AM) where water vapour and air pollutants (ozone) become stacked in layers, up to 4000(+) m, over the WMB. The AM cycle can last 3-5 consecutive days, and recur several times each month from mid May to late August. At the end of each cycle the accumulated water vapour can feed Vb track events and generate intense rainfall and summer floods in Central Europe. Venting out of the water vapour that should have precipitated within the WMB increases the salinity of the sea and affects the Atlantic-Mediterranean Salinity valve at Gibraltar. This, in turn, can alter the tracks of Atlantic Depressions and their frontal systems over Atlantic Europe. Another effect is the greenhouse heating by water vapour and photo-oxidants (e.g., O3) when layered over the Basin during the AM cycle. This increases the Sea Surface Temperature (SST), and the higher SST intensifies torrential rain events over the Mediterranean coasts in autumn. All these processes raise research questions that must be addressed to improve the meteorological forecasting of extreme events, as well as climate model predictions.

  1. Predicting biodiversity change and averting collapse in agricultural landscapes.

    PubMed

    Mendenhall, Chase D; Karp, Daniel S; Meyer, Christoph F J; Hadly, Elizabeth A; Daily, Gretchen C

    2014-05-01

    The equilibrium theory of island biogeography is the basis for estimating extinction rates and a pillar of conservation science. The default strategy for conserving biodiversity is the designation of nature reserves, treated as islands in an inhospitable sea of human activity. Despite the profound influence of islands on conservation theory and practice, their mainland analogues, forest fragments in human-dominated landscapes, consistently defy expected biodiversity patterns based on island biogeography theory. Countryside biogeography is an alternative framework, which recognizes that the fate of the world's wildlife will be decided largely by the hospitality of agricultural or countryside ecosystems. Here we directly test these biogeographic theories by comparing a Neotropical countryside ecosystem with a nearby island ecosystem, and show that each supports similar bat biodiversity in fundamentally different ways. The island ecosystem conforms to island biogeographic predictions of bat species loss, in which the water matrix is not habitat. In contrast, the countryside ecosystem has high species richness and evenness across forest reserves and smaller forest fragments. Relative to forest reserves and fragments, deforested countryside habitat supports a less species-rich, yet equally even, bat assemblage. Moreover, the bat assemblage associated with deforested habitat is compositionally novel because of predictable changes in abundances by many species using human-made habitat. Finally, we perform a global meta-analysis of bat biogeographic studies, spanning more than 700 species. It generalizes our findings, showing that separate biogeographic theories for countryside and island ecosystems are necessary. A theory of countryside biogeography is essential to conservation strategy in the agricultural ecosystems that comprise roughly half of the global land surface and are likely to increase even further. PMID:24739971

  2. Pacific Walrus and climate change: observations and predictions

    PubMed Central

    MacCracken, James G

    2012-01-01

    The extent and duration of sea-ice habitats used by Pacific walrus (Odobenus rosmarus divergens) are diminishing resulting in altered walrus behavior, mortality, and distribution. I document changes that have occurred over the past several decades and make predictions to the end of the 21st century. Climate models project that sea ice will monotonically decline resulting in more ice-free summers of longer duration. Several stressors that may impact walruses are directly influenced by sea ice. How these stressors materialize were modeled as most likely-case, worst-case, and best-case scenarios for the mid- and late-21st century, resulting in four comprehensive working hypotheses that can help identify and prioritize management and research projects, identify comprehensive mitigation actions, and guide monitoring programs to track future developments and adjust programs as needed. In the short term, the most plausible hypotheses predict a continuing northward shift in walrus distribution, increasing use of coastal haulouts in summer and fall, and a population reduction set by the carrying capacity of the near shore environment and subsistence hunting. Alternatively, under worst-case conditions, the population will decline to a level where the probability of extinction is high. In the long term, walrus may seasonally abandon the Bering and Chukchi Seas for sea-ice refugia to the northwest and northeast, ocean warming and pH decline alter walrus food resources, and subsistence hunting exacerbates a large population decline. However, conditions that reverse current trends in sea ice loss cannot be ruled out. Which hypothesis comes to fruition depends on how the stressors develop and the success of mitigation measures. Best-case scenarios indicate that successful mitigation of unsustainable harvests and terrestrial haulout-related mortalities can be effective. Management and research should focus on monitoring, elucidating effects, and mitigation, while ultimately

  3. Prediction of glass durability as a function of environmental conditions

    SciTech Connect

    Jantzen, C M

    1988-01-01

    A thermodynamic model of glass durability is applied to natural, ancient, and nuclear waste glasses. The durabilities of over 150 different natural and man-made glasses, including actual ancient Roman and Islamic glasses (Jalame ca. 350 AD, Nishapur 10-11th century AD and Gorgon 9-11th century AD), are compared. Glass durability is a function of the thermodynamic hydration free energy, ..delta..G/sub hyd/, which can be calculated from glass composition and solution pH. The durability of the most durable nuclear waste glasses examined was /approximately/10/sup 6/ years. The least durable waste glass formulations were comparable in durability to the most durable simulated medieval window glasses of /approximately/10/sup 3/ years. In this manner, the durability of nuclear waste glasses has been interpolated to be /approximately/10/sup 6/ years and no less than 10/sup 3/ years. Hydration thermodynamics have been shown to be applicable to the dissolution of glass in various natural environments. Groundwater-glass interactions relative to geologic disposal of nuclear waste, hydration rind dating of obsidians, andor other archeological studies can be modeled, e.g., the relative durabilities of six simulated medieval window glasses have been correctly predicted for both laboratory (one month) and burial (5 years) experiments. Effects of solution pH on glass dissolution has been determined experimentally for the 150 different glasses and can be predicted theoretically by hydration thermodynamics. The effects of solution redox on dissolution of glass matrix elements such as SI and B have shown to be minimal. The combined effects of solution pH and Eh have been described and unified by construction of thermodynamically calculated Pourbaix (pH-Eh) diagrams for glass dissolution. The Pourbaix diagrams have been quantified to describe glass dissolution as a function of environmental conditions by use of the data derived from hydration thermodynamics. 56 refs., 7 figs.

  4. Prediction of Land use changes using CA in GIS Environment

    NASA Astrophysics Data System (ADS)

    Kiavarz Moghaddam, H.; Samadzadegan, F.

    2009-04-01

    GIS extention. A set of crisp rules are defined and calibrated based on real urban growth pattern. Uncertainty analysis is performed to evaluate the accuracy of the simulated results as compared to the historical real data. Evaluation shows promising results represented by the high average accuracies achieved. The average accuracy for the predicted growth images 1964 and 2002 is over 80 %. Modifying CA growth rules over time to match the growth pattern changes is important to obtain accurate simulation. This modification is based on the urban growth relationship for Tehran over time as can be seen in the historical raster data. The feedback obtained from comparing the simulated and real data is crucial in identifying the optimal set of CA rules for reliable simulation and calibrating growth steps.

  5. Predicted changes in energy demands for heating and cooling due to climate change

    NASA Astrophysics Data System (ADS)

    Dolinar, Mojca; Vidrih, Boris; Kajfež-Bogataj, Lučka; Medved, Sašo

    In the last 3 years in Slovenia we experienced extremely hot summers and demand for cooling the buildings have risen significantly. Since climate change scenarios predict higher temperatures for the whole country and for all seasons, we expect that energy demand for heating would decrease while demand for cooling would increase. An analysis for building with permitted energy demand and for low-energy demand building in two typical urban climates in Slovenia was performed. The transient systems simulation program (TRNSYS) was used for simulation of the indoor conditions and the energy use for heating and cooling. Climate change scenarios were presented in form of “future” Test Reference Years (TRY). The time series of hourly data for all meteorological variables for different scenarios were chosen from actual measurements, using the method of highest likelihood. The climate change scenarios predicted temperature rise (+1 °C and +3 °C) and solar radiation increase (+3% and +6%). With the selection of these scenarios we covered the spectra of possible predicted climate changes in Slovenia. The results show that energy use for heating would decrease from 16% to 25% (depends on the intensity of warming) in subalpine region, while in Mediterranean region the rate of change would not be significant. In summer time we would need up to six times more energy for cooling in subalpine region and approximately two times more in Mediterranean region. low-energy building proved to be very economical in wintertime while on average higher energy consumption for cooling is expected in those buildings in summertime. In case of significant warmer and more solar energy intensive climate, the good isolated buildings are more efficient than standard buildings. TRY proved not to be efficient for studying extreme conditions like installed power of the cooling system.

  6. Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach.

    PubMed

    Le Roux, Xavier; Bouskill, Nicholas J; Niboyet, Audrey; Barthes, Laure; Dijkstra, Paul; Field, Chris B; Hungate, Bruce A; Lerondelle, Catherine; Pommier, Thomas; Tang, Jinyun; Terada, Akihiko; Tourna, Maria; Poly, Franck

    2016-01-01

    Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO2+Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes. PMID:27242680

  7. Predicting the Responses of Soil Nitrite-Oxidizers to Multi-Factorial Global Change: A Trait-Based Approach

    PubMed Central

    Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey; Barthes, Laure; Dijkstra, Paul; Field, Chris B.; Hungate, Bruce A.; Lerondelle, Catherine; Pommier, Thomas; Tang, Jinyun; Terada, Akihiko; Tourna, Maria; Poly, Franck

    2016-01-01

    Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundance and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the ‘High CO2+Nitrogen+Precipitation’ treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes. PMID:27242680

  8. Soda consumption during ad libitum food intake predicts weight change.

    PubMed

    Bundrick, Sarah C; Thearle, Marie S; Venti, Colleen A; Krakoff, Jonathan; Votruba, Susanne B

    2014-03-01

    Soda consumption may contribute to weight gain over time. Objective data were used to determine whether soda consumption predicts weight gain or changes in glucose regulation over time. Subjects without diabetes (128 men, 75 women; mean age 34.3±8.9 years; mean body mass index 32.5±7.4; mean percentage body fat 31.6%±8.6%) self-selected their food from an ad libitum vending machine system for 3 days. Mean daily energy intake was calculated from food weight. Energy consumed from soda was recorded as were food choices that were low in fat (<20% of calories from fat) or high in simple sugars (>30%). Food choices were expressed as percentage of daily energy intake. A subset of 85 subjects had measurement of follow-up weights and oral glucose tolerance (57 men, 28 women; mean follow-up time=2.5±2.1 years, range 6 months to 9.9 years). Energy consumed from soda was negatively related to age (r=-0.27, P=0.0001) and choosing low-fat foods (r=-0.35, P<0.0001), but positively associated with choosing solid foods high in simple sugars (r=0.45, P<0.0001) and overall average daily energy intake (r=0.46, P<0.0001). Energy intake from food alone did not differ between individuals who did and did not consume beverage calories (P=0.11). Total daily energy intake had no relationship with change in weight (P=0.29) or change in glucose regulation (P=0.38) over time. However, energy consumed from soda correlated with change in weight (r=0.21, P=0.04). This relationship was unchanged after adjusting for follow-up time and initial weight. Soda consumption is a marker for excess energy consumption and is associated with weight gain. PMID:24321742

  9. Grade 12 French Students' use of a Thermodynamic Model for Predicting the Direction of Incomplete Chemical Changes

    NASA Astrophysics Data System (ADS)

    Kermen, Isabelle; Méheut, Martine

    2011-09-01

    The authors of the current chemistry curriculum-implemented in Grade 12 in France-provided a criterion of change allowing prediction of direction of chemical changes and pointed out the difference to be made between experimental facts and models. A study analysing part of the curriculum content and the effects of teaching this content on students' reasoning was conducted. The content analysis presents the functioning of the thermodynamic model, which highlights the links to be made between the experimental situation and the model when predicting the direction of a chemical change. This functioning specifies the role of the chemical equation and that of the criterion of change (comparing the reaction quotient to the equilibrium constant) and stresses the crucial points that may lead to misunderstandings. Written tests were administered to students after teaching them to determine how they predicted the direction of a chemical change, and whether they made a relevant choice between using the chemical equation and using the criterion of change and a clear distinction between the experimental situation and the thermodynamic model. Few students had a good understanding of the respective roles of the criterion and the chemical equation. A majority used the criterion to predict the direction of chemical changes relevantly, but correct answers were not widespread. Two particular mistakes, the modification of the expression of the reaction quotient and the prediction of a change despite a missing reactant, revealed that students do not properly understand the difference and the relationship between the experimental situation and the thermodynamic model.

  10. A continuous function model for path prediction of entities

    NASA Astrophysics Data System (ADS)

    Nanda, S.; Pray, R.

    2007-04-01

    As militaries across the world continue to evolve, the roles of humans in various theatres of operation are being increasingly targeted by military planners for substitution with automation. Forward observation and direction of supporting arms to neutralize threats from dynamic adversaries is one such example. However, contemporary tracking and targeting systems are incapable of serving autonomously for they do not embody the sophisticated algorithms necessary to predict the future positions of adversaries with the accuracy offered by the cognitive and analytical abilities of human operators. The need for these systems to incorporate methods characterizing such intelligence is therefore compelling. In this paper, we present a novel technique to achieve this goal by modeling the path of an entity as a continuous polynomial function of multiple variables expressed as a Taylor series with a finite number of terms. We demonstrate the method for evaluating the coefficient of each term to define this function unambiguously for any given entity, and illustrate its use to determine the entity's position at any point in time in the future.

  11. Local functional descriptors for surface comparison based binding prediction

    PubMed Central

    2012-01-01

    Background Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. Results We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. Conclusions Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications. PMID:23176080

  12. Prediction of stability changes upon mutation in an icosahedral capsid

    PubMed Central

    Hickman, Samuel J.

    2015-01-01

    ABSTRACT Identifying the contributions to thermodynamic stability of capsids is of fundamental and practical importance. Here we use simulation to assess how mutations affect the stability of lumazine synthase from the hyperthermophile Aquifex aeolicus, a T = 1 icosahedral capsid; in the simulations the icosahedral symmetry of the capsid is preserved by simulating a single pentamer and imposing crystal symmetry, in effect simulating an infinite cubic lattice of icosahedral capsids. The stability is assessed by estimating the free energy of association using an empirical method previously proposed to identify biological units in crystal structures. We investigate the effect on capsid formation of seven mutations, for which it has been experimentally assessed whether they disrupt capsid formation or not. With one exception, our approach predicts the effect of the mutations on the capsid stability. The method allows the identification of interaction networks, which drive capsid assembly, and highlights the plasticity of the interfaces between subunits in the capsid. Proteins 2015; 83:1733–1741. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc PMID:26178267

  13. Predicting the Effect of Changing Precipitation Extremes and Land Cover Change on Urban Water Quality

    NASA Astrophysics Data System (ADS)

    SUN, N.; Yearsley, J. R.; Lettenmaier, D. P.

    2013-12-01

    Recent research shows that precipitation extremes in many of the largest U.S. urban areas have increased over the last 60 years. These changes have important implications for stormwater runoff and water quality, which in urban areas are dominated by the most extreme precipitation events. We assess the potential implications of changes in extreme precipitation and changing land cover in urban and urbanizing watersheds at the regional scale using a combination of hydrology and water quality models. Specifically, we describe the integration of a spatially distributed hydrological model - the Distributed Hydrology Soil Vegetation Model (DHSVM), the urban water quality model in EPA's Storm Water Management Model (SWMM), the semi-Lagrangian stream temperature model RBM10, and dynamical and statistical downscaling methods applied to global climate predictions. Key output water quality parameters include total suspended solids (TSS), toal nitrogen, total phosphorous, fecal coliform bacteria and stream temperature. We have evaluated the performance of the modeling system in the highly urbanized Mercer Creek watershed in the rapidly growing Bellevue urban area in WA, USA. The results suggest that the model is able to (1) produce reasonable streamflow predictions at fine temporal and spatial scales; (2) provide spatially distributed water temperature predictions that mostly agree with observations throughout a complex stream network, and characterize impacts of climate, landscape, near-stream vegetation change on stream temperature at local and regional scales; and (3) capture plausibly the response of water quality constituents to varying magnitude of precipitation events in urban environments. Next we will extend the scope of the study from the Mercer Creek watershed to include the entire Puget Sound Basin, WA, USA.

  14. Sensitivity and specificity of the functional hallux limitus test to predict foot function.

    PubMed

    Payne, Craig; Chuter, Vivienne; Miller, Kathryn

    2002-05-01

    Functional hallux limitus is an underrecognized entity that generally does not produce symptoms but can result in a variety of compensatory mechanisms that can produce symptoms. Clinically, hallux limitus can be determined by assessing the range of motion available at the first metatarsophalangeal joint while the first ray is prevented from plantarflexing. The aim of this study was to determine the sensitivity and specificity of this clinical test to predict abnormal excessive midtarsal joint function during gait. A total of 86 feet were examined for functional hallux limitus and abnormal pronation of the midtarsal joint during late midstance. The test had a sensitivity of 0.72 and a specificity of 0.66, suggesting that clinicians should consider functional hallux limitus when there is late midstance pronation of the midtarsal joint during gait. PMID:12015407

  15. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    DOE PAGESBeta

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-19

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict themore » effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.« less

  16. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    DOE PAGESBeta

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-19

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Kohler theory to predict themore » effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. Furthermore, the model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.« less

  17. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    NASA Astrophysics Data System (ADS)

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-01

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict the effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid-liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.

  18. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    SciTech Connect

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2016-01-01

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict the effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.

  19. Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

    NASA Astrophysics Data System (ADS)

    Petters, M. D.; Kreidenweis, S. M.; Ziemann, P. J.

    2015-09-01

    A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. The model combines Köhler theory with semi-empirical group contribution methods to estimate molar volumes, activity coefficients and liquid-liquid phase boundaries to predict the effective hygroscopicity parameter, kappa. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of two. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging testbeds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger scale models.

  20. Age Related Changes in Autonomic Functions

    PubMed Central

    Amir, Mohammed; Pakhare, Abhijit; Rathi, Preeti; Chaudhary, Lalita

    2016-01-01

    Introduction Autonomic Nervous System (ANS) imbalance may trigger or enhance pathology in different organ systems that varies in different age groups hence objective of present study was to evaluate association of different Age-groups with autonomic functions. Materials and Methods A cross-sectional study was conducted in 62 healthy volunteers in Department of Physiology LLRM Medical College Meerut, India. Volunteers were divided into three groups as younger (15-45 years), middle (45-60) and elder age (above 60), Autonomic functions were tested in three domains viz. Cardio-vagal, adrenergic and sudomotor functions. Numerical data was summarized as mean and standard deviation and categorical data as count and percentage. ANOVA and Chi-square test were used to find difference among groups, p<0.05 was considered statistically significant. Results Mean ± standard deviation OHT(Orthostatic Hypotension Test) among of younger, middle and elder age groups were 8.80±2.28, 13.40±4.64 and 21.82±6.04 respectively which represent decrease in sympathetic functions with age (p<0.001). Cardio-vagal or parasympathetic responses indicated by DBT (Deep Breathing Test) Valsalva and 30:15 ratio of HR response to standing tests has shown statistically significant (p<0.001) decrease in mean response with increasing age. Sudomotor response appeared normal in younger and middle group but was interrupted in more than half of elderly people (p<0.001). Conclusion Sympathetic responses & para-sympathetic responses have shown the significant decline with increasing age group. Sudomotor responses were partially interrupted in elderly age group. PMID:27134865

  1. Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

    PubMed

    Kiviniemi, Marc T; Brown-Kramer, Carolyn R

    2015-05-01

    Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. PMID:25903243

  2. Predicting floodplain boundary changes following the Cerro Grande wildfire

    NASA Astrophysics Data System (ADS)

    McLin, Stephen G.; Springer, Everett P.; Lane, Leonard J.

    2001-10-01

    A combined ArcView GIS-HEC modelling application for floodplain analysis of pre- and post-burned watersheds is described. The burned study area is located on Pajarito Plateau near Los Alamos National Laboratory (the Laboratory), where the Cerro Grande Wildfire burned 42 878 acres (17 352 ha) in May 2000. This area is dominated by rugged mountains that are dissected by numerous steep canyons having both ephemeral and perennial channel reaches. Vegetation consists of pinon-juniper woodlands located between 6000 and 7000 ft (1829-2134 m) above mean sea level (MSL), and Ponderosa pine stands between 7000 and 10000 ft MSL (2134-3048 m). Approximately 17% of the burned area is located within the Laboratory, and the remainder is located in upstream or adjacent watersheds. Pre-burn floodplains were previously mapped in 1990-91 using early HEC models as part of the hazardous waste site permitting process. Precipitation and stream gauge data provide essential information characterizing rainfall-runoff relationships before and after the fire. They also provide a means of monitoring spatial and temporal changes as forest recovery progresses. The 2000 summer monsoon began in late June and provided several significant runoff events for model calibration. HEC-HMS modelled responses were sequentially refined so that observed and predicted hydrograph peaks were matched at numerous channel locations. The 100 year, 6 h design storm was eventually used to predict peak hydrographs at critical sites. These results were compared with pre-fire simulations so that new flood-prone areas could be systematically identified. Stream channel cross-sectional geometries were extracted from a gridded 1 ft (0·3 m) digital elevation model (DEM) using ArcView GIS. Then floodpool topwidths, depths, and flow velocities were remapped using the HEC-RAS model. Finally, numerous surveyed channel sections were selectively made at crucial sites for DEM verification. These evaluations provided timely guidance

  3. Functional changes in littoral macroinvertebrate communities in response to watershed-level anthropogenic stress.

    PubMed

    Kovalenko, Katya E; Brady, Valerie J; Ciborowski, Jan J H; Ilyushkin, Sergey; Johnson, Lucinda B

    2014-01-01

    Watershed-scale anthropogenic stressors have profound effects on aquatic communities. Although several functional traits of stream macroinvertebrates change predictably in response to land development and urbanization, little is known about macroinvertebrate functional responses in lakes. We assessed functional community structure, functional diversity (Rao's quadratic entropy) and voltinism in macroinvertebrate communities sampled across the full gradient of anthropogenic stress in Laurentian Great Lakes coastal wetlands. Functional diversity and voltinism significantly decreased with increasing development, whereas agriculture had smaller or non-significant effects. Functional community structure was affected by watershed-scale development, as demonstrated by an ordination analysis followed by regression. Because functional community structure affects energy flow and ecosystem function, and functional diversity is known to have important implications for ecosystem resilience to further environmental change, these results highlight the necessity of finding ways to remediate or at least ameliorate these effects. PMID:25006811

  4. Functional Changes in Littoral Macroinvertebrate Communities in Response to Watershed-Level Anthropogenic Stress

    PubMed Central

    Kovalenko, Katya E.; Brady, Valerie J.; Ciborowski, Jan J. H.; Ilyushkin, Sergey; Johnson, Lucinda B.

    2014-01-01

    Watershed-scale anthropogenic stressors have profound effects on aquatic communities. Although several functional traits of stream macroinvertebrates change predictably in response to land development and urbanization, little is known about macroinvertebrate functional responses in lakes. We assessed functional community structure, functional diversity (Rao’s quadratic entropy) and voltinism in macroinvertebrate communities sampled across the full gradient of anthropogenic stress in Laurentian Great Lakes coastal wetlands. Functional diversity and voltinism significantly decreased with increasing development, whereas agriculture had smaller or non-significant effects. Functional community structure was affected by watershed-scale development, as demonstrated by an ordination analysis followed by regression. Because functional community structure affects energy flow and ecosystem function, and functional diversity is known to have important implications for ecosystem resilience to further environmental change, these results highlight the necessity of finding ways to remediate or at least ameliorate these effects. PMID:25006811

  5. Genomic islands predict functional adaptation in marine actinobacteria

    SciTech Connect

    Penn, Kevin; Jenkins, Caroline; Nett, Markus; Udwary, Daniel; Gontang, Erin; McGlinchey, Ryan; Foster, Brian; Lapidus, Alla; Podell, Sheila; Allen, Eric; Moore, Bradley; Jensen, Paul

    2009-04-01

    Linking functional traits to bacterial phylogeny remains a fundamental but elusive goal of microbial ecology 1. Without this information, it becomes impossible to resolve meaningful units of diversity and the mechanisms by which bacteria interact with each other and adapt to environmental change. Ecological adaptations among bacterial populations have been linked to genomic islands, strain-specific regions of DNA that house functionally adaptive traits 2. In the case of environmental bacteria, these traits are largely inferred from bioinformatic or gene expression analyses 2, thus leaving few examples in which the functions of island genes have been experimentally characterized. Here we report the complete genome sequences of Salinispora tropica and S. arenicola, the first cultured, obligate marine Actinobacteria 3. These two species inhabit benthic marine environments and dedicate 8-10percent of their genomes to the biosynthesis of secondary metabolites. Despite a close phylogenetic relationship, 25 of 37 secondary metabolic pathways are species-specific and located within 21 genomic islands, thus providing new evidence linking secondary metabolism to ecological adaptation. Species-specific differences are also observed in CRISPR sequences, suggesting that variations in phage immunity provide fitness advantages that contribute to the cosmopolitan distribution of S. arenicola 4. The two Salinispora genomes have evolved by complex processes that include the duplication and acquisition of secondary metabolite genes, the products of which provide immediate opportunities for molecular diversification and ecological adaptation. Evidence that secondary metabolic pathways are exchanged by Horizontal Gene Transfer (HGT) yet are fixed among globally distributed populations 5 supports a functional role for their products and suggests that pathway acquisition represents a previously unrecognized force driving bacterial diversification

  6. Prediction Accuracy of a Novel Dynamic Structure–Function Model for Glaucoma Progression

    PubMed Central

    Hu, Rongrong; Marín-Franch, Iván; Racette, Lyne

    2014-01-01

    Purpose. To assess the prediction accuracy of a novel dynamic structure–function (DSF) model to monitor glaucoma progression. Methods. Longitudinal data of paired rim area (RA) and mean sensitivity (MS) from 220 eyes with ocular hypertension or primary open-angle glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. Rim area and MS were expressed as percent of mean normal based on an independent dataset of 91 healthy eyes. The DSF model uses centroids as estimates of the current state of the disease and velocity vectors as estimates of direction and rate of change over time. The first three visits were used to predict the fourth visit; the first four visits were used to predict the fifth visit, and so on up to the 11th visit. The prediction error (PE) was compared to that of ordinary least squares linear regression (OLSLR) using Wilcoxon signed-rank test. Results. For predictions at visit 4 to visit 7, the average PE for the DSF model was significantly lower than OLSLR by 1.19% to 3.42% of mean normal. No significant difference was observed for the predictions at visit 8 to visit 11. The DSF model had lower PE than OLSLR for 70% of eyes in predicting visit 4 and approximately 60% in predicting visits 5, 6, and 7. Conclusions. The two models had similar prediction capabilities, and the DSF model performed better in shorter time series. The DSF model could be clinically useful when only limited follow-ups are available. (ClinicalTrials.gov numbers, NCT00221923, NCT00221897.) PMID:25358735

  7. Investigating repetition and change in musical rhythm by functional MRI.

    PubMed

    Danielsen, A; Otnæss, M K; Jensen, J; Williams, S C R; Ostberg, B C

    2014-09-01

    Groove-based rhythm is a basic and much appreciated feature of Western popular music. It is commonly associated with dance, movement and pleasure and is characterized by the repetition of a basic rhythmic pattern. At various points in the musical course, drum breaks occur, representing a change compared to the repeated pattern of the groove. In the present experiment, we investigated the brain response to such drum breaks in a repetitive groove. Participants were scanned with functional magnetic resonance imaging (fMRI) while listening to a previously unheard naturalistic groove with drum breaks at uneven intervals. The rhythmic pattern and the timing of its different parts as performed were the only aspects that changed from the repetitive sections to the breaks. Differences in blood oxygen level-dependent activation were analyzed. In contrast to the repetitive parts, the drum breaks activated the left cerebellum, the right inferior frontal gyrus (RIFG), and the superior temporal gyri (STG) bilaterally. A tapping test using the same stimulus showed an increase in the standard deviation of inter-tap-intervals in the breaks versus the repetitive parts, indicating extra challenges for auditory-motor integration in the drum breaks. Both the RIFG and STG have been associated with structural irregularity and increase in musical-syntactical complexity in several earlier studies, whereas the left cerebellum is known to play a part in timing. Together these areas may be recruited in the breaks due to a prediction error process whereby the internal model is being updated. This concurs with previous research suggesting a network for predictive feed-forward control that comprises the cerebellum and the cortical areas that were activated in the breaks. PMID:24972303

  8. Multi-parameter prediction of drivers' lane-changing behaviour with neural network model.

    PubMed

    Peng, Jinshuan; Guo, Yingshi; Fu, Rui; Yuan, Wei; Wang, Chang

    2015-09-01

    Accurate prediction of driving behaviour is essential for an active safety system to ensure driver safety. A model for predicting lane-changing behaviour is developed from the results of naturalistic on-road experiment for use in a lane-changing assistance system. Lane changing intent time window is determined via visual characteristics extraction of rearview mirrors. A prediction index system for left lane changes was constructed by considering drivers' visual search behaviours, vehicle operation behaviours, vehicle motion states, and driving conditions. A back-propagation neural network model was developed to predict lane-changing behaviour. The lane-change-intent time window is approximately 5 s long, depending on the subjects. The proposed model can accurately predict drivers' lane changing behaviour for at least 1.5 s in advance. The accuracy and time series characteristics of the model are superior to the use of turn signals in predicting lane-changing behaviour. PMID:25959336

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

    PubMed Central

    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

    2015-01-01

    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. PMID:25567651

  10. Disparities between observed and predicted impacts of climate change on winter bird assemblages.

    PubMed

    La Sorte, Frank A; Lee, Tien Ming; Wilman, Hamish; Jetz, Walter

    2009-09-01

    Understanding how climate change affects the structure and function of communities is critical for gauging its full impact on biodiversity. To date, community-level changes have been poorly documented, owing, in part, to the paucity of long-term datasets. To circumvent this, the use of 'space-for-time' substitution--the forecasting of temporal trends from spatial climatic gradients--has increasingly been adopted, often with little empirical support. Here we examine changes from 1975 to 2001 in three community attributes (species richness, body mass and occupancy) for 404 assemblages of terrestrial winter avifauna in North America containing a total of 227 species. We examine the accuracy of space-for-time substitution and assess causal associations between community attributes and observed changes in annual temperature using a longitudinal study design. Annual temperature and all three community attributes increased over time. The trends for the three community attributes differed significantly from the spatially derived predictions, although richness showed broad congruence. Correlations with trends in temperature were found with richness and body mass. In the face of rapid climate change, applying space-for-time substitution as a predictive tool could be problematic with communities developing patterns not reflected by spatial ecological associations. PMID:19520804

  11. Prediction of Functional Outcome in Axonal Guillain-Barre Syndrome

    PubMed Central

    2016-01-01

    Objective To identify the factors that could predict the functional outcome in patients with the axonal type of Guillain-Barre syndrome (GBS). Methods Two hundred and two GBS patients admitted to our university hospital between 2003 and 2014 were reviewed retrospectively. We defined a good outcome as being "able to walk independently at 1 month after onset" and a poor outcome as being "unable to walk independently at 1 month after onset". We evaluated the factors that differed between the good and poor outcome groups. Results Twenty-four patients were classified into the acute motor axonal neuropathy type. There was a statistically significant difference between the good and poor outcome groups in terms of the GBS disability score at admission, and GBS disability score and Medical Research Council sum score at 1 month after admission. In an electrophysiologic analysis, the good outcome group showed greater amplitude of median, ulnar, deep peroneal, and posterior tibial nerve compound muscle action potentials (CMAP) and greater amplitude of median, ulnar, and superficial peroneal sensory nerve action potentials (SNAP) than the poor outcome group. Conclusion A lower GBS disability score at admission, high amplitude of median, ulnar, deep peroneal, and posterior tibial CMAPs, and high amplitude of median, ulnar, and superficial peroneal SNAPs were associated with being able to walk at 1 month in patients with axonal GBS. PMID:27446785

  12. Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

    NASA Technical Reports Server (NTRS)

    Crane, Robert G.; Hewitson, Bruce

    1990-01-01

    Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

  13. A data mining based approach to predict spatiotemporal changes in satellite images

    NASA Astrophysics Data System (ADS)

    Boulila, W.; Farah, I. R.; Ettabaa, K. Saheb; Solaiman, B.; Ghézala, H. Ben

    2011-06-01

    The interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited. This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.

  14. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    SciTech Connect

    Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J.-P.

    2014-09-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

  15. The role of plant functional trade-offs for biodiversity changes and biome shifts under scenarios of global climatic change

    NASA Astrophysics Data System (ADS)

    Reu, B.; Zaehle, S.; Proulx, R.; Bohn, K.; Kleidon, A.; Pavlick, R.; Schmidtlein, S.

    2010-10-01

    The global geographic distribution of biodiversity and biomes is determined by species-specific physiological tolerances to climatic constraints. Current models implement empirical bioclimatic relationships to predict present-day vegetation patterns and to forecast biodiversity changes and biome shifts under climatic change. In this paper, we consider plant functional trade-offs and their interactions with climatic changes to forecast and explain biodiversity changes and biome shifts. The Jena Diversity model (JeDi) simulates plant survival according to essential plant functional trade-offs, including eco-physiological processes such as water uptake, photosynthesis, allocation, reproduction and phenology. We apply JeDi to quantify biodiversity changes and biome shifts between present-day and a range of possible future climates from two scenarios (A2 and B1) and seven global climate models using metrics of plant functional richness and functional identity. Our results show (i) a significant biodiversity loss in the tropics, (ii) an increase in biodiversity at mid and high latitudes, and (iii) a poleward shift of biomes. While these results are consistent with the findings of empirical approaches, we are able to explain them in terms of the plant functional trade-offs involved in the allocation, metabolic and reproduction strategies of plants. We conclude that general aspects of plant physiological tolerances can be derived from plant functional trade-offs, which may provide a useful process- and trait-based alternative to bioclimatic relationships in order to address questions about the causes of biodiversity changes and biome shifts.

  16. Importance of atmospheric angular momentum function in non-linear prediction of length of day variation

    NASA Astrophysics Data System (ADS)

    Wang, Q. J.; Liao, D. C.; Zhou, Y. H.; Liao, X. H.

    2008-01-01

    Prediction of the variations of the length of day (LOD) is of great importance in both scientific issues and practical applications. However, due to the complex time-variable characteristics of the LOD variation, it's usually difficult to obtain satisfied prediction results by conventional linear time series analysis methods. The artificial neural networks (ANN) is a non-linear information processing system. This study employs the ANN to predict the LOD change. The topology of the ANN model is determined based on the criterion of minimization of the root mean square error (RMSE). For most of the studies that use ANN to predict the LOD, the influence of global atmospheric movements on the variations of the LOD hasn't been considered. Considering the close connection between the LOD variation and the atmospheric circulation movement, and the capability of simulating and forecasting the axial atmospheric angular momentum (AAM) function with global atmospheric circulation pattern, the axial AAM is added into the ANN model as an additional input parameter to predict the LOD variation. The daily LOD series in this study are from the C04 series of the International Earth rotation and reference systems service (IERS), spanning from 1962 to 2005. We first removed the contributions of the 62 zonal Earth tides from the LOD changes with periods from 5 days to 18.6 years according to IERS Convention 2003, and the effects that can be described by functional models, e.g. the annual and semi-annual oscillations, the terms whose periods are 1, 1/2, 1/3 of the length of the whole data set. Only the residuals between the modeled and the observed LODRs, are used for training. Likewise, the axial AAM series are also de-trended. The residuals of LODR and axial AAM series are used to train the networks. The trained networks are applied to predict the LODR variation for time interval of 1 to 40 days. For comparisons, we also use the LODR only to construct the ANN model and to predict the

  17. Relating Phylogenetic and Functional Diversity among Denitrifiers and Quantifying their Capacity to Predict Community Functioning

    PubMed Central

    Salles, Joana Falcão; Le Roux, Xavier; Poly, Franck

    2012-01-01

    Genetic diversity of phylogenetic or functional markers is widely used as a proxy of microbial diversity. However, it remains unclear to what extent functional diversity (FD), gene sequence diversity and community functioning are linked. For a range of denitrifying bacteria, we analyzed the relationships between (i) the similarity of functional traits evaluated from metabolic profiles (BIOLOG plates) or from N2O accumulation patterns on different carbon sources and (ii) the similarity of phylogenetic (16S rRNA gene) or functional (nir gene) markers. We also calculated different proxies for the diversity of denitrifier community based on taxa richness, phylogenetic (16S rRNA gene) or functional similarities (based either on metabolic profiles or N2O accumulation patterns), and evaluated their performance in inferring the functioning of assembled denitrifying communities. For individual strains, the variation in the 16S rRNA gene sequence was weakly correlated with the variation in metabolic patterns (ρ = 0.35) and was not related to N2O accumulation. The latter was correlated with the similarity of nitrite reductase residues. When nir genes were analyzed separately, the similarity in amino acids coded by the nirS genes was highly correlated with the observed patterns of N2O accumulation (ρ = 0.62), whereas nirK amino acid residues were unrelated to N2O accumulation. For bacterial assemblages, phylogenetic diversity (average similarity among species in a community) and mean community dissimilarity (average distance between species) calculated using 16S rRNA gene sequences, and FD measures associated with metabolic profiles, poorly predicted the variation in the functioning of assembled communities (≤15%). In contrast, the proxies of FD based on N2O accumulation patterns performed better and explained from 23 to 42% of the variation in denitrification. Amongst those, community niche was the best metric, indicating the importance of complementarity for

  18. Changing currents: a strategy for understanding and predicting the changing ocean circulation.

    PubMed

    Bryden, Harry L; Robinson, Carol; Griffiths, Gwyn

    2012-12-13

    Within the context of UK marine science, we project a strategy for ocean circulation research over the next 20 years. We recommend a focus on three types of research: (i) sustained observations of the varying and evolving ocean circulation, (ii) careful analysis and interpretation of the observed climate changes for comparison with climate model projections, and (iii) the design and execution of focused field experiments to understand ocean processes that are not resolved in coupled climate models so as to be able to embed these processes realistically in the models. Within UK-sustained observations, we emphasize smart, cost-effective design of the observational network to extract maximum information from limited field resources. We encourage the incorporation of new sensors and new energy sources within the operational environment of UK-sustained observational programmes to bridge the gap that normally separates laboratory prototype from operational instrument. For interpreting the climate-change records obtained through a variety of national and international sustained observational programmes, creative and dedicated UK scientists should lead efforts to extract the meaningful signals and patterns of climate change and to interpret them so as to project future changes. For the process studies, individual scientists will need to work together in team environments to combine observational and process modelling results into effective improvements in the coupled climate models that will lead to more accurate climate predictions. PMID:23129709

  19. Prediction of Changes in Vegetation Distribution Under Climate Change Scenarios Using Modis Dataset

    NASA Astrophysics Data System (ADS)

    Hirayama, Hidetake; Tomita, Mizuki; Hara, Keitarou

    2016-06-01

    The distribution of vegetation is expected to change under the influence of climate change. This study utilizes vegetation maps derived from Terra/MODIS data to generate a model of current climate conditions suitable to beech-dominated deciduous forests, which are the typical vegetation of Japan's cool temperate zone. This model will then be coordinated with future climate change scenarios to predict the future distribution of beech forests. The model was developed by using the presence or absence of beech forest as the dependent variable. Four climatic variables; mean minimum daily temperature of the coldest month (TMC) warmth index (WI) winter precipitation (PRW) and summer precipitation (PRS): and five geophysical variables; topography (TOPO), surface geology (GEOL), soil (SOIL), slope aspect (ASP), and inclination (INCL); were adopted as independent variables. Previous vegetation distribution studies used point data derived from field surveys. The remote sensing data utilized in this study, however, should permit collecting of greater amounts of data, and also frequent updating of data and distribution maps. These results will hopefully show that use of remote sensing data can provide new insights into our understanding of how vegetation distribution will be influenced by climate change.

  20. Changes in social functioning and circulating oxytocin and vasopressin following the migration to a new country.

    PubMed

    Gouin, Jean-Philippe; Pournajafi-Nazarloo, Hossein; Carter, C Sue

    2015-02-01

    Prior studies have reported associations between plasma oxytocin and vasopressin and markers of social functioning. However, because most human studies have used cross-sectional designs, it is unclear whether plasma oxytocin and vasopressin influences social functioning or whether social functioning modulates the production and peripheral release of these peptides. In order to address this question, we followed individuals who experienced major changes in social functioning subsequent to the migration to a new country. In this study, 59 new international students were recruited shortly after arrival in the host country and reassessed 2 and 5 months later. At each assessment participants provided information on their current social functioning and blood samples for oxytocin and vasopressin analysis. Results indicated that changes in social functioning were not related to changes in plasma oxytocin. Instead, baseline oxytocin predicted changes in social relationship satisfaction, social support, and loneliness over time. In contrast, plasma vasopressin changed as a function of social integration. Baseline vasopressin was not related to changes in social functioning over time. These results emphasize the different roles of plasma oxytocin and vasopressin in responses to changes in social functioning in humans. PMID:25446216

  1. Changes in cardiovascular function with aging.

    PubMed

    Lakatta, E G

    1990-05-01

    Overall cardiovascular function at rest in most healthy elderly individuals is adequate to meet the body's need for pressure and flow. The resting heart rate is unchanged. Heart size is essentially not different in younger vs older adults, but heart wall thickness increases modestly, due largely to an increase in myocyte size. While the early diastolic filling rate is reduced, an enhanced atrial contribution to ventricular filling in elderly individuals maintains filling volume at a normal level. Although systolic pressure at rest increases with age, the resting end-systolic volume and election fraction are not altered, due partly to the increase in left ventricular thickness. Physical work capacity declines with advancing age, but the extent to which this can be attributed to a decrement in cardiac reserve is not certain. Part of the age-related decline in maximum oxygen consumption appears to be due to peripheral rather than central circulatory factors, e.g. to a decrease in muscle mass with age during exercise, the ability to direct blood flow to muscles, and the ability of muscle to utilize oxygen. Some elderly individuals exhibit cardiac dilatation which produces an increased stroke volume sufficient to counter the well-known age-related decrease in exercise heart rate, such that high levels of cardiac output can be maintained during exercise. Still, in these individuals, the exercise-induced reduction in end-systolic volume and increase in ejection fraction is less than in younger individuals. A similar haemodynamic profile occurs in individuals of any age who exercise in the presence of beta-adrenergic blockade.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:2188839

  2. The conformational signature of β-arrestin2 predicts its trafficking and signalling functions.

    PubMed

    Lee, Mi-Hye; Appleton, Kathryn M; Strungs, Erik G; Kwon, Joshua Y; Morinelli, Thomas A; Peterson, Yuri K; Laporte, Stephane A; Luttrell, Louis M

    2016-03-31

    Arrestins are cytosolic proteins that regulate G-protein-coupled receptor (GPCR) desensitization, internalization, trafficking and signalling. Arrestin recruitment uncouples GPCRs from heterotrimeric G proteins, and targets the proteins for internalization via clathrin-coated pits. Arrestins also function as ligand-regulated scaffolds that recruit multiple non-G-protein effectors into GPCR-based 'signalsomes'. Although the dominant function(s) of arrestins vary between receptors, the mechanism whereby different GPCRs specify these divergent functions is unclear. Using a panel of intramolecular fluorescein arsenical hairpin (FlAsH) bioluminescence resonance energy transfer (BRET) reporters to monitor conformational changes in β-arrestin2, here we show that GPCRs impose distinctive arrestin 'conformational signatures' that reflect the stability of the receptor-arrestin complex and role of β-arrestin2 in activating or dampening downstream signalling events. The predictive value of these signatures extends to structurally distinct ligands activating the same GPCR, such that the innate properties of the ligand are reflected as changes in β-arrestin2 conformation. Our findings demonstrate that information about ligand-receptor conformation is encoded within the population average β-arrestin2 conformation, and provide insight into how different GPCRs can use a common effector for different purposes. This approach may have application in the characterization and development of functionally selective GPCR ligands and in identifying factors that dictate arrestin conformation and function. PMID:27007854

  3. Stages of change or changes of stage? Predicting transitions in transtheoretical model stages in relation to healthy food choice.

    PubMed

    Armitage, Christopher J; Sheeran, Paschal; Conner, Mark; Arden, Madelynne A

    2004-06-01

    Relatively little research has examined factors that account for transitions between transtheoretical model (TTM) stages of change. The present study (N = 787) used sociodemographic, TTM, and theory of planned behavior (TPB) variables, as well as theory-driven interventions to predict changes in stage. Longitudinal analyses revealed that sociodemographic, TPB, and 1 of the interventions predicted transitions between most stages of change. In fact, only progression from the preparation stage was not predictable. However, given that this change of stage marks the transition between cognition and actual behavior, the identification of variables that bridge this gap is crucial for the development of interventions to promote stage transitions. PMID:15279532

  4. An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

    PubMed Central

    Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

    2015-01-01

    The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

  5. Predicting ecological changes on benthic estuarine assemblages through decadal climate trends along Brazilian Marine Ecoregions

    NASA Astrophysics Data System (ADS)

    Bernardino, Angelo F.; Netto, Sérgio A.; Pagliosa, Paulo R.; Barros, Francisco; Christofoletti, Ronaldo A.; Rosa Filho, José S.; Colling, André; Lana, Paulo C.

    2015-12-01

    Estuaries are threatened coastal ecosystems that support relevant ecological functions worldwide. The predicted global climate changes demand actions to understand, anticipate and avoid further damage to estuarine habitats. In this study we reviewed data on polychaete assemblages, as a surrogate for overall benthic communities, from 51 estuaries along five Marine Ecoregions of Brazil (Amazonia, NE Brazil, E Brazil, SE Brazil and Rio Grande). We critically evaluated the adaptive capacity and ultimately the resilience to decadal changes in temperature and rainfall of the polychaete assemblages. As a support for theoretical predictions on changes linked to global warming we compared the variability of benthic assemblages across the ecoregions with a 40-year time series of temperature and rainfall data. We found a significant upward trend in temperature during the last four decades at all marine ecoregions of Brazil, while rainfall increase was restricted to the SE Brazil ecoregion. Benthic assemblages and climate trends varied significantly among and within ecoregions. The high variability in climate patterns in estuaries within the same ecoregion may lead to correspondingly high levels of noise on the expected responses of benthic fauna. Nonetheless, we expect changes in community structure and productivity of benthic species at marine ecoregions under increasing influence of higher temperatures, extreme events and pollution.

  6. SIFT Indel: Predictions for the Functional Effects of Amino Acid Insertions/Deletions in Proteins

    PubMed Central

    Hu, Jing; Ng, Pauline C.

    2013-01-01

    Indels in the coding regions of a gene can either cause frameshifts or amino acid insertions/deletions. Frameshifting indels are indels that have a length that is not divisible by 3 and subsequently cause frameshifts. Indels that have a length divisible by 3 cause amino acid insertions/deletions or block substitutions; we call these 3n indels. The new amino acid changes resulting from 3n indels could potentially affect protein function. Therefore, we construct a SIFT Indel prediction algorithm for 3n indels which achieves 82% accuracy, 81% sensitivity, 82% specificity, 82% precision, 0.63 MCC, and 0.87 AUC by 10-fold cross-validation. We have previously published a prediction algorithm for frameshifting indels. The rules for the prediction of 3n indels are different from the rules for the prediction of frameshifting indels and reflect the biological differences of these two different types of variations. SIFT Indel was applied to human 3n indels from the 1000 Genomes Project and the Exome Sequencing Project. We found that common variants are less likely to be deleterious than rare variants. The SIFT indel prediction algorithm for 3n indels is available at http://sift-dna.org/ PMID:24194902

  7. Reduced Cognitive Function Predicts Functional Decline in Patients with Heart Failure over 12 months

    PubMed Central

    Alosco, Michael L.; Spitznagel, Mary Beth; Cohen, Ronald; Sweet, Lawrence H.; Colbert, Lisa H.; Josephson, Richard; Hughes, Joel; Rosneck, Jim; Gunstad, John

    2016-01-01

    Background Impaired activities of daily living (ADL) are common in heart failure (HF) patients and contribute to the elevated mortality and hospitalization rates in this population. Cognitive impairment is also prevalent in HF, though its ability to predict functional decline over time is unknown. Aims This study examined the longitudinal pattern of activities of daily living in HF persons and whether reduced baseline cognitive status predicts functional decline in this population. Methods 110 persons with HF completed the Lawton-Brody Activities of Daily Living Scale and were administered the Modified Mini-Mental Status Examination (3MS) at baseline and a 12-month follow-up. Three composite scores were derived from the Lawton-Brody, including total, instrumental, and basic ADLs. Results HF patients reported high rates of baseline impairments in instrumental ADLs, including shopping, food preparation, housekeeping duties, laundry, among others. Repeated measures analyses showed significant declines in total and instrumental ADLs from baseline to the 12-month follow-up in HF (p < .05). Hierarchical regression analyses showed that poorer baseline performance on the 3MS predicted worse total ADL performance at 12-months (β = .15, p = .049), including greater dependence in shopping, driving, feeding, and physical ambulation (p < .05 for all). Conclusion The current results show that HF patients report significant functional decline over a 12-month period and brief cognitive tests can identify those patients at highest risk for decline. If replicated, such findings encourage the use of cognitive screening measures to identify HF patients most likely to require assistance with ADL tasks. PMID:23754840

  8. Ways that Social Change Predicts Personal Quality of Life

    ERIC Educational Resources Information Center

    Cheung, Chau-Kiu; Leung, Kwok

    2010-01-01

    A notable way that social change affects personal quality of life would rely on the person's experience with social change. This experience may influence societal quality of life and quality of work life, which may in turn affect personal quality of life. Additionally, the experience of social change is possibly less detrimental to personal…

  9. The role of plant functional trade-offs for biodiversity changes and biome shifts under scenarios of global climatic change

    NASA Astrophysics Data System (ADS)

    Reu, B.; Zaehle, S.; Proulx, R.; Bohn, K.; Kleidon, A.; Pavlick, R.; Schmidtlein, S.

    2011-05-01

    The global geographic distribution of biodiversity and biomes is determined by species-specific physiological tolerances to climatic constraints. Current vegetation models employ empirical bioclimatic relationships to predict present-day vegetation patterns and to forecast biodiversity changes and biome shifts under climatic change. In this paper, we consider trade-offs in plant functioning and their responses under climatic changes to forecast and explain changes in plant functional richness and shifts in biome geographic distributions. The Jena Diversity model (JeDi) simulates plant survival according to essential plant functional trade-offs, including ecophysiological processes such as water uptake, photosynthesis, allocation, reproduction and phenology. We use JeDi to quantify changes in plant functional richness and biome shifts between present-day and a range of possible future climates from two SRES emission scenarios (A2 and B1) and seven global climate models using metrics of plant functional richness and functional identity. Our results show (i) a significant loss of plant functional richness in the tropics, (ii) an increase in plant functional richness at mid and high latitudes, and (iii) a pole-ward shift of biomes. While these results are consistent with the findings of empirical approaches, we are able to explain them in terms of the plant functional trade-offs involved in the allocation, metabolic and reproduction strategies of plants. We conclude that general aspects of plant physiological tolerances can be derived from functional trade-offs, which may provide a useful process- and trait-based alternative to bioclimatic relationships. Such a mechanistic approach may be particularly relevant when addressing vegetation responses to climatic changes that encounter novel combinations of climate parameters that do not exist under contemporary climate.

  10. Plant physiological models of heat, water and photoinhibition stress for climate change modelling and agricultural prediction

    NASA Astrophysics Data System (ADS)

    Nicolas, B.; Gilbert, M. E.; Paw U, K. T.

    2015-12-01

    Soil-Vegetation-Atmosphere Transfer (SVAT) models are based upon well understood steady state photosynthetic physiology - the Farquhar-von Caemmerer-Berry model (FvCB). However, representations of physiological stress and damage have not been successfully integrated into SVAT models. Generally, it has been assumed that plants will strive to conserve water at higher temperatures by reducing stomatal conductance or adjusting osmotic balance, until potentially damaging temperatures and the need for evaporative cooling become more important than water conservation. A key point is that damage is the result of combined stresses: drought leads to stomatal closure, less evaporative cooling, high leaf temperature, less photosynthetic dissipation of absorbed energy, all coupled with high light (photosynthetic photon flux density; PPFD). This leads to excess absorbed energy by Photosystem II (PSII) and results in photoinhibition and damage, neither are included in SVAT models. Current representations of photoinhibition are treated as a function of PPFD, not as a function of constrained photosynthesis under heat or water. Thus, it seems unlikely that current models can predict responses of vegetation to climate variability and change. We propose a dynamic model of damage to Rubisco and RuBP-regeneration that accounts, mechanistically, for the interactions between high temperature, light, and constrained photosynthesis under drought. Further, these predictions are illustrated by key experiments allowing model validation. We also integrated this new framework within the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA). Preliminary results show that our approach can be used to predict reasonable photosynthetic dynamics. For instances, a leaf undergoing one day of drought stress will quickly decrease its maximum quantum yield of PSII (Fv/Fm), but it won't recover to unstressed levels for several days. Consequently, cumulative effect of photoinhibition on photosynthesis can cause