Imbalanced target prediction with pattern discovery on clinical data repositories.

PubMed

Chan, Tak-Ming; Li, Yuxi; Chiau, Choo-Chiap; Zhu, Jane; Jiang, Jie; Huo, Yong

2017-04-20

Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice. We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed. We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance. Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.

[Prediction method of rural landscape pattern evolution based on life cycle: a case study of Jinjing Town, Hunan Province, China].

PubMed

Ji, Xiang; Liu, Li-Ming; Li, Hong-Qing

2014-11-01

Taking Jinjing Town in Dongting Lake area as a case, this paper analyzed the evolution of rural landscape patterns by means of life cycle theory, simulated the evolution cycle curve, and calculated its evolution period, then combining CA-Markov model, a complete prediction model was built based on the rule of rural landscape change. The results showed that rural settlement and paddy landscapes of Jinjing Town would change most in 2020, with the rural settlement landscape increased to 1194.01 hm2 and paddy landscape greatly reduced to 3090.24 hm2. The quantitative and spatial prediction accuracies of the model were up to 99.3% and 96.4%, respectively, being more explicit than single CA-Markov model. The prediction model of rural landscape patterns change proposed in this paper would be helpful for rural landscape planning in future.

Alternative mechanisms alter the emergent properties of self-organization in mussel beds

PubMed Central

Liu, Quan-Xing; Weerman, Ellen J.; Herman, Peter M. J.; Olff, Han; van de Koppel, Johan

2012-01-01

Theoretical models predict that spatial self-organization can have important, unexpected implications by affecting the functioning of ecosystems in terms of resilience and productivity. Whether and how these emergent effects depend on specific formulations of the underlying mechanisms are questions that are often ignored. Here, we compare two alternative models of regular spatial pattern formation in mussel beds that have different mechanistic descriptions of the facilitative interactions between mussels. The first mechanism involves a reduced mussel loss rate at high density owing to mutual protection between the mussels, which is the basis of prior studies on the pattern formation in mussels. The second mechanism assumes, based on novel experimental evidence, that mussels feed more efficiently on top of mussel-generated hummocks. Model simulations point out that the second mechanism produces very similar types of spatial patterns in mussel beds. Yet the mechanisms predict a strikingly contrasting effect of these spatial patterns on ecosystem functioning, in terms of productivity and resilience. In the first model, where high mussel densities reduce mussel loss rates, patterns are predicted to strongly increase productivity and decrease the recovery time of the bed following a disturbance. When pattern formation is generated by increased feeding efficiency on hummocks, only minor emergent effects of pattern formation on ecosystem functioning are predicted. Our results provide a warning against predictions of the implications and emergent properties of spatial self-organization, when the mechanisms that underlie self-organization are incompletely understood and not based on the experimental study. PMID:22418256

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

PubMed

Wang, Xun-Heng; Jiao, Yun; Li, Lihua

2017-10-24

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals.

PubMed

Latash, M L; Goodman, S R

1994-01-01

The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns. Copyright © 1994. Published by Elsevier Ltd.

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017.

PubMed

Olugasa, Babasola O; Odigie, Eugene A; Lawani, Mike; Ojo, Johnson F

2015-01-01

The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country. A retrospective 5 years data of LF distribution countrywide among humans were used to train a time-trend model of the disease in Liberia. A time-trend quadratic model was selected due to its goodness-of-fit (R2 = 0.89, and P < 0.05) and best performance compared to linear and exponential models. Parameter predictors were run on least square method to predict LF cases for a prospective 5 years period, covering 2013-2017. The two-stage predictive model of LF case-pattern between 2013 and 2017 was characterized by a prospective decline within the South-coast County of Grand Bassa over the forecast period and an upward case-trend within the Northern County of Nimba. Case specific exponential increase was predicted for the first 2 years (2013-2014) with a geometric increase over the next 3 years (2015-2017) in Nimba County. This paper describes a translational application of the space-time distribution pattern of LF epidemics, 2008-2012 reported in Liberia, on which a predictive model was developed. We proposed a computationally feasible two-stage space-time permutation approach to estimate the time-trend parameters and conduct predictive inference on LF in Liberia.

The predictability of consumer visitation patterns

NASA Astrophysics Data System (ADS)

Krumme, Coco; Llorente, Alejandro; Cebrian, Manuel; Pentland, Alex ("Sandy"); Moro, Esteban

2013-04-01

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.

The predictability of consumer visitation patterns

PubMed Central

Krumme, Coco; Llorente, Alejandro; Cebrian, Manuel; Pentland, Alex ("Sandy"); Moro, Esteban

2013-01-01

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population. PMID:23598917

Predicting summer monsoon of Bhutan based on SST and teleconnection indices

NASA Astrophysics Data System (ADS)

Dorji, Singay; Herath, Srikantha; Mishra, Binaya Kumar; Chophel, Ugyen

2018-02-01

The paper uses a statistical method of predicting summer monsoon over Bhutan using the ocean-atmospheric circulation variables of sea surface temperature (SST), mean sea-level pressure (MSLP), and selected teleconnection indices. The predictors are selected based on the correlation. They are the SST and MSLP of the Bay of Bengal and the Arabian Sea and the MSLP of Bangladesh and northeast India. The Northern Hemisphere teleconnections of East Atlantic Pattern (EA), West Pacific Pattern (WP), Pacific/North American Pattern, and East Atlantic/West Russia Pattern (EA/WR). The rainfall station data are grouped into two regions with principal components analysis and Ward's hierarchical clustering algorithm. A support vector machine for regression model is proposed to predict the monsoon. The model shows improved skills over traditional linear regression. The model was able to predict the summer monsoon for the test data from 2011 to 2015 with a total monthly root mean squared error of 112 mm for region A and 33 mm for region B. Model could also forecast the 2016 monsoon of the South Asia Monsoon Outlook of World Meteorological Organization (WMO) for Bhutan. The reliance on agriculture and hydropower economy makes the prediction of summer monsoon highly valuable information for farmers and various other sectors. The proposed method can predict summer monsoon for operational forecasting.

Predictable patterns of the May-June rainfall anomaly over East Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young; Ha, Kyung-Ja

2017-02-01

During early summer (May-June, MJ), East Asia (EA) subtropical front is a defining feature of Asian monsoon, which produces the most prominent precipitation band in the global subtropics. Here we show that dynamical prediction of early summer EA (20°N-45°N, 100°E-130°E) rainfall made by four coupled climate models' ensemble hindcast (1979-2010) yields only a moderate skill and cannot be used to estimate predictability. The present study uses an alternative, empirical orthogonal function (EOF)-based physical-empirical (P-E) model approach to predict rainfall anomaly pattern and estimate its potential predictability. The first three leading modes are physically meaningful and can be, respectively, attributed to (a) the interaction between the anomalous western North Pacific subtropical high and underlying Indo-Pacific warm ocean, (b) the forcing associated with North Pacific sea surface temperature (SST) anomaly, and (c) the development of equatorial central Pacific SST anomalies. A suite of P-E models is established to forecast the first three leading principal components. All predictors are 0 month ahead of May, so the prediction here is named as a 0 month lead prediction. The cross-validated hindcast results demonstrate that these modes may be predicted with significant temporal correlation skills (0.48-0.72). Using the predicted principal components and the corresponding EOF patterns, the total MJ rainfall anomaly was hindcasted for the period of 1979-2015. The time-mean pattern correlation coefficient (PCC) score reaches 0.38, which is significantly higher than dynamical models' multimodel ensemble skill (0.21). The estimated potential maximum attainable PCC is around 0.65, suggesting that the dynamical prediction models may have large rooms to improve. Limitations and future work are discussed.

Development of time-trend model for analysing and predicting case pattern of dog bite injury induced rabies-like-illness in Liberia, 2014-2017.

PubMed

Jomah, N D; Ojo, J F; Odigie, E A; Olugasa, B O

2014-12-01

The post-civil war records of dog bite injuries (DBI) and rabies-like-illness (RLI) among humans in Liberia is a vital epidemiological resource for developing a predictive model to guide the allocation of resources towards human rabies control. Whereas DBI and RLI are high, they are largely under-reported. The objective of this study was to develop a time model of the case-pattern and apply it to derive predictors of time-trend point distribution of DBI-RLI cases. A retrospective 6 years data of DBI distribution among humans countrywide were converted to quarterly series using a transformation technique of Minimizing Squared First Difference statistic. The generated dataset was used to train a time-trend model of the DBI-RLI syndrome in Liberia. An additive detenninistic time-trend model was selected due to its performance compared to multiplication model of trend and seasonal movement. Parameter predictors were run on least square method to predict DBI cases for a prospective 4 years period, covering 2014-2017. The two-stage predictive model of DBI case-pattern between 2014 and 2017 was characterised by a uniform upward trend within Liberia's coastal and hinterland Counties over the forecast period. This paper describes a translational application of the time-trend distribution pattern of DBI epidemics, 2008-2013 reported in Liberia, on which a predictive model was developed. A computationally feasible two-stage time-trend permutation approach is proposed to estimate the time-trend parameters and conduct predictive inference on DBI-RLI in Liberia.

COMPARISON OF SPATIAL PATTERNS OF POLLUTANT DISTRIBUTION WITH CMAQ PREDICTIONS

EPA Science Inventory

To evaluate the Models-3/Community Multiscale Air Quality (CMAQ) modeling system in reproducing the spatial patterns of aerosol concentrations over the country on timescales of months and years, the spatial patterns of model output are compared with those derived from observation...

Pattern formation in a model for mountain pine beetle dispersal: linking model predictions to data.

PubMed

Strohm, S; Tyson, R C; Powell, J A

2013-10-01

Pattern formation occurs in a wide range of biological systems. This pattern formation can occur in mathematical models because of diffusion-driven instability or due to the interaction between reaction, diffusion, and chemotaxis. In this paper, we investigate the spatial pattern formation of attack clusters in a system for Mountain Pine Beetle. The pattern formation (aggregation) of the Mountain Pine Beetle in order to attack susceptible trees is crucial for their survival and reproduction. We use a reaction-diffusion equation with chemotaxis to model the interaction between Mountain Pine Beetle, Mountain Pine Beetle pheromones, and susceptible trees. Mathematical analysis is utilized to discover the spacing in-between beetle attacks on the susceptible landscape. The model predictions are verified by analysing aerial detection survey data of Mountain Pine Beetle Attack from the Sawtooth National Recreation Area. We find that the distance between Mountain Pine Beetle attack clusters predicted by our model closely corresponds to the observed attack data in the Sawtooth National Recreation Area. These results clarify the spatial mechanisms controlling the transition from incipient to epidemic populations and may lead to control measures which protect forests from Mountain Pine Beetle outbreak.

Specimen-specific modeling of hip fracture pattern and repair.

PubMed

Ali, Azhar A; Cristofolini, Luca; Schileo, Enrico; Hu, Haixiang; Taddei, Fulvia; Kim, Raymond H; Rullkoetter, Paul J; Laz, Peter J

2014-01-22

Hip fracture remains a major health problem for the elderly. Clinical studies have assessed fracture risk based on bone quality in the aging population and cadaveric testing has quantified bone strength and fracture loads. Prior modeling has primarily focused on quantifying the strain distribution in bone as an indicator of fracture risk. Recent advances in the extended finite element method (XFEM) enable prediction of the initiation and propagation of cracks without requiring a priori knowledge of the crack path. Accordingly, the objectives of this study were to predict femoral fracture in specimen-specific models using the XFEM approach, to perform one-to-one comparisons of predicted and in vitro fracture patterns, and to develop a framework to assess the mechanics and load transfer in the fractured femur when it is repaired with an osteosynthesis implant. Five specimen-specific femur models were developed from in vitro experiments under a simulated stance loading condition. Predicted fracture patterns closely matched the in vitro patterns; however, predictions of fracture load differed by approximately 50% due to sensitivity to local material properties. Specimen-specific intertrochanteric fractures were induced by subjecting the femur models to a sideways fall and repaired with a contemporary implant. Under a post-surgical stance loading, model-predicted load sharing between the implant and bone across the fracture surface varied from 59%:41% to 89%:11%, underscoring the importance of considering anatomic and fracture variability in the evaluation of implants. XFEM modeling shows potential as a macro-level analysis enabling fracture investigations of clinical cohorts, including at-risk groups, and the design of robust implants. © 2013 Published by Elsevier Ltd.

Self-supervised ARTMAP.

PubMed

Amis, Gregory P; Carpenter, Gail A

2010-03-01

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semi-supervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative low-dimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.eu.edu/SSART/. Copyright 2009 Elsevier Ltd. All rights reserved.

A reexamination of age-related variation in body weight and morphometry of Maryland nutria

USGS Publications Warehouse

Sherfy, M.H.; Mollett, T.A.; McGowan, K.R.; Daugherty, S.L.

2006-01-01

Age-related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross-validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model-building data set (n = 192-193 M, 217-222 F) and evaluated them with the remaining animals (n = 487-488 M, 642-647 F). We used nonlinear regression to develop Gompertz growth-curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log-transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age-related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live-captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live-captured animals.

Sparse network-based models for patient classification using fMRI

PubMed Central

Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

2015-01-01

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

PubMed

Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

2018-04-17

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

The representation of order information in auditory-verbal short-term memory.

PubMed

Kalm, Kristjan; Norris, Dennis

2014-05-14

Here we investigate how order information is represented in auditory-verbal short-term memory (STM). We used fMRI and a serial recall task to dissociate neural activity patterns representing the phonological properties of the items stored in STM from the patterns representing their order. For this purpose, we analyzed fMRI activity patterns elicited by different item sets and different orderings of those items. These fMRI activity patterns were compared with the predictions made by positional and chaining models of serial order. The positional models encode associations between items and their positions in a sequence, whereas the chaining models encode associations between successive items and retain no position information. We show that a set of brain areas in the postero-dorsal stream of auditory processing store associations between items and order as predicted by a positional model. The chaining model of order representation generates a different pattern similarity prediction, which was shown to be inconsistent with the fMRI data. Our results thus favor a neural model of order representation that stores item codes, position codes, and the mapping between them. This study provides the first fMRI evidence for a specific model of order representation in the human brain. Copyright © 2014 the authors 0270-6474/14/346879-08$15.00/0.

Track-pattern-based seasonal prediction model for intense tropical cyclone activities over the North Atlantic and the western North Pacific basins

NASA Astrophysics Data System (ADS)

Choi, W.; Ho, C. H.

2015-12-01

Intense tropical cyclones (TCs) accompanying heavy rainfall and destructive wind gusts sometimes cause incredible socio-economic damages in the regions near their landfall. This study aims to analyze intense TC activities in the North Atlantic (NA) and the western North Pacific (WNP) basins and develop their track propensity seasonal prediction model. Considering that the number of TCs in the NA basin is much smaller than that in the WNP basin, different intensity criteria are used; category 1 and above for NA and category 3 and above for WNP based on Saffir-Simpson hurricane wind scale. By using a fuzzy clustering method, intense TC tracks in the NA and the WNP basins are classified into two and three representative patterns, respectively. Each pattern shows empirical relationships with climate variabilities such as sea surface temperature distribution associated with El Niño/La Niña or Atlantic Meridional Mode, Pacific decadal oscillation, upper and low level zonal wind, and strength of subtropical high. The hybrid statistical-dynamical method has been used to develop the seasonal prediction model for each pattern based on statistical relationships between the intense TC activity and seasonal averaged key predictors. The model performance is statistically assessed by cross validation for the training period (1982-2013) and has been applied for the 2014 and 2015 prediction. This study suggests applicability of this model to real prediction work and provide bridgehead of attempt for intense TC prediction.

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

PubMed

Werneke, Mark W; Edmond, Susan; Deutscher, Daniel; Ward, Jason; Grigsby, David; Young, Michelle; McGill, Troy; McClenahan, Brian; Weinberg, Jon; Davidow, Amy L

2016-09-01

Study Design Retrospective cohort. Background Patient-classification subgroupings may be important prognostic factors explaining outcomes. Objectives To determine effects of adding classification variables (McKenzie syndrome and pain patterns, including centralization and directional preference; Symptom Checklist Back Pain Prediction Model [SCL BPPM]; and the Fear-Avoidance Beliefs Questionnaire subscales of work and physical activity) to a baseline risk-adjusted model predicting functional status (FS) outcomes. Methods Consecutive patients completed a battery of questionnaires that gathered information on 11 risk-adjustment variables. Physical therapists trained in Mechanical Diagnosis and Therapy methods classified each patient by McKenzie syndromes and pain pattern. Functional status was assessed at discharge by patient-reported outcomes. Only patients with complete data were included. Risk of selection bias was assessed. Prediction of discharge FS was assessed using linear stepwise regression models, allowing 13 variables to enter the model. Significant variables were retained in subsequent models. Model power (R(2)) and beta coefficients for model variables were estimated. Results Two thousand sixty-six patients with lumbar impairments were evaluated. Of those, 994 (48%), 10 (<1%), and 601 (29%) were excluded due to incomplete psychosocial data, McKenzie classification data, and missing FS at discharge, respectively. The final sample for analyses was 723 (35%). Overall R(2) for the baseline prediction FS model was 0.40. Adding classification variables to the baseline model did not result in significant increases in R(2). McKenzie syndrome or pain pattern explained 2.8% and 3.0% of the variance, respectively. When pain pattern and SCL BPPM were added simultaneously, overall model R(2) increased to 0.44. Although none of these increases in R(2) were significant, some classification variables were stronger predictors compared with some other variables included in the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

Current and Future Patterns of Global Marine Mammal Biodiversity

PubMed Central

Kaschner, Kristin; Tittensor, Derek P.; Ready, Jonathan; Gerrodette, Tim; Worm, Boris

2011-01-01

Quantifying the spatial distribution of taxa is an important prerequisite for the preservation of biodiversity, and can provide a baseline against which to measure the impacts of climate change. Here we analyse patterns of marine mammal species richness based on predictions of global distributional ranges for 115 species, including all extant pinnipeds and cetaceans. We used an environmental suitability model specifically designed to address the paucity of distributional data for many marine mammal species. We generated richness patterns by overlaying predicted distributions for all species; these were then validated against sightings data from dedicated long-term surveys in the Eastern Tropical Pacific, the Northeast Atlantic and the Southern Ocean. Model outputs correlated well with empirically observed patterns of biodiversity in all three survey regions. Marine mammal richness was predicted to be highest in temperate waters of both hemispheres with distinct hotspots around New Zealand, Japan, Baja California, the Galapagos Islands, the Southeast Pacific, and the Southern Ocean. We then applied our model to explore potential changes in biodiversity under future perturbations of environmental conditions. Forward projections of biodiversity using an intermediate Intergovernmental Panel for Climate Change (IPCC) temperature scenario predicted that projected ocean warming and changes in sea ice cover until 2050 may have moderate effects on the spatial patterns of marine mammal richness. Increases in cetacean richness were predicted above 40° latitude in both hemispheres, while decreases in both pinniped and cetacean richness were expected at lower latitudes. Our results show how species distribution models can be applied to explore broad patterns of marine biodiversity worldwide for taxa for which limited distributional data are available. PMID:21625431

Neuronal pattern separation of motion-relevant input in LIP activity

PubMed Central

Berberian, Nareg; MacPherson, Amanda; Giraud, Eloïse; Richardson, Lydia

2016-01-01

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination. PMID:27881719

Adolescent Psychosocial Development: A Review of Longitudinal Models and Research

ERIC Educational Resources Information Center

Meeus, Wim

2016-01-01

This review used 4 types of longitudinal models (descriptive models, prediction models, developmental sequence models and longitudinal mediation models) to identify regular patterns of psychosocial development in adolescence. Eight patterns of adolescent development were observed across countries: (1) adolescent maturation in multiple…

Effect of topological patterning on self-rolling of nanomembranes.

PubMed

Chen, Cheng; Song, Pengfei; Meng, Fanchao; Ou, Pengfei; Liu, Xinyu; Song, Jun

2018-08-24

The effects of topological patterning (i.e., grating and rectangular patterns) on the self-rolling behaviors of heteroepitaxial strained nanomembranes have been systematically studied. An analytical modeling framework, validated through finite-element simulations, has been formulated to predict the resultant curvature of the patterned nanomembrane as the pattern thickness and density vary. The effectiveness of the grating pattern in regulating the rolling direction of the nanomembrane has been demonstrated and quantitatively assessed. Further to the rolling of nanomembranes, a route to achieve predictive design of helical structures has been proposed and showcased. The present study provides new knowledge and mechanistic guidance towards predictive control and tuning of roll-up nanostructures via topological patterning.

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

PubMed Central

Yang, Pei-Ching; Chang, Chia-Chi; Chiang, Jung-Hsien; Chen, Ying-Yeh

2016-01-01

Background Prompt recognition and intervention of negative emotions is crucial for patients with depression. Mobile phones and mobile apps are suitable technologies that can be used to recognize negative emotions and intervene if necessary. Objective Mobile phone usage patterns can be associated with concurrent emotional states. The objective of this study is to adapt machine-learning methods to analyze such patterns for the prediction of negative emotion. Methods We developed an Android-based app to capture emotional states and mobile phone usage patterns, which included call logs (and use of apps). Visual analog scales (VASs) were used to report negative emotions in dimensions of depression, anxiety, and stress. In the system-training phase, participants were requested to tag their emotions for 14 consecutive days. Five feature-selection methods were used to determine individual usage patterns and four machine-learning methods were tested. Finally, rank product scoring was used to select the best combination to construct the prediction model. In the system evaluation phase, participants were then requested to verify the predicted negative emotions for at least 5 days. Results Out of 40 enrolled healthy participants, we analyzed data from 28 participants, including 30% (9/28) women with a mean (SD) age of 29.2 (5.1) years with sufficient emotion tags. The combination of time slots of 2 hours, greedy forward selection, and Naïve Bayes method was chosen for the prediction model. We further validated the personalized models in 18 participants who performed at least 5 days of model evaluation. Overall, the predictive accuracy for negative emotions was 86.17%. Conclusion We developed a system capable of predicting negative emotions based on mobile phone usage patterns. This system has potential for ecological momentary intervention (EMI) for depressive disorders by automatically recognizing negative emotions and providing people with preventive treatments before it escalates to clinical depression. PMID:27511748

Respiratory morbidity of pattern and model makers exposed to wood, plastic, and metal products

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

Robins, T.G.; Haboubi, G.; Demers, R.Y.

Pattern and model makers are skilled tradespersons who may be exposed to hardwoods, softwoods, phenol-formaldehyde resin-impregnated woods, epoxy and polyester/styrene resin systems, and welding and metal-casting fumes. The relationship of respiratory symptoms (wheezing, chronic bronchitis, dyspnea) and pulmonary function (FVC% predicted, FEV1% predicted, FEV1/FVC% predicted) with interview-derived cumulative exposure estimates to specific workplace agents and to all work with wood, plastic, or metal products was investigated in 751 pattern and model makers in southeast Michigan. In stratified analyses and age- and smoking-adjusted linear and logistic regression models, measures of cumulative wood exposures were associated with decrements in pulmonary function andmore » dyspnea, but not with other symptoms. In similar analyses, measures of cumulative plastic exposures were associated with wheezing, chronic bronchitis, and dyspnea, but not with decrements in pulmonary function. Prior studies of exposure levels among pattern and model makers and of respiratory health effects of specific agents among other occupational groups support the plausibility of wood-related effects more strongly than that of plastic-related effects.« less

DECHLORINATION-CONTROLLED POLYCHLORINATED DIBENZOFURAN FROM MUNICIPAL WASTE INCINERATORS

EPA Science Inventory

The ability to predict polychlorinated dibenzofuran (PCDF) isomer patterns from municipal waste incinerators (MWIs) enables an understanding of PCDF formation that may provide preventive measures. This work develops a model for the pattern prediction, assuming that the peak rati...

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex.

PubMed

Tong, Frank; Harrison, Stephenie A; Dewey, John A; Kamitani, Yukiyasu

2012-11-15

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. Copyright © 2012 Elsevier Inc. All rights reserved.

Relationship between BOLD amplitude and pattern classification of orientation-selective activity in the human visual cortex

PubMed Central

Tong, Frank; Harrison, Stephenie A.; Dewey, John A.; Kamitani, Yukiyasu

2012-01-01

Orientation-selective responses can be decoded from fMRI activity patterns in the human visual cortex, using multivariate pattern analysis (MVPA). To what extent do these feature-selective activity patterns depend on the strength and quality of the sensory input, and might the reliability of these activity patterns be predicted by the gross amplitude of the stimulus-driven BOLD response? Observers viewed oriented gratings that varied in luminance contrast (4, 20 or 100%) or spatial frequency (0.25, 1.0 or 4.0 cpd). As predicted, activity patterns in early visual areas led to better discrimination of orientations presented at high than low contrast, with greater effects of contrast found in area V1 than in V3. A second experiment revealed generally better decoding of orientations at low or moderate as compared to high spatial frequencies. Interestingly however, V1 exhibited a relative advantage at discriminating high spatial frequency orientations, consistent with the finer scale of representation in the primary visual cortex. In both experiments, the reliability of these orientation-selective activity patterns was well predicted by the average BOLD amplitude in each region of interest, as indicated by correlation analyses, as well as decoding applied to a simple model of voxel responses to simulated orientation columns. Moreover, individual differences in decoding accuracy could be predicted by the signal-to-noise ratio of an individual's BOLD response. Our results indicate that decoding accuracy can be well predicted by incorporating the amplitude of the BOLD response into simple simulation models of cortical selectivity; such models could prove useful in future applications of fMRI pattern classification. PMID:22917989

Brain regions engaged by part- and whole-task performance in a video game: a model-based test of the decomposition hypothesis.

PubMed

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Anderson, Abraham R; Poole, Ben; Qin, Yulin

2011-12-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

Is pigment patterning in fish skin determined by the Turing mechanism?

PubMed

Watanabe, Masakatsu; Kondo, Shigeru

2015-02-01

More than half a century ago, Alan Turing postulated that pigment patterns may arise from a mechanism that could be mathematically modeled based on the diffusion of two substances that interact with each other. Over the past 15 years, the molecular and genetic tools to verify this prediction have become available. Here, we review experimental studies aimed at identifying the mechanism underlying pigment pattern formation in zebrafish. Extensive molecular genetic studies in this model organism have revealed the interactions between the pigment cells that are responsible for the patterns. The mechanism discovered is substantially different from that predicted by the mathematical model, but it retains the property of 'local activation and long-range inhibition', a necessary condition for Turing pattern formation. Although some of the molecular details of pattern formation remain to be elucidated, current evidence confirms that the underlying mechanism is mathematically equivalent to the Turing mechanism. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Linear Stochastic Dynamical Model of ENSO. Part II: Analysis.

NASA Astrophysics Data System (ADS)

Thompson, C. J.; Battisti, D. S.

2001-02-01

In this study the behavior of a linear, intermediate model of ENSO is examined under stochastic forcing. The model was developed in a companion paper (Part I) and is derived from the Zebiak-Cane ENSO model. Four variants of the model are used whose stabilities range from slightly damped to moderately damped. Each model is run as a simulation while being perturbed by noise that is uncorrelated (white) in space and time. The statistics of the model output show the moderately damped models to be more realistic than the slightly damped models. The moderately damped models have power spectra that are quantitatively quite similar to observations, and a seasonal pattern of variance that is qualitatively similar to observations. All models produce ENSOs that are phase locked to the annual cycle, and all display the `spring barrier' characteristic in their autocorrelation patterns, though in the models this `barrier' occurs during the summer and is less intense than in the observations (inclusion of nonlinear effects is shown to partially remedy this deficiency). The more realistic models also show a decadal variability in the lagged autocorrelation pattern that is qualitatively similar to observations.Analysis of the models shows that the greatest part of the variability comes from perturbations that project onto the first singular vector, which then grow rapidly into the ENSO mode. Essentially, the model output represents many instances of the ENSO mode, with random phase and amplitude, stimulated by the noise through the optimal transient growth of the singular vectors.The limit of predictability for each model is calculated and it is shown that the more realistic (moderately damped) models have worse potential predictability (9-15 months) than the deterministic chaotic models that have been studied widely in the literature. The predictability limits are strongly correlated with the stability of the models' ENSO mode-the more highly damped models having much shorter limits of predictability. A comparison of the two most realistic models shows that even though these models have similar statistics, they have very different predictability limits. The models have a strong seasonal dependence to their predictability limits.The results of this study (with the companion paper) suggest that the linear, stable dynamical model of ENSO is indeed a plausible hypothesis for the observed ENSO. With very reasonable levels of stochastic forcing, the model produces realistic levels of variance, has a realistic spectrum, and qualitatively reproduces the observed seasonal pattern of variance, the autocorrelation pattern, and the ENSO-like decadal variability.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

Bioconvective patterns, synchrony, and survival. [in light-limited growth model of motile algae culture

NASA Technical Reports Server (NTRS)

Noever, David A.

1990-01-01

With and without bioconvective pattern formation, a theoretical model predicts growth in light-limited cultures of motile algae. At the critical density for pattern formation, the resulting doubly exponential population curves show an inflection. Such growth corresponds quantitatively to experiments in mechanically unstirred cultures. This attaches survival value to synchronized pattern formation.

A Factor Analytic Model of Drug-Related Behavior in Adolescence and Its Impact on Arrests at Multiple Stages of the Life Course

PubMed Central

2016-01-01

Objectives Recognizing the inherent variability of drug-related behaviors, this study develops an empirically-driven and holistic model of drug-related behavior during adolescence using factor analysis to simultaneously model multiple drug behaviors. Methods The factor analytic model uncovers latent dimensions of drug-related behaviors, rather than patterns of individuals. These latent dimensions are treated as empirical typologies which are then used to predict an individual’s number of arrests accrued at multiple phases of the life course. The data are robust enough to simultaneously capture drug behavior measures typically considered in isolation in the literature, and to allow for behavior to change and evolve over the period of adolescence. Results Results show that factor analysis is capable of developing highly descriptive patterns of drug offending, and that these patterns have great utility in predicting arrests. Results further demonstrate that while drug behavior patterns are predictive of arrests at the end of adolescence for both males and females, the impacts on arrests are longer lasting for females. Conclusions The various facets of drug behaviors have been a long-time concern of criminological research. However, the ability to model multiple behaviors simultaneously is often constrained by data that do not measure the constructs fully. Factor analysis is shown to be a useful technique for modeling adolescent drug involvement patterns in a way that accounts for the multitude and variability of possible behaviors, and in predicting future negative life outcomes, such as arrests. PMID:28435183

Predicting the performance of airborne antennas in the microwave regime

NASA Astrophysics Data System (ADS)

Carroll, David P.

1990-12-01

This study investigated the application of a high-frequency model (Uniform Geometrical Theory of Diffraction) of electromagnetic sources mounted on a curved surface of a complex structure. In particular, the purpose of the study was to determine if the model could be used to predict the radiation patterns of cavity-backed spiral antennas mounted on aircraft fuselages so that the optimum locations for the antennas could be chosen during the aircraft design phase. A review of literature revealed a good deal of work in modeling communications, navigation, identification antennas (blade monopoles and aperture slots) mounted on a wide variety of aircraft fuselages and successful validation against quarter-scale model measurements. This study developed a monopole-array model of a spiral antenna's radiation at vertical polarization and an ellipsoid-plate model of the FB-111A. Using the antenna and aircraft models, the existing Uniform Geometrical Theory of Diffraction model generated radiation patterns which agreed favorably with full-scale measured data. The study includes plots of predicted and measured radiation patterns from 2.5 to 15 Gigahertz.

SSME seal test program: Test results for smooth, hole-pattern and helically-grooved stators

NASA Technical Reports Server (NTRS)

Childs, Dara W.

1987-01-01

All of the listed seals were tested in a liquid Halon test facility at high Reynolds numbers. In addition, a helically-grooved-stator seal was tested in an air seal facility. An analysis of the test results with comparisons to theoretical predictions supports the following conclusions: (1) For small seals, the Hirs' friction-factor model is more restricted than had been thought; (2) For smooth seals, predictions of stiffness and damping improve markedly as the radical clearance is reduced; (3) Friction-factor data for hole-pattern-seal stators frequently deviates from the Hirs model; (4) Predictions of stiffness and damping coefficients for hole-pattern-stator seals is generally reasonable; (5) Tests for the hole-pattern stators at reduced clearances show no clear optimum for hole-pattern seals with respect to either hole-area ratio or hole depth to minimum clearance ratios; (6) Tests of these hole-pattern stators show no significant advantage in net damping over smooth seals; (7) Tests of helically-grooved seal stators in Halon show reasonable agreement between theory and prediction for leakage and direct stiffness but poor agreement for the net damping coefficient.

Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416

Attribution of Large-Scale Climate Patterns to Seasonal Peak-Flow and Prospects for Prediction Globally

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Ward, Philip; Block, Paul

2018-02-01

Flood-related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large-scale climate drivers in streamflow (or high-flow) prediction has been widely studied, an explicit link to global-scale long-lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak-flow to large-scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR-GLOBWB, a global-scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global-scale season-ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair-to-good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data-poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local-scale seasonal peak-flow prediction by identifying relevant global-scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.

Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.

PubMed

Taslimitehrani, Vahid; Dong, Guozhu; Pereira, Naveen L; Panahiazar, Maryam; Pathak, Jyotishman

2016-04-01

Computerized survival prediction in healthcare identifying the risk of disease mortality, helps healthcare providers to effectively manage their patients by providing appropriate treatment options. In this study, we propose to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5year survival in heart failure (HF) using data from electronic health records (EHRs) at Mayo Clinic. The CPXR(Log) constructs a pattern aided logistic regression model defined by several patterns and corresponding local logistic regression models. One of the models generated by CPXR(Log) achieved an AUC and accuracy of 0.94 and 0.91, respectively, and significantly outperformed prognostic models reported in prior studies. Data extracted from EHRs allowed incorporation of patient co-morbidities into our models which helped improve the performance of the CPXR(Log) models (15.9% AUC improvement), although did not improve the accuracy of the models built by other classifiers. We also propose a probabilistic loss function to determine the large error and small error instances. The new loss function used in the algorithm outperforms other functions used in the previous studies by 1% improvement in the AUC. This study revealed that using EHR data to build prediction models can be very challenging using existing classification methods due to the high dimensionality and complexity of EHR data. The risk models developed by CPXR(Log) also reveal that HF is a highly heterogeneous disease, i.e., different subgroups of HF patients require different types of considerations with their diagnosis and treatment. Our risk models provided two valuable insights for application of predictive modeling techniques in biomedicine: Logistic risk models often make systematic prediction errors, and it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases. Copyright © 2016 Elsevier Inc. All rights reserved.

Logic programming to predict cell fate patterns and retrodict genotypes in organogenesis.

PubMed

Hall, Benjamin A; Jackson, Ethan; Hajnal, Alex; Fisher, Jasmin

2014-09-06

Caenorhabditis elegans vulval development is a paradigm system for understanding cell differentiation in the process of organogenesis. Through temporal and spatial controls, the fate pattern of six cells is determined by the competition of the LET-23 and the Notch signalling pathways. Modelling cell fate determination in vulval development using state-based models, coupled with formal analysis techniques, has been established as a powerful approach in predicting the outcome of combinations of mutations. However, computing the outcomes of complex and highly concurrent models can become prohibitive. Here, we show how logic programs derived from state machines describing the differentiation of C. elegans vulval precursor cells can increase the speed of prediction by four orders of magnitude relative to previous approaches. Moreover, this increase in speed allows us to infer, or 'retrodict', compatible genomes from cell fate patterns. We exploit this technique to predict highly variable cell fate patterns resulting from dig-1 reduced-function mutations and let-23 mosaics. In addition to the new insights offered, we propose our technique as a platform for aiding the design and analysis of experimental data. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Population Dynamics and Flight Phenology Model of Codling Moth Differ between Commercial and Abandoned Apple Orchard Ecosystems.

PubMed

Joshi, Neelendra K; Rajotte, Edwin G; Naithani, Kusum J; Krawczyk, Greg; Hull, Larry A

2016-01-01

Apple orchard management practices may affect development and phenology of arthropod pests, such as the codling moth (CM), Cydia pomonella (L.) (Lepidoptera: Tortricidae), which is a serious internal fruit-feeding pest of apples worldwide. Estimating population dynamics and accurately predicting the timing of CM development and phenology events (for instance, adult flight, and egg-hatch) allows growers to understand and control local populations of CM. Studies were conducted to compare the CM flight phenology in commercial and abandoned apple orchard ecosystems using a logistic function model based on degree-days accumulation. The flight models for these orchards were derived from the cumulative percent moth capture using two types of commercially available CM lure baited traps. Models from both types of orchards were also compared to another model known as PETE (prediction extension timing estimator) that was developed in 1970s to predict life cycle events for many fruit pests including CM across different fruit growing regions of the United States. We found that the flight phenology of CM was significantly different in commercial and abandoned orchards. CM male flight patterns for first and second generations as predicted by the constrained and unconstrained PCM (Pennsylvania Codling Moth) models in commercial and abandoned orchards were different than the flight patterns predicted by the currently used CM model (i.e., PETE model). In commercial orchards, during the first and second generations, the PCM unconstrained model predicted delays in moth emergence compared to current model. In addition, the flight patterns of females were different between commercial and abandoned orchards. Such differences in CM flight phenology between commercial and abandoned orchard ecosystems suggest potential impact of orchard environment and crop management practices on CM biology.

Population Dynamics and Flight Phenology Model of Codling Moth Differ between Commercial and Abandoned Apple Orchard Ecosystems

PubMed Central

Joshi, Neelendra K.; Rajotte, Edwin G.; Naithani, Kusum J.; Krawczyk, Greg; Hull, Larry A.

2016-01-01

Apple orchard management practices may affect development and phenology of arthropod pests, such as the codling moth (CM), Cydia pomonella (L.) (Lepidoptera: Tortricidae), which is a serious internal fruit-feeding pest of apples worldwide. Estimating population dynamics and accurately predicting the timing of CM development and phenology events (for instance, adult flight, and egg-hatch) allows growers to understand and control local populations of CM. Studies were conducted to compare the CM flight phenology in commercial and abandoned apple orchard ecosystems using a logistic function model based on degree-days accumulation. The flight models for these orchards were derived from the cumulative percent moth capture using two types of commercially available CM lure baited traps. Models from both types of orchards were also compared to another model known as PETE (prediction extension timing estimator) that was developed in 1970s to predict life cycle events for many fruit pests including CM across different fruit growing regions of the United States. We found that the flight phenology of CM was significantly different in commercial and abandoned orchards. CM male flight patterns for first and second generations as predicted by the constrained and unconstrained PCM (Pennsylvania Codling Moth) models in commercial and abandoned orchards were different than the flight patterns predicted by the currently used CM model (i.e., PETE model). In commercial orchards, during the first and second generations, the PCM unconstrained model predicted delays in moth emergence compared to current model. In addition, the flight patterns of females were different between commercial and abandoned orchards. Such differences in CM flight phenology between commercial and abandoned orchard ecosystems suggest potential impact of orchard environment and crop management practices on CM biology. PMID:27713702

Introducing etch kernels for efficient pattern sampling and etch bias prediction

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels, as well as the choice of calibration patterns, is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels-"internal, external, curvature, Gaussian, z_profile"-designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.

A new prospect in magnetic nanoparticle-based cancer therapy: Taking credit from mathematical tissue-mimicking phantom brain models.

PubMed

Saeedi, Mostafa; Vahidi, Omid; Goodarzi, Vahabodin; Saeb, Mohammad Reza; Izadi, Leila; Mozafari, Masoud

2017-11-01

Distribution patterns/performance of magnetic nanoparticles (MNPs) was visualized by computer simulation and experimental validation on agarose gel tissue-mimicking phantom (AGTMP) models. The geometry of a complex three-dimensional mathematical phantom model of a cancer tumor was examined by tomography imaging. The capability of mathematical model to predict distribution patterns/performance in AGTMP model was captured. The temperature profile vs. hyperthermia duration was obtained by solving bio-heat equations for four different MNPs distribution patterns and correlated with cell death rate. The outcomes indicated that bio-heat model was able to predict temperature profile throughout the tissue model with a reasonable precision, to be applied for complex tissue geometries. The simulation results on the cancer tumor model shed light on the effectiveness of the studied parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar.

PubMed

Brown, Jason L; Cameron, Alison; Yoder, Anne D; Vences, Miguel

2014-10-09

Pattern and process are inextricably linked in biogeographic analyses, though we can observe pattern, we must infer process. Inferences of process are often based on ad hoc comparisons using a single spatial predictor. Here, we present an alternative approach that uses mixed-spatial models to measure the predictive potential of combinations of hypotheses. Biodiversity patterns are estimated from 8,362 occurrence records from 745 species of Malagasy amphibians and reptiles. By incorporating 18 spatially explicit predictions of 12 major biogeographic hypotheses, we show that mixed models greatly improve our ability to explain the observed biodiversity patterns. We conclude that patterns are influenced by a combination of diversification processes rather than by a single predominant mechanism. A 'one-size-fits-all' model does not exist. By developing a novel method for examining and synthesizing spatial parameters such as species richness, endemism and community similarity, we demonstrate the potential of these analyses for understanding the diversification history of Madagascar's biota.

Modeling the Hydration Layer around Proteins: Applications to Small- and Wide-Angle X-Ray Scattering

PubMed Central

Virtanen, Jouko Juhani; Makowski, Lee; Sosnick, Tobin R.; Freed, Karl F.

2011-01-01

Small-/wide-angle x-ray scattering (SWAXS) experiments can aid in determining the structures of proteins and protein complexes, but success requires accurate computational treatment of solvation. We compare two methods by which to calculate SWAXS patterns. The first approach uses all-atom explicit-solvent molecular dynamics (MD) simulations. The second, far less computationally expensive method involves prediction of the hydration density around a protein using our new HyPred solvation model, which is applied without the need for additional MD simulations. The SWAXS patterns obtained from the HyPred model compare well to both experimental data and the patterns predicted by the MD simulations. Both approaches exhibit advantages over existing methods for analyzing SWAXS data. The close correspondence between calculated and observed SWAXS patterns provides strong experimental support for the description of hydration implicit in the HyPred model. PMID:22004761

A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

PubMed

Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha; Kumar, C S

2018-05-19

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

Volumetric pattern analysis of fuselage-mounted airborne antennas. Ph.D. Thesis; [prediction analysis techniques for antenna radiation patterns of microwave antennas on commercial aircraft

NASA Technical Reports Server (NTRS)

Yu, C. L.

1976-01-01

A volumetric pattern analysis of fuselage-mounted airborne antennas at high frequencies was investigated. The primary goal of the investigation was to develop a numerical solution for predicting radiation patterns of airborne antennas in an accurate and efficient manner. An analytical study of airborne antenna pattern problems is presented in which the antenna is mounted on the fuselage near the top or bottom. Since this is a study of general-type commercial aircraft, the aircraft was modeled in its most basic form. The fuselage was assumed to be an infinitely long perfectly conducting elliptic cylinder in its cross-section and a composite elliptic cylinder in its elevation profile. The wing, cockpit, stabilizers (horizontal and vertical) and landing gear are modeled by "N" sided bent or flat plates which can be arbitrarily attached to the fuselage. The volumetric solution developed utilizes two elliptic cylinders, namely, the roll plane and elevation plane models to approximate the principal surface profile (longitudinal and transverse) at the antenna location. With the belt concept and the aid of appropriate coordinate system transformations the solution can be used to predict the volumetric patterns of airborne antennas in an accurate and efficient manner. Applications of this solution to various airborne antenna problems show good agreement with scale model measurements. Extensive data are presented for a microwave landing antenna system.

Diatom-sedimentation feedback generates a self-organized geomorphic landscape on intertidal mudflats (Invited)

NASA Astrophysics Data System (ADS)

van de Koppel, J.; Weerman, E.; Herman, P.

2010-12-01

During spring, intertidal flats can exhibit strikingly regular spatial patterns of diatom-covered hummocks alternating with almost bare, water-filled hollows. We hypothesize that 1) the formation of this geomorphic landscape is caused by a strong interaction between benthic diatoms and sediment dynamics, inducing spatial self-organization, and 2) that self-organization affects ecosystem functioning by increasing the net average sedimentation on the tidal flat. We present a combined empirical and mathematical study to test the first hypothesis. We determined how the sediment erosion threshold varied with diatom cover and elevation. Our results were incorporated into a mathematical model to investigate whether the proposed mechanism could explain the formation of the observed patterns. Our mathematical model confirmed that the interaction between sedimentation, diatom growth and water redistribution could induce the formation of regular patterns on the intertidal mudflat. The model predicts that areas exhibiting spatially-self-organized patterns have increased sediment accretion and diatom biomass compared with areas lacking spatial patterns. We tested this prediction by following the sediment elevation during the season on both patterned and unpatterned parts of the mudflat. The results of our study confirmed our model prediction, as more sediment was found to accumulate in patterned parts of the mudflat, revealing how self-organization affected the functioning of mudflat ecosystems. Our study on intertidal mudflats provides a simple but clear-cut example of how the interaction between biological and geomorphological processes, through the process of self-organization, induces a self-organized geomorphic landscape.

Spatial Pattern of Standing Timber Value across the Brazilian Amazon

PubMed Central

Ahmed, Sadia E.; Ewers, Robert M.

2012-01-01

The Amazon is a globally important system, providing a host of ecosystem services from climate regulation to food sources. It is also home to a quarter of all global diversity. Large swathes of forest are removed each year, and many models have attempted to predict the spatial patterns of this forest loss. The spatial patterns of deforestation are determined largely by the patterns of roads that open access to frontier areas and expansion of the road network in the Amazon is largely determined by profit seeking logging activities. Here we present predictions for the spatial distribution of standing value of timber across the Amazon. We show that the patterns of timber value reflect large-scale ecological gradients, determining the spatial distribution of functional traits of trees which are, in turn, correlated with timber values. We expect that understanding the spatial patterns of timber value across the Amazon will aid predictions of logging movements and thus predictions of potential future road developments. These predictions in turn will be of great use in estimating the spatial patterns of deforestation in this globally important biome. PMID:22590520

Reconfiguration of the pontomedullary respiratory network: a computational modeling study with coordinated in vivo experiments.

PubMed

Rybak, I A; O'Connor, R; Ross, A; Shevtsova, N A; Nuding, S C; Segers, L S; Shannon, R; Dick, T E; Dunin-Barkowski, W L; Orem, J M; Solomon, I C; Morris, K F; Lindsey, B G

2008-10-01

A large body of data suggests that the pontine respiratory group (PRG) is involved in respiratory phase-switching and the reconfiguration of the brain stem respiratory network. However, connectivity between the PRG and ventral respiratory column (VRC) in computational models has been largely ad hoc. We developed a network model with PRG-VRC connectivity inferred from coordinated in vivo experiments. Neurons were modeled in the "integrate-and-fire" style; some neurons had pacemaker properties derived from the model of Breen et al. We recapitulated earlier modeling results, including reproduction of activity profiles of different respiratory neurons and motor outputs, and their changes under different conditions (vagotomy, pontine lesions, etc.). The model also reproduced characteristic changes in neuronal and motor patterns observed in vivo during fictive cough and during hypoxia in non-rapid eye movement sleep. Our simulations suggested possible mechanisms for respiratory pattern reorganization during these behaviors. The model predicted that network- and pacemaker-generated rhythms could be co-expressed during the transition from gasping to eupnea, producing a combined "burst-ramp" pattern of phrenic discharges. To test this prediction, phrenic activity and multiple single neuron spike trains were monitored in vagotomized, decerebrate, immobilized, thoracotomized, and artificially ventilated cats during hypoxia and recovery. In most experiments, phrenic discharge patterns during recovery from hypoxia were similar to those predicted by the model. We conclude that under certain conditions, e.g., during recovery from severe brain hypoxia, components of a distributed network activity present during eupnea can be co-expressed with gasp patterns generated by a distinct, functionally "simplified" mechanism.

Pattern Separation Deficits Following Damage to the Hippocampus

ERIC Educational Resources Information Center

Kirwan, C. Brock; Hartshorn, Andrew; Stark, Shauna M.; Goodrich-Hunsaker, Naomi J.; Hopkins, Ramona O.; Stark, Craig E. L.

2012-01-01

Computational models of hippocampal function propose that the hippocampus is capable of rapidly storing distinct representations through a process known as pattern separation. This prediction is supported by electrophysiological data from rodents and neuroimaging data from humans. Here, we test the prediction that damage to the hippocampus would…

Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

NASA Astrophysics Data System (ADS)

Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

2015-07-01

Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

Climate change likely to reduce orchid bee abundance even in climatic suitable sites.

PubMed

Faleiro, Frederico Valtuille; Nemésio, André; Loyola, Rafael

2018-06-01

Studies have tested whether model predictions based on species' occurrence can predict the spatial pattern of population abundance. The relationship between predicted environmental suitability and population abundance varies in shape, strength and predictive power. However, little attention has been paid to the congruence in predictions of different models fed with occurrence or abundance data, in particular when comparing metrics of climate change impact. Here, we used the ecological niche modeling fit with presence-absence and abundance data of orchid bees to predict the effect of climate change on species and assembly level distribution patterns. In addition, we assessed whether predictions of presence-absence models can be used as a proxy to abundance patterns. We obtained georeferenced abundance data of orchid bees (Hymenoptera: Apidae: Euglossina) in the Brazilian Atlantic Forest. Sampling method consisted in attracting male orchid bees to baits of at least five different aromatic compounds and collecting the individuals with entomological nets or bait traps. We limited abundance data to those obtained by similar standard sampling protocol to avoid bias in abundance estimation. We used boosted regression trees to model ecological niches and project them into six climate models and two Representative Concentration Pathways. We found that models based on species occurrences worked as a proxy for changes in population abundance when the output of the models were continuous; results were very different when outputs were discretized to binary predictions. We found an overall trend of diminishing abundance in the future, but a clear retention of climatically suitable sites too. Furthermore, geographic distance to gained climatic suitable areas can be very short, although it embraces great variation. Changes in species richness and turnover would be concentrated in western and southern Atlantic Forest. Our findings offer support to the ongoing debate of suitability-abundance models and can be used to support spatial conservation prioritization schemes and species triage in Atlantic Forest. © 2018 John Wiley & Sons Ltd.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning.

PubMed

Kim Oanh, Nguyen Thi; Leelasakultum, Ketsiri

2011-05-01

This study investigated the main causes of haze episodes in the northwestern Thailand to provide early warning and prediction. In an absence of emission input data required for chemical transport modeling to predict the haze, the climatological approach in combination with statistical analysis was used. An automatic meteorological classification scheme was developed using regional meteorological station data of 8years (2001-2008) which classified the prevailing synoptic patterns over Northern Thailand into 4 patterns. Pattern 2, occurring with high frequency in March, was found to associate with the highest levels of 24h PM(10) in Chiangmai, the largest city in Northern Thailand. Typical features of this pattern were the dominance of thermal lows over India, Western China and Northern Thailand with hot, dry and stagnant air in Northern Thailand. March 2007, the month with the most severe haze episode in Chiangmai, was found to have a high frequency of occurrence of pattern 2 coupled with the highest emission intensities from biomass open burning. Backward trajectories showed that, on haze episode days, air masses passed over the region of dense biomass fire hotspots before arriving at Chiangmai. A stepwise regression model was developed to predict 24h PM(10) for days of meteorology pattern 2 using February-April data of 2007-2009 and tested with 2004-2010 data. The model performed satisfactorily for the model development dataset (R(2)=87%) and test dataset (R(2)=81%), which appeared to be superior over a simple persistence regression of 24h PM(10) (R(2)=76%). Our developed model had an accuracy over 90% for the categorical forecast of PM(10)>120μg/m(3). The episode warning procedure would identify synoptic pattern 2 and predict 24h PM(10) in Chiangmai 24h in advance. This approach would be applicable for air pollution episode management in other areas with complex terrain where similar conditions exist. Copyright © 2011 Elsevier B.V. All rights reserved.

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

An integral equation formulation for predicting radiation patterns of a space shuttle annular slot antenna

NASA Technical Reports Server (NTRS)

Jones, J. E.; Richmond, J. H.

1974-01-01

An integral equation formulation is applied to predict pitch- and roll-plane radiation patterns of a thin VHF/UHF (very high frequency/ultra high frequency) annular slot communications antenna operating at several locations in the nose region of the space shuttle orbiter. Digital computer programs used to compute radiation patterns are given and the use of the programs is illustrated. Experimental verification of computed patterns is given from measurements made on 1/35-scale models of the orbiter.

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

NASA Astrophysics Data System (ADS)

Ye, Liming; Yang, Guixia; Van Ranst, Eric; Tang, Huajun

2013-03-01

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (˜10-year) environmental planning and decision making.

COMPARISONS OF SPATIAL PATTERNS OF WET DEPOSITION TO MODEL PREDICTIONS

EPA Science Inventory

The Community Multiscale Air Quality model, (CMAQ), is a "one-atmosphere" model, in that it uses a consistent set of chemical reactions and physical principles to predict concentrations of primary pollutants, photochemical smog, and fine aerosols, as well as wet and dry depositi...

Numerical Simulation for Predicting Fatigue Damage Progress in Notched CFRP Laminates by Using Cohesive Elements

NASA Astrophysics Data System (ADS)

Okabe, Tomonaga; Yashiro, Shigeki

This study proposes the cohesive zone model (CZM) for predicting fatigue damage growth in notched carbon-fiber-reinforced composite plastic (CFRP) cross-ply laminates. In this model, damage growth in the fracture process of cohesive elements due to cyclic loading is represented by the conventional damage mechanics model. We preliminarily investigated whether this model can appropriately express fatigue damage growth for a circular crack embedded in isotropic solid material. This investigation demonstrated that this model could reproduce the results with the well-established fracture mechanics model plus the Paris' law by tuning adjustable parameters. We then numerically investigated the damage process in notched CFRP cross-ply laminates under tensile cyclic loading and compared the predicted damage patterns with those in experiments reported by Spearing et al. (Compos. Sci. Technol. 1992). The predicted damage patterns agreed with the experiment results, which exhibited the extension of multiple types of damage (e.g., splits, transverse cracks and delaminations) near the notches.

Observational evidence of European summer weather patterns predictable from spring

NASA Astrophysics Data System (ADS)

Ossó, Albert; Sutton, Rowan; Shaffrey, Len; Dong, Buwen

2018-01-01

Forecasts of summer weather patterns months in advance would be of great value for a wide range of applications. However, seasonal dynamical model forecasts for European summers have very little skill, particularly for rainfall. It has not been clear whether this low skill reflects inherent unpredictability of summer weather or, alternatively, is a consequence of weaknesses in current forecast systems. Here we analyze atmosphere and ocean observations and identify evidence that a specific pattern of summertime atmospheric circulation––the summer East Atlantic (SEA) pattern––is predictable from the previous spring. An index of North Atlantic sea-surface temperatures in March–April can predict the SEA pattern in July–August with a cross-validated correlation skill above 0.6. Our analyses show that the sea-surface temperatures influence atmospheric circulation and the position of the jet stream over the North Atlantic. The SEA pattern has a particularly strong influence on rainfall in the British Isles, which we find can also be predicted months ahead with a significant skill of 0.56. Our results have immediate application to empirical forecasts of summer rainfall for the United Kingdom, Ireland, and northern France and also suggest that current dynamical model forecast systems have large potential for improvement.

Activity Patterns in Response to Symptoms in Patients Being Treated for Chronic Fatigue Syndrome: An Experience Sampling Methodology Study

PubMed Central

2016-01-01

Objective: Cognitive–behavioral models of chronic fatigue syndrome (CFS) propose that patients respond to symptoms with 2 predominant activity patterns—activity limitation and all-or-nothing behaviors—both of which may contribute to illness persistence. The current study investigated whether activity patterns occurred at the same time as, or followed on from, patient symptom experience and affect. Method: Twenty-three adults with CFS were recruited from U.K. CFS services. Experience sampling methodology (ESM) was used to assess fluctuations in patient symptom experience, affect, and activity management patterns over 10 assessments per day for a total of 6 days. Assessments were conducted within patients’ daily life and were delivered through an app on touchscreen Android mobile phones. Multilevel model analyses were conducted to examine the role of self-reported patient fatigue, pain, and affect as predictors of change in activity patterns at the same and subsequent assessment. Results: Current experience of fatigue-related symptoms and pain predicted higher patient activity limitation at the current and subsequent assessments whereas subjective wellness predicted higher all-or-nothing behavior at both times. Current pain predicted less all-or-nothing behavior at the subsequent assessment. In contrast to hypotheses, current positive affect was predictive of current activity limitation whereas current negative affect was predictive of current all-or-nothing behavior. Both activity patterns varied at the momentary level. Conclusions: Patient symptom experiences appear to be driving patient activity management patterns in line with the cognitive–behavioral model of CFS. ESM offers a useful method for examining multiple interacting variables within the context of patients’ daily life. PMID:27819461

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

Transregional Collaborative Research Centre 32: Patterns in Soil-Vegetation-Atmosphere-Systems

NASA Astrophysics Data System (ADS)

Masbou, M.; Simmer, C.; Kollet, S.; Boessenkool, K.; Crewell, S.; Diekkrüger, B.; Huber, K.; Klitzsch, N.; Koyama, C.; Vereecken, H.

2012-04-01

The soil-vegetation-atmosphere system is characterized by non-linear exchanges of mass, momentum and energy with complex patterns, structures and processes that act at different temporal and spatial scales. Under the TR32 framework, the characterisation of these structures and patterns will lead to a deeper qualitative and quantitative understanding of the SVA system, and ultimately to better predictions of the SVA state. Research in TR32 is based on three methodological pillars: Monitoring, Modelling and Data Assimilation. Focusing our research on the Rur Catchment (Germany), patterns are monitored since 2006 continuously using existing and novel geophysical and remote sensing techniques from the local to the catchment scale based on ground penetrating radar methods, induced polarization, radiomagnetotellurics, electrical resistivity tomography, boundary layer scintillometry, lidar techniques, cosmic-ray, microwave radiometry, and precipitation radars with polarization diversity. Modelling approaches involve development of scaled consistent coupled model platform: high resolution numerical weather prediction (NWP; 400m) and hydrological models (few meters). In the second phase (2011-2014), the focus is on the integration of models from the groundwater to the atmosphere for both the m- and km-scale and the extension of the experimental monitoring in respect to vegetation. The coupled modelling platform is based on the atmospheric model COSMO, the land surface model CLM and the hydrological model ParFlow. A scale consistent two-way coupling is performed using the external OASIS coupler. Example work includes the transfer of laboratory methods to the field; the measurements of patterns of soil-carbon, evapotranspiration and respiration measured in the field; catchment-scale modeling of exchange processes and the setup of an atmospheric boundary layer monitoring network. These modern and predominantly non-invasive measurement techniques are exploited in combination with advanced modelling systems by data assimilation to yield improved numerical models for the prediction of water-, energy and CO2-transfer by accounting for the patterns occurring at various scales.

The effects of school closures on influenza outbreaks and pandemics: systematic review of simulation studies.

PubMed

Jackson, Charlotte; Mangtani, Punam; Hawker, Jeremy; Olowokure, Babatunde; Vynnycky, Emilia

2014-01-01

School closure is a potential intervention during an influenza pandemic and has been investigated in many modelling studies. To systematically review the effects of school closure on influenza outbreaks as predicted by simulation studies. We searched Medline and Embase for relevant modelling studies published by the end of October 2012, and handsearched key journals. We summarised the predicted effects of school closure on the peak and cumulative attack rates and the duration of the epidemic. We investigated how these predictions depended on the basic reproduction number, the timing and duration of closure and the assumed effects of school closures on contact patterns. School closures were usually predicted to be most effective if they caused large reductions in contact, if transmissibility was low (e.g. a basic reproduction number <2), and if attack rates were higher in children than in adults. The cumulative attack rate was expected to change less than the peak, but quantitative predictions varied (e.g. reductions in the peak were frequently 20-60% but some studies predicted >90% reductions or even increases under certain assumptions). This partly reflected differences in model assumptions, such as those regarding population contact patterns. Simulation studies suggest that school closure can be a useful control measure during an influenza pandemic, particularly for reducing peak demand on health services. However, it is difficult to accurately quantify the likely benefits. Further studies of the effects of reactive school closures on contact patterns are needed to improve the accuracy of model predictions.

Developing a musculoskeletal model of the primate skull: predicting muscle activations, bite force, and joint reaction forces using multibody dynamics analysis and advanced optimisation methods.

PubMed

Shi, Junfen; Curtis, Neil; Fitton, Laura C; O'Higgins, Paul; Fagan, Michael J

2012-10-07

An accurate, dynamic, functional model of the skull that can be used to predict muscle forces, bite forces, and joint reaction forces would have many uses across a broad range of disciplines. One major issue however with musculoskeletal analyses is that of muscle activation pattern indeterminacy. A very large number of possible muscle force combinations will satisfy a particular functional task. This makes predicting physiological muscle recruitment patterns difficult. Here we describe in detail the process of development of a complex multibody computer model of a primate skull (Macaca fascicularis), that aims to predict muscle recruitment patterns during biting. Using optimisation criteria based on minimisation of muscle stress we predict working to balancing side muscle force ratios, peak bite forces, and joint reaction forces during unilateral biting. Validation of such models is problematic; however we have shown comparable working to balancing muscle activity and TMJ reaction ratios during biting to those observed in vivo and that peak predicted bite forces compare well to published experimental data. To our knowledge the complexity of the musculoskeletal model is greater than any previously reported for a primate. This complexity, when compared to more simple representations provides more nuanced insights into the functioning of masticatory muscles. Thus, we have shown muscle activity to vary throughout individual muscle groups, which enables them to function optimally during specific masticatory tasks. This model will be utilised in future studies into the functioning of the masticatory apparatus. Copyright © 2012 Elsevier Ltd. All rights reserved.

Brain Regions Engaged by Part- and Whole-task Performance in a Video Game: A Model-based Test of the Decomposition Hypothesis

PubMed Central

Anderson, John R.; Bothell, Daniel; Fincham, Jon M.; Anderson, Abraham R.; Poole, Ben; Qin, Yulin

2013-01-01

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model’s predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits. PMID:21557648

Domain Selectivity in the Parahippocampal Gyrus Is Predicted by the Same Structural Connectivity Patterns in Blind and Sighted Individuals.

PubMed

Wang, Xiaoying; He, Chenxi; Peelen, Marius V; Zhong, Suyu; Gong, Gaolang; Caramazza, Alfonso; Bi, Yanchao

2017-05-03

Human ventral occipital temporal cortex contains clusters of neurons that show domain-preferring responses during visual perception. Recent studies have reported that some of these clusters show surprisingly similar domain selectivity in congenitally blind participants performing nonvisual tasks. An important open question is whether these functional similarities are driven by similar innate connections in blind and sighted groups. Here we addressed this question focusing on the parahippocampal gyrus (PHG), a region that is selective for large objects and scenes. Based on the assumption that patterns of long-range connectivity shape local computation, we examined whether domain selectivity in PHG is driven by similar structural connectivity patterns in the two populations. Multiple regression models were built to predict the selectivity of PHG voxels for large human-made objects from white matter (WM) connectivity patterns in both groups. These models were then tested using independent data from participants with similar visual experience (two sighted groups) and using data from participants with different visual experience (blind and sighted groups). Strikingly, the WM-based predictions between blind and sighted groups were as successful as predictions between two independent sighted groups. That is, the functional selectivity for large objects of a PHG voxel in a blind participant could be accurately predicted by its WM pattern using the connection-to-function model built from the sighted group data, and vice versa. Regions that significantly predicted PHG selectivity were located in temporal and frontal cortices in both sighted and blind populations. These results show that the large-scale network driving domain selectivity in PHG is independent of vision. SIGNIFICANCE STATEMENT Recent studies have reported intriguingly similar domain selectivity in sighted and congenitally blind individuals in regions within the ventral visual cortex. To examine whether these similarities originate from similar innate connectional roots, we investigated whether the domain selectivity in one population could be predicted by the structural connectivity pattern of the other. We found that the selectivity for large objects of a PHG voxel in a blind participant could be predicted by its structural connectivity pattern using the connection-to-function model built from the sighted group data, and vice versa. These results reveal that the structural connectivity underlying domain selectivity in the PHG is independent of visual experience, providing evidence for nonvisual representations in this region. Copyright © 2017 the authors 0270-6474/17/374706-12$15.00/0.

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

USGS Publications Warehouse

Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.

2015-01-01

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

Vegetation pattern formation in a fog-dependent ecosystem.

PubMed

Borthagaray, Ana I; Fuentes, Miguel A; Marquet, Pablo A

2010-07-07

Vegetation pattern formation is a striking characteristic of several water-limited ecosystems around the world. Typically, they have been described on runoff-based ecosystems emphasizing local interactions between water, biomass interception, growth and dispersal. Here, we show that this situation is by no means general, as banded patterns in vegetation can emerge in areas without rainfall and in plants without functional root (the Bromeliad Tillandsia landbeckii) and where fog is the principal source of moisture. We show that a simple model based on the advection of fog-water by wind and its interception by the vegetation can reproduce banded patterns which agree with empirical patterns observed in the Coastal Atacama Desert. Our model predicts how the parameters may affect the conditions to form the banded pattern, showing a transition from a uniform vegetated state, at high water input or terrain slope to a desert state throughout intermediate banded states. Moreover, the model predicts that the pattern wavelength is a decreasing non-linear function of fog-water input and slope, and an increasing function of plant loss and fog-water flow speed. Finally, we show that the vegetation density is increased by the formation of the regular pattern compared to the density expected by the spatially homogeneous model emphasizing the importance of self-organization in arid ecosystems. (c) 2010 Elsevier Ltd. All rights reserved.

A Physics-Inspired Mechanistic Model of Migratory Movement Patterns in Birds.

PubMed

Revell, Christopher; Somveille, Marius

2017-08-29

In this paper, we introduce a mechanistic model of migratory movement patterns in birds, inspired by ideas and methods from physics. Previous studies have shed light on the factors influencing bird migration but have mainly relied on statistical correlative analysis of tracking data. Our novel method offers a bottom up explanation of population-level migratory movement patterns. It differs from previous mechanistic models of animal migration and enables predictions of pathways and destinations from a given starting location. We define an environmental potential landscape from environmental data and simulate bird movement within this landscape based on simple decision rules drawn from statistical mechanics. We explore the capacity of the model by qualitatively comparing simulation results to the non-breeding migration patterns of a seabird species, the Black-browed Albatross (Thalassarche melanophris). This minimal, two-parameter model was able to capture remarkably well the previously documented migration patterns of the Black-browed Albatross, with the best combination of parameter values conserved across multiple geographically separate populations. Our physics-inspired mechanistic model could be applied to other bird and highly-mobile species, improving our understanding of the relative importance of various factors driving migration and making predictions that could be useful for conservation.

Model uncertainties do not affect observed patterns of species richness in the Amazon.

PubMed

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo; Loyola, Rafael

2017-01-01

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution. We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

Model uncertainties do not affect observed patterns of species richness in the Amazon

PubMed Central

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

2017-01-01

Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. PMID:29023503

Tracking children's mental states while solving algebra equations.

PubMed

Anderson, John R; Betts, Shawn; Ferris, Jennifer L; Fincham, Jon M

2012-11-01

Behavioral and function magnetic resonance imagery (fMRI) data were combined to infer the mental states of students as they interacted with an intelligent tutoring system. Sixteen children interacted with a computer tutor for solving linear equations over a six-day period (days 0-5), with days 1 and 5 occurring in an fMRI scanner. Hidden Markov model algorithms combined a model of student behavior with multi-voxel imaging pattern data to predict the mental states of students. We separately assessed the algorithms' ability to predict which step in a problem-solving sequence was performed and whether the step was performed correctly. For day 1, the data patterns of other students were used to predict the mental states of a target student. These predictions were improved on day 5 by adding information about the target student's behavioral and imaging data from day 1. Successful tracking of mental states depended on using the combination of a behavioral model and multi-voxel pattern analysis, illustrating the effectiveness of an integrated approach to tracking the cognition of individuals in real time as they perform complex tasks. Copyright © 2011 Wiley Periodicals, Inc.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Diagnostic, Predictive and Compositional Modeling with Data Mining in Integrated Learning Environments

ERIC Educational Resources Information Center

Lee, Chien-Sing

2007-01-01

Models represent a set of generic patterns to test hypotheses. This paper presents the CogMoLab student model in the context of an integrated learning environment. Three aspects are discussed: diagnostic and predictive modeling with respect to the issues of credit assignment and scalability and compositional modeling of the student profile in the…

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

PubMed Central

Yoshioka, Nayuta; Zangerl, Barbara; Nivison-Smith, Lisa; Khuu, Sieu K.; Jones, Bryan W.; Pfeiffer, Rebecca L.; Marc, Robert E.; Kalloniatis, Michael

2017-01-01

Purpose To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. Methods Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. Results Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm). Conclusions Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease. PMID:28632847

Model-Derived Dispersal Pathways from Multiple Source Populations Explain Variability of Invertebrate Larval Supply

PubMed Central

Domingues, Carla P.; Nolasco, Rita; Dubert, Jesus; Queiroga, Henrique

2012-01-01

Background Predicting the spatial and temporal patterns of marine larval dispersal and supply is a challenging task due to the small size of the larvae and the variability of oceanographic processes. Addressing this problem requires the use of novel approaches capable of capturing the inherent variability in the mechanisms involved. Methodology/Principal Findings In this study we test whether dispersal and connectivity patterns generated from a bio-physical model of larval dispersal of the crab Carcinus maenas, along the west coast of the Iberian Peninsula, can predict the highly variable daily pattern of wind-driven larval supply to an estuary observed during the peak reproductive season (March–June) in 2006 and 2007. Cross-correlations between observed and predicted supply were significant (p<0.05) and strong, ranging from 0.34 to 0.81 at time lags of −6 to +5 d. Importantly, the model correctly predicted observed cross-shelf distributions (Pearson r = 0.82, p<0.001, and r = 0.79, p<0.01, in 2006 and 2007) and indicated that all supply events were comprised of larvae that had been retained within the inner shelf; larvae transported to the outer shelf and beyond never recruited. Estimated average dispersal distances ranged from 57 to 198 km and were only marginally affected by mortality. Conclusions/Significance The high degree of predicted demographic connectivity over relatively large geographic scales is consistent with the lack of genetic structuring in C. maenas along the Iberian Peninsula. These findings indicate that the dynamic nature of larval dispersal can be captured by mechanistic biophysical models, which can be used to provide meaningful predictions of the patterns and causes of fine-scale variability in larval supply to marine populations. PMID:22558225

Predicting perturbation patterns from the topology of biological networks.

PubMed

Santolini, Marc; Barabási, Albert-László

2018-06-20

High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

Predictability of spatio-temporal patterns in a lattice of coupled FitzHugh–Nagumo oscillators

PubMed Central

Grace, Miriam; Hütt, Marc-Thorsten

2013-01-01

In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a ‘developmental path’), together with differences in cell properties (‘desynchronization’ of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh–Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or ‘spread’) of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system. PMID:23349439

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks.

PubMed

Khan, Taimoor; De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

PubMed Central

De, Asok

2014-01-01

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results. PMID:27382616

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns.

PubMed

Shen, Lu; Mickley, Loretta J

2017-03-07

We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.

Biomes computed from simulated climatologies

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

Claussen, M.; Esch, M.

1994-01-01

The biome model of Prentice et al. is used to predict global patterns of potential natural plant formations, or biomes, from climatologies simulated by ECHAM, a model used for climate simulations at the Max-Planck-Institut fuer Meteorologie. This study undertaken in order to show the advantage of this biome model in diagnosing the performance of a climate model and assessing effects of past and future climate changes predicted by a climate model. Good overall agreement is found between global patterns of biomes computed from observed and simulated data of present climate. But there are also major discrepancies indicated by a differencemore » in biomes in Australia, in the Kalahari Desert, and in the Middle West of North America. These discrepancies can be traced back to in simulated rainfall as well as summer or winter temperatures. Global patterns of biomes computed from an ice age simulation reveal that North America, Europe, and Siberia should have been covered largely by tundra and taiga, whereas only small differences are for the tropical rain forests. A potential northeast shift of biomes is expected from a simulation with enhanced CO{sub 2} concentration according to the IPCC Scenario A. Little change is seen in the tropical rain forest and the Sahara. Since the biome model used is not capable of predicting chances in vegetation patterns due to a rapid climate change, the latter simulation to be taken as a prediction of chances in conditions favourable for the existence of certain biomes, not as a reduction of a future distribution of biomes. 15 refs., 8 figs., 2 tabs.« less

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

PubMed Central

Mickley, Loretta J.

2017-01-01

We develop a statistical model to predict June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean–atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean–atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region. PMID:28223483

Field-scale prediction of enhanced DNAPL dissolution based on partitioning tracers.

PubMed

Wang, Fang; Annable, Michael D; Jawitz, James W

2013-09-01

The equilibrium streamtube model (EST) has demonstrated the ability to accurately predict dense nonaqueous phase liquid (DNAPL) dissolution in laboratory experiments and numerical simulations. Here the model is applied to predict DNAPL dissolution at a tetrachloroethylene (PCE)-contaminated dry cleaner site, located in Jacksonville, Florida. The EST model is an analytical solution with field-measurable input parameters. Measured data from a field-scale partitioning tracer test were used to parameterize the EST model and the predicted PCE dissolution was compared to measured data from an in-situ ethanol flood. In addition, a simulated partitioning tracer test from a calibrated, three-dimensional, spatially explicit multiphase flow model (UTCHEM) was also used to parameterize the EST analytical solution. The EST ethanol prediction based on both the field partitioning tracer test and the simulation closely matched the total recovery well field ethanol data with Nash-Sutcliffe efficiency E=0.96 and 0.90, respectively. The EST PCE predictions showed a peak shift to earlier arrival times for models based on either field-measured or simulated partitioning tracer tests, resulting in poorer matches to the field PCE data in both cases. The peak shifts were concluded to be caused by well screen interval differences between the field tracer test and ethanol flood. Both the EST model and UTCHEM were also used to predict PCE aqueous dissolution under natural gradient conditions, which has a much less complex flow pattern than the forced-gradient double five spot used for the ethanol flood. The natural gradient EST predictions based on parameters determined from tracer tests conducted with a complex flow pattern underestimated the UTCHEM-simulated natural gradient total mass removal by 12% after 170 pore volumes of water flushing indicating that some mass was not detected by the tracers likely due to stagnation zones in the flow field. These findings highlight the important influence of well configuration and the associated flow patterns on dissolution. © 2013.

Field-scale prediction of enhanced DNAPL dissolution based on partitioning tracers

NASA Astrophysics Data System (ADS)

Wang, Fang; Annable, Michael D.; Jawitz, James W.

2013-09-01

The equilibrium streamtube model (EST) has demonstrated the ability to accurately predict dense nonaqueous phase liquid (DNAPL) dissolution in laboratory experiments and numerical simulations. Here the model is applied to predict DNAPL dissolution at a tetrachloroethylene (PCE)-contaminated dry cleaner site, located in Jacksonville, Florida. The EST model is an analytical solution with field-measurable input parameters. Measured data from a field-scale partitioning tracer test were used to parameterize the EST model and the predicted PCE dissolution was compared to measured data from an in-situ ethanol flood. In addition, a simulated partitioning tracer test from a calibrated, three-dimensional, spatially explicit multiphase flow model (UTCHEM) was also used to parameterize the EST analytical solution. The EST ethanol prediction based on both the field partitioning tracer test and the simulation closely matched the total recovery well field ethanol data with Nash-Sutcliffe efficiency E = 0.96 and 0.90, respectively. The EST PCE predictions showed a peak shift to earlier arrival times for models based on either field-measured or simulated partitioning tracer tests, resulting in poorer matches to the field PCE data in both cases. The peak shifts were concluded to be caused by well screen interval differences between the field tracer test and ethanol flood. Both the EST model and UTCHEM were also used to predict PCE aqueous dissolution under natural gradient conditions, which has a much less complex flow pattern than the forced-gradient double five spot used for the ethanol flood. The natural gradient EST predictions based on parameters determined from tracer tests conducted with a complex flow pattern underestimated the UTCHEM-simulated natural gradient total mass removal by 12% after 170 pore volumes of water flushing indicating that some mass was not detected by the tracers likely due to stagnation zones in the flow field. These findings highlight the important influence of well configuration and the associated flow patterns on dissolution.

Magnitude and frequency variations of vector-borne infection outbreaks using the Ross-Macdonald model: explaining and predicting outbreaks of dengue fever.

PubMed

Amaku, M; Azevedo, F; Burattini, M N; Coelho, G E; Coutinho, F A B; Greenhalgh, D; Lopez, L F; Motitsuki, R S; Wilder-Smith, A; Massad, E

2016-08-19

The classical Ross-Macdonald model is often utilized to model vector-borne infections; however, this model fails on several fronts. First, using measured (or estimated) parameters, which values are accepted from the literature, the model predicts a much greater number of cases than what is usually observed. Second, the model predicts a single large outbreak that is followed by decades of much smaller outbreaks, which is not consistent with what is observed. Usually towns or cities report a number of recurrences for many years, even when environmental changes cannot explain the disappearance of the infection between the peaks. In this paper, we continue to examine the pitfalls in modelling this class of infections, and explain that, if properly used, the Ross-Macdonald model works and can be used to understand the patterns of epidemics and even, to some extent, be used to make predictions. We model several outbreaks of dengue fever and show that the variable pattern of yearly recurrence (or its absence) can be understood and explained by a simple Ross-Macdonald model modified to take into account human movement across a range of neighbourhoods within a city. In addition, we analyse the effect of seasonal variations in the parameters that determine the number, longevity and biting behaviour of mosquitoes. Based on the size of the first outbreak, we show that it is possible to estimate the proportion of the remaining susceptible individuals and to predict the likelihood and magnitude of the eventual subsequent outbreaks. This approach is described based on actual dengue outbreaks with different recurrence patterns from some Brazilian regions.

Resource competition model predicts zonation and increasing nutrient use efficiency along a wetland salinity gradient

USGS Publications Warehouse

Schoolmaster, Donald; Stagg, Camille L.

2018-01-01

A trade-off between competitive ability and stress tolerance has been hypothesized and empirically supported to explain the zonation of species across stress gradients for a number of systems. Since stress often reduces plant productivity, one might expect a pattern of decreasing productivity across the zones of the stress gradient. However, this pattern is often not observed in coastal wetlands that show patterns of zonation along a salinity gradient. To address the potentially complex relationship between stress, zonation, and productivity in coastal wetlands, we developed a model of plant biomass as a function of resource competition and salinity stress. Analysis of the model confirms the conventional wisdom that a trade-off between competitive ability and stress tolerance is a necessary condition for zonation. It also suggests that a negative relationship between salinity and production can be overcome if (1) the supply of the limiting resource increases with greater salinity stress or (2) nutrient use efficiency increases with increasing salinity. We fit the equilibrium solution of the dynamic model to data from Louisiana coastal wetlands to test its ability to explain patterns of production across the landscape gradient and derive predictions that could be tested with independent data. We found support for a number of the model predictions, including patterns of decreasing competitive ability and increasing nutrient use efficiency across a gradient from freshwater to saline wetlands. In addition to providing a quantitative framework to support the mechanistic hypotheses of zonation, these results suggest that this simple model is a useful platform to further build upon, simulate and test mechanistic hypotheses of more complex patterns and phenomena in coastal wetlands.

Predictive modeling of complications.

PubMed

Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

2016-09-01

Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images

PubMed Central

Biewener, Andrew A.; Wakeling, James M.

2017-01-01

ABSTRACT Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force–length and force–velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasound-based measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed–high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types. PMID:28202584

AN ISOMER PREDICTION MODEL FOR PCNS, PCDD/FS, AND PCBS FROM MUNICIPAL WASTE INCINERATORS

EPA Science Inventory

Isomer patterns of polychlorinated naphthalenes (PCNs), polychlorinated dibenzo-p-dioxins (PCDDs), and polychlorinated biphenyls (PCBs) from municipal waste incinerators (MWIs) were predicted by a model based on dechlorination kinetics from the most-chlorinated species. Successfu...

Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns

PubMed Central

Chérel, Guillaume; Cottineau, Clémentine; Reuillon, Romain

2015-01-01

Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic. PMID:26368917

Predicting spiral wave patterns from cell properties in a model of biological self-organization.

PubMed

Geberth, Daniel; Hütt, Marc-Thorsten

2008-09-01

In many biological systems, biological variability (i.e., systematic differences between the system components) can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In principle, the distribution of single-element properties should thus allow predicting features of such patterns. For a mathematical model of a paradigmatic and well-studied pattern formation process, spiral waves of cAMP signaling in colonies of the slime mold Dictyostelium discoideum, we explore this possibility and observe a pronounced anticorrelation between spiral waves and cell properties (namely, the firing rate) and particularly a clustering of spiral wave tips in regions devoid of spontaneously firing (pacemaker) cells. Furthermore, we observe local inhomogeneities in the distribution of spiral chiralities, again induced by the pacemaker distribution. We show that these findings can be explained by a simple geometrical model of spiral wave generation.

Predicting spiral wave patterns from cell properties in a model of biological self-organization

NASA Astrophysics Data System (ADS)

Geberth, Daniel; Hütt, Marc-Thorsten

2008-09-01

In many biological systems, biological variability (i.e., systematic differences between the system components) can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In principle, the distribution of single-element properties should thus allow predicting features of such patterns. For a mathematical model of a paradigmatic and well-studied pattern formation process, spiral waves of cAMP signaling in colonies of the slime mold Dictyostelium discoideum, we explore this possibility and observe a pronounced anticorrelation between spiral waves and cell properties (namely, the firing rate) and particularly a clustering of spiral wave tips in regions devoid of spontaneously firing (pacemaker) cells. Furthermore, we observe local inhomogeneities in the distribution of spiral chiralities, again induced by the pacemaker distribution. We show that these findings can be explained by a simple geometrical model of spiral wave generation.

Morphoelastic control of gastro-intestinal organogenesis: Theoretical predictions and numerical insights

NASA Astrophysics Data System (ADS)

Balbi, V.; Kuhl, E.; Ciarletta, P.

2015-05-01

With nine meters in length, the gastrointestinal tract is not only our longest, but also our structurally most diverse organ. During embryonic development, it evolves as a bilayered tube with an inner endodermal lining and an outer mesodermal layer. Its inner surface displays a wide variety of morphological patterns, which are closely correlated to digestive function. However, the evolution of these intestinal patterns remains poorly understood. Here we show that geometric and mechanical factors can explain intestinal pattern formation. Using the nonlinear field theories of mechanics, we model surface morphogenesis as the instability problem of constrained differential growth. To allow for internal and external expansion, we model the gastrointestinal tract with homogeneous Neumann boundary conditions. To establish estimates for the folding pattern at the onset of folding, we perform a linear stability analysis supplemented by the perturbation theory. To predict pattern evolution in the post-buckling regime, we perform a series of nonlinear finite element simulations. Our model explains why longitudinal folds emerge in the esophagus with a thick and stiff outer layer, whereas circumferential folds emerge in the jejunum with a thinner and softer outer layer. In intermediate regions like the feline esophagus, longitudinal and circumferential folds emerge simultaneously. Our model could serve as a valuable tool to explain and predict alterations in esophageal morphology as a result of developmental disorders or certain digestive pathologies including food allergies.

Neutrino mass matrices with two vanishing cofactors and Fritzsch texture for charged lepton mass matrix

NASA Astrophysics Data System (ADS)

Wang, Weijian; Guo, Shu-Yuan; Wang, Zhi-Gang

2016-04-01

In this paper, we study the cofactor 2 zero neutrino mass matrices with the Fritzsch-type structure in charged lepton mass matrix (CLMM). In the numerical analysis, we perform a scan over the parameter space of all the 15 possible patterns to get a large sample of viable scattering points. Among the 15 possible patterns, three of them can accommodate the latest lepton mixing and neutrino mass data. We compare the predictions of the allowed patterns with their counterparts with diagonal CLMM. In this case, the severe cosmology bound on the neutrino mass set a strong constraint on the parameter space, rendering two patterns only marginally allowed. The Fritzsch-type CLMM will have impact on the viable parameter space and give rise to different phenomenological predictions. Each allowed pattern predicts the strong correlations between physical variables, which is essential for model selection and can be probed in future experiments. It is found that under the no-diagonal CLMM, the cofactor zeros structure in neutrino mass matrix is unstable as the running of renormalization group (RG) from seesaw scale to the electroweak scale. A way out of the problem is to propose the flavor symmetry under the models with a TeV seesaw scale. The inverse seesaw model and a loop-induced model are given as two examples.

A Muscle’s Force Depends on the Recruitment Patterns of Its Fibers

PubMed Central

Wakeling, James M.; Lee, Sabrina S. M.; Arnold, Allison S.; de Boef Miara, Maria; Biewener, Andrew A.

2012-01-01

Biomechanical models of whole muscles commonly used in simulations of musculoskeletal function and movement typically assume that the muscle generates force as a scaled-up muscle fiber. However, muscles are comprised of motor units that have different intrinsic properties and that can be activated at different times. This study tested whether a muscle model comprised of motor units that could be independently activated resulted in more accurate predictions of force than traditional Hill-type models. Forces predicted by the models were evaluated by direct comparison with the muscle forces measured in situ from the gastrocnemii in goats. The muscle was stimulated tetanically at a range of frequencies, muscle fiber strains were measured using sonomicrometry, and the activation patterns of the different types of motor unit were calculated from electromyographic recordings. Activation patterns were input into five different muscle models. Four models were traditional Hill-type models with different intrinsic speeds and fiber-type properties. The fifth model incorporated differential groups of fast and slow motor units. For all goats, muscles and stimulation frequencies the differential model resulted in the best predictions of muscle force. The in situ muscle output was shown to depend on the recruitment of different motor units within the muscle. PMID:22350666

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Tozan, Yesim; Ahlm, Clas; Rocklöv, Joacim

2017-06-01

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

Modelling spatial patterns of urban growth in Africa

PubMed Central

Linard, Catherine; Tatem, Andrew J.; Gilbert, Marius

2013-01-01

The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers. PMID:25152552

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies: application to regional drought processes in China

NASA Astrophysics Data System (ADS)

Liu, Zhenchen; Lu, Guihua; He, Hai; Wu, Zhiyong; He, Jian

2018-01-01

Reliable drought prediction is fundamental for water resource managers to develop and implement drought mitigation measures. Considering that drought development is closely related to the spatial-temporal evolution of large-scale circulation patterns, we developed a conceptual prediction model of seasonal drought processes based on atmospheric and oceanic standardized anomalies (SAs). Empirical orthogonal function (EOF) analysis is first applied to drought-related SAs at 200 and 500 hPa geopotential height (HGT) and sea surface temperature (SST). Subsequently, SA-based predictors are built based on the spatial pattern of the first EOF modes. This drought prediction model is essentially the synchronous statistical relationship between 90-day-accumulated atmospheric-oceanic SA-based predictors and SPI3 (3-month standardized precipitation index), calibrated using a simple stepwise regression method. Predictor computation is based on forecast atmospheric-oceanic products retrieved from the NCEP Climate Forecast System Version 2 (CFSv2), indicating the lead time of the model depends on that of CFSv2. The model can make seamless drought predictions for operational use after a year-to-year calibration. Model application to four recent severe regional drought processes in China indicates its good performance in predicting seasonal drought development, despite its weakness in predicting drought severity. Overall, the model can be a worthy reference for seasonal water resource management in China.

Stochastic many-body problems in ecology, evolution, neuroscience, and systems biology

NASA Astrophysics Data System (ADS)

Butler, Thomas C.

Using the tools of many-body theory, I analyze problems in four different areas of biology dominated by strong fluctuations: The evolutionary history of the genetic code, spatiotemporal pattern formation in ecology, spatiotemporal pattern formation in neuroscience and the robustness of a model circadian rhythm circuit in systems biology. In the first two research chapters, I demonstrate that the genetic code is extremely optimal (in the sense that it manages the effects of point mutations or mistranslations efficiently), more than an order of magnitude beyond what was previously thought. I further show that the structure of the genetic code implies that early proteins were probably only loosely defined. Both the nature of early proteins and the extreme optimality of the genetic code are interpreted in light of recent theory [1] as evidence that the evolution of the genetic code was driven by evolutionary dynamics that were dominated by horizontal gene transfer. I then explore the optimality of a proposed precursor to the genetic code. The results show that the precursor code has only limited optimality, which is interpreted as evidence that the precursor emerged prior to translation, or else never existed. In the next part of the dissertation, I introduce a many-body formalism for reaction-diffusion systems described at the mesoscopic scale with master equations. I first apply this formalism to spatially-extended predator-prey ecosystems, resulting in the prediction that many-body correlations and fluctuations drive population cycles in time, called quasicycles. Most of these results were previously known, but were derived using the system size expansion [2, 3]. I next apply the analytical techniques developed in the study of quasi-cycles to a simple model of Turing patterns in a predator-prey ecosystem. This analysis shows that fluctuations drive the formation of a new kind of spatiotemporal pattern formation that I name "quasi-patterns." These quasi-patterns exist over a much larger range of physically accessible parameters than the patterns predicted in mean field theory and therefore account for the apparent observations in ecology of patterns in regimes where Turing patterns do not occur. I further show that quasi-patterns have statistical properties that allow them to be distinguished empirically from mean field Turing patterns. I next analyze a model of visual cortex in the brain that has striking similarities to the activator-inhibitor model of ecosystem quasi-pattern formation. Through analysis of the resulting phase diagram, I show that the architecture of the neural network in the visual cortex is configured to make the visual cortex robust to unwanted internally generated spatial structure that interferes with normal visual function. I also predict that some geometric visual hallucinations are quasi-patterns and that the visual cortex supports a new phase of spatially scale invariant behavior present far from criticality. In the final chapter, I explore the effects of fluctuations on cycles in systems biology, specifically the pervasive phenomenon of circadian rhythms. By exploring the behavior of a generic stochastic model of circadian rhythms, I show that the circadian rhythm circuit exploits leaky mRNA production to safeguard the cycle from failure. I also show that this safeguard mechanism is highly robust to changes in the rate of leaky mRNA production. Finally, I explore the failure of the deterministic model in two different contexts, one where the deterministic model predicts cycles where they do not exist, and another context in which cycles are not predicted by the deterministic model.

Analytical modeling and sensor monitoring for optimal processing of advanced textile structural composites by resin transfer molding

NASA Technical Reports Server (NTRS)

Loos, Alfred C.; Macrae, John D.; Hammond, Vincent H.; Kranbuehl, David E.; Hart, Sean M.; Hasko, Gregory H.; Markus, Alan M.

1993-01-01

A two-dimensional model of the resin transfer molding (RTM) process was developed which can be used to simulate the infiltration of resin into an anisotropic fibrous preform. Frequency dependent electromagnetic sensing (FDEMS) has been developed for in situ monitoring of the RTM process. Flow visualization tests were performed to obtain data which can be used to verify the sensor measurements and the model predictions. Results of the tests showed that FDEMS can accurately detect the position of the resin flow-front during mold filling, and that the model predicted flow-front patterns agreed well with the measured flow-front patterns.

Predictive model for CO2 generation and decay in building envelopes

NASA Astrophysics Data System (ADS)

Aglan, Heshmat A.

2003-01-01

Understanding carbon dioxide generation and decay patterns in buildings with high occupancy levels is useful to identify their indoor air quality, air change rates, percent fresh air makeup, occupancy pattern, and how a variable air volume system to off-set undesirable CO2 level can be modulated. A mathematical model governing the generation and decay of CO2 in building envelopes with forced ventilation due to high occupancy is developed. The model has been verified experimentally in a newly constructed energy efficient healthy house. It was shown that the model accurately predicts the CO2 concentration at any time during the generation and decay processes.

Peak-summer East Asian rainfall predictability and prediction part II: extratropical East Asia

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen

2016-07-01

The part II of the present study focuses on northern East Asia (NEA: 26°N-50°N, 100°-140°E), exploring the source and limit of the predictability of the peak summer (July-August) rainfall. Prediction of NEA peak summer rainfall is extremely challenging because of the exposure of the NEA to midlatitude influence. By examining four coupled climate models' multi-model ensemble (MME) hindcast during 1979-2010, we found that the domain-averaged MME temporal correlation coefficient (TCC) skill is only 0.13. It is unclear whether the dynamical models' poor skills are due to limited predictability of the peak-summer NEA rainfall. In the present study we attempted to address this issue by applying predictable mode analysis method using 35-year observations (1979-2013). Four empirical orthogonal modes of variability and associated major potential sources of variability are identified: (a) an equatorial western Pacific (EWP)-NEA teleconnection driven by EWP sea surface temperature (SST) anomalies, (b) a western Pacific subtropical high and Indo-Pacific dipole SST feedback mode, (c) a central Pacific-El Nino-Southern Oscillation mode, and (d) a Eurasian wave train pattern. Physically meaningful predictors for each principal component (PC) were selected based on analysis of the lead-lag correlations with the persistent and tendency fields of SST and sea-level pressure from March to June. A suite of physical-empirical (P-E) models is established to predict the four leading PCs. The peak summer rainfall anomaly pattern is then objectively predicted by using the predicted PCs and the corresponding observed spatial patterns. A 35-year cross-validated hindcast over the NEA yields a domain-averaged TCC skill of 0.36, which is significantly higher than the MME dynamical hindcast (0.13). The estimated maximum potential attainable TCC skill averaged over the entire domain is around 0.61, suggesting that the current dynamical prediction models may have large rooms to improve. Limitations and future work are also discussed.

A Theory of Age-Dependent Mutation and Senescence

PubMed Central

Moorad, Jacob A.; Promislow, Daniel E. L.

2008-01-01

Laboratory experiments show us that the deleterious character of accumulated novel age-specific mutations is reduced and made less variable with increased age. While theories of aging predict that the frequency of deleterious mutations at mutation–selection equilibrium will increase with the mutation's age of effect, they do not account for these age-related changes in the distribution of de novo mutational effects. Furthermore, no model predicts why this dependence of mutational effects upon age exists. Because the nature of mutational distributions plays a critical role in shaping patterns of senescence, we need to develop aging theory that explains and incorporates these effects. Here we propose a model that explains the age dependency of mutational effects by extending Fisher's geometrical model of adaptation to include a temporal dimension. Using a combination of simple analytical arguments and simulations, we show that our model predicts age-specific mutational distributions that are consistent with observations from mutation-accumulation experiments. Simulations show us that these age-specific mutational effects may generate patterns of senescence at mutation–selection equilibrium that are consistent with observed demographic patterns that are otherwise difficult to explain. PMID:18660535

Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan.

PubMed

Elshayeb, Ayman A; Ahmed, Abdelazim A; El Siddig, Marmar A; El Hussien, Adil A

2017-11-14

Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum. The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks. Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models. A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax - b). Minimum bactericidal concentration's predication of resistance was given the exponential trend (y = n e x ) and the predictive coefficient R 2  > 0 < 1 are approximately alike. It was assumed that resistant bacteria occurred with a constant rate of antibiotic doses during the whole experimental period. Thus, the number of sensitive bacteria decreases at the same rate as resistant occur following term to the modified predictive model which solved computationally. This study assesses the prediction of multi-drug resistance among S. Typhi isolates by applying low cost materials and simple statistical methods suitable for the most frequently used antibiotics as typhoid empirical therapy. Therefore, bacterial surveillance systems should be implemented to present data on the aetiology and current antimicrobial drug resistance patterns of community-acquired agents causing outbreaks.

Classification and Prediction of RF Coupling inside A-320 and A-319 Airplanes using Feed Forward Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha; Ely, Jay; Vahala, Linda

2006-01-01

Neural Network Modeling is introduced in this paper to classify and predict Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data and a plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Seasonal prediction of East Asian summer rainfall using a multi-model ensemble system

NASA Astrophysics Data System (ADS)

Ahn, Joong-Bae; Lee, Doo-Young; Yoo, Jin‑Ho

2015-04-01

Using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers, the prediction skills of climate models in the western tropical Pacific (WTP) and East Asian region are assessed. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical-statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical-statistical approach compared to the dynamical forecast alone. Acknowledgements This work was carried out with the support of Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under grant project PJ009353 and Korea Meteorological Administration Research and Development Program under grant CATER 2012-3100, Republic of Korea.

Fetal Substance Exposure and Cumulative Environmental Risk in an African American Cohort

ERIC Educational Resources Information Center

Yumoto, Chie; Jacobson, Sandra W.; Jacobson, Joseph L.

2008-01-01

Two models of vulnerability to socioenvironmental risk were examined in 337 African American children (M = 7.8 years) recruited to overrepresent prenatal alcohol or cocaine exposure: The cumulative risk model predicted synergistic effects from exposure to multiple risk factors, and the fetal patterning of disease model predicted that prenatal…

Invasive Species Distribution Modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Treesearch

Tomáš Václavík; Ross K. Meentemeyer

2009-01-01

Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges...

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

Modeling and predicting urban growth pattern of the Tokyo metropolitan area based on cellular automata

NASA Astrophysics Data System (ADS)

Zhao, Yaolong; Zhao, Junsan; Murayama, Yuji

2008-10-01

The period of high economic growth in Japan which began in the latter half of the 1950s led to a massive migration of population from rural regions to the Tokyo metropolitan area. This phenomenon brought about rapid urban growth and urban structure changes in this area. Purpose of this study is to establish a constrained CA (Cellular Automata) model with GIS (Geographical Information Systems) to simulate urban growth pattern in the Tokyo metropolitan area towards predicting urban form and landscape for the near future. Urban land-use is classified into multi-categories for interpreting the effect of interaction among land-use categories in the spatial process of urban growth. Driving factors of urban growth pattern, such as land condition, railway network, land-use zoning, random perturbation, and neighborhood interaction and so forth, are explored and integrated into this model. These driving factors are calibrated based on exploratory spatial data analysis (ESDA), spatial statistics, logistic regression, and "trial and error" approach. The simulation is assessed at both macro and micro classification levels in three ways: visual approach; fractal dimension; and spatial metrics. Results indicate that this model provides an effective prototype to simulate and predict urban growth pattern of the Tokyo metropolitan area.

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Tropospheric biennial oscillation and south Asian summer monsoon rainfall in a coupled model

NASA Astrophysics Data System (ADS)

Konda, Gopinadh; Chowdary, J. S.; Srinivas, G.; Gnanaseelan, C.; Parekh, Anant; Attada, Raju; Rama Krishna, S. S. V. S.

2018-06-01

In this study Tropospheric Biennial Oscillation (TBO) and south Asian summer monsoon rainfall are examined in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. High correlation between the observations and model TBO index suggests that the model is able to capture most of the TBO years. Spatial patterns of rainfall anomalies associated with positive TBO over the south Asian region are better represented in the model as in the observations. However, the model predicted rainfall anomaly patterns associated with negative TBO years are improper and magnitudes are underestimated compared to the observations. It is noted that positive (negative) TBO is associated with La Niña (El Niño) like Sea surface temperature (SST) anomalies in the model. This leads to the fact that model TBO is El Niño-Southern Oscillation (ENSO) driven, while in the observations Indian Ocean Dipole (IOD) also plays a role in the negative TBO phase. Detailed analysis suggests that the negative TBO rainfall anomaly pattern in the model is highly influenced by improper teleconnections allied to IOD. Unlike in the observations, rainfall anomalies over the south Asian region are anti-correlated with IOD index in CFSv2. Further, summer monsoon rainfall over south Asian region is highly correlated with IOD western pole than eastern pole in CFSv2 in contrast to the observations. Altogether, the present study highlights the importance of improving Indian Ocean SST teleconnections to south Asian summer rainfall in the model by enhancing the predictability of TBO. This in turn would improve monsoon rainfall prediction skill of the model.

Predictability of Zonal Means During Boreal Summer

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Suarez, Max J.; Pegion, Philip J.; Kistler, Michael A.; Kumar, Arun; Einaudi, Franco (Technical Monitor)

2001-01-01

This study examines the predictability of seasonal means during boreal summer. The results are based on ensembles of June-July-August (JJA) simulations (started in mid May) carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTS) and sea ice for the years 1980-1999. We find that the predictability of the JJA extra-tropical height field is primarily in the zonal mean component of the response to the SST anomalies. This contrasts with the cold season (January-February-March) when the predictability of seasonal means in the boreal extratropics is primarily in the wave component of the El Nino/Southern Oscillation (ENSO) response. Two patterns dominate the interannual variability of the ensemble mean JJA zonal mean height field. One has maximum variance in the tropical/subtropical upper troposphere, while the other has substantial variance in middle latitudes of both hemispheres. Both are symmetric with respect to the equator. A regression analysis suggests that the tropical/subtropical pattern is associated with SST anomalies in the far eastern tropical Pacific and the Indian Ocean, while the middle latitude pattern is forced by SST anomalies in the tropical Pacific just east of the dateline. The two leading zonal height patterns are reproduced in model runs forced with the two leading JJA SST patterns of variability. A comparison with observations shows a signature of the middle latitude pattern that is consistent with the occurrence of dry and wet summers over the United States. We hypothesize that both patterns, while imposing only weak constraints on extratropical warm season continental-scale climates, may play a role in the predilection for drought or pluvial conditions.

Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

NASA Astrophysics Data System (ADS)

Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

2015-03-01

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

2016-10-01

Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

Category-Specific Neural Oscillations Predict Recall Organization During Memory Search

PubMed Central

Morton, Neal W.; Kahana, Michael J.; Rosenberg, Emily A.; Baltuch, Gordon H.; Litt, Brian; Sharan, Ashwini D.; Sperling, Michael R.; Polyn, Sean M.

2013-01-01

Retrieved-context models of human memory propose that as material is studied, retrieval cues are constructed that allow one to target particular aspects of past experience. We examined the neural predictions of these models by using electrocorticographic/depth recordings and scalp electroencephalography (EEG) to characterize category-specific oscillatory activity, while participants studied and recalled items from distinct, neurally discriminable categories. During study, these category-specific patterns predict whether a studied item will be recalled. In the scalp EEG experiment, category-specific activity during study also predicts whether a given item will be recalled adjacent to other same-category items, consistent with the proposal that a category-specific retrieval cue is used to guide memory search. Retrieved-context models suggest that integrative neural circuitry is involved in the construction and maintenance of the retrieval cue. Consistent with this hypothesis, we observe category-specific patterns that rise in strength as multiple same-category items are studied sequentially, and find that individual differences in this category-specific neural integration during study predict the degree to which a participant will use category information to organize memory search. Finally, we track the deployment of this retrieval cue during memory search: Category-specific patterns are stronger when participants organize their responses according to the category of the studied material. PMID:22875859

Multivariate Cholesky models of human female fertility patterns in the NLSY.

PubMed

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

PubMed

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Strike-Slip Fault Patterns on Europa: Obliquity or Polar Wander?

NASA Technical Reports Server (NTRS)

Rhoden, Alyssa Rose; Hurford, Terry A.; Manga, Michael

2011-01-01

Variations in diurnal tidal stress due to Europa's eccentric orbit have been considered as the driver of strike-slip motion along pre-existing faults, but obliquity and physical libration have not been taken into account. The first objective of this work is to examine the effects of obliquity on the predicted global pattern of fault slip directions based on a tidal-tectonic formation model. Our second objective is to test the hypothesis that incorporating obliquity can reconcile theory and observations without requiring polar wander, which was previously invoked to explain the mismatch found between the slip directions of 192 faults on Europa and the global pattern predicted using the eccentricity-only model. We compute predictions for individual, observed faults at their current latitude, longitude, and azimuth with four different tidal models: eccentricity only, eccentricity plus obliquity, eccentricity plus physical libration, and a combination of all three effects. We then determine whether longitude migration, presumably due to non-synchronous rotation, is indicated in observed faults by repeating the comparisons with and without obliquity, this time also allowing longitude translation. We find that a tidal model including an obliquity of 1.2?, along with longitude migration, can predict the slip directions of all observed features in the survey. However, all but four faults can be fit with only 1? of obliquity so the value we find may represent the maximum departure from a lower time-averaged obliquity value. Adding physical libration to the obliquity model improves the accuracy of predictions at the current locations of the faults, but fails to predict the slip directions of six faults and requires additional degrees of freedom. The obliquity model with longitude migration is therefore our preferred model. Although the polar wander interpretation cannot be ruled out from these results alone, the obliquity model accounts for all observations with a value consistent with theoretical expectations and cycloid modeling.

An Analysis of Document Category Prediction Responses to Classifier Model Parameter Treatment Permutations within the Software Design Patterns Subject Domain

ERIC Educational Resources Information Center

Pankau, Brian L.

2009-01-01

This empirical study evaluates the document category prediction effectiveness of Naive Bayes (NB) and K-Nearest Neighbor (KNN) classifier treatments built from different feature selection and machine learning settings and trained and tested against textual corpora of 2300 Gang-Of-Four (GOF) design pattern documents. Analysis of the experiment's…

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Soft tissue modelling with conical springs.

PubMed

Omar, Nadzeri; Zhong, Yongmin; Jazar, Reza N; Subic, Aleksandar; Smith, Julian; Shirinzadeh, Bijan

2015-01-01

This paper presents a new method for real-time modelling soft tissue deformation. It improves the traditional mass-spring model with conical springs to deal with nonlinear mechanical behaviours of soft tissues. A conical spring model is developed to predict soft tissue deformation with reference to deformation patterns. The model parameters are formulated according to tissue deformation patterns and the nonlinear behaviours of soft tissues are modelled with the stiffness variation of conical spring. Experimental results show that the proposed method can describe different tissue deformation patterns using one single equation and also exhibit the typical mechanical behaviours of soft tissues.

Comparison of human gastrocnemius forces predicted by Hill-type muscle models and estimated from ultrasound images.

PubMed

Dick, Taylor J M; Biewener, Andrew A; Wakeling, James M

2017-05-01

Hill-type models are ubiquitous in the field of biomechanics, providing estimates of a muscle's force as a function of its activation state and its assumed force-length and force-velocity properties. However, despite their routine use, the accuracy with which Hill-type models predict the forces generated by muscles during submaximal, dynamic tasks remains largely unknown. This study compared human gastrocnemius forces predicted by Hill-type models with the forces estimated from ultrasound-based measures of tendon length changes and stiffness during cycling, over a range of loads and cadences. We tested both a traditional model, with one contractile element, and a differential model, with two contractile elements that accounted for independent contributions of slow and fast muscle fibres. Both models were driven by subject-specific, ultrasound-based measures of fascicle lengths, velocities and pennation angles and by activation patterns of slow and fast muscle fibres derived from surface electromyographic recordings. The models predicted, on average, 54% of the time-varying gastrocnemius forces estimated from the ultrasound-based methods. However, differences between predicted and estimated forces were smaller under low speed-high activation conditions, with models able to predict nearly 80% of the gastrocnemius force over a complete pedal cycle. Additionally, the predictions from the Hill-type muscle models tested here showed that a similar pattern of force production could be achieved for most conditions with and without accounting for the independent contributions of different muscle fibre types. © 2017. Published by The Company of Biologists Ltd.

Soviet Economic Policy Towards Eastern Europe

DTIC Science & Technology

1988-11-01

high. Without specifying the determinants of Soviet demand for "allegiance" in more detail, the model is not testable; we cannot predict how subsidy...trade inside (Czechoslovakia, Bulgaria). These countries are behaving as predicted by the model . If this hypothesis is true, the pattern of subsidies...also compares the sum of per capita subsidies by country between 1970 and 1982 with the sum of subsidies predicted by the model . Because of the poor

SPATIAL PREDICTION USING COMBINED SOURCES OF DATA

EPA Science Inventory

For improved environmental decision-making, it is important to develop new models for spatial prediction that accurately characterize important spatial and temporal patterns of air pollution. As the U .S. Environmental Protection Agency begins to use spatial prediction in the reg...

Pattern placement errors: application of in-situ interferometer-determined Zernike coefficients in determining printed image deviations

NASA Astrophysics Data System (ADS)

Roberts, William R.; Gould, Christopher J.; Smith, Adlai H.; Rebitz, Ken

2000-08-01

Several ideas have recently been presented which attempt to measure and predict lens aberrations for new low k1 imaging systems. Abbreviated sets of Zernike coefficients have been produced and used to predict Across Chip Linewidth Variation. Empirical use of the wavefront aberrations can now be used in commercially available lithography simulators to predict pattern distortion and placement errors. Measurement and Determination of Zernike coefficients has been a significant effort of many. However the use of this data has generally been limited to matching lenses or picking best fit lense pairs. We will use wavefront aberration data collected using the Litel InspecStep in-situ Interferometer as input data for Prolith/3D to model and predict pattern placement errors and intrafield overlay variation. Experiment data will be collected and compared to the simulated predictions.

Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

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

Rossi, R; Gallagher, B; Neville, J

Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied ourmore » model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.« less

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

PubMed Central

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

2016-01-01

Abstract Aim It has been recently suggested that different ‘unified theories of biodiversity and biogeography’ can be characterized by three common ‘minimal sufficient rules’: (1) species abundance distributions follow a hollow curve, (2) species show intraspecific aggregation, and (3) species are independently placed with respect to other species. Here, we translate these qualitative rules into a quantitative framework and assess if these minimal rules are indeed sufficient to predict multiple macroecological biodiversity patterns simultaneously. Location Tropical forest plots in Barro Colorado Island (BCI), Panama, and in Sinharaja, Sri Lanka. Methods We assess the predictive power of the three rules using dynamic and spatial simulation models in combination with census data from the two forest plots. We use two different versions of the model: (1) a neutral model and (2) an extended model that allowed for species differences in dispersal distances. In a first step we derive model parameterizations that correctly represent the three minimal rules (i.e. the model quantitatively matches the observed species abundance distribution and the distribution of intraspecific aggregation). In a second step we applied the parameterized models to predict four additional spatial biodiversity patterns. Results Species‐specific dispersal was needed to quantitatively fulfil the three minimal rules. The model with species‐specific dispersal correctly predicted the species–area relationship, but failed to predict the distance decay, the relationship between species abundances and aggregations, and the distribution of a spatial co‐occurrence index of all abundant species pairs. These results were consistent over the two forest plots. Main conclusions The three ‘minimal sufficient’ rules only provide an incomplete approximation of the stochastic spatial geometry of biodiversity in tropical forests. The assumption of independent interspecific placements is most likely violated in many forests due to shared or distinct habitat preferences. Furthermore, our results highlight missing knowledge about the relationship between species abundances and their aggregation. PMID:27667967

A model of the transverse modes of stable and unstable porro-prism resonators using symmetry considerations

NASA Astrophysics Data System (ADS)

Burger, Liesl; Forbes, Andrew

2007-09-01

A simple model of a Porro prism laser resonator has been found to correctly predict the formation of the "petal" mode patterns typical of these resonators. A geometrical analysis of the petals suggests that these petals are the lowest-order modes of this type of resonator. Further use of the model reveals the formation of more complex beam patterns, and the nature of these patterns is investigated. Also, the output of stable and unstable resonator modes is presented.

Modelling survival: exposure pattern, species sensitivity and uncertainty.

PubMed

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B; Van den Brink, Paul J; Veltman, Karin; Vogel, Sören; Zimmer, Elke I; Preuss, Thomas G

2016-07-06

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Modelling survival: exposure pattern, species sensitivity and uncertainty

NASA Astrophysics Data System (ADS)

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I.; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B.; van den Brink, Paul J.; Veltman, Karin; Vogel, Sören; Zimmer, Elke I.; Preuss, Thomas G.

2016-07-01

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Process-morphology scaling relations quantify self-organization in capillary densified nanofiber arrays.

PubMed

Kaiser, Ashley L; Stein, Itai Y; Cui, Kehang; Wardle, Brian L

2018-02-07

Capillary-mediated densification is an inexpensive and versatile approach to tune the application-specific properties and packing morphology of bulk nanofiber (NF) arrays, such as aligned carbon nanotubes. While NF length governs elasto-capillary self-assembly, the geometry of cellular patterns formed by capillary densified NFs cannot be precisely predicted by existing theories. This originates from the recently quantified orders of magnitude lower than expected NF array effective axial elastic modulus (E), and here we show via parametric experimentation and modeling that E determines the width, area, and wall thickness of the resulting cellular pattern. Both experiments and models show that further tuning of the cellular pattern is possible by altering the NF-substrate adhesion strength, which could enable the broad use of this facile approach to predictably pattern NF arrays for high value applications.

Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

NASA Astrophysics Data System (ADS)

Lee, Doo Young; Ahn, Joong-Bae; Yoo, Jin-Ho

2015-08-01

The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June-August) during the period 1983-2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical-statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical-statistical approach compared to the dynamical forecast alone.

Benchmarking test of empirical root water uptake models

NASA Astrophysics Data System (ADS)

dos Santos, Marcos Alex; de Jong van Lier, Quirijn; van Dam, Jos C.; Freire Bezerra, Andre Herman

2017-01-01

Detailed physical models describing root water uptake (RWU) are an important tool for the prediction of RWU and crop transpiration, but the hydraulic parameters involved are hardly ever available, making them less attractive for many studies. Empirical models are more readily used because of their simplicity and the associated lower data requirements. The purpose of this study is to evaluate the capability of some empirical models to mimic the RWU distribution under varying environmental conditions predicted from numerical simulations with a detailed physical model. A review of some empirical models used as sub-models in ecohydrological models is presented, and alternative empirical RWU models are proposed. All these empirical models are analogous to the standard Feddes model, but differ in how RWU is partitioned over depth or how the transpiration reduction function is defined. The parameters of the empirical models are determined by inverse modelling of simulated depth-dependent RWU. The performance of the empirical models and their optimized empirical parameters depends on the scenario. The standard empirical Feddes model only performs well in scenarios with low root length density R, i.e. for scenarios with low RWU compensation. For medium and high R, the Feddes RWU model cannot mimic properly the root uptake dynamics as predicted by the physical model. The Jarvis RWU model in combination with the Feddes reduction function (JMf) only provides good predictions for low and medium R scenarios. For high R, it cannot mimic the uptake patterns predicted by the physical model. Incorporating a newly proposed reduction function into the Jarvis model improved RWU predictions. Regarding the ability of the models to predict plant transpiration, all models accounting for compensation show good performance. The Akaike information criterion (AIC) indicates that the Jarvis (2010) model (JMII), with no empirical parameters to be estimated, is the best model. The proposed models are better in predicting RWU patterns similar to the physical model. The statistical indices point to them as the best alternatives for mimicking RWU predictions of the physical model.

An eco-evolutionary IBM improves predictions of future geneticconnectivity for American Pikas (Ochotona princeps) in Crater Lake National Park, Oregon

EPA Science Inventory

In the face of rapid, contemporary climate change, conservationbiologists are relying heavily on species distribution models (SDMs)to predict shifting occupancy and distribution patterns in responseto future conditions. These models are critical tools for assessingvulnerability t...

Effects of soil moisture on the diurnal pattern of pesticide emission: Numerical simulation and sensitivity analysis

USDA-ARS?s Scientific Manuscript database

Accurate prediction of pesticide volatilization is important for the protection of human and environmental health. Due to the complexity of the volatilization process, sophisticated predictive models are needed, especially for dry soil conditions. A mathematical model was developed to allow simulati...

Crime prediction modeling

NASA Technical Reports Server (NTRS)

1971-01-01

A study of techniques for the prediction of crime in the City of Los Angeles was conducted. Alternative approaches to crime prediction (causal, quasicausal, associative, extrapolative, and pattern-recognition models) are discussed, as is the environment within which predictions were desired for the immediate application. The decision was made to use time series (extrapolative) models to produce the desired predictions. The characteristics of the data and the procedure used to choose equations for the extrapolations are discussed. The usefulness of different functional forms (constant, quadratic, and exponential forms) and of different parameter estimation techniques (multiple regression and multiple exponential smoothing) are compared, and the quality of the resultant predictions is assessed.

The need for data science in epidemic modelling. Comment on: "Mathematical models to characterize early epidemic growth: A review" by Gerardo Chowell et al.

NASA Astrophysics Data System (ADS)

Danon, Leon; Brooks-Pollock, Ellen

2016-09-01

In their review, Chowell et al. consider the ability of mathematical models to predict early epidemic growth [1]. In particular, they question the central prediction of classical differential equation models that the number of cases grows exponentially during the early stages of an epidemic. Using examples including HIV and Ebola, they argue that classical models fail to capture key qualitative features of early growth and describe a selection of models that do capture non-exponential epidemic growth. An implication of this failure is that predictions may be inaccurate and unusable, highlighting the need for care when embarking upon modelling using classical methodology. There remains a lack of understanding of the mechanisms driving many observed epidemic patterns; we argue that data science should form a fundamental component of epidemic modelling, providing a rigorous methodology for data-driven approaches, rather than trying to enforce established frameworks. The need for refinement of classical models provides a strong argument for the use of data science, to identify qualitative characteristics and pinpoint the mechanisms responsible for the observed epidemic patterns.

A robust operational model for predicting where tropical cyclone waves damage coral reefs

NASA Astrophysics Data System (ADS)

Puotinen, Marji; Maynard, Jeffrey A.; Beeden, Roger; Radford, Ben; Williams, Gareth J.

2016-05-01

Tropical cyclone (TC) waves can severely damage coral reefs. Models that predict where to find such damage (the ‘damage zone’) enable reef managers to: 1) target management responses after major TCs in near-real time to promote recovery at severely damaged sites; and 2) identify spatial patterns in historic TC exposure to explain habitat condition trajectories. For damage models to meet these needs, they must be valid for TCs of varying intensity, circulation size and duration. Here, we map damage zones for 46 TCs that crossed Australia’s Great Barrier Reef from 1985-2015 using three models - including one we develop which extends the capability of the others. We ground truth model performance with field data of wave damage from seven TCs of varying characteristics. The model we develop (4MW) out-performed the other models at capturing all incidences of known damage. The next best performing model (AHF) both under-predicted and over-predicted damage for TCs of various types. 4MW and AHF produce strikingly different spatial and temporal patterns of damage potential when used to reconstruct past TCs from 1985-2015. The 4MW model greatly enhances both of the main capabilities TC damage models provide to managers, and is useful wherever TCs and coral reefs co-occur.

A robust operational model for predicting where tropical cyclone waves damage coral reefs.

PubMed

Puotinen, Marji; Maynard, Jeffrey A; Beeden, Roger; Radford, Ben; Williams, Gareth J

2016-05-17

Tropical cyclone (TC) waves can severely damage coral reefs. Models that predict where to find such damage (the 'damage zone') enable reef managers to: 1) target management responses after major TCs in near-real time to promote recovery at severely damaged sites; and 2) identify spatial patterns in historic TC exposure to explain habitat condition trajectories. For damage models to meet these needs, they must be valid for TCs of varying intensity, circulation size and duration. Here, we map damage zones for 46 TCs that crossed Australia's Great Barrier Reef from 1985-2015 using three models - including one we develop which extends the capability of the others. We ground truth model performance with field data of wave damage from seven TCs of varying characteristics. The model we develop (4MW) out-performed the other models at capturing all incidences of known damage. The next best performing model (AHF) both under-predicted and over-predicted damage for TCs of various types. 4MW and AHF produce strikingly different spatial and temporal patterns of damage potential when used to reconstruct past TCs from 1985-2015. The 4MW model greatly enhances both of the main capabilities TC damage models provide to managers, and is useful wherever TCs and coral reefs co-occur.

Calculation of Debye-Scherrer diffraction patterns from highly stressed polycrystalline materials

DOE PAGES

MacDonald, M. J.; Vorberger, J.; Gamboa, E. J.; ...

2016-06-07

Calculations of Debye-Scherrer diffraction patterns from polycrystalline materials have typically been done in the limit of small deviatoric stresses. Although these methods are well suited for experiments conducted near hydrostatic conditions, more robust models are required to diagnose the large strain anisotropies present in dynamic compression experiments. A method to predict Debye-Scherrer diffraction patterns for arbitrary strains has been presented in the Voigt (iso-strain) limit. Here, we present a method to calculate Debye-Scherrer diffraction patterns from highly stressed polycrystalline samples in the Reuss (iso-stress) limit. This analysis uses elastic constants to calculate lattice strains for all initial crystallite orientations, enablingmore » elastic anisotropy and sample texture effects to be modeled directly. Furthermore, the effects of probing geometry, deviatoric stresses, and sample texture are demonstrated and compared to Voigt limit predictions. An example of shock-compressed polycrystalline diamond is presented to illustrate how this model can be applied and demonstrates the importance of including material strength when interpreting diffraction in dynamic compression experiments.« less

Environmental drivers and spatial dependency in wildfire ignition patterns of northwestern Patagonia.

PubMed

Mundo, Ignacio A; Wiegand, Thorsten; Kanagaraj, Rajapandian; Kitzberger, Thomas

2013-07-15

Fire management requires an understanding of the spatial characteristics of fire ignition patterns and how anthropogenic and natural factors influence ignition patterns across space. In this study we take advantage of a recent fire ignition database (855 points) to conduct a comprehensive analysis of the spatial pattern of fire ignitions in the western area of Neuquén province (57,649 km(2)), Argentina, for the 1992-2008 period. The objectives of our study were to better understand the spatial pattern and the environmental drivers of the fire ignitions, with the ultimate aim of supporting fire management. We conducted our analyses on three different levels: statistical "habitat" modelling of fire ignition (natural, anthropogenic, and all causes) based on an information theoretic approach to test several competing hypotheses on environmental drivers (i.e. topographic, climatic, anthropogenic, land cover, and their combinations); spatial point pattern analysis to quantify additional spatial autocorrelation in the ignition patterns; and quantification of potential spatial associations between fires of different causes relative to towns using a novel implementation of the independence null model. Anthropogenic fire ignitions were best predicted by the most complex habitat model including all groups of variables, whereas natural ignitions were best predicted by topographic, climatic and land-cover variables. The spatial pattern of all ignitions showed considerable clustering at intermediate distances (<40 km) not captured by the probability of fire ignitions predicted by the habitat model. There was a strong (linear) and highly significant increase in the density of fire ignitions with decreasing distance to towns (<5 km), but fire ignitions of natural and anthropogenic causes were statistically independent. A two-dimensional habitat model that quantifies differences between ignition probabilities of natural and anthropogenic causes allows fire managers to delineate target areas for consideration of major preventive treatments, strategic placement of fuel treatments, and forecasting of fire ignition. The techniques presented here can be widely applied to situations where a spatial point pattern is jointly influenced by extrinsic environmental factors and intrinsic point interactions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Array Simulations Platform (ASP) predicts NASA Data Link Module (NDLM) performance

NASA Technical Reports Server (NTRS)

Snook, Allen David

1993-01-01

Through a variety of imbedded theoretical and actual antenna patterns, the array simulation platform (ASP) enhanced analysis of the array antenna pattern effects for the KTx (Ku-Band Transmit) service of the NDLM (NASA Data Link Module). The ASP utilizes internally stored models of the NDLM antennas and can develop the overall pattern of antenna arrays through common array calculation techniques. ASP expertly assisted in the diagnosing of element phase shifter errors during KTx testing and was able to accurately predict the overall array pattern from combinations of the four internally held element patterns. This paper provides an overview of the use of the ASP software in the solving of array mis-phasing problems.

SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

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

Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.

The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

SALSA3D: A Tomographic Model of Compressional Wave Slowness in the Earth’s Mantle for Improved Travel-Time Prediction and Travel-Time Prediction Uncertainty

DOE PAGES

Ballard, Sanford; Hipp, James R.; Begnaud, Michael L.; ...

2016-10-11

The task of monitoring the Earth for nuclear explosions relies heavily on seismic data to detect, locate, and characterize suspected nuclear tests. In this study, motivated by the need to locate suspected explosions as accurately and precisely as possible, we developed a tomographic model of the compressional wave slowness in the Earth’s mantle with primary focus on the accuracy and precision of travel-time predictions for P and Pn ray paths through the model. Path-dependent travel-time prediction uncertainties are obtained by computing the full 3D model covariance matrix and then integrating slowness variance and covariance along ray paths from source tomore » receiver. Path-dependent travel-time prediction uncertainties reflect the amount of seismic data that was used in tomography with very low values for paths represented by abundant data in the tomographic data set and very high values for paths through portions of the model that were poorly sampled by the tomography data set. The pattern of travel-time prediction uncertainty is a direct result of the off-diagonal terms of the model covariance matrix and underscores the importance of incorporating the full model covariance matrix in the determination of travel-time prediction uncertainty. In addition, the computed pattern of uncertainty differs significantly from that of 1D distance-dependent travel-time uncertainties computed using traditional methods, which are only appropriate for use with travel times computed through 1D velocity models.« less

Prediction of penetration rate of rotary-percussive drilling using artificial neural networks - a case study / Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych - studium przypadku

NASA Astrophysics Data System (ADS)

Aalizad, Seyed Ali; Rashidinejad, Farshad

2012-12-01

Penetration rate in rocks is one of the most important parameters of determination of drilling economics. Total drilling costs can be determined by predicting the penetration rate and utilized for mine planning. The factors which affect penetration rate are exceedingly numerous and certainly are not completely understood. For the prediction of penetration rate in rotary-percussive drilling, four types of rocks in Sangan mine have been chosen. Sangan is situated in Khorasan-Razavi province in Northeastern Iran. The selected parameters affect penetration rate is divided in three categories: rock properties, drilling condition and drilling pattern. The rock properties are: density, rock quality designation (RQD), uni-axial compressive strength, Brazilian tensile strength, porosity, Mohs hardness, Young modulus, P-wave velocity. Drilling condition parameters are: percussion, rotation, feed (thrust load) and flushing pressure; and parameters for drilling pattern are: blasthole diameter and length. Rock properties were determined in the laboratory, and drilling condition and drilling pattern were determined in the field. For create a correlation between penetration rate and rock properties, drilling condition and drilling pattern, artificial neural networks (ANN) were used. For this purpose, 102 blastholes were observed and drilling condition, drilling pattern and time of drilling in each blasthole were recorded. To obtain a correlation between this data and prediction of penetration rate, MATLAB software was used. To train the pattern of ANN, 77 data has been used and 25 of them found for testing the pattern. Performance of ANN models was assessed through the root mean square error (RMSE) and correlation coefficient (R2). For optimized model (14-14-10-1) RMSE and R2 is 0.1865 and 86%, respectively, and its sensitivity analysis showed that there is a strong correlation between penetration rate and RQD, rotation and blasthole diameter. High correlation coefficient and low root mean square error of these models showed that the ANN is a suitable tool for penetration rate prediction.

Predictive modelling of contagious deforestation in the Brazilian Amazon.

PubMed

Rosa, Isabel M D; Purves, Drew; Souza, Carlos; Ewers, Robert M

2013-01-01

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges "bottom up", as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated-pre- and post-PPCDAM ("Plano de Ação para Proteção e Controle do Desmatamento na Amazônia")-the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Predictive Modelling of Contagious Deforestation in the Brazilian Amazon

PubMed Central

Rosa, Isabel M. D.; Purves, Drew; Souza, Carlos; Ewers, Robert M.

2013-01-01

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation. PMID:24204776

Use of a machine learning algorithm to classify expertise: analysis of hand motion patterns during a simulated surgical task.

PubMed

Watson, Robert A

2014-08-01

To test the hypothesis that machine learning algorithms increase the predictive power to classify surgical expertise using surgeons' hand motion patterns. In 2012 at the University of North Carolina at Chapel Hill, 14 surgical attendings and 10 first- and second-year surgical residents each performed two bench model venous anastomoses. During the simulated tasks, the participants wore an inertial measurement unit on the dorsum of their dominant (right) hand to capture their hand motion patterns. The pattern from each bench model task performed was preprocessed into a symbolic time series and labeled as expert (attending) or novice (resident). The labeled hand motion patterns were processed and used to train a Support Vector Machine (SVM) classification algorithm. The trained algorithm was then tested for discriminative/predictive power against unlabeled (blinded) hand motion patterns from tasks not used in the training. The Lempel-Ziv (LZ) complexity metric was also measured from each hand motion pattern, with an optimal threshold calculated to separately classify the patterns. The LZ metric classified unlabeled (blinded) hand motion patterns into expert and novice groups with an accuracy of 70% (sensitivity 64%, specificity 80%). The SVM algorithm had an accuracy of 83% (sensitivity 86%, specificity 80%). The results confirmed the hypothesis. The SVM algorithm increased the predictive power to classify blinded surgical hand motion patterns into expert versus novice groups. With further development, the system used in this study could become a viable tool for low-cost, objective assessment of procedural proficiency in a competency-based curriculum.

Predictive Modeling of Rice Yellow Stem Borer Population Dynamics under Climate Change Scenarios in Indramayu

NASA Astrophysics Data System (ADS)

Nurhayati, E.; Koesmaryono, Y.; Impron

2017-03-01

Rice Yellow Stem Borer (YSB) is one of the major insect pests in rice plants that has high attack intensity in rice production center areas, especially in West Java. This pest is consider as holometabola insects that causes rice damage in the vegetative phase (deadheart) as well as generative phase (whitehead). Climatic factor is one of the environmental factors influence the pattern of dynamics population. The purpose of this study was to develop a predictive modeling of YSB pest dynamics population under climate change scenarios (2016-2035 period) using Dymex Model in Indramayu area, West Java. YSB modeling required two main components, namely climate parameters and YSB development lower threshold of temperature (To) to describe YSB life cycle in every phase. Calibration and validation test of models showed the coefficient of determination (R2) between the predicted results and observations of the study area were 0.74 and 0.88 respectively, which was able to illustrate the development, mortality, transfer of individuals from one stage to the next life also fecundity and YSB reproduction. On baseline climate condition, there was a tendency of population abundance peak (outbreak) occured when a change of rainfall intensity in the rainy season transition to dry season or the opposite conditions was happen. In both of application of climate change scenarios, the model outputs were generated well and able to predict the pattern of YSB population dynamics with a the increasing trend of specific population numbers, generation numbers per season and also shifting pattern of populations abundance peak in the future climatic conditions. These results can be adopted as a tool to predict outbreak and to give early warning to control YSB pest more effectively.

Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps

NASA Astrophysics Data System (ADS)

Obermayer, K.; Blasdel, G. G.; Schulten, K.

1992-05-01

We report a detailed analytical and numerical model study of pattern formation during the development of visual maps, namely, the formation of topographic maps and orientation and ocular dominance columns in the striate cortex. Pattern formation is described by a stimulus-driven Markovian process, the self-organizing feature map. This algorithm generates topologically correct maps between a space of (visual) input signals and an array of formal ``neurons,'' which in our model represents the cortex. We define order parameters that are a function of the set of visual stimuli an animal perceives, and we demonstrate that the formation of orientation and ocular dominance columns is the result of a global instability of the retinoptic projection above a critical value of these order parameters. We characterize the spatial structure of the emerging patterns by power spectra, correlation functions, and Gabor transforms, and we compare model predictions with experimental data obtained from the striate cortex of the macaque monkey with optical imaging. Above the critical value of the order parameters the model predicts a lateral segregation of the striate cortex into (i) binocular regions with linear changes in orientation preference, where iso-orientation slabs run perpendicular to the ocular dominance bands, and (ii) monocular regions with low orientation specificity, which contain the singularities of the orientation map. Some of these predictions have already been verified by experiments.

An Electrophysiological Index of Perceptual Goodness

PubMed Central

Makin, Alexis D.J.; Wright, Damien; Rampone, Giulia; Palumbo, Letizia; Guest, Martin; Sheehan, Rhiannon; Cleaver, Helen; Bertamini, Marco

2016-01-01

A traditional line of work starting with the Gestalt school has shown that patterns vary in strength and salience; a difference in “Perceptual goodness.” The Holographic weight of evidence model quantifies goodness of visual regularities. The key formula states that W = E/N, where E is number of holographic identities in a pattern and N is number of elements. We tested whether W predicts the amplitude of the neural response to regularity in an extrastriate symmetry-sensitive network. We recorded an Event Related Potential (ERP) generated by symmetry called the Sustained Posterior Negativity (SPN). First, we reanalyzed the published work and found that W explained most variance in SPN amplitude. Then in four new studies, we confirmed specific predictions of the holographic model regarding 1) the differential effects of numerosity on reflection and repetition, 2) the similarity between reflection and Glass patterns, 3) multiple symmetries, and 4) symmetry and anti-symmetry. In all cases, the holographic approach predicted SPN amplitude remarkably well; particularly in an early window around 300–400 ms post stimulus onset. Although the holographic model was not conceived as a model of neural processing, it captures many details of the brain response to symmetry. PMID:27702812

The role of model errors represented by nonlinear forcing singular vector tendency error in causing the "spring predictability barrier" within ENSO predictions

NASA Astrophysics Data System (ADS)

Duan, Wansuo; Zhao, Peng

2017-04-01

Within the Zebiak-Cane model, the nonlinear forcing singular vector (NFSV) approach is used to investigate the role of model errors in the "Spring Predictability Barrier" (SPB) phenomenon within ENSO predictions. NFSV-related errors have the largest negative effect on the uncertainties of El Niño predictions. NFSV errors can be classified into two types: the first is characterized by a zonal dipolar pattern of SST anomalies (SSTA), with the western poles centered in the equatorial central-western Pacific exhibiting positive anomalies and the eastern poles in the equatorial eastern Pacific exhibiting negative anomalies; and the second is characterized by a pattern almost opposite the first type. The first type of error tends to have the worst effects on El Niño growth-phase predictions, whereas the latter often yields the largest negative effects on decaying-phase predictions. The evolution of prediction errors caused by NFSV-related errors exhibits prominent seasonality, with the fastest error growth in the spring and/or summer seasons; hence, these errors result in a significant SPB related to El Niño events. The linear counterpart of NFSVs, the (linear) forcing singular vector (FSV), induces a less significant SPB because it contains smaller prediction errors. Random errors cannot generate a SPB for El Niño events. These results show that the occurrence of an SPB is related to the spatial patterns of tendency errors. The NFSV tendency errors cause the most significant SPB for El Niño events. In addition, NFSVs often concentrate these large value errors in a few areas within the equatorial eastern and central-western Pacific, which likely represent those areas sensitive to El Niño predictions associated with model errors. Meanwhile, these areas are also exactly consistent with the sensitive areas related to initial errors determined by previous studies. This implies that additional observations in the sensitive areas would not only improve the accuracy of the initial field but also promote the reduction of model errors to greatly improve ENSO forecasts.

Forecasting of monsoon heavy rains: challenges in NWP

NASA Astrophysics Data System (ADS)

Sharma, Kuldeep; Ashrit, Raghavendra; Iyengar, Gopal; Bhatla, R.; Rajagopal, E. N.

2016-05-01

Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator -- Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.

Evaluation of two models for predicting elemental accumulation by arthropods

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

Webster, J.R.; Crossley, D.A. Jr.

1978-06-15

Two different models have been proposed for predicting elemental accumulation by arthropods. Parameters of both models can be quantified from radioisotope elimination experiments. Our analysis of the 2 models shows that both predict identical elemental accumulation for a whole organism, though differing in the accumulation in body and gut. We quantified both models with experimental data from /sup 134/Cs and /sup 85/Sr elimination by crickets. Computer simulations of radioisotope accumulation were then compared with actual accumulation experiments. Neither model showed exact fit to the experimental data, though both showed the general pattern of elemental accumulation.

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

PubMed Central

Safiuddin, Md.; Raman, Sudharshan N.; Abdus Salam, Md.; Jumaat, Mohd. Zamin

2016-01-01

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN. PMID:28773520

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.

PubMed

Safiuddin, Md; Raman, Sudharshan N; Abdus Salam, Md; Jumaat, Mohd Zamin

2016-05-20

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination ( R ²) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

A discrete model of Drosophila eggshell patterning reveals cell-autonomous and juxtacrine effects.

PubMed

Fauré, Adrien; Vreede, Barbara M I; Sucena, Elio; Chaouiya, Claudine

2014-03-01

The Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.

Thermokinetic Modeling of Phase Transformation in the Laser Powder Deposition Process

NASA Astrophysics Data System (ADS)

Foroozmehr, Ehsan; Kovacevic, Radovan

2009-08-01

A finite element model coupled with a thermokinetic model is developed to predict the phase transformation of the laser deposition of AISI 4140 on a substrate with the same material. Four different deposition patterns, long-bead, short-bead, spiral-in, and spiral-out, are used to cover a similar area. Using a finite element model, the temperature history of the laser powder deposition (LPD) process is determined. The martensite transformation as well as martensite tempering is considered to calculate the final fraction of martensite, ferrite, cementite, ɛ-carbide, and retained austenite. Comparing the surface hardness topography of different patterns reveals that path planning is a critical parameter in laser surface modification. The predicted results are in a close agreement with the experimental results.

Improving Seasonal Climate Predictability in the Colorado River Basin for Enhanced Decision Support

NASA Astrophysics Data System (ADS)

Rajagopal, S.; Mahmoud, M. I.

2016-12-01

The water resource management community is increasingly seeking skillful seasonal climate forecasts with long lead times. But predicting wet or dry climate with sufficient lead time (3 months) for a season (especially winter) in the Colorado River Basin (CRB) is a challenging problem. The typical approach taken to predicting winter climate is based on using climate indices and climate models to predict precipitation or streamflow in the Colorado River Basin. In addition to this approach; which may have a long lead time, water supply forecasts are also generated based on current observations by the Colorado River Forecast Center. Recently, the effects of coupled atmospheric-ocean phenomena such as ENSO over North America, and atmospheric circulation patterns at the 500 mb pressure level, which make the CRB wet or dry, have been studied separately. In the current work we test whether combining climate indices and circulation patterns improve predictability in the CRB. To accomplish this, the atmospheric circulation data from the Earth System Research Laboratory (ESRL) and climate indices data from the Climate Prediction Center were combined using logical functions. To quantify the skill in prediction, statistics such as the hit ratio and false alarm ratio were computed. The results from using a combination of climate indices and atmospheric circulation patterns suggest that there is an improvement in the prediction skill with hit ratios higher than 0.8, as compared to using either predictor individually (which typically produced a hit ratio of 0.6). Based on this result, there is value in using this hybrid approach when compared to a black box statistical model, as the climate index is an analog to the moisture availability and the right atmospheric circulation pattern helps in transporting that moisture to the Basin.

A statistical nanomechanism of biomolecular patterning actuated by surface potential

NASA Astrophysics Data System (ADS)

Lin, Chih-Ting; Lin, Chih-Hao

2011-02-01

Biomolecular patterning on a nanoscale/microscale on chip surfaces is one of the most important techniques used in vitro biochip technologies. Here, we report upon a stochastic mechanics model we have developed for biomolecular patterning controlled by surface potential. The probabilistic biomolecular surface adsorption behavior can be modeled by considering the potential difference between the binding and nonbinding states. To verify our model, we experimentally implemented a method of electroactivated biomolecular patterning technology and the resulting fluorescence intensity matched the prediction of the developed model quite well. Based on this result, we also experimentally demonstrated the creation of a bovine serum albumin pattern with a width of 200 nm in 5 min operations. This submicron noncovalent-binding biomolecular pattern can be maintained for hours after removing the applied electrical voltage. These stochastic understandings and experimental results not only prove the feasibility of submicron biomolecular patterns on chips but also pave the way for nanoscale interfacial-bioelectrical engineering.

Model of an axially strained weakly guiding optical fiber modal pattern

NASA Technical Reports Server (NTRS)

Egalon, Claudio O.; Rogowski, Robert S.

1992-01-01

Axial strain can be determined by monitoring the modal pattern variation of an optical fiber. The results of a numerical model developed to calculate the modal pattern variation at the end of a weakly guiding optical fiber under axial strain is presented. Whenever an optical fiber is under stress, the optical path length, the index of refraction, and the propagation constants of each fiber mode change. In consequence, the modal phase term for the fields and the fiber output pattern are also modified. For multimode fibers, very complicated patterns result. The predicted patterns are presented, and an expression for the phase variation with strain is derived.

An experimental and theoretical investigation of deposition patterns from an agricultural airplane

NASA Technical Reports Server (NTRS)

Morris, D. J.; Croom, C. C.; Vandam, C. P.; Holmes, B. J.

1984-01-01

A flight test program has been conducted with a representative agricultural airplane to provide data for validating a computer program model which predicts aerially applied particle deposition. Test procedures and the data from this test are presented and discussed. The computer program features are summarized, and comparisons of predicted and measured particle deposition are presented. Applications of the computer program for spray pattern improvement are illustrated.

A general framework for predicting delayed responses of ecological communities to habitat loss.

PubMed

Chen, Youhua; Shen, Tsung-Jen

2017-04-20

Although biodiversity crisis at different spatial scales has been well recognised, the phenomena of extinction debt and immigration credit at a crossing-scale context are, at best, unclear. Based on two community patterns, regional species abundance distribution (SAD) and spatial abundance distribution (SAAD), Kitzes and Harte (2015) presented a macroecological framework for predicting post-disturbance delayed extinction patterns in the entire ecological community. In this study, we further expand this basic framework to predict diverse time-lagged effects of habitat destruction on local communities. Specifically, our generalisation of KH's model could address the questions that could not be answered previously: (1) How many species are subjected to delayed extinction in a local community when habitat is destructed in other areas? (2) How do rare or endemic species contribute to extinction debt or immigration credit of the local community? (3) How will species differ between two local areas? From the demonstrations using two SAD models (single-parameter lognormal and logseries), the predicted patterns of the debt, credit, and change in the fraction of unique species can vary, but with consistencies and depending on several factors. The general framework deepens the understanding of the theoretical effects of habitat loss on community dynamic patterns in local samples.

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection

NASA Astrophysics Data System (ADS)

Kashid, S. S.; Ghosh, Subimal; Maity, Rajib

2010-12-01

SummarySimultaneous variations in weather and climate over widely separated regions are commonly known as "hydroclimatic teleconnections". Rainfall and runoff patterns, over continents, are found to be significantly teleconnected, with large-scale circulation patterns, through such hydroclimatic teleconnections. Though such teleconnections exist in nature, it is very difficult to model them, due to their inherent complexity. Statistical techniques and Artificial Intelligence (AI) tools gain popularity in modeling hydroclimatic teleconnection, based on their ability, in capturing the complicated relationship between the predictors (e.g. sea surface temperatures) and predictand (e.g., rainfall). Genetic Programming is such an AI tool, which is capable of capturing nonlinear relationship, between predictor and predictand, due to its flexible functional structure. In the present study, gridded multi-site weekly rainfall is predicted from El Niño Southern Oscillation (ENSO) indices, Equatorial Indian Ocean Oscillation (EQUINOO) indices, Outgoing Longwave Radiation (OLR) and lag rainfall at grid points, over the catchment, using Genetic Programming. The predicted rainfall is further used in a Genetic Programming model to predict streamflows. The model is applied for weekly forecasting of streamflow in Mahanadi River, India, and satisfactory performance is observed.

Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns - an investigation using modelled scattering data

NASA Astrophysics Data System (ADS)

Salawu, Emmanuel Oluwatobi; Hesse, Evelyn; Stopford, Chris; Davey, Neil; Sun, Yi

2017-11-01

Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles' orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle's size and size PADs.

Validating a spatially distributed hydrological model with soil morphology data

NASA Astrophysics Data System (ADS)

Doppler, T.; Honti, M.; Zihlmann, U.; Weisskopf, P.; Stamm, C.

2013-10-01

Spatially distributed hydrological models are popular tools in hydrology and they are claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time-series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for the transport of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography. Around 40% of the catchment area are artificially drained. We measured weather data, discharge and groundwater levels in 11 piezometers for 1.5 yr. For broadening the spatially distributed data sets that can be used for model calibration and validation, we translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. We used redox-morphology signs for these estimates. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to the groundwater levels in the piezometers and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the accuracy of the groundwater level predictions was not high enough to be used for the prediction of saturated areas. The groundwater level dynamics were not adequately reproduced and the predicted spatial patterns of soil saturation did not correspond to the patterns estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a more complex model. Especially high spatial resolution and very detailed process representations at the boundary between the unsaturated and the saturated zone are expected to be crucial. The data needed for such a detailed model are not generally available. The high computational demand and the complex model setup would require more resources than the direct identification of saturated areas in the field. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

Predicting vehicle fuel consumption patterns using floating vehicle data.

PubMed

Du, Yiman; Wu, Jianping; Yang, Senyan; Zhou, Liutong

2017-09-01

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model. Copyright © 2017. Published by Elsevier B.V.

A study of the relationship between parental bonding, self-concept and eating disturbances in Norwegian and American college populations.

PubMed

Perry, Judith A; Silvera, David H; Neilands, Torsten B; Rosenvinge, Jan H; Hanssen, Tina

2008-01-01

This study investigated the relationship between bonding patterns and self-concept, and the influence of these constructs on a measure of sub-clinical eating disturbances. Undergraduate students from the United States (N=166) and Norway (N=233) were given self-report questionnaires that included measures of parental bonding, locus of control, self-concept clarity, self-esteem, and disturbed cognitions associated with eating. A structural equation model showed the expected pattern, with bonding predicting self-concept and self-concept predicting eating disturbances. The model fit equally well for samples from both countries and for both genders. This model links the pattern of low care and overprotective parental bonding indicators mediated through a self-concept defined by a lack of self-understanding, low self-esteem, and external locus of control to increased risk of eating disturbances for college aged men and women.

Development of theoretical models of integrated millimeter wave antennas

NASA Technical Reports Server (NTRS)

Yngvesson, K. Sigfrid; Schaubert, Daniel H.

1991-01-01

Extensive radiation patterns for Linear Tapered Slot Antenna (LTSA) Single Elements are presented. The directivity of LTSA elements is predicted correctly by taking the cross polarized pattern into account. A moment method program predicts radiation patterns for air LTSAs with excellent agreement with experimental data. A moment method program was also developed for the task LTSA Array Modeling. Computations performed with this program are in excellent agreement with published results for dipole and monopole arrays, and with waveguide simulator experiments, for more complicated structures. Empirical modeling of LTSA arrays demonstrated that the maximum theoretical element gain can be obtained. Formulations were also developed for calculating the aperture efficiency of LTSA arrays used in reflector systems. It was shown that LTSA arrays used in multibeam systems have a considerable advantage in terms of higher packing density, compared with waveguide feeds. Conversion loss of 10 dB was demonstrated at 35 GHz.

Enhancement of seasonal prediction of East Asian summer rainfall related to the western tropical Pacific convection

NASA Astrophysics Data System (ADS)

Lee, D. Y.; Ahn, J. B.; Yoo, J. H.

2014-12-01

The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June-August) during the period 1983-2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation (ENSO) developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index (EASMI) or each MP index (MPI). Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by statistical-empirical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using the statistical-empirical method compared to the dynamical models. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953, Republic of Korea.

Modeling soil erosion and transport on forest landscape

Treesearch

Ge Sun; Steven G McNulty

1998-01-01

Century-long studies on the impacts of forest management in North America suggest sediment can cause major reduction on stream water quality. Soil erosion patterns in forest watersheds are patchy and heterogeneous. Therefore, patterns of soil erosion are difficult to model and predict. The objective of this study is to develop a user friendly management tool for land...

Maps and models of density and stiffness within individual Douglas-fir trees

Treesearch

Christine L. Todoroki; Eini C. Lowell; Dennis P. Dykstra; David G. Briggs

2012-01-01

Spatial maps of density and stiffness patterns within individual trees were developed using two methods: (1) measured wood properties of veneer sheets; and (2) mixed effects models, to test the hypothesis that within-tree patterns could be predicted from easily measurable tree variables (height, taper, breast-height diameter, and acoustic velocity). Sample trees...

Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.

PubMed

Ma, Handong; Weng, Chunhua

2016-04-01

To link public data resources for predicting post-marketing drug safety label changes by analyzing the Convergent Focus Shift patterns among drug testing trials. We identified 256 top-selling prescription drugs between 2003 and 2013 and divided them into 83 BBW drugs (drugs with at least one black box warning label) and 173 ROBUST drugs (drugs without any black box warning label) based on their FDA black box warning (BBW) records. We retrieved 7499 clinical trials that each had at least one of these drugs for intervention from the ClinicalTrials.gov. We stratified all the trials by pre-marketing or post-marketing status, study phase, and study start date. For each trial, we retrieved drug and disease concepts from clinical trial summaries to model its study population using medParser and SNOMED-CT. Convergent Focus Shift (CFS) pattern was calculated and used to assess the temporal changes in study populations from pre-marketing to post-marketing trials for each drug. Then we selected 68 candidate drugs, 18 with BBW warning and 50 without, that each had at least nine pre-marketing trials and nine post-marketing trials for predictive modeling. A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. Pre- and post-marketing trials of BBW and ROBUST drugs were compared to look for their differences in CFS patterns. Among the 18 BBW drugs, we consistently observed that the post-marketing trials focused more on recruiting patients with medical conditions previously unconsidered in the pre-marketing trials. In contrast, among the 50 ROBUST drugs, the post-marketing trials involved a variety of medications for testing their associations with target intervention(s). We found it feasible to predict BBW acquisitions using different CFS patterns between the two groups of drugs. Our random forest predictor achieved an AUC of 0.77. We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy. This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance. Copyright © 2016 Elsevier Inc. All rights reserved.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

A mechanistic modeling system for estimating large scale emissions and transport of pollen and co-allergens

PubMed Central

Efstathiou, Christos; Isukapalli, Sastry

2011-01-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants. PMID:21516207

A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens

NASA Astrophysics Data System (ADS)

Efstathiou, Christos; Isukapalli, Sastry; Georgopoulos, Panos

2011-04-01

Allergic airway diseases represent a complex health problem which can be exacerbated by the synergistic action of pollen particles and air pollutants such as ozone. Understanding human exposures to aeroallergens requires accurate estimates of the spatial distribution of airborne pollen levels as well as of various air pollutants at different times. However, currently there are no established methods for estimating allergenic pollen emissions and concentrations over large geographic areas such as the United States. A mechanistic modeling system for describing pollen emissions and transport over extensive domains has been developed by adapting components of existing regional scale air quality models and vegetation databases. First, components of the Biogenic Emissions Inventory System (BEIS) were adapted to predict pollen emission patterns. Subsequently, the transport module of the Community Multiscale Air Quality (CMAQ) modeling system was modified to incorporate description of pollen transport. The combined model, CMAQ-pollen, allows for simultaneous prediction of multiple air pollutants and pollen levels in a single model simulation, and uses consistent assumptions related to the transport of multiple chemicals and pollen species. Application case studies for evaluating the combined modeling system included the simulation of birch and ragweed pollen levels for the year 2002, during their corresponding peak pollination periods (April for birch and September for ragweed). The model simulations were driven by previously evaluated meteorological model outputs and emissions inventories for the eastern United States for the simulation period. A semi-quantitative evaluation of CMAQ-pollen was performed using tree and ragweed pollen counts in Newark, NJ for the same time periods. The peak birch pollen concentrations were predicted to occur within two days of the peak measurements, while the temporal patterns closely followed the measured profiles of overall tree pollen. For the case of ragweed pollen, the model was able to capture the patterns observed during September 2002, but did not predict an early peak; this can be associated with a wider species pollination window and inadequate spatial information in current land cover databases. An additional sensitivity simulation was performed to comparatively evaluate the dispersion patterns predicted by CMAQ-pollen with those predicted by the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which is used extensively in aerobiological studies. The CMAQ estimated concentration plumes matched the equivalent pollen scenario modeled with HYSPLIT. The novel pollen modeling approach presented here allows simultaneous estimation of multiple airborne allergens and other air pollutants, and is being developed as a central component of an integrated population exposure modeling system, the Modeling Environment for Total Risk studies (MENTOR) for multiple, co-occurring contaminants that include aeroallergens and irritants.

A conceptual prediction model for seasonal drought processes using atmospheric and oceanic standardized anomalies and its application to four recent severe regional drought events in China

NASA Astrophysics Data System (ADS)

Liu, Z.; LU, G.; He, H.; Wu, Z.; He, J.

2017-12-01

Reliable drought prediction is fundamental for seasonal water management. Considering that drought development is closely related to the spatio-temporal evolution of large-scale circulation patterns, we develop a conceptual prediction model of seasonal drought processes based on atmospheric/oceanic Standardized Anomalies (SA). It is essentially the synchronous stepwise regression relationship between 90-day-accumulated atmospheric/oceanic SA-based predictors and 3-month SPI updated daily (SPI3). It is forced with forecasted atmospheric and oceanic variables retrieved from seasonal climate forecast systems, and it can make seamless drought prediction for operational use after a year-to-year calibration. Simulation and prediction of four severe seasonal regional drought processes in China were forced with the NCEP/NCAR reanalysis datasets and the NCEP Climate Forecast System Version 2 (CFSv2) operationally forecasted datasets, respectively. With the help of real-time correction for operational application, model application during four recent severe regional drought events in China revealed that the model is good at development prediction but weak in severity prediction. In addition to weakness in prediction of drought peak, the prediction of drought relief is possible to be predicted as drought recession. This weak performance may be associated with precipitation-causing weather patterns during drought relief. Based on initial virtual analysis on predicted 90-day prospective SPI3 curves, it shows that the 2009/2010 drought in Southwest China and 2014 drought in North China can be predicted and simulated well even for the prospective 1-75 day. In comparison, the prospective 1-45 day may be a feasible and acceptable lead time for simulation and prediction of the 2011 droughts in Southwest China and East China, after which the simulated and predicted developments clearly change.

A gene network model accounting for development and evolution of mammalian teeth

PubMed Central

Salazar-Ciudad, Isaac; Jernvall, Jukka

2002-01-01

Generation of morphological diversity remains a challenge for evolutionary biologists because it is unclear how an ultimately finite number of genes involved in initial pattern formation integrates with morphogenesis. Ideally, models used to search for the simplest developmental principles on how genes produce form should account for both developmental process and evolutionary change. Here we present a model reproducing the morphology of mammalian teeth by integrating experimental data on gene interactions and growth into a morphodynamic mechanism in which developing morphology has a causal role in patterning. The model predicts the course of tooth-shape development in different mammalian species and also reproduces key transitions in evolution. Furthermore, we reproduce the known expression patterns of several genes involved in tooth development and their dynamics over developmental time. Large morphological effects frequently can be achieved by small changes, according to this model, and similar morphologies can be produced by different changes. This finding may be consistent with why predicting the morphological outcomes of molecular experiments is challenging. Nevertheless, models incorporating morphology and gene activity show promise for linking genotypes to phenotypes. PMID:12048258

The modelling of heat, mass and solute transport in solidification systems

NASA Technical Reports Server (NTRS)

Voller, V. R.; Brent, A. D.; Prakash, C.

1989-01-01

The aim of this paper is to explore the range of possible one-phase models of binary alloy solidification. Starting from a general two-phase description, based on the two-fluid model, three limiting cases are identified which result in one-phase models of binary systems. Each of these models can be readily implemented in standard single phase flow numerical codes. Differences between predictions from these models are examined. In particular, the effects of the models on the predicted macro-segregation patterns are evaluated.

Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes.

PubMed

Catherine, Arnaud; Selma, Maloufi; Mouillot, David; Troussellier, Marc; Bernard, Cécile

2016-07-15

Local species richness (SR) is a key characteristic affecting ecosystem functioning. Yet, the mechanisms regulating phytoplankton diversity in freshwater ecosystems are not fully understood, especially in peri-urban environments where anthropogenic pressures strongly impact the quality of aquatic ecosystems. To address this issue, we sampled the phytoplankton communities of 50 lakes in the Paris area (France) characterized by a large gradient of physico-chemical and catchment-scale characteristics. We used large phytoplankton datasets to describe phytoplankton diversity patterns and applied a machine-learning algorithm to test the degree to which species richness patterns are potentially controlled by environmental factors. Selected environmental factors were studied at two scales: the lake-scale (e.g. nutrients concentrations, water temperature, lake depth) and the catchment-scale (e.g. catchment, landscape and climate variables). Then, we used a variance partitioning approach to evaluate the interaction between lake-scale and catchment-scale variables in explaining local species richness. Finally, we analysed the residuals of predictive models to identify potential vectors of improvement of phytoplankton species richness predictive models. Lake-scale and catchment-scale drivers provided similar predictive accuracy of local species richness (R(2)=0.458 and 0.424, respectively). Both models suggested that seasonal temperature variations and nutrient supply strongly modulate local species richness. Integrating lake- and catchment-scale predictors in a single predictive model did not provide increased predictive accuracy; therefore suggesting that the catchment-scale model probably explains observed species richness variations through the impact of catchment-scale variables on in-lake water quality characteristics. Models based on catchment characteristics, which include simple and easy to obtain variables, provide a meaningful way of predicting phytoplankton species richness in temperate lakes. This approach may prove useful and cost-effective for the management and conservation of aquatic ecosystems. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of climate change impact on rainfall pattern of Sambas district, West Kalimantan

NASA Astrophysics Data System (ADS)

Berliana Sipayung, Sinta; Nurlatifah, Amalia; Siswanto, Bambang; Slamet S, Lilik

2018-05-01

Climate change is one of the most important issues being discussed globally. It caused by global warming and indirectly affecting the world climate cycle. This research discussed the effect of climate change on rainfall pattern of Sambas District and predicted the future rainfall pattern due to climate change. CRU and TRMM were used and has been validated using in situ data. This research was used Climate Modelling and Prediction using CCAM (Conformal Cubic Atmospheric Model) which also validated by in situ data (correlation= 0.81). The results show that temperature trends in Sambas regency increased to 0.082°C/yr from 1991-2014 according to CRU data. High temperature trigger changes in rainfall patterns. Rainfall pattern in Sambas District has an equatorial type where the peak occurs when the sun is right on the equator. Rainfall in Sambas reaches the maximum in March and September when the equinox occurs. The CCAM model is used to project rainfall in Sambas District in the future. The model results show that rainfall in Sambas District is projected to increase to 0.018 mm/month until 2055 so the flow rate increase 0.006 m3/month and the water balance increase 0.009 mm/month.

Modelling survival: exposure pattern, species sensitivity and uncertainty

PubMed Central

Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I.; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B.; Van den Brink, Paul J.; Veltman, Karin; Vogel, Sören; Zimmer, Elke I.; Preuss, Thomas G.

2016-01-01

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans. PMID:27381500

Optimality Principles for Model-Based Prediction of Human Gait

PubMed Central

Ackermann, Marko; van den Bogert, Antonie J.

2010-01-01

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient’s gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait. PMID:20074736

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

A Mathematical Model of the Olfactory Bulb for the Selective Adaptation Mechanism in the Rodent Olfactory System.

PubMed

Soh, Zu; Nishikawa, Shinya; Kurita, Yuichi; Takiguchi, Noboru; Tsuji, Toshio

2016-01-01

To predict the odor quality of an odorant mixture, the interaction between odorants must be taken into account. Previously, an experiment in which mice discriminated between odorant mixtures identified a selective adaptation mechanism in the olfactory system. This paper proposes an olfactory model for odorant mixtures that can account for selective adaptation in terms of neural activity. The proposed model uses the spatial activity pattern of the mitral layer obtained from model simulations to predict the perceptual similarity between odors. Measured glomerular activity patterns are used as input to the model. The neural interaction between mitral cells and granular cells is then simulated, and a dissimilarity index between odors is defined using the activity patterns of the mitral layer. An odor set composed of three odorants is used to test the ability of the model. Simulations are performed based on the odor discrimination experiment on mice. As a result, we observe that part of the neural activity in the glomerular layer is enhanced in the mitral layer, whereas another part is suppressed. We find that the dissimilarity index strongly correlates with the odor discrimination rate of mice: r = 0.88 (p = 0.019). We conclude that our model has the ability to predict the perceptual similarity of odorant mixtures. In addition, the model also accounts for selective adaptation via the odor discrimination rate, and the enhancement and inhibition in the mitral layer may be related to this selective adaptation.

Growth response of conifers in Adirondack plantations to changing environment: Model approaches based on stem-analysis

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

Pan, Y.

1993-01-01

Based on model approaches, three conifer species, red pine, Norway spruce and Scots pine grown in plantations at Pack Demonstration Forest, in the southeastern Adirondack mountains of New York, were chosen to study growth response to different environmental changes, including silvicultural treatments and changes in climate and chemical environment. Detailed stem analysis data provided a basis for constructing tree growth models. These models were organized into three groups: morphological, dynamic and predictive. The morphological model was designed to evaluate relationship between tree attributes and interactive influences of intrinsic and extrinsic factors on the annual increments. Three types of morphological patternsmore » have been characterized: space-time patterns of whole-stem rings, intrinsic wood deposition pattern along the tree-stem, and bolewood allocation ratio patterns along the tree-stem. The dynamic model reflects the growth process as a system which responds to extrinsic signal inputs, including fertilization pulses, spacing effects and climatic disturbance, as well as intrinsic feedback. Growth signals indicative of climatic effects were used to construct growth-climate models using both multivariate analysis and Kalman filter methods. The predictive model utilized GCMs and growth-climate relationships to forecast tree growth responses in relation to future scenarios of CO[sub 2]-induced climate change. Prediction results indicate that different conifer species have individualistic growth response to future climatic change and suggest possible changes in future growth and distribution of naturally occurring conifers in this region.« less

Seasonal to interannual Arctic sea ice predictability in current global climate models

NASA Astrophysics Data System (ADS)

Tietsche, S.; Day, J. J.; Guemas, V.; Hurlin, W. J.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Collins, M.; Hawkins, E.

2014-02-01

We establish the first intermodel comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea ice extent and volume, there is potential predictive skill for lead times of up to 3 years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate.

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

PubMed Central

Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

2015-01-01

Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

On the mechanochemical theory of biological pattern formation with application to vasculogenesis.

PubMed

Murray, James D

2003-02-01

We first describe the Murray-Oster mechanical theory of pattern formation, the biological basis of which is experimentally well documented. The model quantifies the interaction of cells and the extracellular matrix via the cell-generated forces. The model framework is described in quantitative detail. Vascular endothelial cells, when cultured on gelled basement membrane matrix, rapidly aggregate into clusters while deforming the matrix into a network of cord-like structures tessellating the planar culture. We apply the mechanical theory of pattern formation to this culture system and show that neither strain-biased anisotropic cell traction nor cell migration are necessary for pattern formation: isotropic, strain-stimulated cell traction is sufficient to form the observed patterns. Predictions from the model were confirmed experimentally.

Looking Past Primary Productivity: Benchmarking System Processes that Drive Ecosystem Level Responses in Models

NASA Astrophysics Data System (ADS)

Cowdery, E.; Dietze, M.

2017-12-01

As atmospheric levels of carbon dioxide levels continue to increase, it is critical that terrestrial ecosystem models can accurately predict ecological responses to the changing environment. Current predictions of net primary productivity (NPP) in response to elevated atmospheric CO2 concentration are highly variable and contain a considerable amount of uncertainty. Benchmarking model predictions against data are necessary to assess their ability to replicate observed patterns, but also to identify and evaluate the assumptions causing inter-model differences. We have implemented a novel benchmarking workflow as part of the Predictive Ecosystem Analyzer (PEcAn) that is automated, repeatable, and generalized to incorporate different sites and ecological models. Building on the recent Free-Air CO2 Enrichment Model Data Synthesis (FACE-MDS) project, we used observational data from the FACE experiments to test this flexible, extensible benchmarking approach aimed at providing repeatable tests of model process representation that can be performed quickly and frequently. Model performance assessments are often limited to traditional residual error analysis; however, this can result in a loss of critical information. Models that fail tests of relative measures of fit may still perform well under measures of absolute fit and mathematical similarity. This implies that models that are discounted as poor predictors of ecological productivity may still be capturing important patterns. Conversely, models that have been found to be good predictors of productivity may be hiding error in their sub-process that result in the right answers for the wrong reasons. Our suite of tests have not only highlighted process based sources of uncertainty in model productivity calculations, they have also quantified the patterns and scale of this error. Combining these findings with PEcAn's model sensitivity analysis and variance decomposition strengthen our ability to identify which processes need further study and additional data constraints. This can be used to inform future experimental design and in turn can provide an informative starting point for data assimilation.

Advances in the use of observed spatial patterns of catchment hydrological response

NASA Astrophysics Data System (ADS)

Grayson, Rodger B.; Blöschl, Günter; Western, Andrew W.; McMahon, Thomas A.

Over the past two decades there have been repeated calls for the collection of new data for use in developing hydrological science. The last few years have begun to bear fruit from the seeds sown by these calls, through increases in the availability and utility of remote sensing data, as well as the execution of campaigns in research catchments aimed at providing new data for advancing hydrological understanding and predictive capability. In this paper we discuss some philosophical considerations related to model complexity, data availability and predictive performance, highlighting the potential of observed patterns in moving the science and practice of catchment hydrology forward. We then review advances that have arisen from recent work on spatial patterns, including in the characterisation of spatial structure and heterogeneity, and the use of patterns for developing, calibrating and testing distributed hydrological models. We illustrate progress via examples using observed patterns of snow cover, runoff occurrence and soil moisture. Methods for the comparison of patterns are presented, illustrating how they can be used to assess hydrologically important characteristics of model performance. These methods include point-to-point comparisons, spatial relationships between errors and landscape parameters, transects, and optimal local alignment. It is argued that the progress made to date augers well for future developments, but there is scope for improvements in several areas. These include better quantitative methods for pattern comparisons, better use of pattern information in data assimilation and modelling, and a call for improved archiving of data from field studies to assist in comparative studies for generalising results and developing fundamental understanding.

Multivariate neural biomarkers of emotional states are categorically distinct

PubMed Central

Kragel, Philip A.

2015-01-01

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories. PMID:25813790

Predicting Perceptual Success with Segments: A Test of Japanese Speakers of Russian

ERIC Educational Resources Information Center

Larson-Hall, J.

2004-01-01

A perception experiment involving a novel language pairing, that of Japanese as a first language (L1) and Russian as a second language (L2), was conducted with 33 Japanese learners of Russian to determine whether two phonological models could successfully predict patterns of perceptual difficulty with eight Russian segments. The Featural Model of…

Testing Predictions of the Interactive Activation Model in Recovery from Aphasia after Treatment

ERIC Educational Resources Information Center

Jokel, Regina; Rochon, Elizabeth; Leonard, Carol

2004-01-01

This paper presents preliminary results of pre- and post-treatment error analysis from an aphasic patient with anomia. The Interactive Activation (IA) model of word production (Dell, Schwartz, Martin, Saffran, & Gagnon, 1997) is utilized to make predictions about the anticipated changes on a picture naming task and to explain emerging patterns.…

Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

NASA Astrophysics Data System (ADS)

Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

2017-09-01

In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

NASA Technical Reports Server (NTRS)

Carlson, T. N.

1986-01-01

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Validating a spatially distributed hydrological model with soil morphology data

NASA Astrophysics Data System (ADS)

Doppler, T.; Honti, M.; Zihlmann, U.; Weisskopf, P.; Stamm, C.

2014-09-01

Spatially distributed models are popular tools in hydrology claimed to be useful to support management decisions. Despite the high spatial resolution of the computed variables, calibration and validation is often carried out only on discharge time series at specific locations due to the lack of spatially distributed reference data. Because of this restriction, the predictive power of these models, with regard to predicted spatial patterns, can usually not be judged. An example of spatial predictions in hydrology is the prediction of saturated areas in agricultural catchments. These areas can be important source areas for inputs of agrochemicals to the stream. We set up a spatially distributed model to predict saturated areas in a 1.2 km2 catchment in Switzerland with moderate topography and artificial drainage. We translated soil morphological data available from soil maps into an estimate of the duration of soil saturation in the soil horizons. This resulted in a data set with high spatial coverage on which the model predictions were validated. In general, these saturation estimates corresponded well to the measured groundwater levels. We worked with a model that would be applicable for management decisions because of its fast calculation speed and rather low data requirements. We simultaneously calibrated the model to observed groundwater levels and discharge. The model was able to reproduce the general hydrological behavior of the catchment in terms of discharge and absolute groundwater levels. However, the the groundwater level predictions were not accurate enough to be used for the prediction of saturated areas. Groundwater level dynamics were not adequately reproduced and the predicted spatial saturation patterns did not correspond to those estimated from the soil map. Our results indicate that an accurate prediction of the groundwater level dynamics of the shallow groundwater in our catchment that is subject to artificial drainage would require a model that better represents processes at the boundary between the unsaturated and the saturated zone. However, data needed for such a more detailed model are not generally available. This severely hampers the practical use of such models despite their usefulness for scientific purposes.

Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming

NASA Astrophysics Data System (ADS)

Kashid, Satishkumar S.; Maity, Rajib

2012-08-01

SummaryPrediction of Indian Summer Monsoon Rainfall (ISMR) is of vital importance for Indian economy, and it has been remained a great challenge for hydro-meteorologists due to inherent complexities in the climatic systems. The Large-scale atmospheric circulation patterns from tropical Pacific Ocean (ENSO) and those from tropical Indian Ocean (EQUINOO) are established to influence the Indian Summer Monsoon Rainfall. The information of these two large scale atmospheric circulation patterns in terms of their indices is used to model the complex relationship between Indian Summer Monsoon Rainfall and the ENSO as well as EQUINOO indices. However, extracting the signal from such large-scale indices for modeling such complex systems is significantly difficult. Rainfall predictions have been done for 'All India' as one unit, as well as for five 'homogeneous monsoon regions of India', defined by Indian Institute of Tropical Meteorology. Recent 'Artificial Intelligence' tool 'Genetic Programming' (GP) has been employed for modeling such problem. The Genetic Programming approach is found to capture the complex relationship between the monthly Indian Summer Monsoon Rainfall and large scale atmospheric circulation pattern indices - ENSO and EQUINOO. Research findings of this study indicate that GP-derived monthly rainfall forecasting models, that use large-scale atmospheric circulation information are successful in prediction of All India Summer Monsoon Rainfall with correlation coefficient as good as 0.866, which may appears attractive for such a complex system. A separate analysis is carried out for All India Summer Monsoon rainfall for India as one unit, and five homogeneous monsoon regions, based on ENSO and EQUINOO indices of months of March, April and May only, performed at end of month of May. In this case, All India Summer Monsoon Rainfall could be predicted with 0.70 as correlation coefficient with somewhat lesser Correlation Coefficient (C.C.) values for different 'homogeneous monsoon regions'.

DEPOSITION PATTERNS OF RAGWEED POLLEN IN THE HUMAN RESPIRATORY TRACT

EPA Science Inventory

Inhaled particle deposition sites must be identified to effectively treat human airway diseases. e have determined distribution patterns of a selected aeroallergen, ragweed pollen, among human extrathoracic (ET: .e., oro-nasopharyngeal) regions and the lung. A predictive model va...

Topics in Complexity: Dynamical Patterns in the Cyberworld

NASA Astrophysics Data System (ADS)

Qi, Hong

Quantitative understanding of mechanism in complex systems is a common "difficult" problem across many fields such as physical, biological, social and economic sciences. Investigation on underlying dynamics of complex systems and building individual-based models have recently been fueled by big data resulted from advancing information technology. This thesis investigates complex systems in social science, focusing on civil unrests on streets and relevant activities online. Investigation consists of collecting data of unrests from open digital source, featuring dynamical patterns underlying, making predictions and constructing models. A simple law governing the progress of two-sided confrontations is proposed with data of activities at micro-level. Unraveling the connections between activity of organizing online and outburst of unrests on streets gives rise to a further meso-level pattern of human behavior, through which adversarial groups evolve online and hyper-escalate ahead of real-world uprisings. Based on the patterns found, noticeable improvement of prediction of civil unrests is achieved. Meanwhile, novel model created from combination of mobility dynamics in the cyberworld and a traditional contagion model can better capture the characteristics of modern civil unrests and other contagion-like phenomena than the original one.

Longitudinal Associations Between Marital Instability and Child Sleep Problems across Infancy and Toddlerhood in Adoptive Families

PubMed Central

Mannering, Anne M.; Harold, Gordon T.; Leve, Leslie D.; Shelton, Katherine H.; Shaw, Daniel S.; Conger, Rand D.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Reiss, David

2009-01-01

This study examined the longitudinal association between marital instability and child sleep problems at ages 9 and 18 months in 357 families with a genetically unrelated infant adopted at birth. This design eliminates shared genes as an explanation for similarities between parent and child. Structural equation modeling indicated that T1 marital instability predicted T2 child sleep problems, but T1 child sleep problems did not predict T2 marital instability. This pattern of results was replicated when models were estimated separately for mothers and children and for fathers and children. Thus, even after controlling for stability in sleep problems and marital instability and eliminating shared genetic influences on associations using a longitudinal adoption design, marital instability prospectively predicts early childhood sleep patterns. PMID:21557740

Multi-nutrient, multi-group model of present and future oceanic phytoplankton communities

NASA Astrophysics Data System (ADS)

Litchman, E.; Klausmeier, C. A.; Miller, J. R.; Schofield, O. M.; Falkowski, P. G.

2006-11-01

Phytoplankton community composition profoundly affects patterns of nutrient cycling and the dynamics of marine food webs; therefore predicting present and future phytoplankton community structure is crucial to understand how ocean ecosystems respond to physical forcing and nutrient limitations. We develop a mechanistic model of phytoplankton communities that includes multiple taxonomic groups (diatoms, coccolithophores and prasinophytes), nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was parameterized based on an extensive literature survey. We test the model at two contrasting sites in the modern ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarctic North Pacific (ocean station Papa, OSP). The model successfully predicts general patterns of community composition and succession at both sites: In the North Atlantic, the model predicts a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in the season. In the North Pacific, the model reproduces the low chlorophyll community dominated by prasinophytes and coccolithophores, with low total biomass variability and high nutrient concentrations throughout the year. Sensitivity analysis revealed that the identity of the most sensitive parameters and the range of acceptable parameters differed between the two sites. We then use the model to predict community reorganization under different global change scenarios: a later onset and extended duration of stratification, with shallower mixed layer depths due to increased greenhouse gas concentrations; increase in deep water nitrogen; decrease in deep water phosphorus and increase or decrease in iron concentration. To estimate uncertainty in our predictions, we used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced acceptable modern day outcomes and the robustness of the predictions was determined. Change in the onset and duration of stratification altered the timing and the magnitude of the spring diatom bloom in the North Atlantic and increased total phytoplankton and zooplankton biomass in the North Pacific. Changes in nutrient concentrations in some cases changed dominance patterns of major groups, as well as total chlorophyll and zooplankton biomass. Based on these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical cycles.

Calculation of Debye-Scherrer diffraction patterns from highly stressed polycrystalline materials

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

MacDonald, M. J., E-mail: macdonm@umich.edu; SLAC National Accelerator Laboratory, Menlo Park, California 94025; Vorberger, J.

2016-06-07

Calculations of Debye-Scherrer diffraction patterns from polycrystalline materials have typically been done in the limit of small deviatoric stresses. Although these methods are well suited for experiments conducted near hydrostatic conditions, more robust models are required to diagnose the large strain anisotropies present in dynamic compression experiments. A method to predict Debye-Scherrer diffraction patterns for arbitrary strains has been presented in the Voigt (iso-strain) limit [Higginbotham, J. Appl. Phys. 115, 174906 (2014)]. Here, we present a method to calculate Debye-Scherrer diffraction patterns from highly stressed polycrystalline samples in the Reuss (iso-stress) limit. This analysis uses elastic constants to calculate latticemore » strains for all initial crystallite orientations, enabling elastic anisotropy and sample texture effects to be modeled directly. The effects of probing geometry, deviatoric stresses, and sample texture are demonstrated and compared to Voigt limit predictions. An example of shock-compressed polycrystalline diamond is presented to illustrate how this model can be applied and demonstrates the importance of including material strength when interpreting diffraction in dynamic compression experiments.« less

Model of an axially strained weakly guiding optical fiber modal pattern

NASA Technical Reports Server (NTRS)

Egalon, Claudio O.; Rogowski, Robert S.

1991-01-01

Axial strain may be determined by monitoring the modal pattern variation of an optical fiber. In this paper we present the results of a numerical model that has been developed to calculate the modal pattern variation at the end of a weakly guiding optical fiber under axial strain. Whenever an optical fiber is under stress, the optical path length, the index of refraction and the propagation constants of each fiber mode change. In consequence, the modal phase term of the fields and the fiber output pattern are also modified. For multimode fibers, very complicated patterns result. The predicted patterns are presented, and an expression for the phase variation with strain is derived.

Testing the skill of numerical hydraulic modeling to simulate spatiotemporal flooding patterns in the Logone floodplain, Cameroon

NASA Astrophysics Data System (ADS)

Fernández, Alfonso; Najafi, Mohammad Reza; Durand, Michael; Mark, Bryan G.; Moritz, Mark; Jung, Hahn Chul; Neal, Jeffrey; Shastry, Apoorva; Laborde, Sarah; Phang, Sui Chian; Hamilton, Ian M.; Xiao, Ningchuan

2016-08-01

Recent innovations in hydraulic modeling have enabled global simulation of rivers, including simulation of their coupled wetlands and floodplains. Accurate simulations of floodplains using these approaches may imply tremendous advances in global hydrologic studies and in biogeochemical cycling. One such innovation is to explicitly treat sub-grid channels within two-dimensional models, given only remotely sensed data in areas with limited data availability. However, predicting inundated area in floodplains using a sub-grid model has not been rigorously validated. In this study, we applied the LISFLOOD-FP hydraulic model using a sub-grid channel parameterization to simulate inundation dynamics on the Logone River floodplain, in northern Cameroon, from 2001 to 2007. Our goal was to determine whether floodplain dynamics could be simulated with sufficient accuracy to understand human and natural contributions to current and future inundation patterns. Model inputs in this data-sparse region include in situ river discharge, satellite-derived rainfall, and the shuttle radar topography mission (SRTM) floodplain elevation. We found that the model accurately simulated total floodplain inundation, with a Pearson correlation coefficient greater than 0.9, and RMSE less than 700 km2, compared to peak inundation greater than 6000 km2. Predicted discharge downstream of the floodplain matched measurements (Nash-Sutcliffe efficiency of 0.81), and indicated that net flow from the channel to the floodplain was modeled accurately. However, the spatial pattern of inundation was not well simulated, apparently due to uncertainties in SRTM elevations. We evaluated model results at 250, 500 and 1000-m spatial resolutions, and found that results are insensitive to spatial resolution. We also compared the model output against results from a run of LISFLOOD-FP in which the sub-grid channel parameterization was disabled, finding that the sub-grid parameterization simulated more realistic dynamics. These results suggest that analysis of global inundation is feasible using a sub-grid model, but that spatial patterns at sub-kilometer resolutions still need to be adequately predicted.

Reprint of: Initial uncertainty impacts statistical learning in sound sequence processing.

PubMed

Todd, Juanita; Provost, Alexander; Whitson, Lisa; Mullens, Daniel

2018-05-18

This paper features two studies confirming a lasting impact of first learning on how subsequent experience is weighted in early relevance-filtering processes. In both studies participants were exposed to sequences of sound that contained a regular pattern on two different timescales. Regular patterning in sound is readily detected by the auditory system and used to form "prediction models" that define the most likely properties of sound to be encountered in a given context. The presence and strength of these prediction models is inferred from changes in automatically elicited components of auditory evoked potentials. Both studies employed sound sequences that contained both a local and longer-term pattern. The local pattern was defined by a regular repeating pure tone occasionally interrupted by a rare deviating tone (p=0.125) that was physically different (a 30msvs. 60ms duration difference in one condition and a 1000Hz vs. 1500Hz frequency difference in the other). The longer-term pattern was defined by the rate at which the two tones alternated probabilities (i.e., the tone that was first rare became common and the tone that was first common became rare). There was no task related to the tones and participants were asked to ignore them while focussing attention on a movie with subtitles. Auditory-evoked potentials revealed long lasting modulatory influences based on whether the tone was initially encountered as rare and unpredictable or common and predictable. The results are interpreted as evidence that probability (or indeed predictability) assigns a differential information-value to the two tones that in turn affects the extent to which prediction models are updated and imposed. These effects are exposed for both common and rare occurrences of the tones. The studies contribute to a body of work that reveals that probabilistic information is not faithfully represented in these early evoked potentials and instead exposes that predictability (or conversely uncertainty) may trigger value-based learning modulations even in task-irrelevant incidental learning. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model.

PubMed

Sallah, Kankoé; Giorgi, Roch; Bengtsson, Linus; Lu, Xin; Wetter, Erik; Adrien, Paul; Rebaudet, Stanislas; Piarroux, Renaud; Gaudart, Jean

2017-11-22

Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible-infected-recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

GlycoPP: A Webserver for Prediction of N- and O-Glycosites in Prokaryotic Protein Sequences

PubMed Central

Chauhan, Jagat S.; Bhat, Adil H.; Raghava, Gajendra P. S.; Rao, Alka

2012-01-01

Glycosylation is one of the most abundant post-translational modifications (PTMs) required for various structure/function modulations of proteins in a living cell. Although elucidated recently in prokaryotes, this type of PTM is present across all three domains of life. In prokaryotes, two types of protein glycan linkages are more widespread namely, N- linked, where a glycan moiety is attached to the amide group of Asn, and O- linked, where a glycan moiety is attached to the hydroxyl group of Ser/Thr/Tyr. For their biologically ubiquitous nature, significance, and technology applications, the study of prokaryotic glycoproteins is a fast emerging area of research. Here we describe new Support Vector Machine (SVM) based algorithms (models) developed for predicting glycosylated-residues (glycosites) with high accuracy in prokaryotic protein sequences. The models are based on binary profile of patterns, composition profile of patterns, and position-specific scoring matrix profile of patterns as training features. The study employ an extensive dataset of 107 N-linked and 116 O-linked glycosites extracted from 59 experimentally characterized glycoproteins of prokaryotes. This dataset includes validated N-glycosites from phyla Crenarchaeota, Euryarchaeota (domain Archaea), Proteobacteria (domain Bacteria) and validated O-glycosites from phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (domain Bacteria). In view of the current understanding that glycosylation occurs on folded proteins in bacteria, hybrid models have been developed using information on predicted secondary structures and accessible surface area in various combinations with training features. Using these models, N-glycosites and O-glycosites could be predicted with an accuracy of 82.71% (MCC 0.65) and 73.71% (MCC 0.48), respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence. A web server GlycoPP, implementing these models is available freely at http:/www.imtech.res.in/raghava/glycopp/. PMID:22808107

Symptom patterns in dissociative identity disorder patients and the general population.

PubMed

Ross, Colin A; Ness, Laura

2010-01-01

The authors used the Dissociative Disorders Interview Schedule to compare structured interview symptom patterns in a general population sample (N= 502) and a sample of patients with clinical diagnoses of dissociative identity disorder (N= 303). Based on the Trauma Model, the authors predicted that the patterns would be similar in the 2 samples and that symptom scores would be higher in participants reporting childhood sexual abuse in both samples. They predicted that symptom scores would be higher among women with dissociative identity disorder reporting sexual abuse than among women in the general population reporting sexual abuse, with the clinical sample reporting more severe abuse. These predictions were supported by the data. The authors conclude that symptom patterns in dissociative identity disorder are typical of the normal human response to severe, chronic childhood trauma and have ecological validity for the human race in general.

Plant hydraulics improves and topography mediates prediction of aspen mortality in southwestern USA.

PubMed

Tai, Xiaonan; Mackay, D Scott; Anderegg, William R L; Sperry, John S; Brooks, Paul D

2017-01-01

Elevated forest mortality has been attributed to climate change-induced droughts, but prediction of spatial mortality patterns remains challenging. We evaluated whether introducing plant hydraulics and topographic convergence-induced soil moisture variation to land surface models (LSM) can help explain spatial patterns of mortality. A scheme predicting plant hydraulic safety loss from soil moisture was developed using field measurements and a plant physiology-hydraulics model, TREES. The scheme was upscaled to Populus tremuloides forests across Colorado, USA, using LSM-modeled and topography-mediated soil moisture, respectively. The spatial patterns of hydraulic safety loss were compared against aerial surveyed mortality. Incorporating hydraulic safety loss raised the explanatory power of mortality by 40% compared to LSM-modeled soil moisture. Topographic convergence was mostly influential in suppressing mortality in low and concave areas, explaining an additional 10% of the variations in mortality for those regions. Plant hydraulics integrated water stress along the soil-plant continuum and was more closely tied to plant physiological response to drought. In addition to the well-recognized topo-climate influence due to elevation and aspect, we found evidence that topographic convergence mediates tree mortality in certain parts of the landscape that are low and convergent, likely through influences on plant-available water. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Prediction of the Acoustic Field Associated with Instability Wave Source Model for a Compressible Jet

NASA Technical Reports Server (NTRS)

Golubev, Vladimir; Mankbadi, Reda R.; Dahl, Milo D.; Kiraly, L. James (Technical Monitor)

2002-01-01

This paper provides preliminary results of the study of the acoustic radiation from the source model representing spatially-growing instability waves in a round jet at high speeds. The source model is briefly discussed first followed by the analysis of the produced acoustic directivity pattern. Two integral surface techniques are discussed and compared for prediction of the jet acoustic radiation field.

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

NASA Technical Reports Server (NTRS)

Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

2007-01-01

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Sound transmission in the chest under surface excitation - An experimental and computational study with diagnostic applications

PubMed Central

Peng, Ying; Dai, Zoujun; Mansy, Hansen A.; Sandler, Richard H.; Balk, Robert A; Royston, Thomas. J

2014-01-01

Chest physical examination often includes performing chest percussion, which involves introducing sound stimulus to the chest wall and detecting an audible change. This approach relies on observations that underlying acoustic transmission, coupling, and resonance patterns can be altered by chest structure changes due to pathologies. More accurate detection and quantification of these acoustic alterations may provide further useful diagnostic information. To elucidate the physical processes involved, a realistic computer model of sound transmission in the chest is helpful. In the present study, a computational model was developed and validated by comparing its predictions with results from animal and human experiments which involved applying acoustic excitation to the anterior chest while detecting skin vibrations at the posterior chest. To investigate the effect of pathology on sound transmission, the computational model was used to simulate the effects of pneumothorax on sounds introduced at the anterior chest and detected at the posterior. Model predictions and experimental results showed similar trends. The model also predicted wave patterns inside the chest, which may be used to assess results of elastography measurements. Future animal and human tests may expand the predictive power of the model to include acoustic behavior for a wider range of pulmonary conditions. PMID:25001497

Phylogenies support out-of-equilibrium models of biodiversity.

PubMed

Manceau, Marc; Lambert, Amaury; Morlon, Hélène

2015-04-01

There is a long tradition in ecology of studying models of biodiversity at equilibrium. These models, including the influential Neutral Theory of Biodiversity, have been successful at predicting major macroecological patterns, such as species abundance distributions. But they have failed to predict macroevolutionary patterns, such as those captured in phylogenetic trees. Here, we develop a model of biodiversity in which all individuals have identical demographic rates, metacommunity size is allowed to vary stochastically according to population dynamics, and speciation arises naturally from the accumulation of point mutations. We show that this model generates phylogenies matching those observed in nature if the metacommunity is out of equilibrium. We develop a likelihood inference framework that allows fitting our model to empirical phylogenies, and apply this framework to various mammalian families. Our results corroborate the hypothesis that biodiversity dynamics are out of equilibrium. © 2015 John Wiley & Sons Ltd/CNRS.

Strategy Plan A Methodology to Predict the Uniformity of Double-Shell Tank Waste Slurries Based on Mixing Pump Operation

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

J.A. Bamberger; L.M. Liljegren; P.S. Lowery

This document presents an analysis of the mechanisms influencing mixing within double-shell slurry tanks. A research program to characterize mixing of slurries within tanks has been proposed. The research program presents a combined experimental and computational approach to produce correlations describing the tank slurry concentration profile (and therefore uniformity) as a function of mixer pump operating conditions. The TEMPEST computer code was used to simulate both a full-scale (prototype) and scaled (model) double-shell waste tank to predict flow patterns resulting from a stationary jet centered in the tank. The simulation results were used to evaluate flow patterns in the tankmore » and to determine whether flow patterns are similar between the full-scale prototype and an existing 1/12-scale model tank. The flow patterns were sufficiently similar to recommend conducting scoping experiments at 1/12-scale. Also, TEMPEST modeled velocity profiles of the near-floor jet were compared to experimental measurements of the near-floor jet with good agreement. Reported values of physical properties of double-shell tank slurries were analyzed to evaluate the range of properties appropriate for conducting scaled experiments. One-twelfth scale scoping experiments are recommended to confirm the prioritization of the dimensionless groups (gravitational settling, Froude, and Reynolds numbers) that affect slurry suspension in the tank. Two of the proposed 1/12-scale test conditions were modeled using the TEMPEST computer code to observe the anticipated flow fields. This information will be used to guide selection of sampling probe locations. Additional computer modeling is being conducted to model a particulate laden, rotating jet centered in the tank. The results of this modeling effort will be compared to the scaled experimental data to quantify the agreement between the code and the 1/12-scale experiment. The scoping experiment results will guide selection of parameters to be varied in the follow-on experiments. Data from the follow-on experiments will be used to develop correlations to describe slurry concentration profile as a function of mixing pump operating conditions. This data will also be used to further evaluate the computer model applications. If the agreement between the experimental data and the code predictions is good, the computer code will be recommended for use to predict slurry uniformity in the tanks under various operating conditions. If the agreement between the code predictions and experimental results is not good, the experimental data correlations will be used to predict slurry uniformity in the tanks within the range of correlation applicability.« less

A systems approach to college drinking: development of a deterministic model for testing alcohol control policies.

PubMed

Scribner, Richard; Ackleh, Azmy S; Fitzpatrick, Ben G; Jacquez, Geoffrey; Thibodeaux, Jeremy J; Rommel, Robert; Simonsen, Neal

2009-09-01

The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by "wetness" and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately "dry" campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately "wet" campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses.

A Systems Approach to College Drinking: Development of a Deterministic Model for Testing Alcohol Control Policies*

PubMed Central

Scribner, Richard; Ackleh, Azmy S.; Fitzpatrick, Ben G.; Jacquez, Geoffrey; Thibodeaux, Jeremy J.; Rommel, Robert; Simonsen, Neal

2009-01-01

Objective: The misuse and abuse of alcohol among college students remain persistent problems. Using a systems approach to understand the dynamics of student drinking behavior and thus forecasting the impact of campus policy to address the problem represents a novel approach. Toward this end, the successful development of a predictive mathematical model of college drinking would represent a significant advance for prevention efforts. Method: A deterministic, compartmental model of college drinking was developed, incorporating three processes: (1) individual factors, (2) social interactions, and (3) social norms. The model quantifies these processes in terms of the movement of students between drinking compartments characterized by five styles of college drinking: abstainers, light drinkers, moderate drinkers, problem drinkers, and heavy episodic drinkers. Predictions from the model were first compared with actual campus-level data and then used to predict the effects of several simulated interventions to address heavy episodic drinking. Results: First, the model provides a reasonable fit of actual drinking styles of students attending Social Norms Marketing Research Project campuses varying by “wetness” and by drinking styles of matriculating students. Second, the model predicts that a combination of simulated interventions targeting heavy episodic drinkers at a moderately “dry” campus would extinguish heavy episodic drinkers, replacing them with light and moderate drinkers. Instituting the same combination of simulated interventions at a moderately “wet” campus would result in only a moderate reduction in heavy episodic drinkers (i.e., 50% to 35%). Conclusions: A simple, five-state compartmental model adequately predicted the actual drinking patterns of students from a variety of campuses surveyed in the Social Norms Marketing Research Project study. The model predicted the impact on drinking patterns of several simulated interventions to address heavy episodic drinking on various types of campuses. PMID:19737506

Personalized Vehicle Energy Efficiency & Range Predictor/MyGreenCar

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

SAXENA, SAMVEG

MyGreenCar provides users with the ability to predict the range capabilities, fuel economy, and operating costs for any vehicle for their individual driving patterns. Users launce the MyGreeCar mobile app on their smartphones to collect their driving patterns over any duration (e.g. serval days, weeks, months, etc) using a phones's locational capabilities. Using vehicle powertrain models for any user-specified vehicle type, MyGreenCar, calculates the component-level energy and power interactions for the chosen vehicle to predict several important quantities, including: 1. For Evs: Alleviating range anxiety 2. Comparing fuel economy, operating costs, and payback time across models and types.

Geographic patterns of cigarette butt waste in the urban environment.

PubMed

Marah, Maacah; Novotny, Thomas E

2011-05-01

This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities.

Geographic patterns of cigarette butt waste in the urban environment

PubMed Central

Novotny, Thomas E

2011-01-01

Background This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Methods Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. Results We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Conclusions Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities. PMID:21504924

A tool to estimate bar patterns and flow conditions in estuaries when limited data is available

NASA Astrophysics Data System (ADS)

Leuven, J.; Verhoeve, S.; Bruijns, A. J.; Selakovic, S.; van Dijk, W. M.; Kleinhans, M. G.

2017-12-01

The effects of human interventions, natural evolution of estuaries and rising sea-level on food security and flood safety are largely unknown. In addition, ecologists require quantified habitat area to study future evolution of estuaries, but they lack predictive capability of bathymetry and hydrodynamics. For example, crucial input required for ecological models are values of intertidal area, inundation time, peak flow velocities and salinity. While numerical models can reproduce these spatial patterns, their computational times are long and for each case a new model must be developed. Therefore, we developed a comprehensive set of relations that accurately predict the hydrodynamics and the patterns of channels and bars, using a combination of the empirical relations derived from approximately 50 estuaries and theory for bars and estuaries. The first step is to predict local tidal prisms, which is the tidal prism that flows through a given cross-section. Second, the channel geometry is predicted from tidal prism and hydraulic geometry relations. Subsequently, typical flow velocities can be estimated from the channel geometry and tidal prism. Then, an ideal estuary shape is fitted to the measured planform: the deviation from the ideal shape, which is defined as the excess width, gives a measure of the locations where tidal bars form and their summed width (Leuven et al., 2017). From excess width, typical hypsometries can be predicted per cross-section. In the last step, flow velocities are calculated for the full range of occurring depths and salinity is calculated based on the estuary shape. Here, we will present a prototype tool that predicts equilibrium bar patterns and typical flow conditions. The tool is easy to use because the only input required is the estuary outline and tidal amplitude. Therefore it can be used by policy makers and researchers from multiple disciplines, such as ecologists, geologists and hydrologists, for example for paleogeographic reconstructions.

In Situ Observational Constraints on GIA in Antarctica

NASA Astrophysics Data System (ADS)

Wilson, T. J.; Bevis, M. G.; Kendrick, E. C.; Konfal, S.; Dalziel, I. W.; Smalley, R.; Willis, M. J.; Wiens, D. A.; Heeszel, D. S.

2012-12-01

Geodetic and seismologic data sets have been acquired across a significant portion of Antarctica through deployment of autonomous, remote instrumentation by the Antarctic Network (ANET) project of the Polar Earth Observing Network (POLENET). Continuous GPS measurements of bedrock crustal motions are yielding a synoptic picture of vertical and horizontal crustal motion patterns from the Transantarctic Mountains to the Ellsworth-Whitmore Mountains and Marie Byrd Land regions. Vertical motion patterns are broadly compatible with predictions from current GIA models, but the magnitudes of the vertical motions are substantially lower than predicted. Slower rates of uplift due to GIA can be attributed to factors including errors in ice history, a superposed solid earth response to modern ice mass change, and/or the influence of laterally varying earth properties on the GIA response. Patterns of horizontal motions measured by ANET show that the role of laterally varying earth rheology is extremely important in Antarctica. Crustal motion vectors are closely aligned and document motion from East toward West Antarctica, in contradiction to ice sheet reconstructions placing maximum LGM ice mass loss in West Antarctica and GIA models that predict motions in the opposite direction. When compared to earth structure mapped by seismology, the horizontal crustal motions are consistently near-perpendicular to the very strong gradient in crust and mantle properties, perhaps the first confirmation of predictions from modeling studies that horizontal motions can be deflected or even reversed where such a lateral earth property exists. Accurate GIA models for Antarctica clearly require a laterally-varying earth model and tuning based on these new GPS and seismological constraints.

A circular model for song motor control in Serinus canaria

PubMed Central

Alonso, Rodrigo G.; Trevisan, Marcos A.; Amador, Ana; Goller, Franz; Mindlin, Gabriel B.

2015-01-01

Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture. PMID:25904860

Predicting the effect of seine rope layout pattern and haul-in procedure on the effectiveness of demersal seine fishing: A Computer simulation-based approach.

PubMed

Madsen, Nina A H; Aarsæther, Karl G; Herrmann, Bent

2017-01-01

Demersal Seining is an active fishing method applying two long seine ropes and a seine net. Demersal seining relies on fish responding to the seine rope as it moves during the fishing process. The seine ropes and net are deployed in a specific pattern encircling an area on the seabed. In some variants of demersal seining the haul-in procedure includes a towing phase where the fishing vessel moves forward before starting to winch in the seine ropes. The initial seine rope encircled area, the gradual change in it during the haul-in process and the fish's reaction to the moving seine ropes play an important role in the catch performance of demersal seine fishing. The current study investigates this subject by applying computer simulation models for demersal seine fishing. The demersal seine fishing is dynamic in nature and therefore a dynamic model, SeineSolver is applied for simulating the physical behaviour of the seine ropes during the fishing process. Information about the seine rope behaviour is used as input to another simulation tool, SeineFish that predicts the catch performance of the demersal seine fishing process. SeineFish implements a simple model for how fish at the seabed reacts to an approaching seine rope. Here, the SeineSolver and SeineFish tools are applied to investigate catching performance for a Norwegian demersal seine fishery targeting cod (Gadus morhua) in the coastal zone. The effect of seine rope layout pattern and the duration of the towing phase are investigated. Among the four different layout patterns investigated, the square layout pattern was predicted to perform best; catching 69%-86% more fish than would be obtained with the rectangular layout pattern. Inclusion of a towing phase in the fishing process was found to increase the catch performance for all layout patterns. For the square layout pattern, inclusion of a towing phase of 15 or 35 minutes increased the catch performance by respectively 37% and 48% compared to fishing without a towing phase. These results highlights the importance of the selected seine rope layout pattern and the duration of the towing phase when fishermen try to maximize the catch performance of their fishery. To our knowledge this is the first time the combination of models for the physical behaviour of seine ropes and for fish behaviour in response to seine rope movements have been applied to predict catch performance for demersal seining.

Predicting the effect of seine rope layout pattern and haul-in procedure on the effectiveness of demersal seine fishing: A Computer simulation-based approach

PubMed Central

Madsen, Nina A. H.; Aarsæther, Karl G.; Herrmann, Bent

2017-01-01

Demersal Seining is an active fishing method applying two long seine ropes and a seine net. Demersal seining relies on fish responding to the seine rope as it moves during the fishing process. The seine ropes and net are deployed in a specific pattern encircling an area on the seabed. In some variants of demersal seining the haul-in procedure includes a towing phase where the fishing vessel moves forward before starting to winch in the seine ropes. The initial seine rope encircled area, the gradual change in it during the haul-in process and the fish's reaction to the moving seine ropes play an important role in the catch performance of demersal seine fishing. The current study investigates this subject by applying computer simulation models for demersal seine fishing. The demersal seine fishing is dynamic in nature and therefore a dynamic model, SeineSolver is applied for simulating the physical behaviour of the seine ropes during the fishing process. Information about the seine rope behaviour is used as input to another simulation tool, SeineFish that predicts the catch performance of the demersal seine fishing process. SeineFish implements a simple model for how fish at the seabed reacts to an approaching seine rope. Here, the SeineSolver and SeineFish tools are applied to investigate catching performance for a Norwegian demersal seine fishery targeting cod (Gadus morhua) in the coastal zone. The effect of seine rope layout pattern and the duration of the towing phase are investigated. Among the four different layout patterns investigated, the square layout pattern was predicted to perform best; catching 69%-86% more fish than would be obtained with the rectangular layout pattern. Inclusion of a towing phase in the fishing process was found to increase the catch performance for all layout patterns. For the square layout pattern, inclusion of a towing phase of 15 or 35 minutes increased the catch performance by respectively 37% and 48% compared to fishing without a towing phase. These results highlights the importance of the selected seine rope layout pattern and the duration of the towing phase when fishermen try to maximize the catch performance of their fishery. To our knowledge this is the first time the combination of models for the physical behaviour of seine ropes and for fish behaviour in response to seine rope movements have been applied to predict catch performance for demersal seining. PMID:28771583

Development and Current Status of the “Cambridge” Loudness Models

PubMed Central

2014-01-01

This article reviews the evolution of a series of models of loudness developed in Cambridge, UK. The first model, applicable to stationary sounds, was based on modifications of the model developed by Zwicker, including the introduction of a filter to allow for the effects of transfer of sound through the outer and middle ear prior to the calculation of an excitation pattern, and changes in the way that the excitation pattern was calculated. Later, modifications were introduced to the assumed middle-ear transfer function and to the way that specific loudness was calculated from excitation level. These modifications led to a finite calculated loudness at absolute threshold, which made it possible to predict accurately the absolute thresholds of broadband and narrowband sounds, based on the assumption that the absolute threshold corresponds to a fixed small loudness. The model was also modified to give predictions of partial loudness—the loudness of one sound in the presence of another. This allowed predictions of masked thresholds based on the assumption that the masked threshold corresponds to a fixed small partial loudness. Versions of the model for time-varying sounds were developed, which allowed prediction of the masked threshold of any sound in a background of any other sound. More recent extensions incorporate binaural processing to account for the summation of loudness across ears. In parallel, versions of the model for predicting loudness for hearing-impaired ears have been developed and have been applied to the development of methods for fitting multichannel compression hearing aids. PMID:25315375

The effects of topography on magma chamber deformation models: Application to Mt. Etna and radar interferometry

NASA Astrophysics Data System (ADS)

Williams, Charles A.; Wadge, Geoff

We have used a three-dimensional elastic finite element model to examine the effects of topography on the surface deformation predicted by models of magma chamber deflation. We used the topography of Mt. Etna to control the geometry of our model, and compared the finite element results to those predicted by an analytical solution for a pressurized sphere in an elastic half-space. Topography has a significant effect on the predicted surface deformation for both displacement profiles and synthetic interferograms. Not only are the predicted displacement magnitudes significantly different, but also the map-view patterns of displacement. It is possible to match the predicted displacement magnitudes fairly well by adjusting the elevation of a reference surface; however, the horizontal pattern of deformation is still significantly different. Thus, inversions based on constant-elevation reference surfaces may not properly estimate the horizontal position of a magma chamber. We have investigated an approach where the elevation of the reference surface varies for each computation point, corresponding to topography. For vertical displacements and tilts this method provides a good fit to the finite element results, and thus may form the basis for an inversion scheme. For radial displacements, a constant reference elevation provides a better fit to the numerical results.

Correlations between human mobility and social interaction reveal general activity patterns.

PubMed

Mollgaard, Anders; Lehmann, Sune; Mathiesen, Joachim

2017-01-01

A day in the life of a person involves a broad range of activities which are common across many people. Going beyond diurnal cycles, a central question is: to what extent do individuals act according to patterns shared across an entire population? Here we investigate the interplay between different activity types, namely communication, motion, and physical proximity by analyzing data collected from smartphones distributed among 638 individuals. We explore two central questions: Which underlying principles govern the formation of the activity patterns? Are the patterns specific to each individual or shared across the entire population? We find that statistics of the entire population allows us to successfully predict 71% of the activity and 85% of the inactivity involved in communication, mobility, and physical proximity. Surprisingly, individual level statistics only result in marginally better predictions, indicating that a majority of activity patterns are shared across our sample population. Finally, we predict short-term activity patterns using a generalized linear model, which suggests that a simple linear description might be sufficient to explain a wide range of actions, whether they be of social or of physical character.

Gas liquid flow at microgravity conditions - Flow patterns and their transitions

NASA Technical Reports Server (NTRS)

Dukler, A. E.; Fabre, J. A.; Mcquillen, J. B.; Vernon, R.

1987-01-01

The prediction of flow patterns during gas-liquid flow in conduits is central to the modern approach for modeling two phase flow and heat transfer. The mechanisms of transition are reasonably well understood for flow in pipes on earth where it has been shown that body forces largely control the behavior observed. This work explores the patterns which exist under conditions of microgravity when these body forces are suppressed. Data are presented which were obtained for air-water flow in tubes during drop tower experiments and Learjet trajectories. Preliminary models to explain the observed flow pattern map are evolved.

Personality patterns predict the risk of antisocial behavior in Spanish-speaking adolescents.

PubMed

Alcázar-Córcoles, Miguel A; Verdejo-García, Antonio; Bouso-Sáiz, José C; Revuelta-Menéndez, Javier; Ramírez-Lira, Ezequiel

2017-05-01

There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents. For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk. Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior. The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features.

PubMed

Zhou, Jianya; Zhao, Jing; Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

2016-01-01

To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas. Female, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases.

Deposition of aerially applied BT in an oak forest and its prediction with the FSCBG model

USGS Publications Warehouse

Anderson, Dean E.; Miller, David R.; Wang, Yansen; Yendol, William G.; Mierzejewski, Karl; McManus, Michael L.

1992-01-01

Data are provided from 17 single-swath aerial spray trials that were conducted over a fully leafed, 16-m tall, mixed oak forest. The distribution of cross-swath spray deposits was sampled at the top of the canopy and below the canopy. Micrometeorological conditions were measured above and within the canopy during the spray trials. The USDA Forest Service FSCBG (Forest Service-Cramer-Barry-Grim) model was run to predict the target sampler catch for each trial using forest stand, airplane-application-equipment configuration, and micrometeorological conditions as inputs. Observations showed an average cross-swath deposition of 100 IU cm−2 with large run-to-run variability in deposition patterns, magnitudes, and drift. Eleven percent of the spray material that reached the top of the canopy penetrated through the tree canopy to the forest floor.The FSCBG predictions of the ensemble-averaged deposition were within 17% of the measured deposition at the canopy top and within 8% on the ground beneath the canopy. Run-to-run deposit predictions by FSCBG were considerably less variable than the measured deposits. Individual run predictions were much less accurate than the ensemble-averaged predictions as demonstrated by an average root-mean-square-error (rmse) of 27.9 IU CM−2 at the top of the canopy. Comparisons of the differences between predicted and observed deposits indicated that the model accuracy was sensitive to atmospheric stability conditions. In neutral and stable conditions, a regular pattern of error was indicated by overprediction of the canopy-top deposit at distances from 0 to 20 m downwind from the flight line and underprediction of the deposit both farther downwind than 20 m and upwind of the flight line. In unstable conditions the model generally underpredicted the deposit downwind from the flight line, but showed no regular pattern of error.

A comprehensive mechanistic model for upward two-phase flow in wellbores

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

Sylvester, N.D.; Sarica, C.; Shoham, O.

1994-05-01

A comprehensive model is formulated to predict the flow behavior for upward two-phase flow. This model is composed of a model for flow-pattern prediction and a set of independent mechanistic models for predicting such flow characteristics as holdup and pressure drop in bubble, slug, and annular flow. The comprehensive model is evaluated by using a well data bank made up of 1,712 well cases covering a wide variety of field data. Model performance is also compared with six commonly used empirical correlations and the Hasan-Kabir mechanistic model. Overall model performance is in good agreement with the data. In comparison withmore » other methods, the comprehensive model performed the best.« less

Adverse Childhood Experiences Predict Alcohol Consumption Patterns Among Kenyan Mothers.

PubMed

Goodman, Michael L; Grouls, Astrid; Chen, Catherine X; Keiser, Philip H; Gitari, Stanley

2017-04-16

We analyze whether adverse childhood experiences predict weekly alcohol consumption patterns of Kenyan mothers and their partners. Randomly selected respondents (n = 1,976) were asked about adverse childhood experiences and alcohol consumption patterns for themselves and their partners. Fixed effect models were used to determine odds of reporting weekly alcohol consumption and the number of beverages typically consumed, controlling for wealth, age, education, and partner alcohol consumption. Cumulative adverse childhood experiences predicted higher odds of weekly alcohol consumption of the respondent and her partner. Childhood exposure to physical abuse, emotional neglect, and mental illness in the household significantly increased odds of weekly alcohol consumption by the respondent. More drinks consumed per typical session were higher among respondents with more cumulative adversities. Physical and emotional abuse significantly predicted number of drinks typically consumed by the respondent. To our knowledge, this is the first study to explore and find associations between adverse childhood experiences and alcohol consumption in Kenya. Consistent with high-income settings, exposure to childhood adversities predicted greater alcohol consumption among Kenyan women.

Wafer hotspot prevention using etch aware OPC correction

NASA Astrophysics Data System (ADS)

Hamouda, Ayman; Power, Dave; Salama, Mohamed; Chen, Ao

2016-03-01

As technology development advances into deep-sub-wavelength nodes, multiple patterning is becoming more essential to achieve the technology shrink requirements. Recently, Optical Proximity Correction (OPC) technology has proposed simultaneous correction of multiple mask-patterns to enable multiple patterning awareness during OPC correction. This is essential to prevent inter-layer hot-spots during the final pattern transfer. In state-of-art literature, multi-layer awareness is achieved using simultaneous resist-contour simulations to predict and correct for hot-spots during mask generation. However, this approach assumes a uniform etch shrink response for all patterns independent of their proximity, which isn't sufficient for the full prevention of inter-exposure hot-spot, for example different color space violations post etch or via coverage/enclosure post etch. In this paper, we explain the need to include the etch component during multiple patterning OPC. We also introduce a novel approach for Etch-aware simultaneous Multiple-patterning OPC, where we calibrate and verify a lumped model that includes the combined resist and etch responses. Adding this extra simulation condition during OPC is suitable for full chip processing from a computation intensity point of view. Also, using this model during OPC to predict and correct inter-exposures hot-spots is similar to previously proposed multiple-patterning OPC, yet our proposed approach more accurately corrects post-etch defects too.

The role of abiotic conditions in shaping the long-term patterns of a high-elevation Argentine ant invasion

USGS Publications Warehouse

Krushelnycky, P.D.; Joe, S.M.; Medeiros, A.C.; Daehler, C.C.; Loope, L.L.

2005-01-01

Analysis of long-term patterns of invasion can reveal the importance of abiotic factors in influencing invasion dynamics, and can help predict future patterns of spread. In the case of the invasive Argentine ant (Linepithema humile), most prior studies have investigated this species' limitations in hot and dry climates. However, spatial and temporal patterns of spread involving two ant populations over the course of 30 years at a high elevation site in Hawaii suggest that cold and wet conditions have influenced both the ant's distribution and its rate of invasion. In Haleakala National Park on Maui, we found that a population invading at lower elevation is limited by increasing rainfall and presumably by associated decreasing temperatures. A second, higher elevation population has spread outward in all directions, but rates of spread in different directions appear to have been strongly influenced by differences in elevation and temperature. Patterns of foraging activity were strongly tied to soil temperatures, supporting the hypothesis that variation in temperature can influence rates of spread. Based on past patterns of spread, we predicted a total potential range that covers nearly 50% of the park and 75% of the park's subalpine habitats. We compared this rough estimate with point predictions derived from a degree-day model for Argentine ant colony reproduction, and found that the two independent predictions match closely when soil temperatures are used in the model. The cold, wet conditions that have influenced Argentine ant invasion at this site are likely to be influential at other locations in this species' current and future worldwide distribution. ?? 2005 Blackwell Publishing Ltd.

Predicting the spread of all invasive forest pests in the United States

Treesearch

Emma J. Hudgins; Andrew M. Liebhold; Brian Leung; Regan Early

2017-01-01

We tested whether a general spread model could capture macroecological patterns across all damaging invasive forest pests in the United States. We showed that a common constant dispersal kernel model, simulated from the discovery date, explained 67.94% of the variation in range size across all pests, and had 68.00% locational accuracy between predicted and observed...

ECOHAB - HYDROGRAPHY AND BIOLOGY TO PROVIDE INFORMATION FOR THE CONSTRUCTION OF A MODEL TO PREDICT THE INITIATION, MAINTANENCE AND DISPERSAL OF RED TIDE ON THE WEST COAST OF FLORIDA

EPA Science Inventory

This program is part of a larger program called ECOHAB: Florida that includes this study as well as physical oceanography, circulation patterns, and shelf scale modeling for predicting the occurrence and transport of Karenia brevis (=Gymnodinium breve) red tides. The physical par...

Predicting network modules of cell cycle regulators using relative protein abundance statistics.

PubMed

Oguz, Cihan; Watson, Layne T; Baumann, William T; Tyson, John J

2017-02-28

Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network. Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50. By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.

A Systematic Review of Global Drivers of Ant Elevational Diversity

PubMed Central

Szewczyk, Tim; McCain, Christy M.

2016-01-01

Ant diversity shows a variety of patterns across elevational gradients, though the patterns and drivers have not been evaluated comprehensively. In this systematic review and reanalysis, we use published data on ant elevational diversity to detail the observed patterns and to test the predictions and interactions of four major diversity hypotheses: thermal energy, the mid-domain effect, area, and the elevational climate model. Of sixty-seven published datasets from the literature, only those with standardized, comprehensive sampling were used. Datasets included both local and regional ant diversity and spanned 80° in latitude across six biogeographical provinces. We used a combination of simulations, linear regressions, and non-parametric statistics to test multiple quantitative predictions of each hypothesis. We used an environmentally and geometrically constrained model as well as multiple regression to test their interactions. Ant diversity showed three distinct patterns across elevations: most common were hump-shaped mid-elevation peaks in diversity, followed by low-elevation plateaus and monotonic decreases in the number of ant species. The elevational climate model, which proposes that temperature and precipitation jointly drive diversity, and area were partially supported as independent drivers. Thermal energy and the mid-domain effect were not supported as primary drivers of ant diversity globally. The interaction models supported the influence of multiple drivers, though not a consistent set. In contrast to many vertebrate taxa, global ant elevational diversity patterns appear more complex, with the best environmental model contingent on precipitation levels. Differences in ecology and natural history among taxa may be crucial to the processes influencing broad-scale diversity patterns. PMID:27175999

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

PubMed Central

Korolev, Igor O.; Symonds, Laura L.; Bozoki, Andrea C.

2016-01-01

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. PMID:26901338

On Spatially Explicit Models of Epidemic and Endemic Cholera: The Haiti and Lake Kivu Case Studies.

NASA Astrophysics Data System (ADS)

Rinaldo, A.; Bertuzzo, E.; Mari, L.; Finger, F.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.

2014-12-01

The first part of the Lecture deals with the predictive ability of mechanistic models for the Haitian cholera epidemic. Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. A formal model comparison framework provides a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels. Intensive computations and objective model comparisons show that parsimonious spatially explicit models accounting for spatial connections have superior explanatory power than spatially disconnected ones for short-to intermediate calibration windows. In general, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. The second part deals with approaches suitable to describe patterns of endemic cholera. Cholera outbreaks have been reported in the Democratic Republic of the Congo since the 1970s. Here we employ a spatially explicit, inhomogeneous Markov chain model to describe cholera incidence in eight health zones on the shore of lake Kivu. Remotely sensed datasets of chlorophyll a concentration in the lake, precipitation and indices of global climate anomalies are used as environmental drivers in addition to baseline seasonality. The effect of human mobility is also modelled mechanistically. We test several models on a multi-year dataset of reported cholera cases. Fourteen models, accounting for different environmental drivers, are selected in calibration. Among these, the one accounting for seasonality, El Nino Southern Oscillation, precipitation and human mobility outperforms the others in cross-validation.

Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management.

PubMed

Hayn, Dieter; Kreiner, Karl; Ebner, Hubert; Kastner, Peter; Breznik, Nada; Rzepka, Angelika; Hofmann, Axel; Gombotz, Hans; Schreier, Günter

2017-06-14

Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated. It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns. 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another. Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2. We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and relating neural activity to behavior. Copyright 2010 Elsevier Inc. All rights reserved.

Using a spatially-distributed hydrologic biogeochemistry model with nitrogen transport to study the spatial variation of carbon stocks and fluxes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; He, Y.; Davis, K. J.

2017-12-01

Most current biogeochemical models are 1-D and represent one point in space. Therefore, they cannot resolve topographically driven land surface heterogeneity (e.g., lateral water flow, soil moisture, soil temperature, solar radiation) or the spatial pattern of nutrient availability. A spatially distributed forest biogeochemical model with nitrogen transport, Flux-PIHM-BGC, has been developed by coupling a 1-D mechanistic biogeochemical model Biome-BGC (BBGC) with a spatially distributed land surface hydrologic model, Flux-PIHM, and adding an advection dominated nitrogen transport module. Flux-PIHM is a coupled physically based model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model, and is augmented by adding a topographic solar radiation module. Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as land surface heterogeneities caused by topography. In the coupled Flux-PIHM-BGC model, each Flux-PIHM model grid couples a 1-D BBGC model, while nitrogen is transported among model grids via surface and subsurface water flow. In each grid, Flux-PIHM provides BBGC with soil moisture, soil temperature, and solar radiation, while BBGC provides Flux-PIHM with spatially-distributed leaf area index. The coupled Flux-PIHM-BGC model has been implemented at the Susquehanna/Shale Hills Critical Zone Observatory. The model-predicted aboveground vegetation carbon and soil carbon distributions generally agree with the macro patterns observed within the watershed. The importance of abiotic variables (including soil moisture, soil temperature, solar radiation, and soil mineral nitrogen) in predicting aboveground carbon distribution is calculated using a random forest. The result suggests that the spatial pattern of aboveground carbon is controlled by the distribution of soil mineral nitrogen. A Flux-PIHM-BGC simulation without the nitrogen transport module is also executed. The model without nitrogen transport fails in predicting the spatial patterns of vegetation carbon, which indicates the importance of having a nitrogen transport module in spatially distributed ecohydrologic modeling.

Mountaintop island age determines species richness of boreal mammals in the American Southwest

USGS Publications Warehouse

Frey, J.K.; Bogan, M.A.; Yates, Terry L.

2007-01-01

Models that describe the mechanisms responsible for insular patterns of species richness include the equilibrium theory of island biogeography and the nonequilibrium vicariance model. The relative importance of dispersal or vicariance in structuring insular distribution patterns can be inferred from these models. Predictions of the alternative models were tested for boreal mammals in the American Southwest. Age of mountaintop islands of boreal habitat was determined by constructing a geographic cladogram based on characteristics of intervening valley barriers. Other independent variables included area and isolation of mountaintop islands. Island age was the most important predictor of species richness. In contrast with previous studies of species richness patterns in this system, these results supported the nonequilibrium vicariance model, which indicates that vicariance has been the primary determinant of species distribution patterns in this system.

Advanced fast 3D DSA model development and calibration for design technology co-optimization

NASA Astrophysics Data System (ADS)

Lai, Kafai; Meliorisz, Balint; Muelders, Thomas; Welling, Ulrich; Stock, Hans-Jürgen; Marokkey, Sajan; Demmerle, Wolfgang; Liu, Chi-Chun; Chi, Cheng; Guo, Jing

2017-04-01

Direct Optimization (DO) of a 3D DSA model is a more optimal approach to a DTCO study in terms of accuracy and speed compared to a Cahn Hilliard Equation solver. DO's shorter run time (10X to 100X faster) and linear scaling makes it scalable to the area required for a DTCO study. However, the lack of temporal data output, as opposed to prior art, requires a new calibration method. The new method involves a specific set of calibration patterns. The calibration pattern's design is extremely important when temporal data is absent to obtain robust model parameters. A model calibrated to a Hybrid DSA system with a set of device-relevant constructs indicates the effectiveness of using nontemporal data. Preliminary model prediction using programmed defects on chemo-epitaxy shows encouraging results and agree qualitatively well with theoretical predictions from a strong segregation theory.

A single-cell spiking model for the origin of grid-cell patterns

PubMed Central

Kempter, Richard

2017-01-01

Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity. PMID:28968386

Rejecting probability summation for radial frequency patterns, not so Quick!

PubMed

Baldwin, Alex S; Schmidtmann, Gunnar; Kingdom, Frederick A A; Hess, Robert F

2016-05-01

Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresholds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distinguish whether the responses to the parts of an RF pattern are combined by additive or probability summation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advanced in-production hotspot prediction and monitoring with micro-topography

NASA Astrophysics Data System (ADS)

Fanton, P.; Hasan, T.; Lakcher, A.; Le-Gratiet, B.; Prentice, C.; Simiz, J.-G.; La Greca, R.; Depre, L.; Hunsche, S.

2017-03-01

At 28nm technology node and below, hot spot prediction and process window control across production wafers have become increasingly critical to prevent hotspots from becoming yield-limiting defects. We previously established proof of concept for a systematic approach to identify the most critical pattern locations, i.e. hotspots, in a reticle layout by computational lithography and combining process window characteristics of these patterns with across-wafer process variation data to predict where hotspots may become yield impacting defects [1,2]. The current paper establishes the impact of micro-topography on a 28nm metal layer, and its correlation with hotspot best focus variations across a production chip layout. Detailed topography measurements are obtained from an offline tool, and pattern-dependent best focus (BF) shifts are determined from litho simulations that include mask-3D effects. We also establish hotspot metrology and defect verification by SEM image contour extraction and contour analysis. This enables detection of catastrophic defects as well as quantitative characterization of pattern variability, i.e. local and global CD uniformity, across a wafer to establish hotspot defect and variability maps. Finally, we combine defect prediction and verification capabilities for process monitoring by on-product, guided hotspot metrology, i.e. with sampling locations being determined from the defect prediction model and achieved prediction accuracy (capture rate) around 75%

Neon ion beam induced pattern formation on amorphous carbon surfaces

NASA Astrophysics Data System (ADS)

Bobes, Omar; Hofsäss, Hans; Zhang, Kun

2018-02-01

We investigate the ripple pattern formation on amorphous carbon surfaces at room temperature during low energy Ne ion irradiation as a function of the ion incidence angle. Monte Carlo simulations of the curvature coefficients applied to the Bradley-Harper and Cater-Vishnyakov models, including the recent extensions by Harrison-Bradley and Hofsäss predict that pattern formation on amorphous carbon thin films should be possible for low energy Ne ions from 250 eV up to 1500 eV. Moreover, simulations are able to explain the absence of pattern formation in certain cases. Our experimental results are compared with prediction using current linear theoretical models and applying the crater function formalism, as well as Monte Carlo simulations to calculate curvature coefficients using the SDTrimSP program. Calculations indicate that no patterns should be generated up to 45° incidence angle if the dynamic behavior of the thickness of the ion irradiated layer introduced by Hofsäss is taken into account, while pattern formation most pronounced from 50° for ion energy between 250 eV and 1500 eV, which are in good agreement with our experimental data.

A proposed mathematical model for sleep patterning.

PubMed

Lawder, R E

1984-01-01

The simple model of a ramp, intersecting a triangular waveform, yields results which conform with seven generalized observations of sleep patterning; including the progressive lengthening of 'rapid-eye-movement' (REM) sleep periods within near-constant REM/nonREM cycle periods. Predicted values of REM sleep time, and of Stage 3/4 nonREM sleep time, can be computed using the observed values of other parameters. The distributions of the actual REM and Stage 3/4 times relative to the predicted values were closer to normal than the distributions relative to simple 'best line' fits. It was found that sleep onset tends to occur at a particular moment in the individual subject's '90-min cycle' (the use of a solar time-scale masks this effect), which could account for a subject with a naturally short sleep/wake cycle synchronizing to a 24-h rhythm. A combined 'sleep control system' model offers quantitative simulation of the sleep patterning of endogenous depressives and, with a different perturbation, qualitative simulation of the symptoms of narcolepsy.

Geostatistical modeling of riparian forest microclimate and its implications for sampling

USGS Publications Warehouse

Eskelson, B.N.I.; Anderson, P.D.; Hagar, J.C.; Temesgen, H.

2011-01-01

Predictive models of microclimate under various site conditions in forested headwater stream - riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (Tair). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.

Numerical approaches to model perturbation fire in turing pattern formations

NASA Astrophysics Data System (ADS)

Campagna, R.; Brancaccio, M.; Cuomo, S.; Mazzoleni, S.; Russo, L.; Siettos, K.; Giannino, F.

2017-11-01

Turing patterns were observed in chemical, physical and biological systems described by coupled reaction-diffusion equations. Several models have been formulated proposing the water as the causal mechanism of vegetation pattern formation, but this isn't an exhaustive hypothesis in some natural environments. An alternative explanation has been related to the plant-soil negative feedback. In Marasco et al. [1] the authors explored the hypothesis that both mechanisms contribute in the formation of regular and irregular vegetation patterns. The mathematical model consists in three partial differential equations (PDEs) that take into account for a dynamic balance between biomass, water and toxic compounds. A numerical approach is mandatory also to investigate on the predictions of this kind of models. In this paper we start from the mathematical model described in [1], set the model parameters such that the biomass reaches a stable spatial pattern (spots) and present preliminary studies about the occurrence of perturbing events, such as wildfire, that can affect the regularity of the biomass configuration.

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

NASA Astrophysics Data System (ADS)

Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Dewetting of patterned solid films: Towards a predictive modelling approach

NASA Astrophysics Data System (ADS)

Trautmann, M.; Cheynis, F.; Leroy, F.; Curiotto, S.; Pierre-Louis, O.; Müller, P.

2017-06-01

Owing to its ability to produce an assembly of nanoislands with controllable size and locations, the solid state dewetting of patterned films has recently received great attention. A simple Kinetic Monte Carlo model based on two reduced energetic parameters allows one to reproduce experimental observations of the dewetting morphological evolution of patterned films of Si(001) on SiO2 (or SOI for Silicon-on-Insulator) with various pattern designs. Thus, it is now possible to use KMC to drive further experiments and to optimize the pattern shapes to reach a desired dewetted structure. Comparisons between KMC simulations and dewetting experiments, at least for wire-shaped patterns, show that the prevailing dewetting mechanism depends on the wire width.

A Complex Story: Universal Preference vs. Individual Differences Shaping Aesthetic Response to Fractals Patterns.

PubMed

Street, Nichola; Forsythe, Alexandra M; Reilly, Ronan; Taylor, Richard; Helmy, Mai S

2016-01-01

Fractal patterns offer one way to represent the rough complexity of the natural world. Whilst they dominate many of our visual experiences in nature, little large-scale perceptual research has been done to explore how we respond aesthetically to these patterns. Previous research (Taylor et al., 2011) suggests that the fractal patterns with mid-range fractal dimensions (FDs) have universal aesthetic appeal. Perceptual and aesthetic responses to visual complexity have been more varied with findings suggesting both linear (Forsythe et al., 2011) and curvilinear (Berlyne, 1970) relationships. Individual differences have been found to account for many of the differences we see in aesthetic responses but some, such as culture, have received little attention within the fractal and complexity research fields. This two-study article aims to test preference responses to FD and visual complexity, using a large cohort (N = 443) of participants from around the world to allow universality claims to be tested. It explores the extent to which age, culture and gender can predict our preferences for fractally complex patterns. Following exploratory analysis that found strong correlations between FD and visual complexity, a series of linear mixed-effect models were implemented to explore if each of the individual variables could predict preference. The first tested a linear complexity model (likelihood of selecting the more complex image from the pair of images) and the second a mid-range FD model (likelihood of selecting an image within mid-range). Results show that individual differences can reliably predict preferences for complexity across culture, gender and age. However, in fitting with current findings the mid-range models show greater consistency in preference not mediated by gender, age or culture. This article supports the established theory that the mid-range fractal patterns appear to be a universal construct underlying preference but also highlights the fragility of universal claims by demonstrating individual differences in preference for the interrelated concept of visual complexity. This highlights a current stalemate in the field of empirical aesthetics.

The presence of accessory cusps in chimpanzee lower molars is consistent with a patterning cascade model of development

PubMed Central

Skinner, Matthew M; Gunz, Philipp

2010-01-01

Tooth crown morphology is of primary importance in fossil primate systematics and understanding the developmental basis of its variation facilitates phenotypic analyses of fossil teeth. Lower molars of species in the chimp/human clade (including fossil hominins) possess between four and seven cusps and this variability has been implicated in alpha taxonomy and phylogenetic systematics. What is known about the developmental basis of variation in cusp number – based primarily on experimental studies of rodent molars – suggests that cusps form under a morphodynamic, patterning cascade model involving the iterative formation of enamel knots. In this study we test whether variation in cusp 6 (C6) presence in common chimpanzee and bonobo lower molars (n = 55) is consistent with predictions derived from the patterning cascade model. Using microcomputed tomography we imaged the enamel-dentine junction of lower molars and used geometric morphometrics to examine shape variation in the molar crown correlated with variation in C6 presence (in particular the size and spacing of the dentine horns). Results indicate that C6 presence is consistent with predictions of a patterning cascade model, with larger molars exhibiting a higher frequency of C6 and with the location and size of later-forming cusps correlated with C6 variation. These results demonstrate that a patterning cascade model is appropriate for interpreting cusp variation in Pan and have implications for cusp nomenclature and the use of accessory cusp morphology in primate systematics. PMID:20629983

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

Pattern formation in three-dimensional reaction-diffusion systems

NASA Astrophysics Data System (ADS)

Callahan, T. K.; Knobloch, E.

1999-08-01

Existing group theoretic analysis of pattern formation in three dimensions [T.K. Callahan, E. Knobloch, Symmetry-breaking bifurcations on cubic lattices, Nonlinearity 10 (1997) 1179-1216] is used to make specific predictions about the formation of three-dimensional patterns in two models of the Turing instability, the Brusselator model and the Lengyel-Epstein model. Spatially periodic patterns having the periodicity of the simple cubic (SC), face-centered cubic (FCC) or body-centered cubic (BCC) lattices are considered. An efficient center manifold reduction is described and used to identify parameter regimes permitting stable lamellæ, SC, FCC, double-diamond, hexagonal prism, BCC and BCCI states. Both models possess a special wavenumber k* at which the normal form coefficients take on fixed model-independent ratios and both are described by identical bifurcation diagrams. This property is generic for two-species chemical reaction-diffusion models with a single activator and inhibitor.

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model.

PubMed

Jung, Won-Mo; Park, In-Soo; Lee, Ye-Seul; Kim, Chang-Eop; Lee, Hyangsook; Hahm, Dae-Hyun; Park, Hi-Joon; Jang, Bo-Hyoung; Chae, Younbyoung

2018-04-12

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.

Probability, not linear summation, mediates the detection of concentric orientation-defined textures.

PubMed

Schmidtmann, Gunnar; Jennings, Ben J; Bell, Jason; Kingdom, Frederick A A

2015-01-01

Previous studies investigating signal integration in circular Glass patterns have concluded that the information in these patterns is linearly summed across the entire display for detection. Here we test whether an alternative form of summation, probability summation (PS), modeled under the assumptions of Signal Detection Theory (SDT), can be rejected as a model of Glass pattern detection. PS under SDT alone predicts that the exponent β of the Quick- (or Weibull-) fitted psychometric function should decrease with increasing signal area. We measured spatial integration in circular, radial, spiral, and parallel Glass patterns, as well as comparable patterns composed of Gabors instead of dot pairs. We measured the signal-to-noise ratio required for detection as a function of the size of the area containing signal, with the remaining area containing dot-pair or Gabor-orientation noise. Contrary to some previous studies, we found that the strength of summation never reached values close to linear summation for any stimuli. More importantly, the exponent β systematically decreased with signal area, as predicted by PS under SDT. We applied a model for PS under SDT and found that it gave a good account of the data. We conclude that probability summation is the most likely basis for the detection of circular, radial, spiral, and parallel orientation-defined textures.

Mapping Plant Diversity and Composition Across North Carolina Piedmont Forest Landscapes Using Lidar-Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Hakkenberg, Christopher R.

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.

[Development of a predictive program for microbial growth under various temperature conditions].

PubMed

Fujikawa, Hiroshi; Yano, Kazuyoshi; Morozumi, Satoshi; Kimura, Bon; Fujii, Tateo

2006-12-01

A predictive program for microbial growth under various temperature conditions was developed with a mathematical model. The model was a new logistic model recently developed by us. The program predicts Escherichia coli growth in broth, Staphylococcus aureus growth and its enterotoxin production in milk, and Vibrio parahaemolyticus growth in broth at various temperature patterns. The program, which was built with Microsoft Excel (Visual Basic Application), is user-friendly; users can easily input the temperature history of a test food and obtain the prediction instantly on the computer screen. The predicted growth and toxin production can be important indices to determine whether a food is microbiologically safe or not. This program should be a useful tool to confirm the microbial safety of commercial foods.

Predictive spatial modeling of narcotic crop growth patterns

USGS Publications Warehouse

Waltz, Frederick A.; Moore, D.G.

1986-01-01

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

Effects of ignition location models on the burn patterns of simulated wildfires

USGS Publications Warehouse

Bar-Massada, A.; Syphard, A.D.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C.

2011-01-01

Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are difficult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human-caused or natural, is non-random. Thus, predictions from fire simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of fire simulation models has never been systematically explored. Our goal was to assess the difference in fire simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the influence of random and non-random ignition locations and normal and extreme weather conditions on fire size distributions and spatial patterns of burn probability. Under extreme weather conditions, fires were significantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced significantly larger fires than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in fire simulation models may substantially influence the spatial predictions of fire spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the fire simulations are conducted under extreme weather conditions when fire spread is greatest. ?? 2010 Elsevier Ltd.

Neural representations of the concepts in simple sentences: Concept activation prediction and context effects.

PubMed

Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L

2017-08-15

Although it has been possible to identify individual concepts from a concept's brain activation pattern, there have been significant obstacles to identifying a proposition from its fMRI signature. Here we demonstrate the ability to decode individual prototype sentences from readers' brain activation patterns, by using theory-driven regions of interest and semantic properties. It is possible to predict the fMRI brain activation patterns evoked by propositions and words which are entirely new to the model with reliably above-chance rank accuracy. The two core components implemented in the model that reflect the theory were the choice of intermediate semantic features and the brain regions associated with the neurosemantic dimensions. This approach also predicts the neural representation of object nouns across participants, studies, and sentence contexts. Moreover, we find that the neural representation of an agent-verb-object proto-sentence is more accurately characterized by the neural signatures of its components as they occur in a similar context than by the neural signatures of these components as they occur in isolation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes.

PubMed

Lomsadze, Alexandre; Gemayel, Karl; Tang, Shiyuyun; Borodovsky, Mark

2018-05-17

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed "heuristic" models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts. © 2018 Lomsadze et al.; Published by Cold Spring Harbor Laboratory Press.

Forecasting Enrollment in Intensive English Language Programs.

ERIC Educational Resources Information Center

Davidson, Joseph O.; Mead, Linda

The development of a successful short-range forecasting model for predicting enrollment in a university intensive English course began with examination of the characteristics (nationality, enrollment patterns, persistence) of both course participants and no-shows; the resulting figures revealed consistent patterns for country of origin and for…

Formation of banded vegetation patterns resulted from interactions between sediment deposition and vegetation growth.

PubMed

Huang, Tousheng; Zhang, Huayong; Dai, Liming; Cong, Xuebing; Ma, Shengnan

2018-03-01

This research investigates the formation of banded vegetation patterns on hillslopes affected by interactions between sediment deposition and vegetation growth. The following two perspectives in the formation of these patterns are taken into consideration: (a) increased sediment deposition from plant interception, and (b) reduced plant biomass caused by sediment accumulation. A spatial model is proposed to describe how the interactions between sediment deposition and vegetation growth promote self-organization of banded vegetation patterns. Based on theoretical and numerical analyses of the proposed spatial model, vegetation bands can result from a Turing instability mechanism. The banded vegetation patterns obtained in this research resemble patterns reported in the literature. Moreover, measured by sediment dynamics, the variation of hillslope landform can be described. The model predicts how treads on hillslopes evolve with the banded patterns. Thus, we provide a quantitative interpretation for coevolution of vegetation patterns and landforms under effects of sediment redistribution. Copyright © 2018. Published by Elsevier Masson SAS.

From Patterns to Function in Living Systems: Dryland Ecosystems as a Case Study

NASA Astrophysics Data System (ADS)

Meron, Ehud

2018-03-01

Spatial patterns are ubiquitous in animate matter. Besides their intricate structure and beauty they generally play functional roles. The capacity of living systems to remain functional in changing environments is a question of utmost importance, but its intimate relationship to pattern formation is largely unexplored. Here, we address this relationship using dryland vegetation as a case study. Following a brief introduction to pattern-formation theory, we describe a mathematical model that captures several mechanisms of vegetation pattern formation and discuss ecological contexts that showcase different mechanisms. Using this model, we unravel the different vegetation patterns that keep dryland ecosystems viable along the rainfall gradient, identify multistability ranges where fronts separating domains of alternative stable states exist, and highlight the roles of front dynamics in mitigating or reversing desertification. The utility of satellite images in testing model predictions is discussed. An outlook on outstanding open problems concludes this paper.

Winter precipitation forecast in the European and Mediterranean regions using cluster analysis

NASA Astrophysics Data System (ADS)

Molnos, S.

2017-12-01

The European and Mediterranean climates are sensitive to large-scale circulation of the atmosphere andocean making it difficult to forecast precipitation or temperature on seasonal time-scales. In addition, theMediterranean region has been identified as a hotspot for climate change and already today a drying in theMediterranean region is observed.Thus, it is critically important to predict seasonal droughts as early as possible such that water managersand stakeholders can mitigate impacts.We developed a novel cluster-based forecast method to empirically predict winter's precipitationanomalies in European and Mediterranean regions using precursors in autumn. This approach does notonly utilizes the amplitude but also the pattern of the precursors in generating the forecast.Using a toy model we show that it achieves a better forecast skill than more traditional regression models. Furthermore, we compare our algorithm with dynamic forecast models demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Morphogenesis and Complexity of the Tumor Patterns

NASA Astrophysics Data System (ADS)

Izquierdo-Kulich, E.; Nieto-Villar, J. M.

A mechanism to describe the apoptosis process at mesoscopic level through p53 is proposed in this paper. A deterministic model given by three differential equations is deduced from the mesoscopic approach, which exhibits sustained oscillations caused by a supercritical Andronov-Hopf bifurcation. Taking as hypothesis that the p53 sustained oscillation is the fundamental mechanism for apoptosis regulation; the model predicts that it is necessary a strict control of p53 to stimulated it, which is an important consideration to established new therapy strategy to fight cancer. The mathematical modeling of tumor growth allows us to describe the most important regularities of these systems. A stochastic model, based on the most important processes that take place at the level of individual cells, is proposed to predict the dynamical behavior of the expected radius of the tumor and its fractal dimension. It was found that the tumor has a characteristic fractal dimension, which contains the necessary information to predict the tumor growth until it reaches a stationary state. The mathematical modeling of tumor growth is an approach to explain the complex nature of these systems. A model that describes tumor growth was obtained by using a mesoscopic formalism and fractal dimension. This model theoretically predicts the relation between the morphology of the cell pattern and the mitosis/apoptosis quotient that helps to predict tumor growth from tumoral cells fractal dimension. The relation between the tumor macroscopic morphology and the cell pattern morphology is also determined. This could explain why the interface fractal dimension decreases with the increase of the cell pattern fractal dimension and consequently with the increase of the mitosis/apoptosis relation. Indexes to characterize tumoral cell proliferation and invasion capacities are proposed and used to predict the growth of different types of tumors. These indexes also show that the proliferation capacity is directly proportional to the invasion capacity. The proposed model assumes: i) only interface cells proliferate and invade the host, and ii) the fractal dimension of tumoral cell patterns, can reproduce the Gompertzian growth law. A mathematical model was obtained to describe the relation between the tissue morphology of cervix carcinoma and both dynamic processes of mitosis and apoptosis, and an expression to quantify the tumor aggressiveness, which in this context is associated with the tumor growth rate. The proposed model was applied to Stage III cervix carcinoma in vivo studies. In this study we found that the apoptosis rate was significantly smaller in the tumor tissues and both the mitosis rate and aggressiveness index decrease with Stage III patient's age. These quantitative results correspond to observed behavior in clinical and genetics studies. Finally, the entropy production rate was determined for avascular tumor growth. The proposed formula relates the fractal dimension of the tumor contour with the quotient between mitosis and apoptosis rate, which can be used to characterize the degree of proliferation of tumor cells. The entropy production rate was determined for fourteen tumor cell lines as a physical function of cancer robustness. The entropy production rate is a hallmark that allows us the possibility of prognosis of tumor proliferation and invasion capacities, key factors to improve cancer therapy.

Verification by Remote Sensing of an Oil Slick Movement Prediction Model

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Davis, G.; Wang, H.

1975-01-01

The author has identified the following significant results. LANDSAT, aircraft, ships, and air-dropped current drogues were deployed to determine current circulation and to track oil slick movement on four different dates in Delaware Bay. Results were used to verify a predictive model for oil slick movement and dispersion. The model predicts the behavior of oil slicks given their size, location, tidal stage (current), weather (wind), and nature of crude. Both LANDSAT satellites provided valuable data on gross circulation patterns and convergent coastal fronts which by capturing oil slicks significantly influence their movement and dispersion.

Predicting Trophic Interactions and Habitat Utilization in the California Current Ecosystem

DTIC Science & Technology

2014-09-30

on trophic interactions affecting habitat utilization and foraging patterns of California sea lions (CSL) in the California Current Large Marine...middle (sardine and anchovy) and higher (sea lions ) trophic level species. To this end, our numerical experiments are designed to isolate patterns of...NEMURO) embedded in a regional ocean circulation model (ROMS), and both coupled with a multi- species individual-based model (IBM) for forage fish

Modeling apparent color for visual evaluation of camouflage fabrics

NASA Astrophysics Data System (ADS)

Ramsey, S.; Mayo, T.; Shabaev, A.; Lambrakos, S. G.

2017-08-01

As the U.S. Navy, Army, and Special Operations Forces progress towards fielding more advanced uniforms with multi-colored and highly detailed camouflage patterning, additional test methodologies are necessary in evaluating color in these types of camouflage textiles. The apparent color is the combination of all visible wavelengths (380-760 nm) of light reflected from large (>=1m2 ) fabric sample sizes for a given standoff distance (10-25ft). Camouflage patterns lose resolution with increasing standoff distance, and eventually all colors within the pattern appear monotone (the "apparent color" of the pattern). This paper presents an apparent color prediction model that can be used for evaluation of camouflage fabrics.

Population variation in isotopic composition of shorebird feathers: Implications for determining molting grounds

USGS Publications Warehouse

Torres-Dowdall, J.; Farmer, A.H.; Bucher, E.H.; Rye, R.O.; Landis, G.

2009-01-01

Stable isotope analyses have revolutionized the study of migratory connectivity. However, as with all tools, their limitations must be understood in order to derive the maximum benefit of a particular application. The goal of this study was to evaluate the efficacy of stable isotopes of C, N, H, O and S for assigning known-origin feathers to the molting sites of migrant shorebird species wintering and breeding in Argentina. Specific objectives were to: 1) compare the efficacy of the technique for studying shorebird species with different migration patterns, life histories and habitat-use patterns; 2) evaluate the grouping of species with similar migration and habitat use patterns in a single analysis to potentially improve prediction accuracy; and 3) evaluate the potential gains in prediction accuracy that might be achieved from using multiple stable isotopes. The efficacy of stable isotope ratios to determine origin was found to vary with species. While one species (White-rumped Sandpiper, Calidris fuscicollis) had high levels of accuracy assigning samples to known origin (91% of samples correctly assigned), another (Collared Plover, Charadrius collaris) showed low levels of accuracy (52% of samples correctly assigned). Intra-individual variability may account for this difference in efficacy. The prediction model for three species with similar migration and habitat-use patterns performed poorly compared with the model for just one of the species (71% versus 91% of samples correctly assigned). Thus, combining multiple sympatric species may not improve model prediction accuracy. Increasing the number of stable isotopes in the analyses increased the accuracy of assigning shorebirds to their molting origin, but the best combination - involving a subset of all the isotopes analyzed - varied among species.

VARTM Process Modeling of Aerospace Composite Structures

NASA Technical Reports Server (NTRS)

Song, Xiao-Lan; Grimsley, Brian W.; Hubert, Pascal; Cano, Roberto J.; Loos, Alfred C.

2003-01-01

A three-dimensional model was developed to simulate the VARTM composite manufacturing process. The model considers the two important mechanisms that occur during the process: resin flow, and compaction and relaxation of the preform. The model was used to simulate infiltration of a carbon preform with an epoxy resin by the VARTM process. The model predicted flow patterns and preform thickness changes agreed qualitatively with the measured values. However, the predicted total infiltration times were much longer than measured most likely due to the inaccurate preform permeability values used in the simulation.

The Multigroup Multilevel Categorical Latent Growth Curve Models

ERIC Educational Resources Information Center

Hung, Lai-Fa

2010-01-01

Longitudinal data describe developmental patterns and enable predictions of individual changes beyond sampled time points. Major methodological issues in longitudinal data include modeling random effects, subject effects, growth curve parameters, and autoregressive residuals. This study embedded the longitudinal model within a multigroup…

Multivariate neural biomarkers of emotional states are categorically distinct.

PubMed

Kragel, Philip A; LaBar, Kevin S

2015-11-01

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

When should we expect early bursts of trait evolution in comparative data? Predictions from an evolutionary food web model.

PubMed

Ingram, T; Harmon, L J; Shurin, J B

2012-09-01

Conceptual models of adaptive radiation predict that competitive interactions among species will result in an early burst of speciation and trait evolution followed by a slowdown in diversification rates. Empirical studies often show early accumulation of lineages in phylogenetic trees, but usually fail to detect early bursts of phenotypic evolution. We use an evolutionary simulation model to assemble food webs through adaptive radiation, and examine patterns in the resulting phylogenetic trees and species' traits (body size and trophic position). We find that when foraging trade-offs result in food webs where all species occupy integer trophic levels, lineage diversity and trait disparity are concentrated early in the tree, consistent with the early burst model. In contrast, in food webs in which many omnivorous species feed at multiple trophic levels, high levels of turnover of species' identities and traits tend to eliminate the early burst signal. These results suggest testable predictions about how the niche structure of ecological communities may be reflected by macroevolutionary patterns. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

PubMed

Mari, Lorenzo; Bertuzzo, Enrico; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rinaldo, Andrea

2015-03-06

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The wing pattern of Moerarchis Durrant, 1914 (Lepidoptera: Tineidae) clarifies transitions between predictive models

PubMed Central

2017-01-01

The evolution of wing pattern in Lepidoptera is a popular area of inquiry but few studies have examined microlepidoptera, with fewer still focusing on intraspecific variation. The tineid genus Moerarchis Durrant, 1914 includes two species with high intraspecific variation of wing pattern. A subset of the specimens examined here provide, to my knowledge, the first examples of wing patterns that follow both the ‘alternating wing-margin’ and ‘uniform wing-margin’ models in different regions along the costa. These models can also be evaluated along the dorsum of Moerarchis, where a similar transition between the two models can be seen. Fusion of veins is shown not to effect wing pattern, in agreement with previous inferences that the plesiomorphic location of wing veins constrains the development of colour pattern. The significant correlation between wing length and number of wing pattern elements in Moerarchis australasiella shows that wing size can act as a major determinant of wing pattern complexity. Lastly, some M. australasiella specimens have wing patterns that conform entirely to the ‘uniform wing-margin’ model and contain more than six bands, providing new empirical insight into the century-old question of how wing venation constrains wing patterns with seven or more bands. PMID:28405390

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

PubMed

Wei, Qing; La, David; Kihara, Daisuke

2017-01-01

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

Biogeography of Anurans from the Poorly Known and Threatened Coastal Sandplains of Eastern Brazil.

PubMed

Xavier, Ariane Lima; Guedes, Thaís Barreto; Napoli, Marcelo Felgueiras

2015-01-01

The east coast of Brazil comprises an extensive area inserted in the Tropical Atlantic Domain and is represented by sandy plains of beach ridges commonly known as Restingas. The coastal environments are unique and house a rich amphibian fauna, the geographical distribution patterns of which are incipient. Biogeographical studies can explain the current distributional patterns and provide the identification of natural biogeographical units. These areas are important in elucidating the evolutionary history of the taxa and the areas where they occur. The aim of this study was to seek natural biogeographical units in the Brazilian sandy plains of beach ridges by means of distribution data of amphibians and to test the main predictions of the vicariance model to explain the patterns found. We revised and georeferenced data on the geographical distribution of 63 anuran species. We performed a search for latitudinal distribution patterns along the sandy coastal plains of Brazil using the non-metric multidimensional scaling method (NMDS) and the biotic element analysis to identify natural biogeographical units. The results showed a monotonic variation in anuran species composition along the latitudinal gradient with a break in the clinal pattern from 23°S to 25°S latitude (states of Rio de Janeiro to São Paulo). The major predictions of the vicariance model were corroborated by the detection of four biotic elements with significantly clustered distribution and by the presence of congeneric species distributed in distinct biotic elements. The results support the hypothesis that vicariance could be one of the factors responsible for the distribution patterns of the anuran communities along the sandy coastal plains of eastern Brazil. The results of the clusters are also congruent with the predictions of paleoclimatic models made for the Last Glacial Maximum of the Pleistocene, such as the presence of historical forest refugia and biogeographical patterns already detected for amphibians in the Atlantic Rainforest.

Biogeography of Anurans from the Poorly Known and Threatened Coastal Sandplains of Eastern Brazil

PubMed Central

Xavier, Ariane Lima; Guedes, Thaís Barreto; Napoli, Marcelo Felgueiras

2015-01-01

The east coast of Brazil comprises an extensive area inserted in the Tropical Atlantic Domain and is represented by sandy plains of beach ridges commonly known as Restingas. The coastal environments are unique and house a rich amphibian fauna, the geographical distribution patterns of which are incipient. Biogeographical studies can explain the current distributional patterns and provide the identification of natural biogeographical units. These areas are important in elucidating the evolutionary history of the taxa and the areas where they occur. The aim of this study was to seek natural biogeographical units in the Brazilian sandy plains of beach ridges by means of distribution data of amphibians and to test the main predictions of the vicariance model to explain the patterns found. We revised and georeferenced data on the geographical distribution of 63 anuran species. We performed a search for latitudinal distribution patterns along the sandy coastal plains of Brazil using the non-metric multidimensional scaling method (NMDS) and the biotic element analysis to identify natural biogeographical units. The results showed a monotonic variation in anuran species composition along the latitudinal gradient with a break in the clinal pattern from 23°S to 25°S latitude (states of Rio de Janeiro to São Paulo). The major predictions of the vicariance model were corroborated by the detection of four biotic elements with significantly clustered distribution and by the presence of congeneric species distributed in distinct biotic elements. The results support the hypothesis that vicariance could be one of the factors responsible for the distribution patterns of the anuran communities along the sandy coastal plains of eastern Brazil. The results of the clusters are also congruent with the predictions of paleoclimatic models made for the Last Glacial Maximum of the Pleistocene, such as the presence of historical forest refugia and biogeographical patterns already detected for amphibians in the Atlantic Rainforest. PMID:26047484

Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil.

PubMed

de Souza, Fabio Teodoro

2018-05-29

In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

Modeling of urban growth using cellular automata (CA) optimized by Particle Swarm Optimization (PSO)

NASA Astrophysics Data System (ADS)

Khalilnia, M. H.; Ghaemirad, T.; Abbaspour, R. A.

2013-09-01

In this paper, two satellite images of Tehran, the capital city of Iran, which were taken by TM and ETM+ for years 1988 and 2010 are used as the base information layers to study the changes in urban patterns of this metropolis. The patterns of urban growth for the city of Tehran are extracted in a period of twelve years using cellular automata setting the logistic regression functions as transition functions. Furthermore, the weighting coefficients of parameters affecting the urban growth, i.e. distance from urban centers, distance from rural centers, distance from agricultural centers, and neighborhood effects were selected using PSO. In order to evaluate the results of the prediction, the percent correct match index is calculated. According to the results, by combining optimization techniques with cellular automata model, the urban growth patterns can be predicted with accuracy up to 75 %.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.

PubMed

Cichosz, Simon Lebech; Johansen, Mette Dencker; Hejlesen, Ole

2015-10-14

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies. © 2015 Diabetes Technology Society.

Relative sea-level changes and crustal movements in Britain and Ireland since the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Shennan, Ian; Bradley, Sarah L.; Edwards, Robin

2018-05-01

The new sea-level database for Britain and Ireland contains >2100 data points from 86 regions and records relative sea-level (RSL) changes over the last 20 ka and across elevations ranging from ∼+40 to -55 m. It reveals radically different patterns of RSL as we move from regions near the centre of the Celtic ice sheet at the last glacial maximum to regions near and beyond the ice limits. Validated sea-level index points and limiting data show good agreement with the broad patterns of RSL change predicted by current glacial isostatic adjustment (GIA) models. The index points show no consistent pattern of synchronous coastal advance and retreat across different regions, ∼100-500 km scale, indicating that within-estuary processes, rather than decimetre- and centennial-scale oscillations in sea level, produce major controls on the temporal pattern of horizontal shifts in coastal sedimentary environments. Comparisons between the database and GIA model predictions for multiple regions provide potentially powerful constraints on various characteristics of global GIA models, including the magnitude of MWP1A, the final deglaciation of the Laurentide ice sheet and the continued melting of Antarctica after 7 ka BP.

Prediction of the light scattering patterns from bacteria colonies by a time-resolved reaction-diffusion model and the scalar diffraction theory

NASA Astrophysics Data System (ADS)

Bae, Euiwon; Bai, Nan; Aroonnual, Amornrat; Bhunia, Arun K.; Robinson, J. Paul; Hirleman, E. Daniel

2009-05-01

In order to maximize the utility of the optical scattering technology in the area of bacterial colony identification, it is necessary to have a thorough understanding of how bacteria species grow into different morphological aggregation and subsequently function as distinctive optical amplitude and phase modulators to alter the incoming Gaussian laser beam. In this paper, a 2-dimentional reaction-diffusion (RD) model with nutrient concentration, diffusion coefficient, and agar hardness as variables is investigated to explain the correlation between the various environmental parameters and the distinctive morphological aggregations formed by different bacteria species. More importantly, the morphological change of the bacterial colony against time is demonstrated by this model, which is able to characterize the spatio-temporal patterns formed by the bacteria colonies over their entire growth curve. The bacteria population density information obtained from the RD model is mathematically converted to the amplitude/phase modulation factor used in the scalar diffraction theory which predicts the light scattering patterns for bacterial colonies. The conclusions drawn from the RD model combined with the scalar diffraction theory are useful in guiding the design of the optical scattering instrument aiming at bacteria colony detection and classification.

Origin of seasonal predictability for summer climate over the Northwestern Pacific

PubMed Central

Kosaka, Yu; Xie, Shang-Ping; Lau, Ngar-Cheung; Vecchi, Gabriel A.

2013-01-01

Summer climate in the Northwestern Pacific (NWP) displays large year-to-year variability, affecting densely populated Southeast and East Asia by impacting precipitation, temperature, and tropical cyclones. The Pacific–Japan (PJ) teleconnection pattern provides a crucial link of high predictability from the tropics to East Asia. Using coupled climate model experiments, we show that the PJ pattern is the atmospheric manifestation of an air–sea coupled mode spanning the Indo-NWP warm pool. The PJ pattern forces the Indian Ocean (IO) via a westward propagating atmospheric Rossby wave. In response, IO sea surface temperature feeds back and reinforces the PJ pattern via a tropospheric Kelvin wave. Ocean coupling increases both the amplitude and temporal persistence of the PJ pattern. Cross-correlation of ocean–atmospheric anomalies confirms the coupled nature of this PJIO mode. The ocean–atmosphere feedback explains why the last echoes of El Niño–Southern Oscillation are found in the IO-NWP in the form of the PJIO mode. We demonstrate that the PJIO mode is indeed highly predictable; a characteristic that can enable benefits to society. PMID:23610388

HYDRAULIC REDISTRIBUTION OF SOIL WATER IN TWO OLD-GROWTH CONIFEROUS FORESTS: QUANTIFYING PATTERNS AND CONTROLS

EPA Science Inventory

Although hydraulic redistribution of soil water (HR) by roots is a widespread phenomenon, the processes governing spatial and temporal patterns of HR are not well understood. We incorporated soil/plant biophysical properties into a simple model based on Darcy's law to predict sea...

A Computational Model of Event Segmentation from Perceptual Prediction

ERIC Educational Resources Information Center

Reynolds, Jeremy R.; Zacks, Jeffrey M.; Braver, Todd S.

2007-01-01

People tend to perceive ongoing continuous activity as series of discrete events. This partitioning of continuous activity may occur, in part, because events correspond to dynamic patterns that have recurred across different contexts. Recurring patterns may lead to reliable sequential dependencies in observers' experiences, which then can be used…

A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features

PubMed Central

Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

2016-01-01

Aims To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. Methods and Results We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas. Conclusions Female, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases. PMID:27648828

Spatiotemporal dynamics of landscape pattern and hydrologic process in watershed systems

NASA Astrophysics Data System (ADS)

Randhir, Timothy O.; Tsvetkova, Olga

2011-06-01

SummaryLand use change is influenced by spatial and temporal factors that interact with watershed resources. Modeling these changes is critical to evaluate emerging land use patterns and to predict variation in water quantity and quality. The objective of this study is to model the nature and emergence of spatial patterns in land use and water resource impacts using a spatially explicit and dynamic landscape simulation. Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation. The MCMC (Monte Carlo Markov Chain) analysis with cellular automation is linked to hydrologic equations to simulate landscape patterns and processes. The spatiotemporal watershed dynamics (SWD) model is applied to a subwatershed in the Blackstone River watershed of Massachusetts to predict potential land use changes and expected runoff and sediment loading. Changes in watershed land use and water resources are evaluated over 100 years at a yearly time step. Results show high potential for rapid urbanization that could result in lowering of groundwater recharge and increased storm water peaks. The watershed faces potential decreases in agricultural and forest area that affect open space and pervious cover of the watershed system. Water quality deteriorated due to increased runoff which can also impact stream morphology. While overland erosion decreased, instream erosion increased from increased runoff from urban areas. Use of urban best management practices (BMPs) in sensitive locations, preventive strategies, and long-term conservation planning will be useful in sustaining the watershed system.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

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

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

An equilibrium-point model for fast, single-joint movement: I. Emergence of strategy-dependent EMG patterns.

PubMed

Latash, M L; Gottlieb, G L

1991-09-01

We describe a model for the regulation of fast, single-joint movements, based on the equilibrium-point hypothesis. Limb movement follows constant rate shifts of independently regulated neuromuscular variables. The independently regulated variables are tentatively identified as thresholds of a length sensitive reflex for each of the participating muscles. We use the model to predict EMG patterns associated with changes in the conditions of movement execution, specifically, changes in movement times, velocities, amplitudes, and moments of limb inertia. The approach provides a theoretical neural framework for the dual-strategy hypothesis, which considers certain movements to be results of one of two basic, speed-sensitive or speed-insensitive strategies. This model is advanced as an alternative to pattern-imposing models based on explicit regulation of timing and amplitudes of signals that are explicitly manifest in the EMG patterns.

Modeling Menstrual Cycle Length and Variability at the Approach of Menopause Using Hierarchical Change Point Models

PubMed Central

Huang, Xiaobi; Elliott, Michael R.; Harlow, Siobán D.

2013-01-01

SUMMARY As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes. PMID:24729638

Contrasting support for alternative models of genomic variation based on microhabitat preference: species-specific effects of climate change in alpine sedges.

PubMed

Massatti, Rob; Knowles, L Lacey

2016-08-01

Deterministic processes may uniquely affect codistributed species' phylogeographic patterns such that discordant genetic variation among taxa is predicted. Yet, explicitly testing expectations of genomic discordance in a statistical framework remains challenging. Here, we construct spatially and temporally dynamic models to investigate the hypothesized effect of microhabitat preferences on the permeability of glaciated regions to gene flow in two closely related montane species. Utilizing environmental niche models from the Last Glacial Maximum and the present to inform demographic models of changes in habitat suitability over time, we evaluate the relative probabilities of two alternative models using approximate Bayesian computation (ABC) in which glaciated regions are either (i) permeable or (ii) a barrier to gene flow. Results based on the fit of the empirical data to data sets simulated using a spatially explicit coalescent under alternative models indicate that genomic data are consistent with predictions about the hypothesized role of microhabitat in generating discordant patterns of genetic variation among the taxa. Specifically, a model in which glaciated areas acted as a barrier was much more probable based on patterns of genomic variation in Carex nova, a wet-adapted species. However, in the dry-adapted Carex chalciolepis, the permeable model was more probable, although the difference in the support of the models was small. This work highlights how statistical inferences can be used to distinguish deterministic processes that are expected to result in discordant genomic patterns among species, including species-specific responses to climate change. © 2016 John Wiley & Sons Ltd.

A GRAPH PARTITIONING APPROACH TO PREDICTING PATTERNS IN LATERAL INHIBITION SYSTEMS

PubMed Central

RUFINO FERREIRA, ANA S.; ARCAK, MURAT

2017-01-01

We analyze spatial patterns on networks of cells where adjacent cells inhibit each other through contact signaling. We represent the network as a graph where each vertex represents the dynamics of identical individual cells and where graph edges represent cell-to-cell signaling. To predict steady-state patterns we find equitable partitions of the graph vertices and assign them into disjoint classes. We then use results from monotone systems theory to prove the existence of patterns that are structured in such a way that all the cells in the same class have the same final fate. To study the stability properties of these patterns, we rely on the graph partition to perform a block decomposition of the system. Then, to guarantee stability, we provide a small-gain type criterion that depends on the input-output properties of each cell in the reduced system. Finally, we discuss pattern formation in stochastic models. With the help of a modal decomposition we show that noise can enhance the parameter region where patterning occurs. PMID:29225552

Temporal consistency of spatial pattern in growth of the mussel, Mytilus edulis: Implications for predictive modelling

NASA Astrophysics Data System (ADS)

Bergström, Per; Lindegarth, Susanne; Lindegarth, Mats

2013-10-01

Human pressures on coastal seas are increasing and methods for sustainable management, including spatial planning and mitigative actions, are therefore needed. In coastal areas worldwide, the development of mussel farming as an economically and ecologically sustainable industry requires geographic information on the growth and potential production capacity. In practice this means that coherent maps of temporally stable spatial patterns of growth need to be available in the planning process and that maps need to be based on mechanistic or empirical models. Therefore, as a first step towards development of models of growth, we assessed empirically the fundamental requirement that there are temporally consistent spatial patterns of growth in the blue mussel, Mytilus edulis. Using a pilot study we designed and dimensioned a transplant experiment, where the spatial consistency in the growth of mussels was evaluated at two resolutions. We found strong temporal and scale-dependent spatial variability in growth but patterns suggested that spatial patterns were uncoupled between growth of shell and that of soft tissue. Spatial patterns of shell growth were complex and largely inconsistent among years. Importantly, however, the growth of soft tissue was qualitatively consistent among years at the scale of km. The results suggest that processes affecting the whole coastal area cause substantial differences in growth of soft tissue among years but that factors varying at the scale of km create strong and persistent spatial patterns of growth, with a potential doubling of productivity by identifying the most suitable locations. We conclude that the observed spatial consistency provides a basis for further development of predictive modelling and mapping of soft tissue growth in these coastal areas. Potential causes of observed patterns, consequences for mussel-farming as a tool for mitigating eutrophication, aspects of precision of modelling and sampling of mussel growth as well as ecological functions in general are discussed.

A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.

PubMed

Mian, Shahid; Ball, Graham; Hornbuckle, Jo; Holding, Finn; Carmichael, James; Ellis, Ian; Ali, Selman; Li, Geng; McArdle, Stephanie; Creaser, Colin; Rees, Robert

2003-09-01

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.

Congruence in demersal fish, macroinvertebrate, and macroalgal community turnover on shallow temperate reefs.

PubMed

Thomson, Russell J; Hill, Nicole A; Leaper, Rebecca; Ellis, Nick; Pitcher, C Roland; Barrett, Neville S; Edgar, Graham J

2014-03-01

To support coastal planning through improved understanding of patterns of biotic and abiotic surrogacy at broad scales, we used gradient forest modeling (GFM) to analyze and predict spatial patterns of compositional turnover of demersal fishes, macroinvertebrates, and macroalgae on shallow, temperate Australian reefs. Predictive models were first developed using environmental surrogates with estimates of prediction uncertainty, and then the efficacy of the three assemblages as biosurrogates for each other was assessed. Data from underwater visual surveys of subtidal rocky reefs were collected from the southeastern coastline of continental Australia (including South Australia and Victoria) and the northern coastline of Tasmania. These data were combined with 0.01 degree-resolution gridded environmental variables to develop statistical models of compositional turnover (beta diversity) using GFM. GFM extends the machine learning, ensemble tree-based method of random forests (RF), to allow the simultaneous modeling of multiple taxa. The models were used to generate predictions of compositional turnover for each of the three assemblages within unsurveyed areas across the 6600 km of coastline in the region of interest. The most important predictor for all three assemblages was variability in sea surface temperature (measured as standard deviation from measures taken interannually). Spatial predictions of compositional turnover within unsurveyed areas across the region of interest were remarkably congruent across the three taxa. However, the greatest uncertainty in these predictions varied in location among the different assemblages. Pairwise congruency comparisons of observed and predicted turnover among the three assemblages showed that invertebrate and macroalgal biodiversity were most similar, followed by fishes and macroalgae, and lastly fishes and invertebrate biodiversity, suggesting that of the three assemblages, macroalgae would make the best biosurrogate for both invertebrate and fish compositional turnover.

Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties

NASA Astrophysics Data System (ADS)

Eisenhart, T.; Josset, L.; Rising, J. A.; Devineni, N.; Lall, U.

2017-12-01

In the wake of recent water crises, the need to understand and predict the risk of water stress in urban and rural areas has grown. This understanding has the potential to improve decision making in public resource management, policy making, risk management and investment decisions. Assuming an underlying relationship between urban and rural water stress and observable features, we apply Deep Learning and Supervised Learning models to uncover hidden nonlinear patterns from spatiotemporal datasets. Results of interest includes prediction accuracy on extreme categories (i.e. urban areas highly prone to water stress) and not solely the average risk for urban or rural area, which adds complexity to the tuning of model parameters. We first label urban water stressed counties using annual water quality violations and compile a comprehensive spatiotemporal dataset that captures the yearly evolution of climatic, demographic and economic factors of more than 3,000 US counties over the 1980-2010 period. As county-level data reporting is not done on a yearly basis, we test multiple imputation methods to get around the issue of missing data. Using Python libraries, TensorFlow and scikit-learn, we apply and compare the ability of, amongst other methods, Recurrent Neural Networks (testing both LSTM and GRU cells), Convolutional Neural Networks and Support Vector Machines to predict urban water stress. We evaluate the performance of those models over multiple time spans and combine methods to diminish the risk of overfitting and increase prediction power on test sets. This methodology seeks to identify hidden nonlinear patterns to assess the predominant data features that influence urban and rural water stress. Results from this application at the national scale will assess the performance of deep learning models to predict water stress risk areas across all US counties and will highlight a predominant Machine Learning method for modeling water stress risk using spatiotemporal data.

Probability of US Heat Waves Affected by a Subseasonal Planetary Wave Pattern

NASA Technical Reports Server (NTRS)

Teng, Haiyan; Branstator, Grant; Wang, Hailan; Meehl, Gerald A.; Washington, Warren M.

2013-01-01

Heat waves are thought to result from subseasonal atmospheric variability. Atmospheric phenomena driven by tropical convection, such as the Asian monsoon, have been considered potential sources of predictability on subseasonal timescales. Mid-latitude atmospheric dynamics have been considered too chaotic to allow significant prediction skill of lead times beyond the typical 10-day range of weather forecasts. Here we use a 12,000-year integration of an atmospheric general circulation model to identify a pattern of subseasonal atmospheric variability that can help improve forecast skill for heat waves in the United States. We find that heat waves tend to be preceded by 15-20 days by a pattern of anomalous atmospheric planetary waves with a wavenumber of 5. This circulation pattern can arise as a result of internal atmospheric dynamics and is not necessarily linked to tropical heating.We conclude that some mid-latitude circulation anomalies that increase the probability of heat waves are predictable beyond the typical weather forecast range.

26th International Symposium on Ballistics

DTIC Science & Technology

2011-09-16

judicious use of analytical predictions correlated with ballistic testing and post - test failure morphology investigations. •Our approach...ballistic predictions. The numerical predictions correlate well with the damage pattern. Post - Test Morphology Simulation Imbedded Steel Plate Removed Post ... Test •Numerical simulation of damage to embedded steel plate compares well with the post - test plate morphology •Multi-strike modeling in work

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

An Application of the Theory of Planned Behavior to Sorority Alcohol Consumption

PubMed Central

Huchting, Karen; Lac, Andrew; LaBrie, Joseph W.

2008-01-01

Greek-affiliated college students have been found to drink more heavily and frequently than other students. With female student drinking on the rise over the past decade, sorority women may be at particular risk for heavy consumption patterns. The current study is the first to apply the Theory of Planned Behavior (TPB) to examine drinking patterns among a sorority-only sample. Two-hundred and forty-seven sorority members completed questionnaires measuring TPB variables of attitudes, norms, perceived behavioral control, and intentions, with drinking behaviors measured one month later. Latent structural equation modeling examined the pathways of the TPB model. Intentions to drink mediated the relationship between attitudes and norms on drinking behavior. Subjective norms predicted intentions to drink more than attitudes or perceived behavioral control. Perceived behavioral control did not predict intentions but did predict drinking behaviors. Interpretation and suggestions from these findings are discussed. PMID:18055130

[Transmission dynamic model for echinococcosis granulosus: establishment and application].

PubMed

Yang, Shi-Jie; Wu, Wei-Ping

2009-06-01

A dynamic model of disease can be used to quantitatively describe the pattern and characteristics of disease transmission, predict the disease status and evaluate the efficacy of control strategy. This review summarizes the basic transmission dynamic models of echinococcosis granulosus and their application.

Modeling smoke plume patterns in drainage flows

Treesearch

M.A. Fosberg

1985-01-01

A three-dimensional diagnostic wind model for use in complex terrain has been combined with a three-dimensional trajectory and puff air quality model. The wind model utilizes a terrain following coordinate system and conserves both mass and momentum. The wind model provides the winds required by the predictive trajectory and puff dispersion model. Both the wind model...

Landscape prediction and mapping of game fish biomass, an ecosystem service of Michigan rivers

USGS Publications Warehouse

Esselman, Peter C.; Stevenson, R Jan; Lupi, Frank; Riseng, Catherine M.; Wiley, Michael J.

2015-01-01

The increased integration of ecosystem service concepts into natural resource management places renewed emphasis on prediction and mapping of fish biomass as a major provisioning service of rivers. The goals of this study were to predict and map patterns of fish biomass as a proxy for the availability of catchable fish for anglers in rivers and to identify the strongest landscape constraints on fish productivity. We examined hypotheses about fish responses to total phosphorus (TP), as TP is a growth-limiting nutrient known to cause increases (subsidy response) and/or decreases (stress response) in fish biomass depending on its concentration and the species being considered. Boosted regression trees were used to define nonlinear functions that predicted the standing crops of Brook Trout Salvelinus fontinalis, Brown Trout Salmo trutta, Smallmouth Bass Micropterus dolomieu, panfishes (seven centrarchid species), and Walleye Sander vitreus by using landscape and modeled local-scale predictors. Fitted models were highly significant and explained 22–56% of the variation in validation data sets. Nonlinear and threshold responses were apparent for numerous predictors, including TP concentration, which had significant effects on all except the Walleye fishery. Brook Trout and Smallmouth Bass exhibited both subsidy and stress responses, panfish biomass exhibited a subsidy response only, and Brown Trout exhibited a stress response. Maps of reach-specific standing crop predictions showed patterns of predicted fish biomass that corresponded to spatial patterns in catchment area, water temperature, land cover, and nutrient availability. Maps illustrated predictions of higher trout biomass in coldwater streams draining glacial till in northern Michigan, higher Smallmouth Bass and panfish biomasses in warmwater systems of southern Michigan, and high Walleye biomass in large main-stem rivers throughout the state. Our results allow fisheries managers to examine the biomass potential of streams, describe geographic patterns of fisheries, explore possible nutrient management targets, and identify habitats that are candidates for species management.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Predicting driver from front passenger using only the postmortem pattern of injury following a motor vehicle collision.

PubMed

Curtin, Eleanor; Langlois, Neil E I

2007-10-01

This study aimed to establish whether post-mortem injury patterns can assist in distinguishing drivers from front seat passengers among victims of motor vehicle collisions without regard to collision type, vehicle type or if safety equipment had been used. Injuries sustained by 206 drivers and 91 front seat passengers were catalogued from post-mortem reports. Injuries were coded for the body region, depth and location of the injury. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger. Drivers were more likely to sustain the following injuries: brain injury; fractures to the right femur, right posterior ribs, base of skull, right humerus and right shoulder; and superficial wounds at the right lateral and posterior thigh, right face, right and left anterior knee, right anterior shoulder, lateral right arm and forearm and left anterior thigh. Front passengers were more vulnerable to splenic injury; fractures to the left posterior and anterior ribs, left shoulder and left femur; and superficial wounds at the left anterior shoulder region and left lateral neck. Linear discriminant analysis generated a model for predicting seating position based on the presence of injury to certain regions of the body; the overall predictive accuracy of the model was 69.3%. It was found that driver and front passenger fatalities receive different injury patterns from motor vehicle collisions, regardless of collision type. A larger study is required to improve the predictive accuracy of this model and to ascertain its value to forensic medicine.

Enhanced seasonal predictability of the summer mean temperature in Central Europe favored by new dominant weather patterns

NASA Astrophysics Data System (ADS)

Hoffmann, P.

2018-04-01

In this study two complementary approaches have been combined to estimate the reliability of the data-driven seasonal predictability of the meteorological summer mean temperature (T_{JJA}) over Europe. The developed model is based on linear regressions and uses early season predictors to estimate the target value T_{JJA}. We found for the Potsdam (Germany) climate station that the monthly standard deviations (σ) from January to April and the temperature mean ( m) in April are good predictors to describe T_{JJA} after 1990. However, before 1990 the model failed. The core region where this model works is the north-eastern part of Central Europe. We also analyzed long-term trends of monthly Hess/Brezowsky weather types as possible causes of the dynamical changes. In spring, a significant increase of the occurrences for two opposite weather patterns was found: Zonal Ridge across Central Europe (BM) and Trough over Central Europe (TRM). Both currently make up about 30% of the total alternating weather systems over Europe. Other weather types are predominantly decreasing or their trends are not significant. Thus, the predictability may be attributed to these two weather types where the difference between the two Z500 composite patterns is large. This also applies to the north-eastern part of Central Europe. Finally, the detected enhanced seasonal predictability over Europe is alarming, because severe side effects may occur. One of these are more frequent climate extremes in summer half-year.

Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields.

PubMed

Jahandideh, Samad; Abdolmaleki, Parviz; Movahedi, Mohammad Mehdi

2010-02-01

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes. (c) 2009 Wiley-Liss, Inc.

Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

PubMed

Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

2010-07-08

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the remaining nucleosomes follow a statistical positioning model.

Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

PubMed Central

Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

2010-01-01

DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the remaining nucleosomes follow a statistical positioning model. PMID:20628623

Skillful prediction of hot temperature extremes over the source region of ancient Silk Road.

PubMed

Zhang, Jingyong; Yang, Zhanmei; Wu, Lingyun

2018-04-27

The source region of ancient Silk Road (SRASR) in China, a region of around 150 million people, faces a rapidly increased risk of extreme heat in summer. In this study, we develop statistical models to predict summer hot temperature extremes over the SRASR based on a timescale decomposition approach. Results show that after removing the linear trends, the inter-annual components of summer hot days and heatwaves over the SRASR are significantly related with those of spring soil temperature over Central Asia and sea surface temperature over Northwest Atlantic while their inter-decadal components are closely linked to those of spring East Pacific/North Pacific pattern and Atlantic Multidecadal Oscillation for 1979-2016. The physical processes involved are also discussed. Leave-one-out cross-validation for detrended 1979-2016 time series indicates that the statistical models based on identified spring predictors can predict 47% and 57% of the total variances of summer hot days and heatwaves averaged over the SRASR, respectively. When the linear trends are put back, the prediction skills increase substantially to 64% and 70%. Hindcast experiments for 2012-2016 show high skills in predicting spatial patterns of hot temperature extremes over the SRASR. The statistical models proposed herein can be easily applied to operational seasonal forecasting.

How many kinds of reasoning? Inference, probability, and natural language semantics.

PubMed

Lassiter, Daniel; Goodman, Noah D

2015-03-01

The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictability of Road Traffic and Congestion in Urban Areas

PubMed Central

Wang, Jingyuan; Mao, Yu; Li, Jing; Xiong, Zhang; Wang, Wen-Xu

2015-01-01

Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any priori knowledge of drivers' origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers' behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion. PMID:25849534

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

Automated Discovery and Modeling of Sequential Patterns Preceding Events of Interest

NASA Technical Reports Server (NTRS)

Rohloff, Kurt

2010-01-01

The integration of emerging data manipulation technologies has enabled a paradigm shift in practitioners' abilities to understand and anticipate events of interest in complex systems. Example events of interest include outbreaks of socio-political violence in nation-states. Rather than relying on human-centric modeling efforts that are limited by the availability of SMEs, automated data processing technologies has enabled the development of innovative automated complex system modeling and predictive analysis technologies. We introduce one such emerging modeling technology - the sequential pattern methodology. We have applied the sequential pattern methodology to automatically identify patterns of observed behavior that precede outbreaks of socio-political violence such as riots, rebellions and coups in nation-states. The sequential pattern methodology is a groundbreaking approach to automated complex system model discovery because it generates easily interpretable patterns based on direct observations of sampled factor data for a deeper understanding of societal behaviors that is tolerant of observation noise and missing data. The discovered patterns are simple to interpret and mimic human's identifications of observed trends in temporal data. Discovered patterns also provide an automated forecasting ability: we discuss an example of using discovered patterns coupled with a rich data environment to forecast various types of socio-political violence in nation-states.

Constraining geostatistical models with hydrological data to improve prediction realism

NASA Astrophysics Data System (ADS)

Demyanov, V.; Rojas, T.; Christie, M.; Arnold, D.

2012-04-01

Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.

Strategies for Controlling Non-Transmissible Infection Outbreaks Using a Large Human Movement Data Set

PubMed Central

Hancock, Penelope A.; Rehman, Yasmin; Hall, Ian M.; Edeghere, Obaghe; Danon, Leon; House, Thomas A.; Keeling, Matthew J.

2014-01-01

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak. PMID:25211122

A Single-System Model Predicts Recognition Memory and Repetition Priming in Amnesia

PubMed Central

Kessels, Roy P.C.; Wester, Arie J.; Shanks, David R.

2014-01-01

We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. PMID:25122896

Predicting human genetic interactions from cancer genome evolution.

PubMed

Lu, Xiaowen; Megchelenbrink, Wout; Notebaart, Richard A; Huynen, Martijn A

2015-01-01

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification.

PubMed

Wang, Shouyi; Bowen, Stephen R; Chaovalitwongse, W Art; Sandison, George A; Grabowski, Thomas J; Kinahan, Paul E

2014-02-21

The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUV(peak)) over lesions of interest. Relative differences in SUV(peak) between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUV(peak) values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.

Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification

NASA Astrophysics Data System (ADS)

Wang, Shouyi; Bowen, Stephen R.; Chaovalitwongse, W. Art; Sandison, George A.; Grabowski, Thomas J.; Kinahan, Paul E.

2014-02-01

The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUVpeak) over lesions of interest. Relative differences in SUVpeak between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUVpeak values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation when clinicians quantitatively assess PET/CT for therapy target definition and response assessment.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds.

PubMed

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-12-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal-habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities.

Semantic representation in the white matter pathway

PubMed Central

Fang, Yuxing; Wang, Xiaosha; Zhong, Suyu; Song, Luping; Han, Zaizhu; Gong, Gaolang

2018-01-01

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes. PMID:29624578

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

PubMed Central

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-01-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal–habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities. PMID:25558353

a Maximum Entropy Model of the Bearded Capuchin Monkey Habitat Incorporating Topography and Spectral Unmixing Analysis

NASA Astrophysics Data System (ADS)

Howard, A. M.; Bernardes, S.; Nibbelink, N.; Biondi, L.; Presotto, A.; Fragaszy, D. M.; Madden, M.

2012-07-01

Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.

Towards a Statistical Model of Tropical Cyclone Genesis

NASA Astrophysics Data System (ADS)

Fernandez, A.; Kashinath, K.; McAuliffe, J.; Prabhat, M.; Stark, P. B.; Wehner, M. F.

2017-12-01

Tropical Cyclones (TCs) are important extreme weather phenomena that have a strong impact on humans. TC forecasts are largely based on global numerical models that produce TC-like features. Aspects of Tropical Cyclones such as their formation/genesis, evolution, intensification and dissipation over land are important and challenging problems in climate science. This study investigates the environmental conditions associated with Tropical Cyclone Genesis (TCG) by testing how accurately a statistical model can predict TCG in the CAM5.1 climate model. TCG events are defined using TECA software @inproceedings{Prabhat2015teca, title={TECA: Petascale Pattern Recognition for Climate Science}, author={Prabhat and Byna, Surendra and Vishwanath, Venkatram and Dart, Eli and Wehner, Michael and Collins, William D}, booktitle={Computer Analysis of Images and Patterns}, pages={426-436}, year={2015}, organization={Springer}} to extract TC trajectories from CAM5.1. L1-regularized logistic regression (L1LR) is applied to the CAM5.1 output. The predictions have nearly perfect accuracy for data not associated with TC tracks and high accuracy differentiating between high vorticity and low vorticity systems. The model's active variables largely correspond to current hypotheses about important factors for TCG, such as wind field patterns and local pressure minima, and suggests new routes for investigation. Furthermore, our model's predictions of TC activity are competitive with the output of an instantaneous version of Emanuel and Nolan's Genesis Potential Index (GPI) @inproceedings{eman04, title = "Tropical cyclone activity and the global climate system", author = "Kerry Emanuel and Nolan, {David S.}", year = "2004", pages = "240-241", booktitle = "26th Conference on Hurricanes and Tropical Meteorology"}.

Predicting assemblages and species richness of endemic fish in the upper Yangtze River.

PubMed

He, Yongfeng; Wang, Jianwei; Lek-Ang, Sithan; Lek, Sovan

2010-09-01

The present work describes the ability of two modeling methods, Classification and Regression Tree (CART) and Random Forest (RF), to predict endemic fish assemblages and species richness in the upper Yangtze River, and then to identify the determinant environmental factors contributing to the models. The models included 24 predictor variables and 2 response variables (fish assemblage and species richness) for a total of 46 site units. The predictive quality of the modeling approaches was judged with a leave-one-out validation procedure. There was an average success of 60.9% and 71.7% to assign each site unit to the correct assemblage of fish, and 73% and 84% to explain the variance in species richness, by using CART and RF models, respectively. RF proved to be better than CART in terms of accuracy and efficiency in ecological applications. In any case, the mixed models including both land cover and river characteristic variables were more powerful than either individual one in explaining the endemic fish distribution pattern in the upper Yangtze River. For instance, altitude, slope, length, discharge, runoff, farmland and alpine and sub-alpine meadow played important roles in driving the observed endemic fish assemblage structure, while farmland, slope grassland, discharge, runoff, altitude and drainage area in explaining the observed patterns of endemic species richness. Therefore, the various effects of human activity on natural aquatic ecosystems, in particular, the flow modification of the river and the land use changes may have a considerable effect on the endemic fish distribution patterns on a regional scale. Copyright 2010 Elsevier B.V. All rights reserved.

Assessment of a numerical model to reproduce event‐scale erosion and deposition distributions in a braided river

PubMed Central

Measures, R.; Hicks, D. M.; Brasington, J.

2016-01-01

Abstract Numerical morphological modeling of braided rivers, using a physics‐based approach, is increasingly used as a technique to explore controls on river pattern and, from an applied perspective, to simulate the impact of channel modifications. This paper assesses a depth‐averaged nonuniform sediment model (Delft3D) to predict the morphodynamics of a 2.5 km long reach of the braided Rees River, New Zealand, during a single high‐flow event. Evaluation of model performance primarily focused upon using high‐resolution Digital Elevation Models (DEMs) of Difference, derived from a fusion of terrestrial laser scanning and optical empirical bathymetric mapping, to compare observed and predicted patterns of erosion and deposition and reach‐scale sediment budgets. For the calibrated model, this was supplemented with planform metrics (e.g., braiding intensity). Extensive sensitivity analysis of model functions and parameters was executed, including consideration of numerical scheme for bed load component calculations, hydraulics, bed composition, bed load transport and bed slope effects, bank erosion, and frequency of calculations. Total predicted volumes of erosion and deposition corresponded well to those observed. The difference between predicted and observed volumes of erosion was less than the factor of two that characterizes the accuracy of the Gaeuman et al. bed load transport formula. Grain size distributions were best represented using two φ intervals. For unsteady flows, results were sensitive to the morphological time scale factor. The approach of comparing observed and predicted morphological sediment budgets shows the value of using natural experiment data sets for model testing. Sensitivity results are transferable to guide Delft3D applications to other rivers. PMID:27708477

State-space prediction of spring discharge in a karst catchment in southwest China

NASA Astrophysics Data System (ADS)

Li, Zhenwei; Xu, Xianli; Liu, Meixian; Li, Xuezhang; Zhang, Rongfei; Wang, Kelin; Xu, Chaohao

2017-06-01

Southwest China represents one of the largest continuous karst regions in the world. It is estimated that around 1.7 million people are heavily dependent on water derived from karst springs in southwest China. However, there is a limited amount of water supply in this region. Moreover, there is not enough information on temporal patterns of spring discharge in the area. In this context, it is essential to accurately predict spring discharge, as well as understand karst hydrological processes in a thorough manner, so that water shortages in this area could be predicted and managed efficiently. The objectives of this study were to determine the primary factors that govern spring discharge patterns and to develop a state-space model to predict spring discharge. Spring discharge, precipitation (PT), relative humidity (RD), water temperature (WD), and electrical conductivity (EC) were the variables analyzed in the present work, and they were monitored at two different locations (referred to as karst springs A and B, respectively, in this paper) in a karst catchment area in southwest China from May to November 2015. Results showed that a state-space model using any combinations of variables outperformed a classical linear regression, a back-propagation artificial neural network model, and a least square support vector machine in modeling spring discharge time series for karst spring A. The best state-space model was obtained by using PT and RD, which accounted for 99.9% of the total variation in spring discharge. This model was then applied to an independent data set obtained from karst spring B, and it provided accurate spring discharge estimates. Therefore, state-space modeling was a useful tool for predicting spring discharge in karst regions in southwest China, and this modeling procedure may help researchers to obtain accurate results in other karst regions.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

PubMed

Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-05-01

Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

PubMed Central

Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-01-01

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. PMID:27655861

Assessment of a numerical model to reproduce event-scale erosion and deposition distributions in a braided river.

PubMed

Williams, R D; Measures, R; Hicks, D M; Brasington, J

2016-08-01

Numerical morphological modeling of braided rivers, using a physics-based approach, is increasingly used as a technique to explore controls on river pattern and, from an applied perspective, to simulate the impact of channel modifications. This paper assesses a depth-averaged nonuniform sediment model (Delft3D) to predict the morphodynamics of a 2.5 km long reach of the braided Rees River, New Zealand, during a single high-flow event. Evaluation of model performance primarily focused upon using high-resolution Digital Elevation Models (DEMs) of Difference, derived from a fusion of terrestrial laser scanning and optical empirical bathymetric mapping, to compare observed and predicted patterns of erosion and deposition and reach-scale sediment budgets. For the calibrated model, this was supplemented with planform metrics (e.g., braiding intensity). Extensive sensitivity analysis of model functions and parameters was executed, including consideration of numerical scheme for bed load component calculations, hydraulics, bed composition, bed load transport and bed slope effects, bank erosion, and frequency of calculations. Total predicted volumes of erosion and deposition corresponded well to those observed. The difference between predicted and observed volumes of erosion was less than the factor of two that characterizes the accuracy of the Gaeuman et al. bed load transport formula. Grain size distributions were best represented using two φ intervals. For unsteady flows, results were sensitive to the morphological time scale factor. The approach of comparing observed and predicted morphological sediment budgets shows the value of using natural experiment data sets for model testing. Sensitivity results are transferable to guide Delft3D applications to other rivers.

Crustal motion measurements from the POLENET Antarctic Network: comparisons with glacial isostatic adjustment models

NASA Astrophysics Data System (ADS)

Wilson, T. J.; Konfal, S. A.; Bevis, M. G.; Spada, G.; Melini, D.; Barletta, V. R.; Kendrick, E. C.; Saddler, D.; Smalley, R., Jr.; Dalziel, I. W. D.; Willis, M. J.

2016-12-01

Crustal motions measured by GPS provide a unique proxy record of ice mass change, due to the elastic and viscoelastic response of the earth to removal of ice loads. The ANET/POLENET array of bedrock GPS sites spans much of the Antarctic interior, encompassing regions where glacial isostatic adjustment (GIA) models predict large crustal displacements due to LGM ice loss and including coastal West Antarctica where major modern ice mass loss is documented. To isolate the long-term GIA component of measured crustal motions, we computed and removed elastic displacements due to recent ice mass change. We used the annually resolved ice mass balance data from Martín-Español et al. (2016) derived from a statistical inversion of satellite altimetry, gravimetry, and elastic-corrected GPS data for the period 2003-2013. The Regional Elastic Rebound Calculator (REAR) [Melini et al., 2015] was used to compute elastic vertical and horizontal surface displacements. Uplift due to elastic rebound is substantial in West Antarctica, very minimal in East Antarctica, and variable across the Weddell Embayment. The ANET GPS-derived crustal motion patterns ascribed to non-elastic GIA are spatially complex and differ significantly in magnitude from model predictions. We present a systematic comparison of measured and predicted velocities within different sectors of Antarctica, in order to examine spatial patterns relative to modern ice mass changes, ice history model uncertainties, and lateral variations in earth properties. In the Weddell Embayment region most vertical velocities are lower than uplift predicted by GIA models. Several sites in the southernmost Transantarctic Mountains and the Whitmore Mountains, where small ice mass increase occurs, have vertical uplift significantly exceeding GIA model predictions. There is an intriguing spatial correlation of these fast-moving sites with a low-velocity anomaly in the upper mantle documented by analysis of teleseismic Rayleigh waves by Heeszel et al. (2016). Significant non-elastic GIA velocities occur in the Amundsen Sea Embayment sector, with high uplift flanked by subsiding regions. This pattern can be modeled as a viscoelastic response to ice loss on decadal-centennial time scales in a region with weak upper mantle, consistent with seismic results in the region.

Prediction of reinforcement corrosion using corrosion induced cracks width in corroded reinforced concrete beams

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

Khan, Inamullah; François, Raoul; Castel, Arnaud

2014-02-15

This paper studies the evolution of reinforcement corrosion in comparison to corrosion crack width in a highly corroded reinforced concrete beam. Cracking and corrosion maps of the beam were drawn and steel reinforcement was recovered from the beam to observe the corrosion pattern and to measure the loss of mass of steel reinforcement. Maximum steel cross-section loss of the main reinforcement and average steel cross-section loss between stirrups were plotted against the crack width. The experimental results were compared with existing models proposed by Rodriguez et al., Vidal et al. and Zhang et al. Time prediction models for a givenmore » opening threshold are also compared to experimental results. Steel cross-section loss for stirrups was also measured and was plotted against the crack width. It was observed that steel cross-section loss in the stirrups had no relationship with the crack width of longitudinal corrosion cracks. -- Highlights: •Relationship between crack and corrosion of reinforcement was investigated. •Corrosion results of natural process and then corresponds to in-situ conditions. •Comparison with time predicting model is provided. •Prediction of load-bearing capacity from crack pattern was studied.« less

Predicting recreational fishing use of offshore petroleum platforms in the Central Gulf of Mexico

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

Gordon, W.R. Jr.

1987-01-01

This study is based on the premise that properly sited artificial reefs for optimal human recreational use, a predictive model based upon the marine travel patterns and behavior of marine recreational fishermen, is needed. This research used data gathered from a previous study that addressed the recreational fishing use of offshore oil and gas structures (Ditton and Auyong 1984); on-site data were also collected. The primary research objective was to generate a predictive model that can be applied to artificial-reef development efforts elsewhere. This study investigated the recreational-user patterns of selected petroleum platforms structures in the Central Gulf of Mexico.more » The petroleum structures offshore from the Louisiana coastline provide a unique research tool. Although intended to facilitate the exploration and recovery of hydrocarbons, petroleum platforms also serve as defacto artificial reefs, providing habitat for numerous species of fish and other marine life. Petroleum platforms were found to be the principal fishing destinations within the study area. On-site findings reveal that marine recreational fishermen were as mobile on water, as they are on land. On-site findings were used to assist in the development of a predictive model.« less

A new paradigm for predicting zonal-mean climate and climate change

NASA Astrophysics Data System (ADS)

Armour, K.; Roe, G.; Donohoe, A.; Siler, N.; Markle, B. R.; Liu, X.; Feldl, N.; Battisti, D. S.; Frierson, D. M.

2016-12-01

How will the pole-to-equator temperature gradient, or large-scale patterns of precipitation, change under global warming? Answering such questions typically involves numerical simulations with comprehensive general circulation models (GCMs) that represent the complexities of climate forcing, radiative feedbacks, and atmosphere and ocean dynamics. Yet, our understanding of these predictions hinges on our ability to explain them through the lens of simple models and physical theories. Here we present evidence that zonal-mean climate, and its changes, can be understood in terms of a moist energy balance model that represents atmospheric heat transport as a simple diffusion of latent and sensible heat (as a down-gradient transport of moist static energy, with a diffusivity coefficient that is nearly constant with latitude). We show that the theoretical underpinnings of this model derive from the principle of maximum entropy production; that its predictions are empirically supported by atmospheric reanalyses; and that it successfully predicts the behavior of a hierarchy of climate models - from a gray radiation aquaplanet moist GCM, to comprehensive GCMs participating in CMIP5. As an example of the power of this paradigm, we show that, given only patterns of local radiative feedbacks and climate forcing, the moist energy balance model accurately predicts the evolution of zonal-mean temperature and atmospheric heat transport as simulated by the CMIP5 ensemble. These results suggest that, despite all of its dynamical complexity, the atmosphere essentially responds to energy imbalances by simply diffusing latent and sensible heat down-gradient; this principle appears to explain zonal-mean climate and its changes under global warming.

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

EPA Science Inventory

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predic...

Optimizing countershading camouflage.

PubMed

Cuthill, Innes C; Sanghera, N Simon; Penacchio, Olivier; Lovell, Paul George; Ruxton, Graeme D; Harris, Julie M

2016-11-15

Countershading, the widespread tendency of animals to be darker on the side that receives strongest illumination, has classically been explained as an adaptation for camouflage: obliterating cues to 3D shape and enhancing background matching. However, there have only been two quantitative tests of whether the patterns observed in different species match the optimal shading to obliterate 3D cues, and no tests of whether optimal countershading actually improves concealment or survival. We use a mathematical model of the light field to predict the optimal countershading for concealment that is specific to the light environment and then test this prediction with correspondingly patterned model "caterpillars" exposed to avian predation in the field. We show that the optimal countershading is strongly illumination-dependent. A relatively sharp transition in surface patterning from dark to light is only optimal under direct solar illumination; if there is diffuse illumination from cloudy skies or shade, the pattern provides no advantage over homogeneous background-matching coloration. Conversely, a smoother gradation between dark and light is optimal under cloudy skies or shade. The demonstration of these illumination-dependent effects of different countershading patterns on predation risk strongly supports the comparative evidence showing that the type of countershading varies with light environment.

Instrumentality, Expressivity, and Relational Qualities in the Same-Sex Friendships of College Women and Men

ERIC Educational Resources Information Center

Frey, Lisa L.; Beesley, Denise; Hurst, Rebecca; Saldana, Star; Licuanan, Brian

2016-01-01

Using the relational-cultural model (Jordan, Kaplan, Miller, Stiver, & Surrey, 1991), the authors hypothesized that instrumentality, expressivity, and the individual affective experience of same-sex friendships would predict increased relationship mutuality, with college women and men showing different predictive patterns. Overall, results…

Learning Management System with Prediction Model and Course-Content Recommendation Module

ERIC Educational Resources Information Center

Evale, Digna S.

2017-01-01

Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…

Inferring parturition and neonate survival from movement patterns of female ungulates: a case study using woodland caribou

PubMed Central

DeMars, Craig A; Auger-Méthé, Marie; Schlägel, Ulrike E; Boutin, Stan

2013-01-01

Analyses of animal movement data have primarily focused on understanding patterns of space use and the behavioural processes driving them. Here, we analyzed animal movement data to infer components of individual fitness, specifically parturition and neonate survival. We predicted that parturition and neonate loss events could be identified by sudden and marked changes in female movement patterns. Using GPS radio-telemetry data from female woodland caribou (Rangifer tarandus caribou), we developed and tested two novel movement-based methods for inferring parturition and neonate survival. The first method estimated movement thresholds indicative of parturition and neonate loss from population-level data then applied these thresholds in a moving-window analysis on individual time-series data. The second method used an individual-based approach that discriminated among three a priori models representing the movement patterns of non-parturient females, females with surviving offspring, and females losing offspring. The models assumed that step lengths (the distance between successive GPS locations) were exponentially distributed and that abrupt changes in the scale parameter of the exponential distribution were indicative of parturition and offspring loss. Both methods predicted parturition with near certainty (>97% accuracy) and produced appropriate predictions of parturition dates. Prediction of neonate survival was affected by data quality for both methods; however, when using high quality data (i.e., with few missing GPS locations), the individual-based method performed better, predicting neonate survival status with an accuracy rate of 87%. Understanding ungulate population dynamics often requires estimates of parturition and neonate survival rates. With GPS radio-collars increasingly being used in research and management of ungulates, our movement-based methods represent a viable approach for estimating rates of both parameters. PMID:24324866

Inferring parturition and neonate survival from movement patterns of female ungulates: a case study using woodland caribou.

PubMed

Demars, Craig A; Auger-Méthé, Marie; Schlägel, Ulrike E; Boutin, Stan

2013-10-01

Analyses of animal movement data have primarily focused on understanding patterns of space use and the behavioural processes driving them. Here, we analyzed animal movement data to infer components of individual fitness, specifically parturition and neonate survival. We predicted that parturition and neonate loss events could be identified by sudden and marked changes in female movement patterns. Using GPS radio-telemetry data from female woodland caribou (Rangifer tarandus caribou), we developed and tested two novel movement-based methods for inferring parturition and neonate survival. The first method estimated movement thresholds indicative of parturition and neonate loss from population-level data then applied these thresholds in a moving-window analysis on individual time-series data. The second method used an individual-based approach that discriminated among three a priori models representing the movement patterns of non-parturient females, females with surviving offspring, and females losing offspring. The models assumed that step lengths (the distance between successive GPS locations) were exponentially distributed and that abrupt changes in the scale parameter of the exponential distribution were indicative of parturition and offspring loss. Both methods predicted parturition with near certainty (>97% accuracy) and produced appropriate predictions of parturition dates. Prediction of neonate survival was affected by data quality for both methods; however, when using high quality data (i.e., with few missing GPS locations), the individual-based method performed better, predicting neonate survival status with an accuracy rate of 87%. Understanding ungulate population dynamics often requires estimates of parturition and neonate survival rates. With GPS radio-collars increasingly being used in research and management of ungulates, our movement-based methods represent a viable approach for estimating rates of both parameters.

Spatial pattern enhances ecosystem functioning in an African savanna.

PubMed

Pringle, Robert M; Doak, Daniel F; Brody, Alison K; Jocqué, Rudy; Palmer, Todd M

2010-05-25

The finding that regular spatial patterns can emerge in nature from local interactions between organisms has prompted a search for the ecological importance of these patterns. Theoretical models have predicted that patterning may have positive emergent effects on fundamental ecosystem functions, such as productivity. We provide empirical support for this prediction. In dryland ecosystems, termite mounds are often hotspots of plant growth (primary productivity). Using detailed observations and manipulative experiments in an African savanna, we show that these mounds are also local hotspots of animal abundance (secondary and tertiary productivity): insect abundance and biomass decreased with distance from the nearest termite mound, as did the abundance, biomass, and reproductive output of insect-eating predators. Null-model analyses indicated that at the landscape scale, the evenly spaced distribution of termite mounds produced dramatically greater abundance, biomass, and reproductive output of consumers across trophic levels than would be obtained in landscapes with randomly distributed mounds. These emergent properties of spatial pattern arose because the average distance from an arbitrarily chosen point to the nearest feature in a landscape is minimized in landscapes where the features are hyper-dispersed (i.e., uniformly spaced). This suggests that the linkage between patterning and ecosystem functioning will be common to systems spanning the range of human management intensities. The centrality of spatial pattern to system-wide biomass accumulation underscores the need to conserve pattern-generating organisms and mechanisms, and to incorporate landscape patterning in efforts to restore degraded habitats and maximize the delivery of ecosystem services.

Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution

NASA Astrophysics Data System (ADS)

Buettner, Florian; Gulliford, Sarah L.; Webb, Steve; Partridge, Mike

2011-04-01

Many models exist for predicting toxicities based on dose-volume histograms (DVHs) or dose-surface histograms (DSHs). This approach has several drawbacks as firstly the reduction of the dose distribution to a histogram results in the loss of spatial information and secondly the bins of the histograms are highly correlated with each other. Furthermore, some of the complex nonlinear models proposed in the past lack a direct physical interpretation and the ability to predict probabilities rather than binary outcomes. We propose a parameterized representation of the 3D distribution of the dose to the rectal wall which explicitly includes geometrical information in the form of the eccentricity of the dose distribution as well as its lateral and longitudinal extent. We use a nonlinear kernel-based probabilistic model to predict late rectal toxicity based on the parameterized dose distribution and assessed its predictive power using data from the MRC RT01 trial (ISCTRN 47772397). The endpoints under consideration were rectal bleeding, loose stools, and a global toxicity score. We extract simple rules identifying 3D dose patterns related to a specifically low risk of complication. Normal tissue complication probability (NTCP) models based on parameterized representations of geometrical and volumetric measures resulted in areas under the curve (AUCs) of 0.66, 0.63 and 0.67 for predicting rectal bleeding, loose stools and global toxicity, respectively. In comparison, NTCP models based on standard DVHs performed worse and resulted in AUCs of 0.59 for all three endpoints. In conclusion, we have presented low-dimensional, interpretable and nonlinear NTCP models based on the parameterized representation of the dose to the rectal wall. These models had a higher predictive power than models based on standard DVHs and their low dimensionality allowed for the identification of 3D dose patterns related to a low risk of complication.

Predictive Simulations of Neuromuscular Coordination and Joint-Contact Loading in Human Gait.

PubMed

Lin, Yi-Chung; Walter, Jonathan P; Pandy, Marcus G

2018-04-18

We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot-ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3 cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

PubMed Central

Acampora, Giovanni; Brown, David; Rees, Robert C.

2016-01-01

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582). PMID:27258119

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

USGS Publications Warehouse

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

2008-01-01

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

Interpretable Deep Models for ICU Outcome Prediction

PubMed Central

Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

2016-01-01

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2004-05-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels using monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. From these data, the goal is to develop mathematical models that describe the seasonal flux in consumption for each type of fuel, as well as the total emissions for the nation. The time series models have two components. First, the general long-term yearly trend is determined with regression models for the annual totals. After removing the general trend, two alternatives are considered for modeling the seasonality. The first alternative uses the mean of the monthly proportions to predict the seasonal distribution. Because the seasonal patterns are fairly consistent in the United States, this is an effective modeling technique. Such regularity, however, may not be present with data from other nations. Therefore, as a second alternative, an ordinary least squares autoregressive model is used. This model is chosen for its ability to accurately describe dependent data and for its predictive capacity. It also has a meaningful interpretation, as each coefficient in the model quantifies the dependency for each corresponding time lag. Most importantly, it is dynamic, and able to adapt to anomalies and changing patterns. The order of the autoregressive model is chosen by the Akaike Information Criterion (AIC), which minimizes the predicted variance for all models of increasing complexity. To model the monthly fuel consumption, the annual trend is combined with the seasonal model. The models for each fuel type are then summed together to predict the total carbon dioxide emissions. The prediction error is estimated with the root mean square error (RMSE) from the actual estimated emission values. Overall, the models perform very well, with relative RMSE less than 10% for all fuel types, and under 5% for the national total emissions. Development of successful models is important to better understand and predict global environmental impacts from fossil fuel consumption.

Dietary patterns predict changes in two-hour post-oral glucose tolerance test plasma glucose concentrations in middle-aged adults.

PubMed

Lau, Cathrine; Toft, Ulla; Tetens, Inge; Carstensen, Bendix; Jørgensen, Torben; Pedersen, Oluf; Borch-Johnsen, Knut

2009-03-01

We examined whether the adherence to major dietary patterns at baseline of 5824 nondiabetic Danes (30-60 y) enrolled in the nonpharmacological Inter99 intervention predicted changes in fasting plasma glucose (FPG) and postchallenge 2-h plasma glucose (2h-PG) concentrations during a 5 y period and whether a potential association was dependent on baseline glucose tolerance status. Through principal component analysis, a score for a traditional dietary pattern (characterized by higher intakes of high-fat sandwich spreads, red meat, potatoes, butter and lard, low-fat fish, sandwich meat, and sauces) and a score for a modern dietary pattern (characterized by higher intakes of vegetables, fruit, vegetable oil/vinegar dressing, poultry, pasta, rice, and cereals) were estimated for each person at baseline. Random effect models adjusting for relevant confounders were used to estimate changes in repetitive measures of FPG and 2h-PG. A higher modern score (of 1 SD) predicted an annual decrease in 2h-PG of 0.015 mmol/L (P < 0.01) regardless of glucose tolerance status. For individuals with isolated impaired glucose tolerance, a higher traditional score (of 1 SD) predicted an annual increase in 2h-PG of 0.083 mmol/L (P < 0.0001). In conclusion, glucose tolerance status did not, in general, affect the predictive effect of the dietary patterns. The study suggests that the risk of worsening 2h-PG concentrations may be smaller for individuals with a high modern dietary pattern score characterized by high intakes of vegetables, fruit, vegetable oil/vinegar dressing, poultry, pasta, rice, and cereals.

Simulation of South-Asian Summer Monsoon in a GCM

NASA Astrophysics Data System (ADS)

Ajayamohan, R. S.

2007-10-01

Major characteristics of Indian summer monsoon climate are analyzed using simulations from the upgraded version of Florida State University Global Spectral Model (FSUGSM). The Indian monsoon has been studied in terms of mean precipitation and low-level and upper-level circulation patterns and compared with observations. In addition, the model's fidelity in simulating observed monsoon intraseasonal variability, interannual variability and teleconnection patterns is examined. The model is successful in simulating the major rainbelts over the Indian monsoon region. However, the model exhibits bias in simulating the precipitation bands over the South China Sea and the West Pacific region. Seasonal mean circulation patterns of low-level and upper-level winds are consistent with the model's precipitation pattern. Basic features like onset and peak phase of monsoon are realistically simulated. However, model simulation indicates an early withdrawal of monsoon. Northward propagation of rainbelts over the Indian continent is simulated fairly well, but the propagation is weak over the ocean. The model simulates the meridional dipole structure associated with the monsoon intraseasonal variability realistically. The model is unable to capture the observed interannual variability of monsoon and its teleconnection patterns. Estimate of potential predictability of the model reveals the dominating influence of internal variability over the Indian monsoon region.

Modeling urban growth by the use of a multiobjective optimization approach: environmental and economic issues for the Yangtze watershed, China.

PubMed

Zhang, Wenting; Wang, Haijun; Han, Fengxiang; Gao, Juan; Nguyen, Thuminh; Chen, Yarong; Huang, Bo; Zhan, F Benjamin; Zhou, Lequn; Hong, Song

2014-11-01

Urban growth is an unavoidable process caused by economic development and population growth. Traditional urban growth models represent the future urban growth pattern by repeating the historical urban growth regulations, which can lead to a lot of environmental problems. The Yangtze watershed is the largest and the most prosperous economic area in China, and it has been suffering from rapid urban growth from the 1970s. With the built-up area increasing from 23,238 to 31,054 km(2) during the period from 1980 to 2005, the watershed has suffered from serious nonpoint source (NPS) pollution problems, which have been mainly caused by the rapid urban growth. To protect the environment and at the same time maintain the economic development, a multiobjective optimization (MOP) is proposed to tradeoff the multiple objectives during the urban growth process of the Yangtze watershed. In particular, the four objectives of minimization of NPS pollution, maximization of GDP value, minimization of the spatial incompatibility between the land uses, and minimization of the cost of land-use change are considered by the MOP approach. Conventionally, a genetic algorithm (GA) is employed to search the Pareto solution set. In our MOP approach, a two-dimensional GA, rather than the traditional one-dimensional GA, is employed to assist with the search for the spatial optimization solution, where the land-use cells in the two-dimensional space act as genes in the GA. Furthermore, to confirm the superiority of the MOP approach over the traditional prediction approaches, a widely used urban growth prediction model, cellular automata (CA), is also carried out to allow a comparison with the Pareto solution of MOP. The results indicate that the MOP approach can make a tradeoff between the multiple objectives and can achieve an optimal urban growth pattern for Yangtze watershed, while the CA prediction model just represents the historical urban growth pattern as the future growth pattern. Moreover, according to the spatial clustering index, the urban growth pattern predicted through MOP is more reasonable. In summary, the proposed model provides a set of Pareto urban growth solutions, which compromise environmental and economic issues for the Yangtze watershed.

Benzene patterns in different urban environments and a prediction model for benzene rates based on NOx values

NASA Astrophysics Data System (ADS)

Paz, Shlomit; Goldstein, Pavel; Kordova-Biezuner, Levana; Adler, Lea

2017-04-01

Exposure to benzene has been associated with multiple severe impacts on health. This notwithstanding, at most monitoring stations, benzene is not monitored on a regular basis. The aims of the study were to compare benzene rates in different urban environments (region with heavy traffic and industrial region), to analyse the relationship between benzene and meteorological parameters in a Mediterranean climate type, to estimate the linkages between benzene and NOx and to suggest a prediction model for benzene rates based on NOx levels in order contribute to a better estimation of benzene. Data were used from two different monitoring stations, located on the eastern Mediterranean coast: 1) a traffic monitoring station in Tel Aviv, Israel (TLV) located in an urban region with heavy traffic; 2) a general air quality monitoring station in Haifa Bay (HIB), located in Israel's main industrial region. At each station, hourly, daily, monthly, seasonal, and annual data of benzene, NOx, mean temperature, relative humidity, inversion level, and temperature gradient were analysed over three years: 2008, 2009, and 2010. A prediction model for benzene rates based on NOx levels (which are monitored regularly) was developed to contribute to a better estimation of benzene. The severity of benzene pollution was found to be considerably higher at the traffic monitoring station (TLV) than at the general air quality station (HIB), despite the location of the latter in an industrial area. Hourly, daily, monthly, seasonal, and annual patterns have been shown to coincide with anthropogenic activities (traffic), the day of the week, and atmospheric conditions. A strong correlation between NOx and benzene allowed the development of a prediction model for benzene rates, based on NOx, the day of the week, and the month. The model succeeded in predicting the benzene values throughout the year (except for September). The severity of benzene pollution was found to be considerably higher at the traffic station (TLV) than at the general air quality station (HIB), despite being located in an industrial area. Hourly, daily, seasonal, and annual patterns of benzene rates have been shown to coincide with anthropogenic activities (traffic), day of the week, and atmospheric conditions. A prediction model for benzene rates was developed, based on NOx, the day of the week, and the month. The model suggested in this study might be useful for identifying potential risk of benzene in other urban environments.

Predictions of dispersion and deposition of fallout from nuclear testing using the NOAA-HYSPLIT meteorological model.

PubMed

Moroz, Brian E; Beck, Harold L; Bouville, André; Simon, Steven L

2010-08-01

The NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was evaluated as a research tool to simulate the dispersion and deposition of radioactive fallout from nuclear tests. Model-based estimates of fallout can be valuable for use in the reconstruction of past exposures from nuclear testing, particularly where little historical fallout monitoring data are available. The ability to make reliable predictions about fallout deposition could also have significant importance for nuclear events in the future. We evaluated the accuracy of the HYSPLIT-predicted geographic patterns of deposition by comparing those predictions against known deposition patterns following specific nuclear tests with an emphasis on nuclear weapons tests conducted in the Marshall Islands. We evaluated the ability of the computer code to quantitatively predict the proportion of fallout particles of specific sizes deposited at specific locations as well as their time of transport. In our simulations of fallout from past nuclear tests, historical meteorological data were used from a reanalysis conducted jointly by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). We used a systematic approach in testing the HYSPLIT model by simulating the release of a range of particle sizes from a range of altitudes and evaluating the number and location of particles deposited. Our findings suggest that the quantity and quality of meteorological data are the most important factors for accurate fallout predictions and that, when satisfactory meteorological input data are used, HYSPLIT can produce relatively accurate deposition patterns and fallout arrival times. Furthermore, when no other measurement data are available, HYSPLIT can be used to indicate whether or not fallout might have occurred at a given location and provide, at minimum, crude quantitative estimates of the magnitude of the deposited activity. A variety of simulations of the deposition of fallout from atmospheric nuclear tests conducted in the Marshall Islands (mid-Pacific), at the Nevada Test Site (U.S.), and at the Semipalatinsk Nuclear Test Site (Kazakhstan) were performed. The results of the Marshall Islands simulations were used in a limited fashion to support the dose reconstruction described in companion papers within this volume.

PREDICTIONS OF DISPERSION AND DEPOSITION OF FALLOUT FROM NUCLEAR TESTING USING THE NOAA-HYSPLIT METEOROLOGICAL MODEL

PubMed Central

Moroz, Brian E.; Beck, Harold L.; Bouville, André; Simon, Steven L.

2013-01-01

The NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was evaluated as a research tool to simulate the dispersion and deposition of radioactive fallout from nuclear tests. Model-based estimates of fallout can be valuable for use in the reconstruction of past exposures from nuclear testing, particularly, where little historical fallout monitoring data is available. The ability to make reliable predictions about fallout deposition could also have significant importance for nuclear events in the future. We evaluated the accuracy of the HYSPLIT-predicted geographic patterns of deposition by comparing those predictions against known deposition patterns following specific nuclear tests with an emphasis on nuclear weapons tests conducted in the Marshall Islands. We evaluated the ability of the computer code to quantitatively predict the proportion of fallout particles of specific sizes deposited at specific locations as well as their time of transport. In our simulations of fallout from past nuclear tests, historical meteorological data were used from a reanalysis conducted jointly by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). We used a systematic approach in testing the HYSPLIT model by simulating the release of a range of particles sizes from a range of altitudes and evaluating the number and location of particles deposited. Our findings suggest that the quantity and quality of meteorological data are the most important factors for accurate fallout predictions and that when satisfactory meteorological input data are used, HYSPLIT can produce relatively accurate deposition patterns and fallout arrival times. Furthermore, when no other measurement data are available, HYSPLIT can be used to indicate whether or not fallout might have occurred at a given location and provide, at minimum, crude quantitative estimates of the magnitude of the deposited activity. A variety of simulations of the deposition of fallout from atmospheric nuclear tests conducted in the Marshall Islands, at the Nevada Test Site (USA), and at the Semipalatinsk Nuclear Test Site (Kazakhstan) were performed using reanalysis data composed of historic meteorological observations. The results of the Marshall Islands simulations were used in a limited fashion to support the dose reconstruction described in companion papers within this volume. PMID:20622555

Predicting synchrony in heterogeneous pulse coupled oscillators

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Miliotis, Abraham; Carney, Paul R.; Ditto, William L.

2009-08-01

Pulse coupled oscillators (PCOs) represent an ubiquitous model for a number of physical and biological systems. Phase response curves (PRCs) provide a general mathematical framework to analyze patterns of synchrony generated within these models. A general theoretical approach to account for the nonlinear contributions from higher-order PRCs in the generation of synchronous patterns by the PCOs is still lacking. Here, by considering a prototypical example of a PCO network, i.e., two synaptically coupled neurons, we present a general theory that extends beyond the weak-coupling approximation, to account for higher-order PRC corrections in the derivation of an approximate discrete map, the stable fixed point of which can predict the domain of 1:1 phase locked synchronous states generated by the PCO network.

A musculoskeletal model of the elbow joint complex

NASA Technical Reports Server (NTRS)

Gonzalez, Roger V.; Barr, Ronald E.; Abraham, Lawrence D.

1993-01-01

This paper describes a musculoskeletal model that represents human elbow flexion-extension and forearm pronation-supination. Musculotendon parameters and the skeletal geometry were determined for the musculoskeletal model in the analysis of ballistic elbow joint complex movements. The key objective was to develop a computational model, guided by optimal control, to investigate the relationship among patterns of muscle excitation, individual muscle forces, and movement kinematics. The model was verified using experimental kinematic, torque, and electromyographic data from volunteer subjects performing both isometric and ballistic elbow joint complex movements. In general, the model predicted kinematic and muscle excitation patterns similar to what was experimentally measured.

Personality Patterns of Physicians in Person-Oriented and Technique-Oriented Specialties

ERIC Educational Resources Information Center

Borges, Nicole J.; Gibson, Denise D.

2005-01-01

This study investigated differences in personality patterns between person-oriented and technique-oriented physicians. It tested an integrative framework by converting the scores on the Personality Research Form (PRF) to the Big-Five factors and built a predictive model of group membership in clinical specialty area. PRF scores from 238 physicians…

Smart Meter Driven Segmentation: What Your Consumption Says About You

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

Albert, A; Rajagopal, R

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploitedmore » for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.« less

The development of a probabilistic approach to forecast coastal change

USGS Publications Warehouse

Lentz, Erika E.; Hapke, Cheryl J.; Rosati, Julie D.; Wang, Ping; Roberts, Tiffany M.

2011-01-01

This study demonstrates the applicability of a Bayesian probabilistic model as an effective tool in predicting post-storm beach changes along sandy coastlines. Volume change and net shoreline movement are modeled for two study sites at Fire Island, New York in response to two extratropical storms in 2007 and 2009. Both study areas include modified areas adjacent to unmodified areas in morphologically different segments of coast. Predicted outcomes are evaluated against observed changes to test model accuracy and uncertainty along 163 cross-shore transects. Results show strong agreement in the cross validation of predictions vs. observations, with 70-82% accuracies reported. Although no consistent spatial pattern in inaccurate predictions could be determined, the highest prediction uncertainties appeared in locations that had been recently replenished. Further testing and model refinement are needed; however, these initial results show that Bayesian networks have the potential to serve as important decision-support tools in forecasting coastal change.

Experimental and theoretical study of skylight polarization transmitted through Snell's window of a flat water surface.

PubMed

Sabbah, Shai; Barta, András; Gál, József; Horváth, Gábor; Shashar, Nadav

2006-08-01

The celestial polarization pattern may be scrambled by refraction at the air-water interface. This polarization pattern was examined in shallow waters with a submersible polarimeter, and it was calculated by using land measurements ('semiempirical predictions') and models of the skylight polarization. Semiempirically predicted and measured e-vector orientations were significantly similar. Conversely, predicted percent polarization was correlated but lower than measurements. Percent polarization depended on wavelength, where at high sun altitudes maximal percent polarization generally appeared in the UV and red spectral regions. The wavelength dependency of polarization may lead to differential spectral sensitivity in polarization-sensitive animals according to time and type of activity.

Habitat history improves prediction of biodiversity in rainforest fauna

PubMed Central

Graham, Catherine H.; Moritz, Craig; Williams, Stephen E.

2006-01-01

Patterns of biological diversity should be interpreted in light of both contemporary and historical influences; however, to date, most attempts to explain diversity patterns have largely ignored history or have been unable to quantify the influence of historical processes. The historical effects on patterns of diversity have been hypothesized to be most important for taxonomic groups with poor dispersal abilities. We quantified the relative stability of rainforests over the late Quaternary period by modeling rainforest expansion and contraction in 21 biogeographic subregions in northeast Australia across four time periods. We demonstrate that historical habitat stability can be as important, and in endemic low-dispersal taxa even more important, than current habitat area in explaining spatial patterns of species richness. In contrast, patterns of endemic species richness for taxa with high dispersal capacity are best predicted by using current environmental parameters. We also show that contemporary patterns of species turnover across the region are best explained by historical patterns of habitat connectivity. These results clearly demonstrate that spatially explicit analyses of the historical processes of persistence and colonization are both effective and necessary for understanding observed patterns of biodiversity. PMID:16407139

Landscape modeling for Everglades ecosystem restoration

USGS Publications Warehouse

DeAngelis, D.L.; Gross, L.J.; Huston, M.A.; Wolff, W.F.; Fleming, D.M.; Comiskey, E.J.; Sylvester, S.M.

1998-01-01

A major environmental restoration effort is under way that will affect the Everglades and its neighboring ecosystems in southern Florida. Ecosystem and population-level modeling is being used to help in the planning and evaluation of this restoration. The specific objective of one of these modeling approaches, the Across Trophic Level System Simulation (ATLSS), is to predict the responses of a suite of higher trophic level species to several proposed alterations in Everglades hydrology. These include several species of wading birds, the snail kite, Cape Sable seaside sparrow, Florida panther, white-tailed deer, American alligator, and American crocodile. ATLSS is an ecosystem landscape-modeling approach and uses Geographic Information System (GIS) vegetation data and existing hydrology models for South Florida to provide the basic landscape for these species. A method of pseudotopography provides estimates of water depths through time at 28 ?? 28-m resolution across the landscape of southern Florida. Hydrologic model output drives models of habitat and prey availability for the higher trophic level species. Spatially explicit, individual-based computer models simulate these species. ATLSS simulations can compare the landscape dynamic spatial pattern of the species resulting from different proposed water management strategies. Here we compare the predicted effects of one possible change in water management in South Florida with the base case of no change. Preliminary model results predict substantial differences between these alternatives in some biotic spatial patterns. ?? 1998 Springer-Verlag.

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that for temperature, is more predictable over the tropical regions, and less predictable in extropics. Bootstrap and ANOCOVA are in good agreement with each other, both methods generating larger predictability than Katz. The seasonal predictability of evaporation over land bears considerably similarity with that of temperature using ANOCOVA, bootstrap, LSG and Madden. The remote SST forcing and soil moisture reveal substantial seasonality in their relations with the potentially predictable seasonal signals. For selected regions, either SST or soil moisture or both shows significant relationships with predictable signals, hence providing indirect insight on slowly varying boundary processes involved to enable useful seasonal climate predication. A multivariate analysis of covariance (MANOCOVA) model is established to identify distinctive predictable patterns, which are uncorrelated with each other. Generally speaking, the seasonal predictability from multivariate model is consistent with that from ANOCOVA. Besides unveiling the spatial variability of predictability, MANOCOVA model also reveals the temporal variability of each predictable pattern, which could be linked to the periodic oscillations.

A log-sinh transformation for data normalization and variance stabilization

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Shrestha, D. L.; Robertson, D. E.; Pokhrel, P.

2012-05-01

When quantifying model prediction uncertainty, it is statistically convenient to represent model errors that are normally distributed with a constant variance. The Box-Cox transformation is the most widely used technique to normalize data and stabilize variance, but it is not without limitations. In this paper, a log-sinh transformation is derived based on a pattern of errors commonly seen in hydrological model predictions. It is suited to applications where prediction variables are positively skewed and the spread of errors is seen to first increase rapidly, then slowly, and eventually approach a constant as the prediction variable becomes greater. The log-sinh transformation is applied in two case studies, and the results are compared with one- and two-parameter Box-Cox transformations.

Assessing the Importance of Atlantic Basin Tropical Cyclone Steering Currents in Anticipating Landfall Risk

NASA Astrophysics Data System (ADS)

Truchelut, R.; Hart, R. E.

2013-12-01

While a number of research groups offer quantitative pre-seasonal assessments of aggregate annual Atlantic Basin tropical cyclone activity, the literature is comparatively thin concerning methods to meaningfully quantify seasonal U.S. landfall risks. As the example of Hurricane Andrew impacting Southeast Florida in the otherwise quiet 1992 season demonstrates, an accurate probabilistic assessment of seasonal tropical cyclone threat levels would be of immense public utility and economic value; however, the methods used to predict annual activity demonstrate little skill for predicting annual count of landfalling systems of any intensity bin. Therefore, while current models are optimized to predict cumulative seasonal tropical cyclone activity, they are not ideal tools for assessing the potential for sensible impacts of storms on populated areas. This research aims to bridge the utility gap in seasonal tropical cyclone forecasting by shifting the focus of seasonal modelling to the parameters that are most closely linked to creating conditions favorable for U.S. landfalls, particularly those of destructive and costly intense hurricanes. As it is clear from the initial findings of this study that overall activity has a limited influence on sensible outcomes, this project concentrates on detecting predictability and trends in cyclogenesis location and upper-level wind steering patterns. These metrics are demonstrated to have a relationship with landfall activity in the Atlantic Basin climatological record. By aggregating historic seasonally-averaged steering patterns using newly-available reanalysis model datasets, some atmospheric and oceanic precursors to an elevated risk of North American tropical cyclone landfall have been identified. Work is ongoing to quantify the variance, persistence, and predictability of such patterns over seasonal timescales, with the aim of yielding tools that could be incorporated into tropical cyclone risk mitigation strategies.

Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge.

PubMed

Steinman, David A; Hoi, Yiemeng; Fahy, Paul; Morris, Liam; Walsh, Michael T; Aristokleous, Nicolas; Anayiotos, Andreas S; Papaharilaou, Yannis; Arzani, Amirhossein; Shadden, Shawn C; Berg, Philipp; Janiga, Gábor; Bols, Joris; Segers, Patrick; Bressloff, Neil W; Cibis, Merih; Gijsen, Frank H; Cito, Salvatore; Pallarés, Jordi; Browne, Leonard D; Costelloe, Jennifer A; Lynch, Adrian G; Degroote, Joris; Vierendeels, Jan; Fu, Wenyu; Qiao, Aike; Hodis, Simona; Kallmes, David F; Kalsi, Hardeep; Long, Quan; Kheyfets, Vitaly O; Finol, Ender A; Kono, Kenichi; Malek, Adel M; Lauric, Alexandra; Menon, Prahlad G; Pekkan, Kerem; Esmaily Moghadam, Mahdi; Marsden, Alison L; Oshima, Marie; Katagiri, Kengo; Peiffer, Véronique; Mohamied, Yumnah; Sherwin, Spencer J; Schaller, Jens; Goubergrits, Leonid; Usera, Gabriel; Mendina, Mariana; Valen-Sendstad, Kristian; Habets, Damiaan F; Xiang, Jianping; Meng, Hui; Yu, Yue; Karniadakis, George E; Shaffer, Nicholas; Loth, Francis

2013-02-01

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

Comparing Three Patterns of Strengths and Weaknesses Models for the Identification of Specific Learning Disabilities

ERIC Educational Resources Information Center

Miller, Daniel C.; Maricle, Denise E.; Jones, Alicia M.

2016-01-01

Processing Strengths and Weaknesses (PSW) models have been proposed as a method for identifying specific learning disabilities. Three PSW models were examined for their ability to predict expert identified specific learning disabilities cases. The Dual Discrepancy/Consistency Model (DD/C; Flanagan, Ortiz, & Alfonso, 2013) as operationalized by…

Do attachment patterns predict aggression in a context of social rejection? An executive functioning account.

PubMed

Ma, Yuanxiao; Ma, Haijing; Chen, Xu; Ran, Guangming; Zhang, Xing

2017-07-01

People tend to respond to rejection and attack with aggression. The present research examined the modulation role of attachment patterns on provoked aggression following punishment and proposed an executive functioning account of attachment patterns' modulating influence based on the General Aggression Model. Attachment style was measured using the Experiences in Close Relationships inventory. Experiments 1a and b and 2 adopted a social rejection task and assessed subsequent unprovoked and provoked aggression with different attachment patterns. Moreover, Experiment 1b and 2 used a Stroop task to examine whether differences in provoked aggression by attachment patterns are due to the amount of executive functioning following social rejection, or after unprovoked punishment, or even before social rejection. Anxiously attached participants displayed significant more provoked aggression than securely and avoidantly attached participants in provoked aggression following unprovoked punishment in Experiments 1 and 2. Meanwhile, subsequent Stroop tests indicated anxiously attached participants experienced more executive functioning depletion after social rejection and unprovoked aggression. The present findings support the General Aggression Model and suggest that provoked aggression is predicted by attachment patterns in the context of social rejection; different provoked aggression may depend on the degree of executive functioning that individuals preserved in aggressive situations. The current study contributes to our understanding of the importance of the role of attachment patterns in modulating aggressive behavior accompanying unfair social encounters. © 2017 Wiley Periodicals, Inc.

Contribution to an effective design method for stationary reaction-diffusion patterns.

PubMed

Szalai, István; Horváth, Judit; De Kepper, Patrick

2015-06-01

The British mathematician Alan Turing predicted, in his seminal 1952 publication, that stationary reaction-diffusion patterns could spontaneously develop in reacting chemical or biochemical solutions. The first two clear experimental demonstrations of such a phenomenon were not made before the early 1990s when the design of new chemical oscillatory reactions and appropriate open spatial chemical reactors had been invented. Yet, the number of pattern producing reactions had not grown until 2009 when we developed an operational design method, which takes into account the feeding conditions and other specificities of real open spatial reactors. Since then, on the basis of this method, five additional reactions were shown to produce stationary reaction-diffusion patterns. To gain a clearer view on where our methodical approach on the patterning capacity of a reaction stands, numerical studies in conditions that mimic true open spatial reactors were made. In these numerical experiments, we explored the patterning capacity of Rabai's model for pH driven Landolt type reactions as a function of experimentally attainable parameters that control the main time and length scales. Because of the straightforward reversible binding of protons to carboxylate carrying polymer chains, this class of reaction is at the base of the chemistry leading to most of the stationary reaction-diffusion patterns presently observed. We compare our model predictions with experimental observations and comment on agreements and differences.

Development of a program to fit data to a new logistic model for microbial growth.

PubMed

Fujikawa, Hiroshi; Kano, Yoshihiro

2009-06-01

Recently we developed a mathematical model for microbial growth in food. The model successfully predicted microbial growth at various patterns of temperature. In this study, we developed a program to fit data to the model with a spread sheet program, Microsoft Excel. Users can instantly get curves fitted to the model by inputting growth data and choosing the slope portion of a curve. The program also could estimate growth parameters including the rate constant of growth and the lag period. This program would be a useful tool for analyzing growth data and further predicting microbial growth.

Least-cost transportation networks predict spatial interaction of invasion vectors.

PubMed

Drake, D Andrew R; Mandrak, Nicholas E

2010-12-01

Human-mediated dispersal among aquatic ecosystems often results in biotic transfer between drainage basins. Such activities may circumvent biogeographic factors, with considerable ecological, evolutionary, and economic implications. However, the efficacy of predictions concerning community changes following inter-basin movements are limited, often because the dispersal mechanism is poorly understood (e.g., quantified only partially). To date, spatial-interaction models that predict the movement of humans as vectors of biotic transfer have not incorporated patterns of human movement through transportation networks. As a necessary first step to determine the role of anglers as invasion vectors across a land-lake ecosystem, we investigate their movement potential within Ontario, Canada. To determine possible model improvements resulting from inclusion of network travel, spatial-interaction models were constructed using standard Euclidean (e.g., straight-line) distance measures and also with distances derived from least-cost routing of human transportation networks. Model comparisons determined that least-cost routing both provided the most parsimonious model and also excelled at forecasting spatial interactions, with a proportion of 0.477 total movement deviance explained. The distribution of movements was characterized by many relatively short to medium travel distances (median = 292.6 km) with fewer lengthier distances (75th percentile = 484.6 km, 95th percentile = 775.2 km); however, even the shortest movements were sufficient to overcome drainage-basin boundaries. Ranking of variables in order of their contribution within the most parsimonious model determined that distance traveled, origin outflow, lake attractiveness, and sportfish richness significantly influence movement patterns. Model improvements associated with least-cost routing of human transportation networks imply that patterns of human-mediated invasion are fundamentally linked to the spatial configuration and relative impedance of human transportation networks, placing increased importance on understanding their contribution to the invasion process.

A stochastic multicellular model identifies biological watermarks from disorders in self-organized patterns of phyllotaxis

PubMed Central

Refahi, Yassin; Brunoud, Géraldine; Farcot, Etienne; Jean-Marie, Alain; Pulkkinen, Minna; Vernoux, Teva; Godin, Christophe

2016-01-01

Exploration of developmental mechanisms classically relies on analysis of pattern regularities. Whether disorders induced by biological noise may carry information on building principles of developmental systems is an important debated question. Here, we addressed theoretically this question using phyllotaxis, the geometric arrangement of plant aerial organs, as a model system. Phyllotaxis arises from reiterative organogenesis driven by lateral inhibitions at the shoot apex. Motivated by recurrent observations of disorders in phyllotaxis patterns, we revisited in depth the classical deterministic view of phyllotaxis. We developed a stochastic model of primordia initiation at the shoot apex, integrating locality and stochasticity in the patterning system. This stochastic model recapitulates phyllotactic patterns, both regular and irregular, and makes quantitative predictions on the nature of disorders arising from noise. We further show that disorders in phyllotaxis instruct us on the parameters governing phyllotaxis dynamics, thus that disorders can reveal biological watermarks of developmental systems. DOI: http://dx.doi.org/10.7554/eLife.14093.001 PMID:27380805

New battery model considering thermal transport and partial charge stationary effects in photovoltaic off-grid applications

NASA Astrophysics Data System (ADS)

Sanz-Gorrachategui, Iván; Bernal, Carlos; Oyarbide, Estanis; Garayalde, Erik; Aizpuru, Iosu; Canales, Jose María; Bono-Nuez, Antonio

2018-02-01

The optimization of the battery pack in an off-grid Photovoltaic application must consider the minimum sizing that assures the availability of the system under the worst environmental conditions. Thus, it is necessary to predict the evolution of the state of charge of the battery under incomplete daily charging and discharging processes and fluctuating temperatures over day-night cycles. Much of previous development work has been carried out in order to model the short term evolution of battery variables. Many works focus on the on-line parameter estimation of available charge, using standard or advanced estimators, but they are not focused on the development of a model with predictive capabilities. Moreover, normally stable environmental conditions and standard charge-discharge patterns are considered. As the actual cycle-patterns differ from the manufacturer's tests, batteries fail to perform as expected. This paper proposes a novel methodology to model these issues, with predictive capabilities to estimate the remaining charge in a battery after several solar cycles. A new non-linear state space model is proposed as a basis, and the methodology to feed and train the model is introduced. The new methodology is validated using experimental data, providing only 5% of error at higher temperatures than the nominal one.

The role of mechanics during brain development

NASA Astrophysics Data System (ADS)

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-12-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated with neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism.

The role of mechanics during brain development

PubMed Central

Budday, Silvia; Steinmann, Paul; Kuhl, Ellen

2014-01-01

Convolutions are a classical hallmark of most mammalian brains. Brain surface morphology is often associated with intelligence and closely correlated to neurological dysfunction. Yet, we know surprisingly little about the underlying mechanisms of cortical folding. Here we identify the role of the key anatomic players during the folding process: cortical thickness, stiffness, and growth. To establish estimates for the critical time, pressure, and the wavelength at the onset of folding, we derive an analytical model using the Föppl-von-Kármán theory. Analytical modeling provides a quick first insight into the critical conditions at the onset of folding, yet it fails to predict the evolution of complex instability patterns in the post-critical regime. To predict realistic surface morphologies, we establish a computational model using the continuum theory of finite growth. Computational modeling not only confirms our analytical estimates, but is also capable of predicting the formation of complex surface morphologies with asymmetric patterns and secondary folds. Taken together, our analytical and computational models explain why larger mammalian brains tend to be more convoluted than smaller brains. Both models provide mechanistic interpretations of the classical malformations of lissencephaly and polymicrogyria. Understanding the process of cortical folding in the mammalian brain has direct implications on the diagnostics of neurological disorders including severe retardation, epilepsy, schizophrenia, and autism. PMID:25202162

Food web complexity and stability across habitat connectivity gradients.

PubMed

LeCraw, Robin M; Kratina, Pavel; Srivastava, Diane S

2014-12-01

The effects of habitat connectivity on food webs have been studied both empirically and theoretically, yet the question of whether empirical results support theoretical predictions for any food web metric other than species richness has received little attention. Our synthesis brings together theory and empirical evidence for how habitat connectivity affects both food web stability and complexity. Food web stability is often predicted to be greatest at intermediate levels of connectivity, representing a compromise between the stabilizing effects of dispersal via rescue effects and prey switching, and the destabilizing effects of dispersal via regional synchronization of population dynamics. Empirical studies of food web stability generally support both this pattern and underlying mechanisms. Food chain length has been predicted to have both increasing and unimodal relationships with connectivity as a result of predators being constrained by the patch occupancy of their prey. Although both patterns have been documented empirically, the underlying mechanisms may differ from those predicted by models. In terms of other measures of food web complexity, habitat connectivity has been empirically found to generally increase link density but either reduce or have no effect on connectance, whereas a unimodal relationship is expected. In general, there is growing concordance between empirical patterns and theoretical predictions for some effects of habitat connectivity on food webs, but many predictions remain to be tested over a full connectivity gradient, and empirical metrics of complexity are rarely modeled. Closing these gaps will allow a deeper understanding of how natural and anthropogenic changes in connectivity can affect real food webs.

Comparative Study on the Prediction of Aerodynamic Characteristics of Aircraft with Turbulence Models

NASA Astrophysics Data System (ADS)

Jang, Yujin; Huh, Jinbum; Lee, Namhun; Lee, Seungsoo; Park, Youngmin

2018-04-01

The RANS equations are widely used to analyze complex flows over aircraft. The equations require a turbulence model for turbulent flow analyses. A suitable turbulence must be selected for accurate predictions of aircraft aerodynamic characteristics. In this study, numerical analyses of three-dimensional aircraft are performed to compare the results of various turbulence models for the prediction of aircraft aerodynamic characteristics. A 3-D RANS solver, MSAPv, is used for the aerodynamic analysis. The four turbulence models compared are the Sparlart-Allmaras (SA) model, Coakley's q-ω model, Huang and Coakley's k-ɛ model, and Menter's k-ω SST model. Four aircrafts are considered: an ARA-M100, DLR-F6 wing-body, DLR-F6 wing-body-nacelle-pylon from the second drag prediction workshop, and a high wing aircraft with nacelles. The CFD results are compared with experimental data and other published computational results. The details of separation patterns, shock positions, and Cp distributions are discussed to find the characteristics of the turbulence models.

Decoding negative affect personality trait from patterns of brain activation to threat stimuli.

PubMed

Fernandes, Orlando; Portugal, Liana C L; Alves, Rita de Cássia S; Arruda-Sanchez, Tiago; Rao, Anil; Volchan, Eliane; Pereira, Mirtes; Oliveira, Letícia; Mourao-Miranda, Janaina

2017-01-15

Pattern recognition analysis (PRA) applied to functional magnetic resonance imaging (fMRI) has been used to decode cognitive processes and identify possible biomarkers for mental illness. In the present study, we investigated whether the positive affect (PA) or negative affect (NA) personality traits could be decoded from patterns of brain activation in response to a human threat using a healthy sample. fMRI data from 34 volunteers (15 women) were acquired during a simple motor task while the volunteers viewed a set of threat stimuli that were directed either toward them or away from them and matched neutral pictures. For each participant, contrast images from a General Linear Model (GLM) between the threat versus neutral stimuli defined the spatial patterns used as input to the regression model. We applied a multiple kernel learning (MKL) regression combining information from different brain regions hierarchically in a whole brain model to decode the NA and PA from patterns of brain activation in response to threat stimuli. The MKL model was able to decode NA but not PA from the contrast images between threat stimuli directed away versus neutral with a significance above chance. The correlation and the mean squared error (MSE) between predicted and actual NA were 0.52 (p-value=0.01) and 24.43 (p-value=0.01), respectively. The MKL pattern regression model identified a network with 37 regions that contributed to the predictions. Some of the regions were related to perception (e.g., occipital and temporal regions) while others were related to emotional evaluation (e.g., caudate and prefrontal regions). These results suggest that there was an interaction between the individuals' NA and the brain response to the threat stimuli directed away, which enabled the MKL model to decode NA from the brain patterns. To our knowledge, this is the first evidence that PRA can be used to decode a personality trait from patterns of brain activation during emotional contexts. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints.

PubMed

Colwell, Robert K; Gotelli, Nicholas J; Ashton, Louise A; Beck, Jan; Brehm, Gunnar; Fayle, Tom M; Fiedler, Konrad; Forister, Matthew L; Kessler, Michael; Kitching, Roger L; Klimes, Petr; Kluge, Jürgen; Longino, John T; Maunsell, Sarah C; McCain, Christy M; Moses, Jimmy; Noben, Sarah; Sam, Katerina; Sam, Legi; Shapiro, Arthur M; Wang, Xiangping; Novotny, Vojtech

2016-09-01

We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor - a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon-specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness. © 2016 John Wiley & Sons Ltd/CNRS.

PREDICTING ER BINDING AFFINITY FOR EDC RANKING AND PRIORITIZATION: MODEL II

EPA Science Inventory

The training set used to derive a common reactivity pattern (COREPA) model for estrogen receptor (ER) binding affinity in Model I (see Abstract I in this series) was extended to include 47 rat estrogen receptor (rER) relative binding affinity (RBA) measurements in addition to the...

Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies

EPA Science Inventory

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including: air pollution concentrations; human activity patterns, such as the amount of time spent outdoors vs. indoors, commuting, wal...

The Benefits of Scientific Modeling

ERIC Educational Resources Information Center

Kenyon, Lisa; Schwarz, Christina; Hug, Barbara

2008-01-01

When students are engaged in scientific modeling, they are able to notice patterns and develop and revise representations that become useful models to predict and explain--making their own scientific knowledge stronger, helping them to think critically, and helping them know more about the nature of science. To illustrate, this article describes a…

Time series analysis of monthly pulpwood use in the Northeast

Treesearch

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

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

Brünner, F.; Parganlija, D.; Rebhan, A.

We present new results on the decay patterns of scalar and tensor glueballs in the top-down holographic Witten-Sakai-Sugimoto model. This model, which has only one free dimensionless parameter, gives semi-quantitative predictions for the vector meson spectrum, their decay widths, and also a gluon condensate in agreement with SVZ sum rules. The holographic predictions for scalar glueball decay rates are compared with experimental data for the widely discussed gluon candidates f{sub 0}(1500) and f{sub 0}(1710)

Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds

USGS Publications Warehouse

O'Connell, Allan F.; Gardner, Beth; Oppel, Steffen; Meirinho, Ana; Ramírez, Iván; Miller, Peter I.; Louzao, Maite

2012-01-01

Knowledge about the spatial distribution of seabirds at sea is important for conservation. During marine conservation planning, logistical constraints preclude seabird surveys covering the complete area of interest and spatial distribution of seabirds is frequently inferred from predictive statistical models. Increasingly complex models are available to relate the distribution and abundance of pelagic seabirds to environmental variables, but a comparison of their usefulness for delineating protected areas for seabirds is lacking. Here we compare the performance of five modelling techniques (generalised linear models, generalised additive models, Random Forest, boosted regression trees, and maximum entropy) to predict the distribution of Balearic Shearwaters (Puffinus mauretanicus) along the coast of the western Iberian Peninsula. We used ship transect data from 2004 to 2009 and 13 environmental variables to predict occurrence and density, and evaluated predictive performance of all models using spatially segregated test data. Predicted distribution varied among the different models, although predictive performance varied little. An ensemble prediction that combined results from all five techniques was robust and confirmed the existence of marine important bird areas for Balearic Shearwaters in Portugal and Spain. Our predictions suggested additional areas that would be of high priority for conservation and could be proposed as protected areas. Abundance data were extremely difficult to predict, and none of five modelling techniques provided a reliable prediction of spatial patterns. We advocate the use of ensemble modelling that combines the output of several methods to predict the spatial distribution of seabirds, and use these predictions to target separate surveys assessing the abundance of seabirds in areas of regular use.

Climate Response of Direct Radiative Forcing of Anthropogenic Black Carbon

NASA Technical Reports Server (NTRS)

Chung, Serena H.; Seinfeld,John H.

2008-01-01

The equilibrium climate effect of direct radiative forcing of anthropogenic black carbon (BC) is examined by 100-year simulations in the Goddard Institute for Space Studies General Circulation Model II-prime coupled to a mixed-layer ocean model. Anthropogenic BC is predicted to raise globally and annually averaged equilibrium surface air temperature by 0.20 K if BC is assumed to be externally mixed. The predicted increase is significantly greater in the Northern Hemisphere (0.29 K) than in the Southern Hemisphere (0.11 K). If BC is assumed to be internally mixed with the present day level of sulfate aerosol, the predicted annual mean surface temperature increase rises to 0.37 K globally, 0.54 K for the Northern Hemisphere, and 0.20 K for the Southern Hemisphere. The climate sensitivity of BC direct radiative forcing is calculated to be 0.6 K W (sup -1) square meters, which is about 70% of that of CO2, independent of the assumption of BC mixing state. The largest surface temperature response occurs over the northern high latitudes during winter and early spring. In the tropics and midlatitudes, the largest temperature increase is predicted to occur in the upper troposphere. Direct radiative forcing of anthropogenic BC is also predicted to lead to a change of precipitation patterns in the tropics; precipitation is predicted to increase between 0 and 20 N and decrease between 0 and 20 S, shifting the intertropical convergence zone northward. If BC is assumed to be internally mixed with sulfate instead of externally mixed, the change in precipitation pattern is enhanced. The change in precipitation pattern is not predicted to alter the global burden of BC significantly because the change occurs predominantly in regions removed from BC sources.

Flower Development as an Interplay between Dynamical Physical Fields and Genetic Networks

PubMed Central

Barrio, Rafael Ángel; Hernández-Machado, Aurora; Varea, C.; Romero-Arias, José Roberto; Álvarez-Buylla, Elena

2010-01-01

In this paper we propose a model to describe the mechanisms by which undifferentiated cells attain gene configurations underlying cell fate determination during morphogenesis. Despite the complicated mechanisms that surely intervene in this process, it is clear that the fundamental fact is that cells obtain spatial and temporal information that bias their destiny. Our main hypothesis assumes that there is at least one macroscopic field that breaks the symmetry of space at a given time. This field provides the information required for the process of cell differentiation to occur by being dynamically coupled to a signal transduction mechanism that, in turn, acts directly upon the gene regulatory network (GRN) underlying cell-fate decisions within cells. We illustrate and test our proposal with a GRN model grounded on experimental data for cell fate specification during organ formation in early Arabidopsis thaliana flower development. We show that our model is able to recover the multigene configurations characteristic of sepal, petal, stamen and carpel primordial cells arranged in concentric rings, in a similar pattern to that observed during actual floral organ determination. Such pattern is robust to alterations of the model parameters and simulated failures predict altered spatio-temporal patterns that mimic those described for several mutants. Furthermore, simulated alterations in the physical fields predict a pattern equivalent to that found in Lacandonia schismatica, the only flowering species with central stamens surrounded by carpels. PMID:21048956

Māori identity signatures: A latent profile analysis of the types of Māori identity.

PubMed

Greaves, Lara M; Houkamau, Carla; Sibley, Chris G

2015-10-01

Māori are the indigenous peoples of New Zealand. However, the term 'Māori' can refer to a wide range of people of varying ethnic compositions and cultural identity. We present a statistical model identifying 6 distinct types, or 'Māori Identity Signatures,' and estimate their proportion in the Māori population. The model is tested using a Latent Profile Analysis of a national probability sample of 686 Māori drawn from the New Zealand Attitudes and Values Study. We identify 6 distinct signatures: Traditional Essentialists (22.6%), Traditional Inclusives (16%), High Moderates (31.7%), Low Moderates (18.7%), Spiritually Orientated (4.1%), and Disassociated (6.9%). These distinct Identity Signatures predicted variation in deprivation, age, mixed-ethnic affiliation, and religion. This research presents the first formal statistical model assessing how people's identity as Māori is psychologically structured, documents the relative proportion of these different patterns of structures, and shows that these patterns reliably predict differences in core demographics. We identify a range of patterns of Māori identity far more diverse than has been previously proposed based on qualitative data, and also show that the majority of Māori fit a moderate or traditional identity pattern. The application of our model for studying Māori health and identity development is discussed. (c) 2015 APA, all rights reserved).

Flower development as an interplay between dynamical physical fields and genetic networks.

PubMed

Barrio, Rafael Ángel; Hernández-Machado, Aurora; Varea, C; Romero-Arias, José Roberto; Alvarez-Buylla, Elena

2010-10-27

In this paper we propose a model to describe the mechanisms by which undifferentiated cells attain gene configurations underlying cell fate determination during morphogenesis. Despite the complicated mechanisms that surely intervene in this process, it is clear that the fundamental fact is that cells obtain spatial and temporal information that bias their destiny. Our main hypothesis assumes that there is at least one macroscopic field that breaks the symmetry of space at a given time. This field provides the information required for the process of cell differentiation to occur by being dynamically coupled to a signal transduction mechanism that, in turn, acts directly upon the gene regulatory network (GRN) underlying cell-fate decisions within cells. We illustrate and test our proposal with a GRN model grounded on experimental data for cell fate specification during organ formation in early Arabidopsis thaliana flower development. We show that our model is able to recover the multigene configurations characteristic of sepal, petal, stamen and carpel primordial cells arranged in concentric rings, in a similar pattern to that observed during actual floral organ determination. Such pattern is robust to alterations of the model parameters and simulated failures predict altered spatio-temporal patterns that mimic those described for several mutants. Furthermore, simulated alterations in the physical fields predict a pattern equivalent to that found in Lacandonia schismatica, the only flowering species with central stamens surrounded by carpels.

Modelling benthic macrofauna and seagrass distribution patterns in a North Sea tidal basin in response to 2050 climatic and environmental scenarios

NASA Astrophysics Data System (ADS)

Singer, Anja; Millat, Gerald; Staneva, Joanna; Kröncke, Ingrid

2017-03-01

Small-scale spatial distribution patterns of seven macrofauna species, seagrass beds and mixed mussel/oyster reefs were modelled for the Jade Bay (North Sea, Germany) in response to climatic and environmental scenarios (representing 2050). For the species distribution models four presence-absence modelling methods were merged within the ensemble forecasting platform 'biomod2'. The present spatial distribution (representing 2009) was modelled by statistically related species presences, true species absences and six high-resolution environmental grids. The future spatial distribution was then predicted in response to expected climate change-induced ongoing (1) sea-level rise and (2) water temperature increase. Between 2009 and 2050, the present and future prediction maps revealed a significant range gain for two macrofauna species (Macoma balthica, Tubificoides benedii), whereas the species' range sizes of five macrofauna species remained relatively stable across space and time. The predicted probability of occurrence (PO) of two macrofauna species (Cerastoderma edule, Scoloplos armiger) decreased significantly under the potential future habitat conditions. In addition, a clear seagrass bed extension (Zostera noltii) on the lower intertidal flats (mixed sediments) and a decrease in the PO of mixed Mytilus edulis/Crassostrea gigas reefs was predicted for 2050. Until the mid-21st century, our future climatic and environmental scenario revealed significant changes in the range sizes (gains-losses) and/or the PO (increases-decreases) for seven of the 10 modelled species at the study site.

The Relationship between Defense Patterns and DSM-5 Maladaptive Personality Domains.

PubMed

Granieri, Antonella; La Marca, Luana; Mannino, Giuseppe; Giunta, Serena; Guglielmucci, Fanny; Schimmenti, Adriano

2017-01-01

Aim: Research has extensively examined the relationship between defense mechanisms (DM) and personality traits. However, no study to date has explored if specific defenses (alone or in combination) are able to predict dysfunctional variants of personality domains, as conceived in the alternative DSM-5 model for personality disorders. This study aimed to investigate the relationship between DMs and DSM-5 maladaptive personality domains among adults. Materials and Methods: Three hundred and twenty-eight adults aged between 18 and 64 years old completed measures on DMs and maladapive personality domains. Regression analyses were performed to determine which DMs predicted the maladaptive personality domains of negative affectivity, detachment, antagonism, disinhibition, and psychoticism. Results: According to psychoanalytic literature, results showed that immature defenses positively predicted maladaptive personality domain scores, whereas mature defenses were generally related with better personality functioning. Moreover, different defense patterns emerged as significant predictors of the maladaptive personality domains comprised in the alternative DSM-5 model for personality disorder. Discussion: Our findings support the view that defense patterns represent core components of personality and its disorders, and suggest that an increased use of immature defenses and a reduced use of mature defenses have a negative impact on the development of personality.

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods.

PubMed

Cheng, Feixiong; Shen, Jie; Yu, Yue; Li, Weihua; Liu, Guixia; Lee, Philip W; Tang, Yun

2011-03-01

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

The Relationship between Defense Patterns and DSM-5 Maladaptive Personality Domains

PubMed Central

Granieri, Antonella; La Marca, Luana; Mannino, Giuseppe; Giunta, Serena; Guglielmucci, Fanny; Schimmenti, Adriano

2017-01-01

Aim: Research has extensively examined the relationship between defense mechanisms (DM) and personality traits. However, no study to date has explored if specific defenses (alone or in combination) are able to predict dysfunctional variants of personality domains, as conceived in the alternative DSM-5 model for personality disorders. This study aimed to investigate the relationship between DMs and DSM-5 maladaptive personality domains among adults. Materials and Methods: Three hundred and twenty-eight adults aged between 18 and 64 years old completed measures on DMs and maladapive personality domains. Regression analyses were performed to determine which DMs predicted the maladaptive personality domains of negative affectivity, detachment, antagonism, disinhibition, and psychoticism. Results: According to psychoanalytic literature, results showed that immature defenses positively predicted maladaptive personality domain scores, whereas mature defenses were generally related with better personality functioning. Moreover, different defense patterns emerged as significant predictors of the maladaptive personality domains comprised in the alternative DSM-5 model for personality disorder. Discussion: Our findings support the view that defense patterns represent core components of personality and its disorders, and suggest that an increased use of immature defenses and a reduced use of mature defenses have a negative impact on the development of personality. PMID:29163301

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans.

PubMed

Stevens, Jeffrey R; Marewski, Julian N; Schooler, Lael J; Gilby, Ian C

2016-08-01

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees ( Pan troglodytes ) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition.

Reflections of the social environment in chimpanzee memory: applying rational analysis beyond humans

PubMed Central

Marewski, Julian N.; Schooler, Lael J.; Gilby, Ian C.

2016-01-01

In cognitive science, the rational analysis framework allows modelling of how physical and social environments impose information-processing demands onto cognitive systems. In humans, for example, past social contact among individuals predicts their future contact with linear and power functions. These features of the human environment constrain the optimal way to remember information and probably shape how memory records are retained and retrieved. We offer a primer on how biologists can apply rational analysis to study animal behaviour. Using chimpanzees (Pan troglodytes) as a case study, we modelled 19 years of observational data on their social contact patterns. Much like humans, the frequency of past encounters in chimpanzees linearly predicted future encounters, and the recency of past encounters predicted future encounters with a power function. Consistent with the rational analyses carried out for human memory, these findings suggest that chimpanzee memory performance should reflect those environmental regularities. In re-analysing existing chimpanzee memory data, we found that chimpanzee memory patterns mirrored their social contact patterns. Our findings hint that human and chimpanzee memory systems may have evolved to solve similar information-processing problems. Overall, rational analysis offers novel theoretical and methodological avenues for the comparative study of cognition. PMID:27853606

Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling.

PubMed

Vulović, Aleksandra; Šušteršič, Tijana; Cvijić, Sandra; Ibrić, Svetlana; Filipović, Nenad

2018-02-15

One of the critical components of the respiratory drug delivery is the manner in which the inhaled aerosol is deposited in respiratory tract compartments. Depending on formulation properties, device characteristics and breathing pattern, only a certain fraction of the dose will reach the target site in the lungs, while the rest of the drug will deposit in the inhalation device or in the mouth-throat region. The aim of this study was to link the Computational fluid dynamics (CFD) with physiologically-based pharmacokinetic (PBPK) modelling in order to predict aerolisolization of different dry powder formulations, and estimate concomitant in vivo deposition and absorption of amiloride hydrochloride. Drug physicochemical properties were experimentally determined and used as inputs for the CFD simulations of particle flow in the generated 3D geometric model of Aerolizer® dry powder inhaler (DPI). CFD simulations were used to simulate air flow through Aerolizer® inhaler and Discrete Phase Method (DPM) was used to simulate aerosol particles deposition within the fluid domain. The simulated values for the percent emitted dose were comparable to the values obtained using Andersen cascade impactor (ACI). However, CFD predictions indicated that aerosolized DPI have smaller particle size and narrower size distribution than assumed based on ACI measurements. Comparison with the literature in vivo data revealed that the constructed drug-specific PBPK model was able to capture amiloride absorption pattern following oral and inhalation administration. The PBPK simulation results, based on the CFD generated particle distribution data as input, illustrated the influence of formulation properties on the expected drug plasma concentration profiles. The model also predicted the influence of potential changes in physiological parameters on the extent of inhaled amiloride absorption. Overall, this study demonstrated the potential of the combined CFD-PBPK approach to model inhaled drug bioperformance, and suggested that CFD generated results might serve as input for the prediction of drug deposition pattern in vivo. Copyright © 2017 Elsevier B.V. All rights reserved.

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

PubMed Central

Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

2016-01-01

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. PMID:27629707

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

PubMed

Rosenberg, Monica D; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S; Shen, Xilin; Constable, R Todd; Li, Chiang-Shan R; Chun, Marvin M

2016-09-14

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention. Copyright © 2016 the authors 0270-6474/16/369547-11$15.00/0.

Discovery of fairy circles in Australia supports self-organization theory

PubMed Central

Getzin, Stephan; Yizhaq, Hezi; Bell, Bronwyn; Erickson, Todd E.; Postle, Anthony C.; Katra, Itzhak; Tzuk, Omer; Zelnik, Yuval R.; Wiegand, Kerstin; Wiegand, Thorsten; Meron, Ehud

2016-01-01

Vegetation gap patterns in arid grasslands, such as the “fairy circles” of Namibia, are one of nature’s greatest mysteries and subject to a lively debate on their origin. They are characterized by small-scale hexagonal ordering of circular bare-soil gaps that persists uniformly in the landscape scale to form a homogeneous distribution. Pattern-formation theory predicts that such highly ordered gap patterns should be found also in other water-limited systems across the globe, even if the mechanisms of their formation are different. Here we report that so far unknown fairy circles with the same spatial structure exist 10,000 km away from Namibia in the remote outback of Australia. Combining fieldwork, remote sensing, spatial pattern analysis, and process-based mathematical modeling, we demonstrate that these patterns emerge by self-organization, with no correlation with termite activity; the driving mechanism is a positive biomass–water feedback associated with water runoff and biomass-dependent infiltration rates. The remarkable match between the patterns of Australian and Namibian fairy circles and model results indicate that both patterns emerge from a nonuniform stationary instability, supporting a central universality principle of pattern-formation theory. Applied to the context of dryland vegetation, this principle predicts that different systems that go through the same instability type will show similar vegetation patterns even if the feedback mechanisms and resulting soil–water distributions are different, as we indeed found by comparing the Australian and the Namibian fairy-circle ecosystems. These results suggest that biomass–water feedbacks and resultant vegetation gap patterns are likely more common in remote drylands than is currently known. PMID:26976567

Modelling fatigue and the use of fatigue models in work settings.

PubMed

Dawson, Drew; Ian Noy, Y; Härmä, Mikko; Akerstedt, Torbjorn; Belenky, Gregory

2011-03-01

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the 'defenses-in-depth' approach to fatigue risk management. Copyright © 2010 Elsevier Ltd. All rights reserved.

Modeling preferential water flow and solute transport in unsaturated soil using the active region model

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

Sheng, F.; Wang, K.; Zhang, R.

2009-03-15

Preferential flow and solute transport are common processes in the unsaturated soil, in which distributions of soil water content and solute concentrations are often characterized as fractal patterns. An active region model (ARM) was recently proposed to describe the preferential flow and transport patterns. In this study, ARM governing equations were derived to model the preferential soil water flow and solute transport processes. To evaluate the ARM equations, dye infiltration experiments were conducted, in which distributions of soil water content and Cl{sup -} concentration were measured. Predicted results using the ARM and the mobile-immobile region model (MIM) were compared withmore » the measured distributions of soil water content and Cl{sup -} concentration. Although both the ARM and the MIM are two-region models, they are fundamental different in terms of treatments of the flow region. The models were evaluated based on the modeling efficiency (ME). The MIM provided relatively poor prediction results of the preferential flow and transport with negative ME values or positive ME values less than 0.4. On the contrary, predicted distributions of soil water content and Cl- concentration using the ARM agreed reasonably well with the experimental data with ME values higher than 0.8. The results indicated that the ARM successfully captured the macroscopic behavior of preferential flow and solute transport in the unsaturated soil.« less

Serum microRNA expression patterns that predict early treatment failure in prostate cancer patients.

PubMed

Singh, Prashant K; Preus, Leah; Hu, Qiang; Yan, Li; Long, Mark D; Morrison, Carl D; Nesline, Mary; Johnson, Candace S; Koochekpour, Shahriar; Kohli, Manish; Liu, Song; Trump, Donald L; Sucheston-Campbell, Lara E; Campbell, Moray J

2014-02-15

We aimed to identify microRNA (miRNA) expression patterns in the serum of prostate cancer (CaP) patients that predict the risk of early treatment failure following radical prostatectomy (RP). Microarray and Q-RT-PCR analyses identified 43 miRNAs as differentiating disease stages within 14 prostate cell lines and reflectedpublically available patient data. 34 of these miRNA were detectable in the serum of CaP patients. Association with time to biochemical progression was examined in a cohort of CaP patients following RP. A greater than two-fold increase in hazard of biochemical progression associated with altered expression of miR-103, miR-125b and miR-222 (p<.0008) in the serum of CaP patients. Prediction models based on penalized regression analyses showed that the levels of the miRNAs and PSA together were better at detecting false positives than models without miRNAs, for similar level of sensitivity. Analyses of publically available data revealed significant and reciprocal relationships between changes in CpG methylation and miRNA expression patterns suggesting a role for CpG methylation to regulate miRNA. Exploratory validation supported roles for miR-222 and miR-125b to predict progression risk in CaP. The current study established that expression patterns of serum-detectable miRNAs taken at the time of RP are prognostic for men who are at risk of experiencing subsequent early biochemical progression. These non-invasive approaches could be used to augment treatment decisions.

Critical thresholds in species` responses to landscape structure

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

With, K.A.; Crist, T.O.

1995-12-01

Critical thresholds are transition ranges across which small changes in spatial pattern produce abrupt shifts in ecological responses. Habitat fragmentation provides a familiar example of a critical threshold. As the landscape becomes dissected into smaller parcels of habitat. landscape connectivity-the functional linkage among habitat patches - may suddenly become disrupted, which may have important consequences for the distribution and persistence of populations. Landscape connectivity depends not only on the abundance and spatial patterning of habitat. but also on the habitat specificity and dispersal abilities of species. Habitat specialists with limited dispersal capabilities presumably have a much lower threshold to habitatmore » fragmentation than highly vagile species, which may perceive the landscape as functionally connected across a greater range of fragmentation severity. To determine where threshold effects in species, responses to landscape structure are likely to occur, a simulation model modified from percolation theory was developed. Our simulations predicted the distributional patterns of populations in different landscape mosaics, which we tested empirically using two grasshopper species (Orthoptera: Acrididae) that occur in the shortgrass prairie of north-central Colorado. The distribution of these two species in this grassland mosaic matched the predictions from our simulations. By providing quantitative predictions of threshold effects, this modelling approach may prove useful in the formulation of conservation strategies and assessment of land-use changes on species` distributional patterns and persistence.« less

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

PubMed

Baker, Erich J; Walter, Nicole A R; Salo, Alex; Rivas Perea, Pablo; Moore, Sharon; Gonzales, Steven; Grant, Kathleen A

2017-03-01

The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration. Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having increasingly fewer bouts with more drinks. This analysis also identifies characteristic avoidance of intoxicating topographies in future low drinkers, such as increasing number of bouts and waiting longer before the first ethanol drink. Copyright © 2017 The Authors Alcoholism: Clinical & Experimental Research published by Wiley Periodicals, Inc. on behalf of Research Society on Alcoholism.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

A Complex Story: Universal Preference vs. Individual Differences Shaping Aesthetic Response to Fractals Patterns

PubMed Central

Street, Nichola; Forsythe, Alexandra M.; Reilly, Ronan; Taylor, Richard; Helmy, Mai S.

2016-01-01

Fractal patterns offer one way to represent the rough complexity of the natural world. Whilst they dominate many of our visual experiences in nature, little large-scale perceptual research has been done to explore how we respond aesthetically to these patterns. Previous research (Taylor et al., 2011) suggests that the fractal patterns with mid-range fractal dimensions (FDs) have universal aesthetic appeal. Perceptual and aesthetic responses to visual complexity have been more varied with findings suggesting both linear (Forsythe et al., 2011) and curvilinear (Berlyne, 1970) relationships. Individual differences have been found to account for many of the differences we see in aesthetic responses but some, such as culture, have received little attention within the fractal and complexity research fields. This two-study article aims to test preference responses to FD and visual complexity, using a large cohort (N = 443) of participants from around the world to allow universality claims to be tested. It explores the extent to which age, culture and gender can predict our preferences for fractally complex patterns. Following exploratory analysis that found strong correlations between FD and visual complexity, a series of linear mixed-effect models were implemented to explore if each of the individual variables could predict preference. The first tested a linear complexity model (likelihood of selecting the more complex image from the pair of images) and the second a mid-range FD model (likelihood of selecting an image within mid-range). Results show that individual differences can reliably predict preferences for complexity across culture, gender and age. However, in fitting with current findings the mid-range models show greater consistency in preference not mediated by gender, age or culture. This article supports the established theory that the mid-range fractal patterns appear to be a universal construct underlying preference but also highlights the fragility of universal claims by demonstrating individual differences in preference for the interrelated concept of visual complexity. This highlights a current stalemate in the field of empirical aesthetics. PMID:27252634

Evaluation of blocking performance in ensemble seasonal integrations

NASA Astrophysics Data System (ADS)

Casado, M. J.; Doblas-Reyes, F. J.; Pastor, M. A.

2003-04-01

EVALUATION OF BLOCKING PERFOMANCE IN ENSEMBLE SEASONAL INTEGRATIONS M. J. Casado (1), F. J. Doblas-Reyes (2), A. Pastor (1) (1) I Instituto Nacional de Meteorología, c/Leonardo Prieto Castro,8,28071 ,Madrid,Spain, mjcasado@inm.es (2) ECMWF, Shinfield Park,RG2 9AX, Reading, UK, f.doblas-reyes@ecmwf.int Climate models have shown a robust inability to reliably predict blocking onset and frequency. This systematic error has been evaluated using multi-model ensemble seasonal integrations carried out in the framework of the Prediction Of climate Variations On Seasonal and interanual Timescales (PROVOST) project and compared to a blocking features assessment of the NCEP re-analyses. The PROVOST GCMs are able to adequately reproduce the spatial NCEP teleconnection patterns over the Northern Hemisphere, being notorious the great spatial correlation coefficient with some of the corresponding NCEP patterns. In spite of that, the different models show a consistent underestimation of blocking frequency which may impact on the ability to predict the seasonal amplitude of the leading modes of variability over the Northern Hemisphere.

Selective sweeps in growing microbial colonies

NASA Astrophysics Data System (ADS)

Korolev, Kirill S.; Müller, Melanie J. I.; Karahan, Nilay; Murray, Andrew W.; Hallatschek, Oskar; Nelson, David R.

2012-04-01

Evolutionary experiments with microbes are a powerful tool to study mutations and natural selection. These experiments, however, are often limited to the well-mixed environments of a test tube or a chemostat. Since spatial organization can significantly affect evolutionary dynamics, the need is growing for evolutionary experiments in spatially structured environments. The surface of a Petri dish provides such an environment, but a more detailed understanding of microbial growth on Petri dishes is necessary to interpret such experiments. We formulate a simple deterministic reaction-diffusion model, which successfully predicts the spatial patterns created by two competing species during colony expansion. We also derive the shape of these patterns analytically without relying on microscopic details of the model. In particular, we find that the relative fitness of two microbial strains can be estimated from the logarithmic spirals created by selective sweeps. The theory is tested with strains of the budding yeast Saccharomyces cerevisiae for spatial competitions with different initial conditions and for a range of relative fitnesses. The reaction-diffusion model also connects the microscopic parameters like growth rates and diffusion constants with macroscopic spatial patterns and predicts the relationship between fitness in liquid cultures and on Petri dishes, which we confirmed experimentally. Spatial sector patterns therefore provide an alternative fitness assay to the commonly used liquid culture fitness assays.

Evaluation of the synoptic and mesoscale predictive capabilities of a mesoscale atmospheric simulation system

NASA Technical Reports Server (NTRS)

Koch, S. E.; Skillman, W. C.; Kocin, P. J.; Wetzel, P. J.; Brill, K.; Keyser, D. A.; Mccumber, M. C.

1983-01-01

The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.

Model Averaging for Predicting the Exposure to Aflatoxin B1 Using DNA Methylation in White Blood Cells of Infants

NASA Astrophysics Data System (ADS)

Rahardiantoro, S.; Sartono, B.; Kurnia, A.

2017-03-01

In recent years, DNA methylation has been the special issue to reveal the pattern of a lot of human diseases. Huge amount of data would be the inescapable phenomenon in this case. In addition, some researchers interesting to take some predictions based on these huge data, especially using regression analysis. The classical approach would be failed to take the task. Model averaging by Ando and Li [1] could be an alternative approach to face this problem. This research applied the model averaging to get the best prediction in high dimension of data. In the practice, the case study by Vargas et al [3], data of exposure to aflatoxin B1 (AFB1) and DNA methylation in white blood cells of infants in The Gambia, take the implementation of model averaging. The best ensemble model selected based on the minimum of MAPE, MAE, and MSE of predictions. The result is ensemble model by model averaging with number of predictors in model candidate is 15.

Are running speeds maximized with simple-spring stance mechanics?

PubMed

Clark, Kenneth P; Weyand, Peter G

2014-09-15

Are the fastest running speeds achieved using the simple-spring stance mechanics predicted by the classic spring-mass model? We hypothesized that a passive, linear-spring model would not account for the running mechanics that maximize ground force application and speed. We tested this hypothesis by comparing patterns of ground force application across athletic specialization (competitive sprinters vs. athlete nonsprinters, n = 7 each) and running speed (top speeds vs. slower ones). Vertical ground reaction forces at 5.0 and 7.0 m/s, and individual top speeds (n = 797 total footfalls) were acquired while subjects ran on a custom, high-speed force treadmill. The goodness of fit between measured vertical force vs. time waveform patterns and the patterns predicted by the spring-mass model were assessed using the R(2) statistic (where an R(2) of 1.00 = perfect fit). As hypothesized, the force application patterns of the competitive sprinters deviated significantly more from the simple-spring pattern than those of the athlete, nonsprinters across the three test speeds (R(2) <0.85 vs. R(2) ≥ 0.91, respectively), and deviated most at top speed (R(2) = 0.78 ± 0.02). Sprinters attained faster top speeds than nonsprinters (10.4 ± 0.3 vs. 8.7 ± 0.3 m/s) by applying greater vertical forces during the first half (2.65 ± 0.05 vs. 2.21 ± 0.05 body wt), but not the second half (1.71 ± 0.04 vs. 1.73 ± 0.04 body wt) of the stance phase. We conclude that a passive, simple-spring model has limited application to sprint running performance because the swiftest runners use an asymmetrical pattern of force application to maximize ground reaction forces and attain faster speeds. Copyright © 2014 the American Physiological Society.

Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network.

PubMed

Peng, Jiang Chen; Ran, Zhi Hua; Shen, Jun

2015-09-01

Previous research has yielded conflicting data as to whether the natural history of inflammatory bowel disease follows a seasonal pattern. The purpose of this study was (1) to determine whether the frequency of onset and relapse of inflammatory bowel disease follows a seasonal pattern and (2) to establish a model to predict the frequency of onset, relapse, and severity of inflammatory bowel disease (IBD) with meteorological data based on artificial neural network (ANN). Patients with diagnosis of ulcerative colitis (UC) or Crohn's disease (CD) between 2003 and 2011 were investigated according to the occurrence of onset and flares of symptoms. The expected onset or relapse was calculated on a monthly basis over the study period. For artificial neural network (ANN), patients from 2003 to 2010 were assigned as training cohort and patients in 2011 were assigned as validation cohort. Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy. We found no seasonal pattern of onset (P = 0.248) and relapse (P = 0.394) among UC patients. But, the onset (P = 0.015) and relapse (P = 0.004) of CD were associated with seasonal pattern, with a peak in July and August. ANN had average accuracy to predict the frequency of onset (MSE = 0.076, MAPE = 37.58%) and severity of IBD (MSE = 0.065, MAPE = 42.15%) but high accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1%). The frequency of onset and relapse in IBD showed seasonality only in CD, with a peak in July and August, but not in UC. ANN may have its value in predicting the frequency of relapse among patients with IBD.

Seasonal prediction of typhoon genesis frequency and track patterns in the North West Pacific area

NASA Astrophysics Data System (ADS)

Hyoun, Yoosun; Kang, Kiryong; Shin, Do-Shick

2014-05-01

This study is to investigate the performance of the typhoon seasonal predictability using a dynamical model. The check items are the monthly statistics for total number of typhoon genesis in Western North Pacific (WNP) area and possible threat to Korean peninsula among them, and the probability of each categorized track pattern. As the dynamical model the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) was used, and it uses five ensemble members including control run are generated using time-lagged methods and the resolution of T126L27 (a Gaussian grid spacing of 0.94º). The model initial conditions are obtained from the National Center for Enviromental Prediction Global Forecast System (NCEP GFS) and the SST from Climate Forecast System with bias correction was used for ocean surface boundary condition. The summer (Jun-Jul-Aug) season prediction is made one month prior to target season. The detection of tropical cyclone used in this system is based on six criteria. First, the isolated vortex type minimum sea level pressure should be below 1008hPa. Second, the maximum wind speed is larger than 17m s-1. Third, the magnitude of the maximum relative vorticity at 850hPa exceeds 3.5x10-5s-1. Fourth, the average temperature difference from the area mean of surrounding region at 300hPa, 500hPa, 700hPa exceeds 2.5K. Fifth, the maximum wind speed at 850hPa is larger than that at 300hPa. Sixth, this identified vortex should last more than two days. These criteria were chosen after close examination from model-observation comparison. In this study, we will focus on performance of the system typhoon frequency and track pattern in the WNP area during 2004-2013.

Predicting invasiveness of species in trade: Climate match, trophic guild and fecundity influence establishment and impact of non-native freshwater fishes

USGS Publications Warehouse

Howeth, Jennifer G.; Gantz, Crysta A.; Angermeier, Paul; Frimpong, Emmanuel A.; Hoff, Michael H.; Keller, Reuben P.; Mandrak, Nicholas E.; Marchetti, Michael P.; Olden, Julian D.; Romagosa, Christina M.; Lodge, David M.

2016-01-01

AimImpacts of non-native species have motivated development of risk assessment tools for identifying introduced species likely to become invasive. Here, we develop trait-based models for the establishment and impact stages of freshwater fish invasion, and use them to screen non-native species common in international trade. We also determine which species in the aquarium, biological supply, live bait, live food and water garden trades are likely to become invasive. Results are compared to historical patterns of non-native fish establishment to assess the relative importance over time of pathways in causing invasions.LocationLaurentian Great Lakes region.MethodsTrait-based classification trees for the establishment and impact stages of invasion were developed from data on freshwater fish species that established or failed to establish in the Great Lakes. Fishes in trade were determined from import data from Canadian and United States regulatory agencies, assigned to specific trades and screened through the developed models.ResultsClimate match between a species’ native range and the Great Lakes region predicted establishment success with 75–81% accuracy. Trophic guild and fecundity predicted potential harmful impacts of established non-native fishes with 75–83% accuracy. Screening outcomes suggest the water garden trade poses the greatest risk of introducing new invasive species, followed by the live food and aquarium trades. Analysis of historical patterns of introduction pathways demonstrates the increasing importance of these trades relative to other pathways. Comparisons among trades reveal that model predictions parallel historical patterns; all fishes previously introduced from the water garden trade have established. The live bait, biological supply, aquarium and live food trades have also contributed established non-native fishes.Main conclusionsOur models predict invasion risk of potential fish invaders to the Great Lakes region and could help managers prioritize efforts among species and pathways to minimize such risk. Similar approaches could be applied to other taxonomic groups and geographic regions.

Title: Freshwater phytoplankton responses to global warming.

PubMed

Wagner, Heiko; Fanesi, Andrea; Wilhelm, Christian

2016-09-20

Global warming alters species composition and function of freshwater ecosystems. However, the impact of temperature on primary productivity is not sufficiently understood and water quality models need to be improved in order to assess the quantitative and qualitative changes of aquatic communities. On the basis of experimental data, we demonstrate that the commonly used photosynthetic and water chemistry parameters alone are not sufficient for modeling phytoplankton growth under changing temperature regimes. We present some new aspects of the acclimation process with respect to temperature and how contrasting responses may be explained by a more complete physiological knowledge of the energy flow from photons to new biomass. We further suggest including additional bio-markers/traits for algal growth such as carbon allocation patterns to increase the explanatory power of such models. Although carbon allocation patterns are promising and functional cellular traits for growth prediction under different nutrient and light conditions, their predictive power still waits to be tested with respect to temperature. A great challenge for the near future will be the prediction of primary production efficiencies under the global change scenario using a uniform model for phytoplankton assemblages. Copyright © 2016 Elsevier GmbH. All rights reserved.

Modeling Joint Exposures and Health Outcomes for Cumulative Risk Assessment: The Case of Radon and Smoking

PubMed Central

Chahine, Teresa; Schultz, Bradley D.; Zartarian, Valerie G.; Xue, Jianping; Subramanian, SV; Levy, Jonathan I.

2011-01-01

Community-based cumulative risk assessment requires characterization of exposures to multiple chemical and non-chemical stressors, with consideration of how the non-chemical stressors may influence risks from chemical stressors. Residential radon provides an interesting case example, given its large attributable risk, effect modification due to smoking, and significant variability in radon concentrations and smoking patterns. In spite of this fact, no study to date has estimated geographic and sociodemographic patterns of both radon and smoking in a manner that would allow for inclusion of radon in community-based cumulative risk assessment. In this study, we apply multi-level regression models to explain variability in radon based on housing characteristics and geological variables, and construct a regression model predicting housing characteristics using U.S. Census data. Multi-level regression models of smoking based on predictors common to the housing model allow us to link the exposures. We estimate county-average lifetime lung cancer risks from radon ranging from 0.15 to 1.8 in 100, with high-risk clusters in areas and for subpopulations with high predicted radon and smoking rates. Our findings demonstrate the viability of screening-level assessment to characterize patterns of lung cancer risk from radon, with an approach that can be generalized to multiple chemical and non-chemical stressors. PMID:22016710

Predicting vertically-nonsequential wetting patterns with a source-responsive model

USGS Publications Warehouse

Nimmo, John R.; Mitchell, Lara

2013-01-01

Water infiltrating into soil of natural structure often causes wetting patterns that do not develop in an orderly sequence. Because traditional unsaturated flow models represent a water advance that proceeds sequentially, they fail to predict irregular development of water distribution. In the source-responsive model, a diffuse domain (D) represents flow within soil matrix material following traditional formulations, and a source-responsive domain (S), characterized in terms of the capacity for preferential flow and its degree of activation, represents preferential flow as it responds to changing water-source conditions. In this paper we assume water undergoing rapid source-responsive transport at any particular time is of negligibly small volume; it becomes sensible at the time and depth where domain transfer occurs. A first-order transfer term represents abstraction from the S to the D domain which renders the water sensible. In tests with lab and field data, for some cases the model shows good quantitative agreement, and in all cases it captures the characteristic patterns of wetting that proceed nonsequentially in the vertical direction. In these tests we determined the values of the essential characterizing functions by inverse modeling. These functions relate directly to observable soil characteristics, rendering them amenable to evaluation and improvement through hydropedologic development.

Neuronal encoding of object and distance information: a model simulation study on naturalistic optic flow processing

PubMed Central

Hennig, Patrick; Egelhaaf, Martin

2011-01-01

We developed a model of the input circuitry of the FD1 cell, an identified motion-sensitive interneuron in the blowfly's visual system. The model circuit successfully reproduces the FD1 cell's most conspicuous property: its larger responses to objects than to spatially extended patterns. The model circuit also mimics the time-dependent responses of FD1 to dynamically complex naturalistic stimuli, shaped by the blowfly's saccadic flight and gaze strategy: the FD1 responses are enhanced when, as a consequence of self-motion, a nearby object crosses the receptive field during intersaccadic intervals. Moreover, the model predicts that these object-induced responses are superimposed by pronounced pattern-dependent fluctuations during movements on virtual test flights in a three-dimensional environment with systematic modifications of the environmental patterns. Hence, the FD1 cell is predicted to detect not unambiguously objects defined by the spatial layout of the environment, but to be also sensitive to objects distinguished by textural features. These ambiguous detection abilities suggest an encoding of information about objects—irrespective of the features by which the objects are defined—by a population of cells, with the FD1 cell presumably playing a prominent role in such an ensemble. PMID:22461769

A recovery principle provides insight into auxin pattern control in the Arabidopsis root

PubMed Central

Moore, Simon; Liu, Junli; Zhang, Xiaoxian; Lindsey, Keith

2017-01-01

Regulated auxin patterning provides a key mechanism for controlling root growth and development. We have developed a data-driven mechanistic model using realistic root geometry and formulated a principle to theoretically investigate quantitative auxin pattern recovery following auxin transport perturbation. This principle reveals that auxin patterning is potentially controlled by multiple combinations of interlinked levels and localisation of influx and efflux carriers. We demonstrate that (1) when efflux carriers maintain polarity but change levels, maintaining the same auxin pattern requires non-uniform and polar distribution of influx carriers; (2) the emergence of the same auxin pattern, from different levels of influx carriers with the same nonpolar localisation, requires simultaneous modulation of efflux carrier level and polarity; and (3) multiple patterns of influx and efflux carriers for maintaining an auxin pattern do not have spatially proportional correlation. This reveals that auxin pattern formation requires coordination between influx and efflux carriers. We further show that the model makes various predictions that can be experimentally validated. PMID:28220889

Factors influencing time-location patterns and their impact on estimates of exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

PubMed

Spalt, Elizabeth W; Curl, Cynthia L; Allen, Ryan W; Cohen, Martin; Williams, Kayleen; Hirsch, Jana A; Adar, Sara D; Kaufman, Joel D

2016-06-01

We assessed time-location patterns and the role of individual- and residential-level characteristics on these patterns within the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) cohort and also investigated the impact of individual-level time-location patterns on individual-level estimates of exposure to outdoor air pollution. Reported time-location patterns varied significantly by demographic factors such as age, gender, race/ethnicity, income, education, and employment status. On average, Chinese participants reported spending significantly more time indoors and less time outdoors and in transit than White, Black, or Hispanic participants. Using a tiered linear regression approach, we predicted time indoors at home and total time indoors. Our model, developed using forward-selection procedures, explained 43% of the variability in time spent indoors at home, and incorporated demographic, health, lifestyle, and built environment factors. Time-weighted air pollution predictions calculated using recommended time indoors from USEPA overestimated exposures as compared with predictions made with MESA Air participant-specific information. These data fill an important gap in the literature by describing the impact of individual and residential characteristics on time-location patterns and by demonstrating the impact of population-specific data on exposure estimates.

Optical Pattern Formation in Cold Atoms: Explaining the Red-Blue Asymmetry

NASA Astrophysics Data System (ADS)

Schmittberger, Bonnie; Gauthier, Daniel

2013-05-01

The study of pattern formation in atomic systems has provided new insight into fundamental many-body physics and low-light-level nonlinear optics. Pattern formation in cold atoms in particular is of great interest in condensed matter physics and quantum information science because atoms undergo self-organization at ultralow input powers. We recently reported the first observation of pattern formation in cold atoms but found that our results were not accurately described by any existing theoretical model of pattern formation. Previous models describing pattern formation in cold atoms predict that pattern formation should occur using both red and blue-detuned pump beams, favoring a lower threshold for blue detunings. This disagrees with our recent work, in which we only observed pattern formation with red-detuned pump beams. Previous models also assume a two-level atom, which cannot account for the cooling processes that arise when beams counterpropagate through a cold atomic vapor. We describe a new model for pattern formation that accounts for Sisyphus cooling in multi-level atoms, which gives rise to a new nonlinearity via spatial organization of the atoms. This spatial organization causes a sharp red-blue detuning asymmetry, which agrees well with our experimental observations. We gratefully acknowledge the financial support of the NSF through Grant #PHY-1206040.

New Correlation Methods of Evaporation Heat Transfer in Horizontal Microfine Tubes

NASA Astrophysics Data System (ADS)

Makishi, Osamu; Honda, Hiroshi

A stratified flow model and an annular flow model of evaporation heat transfer in horizontal microfin tubes have been proposed. In the stratified flow model, the contributions of thin film evaporation and nucleate boiling in the groove above a stratified liquid were predicted by a previously reported numerical analysis and a newly developed correlation, respectively. The contributions of nucleate boiling and forced convection in the stratified liquid region were predicted by the new correlation and the Carnavos equation, respectively. In the annular flow model, the contributions of nucleate boiling and forced convection were predicted by the new correlation and the Carnavos equation in which the equivalent Reynolds number was introduced, respectively. A flow pattern transition criterion proposed by Kattan et al. was incorporated to predict the circumferential average heat transfer coefficient in the intermediate region by use of the two models. The predictions of the heat transfer coefficient compared well with available experimental data for ten tubes and four refrigerants.

Numerical modeling of overland flow due to rainfall-runoff

USDA-ARS?s Scientific Manuscript database

Runoff is a basic hydrologic process that can be influenced by management activities in agricultural watersheds. Better description of runoff patterns through modeling will help to understand and predict watershed sediment transport and water quality. Normally, runoff is studied with kinematic wave ...

PREDICTING ER BINDING AFFINITY FOR EDC RANKING AND PRIORITIZATION: MODEL I

EPA Science Inventory

A Common Reactivity Pattern (COREPA) model, based on consideration of multiple energetically reasonable conformations of flexible chemicals was developed using a training set of 232 rat estrogen receptor (rER) relative binding affinity (RBA) measurements. The training set include...

Frequency-dependent selection predicts patterns of radiations and biodiversity.

PubMed

Melián, Carlos J; Alonso, David; Vázquez, Diego P; Regetz, James; Allesina, Stefano

2010-08-26

Most empirical studies support a decline in speciation rates through time, although evidence for constant speciation rates also exists. Declining rates have been explained by invoking pre-existing niches, whereas constant rates have been attributed to non-adaptive processes such as sexual selection and mutation. Trends in speciation rate and the processes underlying it remain unclear, representing a critical information gap in understanding patterns of global diversity. Here we show that the temporal trend in the speciation rate can also be explained by frequency-dependent selection. We construct a frequency-dependent and DNA sequence-based model of speciation. We compare our model to empirical diversity patterns observed for cichlid fish and Darwin's finches, two classic systems for which speciation rates and richness data exist. Negative frequency-dependent selection predicts well both the declining speciation rate found in cichlid fish and explains their species richness. For groups like the Darwin's finches, in which speciation rates are constant and diversity is lower, speciation rate is better explained by a model without frequency-dependent selection. Our analysis shows that differences in diversity may be driven by incipient species abundance with frequency-dependent selection. Our results demonstrate that genetic-distance-based speciation and frequency-dependent selection are sufficient to explain the high diversity observed in natural systems and, importantly, predict decay through time in speciation rate in the absence of pre-existing niches.

An application of hybrid downscaling model to forecast summer precipitation at stations in China

NASA Astrophysics Data System (ADS)

Liu, Ying; Fan, Ke

2014-06-01

A pattern prediction hybrid downscaling method was applied to predict summer (June-July-August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.

How social factors and behavioural strategies affect feeding and social interaction patterns in pigs.

PubMed

Boumans, Iris J M M; de Boer, Imke J M; Hofstede, Gert Jan; Bokkers, Eddie A M

2018-04-26

Animals living in groups compete for food resources and face food conflicts. These conflicts are affected by social factors (e.g. competition level) and behavioural strategies (e.g. avoidance). This study aimed to deepen our understanding of the complex interactions between social factors and behavioural strategies affecting feeding and social interaction patterns in animals. We focused on group-housed growing pigs, Sus scrofa, which typically face conflicts around the feeder, and of which patterns in various competitive environments (i.e. pig:feeder ratio) have been documented soundly. An agent-based model was developed to explore how interactions among social factors and behavioural strategies can affect various feeding and social interaction patterns differently under competitive situations. Model results show that pig and diet characteristics interact with group size and affect daily feeding patterns (e.g. feed intake and feeding time) and conflicts around the feeder. The level of competition can cause a turning point in feeding and social interaction patterns. Beyond a certain point of competition, meal-based (e.g. meal frequency) and social interaction patterns (e.g. displacements) are determined mainly by behavioural strategies. The average daily feeding time can be used to predict the group size at which this turning point occurs. Under the model's assumptions, social facilitation was relatively unimportant in the causation of behavioural patterns in pigs. To validate our model, simulated patterns were compared with empirical patterns in conventionally housed pigs. Similarities between empirical and model patterns support the model results. Our model can be used as a tool in further research for studying the effects of social factors and group dynamics on individual variation in feeding and social interaction patterns in pigs, as well as in other animal species. Copyright © 2018 Elsevier Inc. All rights reserved.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution.

PubMed

Sandvik, Hanno; Barrett, Robert T; Erikstad, Kjell Einar; Myksvoll, Mari S; Vikebø, Frode; Yoccoz, Nigel G; Anker-Nilssen, Tycho; Lorentsen, Svein-Håkon; Reiertsen, Tone K; Skarðhamar, Jofrid; Skern-Mauritzen, Mette; Systad, Geir Helge

2016-05-13

Colonial breeding is an evolutionary puzzle, as the benefits of breeding in high densities are still not fully explained. Although the dynamics of existing colonies are increasingly understood, few studies have addressed the initial formation of colonies, and empirical tests are rare. Using a high-resolution larval drift model, we here document that the distribution of seabird colonies along the Norwegian coast can be explained by variations in the availability and predictability of fish larvae. The modelled variability in concentration of fish larvae is, in turn, predicted by the topography of the continental shelf and coastline. The advection of fish larvae along the coast translates small-scale topographic characteristics into a macroecological pattern, viz. the spatial distribution of top-predator breeding sites. Our findings provide empirical corroboration of the hypothesis that seabird colonies are founded in locations that minimize travel distances between breeding and foraging locations, thereby enabling optimal foraging by central-place foragers.

Introducing the VRT gas turbine combustor

NASA Technical Reports Server (NTRS)

Melconian, Jerry O.; Mostafa, Abdu A.; Nguyen, Hung Lee

1990-01-01

An innovative annular combustor configuration is being developed for aircraft and other gas turbine engines. This design has the potential of permitting higher turbine inlet temperatures by reducing the pattern factor and providing a major reduction in NO(x) emission. The design concept is based on a Variable Residence Time (VRT) technique which allows large fuel particles adequate time to completely burn in the circumferentially mixed primary zone. High durability of the combustor is achieved by dual function use of the incoming air. The feasibility of the concept was demonstrated by water analogue tests and 3-D computer modeling. The computer model predicted a 50 percent reduction in pattern factor when compared to a state of the art conventional combustor. The VRT combustor uses only half the number of fuel nozzles of the conventional configuration. The results of the chemical kinetics model require further investigation, as the NO(x) predictions did not correlate with the available experimental and analytical data base.

Comparison of particle tracking algorithms in commercial CFD packages: sedimentation and diffusion.

PubMed

Robinson, Risa J; Snyder, Pam; Oldham, Michael J

2007-05-01

Computational fluid dynamic modeling software has enabled microdosimetry patterns of inhaled toxins and toxicants to be predicted and visualized, and is being used in inhalation toxicology and risk assessment. These predicted microdosimetry patterns in airway structures are derived from predicted airflow patterns within these airways and particle tracking algorithms used in computational fluid dynamics (CFD) software packages. Although these commercial CFD codes have been tested for accuracy under various conditions, they have not been well tested for respiratory flows in general. Nor has their particle tracking algorithm accuracy been well studied. In this study, three software packages, Fluent Discrete Phase Model (DPM), Fluent Fine Particle Model (FPM), and ANSYS CFX, were evaluated. Sedimentation and diffusion were each isolated in a straight tube geometry and tested for accuracy. A range of flow rates corresponding to adult low activity (minute ventilation = 10 L/min) and to heavy exertion (minute ventilation = 60 L/min) were tested by varying the range of dimensionless diffusion and sedimentation parameters found using the Weibel symmetric 23 generation lung morphology. Numerical results for fully developed parabolic and uniform (slip) profiles were compared respectively, to Pich (1972) and Yu (1977) analytical sedimentation solutions. Schum and Yeh (1980) equations for sedimentation were also compared. Numerical results for diffusional deposition were compared to analytical solutions of Ingham (1975) for parabolic and uniform profiles. Significant differences were found among the various CFD software packages and between numerical and analytical solutions. Therefore, it is prudent to validate CFD predictions against analytical solutions in idealized geometry before tackling the complex geometries of the respiratory tract.

Nonrandom community assembly and high temporal turnover promote regional coexistence in tropics but not temperate zone.

PubMed

Freestone, Amy L; Inouye, Brian D

2015-01-01

A persistent challenge for ecologists is understanding the ecological mechanisms that maintain global patterns of biodiversity, particularly the latitudinal diversity gradient of peak species richness in the tropics. Spatial and temporal variation in community composition contribute to these patterns of biodiversity, but how this variation and its underlying processes change across latitude remains unresolved. Using a model system of sessile marine invertebrates across 25 degrees of latitude, from the temperate zone to the tropics, we tested the prediction that spatial and temporal patterns of taxonomic richness and composition, and the community assembly processes underlying these patterns, will differ across latitude. Specifically, we predicted that high beta diversity (spatial variation in composition) and high temporal turnover contribute to the high species richness of the tropics. Using a standardized experimental approach that controls for several confounding factors that hinder interpretation of prior studies, we present results that support our predictions. In the temperate zone, communities were more similar across spatial scales from centimeters to tens of kilometers and temporal scales up to one year than at lower latitudes. Since the patterns at northern latitudes were congruent with a null model, stochastic assembly processes are implicated. In contrast, the communities in the tropics were a dynamic spatial and temporal mosaic, with low similarity even across small spatial scales and high temporal turnover at both local and regional scales. Unlike the temperate zone, deterministic community assembly processes such as predation likely contributed to the high beta diversity in the tropics. Our results suggest that community assembly processes and temporal dynamics vary across latitude and help structure and maintain latitudinal patterns of diversity.

Buyers and Borrowers: The Application of Consumer Theory to the Study of Library Use.

ERIC Educational Resources Information Center

Emery, Charles D.

Using Ehrenberg's application of mathematical models to the analysis and prediction of repeated buying patterns of consumers, this study focuses on the concept of library use as a form of consumer behavior. The following hypotheses were tested: designated library user groups will tend to exhibit stable behavioral patterns with respect to the…

Influence of forest planning alternatives on landscape pattern and ecosystem processes in northern Wisconsin, USA

Treesearch

Patrick A. Zollner; L. Jay Roberts; Eric J. Gustafson; Hong S. He; Volker Radeloff

2008-01-01

Incorporating an ecosystem management perspective into forest planning requires consideration of the impacts of timber management on a suite of landscape characteristics at broad spatial and long temporal scales. We used the LANDIS forest landscape simulation model to predict forest composition and landscape pattern under seven alternative forest management plans...

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

PubMed Central

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-01-01

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense. PMID:26193275

Self-esteem recognition based on gait pattern using Kinect.

PubMed

Sun, Bingli; Zhang, Zhan; Liu, Xingyun; Hu, Bin; Zhu, Tingshao

2017-10-01

Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem. 178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score. After completing the RRS, each participant walks for two minutes naturally on a rectangular red carpet, and the gait data are recorded using Kinect sensor. After data preprocessing, we extract a few behavioral features to train predicting model by machine learning. Based on these features, we build predicting models to recognize self-esteem. For self-esteem prediction, the best correlation coefficient between predicted score and self-report score is 0.45 (p<0.001). We divide the participants according to gender, and for males, the correlation coefficient is 0.43 (p<0.001), for females, it is 0.59 (p<0.001). Using gait data captured by Kinect sensor, we find that the gait pattern could be used to recognize self-esteem with a fairly good criterion validity. The gait predicting model can be taken as a good supplementary method to measure self-esteem. Copyright © 2017 Elsevier B.V. All rights reserved.

Testing models of parental investment strategy and offspring size in ants.

PubMed

Gilboa, Smadar; Nonacs, Peter

2006-01-01

Parental investment strategies can be fixed or flexible. A fixed strategy predicts making all offspring a single 'optimal' size. Dynamic models predict flexible strategies with more than one optimal size of offspring. Patterns in the distribution of offspring sizes may thus reveal the investment strategy. Static strategies should produce normal distributions. Dynamic strategies should often result in non-normal distributions. Furthermore, variance in morphological traits should be positively correlated with the length of developmental time the traits are exposed to environmental influences. Finally, the type of deviation from normality (i.e., skewed left or right, or platykurtic) should be correlated with the average offspring size. To test the latter prediction, we used simulations to detect significant departures from normality and categorize distribution types. Data from three species of ants strongly support the predicted patterns for dynamic parental investment. Offspring size distributions are often significantly non-normal. Traits fixed earlier in development, such as head width, are less variable than final body weight. The type of distribution observed correlates with mean female dry weight. The overall support for a dynamic parental investment model has implications for life history theory. Predicted conflicts over parental effort, sex investment ratios, and reproductive skew in cooperative breeders follow from assumptions of static parental investment strategies and omnipresent resource limitations. By contrast, with flexible investment strategies such conflicts can be either absent or maladaptive.

Children’s dynamic RSA change during anger and its relations with parenting, temperament, and control of aggression☆

PubMed Central

Miller, Jonas G.; Chocol, Caroline; Nuselovici, Jacob N.; Utendale, William T.; Simard, Melissa; Hastings, Paul D.

2014-01-01

This study examined the moderating effects of child temperament on the association between maternal socialization and 4–6-year-old children’s dynamic respiratory sinus arrhythmia (RSA) change in response to anger-themed emotional materials (N = 180). We used latent growth curve modeling to explore adaptive patterns of dynamic RSA change in response to anger. Greater change in RSA during anger-induction, characterized by more initial RSA suppression and a subsequent return to baseline, was related to children’s better regulation of aggression. For anger-themed materials, low levels of authoritarian parenting predicted more RSA suppression and recovery for more anger-prone children, whereas more authoritative parenting predicted more RSA suppression and recovery for less anger-prone children. These findings suggest that children’s adaptive patterns of dynamic RSA change can be characterized by latent growth curve modeling, and that these patterns may be differentially shaped by parent socialization experiences as a function of child temperament. PMID:23274169

Children's dynamic RSA change during anger and its relations with parenting, temperament, and control of aggression.

PubMed

Miller, Jonas G; Chocol, Caroline; Nuselovici, Jacob N; Utendale, William T; Simard, Melissa; Hastings, Paul D

2013-02-01

This study examined the moderating effects of child temperament on the association between maternal socialization and 4-6-year-old children's dynamic respiratory sinus arrhythmia (RSA) change in response to anger-themed emotional materials (N=180). We used latent growth curve modeling to explore adaptive patterns of dynamic RSA change in response to anger. Greater change in RSA during anger-induction, characterized by more initial RSA suppression and a subsequent return to baseline, was related to children's better regulation of aggression. For anger-themed materials, low levels of authoritarian parenting predicted more RSA suppression and recovery for more anger-prone children, whereas more authoritative parenting predicted more RSA suppression and recovery for less anger-prone children. These findings suggest that children's adaptive patterns of dynamic RSA change can be characterized by latent growth curve modeling, and that these patterns may be differentially shaped by parent socialization experiences as a function of child temperament. Copyright © 2013 Elsevier B.V. All rights reserved.

A selection criterion for patterns in reaction–diffusion systems

PubMed Central

2014-01-01

Background Alan Turing’s work in Morphogenesis has received wide attention during the past 60 years. The central idea behind his theory is that two chemically interacting diffusible substances are able to generate stable spatial patterns, provided certain conditions are met. Ever since, extensive work on several kinds of pattern-generating reaction diffusion systems has been done. Nevertheless, prediction of specific patterns is far from being straightforward, and a great deal of interest in deciphering how to generate specific patterns under controlled conditions prevails. Results Techniques allowing one to predict what kind of spatial structure will emerge from reaction–diffusion systems remain unknown. In response to this need, we consider a generalized reaction diffusion system on a planar domain and provide an analytic criterion to determine whether spots or stripes will be formed. Our criterion is motivated by the existence of an associated energy function that allows bringing in the intuition provided by phase transitions phenomena. Conclusions Our criterion is proved rigorously in some situations, generalizing well-known results for the scalar equation where the pattern selection process can be understood in terms of a potential. In more complex settings it is investigated numerically. Our work constitutes a first step towards rigorous pattern prediction in arbitrary geometries/conditions. Advances in this direction are highly applicable to the efficient design of Biotechnology and Developmental Biology experiments, as well as in simplifying the analysis of morphogenetic models. PMID:24476200

Mathematical modeling and experimental validation of the spatial distribution of boron in the root of Arabidopsis thaliana identify high boron accumulation in the tip and predict a distinct root tip uptake function.

PubMed

Shimotohno, Akie; Sotta, Naoyuki; Sato, Takafumi; De Ruvo, Micol; Marée, Athanasius F M; Grieneisen, Verônica A; Fujiwara, Toru

2015-04-01

Boron, an essential micronutrient, is transported in roots of Arabidopsis thaliana mainly by two different types of transporters, BORs and NIPs (nodulin26-like intrinsic proteins). Both are plasma membrane localized, but have distinct transport properties and patterns of cell type-specific accumulation with different polar localizations, which are likely to affect boron distribution. Here, we used mathematical modeling and an experimental determination to address boron distributions in the root. A computational model of the root is created at the cellular level, describing the boron transporters as observed experimentally. Boron is allowed to diffuse into roots, in cells and cell walls, and to be transported over plasma membranes, reflecting the properties of the different transporters. The model predicts that a region around the quiescent center has a higher concentration of soluble boron than other portions. To evaluate this prediction experimentally, we determined the boron distribution in roots using laser ablation-inductivity coupled plasma-mass spectrometry. The analysis indicated that the boron concentration is highest near the tip and is lower in the more proximal region of the meristem zone, similar to the pattern of soluble boron distribution predicted by the model. Our model also predicts that upward boron flux does not continuously increase from the root tip toward the mature region, indicating that boron taken up in the root tip is not efficiently transported to shoots. This suggests that root tip-absorbed boron is probably used for local root growth, and that instead it is the more mature root regions which have a greater role in transporting boron toward the shoots. © The Author 2015. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

Mathematical Modeling and Experimental Validation of the Spatial Distribution of Boron in the Root of Arabidopsis thaliana Identify High Boron Accumulation in the Tip and Predict a Distinct Root Tip Uptake Function

PubMed Central

Shimotohno, Akie; Sotta, Naoyuki; Sato, Takafumi; De Ruvo, Micol; Marée, Athanasius F.M.; Grieneisen, Verônica A.; Fujiwara, Toru

2015-01-01

Boron, an essential micronutrient, is transported in roots of Arabidopsis thaliana mainly by two different types of transporters, BORs and NIPs (nodulin26-like intrinsic proteins). Both are plasma membrane localized, but have distinct transport properties and patterns of cell type-specific accumulation with different polar localizations, which are likely to affect boron distribution. Here, we used mathematical modeling and an experimental determination to address boron distributions in the root. A computational model of the root is created at the cellular level, describing the boron transporters as observed experimentally. Boron is allowed to diffuse into roots, in cells and cell walls, and to be transported over plasma membranes, reflecting the properties of the different transporters. The model predicts that a region around the quiescent center has a higher concentration of soluble boron than other portions. To evaluate this prediction experimentally, we determined the boron distribution in roots using laser ablation-inductivity coupled plasma-mass spectrometry. The analysis indicated that the boron concentration is highest near the tip and is lower in the more proximal region of the meristem zone, similar to the pattern of soluble boron distribution predicted by the model. Our model also predicts that upward boron flux does not continuously increase from the root tip toward the mature region, indicating that boron taken up in the root tip is not efficiently transported to shoots. This suggests that root tip-absorbed boron is probably used for local root growth, and that instead it is the more mature root regions which have a greater role in transporting boron toward the shoots. PMID:25670713

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Modeling the effect of succimer (DMSA; dimercaptosuccinic acid) chelation therapy in patients poisoned by lead.

PubMed

van Eijkeren, Jan C H; Olie, J Daniël N; Bradberry, Sally M; Vale, J Allister; de Vries, Irma; Clewell, Harvey J; Meulenbelt, Jan; Hunault, Claudine C

2017-02-01

Kinetic models could assist clinicians potentially in managing cases of lead poisoning. Several models exist that can simulate lead kinetics but none of them can predict the effect of chelation in lead poisoning. Our aim was to devise a model to predict the effect of succimer (dimercaptosuccinic acid; DMSA) chelation therapy on blood lead concentrations. We integrated a two-compartment kinetic succimer model into an existing PBPK lead model and produced a Chelation Lead Therapy (CLT) model. The accuracy of the model's predictions was assessed by simulating clinical observations in patients poisoned by lead and treated with succimer. The CLT model calculates blood lead concentrations as the sum of the background exposure and the acute or chronic lead poisoning. The latter was due either to ingestion of traditional remedies or occupational exposure to lead-polluted ambient air. The exposure duration was known. The blood lead concentrations predicted by the CLT model were compared to the measured blood lead concentrations. Pre-chelation blood lead concentrations ranged between 99 and 150 μg/dL. The model was able to simulate accurately the blood lead concentrations during and after succimer treatment. The pattern of urine lead excretion was successfully predicted in some patients, while poorly predicted in others. Our model is able to predict blood lead concentrations after succimer therapy, at least, in situations where the duration of lead exposure is known.

The role of the airline transportation network in the prediction and predictability of global epidemics.

PubMed

Colizza, Vittoria; Barrat, Alain; Barthélemy, Marc; Vespignani, Alessandro

2006-02-14

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.

Predicting species interactions from edge responses: mongoose predation on hawksbill sea turtle nests in fragmented beach habitat.

PubMed

Leighton, Patrick A; Horrocks, Julia A; Krueger, Barry H; Beggs, Jennifer A; Kramer, Donald L

2008-11-07

Because species respond differently to habitat boundaries and spatial overlap affects encounter rates, edge responses should be strong determinants of spatial patterns of species interactions. In the Caribbean, mongooses (Herpestes javanicus) prey on hawksbill sea turtle (Eretmochelys imbricata) eggs. Turtles nest in both open sand and vegetation patches, with a peak in nest abundance near the boundary between the two microhabitats; mongooses rarely leave vegetation. Using both artificial nests and hawksbill nesting data, we examined how the edge responses of these species predict the spatial patterns of nest mortality. Predation risk was strongly related to mongoose abundance but was not affected by nest density or habitat type. The product of predator and prey edge response functions accurately described the observed pattern of total prey mortality. Hawksbill preference for vegetation edge becomes an ecological trap in the presence of mongooses. This is the first study to predict patterns of predation directly from continuous edge response functions of interacting species, establishing a link between models of edge response and species interactions.

Predicting species interactions from edge responses: mongoose predation on hawksbill sea turtle nests in fragmented beach habitat

PubMed Central

Leighton, Patrick A; Horrocks, Julia A; Krueger, Barry H; Beggs, Jennifer A; Kramer, Donald L

2008-01-01

Because species respond differently to habitat boundaries and spatial overlap affects encounter rates, edge responses should be strong determinants of spatial patterns of species interactions. In the Caribbean, mongooses (Herpestes javanicus) prey on hawksbill sea turtle (Eretmochelys imbricata) eggs. Turtles nest in both open sand and vegetation patches, with a peak in nest abundance near the boundary between the two microhabitats; mongooses rarely leave vegetation. Using both artificial nests and hawksbill nesting data, we examined how the edge responses of these species predict the spatial patterns of nest mortality. Predation risk was strongly related to mongoose abundance but was not affected by nest density or habitat type. The product of predator and prey edge response functions accurately described the observed pattern of total prey mortality. Hawksbill preference for vegetation edge becomes an ecological trap in the presence of mongooses. This is the first study to predict patterns of predation directly from continuous edge response functions of interacting species, establishing a link between models of edge response and species interactions. PMID:18647718

Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds

NASA Astrophysics Data System (ADS)

Siler, Nicholas; Po-Chedley, Stephen; Bretherton, Christopher S.

2018-02-01

Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO_2 (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (α) in simulations of the current climate: high-feedback models exhibit lower (higher) α in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean α as temperatures rise and warm regions expand. The spatial pattern of α is found to be strongly predictive (r=0.84) of a model's global cloud feedback, with satellite observations indicating a most-likely value of 0.58± 0.31 Wm^{-2} K^{-1} (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm^{-2} K^{-1}, with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate.

Distributional prediction of Pleistocene forearc minibasin turbidites in the NE Nankai Trough area (off central Japan)

NASA Astrophysics Data System (ADS)

Egawa, K.; Furukawa, T.; Saeki, T.; Suzuki, K.; Narita, H.

2011-12-01

Natural gas hydrate-related sequences commonly provide unclear seismic images due to bottom simulating reflector, a seismic indicator of the theoretical base of gas hydrate stability zone, which usually causes problems for fully analyzing the detailed sedimentary structures and seismic facies. Here we propose an alternative technique to predict the distributional pattern of gas hydrate-related deep-sea turbidites with special reference to a Pleistocene forearc minibasin in the northeastern Nankai Trough area, off central Japan, from the integrated 3D structural and sedimentologic modeling. Structural unfolding and stratigraphic backstripping successively modeled a simple horseshoe-shaped paleobathymetry of the targeted turbidite sequence. Based on best-fit matching of net-to-gross ratio (or sand fraction) between the model and wells, subsequent turbidity current modeling on the restored paleobathymetric surface during a single flow event demonstrated excellent prediction results showing the morphologically controlled turbidity current evolution and selective turbidite sand distribution within the modeled minibasin. Also, multiple turbidity current modeling indicated the stacking sheet turbidites with regression and proximal/distal onlaps in the minibasin due to reflections off an opposing slope, whose sedimentary features are coincident with the seismic interpretation. Such modeling works can help us better understand the depositional pattern of gas hydrate-related, unconsolidated turbidites and also can improve gas hydrate reservoir characterization. This study was financially supported by MH21 Research Consortium.

Integrated CFD modeling of gas turbine combustors

NASA Technical Reports Server (NTRS)

Fuller, E. J.; Smith, C. E.

1993-01-01

3D, curvilinear, multi-domain CFD analysis is becoming a valuable tool in gas turbine combustor design. Used as a supplement to experimental testing. CFD analysis can provide improved understanding of combustor aerodynamics and used to qualitatively assess new combustor designs. This paper discusses recent advancements in CFD combustor methodology, including the timely integration of the design (i.e. CAD) and analysis (i.e. CFD) processes. Allied Signal's F124 combustor was analyzed at maximum power conditions. The assumption of turbulence levels at the nozzle/swirler inlet was shown to be very important in the prediction of combustor exit temperatures. Predicted exit temperatures were compared to experimental rake data, and good overall agreement was seen. Exit radial temperature profiles were well predicted, while the predicted pattern factor was 25 percent higher than the harmonic-averaged experimental pattern factor.

Physical, Cognitive, and Psychosocial Variables from the Disablement Process Model Predict Patterns of Independence and the Transition into Disability for the Oldest-Old

ERIC Educational Resources Information Center

Fauth, Elizabeth Braungart; Zarit, Steven H.; Malmberg, Bo; Johansson, Boo

2007-01-01

Purpose: This study used the Disablement Process Model to predict whether a sample of the oldest-old maintained their disability or disability-free status over a 2- and 4-year follow-up, or whether they transitioned into a state of disability during this time. Design and Methods: We followed a sample of 149 Swedish adults who were 86 years of age…

Waste tyre pyrolysis: modelling of a moving bed reactor.

PubMed

Aylón, E; Fernández-Colino, A; Murillo, R; Grasa, G; Navarro, M V; García, T; Mastral, A M

2010-12-01

This paper describes the development of a new model for waste tyre pyrolysis in a moving bed reactor. This model comprises three different sub-models: a kinetic sub-model that predicts solid conversion in terms of reaction time and temperature, a heat transfer sub-model that calculates the temperature profile inside the particle and the energy flux from the surroundings to the tyre particles and, finally, a hydrodynamic model that predicts the solid flow pattern inside the reactor. These three sub-models have been integrated in order to develop a comprehensive reactor model. Experimental results were obtained in a continuous moving bed reactor and used to validate model predictions, with good approximation achieved between the experimental and simulated results. In addition, a parametric study of the model was carried out, which showed that tyre particle heating is clearly faster than average particle residence time inside the reactor. Therefore, this fast particle heating together with fast reaction kinetics enables total solid conversion to be achieved in this system in accordance with the predictive model. Copyright © 2010 Elsevier Ltd. All rights reserved.

A competitive complex formation mechanism underlies trichome patterning on Arabidopsis leaves

PubMed Central

Digiuni, Simona; Schellmann, Swen; Geier, Florian; Greese, Bettina; Pesch, Martina; Wester, Katja; Dartan, Burcu; Mach, Valerie; Srinivas, Bhylahalli Purushottam; Timmer, Jens; Fleck, Christian; Hulskamp, Martin

2008-01-01

Trichome patterning in Arabidopsis serves as a model system for de novo pattern formation in plants. It is thought to typify the theoretical activator–inhibitor mechanism, although this hypothesis has never been challenged by a combined experimental and theoretical approach. By integrating the key genetic and molecular data of the trichome patterning system, we developed a new theoretical model that allows the direct testing of the effect of experimental interventions and in the prediction of patterning phenotypes. We show experimentally that the trichome inhibitor TRIPTYCHON is transcriptionally activated by the known positive regulators GLABRA1 and GLABRA3. Further, we demonstrate by particle bombardment of protein fusions with GFP that TRIPTYCHON and CAPRICE but not GLABRA1 and GLABRA3 can move between cells. Finally, theoretical considerations suggest promoter swapping and basal overexpression experiments by means of which we are able to discriminate three biologically meaningful variants of the trichome patterning model. Our study demonstrates that the mutual interplay between theory and experiment can reveal a new level of understanding of how biochemical mechanisms can drive biological patterning processes. PMID:18766177

Incorporating Infrastructure and Vegetation Effects on Sea Level Rise Predictions in Low-Gradient Coastal Landscapes

NASA Astrophysics Data System (ADS)

Rodriguez, J. F.; Sandi Rojas, S.; Trivisonno, F.; Saco, P. M.; Riccardi, G.

2015-12-01

At the regional and global scales, coastal management and planning for future sea level rise scenarios is typically supported by modelling tools that predict the expected inundation extent. These tools rely on a number of simplifying assumptions that, in some cases, may result in important overestimation or underestimation of the inundation extent. One of such cases is coastal wetlands, where vegetation strongly affects both the magnitude and the timing of inundation. Many coastal wetlands display other forms of flow restrictions due to, for example, infrastructure or drainage works, which also alters the inundation patterns. In this contribution we explore the effects of flow restrictions on inundation patterns under sea level rise conditions in coastal wetlands. We use a dynamic wetland evolution model that not only incorporates the effects of flow restrictions due to culverts, bridges and weirs as well as vegetation, but also considers that vegetation changes as a consequence of increasing inundation. We apply our model to a coastal wetland in Australia and compare predictions of our model to predictions using conventional approaches. We found that some restrictions accentuate detrimental effects of sea level rise while others moderate them. We also found that some management strategies based on flow redistribution that provide short term solution may result more damaging in the long term if sea level rise is considered.

Modelling the Emergence and Dynamics of Perceptual Organisation in Auditory Streaming

PubMed Central

Mill, Robert W.; Bőhm, Tamás M.; Bendixen, Alexandra; Winkler, István; Denham, Susan L.

2013-01-01

Many sound sources can only be recognised from the pattern of sounds they emit, and not from the individual sound events that make up their emission sequences. Auditory scene analysis addresses the difficult task of interpreting the sound world in terms of an unknown number of discrete sound sources (causes) with possibly overlapping signals, and therefore of associating each event with the appropriate source. There are potentially many different ways in which incoming events can be assigned to different causes, which means that the auditory system has to choose between them. This problem has been studied for many years using the auditory streaming paradigm, and recently it has become apparent that instead of making one fixed perceptual decision, given sufficient time, auditory perception switches back and forth between the alternatives—a phenomenon known as perceptual bi- or multi-stability. We propose a new model of auditory scene analysis at the core of which is a process that seeks to discover predictable patterns in the ongoing sound sequence. Representations of predictable fragments are created on the fly, and are maintained, strengthened or weakened on the basis of their predictive success, and conflict with other representations. Auditory perceptual organisation emerges spontaneously from the nature of the competition between these representations. We present detailed comparisons between the model simulations and data from an auditory streaming experiment, and show that the model accounts for many important findings, including: the emergence of, and switching between, alternative organisations; the influence of stimulus parameters on perceptual dominance, switching rate and perceptual phase durations; and the build-up of auditory streaming. The principal contribution of the model is to show that a two-stage process of pattern discovery and competition between incompatible patterns can account for both the contents (perceptual organisations) and the dynamics of human perception in auditory streaming. PMID:23516340

Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

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

Ghil, M.; Kravtsov, S.; Robertson, A. W.

2008-10-14

This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influencemore » large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.« less

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.

PubMed

Zerlaut, Yann; Chemla, Sandrine; Chavane, Frederic; Destexhe, Alain

2018-02-01

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

PubMed Central

Tataru, Paula; Sand, Andreas; Hobolth, Asger; Mailund, Thomas; Pedersen, Christian N. S.

2013-01-01

Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model. PMID:24833225

A Microfluidic Route to Breaking Chiral Symmetry: Theory and Experiment

NASA Astrophysics Data System (ADS)

Ocko, Samuel; Adams, Laura

A robust route for the biased production of single handed chiral structures has been found in generating non-spherical, multi-component double emulsions using glass microfluidic devices. The specific type of handedness is determined by the final packing geometry of four different inner drops inside an ultra-thin sheath of oil. Before the three dimensional chiral structures are formed, the quasi-one dimensional chain of four inner drops re-arranges in two dimensions into either checkerboard or stripe patterns. We derive an analytical model predicting which pattern is more likely and assembles in the least amount of time. Moreover, our model accurately predicts our experimental results and is based on local bending dynamics, rather than global surface energy minimization. We gratefully acknowledge Professors D. Weitz and L. Mahadevan's support.