Genetic Markers Predict Primary Non-Response and Durable Response To Anti-TNF Biologic Therapies in Crohn's Disease.

PubMed

Barber, Grant E; Yajnik, Vijay; Khalili, Hamed; Giallourakis, Cosmas; Garber, John; Xavier, Ramnik; Ananthakrishnan, Ashwin N

2016-12-01

One-fifth of patients with Crohn's disease (CD) are primary non-responders to anti-tumor necrosis factor (anti-TNF) therapy, and an estimated 10-15% will fail therapy annually. Little is known about the genetics of response to anti-TNF therapy. The aim of our study was to identify genetic factors associated with primary non-response (PNR) and loss of response to anti-TNFs in CD. From a prospective registry, we characterized the response of 427 CD patients to their first anti-TNF therapy. Patients were designated as achieving primary response, durable response, and non-durable response based on clinical, endoscopic, and radiologic criteria. Genotyping was performed on the Illumina Immunochip. Separate genetic scores based on presence of predictive genetic alleles were calculated for PNR and durable response and performance of clinical and genetics models were compared. From 359 patients, 36 were adjudged to have PNR (10%), 200 had durable response, and 74 had non-durable response. PNRs had longer disease duration and were more likely to be smokers. Fifteen risk alleles were associated with PNR. Patients with PNR had a significantly higher genetic risk score (GRS) (P =8 × 10 -12 ). A combined clinical-genetic model more accurately predicted PNR when compared with a clinical only model (0.93 vs. 0.70, P <0.001). Sixteen distinct single nucleotide polymorphisms predicted durable response with a higher GRS (P =7 × 10 -13 ). The GRSs for PNR and durable response were not mutually correlated, suggesting distinct mechanisms. Genetic risk alleles can predict primary non-response and durable response to anti-TNF therapy in CD.

Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials.

PubMed

An, Ming-Wen; Mandrekar, Sumithra J; Branda, Megan E; Hillman, Shauna L; Adjei, Alex A; Pitot, Henry C; Goldberg, Richard M; Sargent, Daniel J

2011-10-15

The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival. Individual patient data from three North Central Cancer Treatment Group trials (N0026, n = 117; N9741, n = 1,109; and N9841, n = 332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response [TriTR: response (complete or partial) vs. stable disease vs. progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12, 16, and 24 weeks postbaseline. Model discrimination was evaluated by the concordance (c) index. The overall best response rates for the three trials were 26%, 45%, and 25%, respectively. Although nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the concordance indices (c-index) for the traditional response metrics ranged from 0.59 to 0.65; for the continuous metrics from 0.60 to 0.66; and for the TriTR metrics from 0.64 to 0.69. The c-indices for TriTR at 12 weeks were comparable with those at 16 and 24 weeks. Continuous tumor measurement-based metrics provided no predictive improvement over traditional response-based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future phase II trials. ©2011 AACR.

Comparison of continuous versus categorical tumor measurement-based metrics to predict overall survival in cancer treatment trials

PubMed Central

An, Ming-Wen; Mandrekar, Sumithra J.; Branda, Megan E.; Hillman, Shauna L.; Adjei, Alex A.; Pitot, Henry; Goldberg, Richard M.; Sargent, Daniel J.

2011-01-01

Purpose The categorical definition of response assessed via the Response Evaluation Criteria in Solid Tumors has documented limitations. We sought to identify alternative metrics for tumor response that improve prediction of overall survival. Experimental Design Individual patient data from three North Central Cancer Treatment Group trials (N0026, n=117; N9741, n=1109; N9841, n=332) were used. Continuous metrics of tumor size based on longitudinal tumor measurements were considered in addition to a trichotomized response (TriTR: Response vs. Stable vs. Progression). Cox proportional hazards models, adjusted for treatment arm and baseline tumor burden, were used to assess the impact of the metrics on subsequent overall survival, using a landmark analysis approach at 12-, 16- and 24-weeks post baseline. Model discrimination was evaluated using the concordance (c) index. Results The overall best response rates for the three trials were 26%, 45%, and 25% respectively. While nearly all metrics were statistically significantly associated with overall survival at the different landmark time points, the c-indices for the traditional response metrics ranged from 0.59-0.65; for the continuous metrics from 0.60-0.66 and for the TriTR metrics from 0.64-0.69. The c-indices for TriTR at 12-weeks were comparable to those at 16- and 24-weeks. Conclusions Continuous tumor-measurement-based metrics provided no predictive improvement over traditional response based metrics or TriTR; TriTR had better predictive ability than best TriTR or confirmed response. If confirmed, TriTR represents a promising endpoint for future Phase II trials. PMID:21880789

Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study.

PubMed

Balconi, Michela; Grippa, Elisabetta; Vanutelli, Maria Elide

2015-12-01

This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Can Mapping Algorithms Based on Raw Scores Overestimate QALYs Gained by Treatment? A Comparison of Mappings Between the Roland-Morris Disability Questionnaire and the EQ-5D-3L Based on Raw and Differenced Score Data.

PubMed

Madan, Jason; Khan, Kamran A; Petrou, Stavros; Lamb, Sarah E

2017-05-01

Mapping algorithms are increasingly being used to predict health-utility values based on responses or scores from non-preference-based measures, thereby informing economic evaluations. We explored whether predictions in the EuroQol 5-dimension 3-level instrument (EQ-5D-3L) health-utility gains from mapping algorithms might differ if estimated using differenced versus raw scores, using the Roland-Morris Disability Questionnaire (RMQ), a widely used health status measure for low back pain, as an example. We estimated algorithms mapping within-person changes in RMQ scores to changes in EQ-5D-3L health utilities using data from two clinical trials with repeated observations. We also used logistic regression models to estimate response mapping algorithms from these data to predict within-person changes in responses to each EQ-5D-3L dimension from changes in RMQ scores. Predicted health-utility gains from these mappings were compared with predictions based on raw RMQ data. Using differenced scores reduced the predicted health-utility gain from a unit decrease in RMQ score from 0.037 (standard error [SE] 0.001) to 0.020 (SE 0.002). Analysis of response mapping data suggests that the use of differenced data reduces the predicted impact of reducing RMQ scores across EQ-5D-3L dimensions and that patients can experience health-utility gains on the EQ-5D-3L 'usual activity' dimension independent from improvements captured by the RMQ. Mappings based on raw RMQ data overestimate the EQ-5D-3L health utility gains from interventions that reduce RMQ scores. Where possible, mapping algorithms should reflect within-person changes in health outcome and be estimated from datasets containing repeated observations if they are to be used to estimate incremental health-utility gains.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

Probabilistic prediction of barrier-island response to hurricanes

USGS Publications Warehouse

Plant, Nathaniel G.; Stockdon, Hilary F.

2012-01-01

Prediction of barrier-island response to hurricane attack is important for assessing the vulnerability of communities, infrastructure, habitat, and recreational assets to the impacts of storm surge, waves, and erosion. We have demonstrated that a conceptual model intended to make qualitative predictions of the type of beach response to storms (e.g., beach erosion, dune erosion, dune overwash, inundation) can be reformulated in a Bayesian network to make quantitative predictions of the morphologic response. In an application of this approach at Santa Rosa Island, FL, predicted dune-crest elevation changes in response to Hurricane Ivan explained about 20% to 30% of the observed variance. An extended Bayesian network based on the original conceptual model, which included dune elevations, storm surge, and swash, but with the addition of beach and dune widths as input variables, showed improved skill compared to the original model, explaining 70% of dune elevation change variance and about 60% of dune and shoreline position change variance. This probabilistic approach accurately represented prediction uncertainty (measured with the log likelihood ratio), and it outperformed the baseline prediction (i.e., the prior distribution based on the observations). Finally, sensitivity studies demonstrated that degrading the resolution of the Bayesian network or removing data from the calibration process reduced the skill of the predictions by 30% to 40%. The reduction in skill did not change conclusions regarding the relative importance of the input variables, and the extended model's skill always outperformed the original model.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

PubMed

Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Therapygenetics: 5-HTTLPR genotype predicts the response to exposure therapy for agoraphobia.

PubMed

Knuts, Inge; Esquivel, Gabriel; Kenis, Gunter; Overbeek, Thea; Leibold, Nicole; Goossens, Lies; Schruers, Koen

2014-08-01

This study was intended to assess the extent to which the low-expression allele of the serotonin transporter gene promoter predicts better response to exposure-based behavior therapy in patients with panic disorder with agoraphobia (PDA). Ninety-nine patients with PDA underwent a 1-week in vivo exposure-based behavior therapy program and provided saliva samples to extract genomic DNA and classify individuals according to four allelic forms (SA, SG, LA, LG) of the 5-HTT-linked polymorphic region (5-HTTLPR). We determined whether the 5-HTTLPR genotype predicted change in avoidance behavior in PDA following treatment. After controlling for pre-treatment avoidance behavior, the 5-HTTLPR low-expression genotypes showed a more favorable response to exposure therapy two weeks following treatment, compared to the other patients. This study suggests a genetic contribution to treatment outcome following behavior therapy and implicates the serotonergic system in response to exposure-based treatments in PDA. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

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

Vedam, S.; Docef, A.; Fix, M.

2005-06-15

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effectsmore » of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.« less

Maternal Responsiveness Predicts Child Language at Ages 3 and 4 in a Community-Based Sample of Slow-to-Talk Toddlers

ERIC Educational Resources Information Center

Hudson, Sophie; Levickis, Penny; Down, Kate; Nicholls, Ruth; Wake, Melissa

2015-01-01

Background: Maternal responsiveness has been shown to predict child language outcomes in clinical samples of children with language delay and non-representative samples of typically developing children. An effective and timely measure of maternal responsiveness for use at the population level has not yet been established. Aims: To determine…

Prediction-based dynamic load-sharing heuristics

NASA Technical Reports Server (NTRS)

Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

1993-01-01

The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

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

DOE PAGES

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey; ...

2016-05-17

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

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

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

Le Roux, Xavier; Bouskill, Nicholas J.; Niboyet, Audrey

Soil microbial diversity is huge and a few grams of soil contain more bacterial taxa than there are bird species on Earth. This high diversity often makes predicting the responses of soil bacteria to environmental change intractable and restricts our capacity to predict the responses of soil functions to global change. Here, using a long-term field experiment in a California grassland, we studied the main and interactive effects of three global change factors (increased atmospheric CO 2 concentration, precipitation and nitrogen addition, and all their factorial combinations, based on global change scenarios for central California) on the potential activity, abundancemore » and dominant taxa of soil nitrite-oxidizing bacteria (NOB). Using a trait-based model, we then tested whether categorizing NOB into a few functional groups unified by physiological traits enables understanding and predicting how soil NOB respond to global environmental change. Contrasted responses to global change treatments were observed between three main NOB functional types. In particular, putatively mixotrophic Nitrobacter, rare under most treatments, became dominant under the 'High CO 2 +Nitrogen+Precipitation' treatment. The mechanistic trait-based model, which simulated ecological niches of NOB types consistent with previous ecophysiological reports, helped predicting the observed effects of global change on NOB and elucidating the underlying biotic and abiotic controls. Our results are a starting point for representing the overwhelming diversity of soil bacteria by a few functional types that can be incorporated into models of terrestrial ecosystems and biogeochemical processes.« less

Responsiveness and predictive validity of the tablet-based symbol digit modalities test in patients with stroke.

PubMed

Hsiao, Pei-Chi; Yu, Wan-Hui; Lee, Shih-Chieh; Chen, Mei-Hsiang; Hsieh, Ching-Lin

2018-06-14

The responsiveness and predictive validity of the Tablet-based Symbol Digit Modalities Test (T-SDMT) are unknown, which limits the utility of the T-SDMT in both clinical and research settings. The purpose of this study was to examine the responsiveness and predictive validity of the T-SDMT in inpatients with stroke. A follow-up, repeated-assessments design. One rehabilitation unit at a local medical center. A total of 50 inpatients receiving rehabilitation completed T-SDMT assessments at admission to and discharge from a rehabilitation ward. The median follow-up period was 14 days. The Barthel index (BI) was assessed at discharge and was used as the criterion of the predictive validity. The mean changes in the T-SDMT scores between admission and discharge were statistically significant (paired t-test = 3.46, p = 0.001). The T-SDMT scores showed a nearly moderate standardized response mean (0.49). A moderate association (Pearson's r = 0.47) was found between the scores of the T-SDMT at admission and those of the BI at discharge, indicating good predictive validity of the T-SDMT. Our results support the responsiveness and predictive validity of the T-SDMT in patients with stroke receiving rehabilitation in hospitals. This study provides empirical evidence supporting the use of the T-SDMT as an outcome measure for assessing processingspeed in inpatients with stroke. The scores of the T-SDMT could be used to predict basic activities of daily living function in inpatients with stroke.

When bad stress goes good: increased threat reactivity predicts improved category learning performance.

PubMed

Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

2011-02-01

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Computational Simulation of the High Strain Rate Tensile Response of Polymer Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.

2002-01-01

A research program is underway to develop strain rate dependent deformation and failure models for the analysis of polymer matrix composites subject to high strain rate impact loads. Under these types of loading conditions, the material response can be highly strain rate dependent and nonlinear. State variable constitutive equations based on a viscoplasticity approach have been developed to model the deformation of the polymer matrix. The constitutive equations are then combined with a mechanics of materials based micromechanics model which utilizes fiber substructuring to predict the effective mechanical and thermal response of the composite. To verify the analytical model, tensile stress-strain curves are predicted for a representative composite over strain rates ranging from around 1 x 10(exp -5)/sec to approximately 400/sec. The analytical predictions compare favorably to experimentally obtained values both qualitatively and quantitatively. Effective elastic and thermal constants are predicted for another composite, and compared to finite element results.

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

NASA Astrophysics Data System (ADS)

Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

2017-03-01

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent.

Early spatiotemporal-specific changes in intermediate signals are predictive of cytotoxic sensitivity to TNFα and co-treatments

PubMed Central

Loo, Lit-Hsin; Bougen-Zhukov, Nicola Michelle; Tan, Wei-Ling Cecilia

2017-01-01

Signaling pathways can generate different cellular responses to the same cytotoxic agents. Current quantitative models for predicting these differential responses are usually based on large numbers of intracellular gene products or signals at different levels of signaling cascades. Here, we report a study to predict cellular sensitivity to tumor necrosis factor alpha (TNFα) using high-throughput cellular imaging and machine-learning methods. We measured and compared 1170 protein phosphorylation events in a panel of human lung cancer cell lines based on different signals, subcellular regions, and time points within one hour of TNFα treatment. We found that two spatiotemporal-specific changes in an intermediate signaling protein, p90 ribosomal S6 kinase (RSK), are sufficient to predict the TNFα sensitivity of these cell lines. Our models could also predict the combined effects of TNFα and other kinase inhibitors, many of which are not known to target RSK directly. Therefore, early spatiotemporal-specific changes in intermediate signals are sufficient to represent the complex cellular responses to these perturbations. Our study provides a general framework for the development of rapid, signaling-based cytotoxicity screens that may be used to predict cellular sensitivity to a cytotoxic agent, or identify co-treatments that may sensitize or desensitize cells to the agent. PMID:28272488

A Four-Step and Four-Criteria Approach for Evaluating Evidence of Dose Addition in Chemical Mixture Toxicity

EPA Science Inventory

Dose addition is the most frequently-used component-based approach for predicting dose response for a mixture of toxicologically-similar chemicals and for statistical evaluation of whether the mixture response is consistent with dose additivity and therefore predictable from the ...

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Treesearch

Lawrence N. Hudson; Joseph Wunderle M.; And Others

2016-01-01

The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to...

Predictive sensor method and apparatus

NASA Technical Reports Server (NTRS)

Nail, William L. (Inventor); Koger, Thomas L. (Inventor); Cambridge, Vivien (Inventor)

1990-01-01

A predictive algorithm is used to determine, in near real time, the steady state response of a slow responding sensor such as hydrogen gas sensor of the type which produces an output current proportional to the partial pressure of the hydrogen present. A microprocessor connected to the sensor samples the sensor output at small regular time intervals and predicts the steady state response of the sensor in response to a perturbation in the parameter being sensed, based on the beginning and end samples of the sensor output for the current sample time interval.

Turbine blade forced response prediction using FREPS

NASA Technical Reports Server (NTRS)

Murthy, Durbha, V.; Morel, Michael R.

1993-01-01

This paper describes a software system called FREPS (Forced REsponse Prediction System) that integrates structural dynamic, steady and unsteady aerodynamic analyses to efficiently predict the forced response dynamic stresses in axial flow turbomachinery blades due to aerodynamic and mechanical excitations. A flutter analysis capability is also incorporated into the system. The FREPS system performs aeroelastic analysis by modeling the motion of the blade in terms of its normal modes. The structural dynamic analysis is performed by a finite element code such as MSC/NASTRAN. The steady aerodynamic analysis is based on nonlinear potential theory and the unsteady aerodynamic analyses is based on the linearization of the non-uniform potential flow mean. The program description and presentation of the capabilities are reported herein. The effectiveness of the FREPS package is demonstrated on the High Pressure Oxygen Turbopump turbine of the Space Shuttle Main Engine. Both flutter and forced response analyses are performed and typical results are illustrated.

Network-based de-noising improves prediction from microarray data.

PubMed

Kato, Tsuyoshi; Murata, Yukio; Miura, Koh; Asai, Kiyoshi; Horton, Paul B; Koji, Tsuda; Fujibuchi, Wataru

2006-03-20

Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson's correlation coefficient between the true and predicted response values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information.

PubMed

Hahn, Tim; Kircher, Tilo; Straube, Benjamin; Wittchen, Hans-Ulrich; Konrad, Carsten; Ströhle, Andreas; Wittmann, André; Pfleiderer, Bettina; Reif, Andreas; Arolt, Volker; Lueken, Ulrike

2015-01-01

Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research. To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG). We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010. Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT. Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%). Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.

A neuronal model of predictive coding accounting for the mismatch negativity.

PubMed

Wacongne, Catherine; Changeux, Jean-Pierre; Dehaene, Stanislas

2012-03-14

The mismatch negativity (MMN) is thought to index the activation of specialized neural networks for active prediction and deviance detection. However, a detailed neuronal model of the neurobiological mechanisms underlying the MMN is still lacking, and its computational foundations remain debated. We propose here a detailed neuronal model of auditory cortex, based on predictive coding, that accounts for the critical features of MMN. The model is entirely composed of spiking excitatory and inhibitory neurons interconnected in a layered cortical architecture with distinct input, predictive, and prediction error units. A spike-timing dependent learning rule, relying upon NMDA receptor synaptic transmission, allows the network to adjust its internal predictions and use a memory of the recent past inputs to anticipate on future stimuli based on transition statistics. We demonstrate that this simple architecture can account for the major empirical properties of the MMN. These include a frequency-dependent response to rare deviants, a response to unexpected repeats in alternating sequences (ABABAA…), a lack of consideration of the global sequence context, a response to sound omission, and a sensitivity of the MMN to NMDA receptor antagonists. Novel predictions are presented, and a new magnetoencephalography experiment in healthy human subjects is presented that validates our key hypothesis: the MMN results from active cortical prediction rather than passive synaptic habituation.

Examining the Predictive Validity of a Dynamic Assessment of Decoding to Forecast Response to Tier 2 Intervention

ERIC Educational Resources Information Center

Cho, Eunsoo; Compton, Donald L.; Fuchs, Douglas; Fuchs, Lynn S.; Bouton, Bobette

2014-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small-group tutoring in a response-to-intervention model. First grade students (n = 134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in…

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy.

PubMed

Hector, Suzanne; Rehm, Markus; Schmid, Jasmin; Kehoe, Joan; McCawley, Niamh; Dicker, Patrick; Murray, Frank; McNamara, Deborah; Kay, Elaine W; Concannon, Caoimhin G; Huber, Heinrich J; Prehn, Jochen H M

2012-05-01

Key to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental systems biology strategy which analyses the susceptibility of cancer cells to undergo caspase activation can be exploited to predict patient responses to 5-fluorouracil-based chemotherapy and to case-specifically identify potential alternative targeted treatments to reactivate apoptosis. We quantified five essential apoptosis-regulating proteins (Pro-Caspases 3 and 9, APAF-1, SMAC and XIAP) in samples of Stage II (n = 13) and III (n=17) tumour and normal colonic (n = 8) tissue using absolute quantitative immunoblotting and employed systems simulations of apoptosis signalling to predict the susceptibility of tumour cells to execute apoptosis. Additional systems analyses assessed the efficacy of novel apoptosis-inducing therapeutics such as XIAP antagonists, proteasome inhibitors and Pro-Caspase-3-activating compounds in restoring apoptosis execution in apoptosis-incompetent tumours. Comparisons of caspase activity profiles demonstrated that the likelihood of colorectal tumours to undergo apoptosis decreases with advancing disease stage. Systems-level analysis correctly predicted positive or negative outcome in 85% (p=0.004) of colorectal cancer patients receiving 5-fluorouracil based chemotherapy and significantly outperformed common uni- and multi-variate statistical approaches. Modelling of individual patient responses to novel apoptosis-inducing therapeutics revealed markedly different inter-individual responses. Our study represents the first proof-of-concept example demonstrating the significant clinical potential of systems biology-based approaches for predicting patient outcome and responsiveness to novel targeted treatment paradigms.

Phenylalanine hydroxylase deficiency in Mexico: genotype-phenotype correlations, BH4 responsiveness and evidence of a founder effect.

PubMed

Vela-Amieva, M; Abreu-González, M; González-del Angel, A; Ibarra-González, I; Fernández-Lainez, C; Barrientos-Ríos, R; Monroy-Santoyo, S; Guillén-López, S; Alcántara-Ortigoza, M A

2015-07-01

The mutational spectrum of the phenylalanine hydroxylase gene (PAH) in Mexico is unknown, although it has been suggested that PKU variants could have a differential geographical distribution. Genotype-phenotype correlations and genotype-based predictions of responsiveness to tetrahydrobiopterin (BH4 ) have never been performed. We sequenced the PAH gene and determined the geographic origin of each allele, mini-haplotype associated, genotype-phenotype correlations and genotype-based prediction of BH4 responsiveness in 48 Mexican patients. The mutational spectrum included 34 variants with c.60+5G>T being the most frequent (20.8%) and linked to haplotype 4.3 possibly because of a founder effect and/or genetic drift. Two new variants were found c.1A>T and c.969+6T>C. The genotype-phenotype correlation was concordant in 70.8%. The genotype-based prediction to BH4 -responsiveness was 41.7%, this information could be useful for the rational selection of candidates for BH4 testing and therapy. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Multiple biomarkers in molecular oncology. II. Molecular diagnostics applications in breast cancer management.

PubMed

Malinowski, Douglas P

2007-05-01

In recent years, the application of genomic and proteomic technologies to the problem of breast cancer prognosis and the prediction of therapy response have begun to yield encouraging results. Independent studies employing transcriptional profiling of primary breast cancer specimens using DNA microarrays have identified gene expression profiles that correlate with clinical outcome in primary breast biopsy specimens. Recent advances in microarray technology have demonstrated reproducibility, making clinical applications more achievable. In this regard, one such DNA microarray device based upon a 70-gene expression signature was recently cleared by the US FDA for application to breast cancer prognosis. These DNA microarrays often employ at least 70 gene targets for transcriptional profiling and prognostic assessment in breast cancer. The use of PCR-based methods utilizing a small subset of genes has recently demonstrated the ability to predict the clinical outcome in early-stage breast cancer. Furthermore, protein-based immunohistochemistry methods have progressed from using gene clusters and gene expression profiling to smaller subsets of expressed proteins to predict prognosis in early-stage breast cancer. Beyond prognostic applications, DNA microarray-based transcriptional profiling has demonstrated the ability to predict response to chemotherapy in early-stage breast cancer patients. In this review, recent advances in the use of multiple markers for prognosis of disease recurrence in early-stage breast cancer and the prediction of therapy response will be discussed.

Prediction of human population responses to toxic compounds by a collaborative competition.

PubMed

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

Interlinking backscatter, grain size and benthic community structure

NASA Astrophysics Data System (ADS)

McGonigle, Chris; Collier, Jenny S.

2014-06-01

The relationship between acoustic backscatter, sediment grain size and benthic community structure is examined using three different quantitative methods, covering image- and angular response-based approaches. Multibeam time-series backscatter (300 kHz) data acquired in 2008 off the coast of East Anglia (UK) are compared with grain size properties, macrofaunal abundance and biomass from 130 Hamon and 16 Clamshell grab samples. Three predictive methods are used: 1) image-based (mean backscatter intensity); 2) angular response-based (predicted mean grain size), and 3) image-based (1st principal component and classification) from Quester Tangent Corporation Multiview software. Relationships between grain size and backscatter are explored using linear regression. Differences in grain size and benthic community structure between acoustically defined groups are examined using ANOVA and PERMANOVA+. Results for the Hamon grab stations indicate significant correlations between measured mean grain size and mean backscatter intensity, angular response predicted mean grain size, and 1st principal component of QTC analysis (all p < 0.001). Results for the Clamshell grab for two of the methods have stronger positive correlations; mean backscatter intensity (r2 = 0.619; p < 0.001) and angular response predicted mean grain size (r2 = 0.692; p < 0.001). ANOVA reveals significant differences in mean grain size (Hamon) within acoustic groups for all methods: mean backscatter (p < 0.001), angular response predicted grain size (p < 0.001), and QTC class (p = 0.009). Mean grain size (Clamshell) shows a significant difference between groups for mean backscatter (p = 0.001); other methods were not significant. PERMANOVA for the Hamon abundance shows benthic community structure was significantly different between acoustic groups for all methods (p ≤ 0.001). Overall these results show considerable promise in that more than 60% of the variance in the mean grain size of the Clamshell grab samples can be explained by mean backscatter or acoustically-predicted grain size. These results show that there is significant predictive capacity for sediment characteristics from multibeam backscatter and that these acoustic classifications can have ecological validity.

Projected effects of Climate-change-induced flow alterations on stream macroinvertebrate abundances.

PubMed

Kakouei, Karan; Kiesel, Jens; Domisch, Sami; Irving, Katie S; Jähnig, Sonja C; Kail, Jochem

2018-03-01

Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species' response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species' abundances were predicted for three periods: (1) baseline (1998-2017), (2) horizon 2050 (2046-2065) and (3) horizon 2090 (2080-2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to -42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.

Thermo-mechanical response predictions for metal matrix composite laminates

NASA Technical Reports Server (NTRS)

Aboudi, J.; Hidde, J. S.; Herakovich, C. T.

1991-01-01

An analytical micromechanical model is employed for prediction of the stress-strain response of metal matrix composite laminates subjected to thermomechanical loading. The predicted behavior of laminates is based upon knowledge of the thermomechanical response of the transversely isotropic, elastic fibers and the elastic-viscoplastic, work-hardening matrix. The method is applied to study the behavior of silicon carbide/titanium metal matrix composite laminates. The response of laminates is compared with that of unidirectional lamina. The results demonstrate the effect of cooling from a stress-free temperature and the mismatch of thermal and mechanical properties of the constituent phases on the laminate's subsequent mechanical response. Typical results are presented for a variety of laminates subjected to monotonic tension, monotonic shear and cyclic tensile/compressive loadings.

Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory

ERIC Educational Resources Information Center

Bergner, Yoav; Droschler, Stefan; Kortemeyer, Gerd; Rayyan, Saif; Seaton, Daniel; Pritchard, David E.

2012-01-01

We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a…

Prediction of adolescents doing physical activity after completing secondary education.

PubMed

Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

2012-03-01

The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

PubMed

Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

2009-02-01

Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

Absolute number of new lesions on 18F-FDG PET/CT is more predictive of clinical response than SUV changes in metastatic melanoma patients receiving ipilimumab.

PubMed

Anwar, Hoda; Sachpekidis, Christos; Winkler, Julia; Kopp-Schneider, Annette; Haberkorn, Uwe; Hassel, Jessica C; Dimitrakopoulou-Strauss, Antonia

2018-03-01

Evaluation of response to immunotherapy is a matter of debate. The aim of the present study was to evaluate the response of metastatic melanoma to treatment with ipilimumab by means of 18 F-FDG PET/CT, using the patients' clinical response as reference. The final cohort included in the analyses consisted of 41 patients with metastatic melanoma who underwent 18 F-FDG PET/CT before and after administration of ipilimumab. After determination of the best clinical response, the PET/CT scans were reviewed and a separate independent analysis was performed, based on the number and functional size of newly emerged 18 F-FDG-avid lesions, as well as on the SUV changes after therapy. The median observation time of the patients after therapy was 21.4 months (range 6.3-41.9 months). Based on their clinical response, patients were dichotomized into those with clinical benefit (CB) and those without CB (No-CB). The CB group (31 patients) included those with stable disease, partial remission and complete remission, and the No-CB group (10 patients) included those with progressive disease. The application of a threshold of four newly emerged 18 F-FDG-avid lesions on the posttherapy PET/CT scan led to a sensitivity (correctly predicting CB) of 84% and a specificity (correctly predicting No-CB) of 100%. This cut-off was lower for lesions with larger functional diameters (three new lesions larger than 1.0 cm and two new lesions larger than 1.5 cm). SUV changes after therapy did not correlate with clinical response. Based on these findings, we developed criteria for predicting clinical response to immunotherapy by means of 18 F-FDG PET/CT (PET Response Evaluation Criteria for Immunotherapy, PERCIMT). Our results show that a cut-off of four newly emerged 18 F-FDG-avid lesions on posttherapy PET/CT gives a reliable indication of treatment failure in patients under ipilimumab treatment. Moreover, the functional size of the new lesions plays an important role in predicting the clinical response. Validation of these results in larger cohorts of patients is warranted.

Mentalizing about emotion and its relationship to empathy.

PubMed

Hooker, Christine I; Verosky, Sara C; Germine, Laura T; Knight, Robert T; D'Esposito, Mark

2008-09-01

Mentalizing involves the ability to predict someone else's behavior based on their belief state. More advanced mentalizing skills involve integrating knowledge about beliefs with knowledge about the emotional impact of those beliefs. Recent research indicates that advanced mentalizing skills may be related to the capacity to empathize with others. However, it is not clear what aspect of mentalizing is most related to empathy. In this study, we used a novel, advanced mentalizing task to identify neural mechanisms involved in predicting a future emotional response based on a belief state. Subjects viewed social scenes in which one character had a False Belief and one character had a True Belief. In the primary condition, subjects were asked to predict what emotion the False Belief Character would feel if they had a full understanding about the situation. We found that neural regions related to both mentalizing and emotion were involved when predicting a future emotional response, including the superior temporal sulcus, medial prefrontal cortex, temporal poles, somatosensory related cortices (SRC), inferior frontal gyrus and thalamus. In addition, greater neural activity in primarily emotion-related regions, including right SRC and bilateral thalamus, when predicting emotional response was significantly correlated with more self-reported empathy. The findings suggest that predicting emotional response involves generating and using internal affective representations and that greater use of these affective representations when trying to understand the emotional experience of others is related to more empathy.

Predicting Social Responsibility and Belonging in Urban After-School Physical Activity Programs with Underserved Children

ERIC Educational Resources Information Center

Martin, Jeffrey J.; Byrd, Brigid; Garn, Alex; McCaughtry, Nate; Kulik, Noel; Centeio, Erin

2016-01-01

The purpose of this cross sectional study was to predict feelings of belonging and social responsibility based on the motivational climate perceptions and contingent self-worth of children participating in urban after-school physical activity programs. Three-hundred and four elementary school students from a major Midwestern city participated.…

Model of the transient neurovascular response based on prompt arterial dilation

PubMed Central

Kim, Jung Hwan; Khan, Reswanul; Thompson, Jeffrey K; Ress, David

2013-01-01

Brief neural stimulation results in a stereotypical pattern of vascular and metabolic response that is the basis for popular brain-imaging methods such as functional magnetic resonance imagine. However, the mechanisms of transient oxygen transport and its coupling to cerebral blood flow (CBF) and oxygen metabolism (CMRO2) are poorly understood. Recent experiments show that brief stimulation produces prompt arterial vasodilation rather than venous vasodilation. This work provides a neurovascular response model for brief stimulation based on transient arterial effects using one-dimensional convection–diffusion transport. Hemoglobin oxygen dissociation is included to enable predictions of absolute oxygen concentrations. Arterial CBF response is modeled using a lumped linear flow model, and CMRO2 response is modeled using a gamma function. Using six parameters, the model successfully fit 161/166 measured extravascular oxygen time courses obtained for brief visual stimulation in cat cerebral cortex. Results show how CBF and CMRO2 responses compete to produce the observed features of the hemodynamic response: initial dip, hyperoxic peak, undershoot, and ringing. Predicted CBF and CMRO2 response amplitudes are consistent with experimental measurements. This model provides a powerful framework to quantitatively interpret oxygen transport in the brain; in particular, its intravascular oxygen concentration predictions provide a new model for fMRI responses. PMID:23756690

Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission: A Multicenter Prospective Study.

PubMed

Mearelli, Filippo; Fiotti, Nicola; Giansante, Carlo; Casarsa, Chiara; Orso, Daniele; De Helmersen, Marco; Altamura, Nicola; Ruscio, Maurizio; Castello, Luigi Mario; Colonetti, Efrem; Marino, Rossella; Barbati, Giulia; Bregnocchi, Andrea; Ronco, Claudio; Lupia, Enrico; Montrucchio, Giuseppe; Muiesan, Maria Lorenza; Di Somma, Salvatore; Avanzi, Gian Carlo; Biolo, Gianni

2018-05-07

To derive and validate a predictive algorithm integrating a nomogram-based prediction of the pretest probability of infection with a panel of serum biomarkers, which could robustly differentiate sepsis/septic shock from noninfectious systemic inflammatory response syndrome. Multicenter prospective study. At emergency department admission in five University hospitals. Nine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort. None. A nomogram, including age, Sequential Organ Failure Assessment score, recent antimicrobial therapy, hyperthermia, leukocytosis, and high C-reactive protein values, was built in order to take data from 716 infected patients and 120 patients with noninfectious systemic inflammatory response syndrome to predict pretest probability of infection. Then, the best combination of procalcitonin, soluble phospholypase A2 group IIA, presepsin, soluble interleukin-2 receptor α, and soluble triggering receptor expressed on myeloid cell-1 was applied in order to categorize patients as "likely" or "unlikely" to be infected. The predictive algorithm required only procalcitonin backed up with soluble phospholypase A2 group IIA determined in 29% of the patients to rule out sepsis/septic shock with a negative predictive value of 93%. In a validation cohort of 158 patients, predictive algorithm reached 100% of negative predictive value requiring biomarker measurements in 18% of the population. We have developed and validated a high-performing, reproducible, and parsimonious algorithm to assist emergency department physicians in distinguishing sepsis/septic shock from noninfectious systemic inflammatory response syndrome.

Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

2016-04-01

The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood function for the signatures is derived from the likelihood for streamflow (rather than using an "ad-hoc" likelihood for the signatures as done in previous approaches). This likelihood is not easily tractable analytically and we therefore cannot apply "simple" MCMC methods. This numerical problem is solved using Approximate Bayesian Computation (ABC). Our result indicate that the proposed approach is suitable for producing reliable streamflow predictive distributions based on calibration to signature data. Moreover, our results provide indications on which signatures are more appropriate to represent the information content of the hydrograph.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time.

PubMed

Limongi, Roberto; Silva, Angélica M

2016-11-01

The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production - where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Species' traits help predict small mammal responses to habitat homogenization by an invasive grass.

PubMed

Ceradini, Joseph P; Chalfoun, Anna D

2017-07-01

Invasive plants can negatively affect native species, however, the strength, direction, and shape of responses may vary depending on the type of habitat alteration and the natural history of native species. To prioritize conservation of vulnerable species, it is therefore critical to effectively predict species' responses to invasive plants, which may be facilitated by a framework based on species' traits. We studied the population and community responses of small mammals and changes in habitat heterogeneity across a gradient of cheatgrass (Bromus tectorum) cover, a widespread invasive plant in North America. We live-trapped small mammals over two summers and assessed the effect of cheatgrass on native small mammal abundance, richness, and species-specific and trait-based occupancy, while accounting for detection probability and other key habitat elements. Abundance was only estimated for the most common species, deer mice (Peromyscus maniculatus). All species were pooled for the trait-based occupancy analysis to quantify the ability of small mammal traits (habitat association, mode of locomotion, and diet) to predict responses to cheatgrass invasion. Habitat heterogeneity decreased with cheatgrass cover. Deer mouse abundance increased marginally with cheatgrass. Species richness did not vary with cheatgrass, however, pocket mouse (Perognathus spp.) and harvest mouse (Reithrodontomys spp.) occupancy tended to decrease and increase, respectively, with cheatgrass cover, suggesting a shift in community composition. Cheatgrass had little effect on occupancy for deer mice, 13-lined ground squirrels (Spermophilus tridecemlineatus), and Ord's kangaroo rat (Dipodomys ordii). Species' responses to cheatgrass primarily corresponded with our a priori predictions based on species' traits. The probability of occupancy varied significantly with a species' habitat association but not with diet or mode of locomotion. When considered within the context of a rapid habitat change, such as caused by invasive plants, relevant species' traits may provide a useful framework for predicting species' responses to a variety of habitat disturbances. Understanding which species are likely to be most affected by exotic plant invasion will help facilitate more efficient, targeted management and conservation of native species and habitats. © 2017 by the Ecological Society of America.

Species’ traits help predict small mammal responses to habitat homogenization by an invasive grass

USGS Publications Warehouse

Ceradini, Joseph P.; Chalfoun, Anna D.

2017-01-01

Invasive plants can negatively affect native species, however, the strength, direction, and shape of responses may vary depending on the type of habitat alteration and the natural history of native species. To prioritize conservation of vulnerable species, it is therefore critical to effectively predict species’ responses to invasive plants, which may be facilitated by a framework based on species’ traits. We studied the population and community responses of small mammals and changes in habitat heterogeneity across a gradient of cheatgrass (Bromus tectorum) cover, a widespread invasive plant in North America. We live-trapped small mammals over two summers and assessed the effect of cheatgrass on native small mammal abundance, richness, and species-specific and trait-based occupancy, while accounting for detection probability and other key habitat elements. Abundance was only estimated for the most common species, deer mice (Peromyscus maniculatus). All species were pooled for the trait-based occupancy analysis to quantify the ability of small mammal traits (habitat association, mode of locomotion, and diet) to predict responses to cheatgrass invasion. Habitat heterogeneity decreased with cheatgrass cover. Deer mouse abundance increased marginally with cheatgrass. Species richness did not vary with cheatgrass, however, pocket mouse (Perognathus spp.) and harvest mouse (Reithrodontomys spp.) occupancy tended to decrease and increase, respectively, with cheatgrass cover, suggesting a shift in community composition. Cheatgrass had little effect on occupancy for deer mice, 13-lined ground squirrels (Spermophilus tridecemlineatus), and Ord's kangaroo rat (Dipodomys ordii). Species’ responses to cheatgrass primarily corresponded with our a priori predictions based on species’ traits. The probability of occupancy varied significantly with a species’ habitat association but not with diet or mode of locomotion. When considered within the context of a rapid habitat change, such as caused by invasive plants, relevant species’ traits may provide a useful framework for predicting species’ responses to a variety of habitat disturbances. Understanding which species are likely to be most affected by exotic plant invasion will help facilitate more efficient, targeted management and conservation of native species and habitats.

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W

PubMed Central

Ma, Chuang; Xin, Mingming; Feldmann, Kenneth A.; Wang, Xiangfeng

2014-01-01

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive “noninformative” genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained “informative” genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes. PMID:24520154

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety.

PubMed

Bunford, Nora; Kujawa, Autumn; Fitzgerald, Kate D; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S; Phan, K Luan

2017-02-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.

Rapid and early α-fetoprotein and des-γ-carboxy prothrombin responses to initial arterial infusion chemotherapy predict treatment outcomes of advanced hepatocellular carcinoma

PubMed Central

OYAMA, KENJI; KODA, MASAHIKO; SUGIHARA, TAKAAKI; KISHINA, MANABU; MIYOSHI, KENICHI; OKAMOTO, TOSHIAKI; HODOTSUKA, MASANORI; FUJISE, YUKI; MATONO, TOMOMITSU; TOKUNAGA, SHIHO; OKAMOTO, KINYA; HOSHO, KEIKO; OKANO, JUNICHI; MURAWAKI, YOSHIKAZU

2015-01-01

The aim of the present study was to predict the effects of transarterial infusion (TAI) chemotherapy based on early changes in α-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) in patients with advanced hepatocellular carcinoma (HCC). Seventy-four patients who underwent TAI with cisplatin, 5-fluorouracil, mitomycin C and epirubicin for advanced HCC were enrolled. Antitumor responses were evaluated 6 months after TAI. Rapid and early responses were defined as the ratio of AFP or DCP after 1 week and 1 month compared to baseline. A total of 5, 10, 17 and 42 patients had complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD), respectively. Early AFP response was significantly lower in the CR+PR compared to the SD+PD groups (P<0.01). The early DCP response was significantly lower in the CR+PR compared to the SD+PD. The sensitivity and specificity of rapid and early AFP responses in the CR+PR were 0.78 and 0.72, and 0.80 and 0.73, respectively, and those of rapid and early DCP responses were 0.67 and 0.65, and 0.77 and 0.71, respectively. The combination of AFP and DCP responses had higher specificity compared to AFP or DCP alone responses. Patients were divided into responder and non-responder groups to evaluate the prediction of survival outcome. Early responders of AFP, DCP and AFP+DCP, who were divided based on the cut-off values of CR+PR survived significantly longer than the non-responders (P<0.05). In conclusion, rapid or early responses of AFP and/or DCP levels 1 and 4 weeks after TAI chemotherapy helped to predict the treatment effects. PMID:26137283

Attenuation of Frontostriatal Connectivity During Reward Processing Predicts Response to Psychotherapy in Major Depressive Disorder.

PubMed

Walsh, Erin; Carl, Hannah; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Smoski, Moria J; Dichter, Gabriel S

2017-03-01

There are few reliable predictors of response to antidepressant treatments. In the present investigation, we examined pretreatment functional brain connectivity during reward processing as a potential predictor of response to Behavioral Activation Treatment for Depression (BATD), a validated psychotherapy that promotes engagement with rewarding stimuli and reduces avoidance behaviors. Thirty-three outpatients with major depressive disorder (MDD) and 20 matched controls completed two runs of the monetary incentive delay task during functional magnetic resonance imaging after which participants with MDD received up to 15 sessions of BATD. Seed-based generalized psychophysiological interaction analyses focused on task-based connectivity across task runs, as well as the attenuation of connectivity from the first to the second run of the task. The average change in Beck Depression Inventory-II scores due to treatment was 10.54 points, a clinically meaningful response. Groups differed in seed-based functional connectivity among multiple frontostriatal regions. Hierarchical linear modeling revealed that improved treatment response to BATD was predicted by greater connectivity between the left putamen and paracingulate gyrus during reward anticipation. In addition, MDD participants with greater attenuation of connectivity between several frontostriatal seeds, and midline subcallosal cortex and left paracingulate gyrus demonstrated improved response to BATD. These findings indicate that pretreatment frontostriatal functional connectivity during reward processing is predictive of response to a psychotherapy modality that promotes improving approach-related behaviors in MDD. Furthermore, connectivity attenuation among reward-processing regions may be a particularly powerful endophenotypic predictor of response to BATD in MDD.

On the prediction of the human response: a recycled mechanistic pharmacokinetic/pharmacodynamic approach.

PubMed

Meno-Tetang, Guy M L; Lowe, Philip J

2005-03-01

Although it is routine to predict the blood or plasma pharmacokinetics of compounds for man based upon preclinical studies, the real value of such predictions only comes when linked to drug effects. In the first example, the immunomodulator, FTY720, the first sphingosine-1-phosphate receptor agonist, stimulates the sequestration of lymphocytes into lymph nodes thus removing cells from blood circulation. A prior physiology-based pharmacokinetic model fitted the concentration-time course of FTY720 in rats. This was connected to an indirect response model of the lymphocyte system to characterise the cell trafficking effects. The IC(50) of FTY720 was different in the rat compared with the monkey; man was assumed to be similar to the monkey. The systemic lymphocyte half-lives were also different between species. To make predictions of the pharmacodynamic behaviour for man, two elements are required, i) systemic exposure, in this case from an upscaled physiology based model, and ii) an estimate of lymphocyte turnover in man, gained from the literature from other drug treatments. Predictions compared well with clinical results. The second example is the monoclonal antibody Xolair, designed to bind immunoglobulin E for atopic diseases. A mechanism based two-site binding model described the kinetics of both Xolair and endogenous IgE. This model has been reused for other monoclonal antibodies designed to bind fluid-phase ligands. Sensitivity analysis shows that if differences across species in the kinetics of the endogenous system are not accounted for, then pharmacokinetic/pharmacodynamic models may give misleading predictions of the time course and extent of the response.

Latency-Based and Psychophysiological Measures of Sexual Interest Show Convergent and Concurrent Validity.

PubMed

Ó Ciardha, Caoilte; Attard-Johnson, Janice; Bindemann, Markus

2018-04-01

Latency-based measures of sexual interest require additional evidence of validity, as do newer pupil dilation approaches. A total of 102 community men completed six latency-based measures of sexual interest. Pupillary responses were recorded during three of these tasks and in an additional task where no participant response was required. For adult stimuli, there was a high degree of intercorrelation between measures, suggesting that tasks may be measuring the same underlying construct (convergent validity). In addition to being correlated with one another, measures also predicted participants' self-reported sexual interest, demonstrating concurrent validity (i.e., the ability of a task to predict a more validated, simultaneously recorded, measure). Latency-based and pupillometric approaches also showed preliminary evidence of concurrent validity in predicting both self-reported interest in child molestation and viewing pornographic material containing children. Taken together, the study findings build on the evidence base for the validity of latency-based and pupillometric measures of sexual interest.

How to personalize ovarian stimulation in clinical practice.

PubMed

Sighinolfi, Giovanna; Grisendi, Valentina; La Marca, Antonio

2017-09-01

Controlled ovarian stimulation (COS) in in vitro fertilization (IVF) cycles is the starting point from which couple's prognosis depends. Individualization in follicle-stimulating hormone (FSH) starting dose and protocol used is based on ovarian response prediction, which depends on ovarian reserve. Anti-Müllerian hormone levels and the antral follicle count are considered the most accurate and reliable markers of ovarian reserve. A literature search was performed for studies that addressed the ability of ovarian reserve markers to predict poor and high ovarian response in assisted reproductive technology cycles. According to the predicted response to ovarian stimulation (poor- normal- or high- response), it is possible to counsel couples before treatment about the prognosis, and also to individualize ovarian stimulation protocols, choosing among GnRH-agonists or antagonists for endogenous FSH suppression, and the FSH starting dose in order to decrease the risk of cycle cancellation and ovarian hyperstimulation syndrome. In this review we discuss how to choose the best COS therapy, based on ovarian reserve markers, in order to enhance chances in IVF.

Quantified degree of eccentricity of aortic valve calcification predicts risk of paravalvular regurgitation and response to balloon post-dilation after self-expandable transcatheter aortic valve replacement.

PubMed

Park, Jun-Bean; Hwang, In-Chang; Lee, Whal; Han, Jung-Kyu; Kim, Chi-Hoon; Lee, Seung-Pyo; Yang, Han-Mo; Park, Eun-Ah; Kim, Hyung-Kwan; Chiam, Paul T L; Kim, Yong-Jin; Koo, Bon-Kwon; Sohn, Dae-Won; Ahn, Hyuk; Kang, Joon-Won; Park, Seung-Jung; Kim, Hyo-Soo

2018-05-15

Limited data exist regarding the impact of aortic valve calcification (AVC) eccentricity on the risk of paravalvular regurgitation (PVR) and response to balloon post-dilation (BPD) after transcatheter aortic valve replacement (TAVR). We investigated the prognostic value of AVC eccentricity in predicting the risk of PVR and response to BPD in patients undergoing TAVR. We analyzed 85 patients with severe aortic stenosis who underwent self-expandable TAVR (43 women; 77.2±7.1years). AVC was quantified as the total amount of calcification (total AVC load) and as the eccentricity of calcium (EoC) using calcium volume scoring with contrast computed tomography angiography (CTA). The EoC was defined as the maximum absolute difference in calcium volume scores between 2 adjacent sectors (bi-partition method) or between sectors based on leaflets (leaflet-based method). Total AVC load and bi-partition EoC, but not leaflet-based EoC, were significant predictors for the occurrence of ≥moderate PVR, and bi-partition EoC had a better predictive value than total AVC load (area under the curve [AUC]=0.863 versus 0.760, p for difference=0.006). In multivariate analysis, bi-partition EoC was an independent predictor for the risk of ≥moderate PVR regardless of perimeter oversizing index. The greater bi-partition EoC was the only significant parameter to predict poor response to BPD (AUC=0.775, p=0.004). Pre-procedural assessment of AVC eccentricity using CTA as "bi-partition EoC" provides useful predictive information on the risk of significant PVR and response to BPD in patients undergoing TAVR with self-expandable valves. Copyright © 2017 Elsevier B.V. All rights reserved.

Prospective validation of immunological infiltrate for prediction of response to neoadjuvant chemotherapy in HER2-negative breast cancer--a substudy of the neoadjuvant GeparQuinto trial.

PubMed

Issa-Nummer, Yasmin; Darb-Esfahani, Silvia; Loibl, Sibylle; Kunz, Georg; Nekljudova, Valentina; Schrader, Iris; Sinn, Bruno Valentin; Ulmer, Hans-Ullrich; Kronenwett, Ralf; Just, Marianne; Kühn, Thorsten; Diebold, Kurt; Untch, Michael; Holms, Frank; Blohmer, Jens-Uwe; Habeck, Jörg-Olaf; Dietel, Manfred; Overkamp, Friedrich; Krabisch, Petra; von Minckwitz, Gunter; Denkert, Carsten

2013-01-01

We have recently described an increased lymphocytic infiltration rate in breast carcinoma tissue is a significant response predictor for anthracycline/taxane-based neoadjuvant chemotherapy (NACT). The aim of this study was to prospectively validate the tumor-associated lymphocyte infiltrate as predictive marker for response to anthracycline/taxane-based NACT. The immunological infiltrate was prospectively evaluated in a total of 313 core biopsies from HER2 negative patients of the multicenter PREDICT study, a substudy of the neoadjuvant GeparQuinto study. Intratumoral lymphocytes (iTuLy), stromal lymphocytes (strLy) as well as lymphocyte-predominant breast cancer (LPBC) were evaluated by histopathological assessment. Pathological complete response (pCR) rates were analyzed and compared between the defined subgroups using the exact test of Fisher. Patients with lymphocyte-predominant breast cancer (LPBC) had a significantly increased pCR rate of 36.6%, compared to non-LPBC patients (14.3%, p<0.001). LPBC and stromal lymphocytes were significantly independent predictors for pCR in multivariate analysis (LPBC: OR 2.7, p = 0.003, strLy: OR 1.2, p = 0.01). The amount of intratumoral lymphocytes was significantly predictive for pCR in univariate (OR 1.2, p = 0.01) but not in multivariate logistic regression analysis (OR 1.2, p = 0.11). Confirming previous investigations of our group, we have prospectively validated in an independent cohort that an increased immunological infiltrate in breast tumor tissue is predictive for response to anthracycline/taxane-based NACT. Patients with LPBC and increased stromal lymphocyte infiltration have significantly increased pCR rates. The lymphocytic infiltrate is a promising additional parameter for histopathological evaluation of breast cancer core biopsies.

High fidelity CFD-CSD aeroelastic analysis of slender bladed horizontal-axis wind turbine

NASA Astrophysics Data System (ADS)

Sayed, M.; Lutz, Th.; Krämer, E.; Shayegan, Sh.; Ghantasala, A.; Wüchner, R.; Bletzinger, K.-U.

2016-09-01

The aeroelastic response of large multi-megawatt slender horizontal-axis wind turbine blades is investigated by means of a time-accurate CFD-CSD coupling approach. A loose coupling approach is implemented and used to perform the simulations. The block- structured CFD solver FLOWer is utilized to obtain the aerodynamic blade loads based on the time-accurate solution of the unsteady Reynolds-averaged Navier-Stokes equations. The CSD solver Carat++ is applied to acquire the blade elastic deformations based on non-linear beam elements. In this contribution, the presented coupling approach is utilized to study the aeroelastic response of the generic DTU 10MW wind turbine. Moreover, the effect of the coupled results on the wind turbine performance is discussed. The results are compared to the aeroelastic response predicted by FLOWer coupled to the MBS tool SIMPACK as well as the response predicted by SIMPACK coupled to a Blade Element Momentum code for aerodynamic predictions. A comparative study among the different modelling approaches for this coupled problem is discussed to quantify the coupling effects of the structural models on the aeroelastic response.

Multi-scale Modeling of the Impact Response of a Strain Rate Sensitive High-Manganese Austenitic Steel

NASA Astrophysics Data System (ADS)

Önal, Orkun; Ozmenci, Cemre; Canadinc, Demircan

2014-09-01

A multi-scale modeling approach was applied to predict the impact response of a strain rate sensitive high-manganese austenitic steel. The roles of texture, geometry and strain rate sensitivity were successfully taken into account all at once by coupling crystal plasticity and finite element (FE) analysis. Specifically, crystal plasticity was utilized to obtain the multi-axial flow rule at different strain rates based on the experimental deformation response under uniaxial tensile loading. The equivalent stress - equivalent strain response was then incorporated into the FE model for the sake of a more representative hardening rule under impact loading. The current results demonstrate that reliable predictions can be obtained by proper coupling of crystal plasticity and FE analysis even if the experimental flow rule of the material is acquired under uniaxial loading and at moderate strain rates that are significantly slower than those attained during impact loading. Furthermore, the current findings also demonstrate the need for an experiment-based multi-scale modeling approach for the sake of reliable predictions of the impact response.

Predicting The Type Of Pregnancy Using Flexible Discriminate Analysis And Artificial Neural Networks: A Comparison Study

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

Hooman, A.; Mohammadzadeh, M

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using three different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression, a neural network and a flexible discrimination based on the data and compared their results using tow statistical indices: Surface under curvemore » (ROC) and kappa coefficient. Based on these tow indices, flexible discrimination proved to be a better fit for prediction on data in comparison to other methods. When the relations among variables are complex, one can use flexible discrimination instead of multinomial logistic regression and neural network to predict the nominal response variables with several levels in order to gain more accurate predictions.« less

Predicting the response of populations to environmental change

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

Ives, A.R.

1995-04-01

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

Reconciled Rat and Human Metabolic Networks for Comparative Toxicogenomics and Biomarker Predictions

DTIC Science & Technology

2017-02-08

compared with the original human GPR rules (Supplementary Fig. 3). The consensus-based approach for filtering orthology annotations was designed to...ARTICLE Received 29 Jan 2016 | Accepted 13 Dec 2016 | Published 8 Feb 2017 Reconciled rat and human metabolic networks for comparative toxicogenomics...predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature- based evidence

Computer modeling of fan-exit-splitter spacing effects on F100 response to distortion

NASA Technical Reports Server (NTRS)

Shaw, M.; Murdoch, R. W.

1982-01-01

The distortion response of the F100(3) engine was effected by the fan exit splitter configuration. The sensitivity for a proximate splitter fan is calculated to be slightly greater than a remote splitter configuration with identical airfoils. Predicted response was based upon a multiple segment parallel compressor Model modified to include a bypass ratio representation that effects the performance characteristics of the last rotor and intermediate case struts. The predicted distortion response required an accurate definition of row pre- and post-stall undistorted operation.

Prediction of Time Response of Electrowetting

NASA Astrophysics Data System (ADS)

Lee, Seung Jun; Hong, Jiwoo; Kang, Kwan Hyoung

2009-11-01

It is very important to predict the time response of electrowetting-based devices, such as liquid lenses, reflective displays, and optical switches. We investigated the time response of electrowetting, based on an analytical and a numerical method, to find out characteristic scales and a scaling law for the switching time. For this, spreading process of a sessile droplet was analyzed based on the domain perturbation method. First, we considered the case of weakly viscous fluids. The analytical result for the spreading process was compared with experimental results, which showed very good agreement in overall time response. It was shown that the overall dynamics is governed by P2 shape mode. We derived characteristic scales combining the droplet volume, density, and surface tension. The overall dynamic process was scaled quite well by the scales. A scaling law was derived from the analytical solution and was verified experimentally. We also suggest a scaling law for highly viscous liquids, based on results of numerical analysis for the electrowetting-actuated spreading process.

Life in the Mosaic: Predicting changes in estuarine nursery production for juvenile fishes in response to sea-level rise with a landscape-based habitat production model

EPA Science Inventory

Identification of critical habitat in estuarine fish nursery areas is an important conservation and management objective, yet response to changes in critical habitat is both equally important and harder to predict. Habitat can be viewed as a mosaic of both temporally variable en...

Scaling environmental change through the community level: a trait-based response-and-effect framework for plants

Treesearch

Katharine N. Suding; Sandra Lavorel; F. Stuart Chapin; Johannes H.C. Cornelissen; Sandra Diaz; Eric Garnier; Deborah Goldberg; David U. Hooper; Stephen T. Jackson; Marie-Laure Navas

2008-01-01

Predicting ecosystem responses to global change is a major challenge in ecology. A critical step in that challenge is to understand how changing environmental conditions influence processes across levels of ecological organization. While direct scaling from individual to ecosystem dynamics can lead to robust and mechanistic predictions, new approaches are needed to...

Scalable and responsive event processing in the cloud

PubMed Central

Suresh, Visalakshmi; Ezhilchelvan, Paul; Watson, Paul

2013-01-01

Event processing involves continuous evaluation of queries over streams of events. Response-time optimization is traditionally done over a fixed set of nodes and/or by using metrics measured at query-operator levels. Cloud computing makes it easy to acquire and release computing nodes as required. Leveraging this flexibility, we propose a novel, queueing-theory-based approach for meeting specified response-time targets against fluctuating event arrival rates by drawing only the necessary amount of computing resources from a cloud platform. In the proposed approach, the entire processing engine of a distinct query is modelled as an atomic unit for predicting response times. Several such units hosted on a single node are modelled as a multiple class M/G/1 system. These aspects eliminate intrusive, low-level performance measurements at run-time, and also offer portability and scalability. Using model-based predictions, cloud resources are efficiently used to meet response-time targets. The efficacy of the approach is demonstrated through cloud-based experiments. PMID:23230164

How We Choose One over Another: Predicting Trial-by-Trial Preference Decision

PubMed Central

Bhushan, Vidya; Saha, Goutam; Lindsen, Job; Shimojo, Shinsuke; Bhattacharya, Joydeep

2012-01-01

Preference formation is a complex problem as it is subjective, involves emotion, is led by implicit processes, and changes depending on the context even within the same individual. Thus, scientific attempts to predict preference are challenging, yet quite important for basic understanding of human decision making mechanisms, but prediction in a group-average sense has only a limited significance. In this study, we predicted preferential decisions on a trial by trial basis based on brain responses occurring before the individuals made their decisions explicit. Participants made a binary preference decision of approachability based on faces while their electrophysiological responses were recorded. An artificial neural network based pattern-classifier was used with time-frequency resolved patterns of a functional connectivity measure as features for the classifier. We were able to predict preference decisions with a mean accuracy of 74.3±2.79% at participant-independent level and of 91.4±3.8% at participant-dependent level. Further, we revealed a causal role of the first impression on final decision and demonstrated the temporal trajectory of preference decision formation. PMID:22912859

Determining the folding and binding free energy of DNA-based nanodevices and nanoswitches using urea titration curves

PubMed Central

Idili, Andrea

2017-01-01

Abstract DNA nanotechnology takes advantage of the predictability of DNA interactions to build complex DNA-based functional nanoscale structures. However, when DNA functional and responsive units that are based on non-canonical DNA interactions are employed it becomes quite challenging to predict, understand and control their thermodynamics. In response to this limitation, here we demonstrate the use of isothermal urea titration experiments to estimate the free energy involved in a set of DNA-based systems ranging from unimolecular DNA-based nanoswitches to more complex DNA folds (e.g. aptamers) and nanodevices. We propose here a set of fitting equations that allow to analyze the urea titration curves of these DNA responsive units based on Watson–Crick and non-canonical interactions (stem-loop, G-quadruplex, triplex structures) and to correctly estimate their relative folding and binding free energy values under different experimental conditions. The results described herein will pave the way toward the use of urea titration experiments in the field of DNA nanotechnology to achieve easier and more reliable thermodynamic characterization of DNA-based functional responsive units. More generally, our results will be of general utility to characterize other complex supramolecular systems based on different biopolymers. PMID:28605461

RNA-based determination of ESR1 and HER2 expression and response to neoadjuvant chemotherapy.

PubMed

Denkert, C; Loibl, S; Kronenwett, R; Budczies, J; von Törne, C; Nekljudova, V; Darb-Esfahani, S; Solbach, C; Sinn, B V; Petry, C; Müller, B M; Hilfrich, J; Altmann, G; Staebler, A; Roth, C; Ataseven, B; Kirchner, T; Dietel, M; Untch, M; von Minckwitz, G

2013-03-01

Hormone and human epidermal growth factor receptor 2 (HER2) receptors are the most important breast cancer biomarkers, and additional objective and quantitative test methods such as messenger RNA (mRNA)-based quantitative analysis are urgently needed. In this study, we investigated the clinical validity of RT-PCR-based evaluation of estrogen receptor (ESR1) and HER2 mRNA expression. A total of 1050 core biopsies from two retrospective (GeparTrio, GeparQuattro) and one prospective (PREDICT) neoadjuvant studies were evaluated by quantitative RT-PCR for ESR1 and HER2. ESR1 mRNA was significantly predictive for reduced response to neoadjuvant chemotherapy in univariate and multivariate analysis in all three cohorts. The complete pathologically documented response (pathological complete response, pCR) rate for ESR1+/HER2- tumors was 7.3%, 8.0% and 8.6%; for ESR1-/HER2- tumors it was 34.4%, 33.7% and 37.3% in GeparTrio, GeparQuattro and PREDICT, respectively (P < 0.001 in each cohort). In the Kaplan-Meier analysis in GeparTrio patients with ESR1+/HER2- tumors had the best prognosis, compared with ESR1-/HER2- and ESR1-/HER2+ tumors [disease-free survival (DFS): P < 0.0005, overall survival (OS): P < 0.0005]. Our results suggest that mRNA levels of ESR1 and HER2 predict response to neoadjuvant chemotherapy and are significantly associated with long-term outcome. As an additional option to standard immunohistochemistry and gene-array-based analysis, quantitative RT-PCR analysis might be useful for determination of the receptor status in breast cancer.

α-Fetoprotein is a surrogate marker for predicting treatment failure in telaprevir-based triple combination therapy for genotype 1b chronic hepatitis C Japanese patients with the IL28B minor genotype.

PubMed

Shimada, Noritomo; Tsubota, Akihito; Atsukawa, Masanori; Abe, Hiroshi; Ika, Makiko; Kato, Keizo; Sato, Yoshiyuki; Kondo, Chisa; Sakamoto, Choitsu; Tanaka, Yasuhito; Aizawa, Yoshio

2014-03-01

Even when treated with telaprevir-based triple therapy, some patients fail to achieve a sustained virological response. This study identified factors related closely to treatment failure. A total of 146 Japanese genotype 1b chronic hepatitis C patients were enrolled in this prospective, multicenter study and received a 24-week regimen of triple therapy. The end-of-treatment response rate was significantly lower in patients with the interleukin 28B (IL28B) (rs8099917) non-TT genotype (85.2%) than in those with the TT genotype (100%, P = 0.0002). Multiple logistic regression analysis identified high α-fetoprotein levels as an independent factor related to non-end-of-treatment response in patients with the non-TT genotype. A cut-off value of 20 ng/ml was determined for a non-end-of-treatment response; sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 75.0%, 95.7%, 75.0%, 75.0%, and 92.6%, respectively. Multiple logistic regression analysis for a sustained virological response identified the IL28B TT genotype, low α-fetoprotein levels, non-responders, and a rapid virological response. The sustained virological response rate was significantly lower in patients with the non-TT genotype (59.3%) than in those with the TT genotype (96.7%, P < 0.0001). In patients with the non-TT genotype, α-fetoprotein was the most significant predictor for non-sustained virological response by univariate analysis. A cut-off value of 7.4 ng/ml α-fetoprotein was determined for non-sustained virological response; sensitivity, specificity, PPV, NPV, and accuracy were 63.6%, 87.5%, 77.8%, 77.8%, and 77.8%, respectively. For the non-TT patients, serum α-fetoprotein levels may be a surrogate marker for predicting treatment failure in telaprevir-based therapy for genotype 1b chronic hepatitis C. © 2013 Wiley Periodicals, Inc.

Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task

NASA Astrophysics Data System (ADS)

Qin, Yulin; Sohn, Myeong-Ho; Anderson, John R.; Stenger, V. Andrew; Fissell, Kate; Goode, Adam; Carter, Cameron S.

2003-04-01

Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising.

PubMed

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

PubMed Central

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M.; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube. PMID:29163251

Prediction of HR/BP response to the spontaneous breathing trial by fluctuation dissipation theory

NASA Astrophysics Data System (ADS)

Chen, Man

2014-03-01

We applied the non-equilibrium fluctuation dissipation theorem to predict how critically-ill patients respond to treatment, based on both heart rate data and blood pressure data collected by standard hospital monitoring devices. The non-equilibrium fluctuation dissipation theorem relates the response of a system to a perturbation to the fluctuations in the stationary state of the system. It is shown that the response of patients to a standard procedure performed on patients, the spontaneous breathing trial (SBT), can be predicted by the non-equilibrium fluctuation dissipation approach. We classify patients into different groups according to the patients' characteristics. For each patient group, we extend the fluctuation dissipation theorem to predict interactions between blood pressure and beat-to-beat dynamics of heart rate in response to a perturbation (SBT), We also extract the form of the perturbation function directly from the physiological data, which may help to reduce the prediction error. We note this method is not limited to the analysis of the heart rate dynamics, but also can be applied to analyze the response of other physiological signals to other clinical interventions.

AAA gunnermodel based on observer theory. [predicting a gunner's tracking response

NASA Technical Reports Server (NTRS)

Kou, R. S.; Glass, B. C.; Day, C. N.; Vikmanis, M. M.

1978-01-01

The Luenberger observer theory is used to develop a predictive model of a gunner's tracking response in antiaircraft artillery systems. This model is composed of an observer, a feedback controller and a remnant element. An important feature of the model is that the structure is simple, hence a computer simulation requires only a short execution time. A parameter identification program based on the least squares curve fitting method and the Gauss Newton gradient algorithm is developed to determine the parameter values of the gunner model. Thus, a systematic procedure exists for identifying model parameters for a given antiaircraft tracking task. Model predictions of tracking errors are compared with human tracking data obtained from manned simulation experiments. Model predictions are in excellent agreement with the empirical data for several flyby and maneuvering target trajectories.

Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells

PubMed Central

Passante, E; Würstle, M L; Hellwig, C T; Leverkus, M; Rehm, M

2013-01-01

Many cancer entities and their associated cell line models are highly heterogeneous in their responsiveness to apoptosis inducers and, despite a detailed understanding of the underlying signaling networks, cell death susceptibility currently cannot be predicted reliably from protein expression profiles. Here, we demonstrate that an integration of quantitative apoptosis protein expression data with pathway knowledge can predict the cell death responsiveness of melanoma cell lines. By a total of 612 measurements, we determined the absolute expression (nM) of 17 core apoptosis regulators in a panel of 11 melanoma cell lines, and enriched these data with systems-level information on apoptosis pathway topology. By applying multivariate statistical analysis and multi-dimensional pattern recognition algorithms, the responsiveness of individual cell lines to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) or dacarbazine (DTIC) could be predicted with very high accuracy (91 and 82% correct predictions), and the most effective treatment option for individual cell lines could be pre-determined in silico. In contrast, cell death responsiveness was poorly predicted when not taking knowledge on protein–protein interactions into account (55 and 36% correct predictions). We also generated mathematical predictions on whether anti-apoptotic Bcl-2 family members or x-linked inhibitor of apoptosis protein (XIAP) can be targeted to enhance TRAIL responsiveness in individual cell lines. Subsequent experiments, making use of pharmacological Bcl-2/Bcl-xL inhibition or siRNA-based XIAP depletion, confirmed the accuracy of these predictions. We therefore demonstrate that cell death responsiveness to TRAIL or DTIC can be predicted reliably in a large number of melanoma cell lines when investigating expression patterns of apoptosis regulators in the context of their network-level interplay. The capacity to predict responsiveness at the cellular level may contribute to personalizing anti-cancer treatments in the future. PMID:23933815

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

PubMed

Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S; Berkowitz, Ross S; Matulonis, Ursula; Jänne, Pasi A; Amrein, Philip C; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

2015-02-26

There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting effects of environmental change on a migratory herbivore

USGS Publications Warehouse

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

2015-01-01

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

Parental Mediation Regarding Children's Smartphone Use: Role of Protection Motivation and Parenting Style.

PubMed

Hwang, Yoori; Choi, Inho; Yum, Jung-Yoon; Jeong, Se-Hoon

2017-06-01

Parental mediation is a type of behavior that could protect children against the negative uses and effects of smartphones. Based on protection motivation theory, this research (a) predicted parental mediation based on parents' threat and efficacy perceptions and (b) predicted threat and efficacy perceptions based on parenting styles and parents' addiction to smartphone use. An online survey of 448 parents of fourth to sixth graders was conducted. Results showed that both restrictive and active parental mediation were predicted by perceived severity, response efficacy, and self-efficacy. With regard to parenting styles, (a) authoritative parenting was positively related to perceived severity as well as response- and self-efficacy, whereas (b) permissive parenting was negatively related to self-efficacy. In addition, parents' addiction was a negative predictor of perceived severity, but a positive predictor of perceived susceptibility.

Climate-based models for pulsed resources improve predictability of consumer population dynamics: outbreaks of house mice in forest ecosystems.

PubMed

Holland, E Penelope; James, Alex; Ruscoe, Wendy A; Pech, Roger P; Byrom, Andrea E

2015-01-01

Accurate predictions of the timing and magnitude of consumer responses to episodic seeding events (masts) are important for understanding ecosystem dynamics and for managing outbreaks of invasive species generated by masts. While models relating consumer populations to resource fluctuations have been developed successfully for a range of natural and modified ecosystems, a critical gap that needs addressing is better prediction of resource pulses. A recent model used change in summer temperature from one year to the next (ΔT) for predicting masts for forest and grassland plants in New Zealand. We extend this climate-based method in the framework of a model for consumer-resource dynamics to predict invasive house mouse (Mus musculus) outbreaks in forest ecosystems. Compared with previous mast models based on absolute temperature, the ΔT method for predicting masts resulted in an improved model for mouse population dynamics. There was also a threshold effect of ΔT on the likelihood of an outbreak occurring. The improved climate-based method for predicting resource pulses and consumer responses provides a straightforward rule of thumb for determining, with one year's advance warning, whether management intervention might be required in invaded ecosystems. The approach could be applied to consumer-resource systems worldwide where climatic variables are used to model the size and duration of resource pulses, and may have particular relevance for ecosystems where global change scenarios predict increased variability in climatic events.

Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels.

PubMed

Al-Samman, A M; Azmi, M H; Rahman, T A; Khan, I; Hindia, M N; Fattouh, A

2016-01-01

This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk-1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method.

Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels

PubMed Central

Al-Samman, A. M.; Azmi, M. H.; Rahman, T. A.; Khan, I.; Hindia, M. N.; Fattouh, A.

2016-01-01

This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk−1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method. PMID:27992445

Direct-to-consumer advertising of predictive genetic tests: a health belief model based examination of consumer response.

PubMed

Rollins, Brent L; Ramakrishnan, Shravanan; Perri, Matthew

2014-01-01

Direct-to-consumer (DTC) advertising of predictive genetic tests (PGTs) has added a new dimension to health advertising. This study used an online survey based on the health belief model framework to examine and more fully understand consumers' responses and behavioral intentions in response to a PGT DTC advertisement. Overall, consumers reported moderate intentions to talk with their doctor and seek more information about PGTs after advertisement exposure, though consumers did not seem ready to take the advertised test or engage in active information search. Those who perceived greater threat from the disease, however, had significantly greater behavioral intentions and information search behavior.

The spatial structure of a nonlinear receptive field.

PubMed

Schwartz, Gregory W; Okawa, Haruhisa; Dunn, Felice A; Morgan, Josh L; Kerschensteiner, Daniel; Wong, Rachel O; Rieke, Fred

2012-11-01

Understanding a sensory system implies the ability to predict responses to a variety of inputs from a common model. In the retina, this includes predicting how the integration of signals across visual space shapes the outputs of retinal ganglion cells. Existing models of this process generalize poorly to predict responses to new stimuli. This failure arises in part from properties of the ganglion cell response that are not well captured by standard receptive-field mapping techniques: nonlinear spatial integration and fine-scale heterogeneities in spatial sampling. Here we characterize a ganglion cell's spatial receptive field using a mechanistic model based on measurements of the physiological properties and connectivity of only the primary excitatory circuitry of the retina. The resulting simplified circuit model successfully predicts ganglion-cell responses to a variety of spatial patterns and thus provides a direct correspondence between circuit connectivity and retinal output.

Proposal of a clinical response score and predictors of clinical response to 2 years of GH replacement therapy in adult GH deficiency.

PubMed

Schneider, Harald J; Buchfelder, Michael; Wallaschofski, Henri; Luger, Anton; Johannsson, Gudmundur; Kann, Peter H; Mattsson, Anders

2015-12-01

There is no single clinical marker to reliably assess the clinical response to growth hormone replacement therapy (GHRT) in adults with growth hormone deficiency (GHD). The objective of this study was to propose a clinical response score to GHRT in adult GHD and to establish clinical factors that predict clinical response. This was a prospective observational cohort study from the international KIMS database (Pfizer International Metabolic Database). We included 3612 adult patients with GHD for proposing the response score and 844 patients for assessing predictors of response. We propose a clinical response score based on changes in total cholesterol, waist circumference and QoL-AGHDA quality of life measurements after 2 years of GHRT. A score point was added for each quintile of change in each variable, resulting in a sum score ranging from 3 to 15. For clinical response at 2 years, we analysed predictors at baseline and after 6 months using logistic regression analyses. In a baseline prediction model, IGF1, QoL-AGHDA, total cholesterol and waist circumference predicted response, with worse baseline parameters being associated with a favourable response (AUC 0.736). In a combined baseline and 6-month prediction model, baseline QoL-AGHDA, total cholesterol and waist circumference, and 6-month change in waist circumference were significant predictors of response (AUC 0.815). A simple clinical response score might be helpful in evaluating the success of GHRT. The baseline prediction model may aid in the decision to initiate GHRT and the combined prediction model may be helpful in the decision to continue GHRT. © 2015 European Society of Endocrinology.

Prediction in the service of comprehension: modulated early brain responses to omitted speech segments.

PubMed

Bendixen, Alexandra; Scharinger, Mathias; Strauß, Antje; Obleser, Jonas

2014-04-01

Speech signals are often compromised by disruptions originating from external (e.g., masking noise) or internal (e.g., inaccurate articulation) sources. Speech comprehension thus entails detecting and replacing missing information based on predictive and restorative neural mechanisms. The present study targets predictive mechanisms by investigating the influence of a speech segment's predictability on early, modality-specific electrophysiological responses to this segment's omission. Predictability was manipulated in simple physical terms in a single-word framework (Experiment 1) or in more complex semantic terms in a sentence framework (Experiment 2). In both experiments, final consonants of the German words Lachs ([laks], salmon) or Latz ([lats], bib) were occasionally omitted, resulting in the syllable La ([la], no semantic meaning), while brain responses were measured with multi-channel electroencephalography (EEG). In both experiments, the occasional presentation of the fragment La elicited a larger omission response when the final speech segment had been predictable. The omission response occurred ∼125-165 msec after the expected onset of the final segment and showed characteristics of the omission mismatch negativity (MMN), with generators in auditory cortical areas. Suggestive of a general auditory predictive mechanism at work, this main observation was robust against varying source of predictive information or attentional allocation, differing between the two experiments. Source localization further suggested the omission response enhancement by predictability to emerge from left superior temporal gyrus and left angular gyrus in both experiments, with additional experiment-specific contributions. These results are consistent with the existence of predictive coding mechanisms in the central auditory system, and suggestive of the general predictive properties of the auditory system to support spoken word recognition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prediction of treatment response and metastatic disease in soft tissue sarcoma

NASA Astrophysics Data System (ADS)

Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

2014-03-01

Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis < 90% post treatment and metastasis.

Vibrational response analysis of tires using a three-dimensional flexible ring-based model

NASA Astrophysics Data System (ADS)

Matsubara, Masami; Tajiri, Daiki; Ise, Tomohiko; Kawamura, Shozo

2017-11-01

Tire vibration characteristics influence noise, vibration, and harshness. Hence, there have been many investigations of the dynamic responses of tires. In this paper, we present new formulations for the prediction of tire tread vibrations below 150 Hz using a three-dimensional flexible ring-based model. The ring represents the tread including the belt, and the springs represent the tire sidewall stiffness. The equations of motion for lateral, longitudinal, and radial vibration on the tread are derived based on the assumption of inextensional deformation. Many of the associated numerical parameters are identified from experimental tests. Unlike most studies of flexible ring models, which mainly discussed radial and circumferential vibration, this study presents steady response functions concerning not only radial and circumferential but also lateral vibration using the three-dimensional flexible ring-based model. The results of impact tests described confirm the theoretical findings. The results show reasonable agreement with the predictions.

Disrupted prediction-error signal in psychosis: evidence for an associative account of delusions

PubMed Central

Corlett, P. R.; Murray, G. K.; Honey, G. D.; Aitken, M. R. F.; Shanks, D. R.; Robbins, T.W.; Bullmore, E.T.; Dickinson, A.; Fletcher, P. C.

2012-01-01

Delusions are maladaptive beliefs about the world. Based upon experimental evidence that prediction error—a mismatch between expectancy and outcome—drives belief formation, this study examined the possibility that delusions form because of disrupted prediction-error processing. We used fMRI to determine prediction-error-related brain responses in 12 healthy subjects and 12 individuals (7 males) with delusional beliefs. Frontal cortex responses in the patient group were suggestive of disrupted prediction-error processing. Furthermore, across subjects, the extent of disruption was significantly related to an individual’s propensity to delusion formation. Our results support a neurobiological theory of delusion formation that implicates aberrant prediction-error signalling, disrupted attentional allocation and associative learning in the formation of delusional beliefs. PMID:17690132

Driver Vision Based Perception-Response Time Prediction and Assistance Model on Mountain Highway Curve.

PubMed

Li, Yi; Chen, Yuren

2016-12-30

To make driving assistance system more humanized, this study focused on the prediction and assistance of drivers' perception-response time on mountain highway curves. Field tests were conducted to collect real-time driving data and driver vision information. A driver-vision lane model quantified curve elements in drivers' vision. A multinomial log-linear model was established to predict perception-response time with traffic/road environment information, driver-vision lane model, and mechanical status (last second). A corresponding assistance model showed a positive impact on drivers' perception-response times on mountain highway curves. Model results revealed that the driver-vision lane model and visual elements did have important influence on drivers' perception-response time. Compared with roadside passive road safety infrastructure, proper visual geometry design, timely visual guidance, and visual information integrality of a curve are significant factors for drivers' perception-response time.

Early PET/CT scan is more effective than RECIST in predicting outcome of patients with liver metastases from colorectal cancer treated with preoperative chemotherapy plus bevacizumab.

PubMed

Lastoria, Secondo; Piccirillo, Maria Carmela; Caracò, Corradina; Nasti, Guglielmo; Aloj, Luigi; Arrichiello, Cecilia; de Lutio di Castelguidone, Elisabetta; Tatangelo, Fabiana; Ottaiano, Alessandro; Iaffaioli, Rosario Vincenzo; Izzo, Francesco; Romano, Giovanni; Giordano, Pasqualina; Signoriello, Simona; Gallo, Ciro; Perrone, Francesco

2013-12-01

Markers predictive of treatment effect might be useful to improve the treatment of patients with metastatic solid tumors. Particularly, early changes in tumor metabolism measured by PET/CT with (18)F-FDG could predict the efficacy of treatment better than standard dimensional Response Evaluation Criteria In Solid Tumors (RECIST) response. We performed PET/CT evaluation before and after 1 cycle of treatment in patients with resectable liver metastases from colorectal cancer, within a phase 2 trial of preoperative FOLFIRI plus bevacizumab. For each lesion, the maximum standardized uptake value (SUV) and the total lesion glycolysis (TLG) were determined. On the basis of previous studies, a ≤ -50% change from baseline was used as a threshold for significant metabolic response for maximum SUV and, exploratively, for TLG. Standard RECIST response was assessed with CT after 3 mo of treatment. Pathologic response was assessed in patients undergoing resection. The association between metabolic and CT/RECIST and pathologic response was tested with the McNemar test; the ability to predict progression-free survival (PFS) and overall survival (OS) was tested with the Log-rank test and a multivariable Cox model. Thirty-three patients were analyzed. After treatment, there was a notable decrease of all the parameters measured by PET/CT. Early metabolic PET/CT response (either SUV- or TLG-based) had a stronger, independent and statistically significant predictive value for PFS and OS than both CT/RECIST and pathologic response at multivariate analysis, although with different degrees of statistical significance. The predictive value of CT/RECIST response was not significant at multivariate analysis. PET/CT response was significantly predictive of long-term outcomes during preoperative treatment of patients with liver metastases from colorectal cancer, and its predictive ability was higher than that of CT/RECIST response after 3 mo of treatment. Such findings need to be confirmed by larger prospective trials.

Spatial Prediction of Coxiella burnetii Outbreak Exposure via Notified Case Counts in a Dose-Response Model.

PubMed

Brooke, Russell J; Kretzschmar, Mirjam E E; Hackert, Volker; Hoebe, Christian J P A; Teunis, Peter F M; Waller, Lance A

2017-01-01

We develop a novel approach to study an outbreak of Q fever in 2009 in the Netherlands by combining a human dose-response model with geostatistics prediction to relate probability of infection and associated probability of illness to an effective dose of Coxiella burnetii. The spatial distribution of the 220 notified cases in the at-risk population are translated into a smooth spatial field of dose. Based on these symptomatic cases, the dose-response model predicts a median of 611 asymptomatic infections (95% range: 410, 1,084) for the 220 reported symptomatic cases in the at-risk population; 2.78 (95% range: 1.86, 4.93) asymptomatic infections for each reported case. The low attack rates observed during the outbreak range from (Equation is included in full-text article.)to (Equation is included in full-text article.). The estimated peak levels of exposure extend to the north-east from the point source with an increasing proportion of asymptomatic infections further from the source. Our work combines established methodology from model-based geostatistics and dose-response modeling allowing for a novel approach to study outbreaks. Unobserved infections and the spatially varying effective dose can be predicted using the flexible framework without assuming any underlying spatial structure of the outbreak process. Such predictions are important for targeting interventions during an outbreak, estimating future disease burden, and determining acceptable risk levels.

Utilization of the Generalized Method of Cells to Analyze the Deformation Response of Laminated Ceramic Matrix Composites

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.

2012-01-01

In order to practically utilize ceramic matrix composites in aircraft engine components, robust analysis tools are required that can simulate the material response in a computationally efficient manner. The MAC/GMC software developed at NASA Glenn Research Center, based on the Generalized Method of Cells micromechanics method, has the potential to meet this need. Utilizing MAC/GMC, the effective stiffness properties, proportional limit stress and ultimate strength can be predicted based on the properties and response of the individual constituents. In this paper, the effective stiffness and strength properties for a representative laminated ceramic matrix composite with a large diameter fiber are predicted for a variety of fiber orientation angles and laminate orientations. As part of the analytical study, methods to determine the in-situ stiffness and strength properties of the constituents required to appropriately simulate the effective composite response are developed. The stiffness properties of the representative composite have been adequately predicted for all of the fiber orientations and laminate configurations examined in this study. The proportional limit stresses and strains and ultimate stresses and strains were predicted with varying levels of accuracy, depending on the laminate orientation. However, for the cases where the predictions did not have the desired level of accuracy, the specific issues related to the micromechanics theory were identified which could lead to difficulties that were encountered that could be addressed in future work.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

Neural correlates of sensory prediction errors in monkeys: evidence for internal models of voluntary self-motion in the cerebellum.

PubMed

Cullen, Kathleen E; Brooks, Jessica X

2015-02-01

During self-motion, the vestibular system makes essential contributions to postural stability and self-motion perception. To ensure accurate perception and motor control, it is critical to distinguish between vestibular sensory inputs that are the result of externally applied motion (exafference) and that are the result of our own actions (reafference). Indeed, although the vestibular sensors encode vestibular afference and reafference with equal fidelity, neurons at the first central stage of sensory processing selectively encode vestibular exafference. The mechanism underlying this reafferent suppression compares the brain's motor-based expectation of sensory feedback with the actual sensory consequences of voluntary self-motion, effectively computing the sensory prediction error (i.e., exafference). It is generally thought that sensory prediction errors are computed in the cerebellum, yet it has been challenging to explicitly demonstrate this. We have recently addressed this question and found that deep cerebellar nuclei neurons explicitly encode sensory prediction errors during self-motion. Importantly, in everyday life, sensory prediction errors occur in response to changes in the effector or world (muscle strength, load, etc.), as well as in response to externally applied sensory stimulation. Accordingly, we hypothesize that altering the relationship between motor commands and the actual movement parameters will result in the updating in the cerebellum-based computation of exafference. If our hypothesis is correct, under these conditions, neuronal responses should initially be increased--consistent with a sudden increase in the sensory prediction error. Then, over time, as the internal model is updated, response modulation should decrease in parallel with a reduction in sensory prediction error, until vestibular reafference is again suppressed. The finding that the internal model predicting the sensory consequences of motor commands adapts for new relationships would have important implications for understanding how responses to passive stimulation endure despite the cerebellum's ability to learn new relationships between motor commands and sensory feedback.

Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille; Moxley, Katherine; Moore, Kathleen; Mannel, Robert; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2017-03-01

Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838+/-0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.

Auditorium acoustics evaluation based on simulated impulse response

NASA Astrophysics Data System (ADS)

Wu, Shuoxian; Wang, Hongwei; Zhao, Yuezhe

2004-05-01

The impulse responses and other acoustical parameters of Huangpu Teenager Palace in Guangzhou were measured. Meanwhile, the acoustical simulation and auralization based on software ODEON were also made. The comparison between the parameters based on computer simulation and measuring is given. This case study shows that auralization technique based on computer simulation can be used for predicting the acoustical quality of a hall at its design stage.

Prediction of visual evoked potentials at any surface location from a set of three recording electrodes.

PubMed

Mazinani, Babac A E; Waberski, Till D; van Ooyen, Andre; Walter, Peter

2008-05-01

Purpose of this study was to introduce a mathematical model which allows the calculation of a source dipole as the origin of the evoked activity based on the data of three simultaneously recorded VEPs from different locations at the scalp surface to predict field potentials at any neighboring location and to validate this model by comparison with actual recordings. In 10 healthy subjects (25-38, mean 29 years) continuous VEPs were recorded via 96 channels. On the base of the recordings at the positions POz', O1' and O2', a source dipole vector was calculated for each time point of the recordings and VEP responses were back projected for any of the 96 electrode positions. Differences between the calculated and the actually recorded responses were quantified by coefficients of variation (CV). The prediction precision and response size depended on the distance between the electrode of the predicted response and the recording electrodes. After compensating this relationship using a polynomial function, the CV of the mean difference between calculated and recorded responses of the 10 subjects was 2.8 +/- 1.2%. In conclusion, the "Mini-Brainmapping" model can provide precise topographical information with minimal additional recording efforts with good reliability. The implementation of this method in a routine diagnostic setting as an "easy-to-do" procedure would allow to examine a large number of patients and normal subjects in a short time, and thus, a solid data base could be created to correlate well defined pathologies with topographical VEP changes.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Ebert, D D; de Jonge, P; Nierenberg, A A; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2017-02-01

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed Central

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-01-01

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-09-28

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC.

Simulated crop yield in response to changes in climate and agricultural practices: results from a simple process based model

NASA Astrophysics Data System (ADS)

Caldararu, S.; Smith, M. J.; Purves, D.; Emmott, S.

2013-12-01

Global agriculture will, in the future, be faced with two main challenges: climate change and an increase in global food demand driven by an increase in population and changes in consumption habits. To be able to predict both the impacts of changes in climate on crop yields and the changes in agricultural practices necessary to respond to such impacts we currently need to improve our understanding of crop responses to climate and the predictive capability of our models. Ideally, what we would have at our disposal is a modelling tool which, given certain climatic conditions and agricultural practices, can predict the growth pattern and final yield of any of the major crops across the globe. We present a simple, process-based crop growth model based on the assumption that plants allocate above- and below-ground biomass to maintain overall carbon optimality and that, to maintain this optimality, the reproductive stage begins at peak nitrogen uptake. The model includes responses to available light, water, temperature and carbon dioxide concentration as well as nitrogen fertilisation and irrigation. The model is data constrained at two sites, the Yaqui Valley, Mexico for wheat and the Southern Great Plains flux site for maize and soybean, using a robust combination of space-based vegetation data (including data from the MODIS and Landsat TM and ETM+ instruments), as well as ground-based biomass and yield measurements. We show a number of climate response scenarios, including increases in temperature and carbon dioxide concentrations as well as responses to irrigation and fertiliser application.

A correlation between extensional displacement and architecture of ionic polymer transducers

NASA Astrophysics Data System (ADS)

Akle, Barbar J.; Duncan, Andrew; Leo, Donald J.

2008-03-01

Ionic polymer transducers (IPT), sometimes referred to as artificial muscles, are known to generate a large bending strain and a moderate stress at low applied voltages (<5V). Bending actuators have limited engineering applications due to the low forcing capabilities and the need for complicated external devices to convert the bending action into rotating or linear motion desired in most devices. Recently Akle and Leo reported extensional actuation in ionic polymer transducers. In this study, extensional IPTs are characterized as a function of transducer architecture. In this study 2 actuators are built and there extensional displacement response is characterized. The transducers have similar electrodes while the middle membrane in the first is a Nafion / ionic liquid and an aluminum oxide - ionic liquid in the second. The first transducer is characterized for constant current input, voltage step input, and sweep voltage input. The model prediction is in agreement in both shape and magnitude for the constant current experiment. The values of α and β used are within the range of values reported in Akle and Leo. Both experiments and model demonstrate that there is a preferred direction of applying the potential so that the transducer will exhibit large deformations. In step response the model well predicted the negative potential and the early part of the step in the positive potential and failed to predict the displacement after approximately 180s has elapsed. The model well predicted the sweep response, and the observed 1st harmonic in the displacement further confirmed the existence of a quadratic in the charge response. Finally the aluminum oxide based transducer is characterized for a step response and compared to the Nafion based transducer. The second actuator demonstrated electromechanical extensional response faster than that in the Nafion based transducer. The Aluminum oxide based transducer is expected to provide larger forces and hence larger energy density.

[Prediction of the molecular response to pertubations from single cell measurements].

PubMed

Remacle, Françoise; Levine, Raphael D

2014-12-01

The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure. © 2014 médecine/sciences – Inserm.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

PubMed Central

Kujawa, Autumn; Fitzgerald, Kate D.; Swain, James E.; Hanna, Gregory L.; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S.; Phan, K. Luan

2018-01-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7–19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post-CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment. PMID:27255517

A comparative study between experimental results and numerical predictions of multi-wall structural response to hypervelocity impact

NASA Technical Reports Server (NTRS)

Schonberg, William P.; Peck, Jeffrey A.

1992-01-01

Over the last three decades, multiwall structures have been analyzed extensively, primarily through experiment, as a means of increasing the protection afforded to spacecraft structure. However, as structural configurations become more varied, the number of tests required to characterize their response increases dramatically. As an alternative, numerical modeling of high-speed impact phenomena is often being used to predict the response of a variety of structural systems under impact loading conditions. This paper presents the results of a preliminary numerical/experimental investigation of the hypervelocity impact response of multiwall structures. The results of experimental high-speed impact tests are compared against the predictions of the HULL hydrodynamic computer code. It is shown that the hypervelocity impact response characteristics of a specific system cannot be accurately predicted from a limited number of HULL code impact simulations. However, if a wide range of impact loadings conditions are considered, then the ballistic limit curve of the system based on the entire series of numerical simulations can be used as a relatively accurate indication of actual system response.

Do circulating long non-coding RNAs (lncRNAs) (LincRNA-p21, GAS 5, HOTAIR) predict the treatment response in patients with head and neck cancer treated with chemoradiotherapy?

PubMed

Fayda, Merdan; Isin, Mustafa; Tambas, Makbule; Guveli, Murat; Meral, Rasim; Altun, Musa; Sahin, Dilek; Ozkan, Gozde; Sanli, Yasemin; Isin, Husniye; Ozgur, Emre; Gezer, Ugur

2016-03-01

Long non-coding RNAs (lncRNAs) have been shown to be aberrantly expressed in head and neck cancer (HNC). The aim of the present study was to evaluate plasma levels of three lncRNA molecules (lincRNA-p21, GAS5, and HOTAIR) in the treatment response in HNC patients treated with radical chemoradiotherapy (CRT). Forty-one patients with HNC were enrolled in the study. Most of the patients had nasopharyngeal carcinoma (n = 27, 65.9 %) and locally advanced disease. Blood was drawn at baseline and treatment evaluation 4.5 months after therapy. lncRNAs in plasma were measured by semiquantitative PCR. Treatment response was evaluated according to clinical examination, RECIST and PERCIST criteria based on magnetic resonance imaging (MRI), and positron emission tomography with computed tomography (PET/CT) findings. Complete response (CR) rates were 73.2, 36.6, and 50 % for clinical investigation, PET/CT-, or MRI-based response evaluation, respectively. Predictive value of lncRNAs was investigated in patients with CR vs. those with partial response (PR)/progressive disease (PD). We found that post-treatment GAS5 levels in patients with PR/PD were significantly higher compared with patients with CR based on clinical investigation (p = 0.01). Receiver operator characteristic (ROC) analysis showed that at a cutoff value of 0.3 of GAS5, sensitivity and specificity for clinical tumor response were 82 and 77 %, respectively. Interestingly, pretreatment GAS5 levels were significantly increased in patients with PR/PD compared to those with CR upon MRI-based response evaluation (p = 0.042). In contrast to GAS5, pretreatment or post-treatment lincRNA-p21 and HOTAIR levels were not informative for treatment response. Our results suggest that circulating GAS5 could be a biomarker in predicting treatment response in HNC patients.

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

PubMed

Patel, Meenal J; Andreescu, Carmen; Price, Julie C; Edelman, Kathryn L; Reynolds, Charles F; Aizenstein, Howard J

2015-10-01

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. Copyright © 2015 John Wiley & Sons, Ltd.

Rostral anterior cingulate cortex morphology predicts treatment response to internet-based CBT for depression

PubMed Central

Webb, Christian A.; Olson, Elizabeth A.; Killgore, William D.S.; Pizzagalli, Diego A.; Rauch, Scott L.; Rosso, Isabelle M.

2018-01-01

Background Rostral and subgenual anterior cingulate cortex (rACC and sgACC) activity and, to a lesser extent, volume have been shown to predict depressive symptom improvement across different antidepressant treatments. This study extends prior work by examining whether rACC and/or sgACC morphology predicts treatment response to internet-based cognitive behavioral therapy (iCBT) for major depressive disorder (MDD). This is the first study to examine neural predictors of response to iCBT. Methods Hierarchical linear modeling tested whether pre-treatment rACC and sgACC volumes predicted depressive symptom improvement during a 6-session (10-week) randomized clinical trial of iCBT (n = 35) vs. a monitored attention control (MAC; n = 38). Analyses also tested whether pre-treatment rACC and sgACC volumes differed between patients who achieved depression remission versus those who did not remit. Results Larger pre-treatment right rACC volume was a significant predictor of greater depressive symptom improvement in iCBT, even when controlling for demographic (age, gender, race) and clinical (baseline depression, anhedonia and anxiety) variables previously linked to treatment response. In addition, pre-treatment right rACC volume was larger among iCBT patients whose depression eventually remitted relative to those who did not remit. Corresponding analyses in the MAC group and for the sgACC were not significant. Conclusions rACC volume prior to iCBT demonstrated incremental predictive validity beyond clinical and demographic variables previously found to predict symptom improvement. Such findings may help inform our understanding of the mediating anatomy of iCBT and, if replicated, may suggest neural targets to augment treatment response (e.g., via modulation of rACC function). ClinicalTrials.gov Identifier NCT01598922 PMID:29486867

Risk of fetal mortality after exposure to Listeria monocytogenes based on dose-response data from pregnant guinea pigs and primates.

PubMed

Williams, Denita; Castleman, Jennifer; Lee, Chi-Ching; Mote, Beth; Smith, Mary Alice

2009-11-01

One-third of the annual cases of listeriosis in the United States occur during pregnancy and can lead to miscarriage or stillbirth, premature delivery, or infection of the newborn. Previous risk assessments completed by the Food and Drug Administration/the Food Safety Inspection Service of the U.S. Department of Agriculture/the Centers for Disease Control and Prevention (FDA/USDA/CDC) and Food and Agricultural Organization/the World Health Organization (FAO/WHO) were based on dose-response data from mice. Recent animal studies using nonhuman primates and guinea pigs have both estimated LD(50)s of approximately 10(7) Listeria monocytogenes colony forming units (cfu). The FAO/WHO estimated a human LD(50) of 1.9 x 10(6) cfu based on data from a pregnant woman consuming contaminated soft cheese. We reevaluated risk based on dose-response curves from pregnant rhesus monkeys and guinea pigs. Using standard risk assessment methodology including hazard identification, exposure assessment, hazard characterization, and risk characterization, risk was calculated based on the new dose-response information. To compare models, we looked at mortality rate per serving at predicted doses ranging from 10(-4) to 10(12) L. monocytogenes cfu. Based on a serving of 10(6) L. monocytogenes cfu, the primate model predicts a death rate of 5.9 x 10(-1) compared to the FDA/USDA/CDC (fig. IV-12) predicted rate of 1.3 x 10(-7). Based on the guinea pig and primate models, the mortality rate calculated by the FDA/USDA/CDC is underestimated for this susceptible population.

Gemcitabine-Based Chemotherapy in Adrenocortical Carcinoma: A Multicenter Study of Efficacy and Predictive Factors.

PubMed

Henning, Judith E K; Deutschbein, Timo; Altieri, Barbara; Steinhauer, Sonja; Kircher, Stefan; Sbiera, Silviu; Wild, Vanessa; Schlötelburg, Wiebke; Kroiss, Matthias; Perotti, Paola; Rosenwald, Andreas; Berruti, Alfredo; Fassnacht, Martin; Ronchi, Cristina L

2017-11-01

Adrenocortical carcinoma (ACC) is rare and confers an unfavorable prognosis in advanced stages. Other than combination chemotherapy with cisplatin, etoposide, doxorubicin, and mitotane, the second- and third-line regimens are not well-established. Gemcitabine (GEM)-based chemotherapy was suggested in a phase 2 clinical trial with 28 patients. In other solid tumors, human equilibrative nucleoside transporter type 1 (hENT1) and/or ribonucleotide reductase catalytic subunit M1 (RRM1) expression have been associated with resistance to GEM. To assess the efficacy of GEM-based chemotherapy in ACC in a real-world setting and the predictive role of molecular parameters. Retrospective multicenter study. Referral centers of university hospitals. A total of 145 patients with advanced ACC were treated with GEM-based chemotherapy (132 with concomitant capecitabine). Formalin-fixed paraffin-embedded tumor material was available for 70 patients for immunohistochemistry. The main outcome measures were progression-free survival (PFS) and an objective response to GEM-based chemotherapy. The secondary objective was the predictive role of hENT1 and RRM1. The median PFS for the patient population was 12 weeks (range, 1 to 94). A partial response or stable disease was achieved in 4.9% and 25.0% of cases, with a median duration of 26.8 weeks. Treatment was generally well tolerated, with adverse events of grade 3 or 4 occurring in 11.0% of cases. No substantial effect of hENT1 and/or RRM1 expression was observed in response to GEM-based chemotherapy. GEM-based chemotherapy is a well-tolerated, but modestly active, regimen against advanced ACC. No reliable molecular predictive factors could be identified. Owing to the scarce alternative therapeutic options, GEM-based chemotherapy remains an important option for salvage treatment for advanced ACC. Copyright © 2017 Endocrine Society

Evaluation of a Progressive Failure Analysis Methodology for Laminated Composite Structures

NASA Technical Reports Server (NTRS)

Sleight, David W.; Knight, Norman F., Jr.; Wang, John T.

1997-01-01

A progressive failure analysis methodology has been developed for predicting the nonlinear response and failure of laminated composite structures. The progressive failure analysis uses C plate and shell elements based on classical lamination theory to calculate the in-plane stresses. Several failure criteria, including the maximum strain criterion, Hashin's criterion, and Christensen's criterion, are used to predict the failure mechanisms. The progressive failure analysis model is implemented into a general purpose finite element code and can predict the damage and response of laminated composite structures from initial loading to final failure.

Integrating predictive information into an agro-economic model to guide agricultural management

NASA Astrophysics Data System (ADS)

Zhang, Y.; Block, P.

2016-12-01

Skillful season-ahead climate predictions linked with responsive agricultural planning and management have the potential to reduce losses, if adopted by farmers, particularly for rainfed-dominated agriculture such as in Ethiopia. Precipitation predictions during the growing season in major agricultural regions of Ethiopia are used to generate predicted climate yield factors, which reflect the influence of precipitation amounts on crop yields and serve as inputs into an agro-economic model. The adapted model, originally developed by the International Food Policy Research Institute, produces outputs of economic indices (GDP, poverty rates, etc.) at zonal and national levels. Forecast-based approaches, in which farmers' actions are in response to forecasted conditions, are compared with no-forecast approaches in which farmers follow business as usual practices, expecting "average" climate conditions. The effects of farmer adoption rates, including the potential for reduced uptake due to poor predictions, and increasing forecast lead-time on economic outputs are also explored. Preliminary results indicate superior gains under forecast-based approaches.

Navy Enhanced Sierra Mechanics (NESM): Toolbox for predicting Navy shock and damage

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

Moyer, Thomas; Stergiou, Jonathan; Reese, Garth

Here, the US Navy is developing a new suite of computational mechanics tools (Navy Enhanced Sierra Mechanics) for the prediction of ship response, damage, and shock environments transmitted to vital systems during threat weapon encounters. NESM includes fully coupled Euler-Lagrange solvers tailored to ship shock/damage predictions. NESM is optimized to support high-performance computing architectures, providing the physics-based ship response/threat weapon damage predictions needed to support the design and assessment of highly survivable ships. NESM is being employed to support current Navy ship design and acquisition programs while being further developed for future Navy fleet needs.

Navy Enhanced Sierra Mechanics (NESM): Toolbox for predicting Navy shock and damage

DOE PAGES

Moyer, Thomas; Stergiou, Jonathan; Reese, Garth; ...

2016-05-25

Here, the US Navy is developing a new suite of computational mechanics tools (Navy Enhanced Sierra Mechanics) for the prediction of ship response, damage, and shock environments transmitted to vital systems during threat weapon encounters. NESM includes fully coupled Euler-Lagrange solvers tailored to ship shock/damage predictions. NESM is optimized to support high-performance computing architectures, providing the physics-based ship response/threat weapon damage predictions needed to support the design and assessment of highly survivable ships. NESM is being employed to support current Navy ship design and acquisition programs while being further developed for future Navy fleet needs.

A combined-slip predictive control of vehicle stability with experimental verification

NASA Astrophysics Data System (ADS)

Jalali, Milad; Hashemi, Ehsan; Khajepour, Amir; Chen, Shih-ken; Litkouhi, Bakhtiar

2018-02-01

In this paper, a model predictive vehicle stability controller is designed based on a combined-slip LuGre tyre model. Variations in the lateral tyre forces due to changes in tyre slip ratios are considered in the prediction model of the controller. It is observed that the proposed combined-slip controller takes advantage of the more accurate tyre model and can adjust tyre slip ratios based on lateral forces of the front axle. This results in an interesting closed-loop response that challenges the notion of braking only the wheels on one side of the vehicle in differential braking. The performance of the proposed controller is evaluated in software simulations and is compared to a similar pure-slip controller. Furthermore, experimental tests are conducted on a rear-wheel drive electric Chevrolet Equinox equipped with differential brakes to evaluate the closed-loop response of the model predictive control controller.

Spatial segregation of adaptation and predictive sensitization in retinal ganglion cells

PubMed Central

Kastner, David B.; Baccus, Stephen A.

2014-01-01

Sensory systems change their sensitivity based upon recent stimuli to adjust their response range to the range of inputs, and to predict future sensory input. Here we report the presence of retinal ganglion cells that have antagonistic plasticity, showing central adaptation and peripheral sensitization. Ganglion cell responses were captured by a spatiotemporal model with independently adapting excitatory and inhibitory subunits, and sensitization requires GABAergic inhibition. Using a simple theory of signal detection we show that the sensitizing surround conforms to an optimal inference model that continually updates the prior signal probability. This indicates that small receptive field regions have dual functionality—to adapt to the local range of signals, but sensitize based upon the probability of the presence of that signal. Within this framework, we show that sensitization predicts the location of a nearby object, revealing prediction as a new functional role for adapting inhibition in the nervous system. PMID:23932000

Application of a Physiologically Based Pharmacokinetic Model to Predict OATP1B1-Related Variability in Pharmacodynamics of Rosuvastatin

PubMed Central

Rose, R H; Neuhoff, S; Abduljalil, K; Chetty, M; Rostami-Hodjegan, A; Jamei, M

2014-01-01

Typically, pharmacokinetic–pharmacodynamic (PK/PD) models use plasma concentration as the input that drives the PD model. However, interindividual variability in uptake transporter activity can lead to variable drug concentrations in plasma without discernible impact on the effect site organ concentration. A physiologically based PK/PD model for rosuvastatin was developed that linked the predicted liver concentration to the PD response model. The model was then applied to predict the effect of genotype-dependent uptake by the organic anion-transporting polypeptide 1B1 (OATP1B1) transporter on the pharmacological response. The area under the plasma concentration–time curve (AUC0–∞) was increased by 63 and 111% for the c.521TC and c.521CC genotypes vs. the c.521TT genotype, while the PD response remained relatively unchanged (3.1 and 5.8% reduction). Using local concentration at the effect site to drive the PD response enabled us to explain the observed disconnect between the effect of the OATP1B1 c521T>C polymorphism on rosuvastatin plasma concentration and the cholesterol synthesis response. PMID:25006781

Spectral response model for a multibin photon-counting spectral computed tomography detector and its applications.

PubMed

Liu, Xuejin; Persson, Mats; Bornefalk, Hans; Karlsson, Staffan; Xu, Cheng; Danielsson, Mats; Huber, Ben

2015-07-01

Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy.

Spectral response model for a multibin photon-counting spectral computed tomography detector and its applications

PubMed Central

Liu, Xuejin; Persson, Mats; Bornefalk, Hans; Karlsson, Staffan; Xu, Cheng; Danielsson, Mats; Huber, Ben

2015-01-01

Abstract. Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy. PMID:26839904

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

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

West, Paul R., E-mail: pwest@stemina.co; Weir, April M.; Smith, Alan M.

2010-08-15

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statisticalmore » analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.« less

Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Yamamoto, Rie; Takayama, Kozo

2013-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared based on a standard formulation. The tensile strength, disintegration time, and stability of these variables were measured as response variables. These responses were predicted quantitatively based on nonlinear TPS. A large amount of data on these tablets was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the tablets were classified into several distinct clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and tablet characteristics. The results of this study suggest that increasing the proportion of microcrystalline cellulose (MCC) improved the tensile strength and the stability of tensile strength of these theophylline tablets. In addition, the proportion of MCC has an optimum value for disintegration time and stability of disintegration. Increasing the proportion of magnesium stearate extended disintegration time. Increasing the compression force improved tensile strength, but degraded the stability of disintegration. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulations.

Effects of environmental change on plant species density: Comparing predictions with experiments

USGS Publications Warehouse

Gough, L.; Grace, J.B.

1999-01-01

Ideally, general ecological relationships may be used to predict responses of natural communities to environmental change, but few attempts have been made to determine the reliability of predictions based on descriptive data. Using a previously published structural equation model (SEM) of descriptive data from a coastal marsh landscape, we compared these predictions against observed changes in plant species density resulting from field experiments (manipulations of soil fertility, flooding, salinity, and mammalian herbivory) in two areas within the same marsh. In general, observed experimental responses were fairly consistent with predictions. The largest discrepancy occurred when sods were transplanted from high- to low-salinity sites and herbivores selectively consumed a particularly palatable plant species in the transplanted sods. Individual plot responses to some treatments were predicted more accurately than others. Individual fertilized plot responses were not consistent with predictions (P > 0.05), nor were fenced plots (herbivore exclosures; R2 = 0.15) compared to unfenced plots (R2 = 0.53). For the remaining treatments, predictions reasonably matched responses (R2 = 0.63). We constructed an SEM for the experimental data; it explained 60% of the variance in species density and showed that fencing and fertilization led to decreases in species density that were not predicted from treatment effects on community biomass or observed disturbance levels. These treatments may have affected the ratio of live to dead biomass, and competitive exclusion likely decreased species density in fenced and fertilized plots. We conclude that experimental validation is required to determine the predictive value of comparative relationships derived from descriptive data.

Recent ecological responses to climate change support predictions of high extinction risk

PubMed Central

Maclean, Ilya M. D.; Wilson, Robert J.

2011-01-01

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity. PMID:21746924

Testing the hypothesis of hierarchical predictability in ecological restoration and succession.

PubMed

Abella, Scott R; Schetter, Timothy A; Walters, Timothy L

2018-02-01

To advance predictive ecology, the hypothesis of hierarchical predictability proposes that community measures for which species are interchangeable (e.g., structure and species richness) are more predictable than measures for which species identity matters (e.g., community composition). Predictability is hypothesized to decrease for response measures in order of the following categories: structure, species richness, function, and species composition. We tested this hypothesis using a 14-year, oak savanna-prairie restoration experiment that removed non-native pine plantations at 24 sites in northwestern Ohio, USA. Based on 24 response measures, the data showed minimal support for the hypothesis, because response measures varied in predictability within categories. Half of response measures had over half their variability modeled using fixed (restoration treatment and year) and random plot effects, and these "predictable" measures occurred in all four categories. Pine basal area, environment (e.g., soil texture), and antecedent vegetation accounted for over half the variation in change within the first three post-restoration years for 77% of response measures. Change between the 3rd and 14th years was less predictable, but most restoration measures increased favorably via sites achieving them in unique ways. We propose that variation will not conform with the hypothesis of hierarchical predictability in ecosystems with vegetation dynamics driven by stochastic processes such as seed dispersal, or where vegetation structure and species richness are influenced by species composition. The ability to predict a community measure may be more driven by the number of combinations of casual factors affecting a measure than by the number of values it can have.

Recent ecological responses to climate change support predictions of high extinction risk.

PubMed

Maclean, Ilya M D; Wilson, Robert J

2011-07-26

Predicted effects of climate change include high extinction risk for many species, but confidence in these predictions is undermined by a perceived lack of empirical support. Many studies have now documented ecological responses to recent climate change, providing the opportunity to test whether the magnitude and nature of recent responses match predictions. Here, we perform a global and multitaxon metaanalysis to show that empirical evidence for the realized effects of climate change supports predictions of future extinction risk. We use International Union for Conservation of Nature (IUCN) Red List criteria as a common scale to estimate extinction risks from a wide range of climate impacts, ecological responses, and methods of analysis, and we compare predictions with observations. Mean extinction probability across studies making predictions of the future effects of climate change was 7% by 2100 compared with 15% based on observed responses. After taking account of possible bias in the type of climate change impact analyzed and the parts of the world and taxa studied, there was less discrepancy between the two approaches: predictions suggested a mean extinction probability of 10% across taxa and regions, whereas empirical evidence gave a mean probability of 14%. As well as mean overall extinction probability, observations also supported predictions in terms of variability in extinction risk and the relative risk associated with broad taxonomic groups and geographic regions. These results suggest that predictions are robust to methodological assumptions and provide strong empirical support for the assertion that anthropogenic climate change is now a major threat to global biodiversity.

A simple prediction tool for inhaled corticosteroid response in asthmatic children.

PubMed

Wu, Yi-Fan; Su, Ming-Wei; Chiang, Bor-Luen; Yang, Yao-Hsu; Tsai, Ching-Hui; Lee, Yungling L

2017-12-07

Inhaled corticosteroids are recommended as the first-line controller medication for childhood asthma owing to their multiple clinical benefits. However, heterogeneity in the response towards these drugs remains a significant clinical problem. Children aged 5 to 18 years with mild to moderate persistent asthma were recruited into the Taiwanese Consortium of Childhood Asthma Study. Their responses to inhaled corticosteroids were assessed based on their improvements in the asthma control test and peak expiratory flow. The predictors of responsiveness were demographic and clinical features that were available in primary care settings. We have developed a prediction model using logistic regression and have simplified it to formulate a practical tool. We assessed its predictive performance using the area under the receiver operating characteristic curve. Of the 73 asthmatic children with baseline and follow-up outcome measurements for inhaled corticosteroids treatment, 24 (33%) were defined as non-responders. The tool we have developed consisted of three predictors yielding a total score between 0 and 5, which are comprised of the following parameters: the age at physician-diagnosis of asthma, sex, and exhaled nitric oxide. Sensitivity and specificity of the tool for prediction of inhaled corticosteroids non-responsiveness, for a score of 3, were 0.75 and 0.69, respectively. The areas under the receiver operating characteristic curve for the prediction tool was 0.763. Our prediction tool represents a simple and low-cost method for predicting the response of inhaled corticosteroids treatment in asthmatic children.

DPYD, TYMS, TYMP, TK1, and TK2 Genetic Expressions as Response Markers in Locally Advanced Rectal Cancer Patients Treated with Fluoropyrimidine-Based Chemoradiotherapy

PubMed Central

Wu, Chan-Han; Huang, Chun-Ming; Chung, Fu-Yen; Huang, Ching-Wen; Tsai, Hsiang-Lin; Chen, Chin-Fan; Wang, Jaw-Yuan

2013-01-01

This study is to investigate multiple chemotherapeutic agent- and radiation-related genetic biomarkers in locally advanced rectal cancer (LARC) patients following fluoropyrimidine-based concurrent chemoradiotherapy (CCRT) for response prediction. We initially selected 6 fluoropyrimidine metabolism-related genes (DPYD, ORPT, TYMS, TYMP, TK1, and TK2) and 3 radiotherapy response-related genes (GLUT1, HIF-1 α, and HIF-2 α) as targets for gene expression identification in 60 LARC cancer specimens. Subsequently, a high-sensitivity weighted enzymatic chip array was designed and constructed to predict responses following CCRT. After CCRT, 39 of 60 (65%) LARC patients were classified as responders (pathological tumor regression grade 2 ~ 4). Using a panel of multiple genetic biomarkers (chip), including DPYD, TYMS, TYMP, TK1, and TK2, at a cutoff value for 3 positive genes, a sensitivity of 89.7% and a specificity of 81% were obtained (AUC: 0.915; 95% CI: 0.840–0.991). Negative chip results were significantly correlated to poor CCRT responses (TRG 0-1) (P = 0.014, hazard ratio: 22.704, 95% CI: 3.055–235.448 in multivariate analysis). Disease-free survival analysis showed significantly better survival rate in patients with positive chip results (P = 0.0001). We suggest that a chip including DPYD, TYMS, TYMP, TK1, and TK2 genes is a potential tool to predict response in LARC following fluoropyrimidine-based CCRT. PMID:24455740

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

PubMed Central

Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

2011-01-01

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning.

PubMed

Cha, Kenny H; Hadjiiski, Lubomir; Chan, Heang-Ping; Weizer, Alon Z; Alva, Ajjai; Cohan, Richard H; Caoili, Elaine M; Paramagul, Chintana; Samala, Ravi K

2017-08-18

Cross-sectional X-ray imaging has become the standard for staging most solid organ malignancies. However, for some malignancies such as urinary bladder cancer, the ability to accurately assess local extent of the disease and understand response to systemic chemotherapy is limited with current imaging approaches. In this study, we explored the feasibility that radiomics-based predictive models using pre- and post-treatment computed tomography (CT) images might be able to distinguish between bladder cancers with and without complete chemotherapy responses. We assessed three unique radiomics-based predictive models, each of which employed different fundamental design principles ranging from a pattern recognition method via deep-learning convolution neural network (DL-CNN), to a more deterministic radiomics feature-based approach and then a bridging method between the two, utilizing a system which extracts radiomics features from the image patterns. Our study indicates that the computerized assessment using radiomics information from the pre- and post-treatment CT of bladder cancer patients has the potential to assist in assessment of treatment response.

Task relevance modulates the behavioural and neural effects of sensory predictions

PubMed Central

Friston, Karl J.; Nobre, Anna C.

2017-01-01

The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants’ brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling. PMID:29206225

Inhibitors to Responsibility-Based Professional Development with In-Service Teachers

ERIC Educational Resources Information Center

Hemphill, Michael A.

2015-01-01

Researchers of continuing professional development (CPD) in physical education have called for new models that move beyond the traditional CPD model. The outcomes of CPD protocols are hard to predict even when they align with the best practices. Responsibility-based CPD has become the focus of recent attention to assist physical educators in…

Ground motions recorded in Rome during the April 2009 L’Aquila seismic sequence: site response and comparison with ground‐motion predictions based on a global dataset

USGS Publications Warehouse

Caserta, Arrigo; Boore, David; Rovelli, Antonio; Govoni, Aladino; Marra, Fabrizio; Monica, Gieseppe Della; Boschi, Enzo

2013-01-01

The mainshock and moderate‐magnitude aftershocks of the 6 April 2009 M 6.3 L’Aquila seismic sequence, about 90 km northeast of Rome, provided the first earthquake ground‐motion recordings in the urban area of Rome. Before those recordings were obtained, the assessments of the seismic hazard in Rome were based on intensity observations and theoretical considerations. The L’Aquila recordings offer an unprecedented opportunity to calibrate the city response to central Apennine earthquakes—earthquakes that have been responsible for the largest damage to Rome in historical times. Using the data recorded in Rome in April 2009, we show that (1) published theoretical predictions of a 1 s resonance in the Tiber valley are confirmed by observations showing a significant amplitude increase in response spectra at that period, (2) the empirical soil‐transfer functions inferred from spectral ratios are satisfactorily fit through 1D models using the available geological, geophysical, and laboratory data, but local variability can be large for individual events, (3) response spectra for the motions recorded in Rome from the L’Aquila earthquakes are significantly amplified in the radial component at periods near 1 s, even at a firm site on volcanic rocks, and (4) short‐period response spectra are smaller than expected when compared to ground‐motion predictions from equations based on a global dataset, whereas the observed response spectra are higher than expected for periods near 1 s.

Adaptive responses and disruptive effects: how major wildfire influences kinship-based social interactions in a forest marsupial.

PubMed

Banks, Sam C; Blyton, Michaela D J; Blair, David; McBurney, Lachlan; Lindenmayer, David B

2012-02-01

Environmental disturbance is predicted to play a key role in the evolution of animal social behaviour. This is because disturbance affects key factors underlying social systems, such as demography, resource availability and genetic structure. However, because natural disturbances are unpredictable there is little information on their effects on social behaviour in wild populations. Here, we investigated how a major wildfire affected cooperation (sharing of hollow trees) by a hollow-dependent marsupial. We based two alternative social predictions on the impacts of fire on population density, genetic structure and resources. We predicted an adaptive social response from previous work showing that kin selection in den-sharing develops as competition for den resources increases. Thus, kin selection should occur in burnt areas because the fire caused loss of the majority of hollow-bearing trees, but no detectable mortality. Alternatively, fire may have a disruptive social effect, whereby postfire home range-shifts 'neutralize' fine-scale genetic structure, thereby removing opportunities for kin selection between neighbours. Both predictions occurred: the disruptive social effect in burnt habitat and the adaptive social response in adjacent unburnt habitat. The latter followed a massive demographic influx to unburnt 'refuge' habitat that increased competition for dens, leading to a density-related kin selection response. Our results show remarkable short-term plasticity of animal social behaviour and demonstrate how the social effects of disturbance extend into undisturbed habitat owing to landscape-scale demographic shifts. We predicted long-term changes in kinship-based cooperative behaviour resulting from the genetic and resource impacts of forecast changes to fire regimes in these forests. © 2011 Blackwell Publishing Ltd.

Screening Magnetic Resonance Imaging-Based Prediction Model for Assessing Immediate Therapeutic Response to Magnetic Resonance Imaging-Guided High-Intensity Focused Ultrasound Ablation of Uterine Fibroids.

PubMed

Kim, Young-sun; Lim, Hyo Keun; Park, Min Jung; Rhim, Hyunchul; Jung, Sin-Ho; Sohn, Insuk; Kim, Tae-Joong; Keserci, Bilgin

2016-01-01

The aim of this study was to fit and validate screening magnetic resonance imaging (MRI)-based prediction models for assessing immediate therapeutic responses of uterine fibroids to MRI-guided high-intensity focused ultrasound (MR-HIFU) ablation. Informed consent from all subjects was obtained for our institutional review board-approved study. A total of 240 symptomatic uterine fibroids (mean diameter, 6.9 cm) in 152 women (mean age, 43.3 years) treated with MR-HIFU ablation were retrospectively analyzed (160 fibroids for training, 80 fibroids for validation). Screening MRI parameters (subcutaneous fat thickness [mm], x1; relative peak enhancement [%] in semiquantitative perfusion MRI, x2; T2 signal intensity ratio of fibroid to skeletal muscle, x3) were used to fit prediction models with regard to ablation efficiency (nonperfused volume/treatment cell volume, y1) and ablation quality (grade 1-5, poor to excellent, y2), respectively, using the generalized estimating equation method. Cutoff values for achievement of treatment intent (efficiency >1.0; quality grade 4/5) were determined based on receiver operating characteristic curve analysis. Prediction performances were validated by calculating positive and negative predictive values. Generalized estimating equation analyses yielded models of y1 = 2.2637 - 0.0415x1 - 0.0011x2 - 0.0772x3 and y2 = 6.8148 - 0.1070x1 - 0.0050x2 - 0.2163x3. Cutoff values were 1.312 for ablation efficiency (area under the curve, 0.7236; sensitivity, 0.6882; specificity, 0.6866) and 4.019 for ablation quality (0.8794; 0.7156; 0.9020). Positive and negative predictive values were 0.917 and 0.500 for ablation efficiency and 0.978 and 0.600 for ablation quality, respectively. Screening MRI-based prediction models for assessing immediate therapeutic responses of uterine fibroids to MR-HIFU ablation were fitted and validated, which may reduce the risk of unsuccessful treatment.

Guidelines and Parameter Selection for the Simulation of Progressive Delamination

NASA Technical Reports Server (NTRS)

Song, Kyongchan; Davila, Carlos G.; Rose, Cheryl A.

2008-01-01

Turon s methodology for determining optimal analysis parameters for the simulation of progressive delamination is reviewed. Recommended procedures for determining analysis parameters for efficient delamination growth predictions using the Abaqus/Standard cohesive element and relatively coarse meshes are provided for single and mixed-mode loading. The Abaqus cohesive element, COH3D8, and a user-defined cohesive element are used to develop finite element models of the double cantilever beam specimen, the end-notched flexure specimen, and the mixed-mode bending specimen to simulate progressive delamination growth in Mode I, Mode II, and mixed-mode fracture, respectively. The predicted responses are compared with their analytical solutions. The results show that for single-mode fracture, the predicted responses obtained with the Abaqus cohesive element correlate well with the analytical solutions. For mixed-mode fracture, it was found that the response predicted using COH3D8 elements depends on the damage evolution criterion that is used. The energy-based criterion overpredicts the peak loads and load-deflection response. The results predicted using a tabulated form of the BK criterion correlate well with the analytical solution and with the results predicted with the user-written element.

Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury

PubMed Central

Field-Fote, Edelle C.; Yang, Jaynie F.; Basso, D. Michele; Gorassini, Monica A.

2017-01-01

Abstract Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans. PMID:27673569

On-Line, Self-Learning, Predictive Tool for Determining Payload Thermal Response

NASA Technical Reports Server (NTRS)

Jen, Chian-Li; Tilwick, Leon

2000-01-01

This paper will present the results of a joint ManTech / Goddard R&D effort, currently under way, to develop and test a computer based, on-line, predictive simulation model for use by facility operators to predict the thermal response of a payload during thermal vacuum testing. Thermal response was identified as an area that could benefit from the algorithms developed by Dr. Jeri for complex computer simulations. Most thermal vacuum test setups are unique since no two payloads have the same thermal properties. This requires that the operators depend on their past experiences to conduct the test which requires time for them to learn how the payload responds while at the same time limiting any risk of exceeding hot or cold temperature limits. The predictive tool being developed is intended to be used with the new Thermal Vacuum Data System (TVDS) developed at Goddard for the Thermal Vacuum Test Operations group. This model can learn the thermal response of the payload by reading a few data points from the TVDS, accepting the payload's current temperature as the initial condition for prediction. The model can then be used as a predictive tool to estimate the future payload temperatures according to a predetermined shroud temperature profile. If the error of prediction is too big, the model can be asked to re-learn the new situation on-line in real-time and give a new prediction. Based on some preliminary tests, we feel this predictive model can forecast the payload temperature of the entire test cycle within 5 degrees Celsius after it has learned 3 times during the beginning of the test. The tool will allow the operator to play "what-if' experiments to decide what is his best shroud temperature set-point control strategy. This tool will save money by minimizing guess work and optimizing transitions as well as making the testing process safer and easier to conduct.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma.

PubMed

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-03-30

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration-time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

PubMed Central

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-01-01

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters (ABCB1 and ABCG2), UGT1A, and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate (P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC (P < 0.0001), and correctly predicted objective response rate (P = 0.0044) as well as adverse events (P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment (P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC. PMID:29682213

Serial MR diffusion to predict treatment response in high-grade pediatric brain tumors: a comparison of regional and voxel-based diffusion change metrics

PubMed Central

Rodriguez Gutierrez, Daniel; Manita, Muftah; Jaspan, Tim; Dineen, Robert A.; Grundy, Richard G.; Auer, Dorothee P.

2013-01-01

Background Assessment of treatment response by measuring tumor size is known to be a late and potentially confounded response index. Serial diffusion MRI has shown potential for allowing earlier and possibly more reliable response assessment in adult patients, with limited experience in clinical settings and in pediatric brain cancer. We present a retrospective study of clinical MRI data in children with high-grade brain tumors to assess and compare the values of several diffusion change metrics to predict treatment response. Methods Eighteen patients (age range, 1.9–20.6 years) with high-grade brain tumors and serial diffusion MRI (pre- and posttreatment interval range, 1–16 weeks posttreatment) were identified after obtaining parental consent. The following diffusion change metrics were compared with the clinical response status assessed at 6 months: (1) regional change in absolute and normalized apparent diffusivity coefficient (ADC), (2) voxel-based fractional volume of increased (fiADC) and decreased ADC (fdADC), and (3) a new metric based on the slope of the first principal component of functional diffusion maps (fDM). Results Responders (n = 12) differed significantly from nonresponders (n = 6) in all 3 diffusional change metrics demonstrating higher regional ADC increase, larger fiADC, and steeper slopes (P < .05). The slope method allowed the best response prediction (P < .01, η2 = 0.78) with a classification accuracy of 83% for a slope of 58° using receiver operating characteristic (ROC) analysis. Conclusions We demonstrate that diffusion change metrics are suitable response predictors for high-grade pediatric tumors, even in the presence of variable clinical diffusion imaging protocols. PMID:23585630

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

PubMed

Wu, Jia; Gong, Guanghua; Cui, Yi; Li, Ruijiang

2016-11-01

To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115. © 2016 International Society for Magnetic Resonance in Medicine.

Tumor-infiltrating lymphocytes predict response to chemotherapy in patients with advance non-small cell lung cancer.

PubMed

Liu, Hui; Zhang, Tiantuo; Ye, Jin; Li, Hongtao; Huang, Jing; Li, Xiaodong; Wu, Benquan; Huang, Xubing; Hou, Jinghui

2012-10-01

Accumulating preclinical evidence suggests that anticancer immune responses contribute to the success of chemotherapy. The predictive significance of tumor-infiltrating lymphocytes (TILs) for response to neoadjuvant chemotherapy in non-small cell lung cancer (NSCLC) remains unknown. The aim of this study was to investigate the prognostic and predictive value of TIL subtypes in patients with advanced NSCLC treated with platinum-based chemotherapy. In total, 159 patients with stage III and IV NSCLC were retrospectively enrolled. The prevalence of CD3(+), CD4(+), CD8(+) and Foxp3(+) TILs was assessed by immunohistochemistry in tumor tissue obtained before chemotherapy. The density of TILs subgroups was treated as dichotomous variables using the median values as cutoff. Survival curves were estimated by the Kaplan-Meier method, and differences in overall survival between groups were determined using the Log-rank test. Prognostic effects of TIL subsets density were evaluated by Cox regression analysis. The presence of CD3(+), CD4(+), CD8(+), and FOXP3(+) TILs was not correlated with any clinicopathological features. Neither the prevalence of TILs nor combined analysis displayed obvious prognostic performances for overall survival in Cox regression model. Instead, higher FOXP3(+)/CD8(+) ratio in tumor sites was an independent factor for poor response to platinum-based chemotherapy in overall cohort. These findings suggest that immunological CD8(+) and FOXP3(+)Tregs cell infiltrate within tumor environment is predictive of response to platinum-based neoadjuvant chemotherapy in advanced NSCLC patients. The understanding of the clinical relevance of the microenvironmental immunological milieu might provide an important clue for the design of novel strategies in cancer immunotherapy.

On Why Targets Evoke P3 Components in Prediction Tasks: Drawing an Analogy between Prediction and Matching Tasks

PubMed Central

Verleger, Rolf; Cäsar, Stephanie; Siller, Bastian; Śmigasiewicz, Kamila

2017-01-01

P3 is the most conspicuous component in recordings of stimulus-evoked EEG potentials from the human scalp, occurring whenever some task has to be performed with the stimuli. The process underlying P3 has been assumed to be the updating of expectancies. More recently, P3 has been related to decision processing and to activation of established stimulus-response associations (S/R-link hypothesis). However, so far this latter approach has not provided a conception about how to explain the occurrence of P3 with predicted stimuli, although P3 was originally discovered in a prediction task. The present article proposes such a conception. We assume that the internal responses right or wrong both become associatively linked to each predicted target and that one of these two response alternatives gets activated as a function of match or mismatch of the target to the preceding prediction. This seems similar to comparison tasks where responses depend on the matching of the target stimulus with a preceding first stimulus (S1). Based on this idea, this study compared the effects of frequencies of first events (predictions or S1) on target-evoked P3s in prediction and comparison tasks. Indeed, frequencies not only of targets but also of first events had similar effects across tasks on target-evoked P3s. These results support the notion that P3 evoked by predicted stimuli reflects activation of appropriate internal “match” or “mismatch” responses, which is compatible with S/R-link hypothesis. PMID:29066965

EPA perspective - exposure and effects prediction and monitoring

EPA Science Inventory

Risk-based decisions for environmental chemicals often rely on estimates of human exposure and biological response. Biomarkers have proven a useful empirical tool for evaluating exposure and hazard predictions. In the United States, the Centers for Disease Control and Preventio...

Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

PubMed

Yoo, Byong Chul; Yeo, Seung-Gu

2017-03-01

Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

Predicting consumer liking and preference based on emotional responses and sensory perception: A study with basic taste solutions.

PubMed

Samant, Shilpa S; Chapko, Matthew J; Seo, Han-Seok

2017-10-01

Traditional methods of sensory testing focus on capturing information about multisensory perceptions, but do not necessarily measure emotions elicited by these food and beverages. The objective of this study was to develop an optimum model of predicting overall liking (rating) and preference (choice) based on taste intensity and evoked emotions. One hundred and two participants (51 females) were asked to taste water, sucrose, citric acid, salt, and caffeine solutions. Their emotional responses toward each sample were measured by a combination of a self-reported emotion questionnaire (EsSense25), facial expressions, and autonomic nervous system (ANS) responses. In addition, their perceived intensity and overall liking were measured. After a break, participants re-tasted the samples and ranked them according to their preference. The results showed that emotional responses measured using self-reported emotion questionnaire and facial expression analysis along with perceived taste intensity performed best to predict overall liking as well as preference, while ANS measures showed limited contribution. Contrary to some previous research, this study demonstrated that not only negative emotions, but also positive ones could help predict consumer liking and preference. In addition, since there were subtle differences in the prediction models of overall liking and preference, both aspects should be taken into account to understand consumer behavior. In conclusion, combination of evoked emotions along with sensory perception could help better understand consumer acceptance as well as preference toward basic taste solutions. Published by Elsevier Ltd.

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks

PubMed Central

Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

2015-01-01

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models. PMID:26355298

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Individualization of controlled ovarian stimulation in vitro fertilization using ovarian reserve markers.

PubMed

Grisendi, Valentina; La Marca, Antonio

2017-06-01

In assisted reproduction technologies (ART) the controlled ovarian stimulation (COS) therapy is the starting point from which a good oocytes retrieval depends. Treatment individualization is based on ovarian response prediction, which largely depends on a woman's ovarian reserve. Anti-Müllerian hormone (AMH) and antral follicle count (AFC) are considered the most accurate and reliable markers of ovarian reserve. A literature search was carried out for studies that addressed the ability of AMH and AFC to predict poor and/or excessive ovarian response in IVF cycles. According to the predicted response to ovarian stimulation (poor- normal- or high-response) is today possible not only to personalize pre-treatment counseling with the couple, but also to individualize the ovarian stimulation protocol, choosing among GnRH-agonists or antagonists for endogenous follicle-stimulating hormone (FSH) suppression and formulating the FSH starting dose most adequate for the single patients. In this review we discuss how to choose the best COS therapy for the single patient, on the basis of the markers-guided ovarian response prediction.

An empirical propellant response function for combustion stability predictions

NASA Technical Reports Server (NTRS)

Hessler, R. O.

1980-01-01

An empirical response function model was developed for ammonium perchlorate propellants to supplant T-burner testing at the preliminary design stage. The model was developed by fitting a limited T-burner data base, in terms of oxidizer size and concentration, to an analytical two parameter response function expression. Multiple peaks are predicted, but the primary effect is of a single peak for most formulations, with notable bulges for the various AP size fractions. The model was extended to velocity coupling with the assumption that dynamic response was controlled primarily by the solid phase described by the two parameter model. The magnitude of velocity coupling was then scaled using an erosive burning law. Routine use of the model for stability predictions on a number of propulsion units indicates that the model tends to overpredict propellant response. It is concluded that the model represents a generally conservative prediction tool, suited especially for the preliminary design stage when T-burner data may not be readily available. The model work included development of a rigorous summation technique for pseudopropellant properties and of a concept for modeling ordered packing of particulates.

Improved partition equilibrium model for predicting analyte response in electrospray ionization mass spectrometry.

PubMed

Du, Lihong; White, Robert L

2009-02-01

A previously proposed partition equilibrium model for quantitative prediction of analyte response in electrospray ionization mass spectrometry is modified to yield an improved linear relationship. Analyte mass spectrometer response is modeled by a competition mechanism between analyte and background electrolytes that is based on partition equilibrium considerations. The correlation between analyte response and solution composition is described by the linear model over a wide concentration range and the improved model is shown to be valid for a wide range of experimental conditions. The behavior of an analyte in a salt solution, which could not be explained by the original model, is correctly predicted. The ion suppression effects of 16:0 lysophosphatidylcholine (LPC) on analyte signals are attributed to a combination of competition for excess charge and reduction of total charge due to surface tension effects. In contrast to the complicated mathematical forms that comprise the original model, the simplified model described here can more easily be employed to predict analyte mass spectrometer responses for solutions containing multiple components. Copyright (c) 2008 John Wiley & Sons, Ltd.

An integrated physiology model to study regional lung damage effects and the physiologic response

PubMed Central

2014-01-01

Background This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. Methods A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. Results The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. Conclusions We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations. PMID:25044032

Stem mortality in surface fires: Part II, experimental methods for characterizing the thermal response of tree stems to heating by fires

Treesearch

D. M. Jimenez; B. W. Butler; J. Reardon

2003-01-01

Current methods for predicting fire-induced plant mortality in shrubs and trees are largely empirical. These methods are not readily linked to duff burning, soil heating, and surface fire behavior models. In response to the need for a physics-based model of this process, a detailed model for predicting the temperature distribution through a tree stem as a function of...

Range position and climate sensitivity: The structure of among-population demographic responses to climatic variation

USGS Publications Warehouse

Amburgey, Staci M.; Miller, David A. W.; Grant, Evan H. Campbell; Rittenhouse, Tracy A. G.; Benard, Michael F.; Richardson, Jonathan L.; Urban, Mark C.; Hughson, Ward; Brand, Adrianne B,; Davis, Christopher J.; Hardin, Carmen R.; Paton, Peter W. C.; Raithel, Christopher J.; Relyea, Rick A.; Scott, A. Floyd; Skelly, David K.; Skidds, Dennis E.; Smith, Charles K.; Werner, Earl E.

2018-01-01

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.

The dynamic-response characteristics of a 35 degree swept-wing airplane as determined from flight measurements

NASA Technical Reports Server (NTRS)

Triplett, William C; Brown, Stuart C; Smith, G Allan

1955-01-01

The longitudinal and lateral-directional dynamic-response characteristics of a 35 degree swept-wing fighter-type airplane determined from flight measurements are presented and compared with predictions based on theoretical studies and wind-tunnel data. Flights were made at an altitude of 35,000 feet covering the Mach number range of 0.50 to 1.04. A limited amount of lateral-directional data were also obtained at 10,000 feet. The flight consisted essentially of recording transient responses to pilot-applied pulsed motions of each of the three primary control surfaces. These transient data were converted into frequency-response form by means of the Fourier transformation and compared with predicted responses calculated from the basic equations. Experimentally determined transfer functions were used for the evaluation of the stability derivatives that have the greatest effect on the dynamic response of the airplane. The values of these derivatives, in most cases, agreed favorably with predictions over the Mach number range of the test.

Testing of the European Union exposure-response relationships and annoyance equivalents model for annoyance due to transportation noises: The need of revised exposure-response relationships and annoyance equivalents model.

PubMed

Gille, Laure-Anne; Marquis-Favre, Catherine; Morel, Julien

2016-09-01

An in situ survey was performed in 8 French cities in 2012 to study the annoyance due to combined transportation noises. As the European Commission recommends to use the exposure-response relationships suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001] to predict annoyance due to single transportation noise, these exposure-response relationships were tested using the annoyance due to each transportation noise measured during the French survey. These relationships only enabled a good prediction in terms of the percentages of people highly annoyed by road traffic noise. For the percentages of people annoyed and a little annoyed by road traffic noise, the quality of prediction is weak. For aircraft and railway noises, prediction of annoyance is not satisfactory either. As a consequence, the annoyance equivalents model of Miedema [The Journal of the Acoustical Society of America, 2004], based on these exposure-response relationships did not enable a good prediction of annoyance due to combined transportation noises. Local exposure-response relationships were derived, following the whole computation suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001]. They led to a better calculation of annoyance due to each transportation noise in the French cities. A new version of the annoyance equivalents model was proposed using these new exposure-response relationships. This model enabled a better prediction of the total annoyance due to the combined transportation noises. These results encourage therefore to improve the annoyance prediction for noise in isolation with local or revised exposure-response relationships, which will also contribute to improve annoyance modeling for combined noises. With this aim in mind, a methodology is proposed to consider noise sensitivity in exposure-response relationships and in the annoyance equivalents model. The results showed that taking into account such variable did not enable to enhance both exposure-response relationships and the annoyance equivalents model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Passenger ride quality determined from commercial airline flights

NASA Technical Reports Server (NTRS)

Richards, L. G.; Kuhlthau, A. R.; Jacobson, I. D.

1975-01-01

The University of Virginia ride-quality research program is reviewed. Data from two flight programs, involving seven types of aircraft, are considered in detail. An apparatus for measuring physical variations in the flight environment and recording the subjective reactions of test subjects is described. Models are presented for predicting the comfort response of test subjects from the physical data, and predicting the overall comfort reaction of test subjects from their moment by moment responses. The correspondence of mean passenger comfort judgments and test subject response is shown. Finally, the models of comfort response based on data from the 5-point and 7-point comfort scales are shown to correspond.

XplOit: An Ontology-Based Data Integration Platform Supporting the Development of Predictive Models for Personalized Medicine.

PubMed

Weiler, Gabriele; Schwarz, Ulf; Rauch, Jochen; Rohm, Kerstin; Lehr, Thorsten; Theobald, Stefan; Kiefer, Stephan; Götz, Katharina; Och, Katharina; Pfeifer, Nico; Handl, Lisa; Smola, Sigrun; Ihle, Matthias; Turki, Amin T; Beelen, Dietrich W; Rissland, Jürgen; Bittenbring, Jörg; Graf, Norbert

2018-01-01

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.

Neural Activity Reveals Preferences Without Choices

PubMed Central

Smith, Alec; Bernheim, B. Douglas; Camerer, Colin

2014-01-01

We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “non-choice” neural responses and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. PMID:25729468

Prediction of interior noise due to random acoustic or turbulent boundary layer excitation using statistical energy analysis

NASA Technical Reports Server (NTRS)

Grosveld, Ferdinand W.

1990-01-01

The feasibility of predicting interior noise due to random acoustic or turbulent boundary layer excitation was investigated in experiments in which a statistical energy analysis model (VAPEPS) was used to analyze measurements of the acceleration response and sound transmission of flat aluminum, lucite, and graphite/epoxy plates exposed to random acoustic or turbulent boundary layer excitation. The noise reduction of the plate, when backed by a shallow cavity and excited by a turbulent boundary layer, was predicted using a simplified theory based on the assumption of adiabatic compression of the fluid in the cavity. The predicted plate acceleration response was used as input in the noise reduction prediction. Reasonable agreement was found between the predictions and the measured noise reduction in the frequency range 315-1000 Hz.

Pseudomonas aeruginosa dose response and bathing water infection.

PubMed

Roser, D J; van den Akker, B; Boase, S; Haas, C N; Ashbolt, N J; Rice, S A

2014-03-01

Pseudomonas aeruginosa is the opportunistic pathogen mostly implicated in folliculitis and acute otitis externa in pools and hot tubs. Nevertheless, infection risks remain poorly quantified. This paper reviews disease aetiologies and bacterial skin colonization science to advance dose-response theory development. Three model forms are identified for predicting disease likelihood from pathogen density. Two are based on Furumoto & Mickey's exponential 'single-hit' model and predict infection likelihood and severity (lesions/m2), respectively. 'Third-generation', mechanistic, dose-response algorithm development is additionally scoped. The proposed formulation integrates dispersion, epidermal interaction, and follicle invasion. The review also details uncertainties needing consideration which pertain to water quality, outbreaks, exposure time, infection sites, biofilms, cerumen, environmental factors (e.g. skin saturation, hydrodynamics), and whether P. aeruginosa is endogenous or exogenous. The review's findings are used to propose a conceptual infection model and identify research priorities including pool dose-response modelling, epidermis ecology and infection likelihood-based hygiene management.

Predictive potential of IL-28B genetic testing for interferon based hepatitis C virus therapy in Pakistan: Current scenario and future perspective.

PubMed

Afzal, Muhammad Sohail

2016-09-18

In Pakistan which ranked second in terms of hepatitis C virus (HCV) infection, it is highly needed to have an established diagnostic test for antiviral therapy response prediction. Interleukin 28B (IL-28B) genetic testing is widely used throughout the world for interferon based therapy prediction for HCV patients and is quite helpful not only for health care workers but also for the patients. There is a strong relationship between single nucleotide polymorphisms at or near the IL-28B gene and the sustained virological response with pegylated interferon plus ribavirin treatment for chronic hepatitis C. Pakistan is a resource limited country, with very low per capita income and there is no proper social security (health insurance) system. The allocated health budget by the government is very low and is used on other health emergencies like polio virus and dengue virus infection. Therefore it is proposed that there should be a well established diagnostic test on the basis of IL-28B which can predict the antiviral therapy response to strengthen health care set-up of Pakistan. This test once established will help in better management of HCV infected patients.

Validation of catchment models for predicting land-use and climate change impacts. 2. Case study for a Mediterranean catchment

NASA Astrophysics Data System (ADS)

Parkin, G.; O'Donnell, G.; Ewen, J.; Bathurst, J. C.; O'Connell, P. E.; Lavabre, J.

1996-02-01

Validation methods commonly used to test catchment models are not capable of demonstrating a model's fitness for making predictions for catchments where the catchment response is not known (including hypothetical catchments, and future conditions of existing catchments which are subject to land-use or climate change). This paper describes the first use of a new method of validation (Ewen and Parkin, 1996. J. Hydrol., 175: 583-594) designed to address these types of application; the method involves making 'blind' predictions of selected hydrological responses which are considered important for a particular application. SHETRAN (a physically based, distributed catchment modelling system) is tested on a small Mediterranean catchment. The test involves quantification of the uncertainty in four predicted features of the catchment response (continuous hydrograph, peak discharge rates, monthly runoff, and total runoff), and comparison of observations with the predicted ranges for these features. The results of this test are considered encouraging.

Quantifying patterns of change in marine ecosystem response to multiple pressures.

PubMed

Large, Scott I; Fay, Gavin; Friedland, Kevin D; Link, Jason S

2015-01-01

The ability to understand and ultimately predict ecosystem response to multiple pressures is paramount to successfully implement ecosystem-based management. Thresholds shifts and nonlinear patterns in ecosystem responses can be used to determine reference points that identify levels of a pressure that may drastically alter ecosystem status, which can inform management action. However, quantifying ecosystem reference points has proven elusive due in large part to the multi-dimensional nature of both ecosystem pressures and ecosystem responses. We used ecological indicators, synthetic measures of ecosystem status and functioning, to enumerate important ecosystem attributes and to reduce the complexity of the Northeast Shelf Large Marine Ecosystem (NES LME). Random forests were used to quantify the importance of four environmental and four anthropogenic pressure variables to the value of ecological indicators, and to quantify shifts in aggregate ecological indicator response along pressure gradients. Anthropogenic pressure variables were critical defining features and were able to predict an average of 8-13% (up to 25-66% for individual ecological indicators) of the variation in ecological indicator values, whereas environmental pressures were able to predict an average of 1-5 % (up to 9-26% for individual ecological indicators) of ecological indicator variation. Each pressure variable predicted a different suite of ecological indicator's variation and the shapes of ecological indicator responses along pressure gradients were generally nonlinear. Threshold shifts in ecosystem response to exploitation, the most important pressure variable, occurred when commercial landings were 20 and 60% of total surveyed biomass. Although present, threshold shifts in ecosystem response to environmental pressures were much less important, which suggests that anthropogenic pressures have significantly altered the ecosystem structure and functioning of the NES LME. Gradient response curves provide ecologically informed transformations of pressure variables to explain patterns of ecosystem structure and functioning. By concurrently identifying thresholds for a suite of ecological indicator responses to multiple pressures, we demonstrate that ecosystem reference points can be evaluated and used to support ecosystem-based management.

Mechanical effort predicts the selection of ankle over hip strategies in nonstepping postural responses

PubMed Central

Jonkers, Ilse; De Schutter, Joris; De Groote, Friedl

2016-01-01

Experimental studies have shown that a continuum of ankle and hip strategies is used to restore posture following an external perturbation. Postural responses can be modeled by feedback control with feedback gains that optimize a specific objective. On the one hand, feedback gains that minimize effort have been used to predict muscle activity during perturbed standing. On the other hand, hip and ankle strategies have been predicted by minimizing postural instability and deviation from upright posture. It remains unclear, however, whether and how effort minimization influences the selection of a specific postural response. We hypothesize that the relative importance of minimizing mechanical work vs. postural instability influences the strategy used to restore upright posture. This hypothesis was investigated based on experiments and predictive simulations of the postural response following a backward support surface translation. Peak hip flexion angle was significantly correlated with three experimentally determined measures of effort, i.e., mechanical work, mean muscle activity and metabolic energy. Furthermore, a continuum of ankle and hip strategies was predicted in simulation when changing the relative importance of minimizing mechanical work and postural instability, with increased weighting of mechanical work resulting in an ankle strategy. In conclusion, the combination of experimental measurements and predictive simulations of the postural response to a backward support surface translation showed that the trade-off between effort and postural instability minimization can explain the selection of a specific postural response in the continuum of potential ankle and hip strategies. PMID:27489362

Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

PubMed

Kuo, Ho-Chang; Wong, Henry Sung-Ching; Chang, Wei-Pin; Chen, Ben-Kuen; Wu, Mei-Shin; Yang, Kuender D; Hsieh, Kai-Sheng; Hsu, Yu-Wen; Liu, Shih-Feng; Liu, Xiao; Chang, Wei-Chiao

2017-10-01

Intravenous immunoglobulin (IVIG) is the treatment of choice in Kawasaki disease (KD). IVIG is used to prevent cardiovascular complications related to KD. However, a proportion of KD patients have persistent fever after IVIG treatment and are defined as IVIG resistant. To develop a risk scoring system based on genetic markers to predict IVIG responsiveness in KD patients, a total of 150 KD patients (126 IVIG responders and 24 IVIG nonresponders) were recruited for this study. A genome-wide association analysis was performed to compare the 2 groups and identified risk alleles for IVIG resistance. A weighted genetic risk score was calculated by the natural log of the odds ratio multiplied by the number of risk alleles. Eleven single-nucleotide polymorphisms were identified by genome-wide association study. The KD patients were categorized into 3 groups based on their calculated weighted genetic risk score. Results indicated a significant association between weighted genetic risk score (groups 3 and 4 versus group 1) and the response to IVIG (Fisher's exact P value 4.518×10 - 03 and 8.224×10 - 10 , respectively). This is the first weighted genetic risk score study based on a genome-wide association study in KD. The predictive model integrated the additive effects of all 11 single-nucleotide polymorphisms to provide a prediction of the responsiveness to IVIG. © 2017 The Authors.

Translating Addictions Research into Evidence-based Practice: the Polaris CD Outcomes Management System

PubMed Central

Toche-Manley, L.; Grissom, G.; Dietzen, L.; Sangsland, S.

2011-01-01

Converting the findings from addictions studies into information actionable by (non-research) treatment programs is important to improving program outcomes. This paper describes the translation of the findings of studies on Patient-Services matching, prediction of patient response to treatment (Expected Treatment Response) and prediction of dropout to provide evidence-based decision support in routine treatment. The findings of the studies and their application to the development of an outcomes management system are described. Implementation issues in a network of addictions treatment programs are discussed. The work illustrates how outcomes management systems can play an important role in translating research into practice. PMID:21324606

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

PubMed Central

Everett, Jeremy R.

2016-01-01

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments. PMID:27660611

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Implicit Coupling Approach for Simulation of Charring Carbon Ablators

NASA Technical Reports Server (NTRS)

Chen, Yih-Kanq; Gokcen, Tahir

2013-01-01

This study demonstrates that coupling of a material thermal response code and a flow solver with nonequilibrium gas/surface interaction for simulation of charring carbon ablators can be performed using an implicit approach. The material thermal response code used in this study is the three-dimensional version of Fully Implicit Ablation and Thermal response program, which predicts charring material thermal response and shape change on hypersonic space vehicles. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation method. Coupling between the material response and flow codes is performed by solving the surface mass balance in flow solver and the surface energy balance in material response code. Thus, the material surface recession is predicted in flow code, and the surface temperature and pyrolysis gas injection rate are computed in material response code. It is demonstrated that the time-lagged explicit approach is sufficient for simulations at low surface heating conditions, in which the surface ablation rate is not a strong function of the surface temperature. At elevated surface heating conditions, the implicit approach has to be taken, because the carbon ablation rate becomes a stiff function of the surface temperature, and thus the explicit approach appears to be inappropriate resulting in severe numerical oscillations of predicted surface temperature. Implicit coupling for simulation of arc-jet models is performed, and the predictions are compared with measured data. Implicit coupling for trajectory based simulation of Stardust fore-body heat shield is also conducted. The predicted stagnation point total recession is compared with that predicted using the chemical equilibrium surface assumption

Pre-treatment neutrophil to lymphocyte ratio as a predictive marker for pathological response to preoperative chemoradiotherapy in pancreatic cancer

PubMed Central

HASEGAWA, SHINICHIRO; EGUCHI, HIDETOSHI; TOMOKUNI, AKIRA; TOMIMARU, YOSHITO; ASAOKA, TADAFUMI; WADA, HIROSHI; HAMA, NAOKI; KAWAMOTO, KOICHI; KOBAYASHI, SHOGO; MARUBASHI, SHIGERU; KONNNO, MASAMITSU; ISHII, HIDESHI; MORI, MASAKI; DOKI, YUICHIRO; NAGANO, HIROAKI

2016-01-01

An elevated neutrophil to lymphocyte ratio (NLR) has been reported to be associated with the pathological response to neoadjuvant therapies in numerous types of cancer. The aim of the current study was to clarify the association between pre-treatment NLR and the pathological response to preoperative chemoradiotherapy in pancreatic cancer patients. This retrospective analysis included data from 56 consecutive patients whose tumors were completely surgically resected. All patients received preoperative therapy, consisting of gemcitabine-based chemotherapy (alone or in combination with S-1) combined with 40 or 50.4 Gy irradiation, prior to surgery. Predictive factors, including NLR, platelet to lymphocyte ratio (PLR), modified Glasgow prognostic score and prognostic nutrition index, were measured prior to treatment. A comparison was made between those who responded well pathologically (good response group, Evans classification IIb/III) and those with a poor response (Evans I/IIa). NLR was determined to be significantly higher in the poor response group. Multivariate analysis identified an elevated NLR as an independent risk factor for the poor pathological response [odds ratio (OR), 5.35; P=0.0257]. The pre-treatment NLR (≥2.2/<2.2) was found to be a statistically significant predictive indicator of pathological response (P=0.00699). The results demonstrate that pre-treatment NLR may be a useful predictive marker for the pathological response to preoperative therapy in pancreatic cancer patients. PMID:26893780

Alexithymia predicts arousal-based processing deficits and discordance between emotion response systems during emotional imagery.

PubMed

Peasley-Miklus, Catherine E; Panayiotou, Georgia; Vrana, Scott R

2016-03-01

Alexithymia is believed to involve deficits in emotion processing and imagery ability. Previous findings suggest that it is especially related to deficits in processing the arousal dimension of emotion, and that discordance may exist between self-report and physiological responses to emotional stimuli in alexithymia. The current study used a well-established emotional imagery paradigm to examine emotion processing deficits and discordance in participants (N = 86) selected based on their extreme scores on the Toronto Alexithymia Scale-20. Physiological (skin conductance, heart rate, and corrugator and zygomaticus electromyographic responses) and self-report (valence, arousal ratings) responses were monitored during imagery of anger, fear, joy, and neutral scenes and emotionally neutral high arousal (action) scenes. Results from regression analyses indicated that alexithymia was largely unrelated to responses on valence-based measures (facial electromyography, valence ratings), but that it was related to arousal-based measures. Specifically, alexithymia was related to higher heart rate during neutral and lower heart rate during fear imagery. Alexithymia did not predict differential responses to action versus neutral imagery, suggesting specificity of deficits to emotional contexts. Evidence for discordance between physiological responses and self-report in alexithymia was obtained from within-person analyses using multilevel modeling. Results are consistent with the idea that alexithymic deficits are specific to processing emotional arousal, and suggest difficulties with parasympathetic control and emotion regulation. Alexithymia is also associated with discordance between self-reported emotional experience and physiological response to emotion, consistent with prior evidence. (c) 2016 APA, all rights reserved).

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, Mark; Wootton, J. Timothy; Doak, Daniel F.; Emmerson, Mark; Estes, James A.; Tinker, M. Timothy

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (∼25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities.

Self-organizing map analysis using multivariate data from theophylline powders predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Kikuchi, Shingo; Takayama, Kozo

2010-11-01

The quality by design concept in pharmaceutical formulation development requires establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline powders were prepared based on the standard formulation. The angle of repose, compressibility, cohesion, and dispersibility were measured as the response variables. These responses were predicted quantitatively on the basis of a nonlinear TPS. A large amount of data on these powders was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the powders could be classified into several distinctive clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and powder characteristics. For instance, the quantities of microcrystalline cellulose (MCC) and magnesium stearate (Mg-St) were classified distinctly into each cluster, indicating that the quantities of MCC and Mg-St were crucial for determining the powder characteristics. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline powder formulations. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

A Prospective Study of Bone Tumor Response Assessment in Metastatic Breast Cancer

PubMed Central

Hayashi, Naoki; Costelloe, Colleen M.; Hamaoka, Tsuyoshi; Wei, Caimiao; Niikura, Naoki; Theriault, Richard L.; Hortobagyi, Gabriel N.; Madewell, John E.; Ueno, Naoto T.

2013-01-01

In this pilot study, we prospectively compared the response of bone metastasis assessed by our MD Anderson (MDA) bone tumor response criteria (computed tomography [CT], plain radiography [XR], and skeletal scintigraphy [SS]) with the response assessed by the World Health Organization (WHO) criteria (XR and SS). Both MDA and WHO criteria predicted progression-free survival (PFS) of patients at 6 months but not at an earlier time point. Background In our previous study, new MD Anderson (MDA) bone tumor response criteria (based on computed tomography [CT], plain radiography [XR], and skeletal scintigraphy [SS]) predicted progression-free survival (PFS) better than did World Health Organization (WHO) bone tumor response criteria (plain radiography [XR] and SS) among patients with breast cancer and bone-only metastases. In this pilot study, we tested whether MDA criteria could reveal bone metastasis response earlier than WHO criteria in patients with newly diagnosed breast cancer with osseous and measurable nonosseous metastases. Methods We prospectively analyzed bone metastasis response using each imaging modality and set of bone response criteria to distinguish progressive disease (PD) from non-PD and their association with PFS and overall survival (OS). We also compared the response of osseous metastases assessed by both criteria with the response of nonosseous measurable lesions. Results The median follow-up period was 26.7 months (range, 6.1–53.3 months) in 29 patients. PFS rates differed at 6 months based on the classification of PD or non-PD using either set of criteria (MDA, P = .002; WHO, P = .014), but these rates, as well as OS, did not differ at 3 months. Response in osseous metastases by either set of criteria did not correlate with the response in nonosseous metastases. Conclusion MDA and WHO criteria predicted PFS of patients with osseous metastases at 6 months but not at an earlier time point. We plan a well-powered study to determine the role of MDA criteria in predicting bone tumor response by incorporating 18-fluorodeoxyglucose (18F) positron emission tomography (FDG-PET)/CT to see if findings using this modality are earlier than those with WHO criteria. PMID:23098575

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

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

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

DOE PAGES

Liu, Yushan; Abu-Rub, Haitham; Xue, Yaosuo; ...

2017-12-25

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require anmore » elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.« less

High Cycle Fatigue Prediction for Mistuned Bladed Disks with Fully Coupled Fluid-Structural Interaction

DTIC Science & Technology

2006-06-01

response (time domain) structural vibration model for mistuned rotor bladed disk based on the efficient SNM model has been developed. The vi- bration...airfoil and 3D wing, unsteady vortex shedding of a stationary cylinder, induced vibration of a cylinder, forced vibration of a pitching airfoil, induced... vibration and flutter boundary of 2D NACA 64A010 transonic airfoil, 3D plate wing structural response. The predicted results agree well with benchmark

Prediction of lung density changes after radiotherapy by cone beam computed tomography response markers and pre-treatment factors for non-small cell lung cancer patients.

PubMed

Bernchou, Uffe; Hansen, Olfred; Schytte, Tine; Bertelsen, Anders; Hope, Andrew; Moseley, Douglas; Brink, Carsten

2015-10-01

This study investigates the ability of pre-treatment factors and response markers extracted from standard cone-beam computed tomography (CBCT) images to predict the lung density changes induced by radiotherapy for non-small cell lung cancer (NSCLC) patients. Density changes in follow-up computed tomography scans were evaluated for 135 NSCLC patients treated with radiotherapy. Early response markers were obtained by analysing changes in lung density in CBCT images acquired during the treatment course. The ability of pre-treatment factors and CBCT markers to predict lung density changes induced by radiotherapy was investigated. Age and CBCT markers extracted at 10th, 20th, and 30th treatment fraction significantly predicted lung density changes in a multivariable analysis, and a set of response models based on these parameters were established. The correlation coefficient for the models was 0.35, 0.35, and 0.39, when based on the markers obtained at the 10th, 20th, and 30th fraction, respectively. The study indicates that younger patients without lung tissue reactions early into their treatment course may have minimal radiation induced lung density increase at follow-up. Further investigations are needed to examine the ability of the models to identify patients with low risk of symptomatic toxicity. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Influence of the Spatial Dimensions of Ultrasonic Transducers on the Frequency Spectrum of Guided Waves.

PubMed

Samaitis, Vykintas; Mažeika, Liudas

2017-08-08

Ultrasonic guided wave (UGW)-based condition monitoring has shown great promise in detecting, localizing, and characterizing damage in complex systems. However, the application of guided waves for damage detection is challenging due to the existence of multiple modes and dispersion. This results in distorted wave packets with limited resolution and the interference of multiple reflected modes. To develop reliable inspection systems, either the transducers have to be optimized to generate a desired single mode of guided waves with known dispersive properties, or the frequency responses of all modes present in the structure must be known to predict wave interaction. Currently, there is a lack of methods to predict the response spectrum of guided wave modes, especially in cases when multiple modes are being excited simultaneously. Such methods are of vital importance for further understanding wave propagation within the structures as well as wave-damage interaction. In this study, a novel method to predict the response spectrum of guided wave modes was proposed based on Fourier analysis of the particle velocity distribution on the excitation area. The method proposed in this study estimates an excitability function based on the spatial dimensions of the transducer, type of vibration, and dispersive properties of the medium. As a result, the response amplitude as a function of frequency for each guided wave mode present in the structure can be separately obtained. The method was validated with numerical simulations on the aluminum and glass fiber composite samples. The key findings showed that it can be applied to estimate the response spectrum of a guided wave mode on any type of material (either isotropic structures, or multi layered anisotropic composites) and under any type of excitation if the phase velocity dispersion curve and the particle velocity distribution of the wave source was known initially. Thus, the proposed method may be a beneficial tool to explain and predict the response spectrum of guided waves throughout the development of any structural health monitoring system.

Influence of the Spatial Dimensions of Ultrasonic Transducers on the Frequency Spectrum of Guided Waves

PubMed Central

Samaitis, Vykintas; Mažeika, Liudas

2017-01-01

Ultrasonic guided wave (UGW)-based condition monitoring has shown great promise in detecting, localizing, and characterizing damage in complex systems. However, the application of guided waves for damage detection is challenging due to the existence of multiple modes and dispersion. This results in distorted wave packets with limited resolution and the interference of multiple reflected modes. To develop reliable inspection systems, either the transducers have to be optimized to generate a desired single mode of guided waves with known dispersive properties, or the frequency responses of all modes present in the structure must be known to predict wave interaction. Currently, there is a lack of methods to predict the response spectrum of guided wave modes, especially in cases when multiple modes are being excited simultaneously. Such methods are of vital importance for further understanding wave propagation within the structures as well as wave-damage interaction. In this study, a novel method to predict the response spectrum of guided wave modes was proposed based on Fourier analysis of the particle velocity distribution on the excitation area. The method proposed in this study estimates an excitability function based on the spatial dimensions of the transducer, type of vibration, and dispersive properties of the medium. As a result, the response amplitude as a function of frequency for each guided wave mode present in the structure can be separately obtained. The method was validated with numerical simulations on the aluminum and glass fiber composite samples. The key findings showed that it can be applied to estimate the response spectrum of a guided wave mode on any type of material (either isotropic structures, or multi layered anisotropic composites) and under any type of excitation if the phase velocity dispersion curve and the particle velocity distribution of the wave source was known initially. Thus, the proposed method may be a beneficial tool to explain and predict the response spectrum of guided waves throughout the development of any structural health monitoring system. PMID:28786924

Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

PubMed

Fleck, David E; Ernest, Nicholas; Adler, Caleb M; Cohen, Kelly; Eliassen, James C; Norris, Matthew; Komoroski, Richard A; Chu, Wen-Jang; Welge, Jeffrey A; Blom, Thomas J; DelBello, Melissa P; Strakowski, Stephen M

2017-06-01

Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy ( 1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Decadal prediction of Sahel rainfall: where does the skill (or lack thereof) come from?

NASA Astrophysics Data System (ADS)

Mohino, Elsa; Keenlyside, Noel; Pohlmann, Holger

2016-12-01

Previous works suggest decadal predictions of Sahel rainfall could be skillful. However, the sources of such skill are still under debate. In addition, previous results are based on short validation periods (i.e. less than 50 years). In this work we propose a framework based on multi-linear regression analysis to study the potential sources of skill for predicting Sahel trends several years ahead. We apply it to an extended decadal hindcast performed with the MPI-ESM-LR model that span from 1901 to 2010 with 1 year sampling interval. Our results show that the skill mainly depends on how well we can predict the timing of the global warming (GW), the Atlantic multidecadal variability (AMV) and, to a lesser extent, the inter-decadal Pacific oscillation signals, and on how well the system simulates the associated SST and West African rainfall response patterns. In the case of the MPI-ESM-LR decadal extended hindcast, the observed timing is well reproduced only for the GW and AMV signals. However, only the West African rainfall response to the AMV is correctly reproduced. Thus, for most of the lead times the main source of skill in the decadal hindcast of West African rainfall is from the AMV. The GW signal degrades skill because the response of West African rainfall to GW is incorrectly captured. Our results also suggest that initialized decadal predictions of West African rainfall can be further improved by better simulating the response of global SST to GW and AMV. Furthermore, our approach may be applied to understand and attribute prediction skill for other variables and regions.

Development of Design Analysis Methods for C/SiC Composite Structures

NASA Technical Reports Server (NTRS)

Sullivan, Roy M.; Mital, Subodh K.; Murthy, Pappu L. N.; Palko, Joseph L.; Cueno, Jacques C.; Koenig, John R.

2006-01-01

The stress-strain behavior at room temperature and at 1100 C (2000 F) was measured for two carbon-fiber-reinforced silicon carbide (C/SiC) composite materials: a two-dimensional plain-weave quasi-isotropic laminate and a three-dimensional angle-interlock woven composite. Micromechanics-based material models were developed for predicting the response properties of these two materials. The micromechanics based material models were calibrated by correlating the predicted material property values with the measured values. Four-point beam bending sub-element specimens were fabricated with these two fiber architectures and four-point bending tests were performed at room temperature and at 1100 C. Displacements and strains were measured at various locations along the beam and recorded as a function of load magnitude. The calibrated material models were used in concert with a nonlinear finite element solution to simulate the structural response of these two materials in the four-point beam bending tests. The structural response predicted by the nonlinear analysis method compares favorably with the measured response for both materials and for both test temperatures. Results show that the material models scale up fairly well from coupon to subcomponent level.

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading

NASA Technical Reports Server (NTRS)

Gentz, Steven J.; Ordway, David O; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

2015-01-01

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This report documents the outcome of the assessment.

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading. [Appendices

NASA Technical Reports Server (NTRS)

Gentz, Steven J.; Ordway, David O.; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

2015-01-01

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (9 inches from the source) dominated by direct wave propagation, mid-field environment (approximately 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading: Appendices

NASA Technical Reports Server (NTRS)

Gentz, Steven J.; Ordway, David O.; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

2015-01-01

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approx. 9 inches from the source) dominated by direct wave propagation, mid-field environment (approx. 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Pharmacogenetics Biomarkers and Their Specific Role in Neoadjuvant Chemoradiotherapy Treatments: An Exploratory Study on Rectal Cancer Patients

PubMed Central

Dreussi, Eva; Cecchin, Erika; Polesel, Jerry; Canzonieri, Vincenzo; Agostini, Marco; Boso, Caterina; Belluco, Claudio; Buonadonna, Angela; Lonardi, Sara; Bergamo, Francesca; Gagno, Sara; De Mattia, Elena; Pucciarelli, Salvatore; De Paoli, Antonino; Toffoli, Giuseppe

2016-01-01

Background: Pathological complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC) is still ascribed to a minority of patients. A pathway based-approach could highlight the predictive role of germline single nucleotide polymorphisms (SNPs). The primary aim of this study was to define new predictive biomarkers considering treatment specificities. Secondary aim was to determine new potential predictive biomarkers independent from radiotherapy (RT) dosage and cotreatment with oxaliplatin. Methods: Thirty germ-line SNPs in twenty-one genes were selected according to a pathway-based approach. Genetic analyses were performed on 280 LARC patients who underwent fluoropyrimidine-based CRT. The potential predictive role of these SNPs in determining pathological tumor response was tested in Group 1 (94 patients undergoing also oxaliplatin), Group 2 (73 patients treated with high RT dosage), Group 3 (113 patients treated with standard RT dosage), and in the pooled population (280 patients). Results: Nine new predictive biomarkers were identified in the three groups. The most promising one was rs3136228-MSH6 (p = 0.004) arising from Group 3. In the pooled population, rs1801133-MTHFR showed only a trend (p = 0.073). Conclusion: This exploratory study highlighted new potential predictive biomarkers of neoadjuvant CRT and underlined the importance to strictly define treatment peculiarities in pharmacogenetic analyses. PMID:27608007

Child-Level Predictors of Responsiveness to Evidence-Based Mathematics Intervention.

PubMed

Powell, Sarah R; Cirino, Paul T; Malone, Amelia S

2017-07-01

We identified child-level predictors of responsiveness to 2 types of mathematics (calculation and word-problem) intervention among 2nd-grade children with mathematics difficulty. Participants were 250 children in 107 classrooms in 23 schools pretested on mathematics and general cognitive measures and posttested on mathematics measures. We assigned classrooms randomly assigned to calculation intervention, word-problem intervention, or business-as-usual control. Intervention lasted 17 weeks. Path analyses indicated that scores on working memory and language comprehension assessments moderated responsiveness to calculation intervention. No moderators were identified for responsiveness to word-problem intervention. Across both intervention groups and the control group, attentive behavior predicted both outcomes. Initial calculation skill predicted the calculation outcome, and initial language comprehension predicted word-problem outcomes. These results indicate that screening for calculation intervention should include a focus on working memory, language comprehension, attentive behavior, and calculations. Screening for word-problem intervention should focus on attentive behavior and word problems.

A System Computational Model of Implicit Emotional Learning

PubMed Central

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation. PMID:27378898

A System Computational Model of Implicit Emotional Learning.

PubMed

Puviani, Luca; Rama, Sidita

2016-01-01

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

The Mechanisms for Within-Host Influenza Virus Control Affect Model-Based Assessment and Prediction of Antiviral Treatment

PubMed Central

Cao, Pengxing

2017-01-01

Models of within-host influenza viral dynamics have contributed to an improved understanding of viral dynamics and antiviral effects over the past decade. Existing models can be classified into two broad types based on the mechanism of viral control: models utilising target cell depletion to limit the progress of infection and models which rely on timely activation of innate and adaptive immune responses to control the infection. In this paper, we compare how two exemplar models based on these different mechanisms behave and investigate how the mechanistic difference affects the assessment and prediction of antiviral treatment. We find that the assumed mechanism for viral control strongly influences the predicted outcomes of treatment. Furthermore, we observe that for the target cell-limited model the assumed drug efficacy strongly influences the predicted treatment outcomes. The area under the viral load curve is identified as the most reliable predictor of drug efficacy, and is robust to model selection. Moreover, with support from previous clinical studies, we suggest that the target cell-limited model is more suitable for modelling in vitro assays or infection in some immunocompromised/immunosuppressed patients while the immune response model is preferred for predicting the infection/antiviral effect in immunocompetent animals/patients. PMID:28933757

Before the N400: effects of lexical-semantic violations in visual cortex.

PubMed

Dikker, Suzanne; Pylkkanen, Liina

2011-07-01

There exists an increasing body of research demonstrating that language processing is aided by context-based predictions. Recent findings suggest that the brain generates estimates about the likely physical appearance of upcoming words based on syntactic predictions: words that do not physically look like the expected syntactic category show increased amplitudes in the visual M100 component, the first salient MEG response to visual stimulation. This research asks whether violations of predictions based on lexical-semantic information might similarly generate early visual effects. In a picture-noun matching task, we found early visual effects for words that did not accurately describe the preceding pictures. These results demonstrate that, just like syntactic predictions, lexical-semantic predictions can affect early visual processing around ∼100ms, suggesting that the M100 response is not exclusively tuned to recognizing visual features relevant to syntactic category analysis. Rather, the brain might generate predictions about upcoming visual input whenever it can. However, visual effects of lexical-semantic violations only occurred when a single lexical item could be predicted. We argue that this may be due to the fact that in natural language processing, there is typically no straightforward mapping between lexical-semantic fields (e.g., flowers) and visual or auditory forms (e.g., tulip, rose, magnolia). For syntactic categories, in contrast, certain form features do reliably correlate with category membership. This difference may, in part, explain why certain syntactic effects typically occur much earlier than lexical-semantic effects. Copyright © 2011 Elsevier Inc. All rights reserved.

Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers: from theory to practice.

PubMed

La Marca, Antonio; Sunkara, Sesh Kamal

2014-01-01

The main objective of individualization of treatment in IVF is to offer every single woman the best treatment tailored to her own unique characteristics, thus maximizing the chances of pregnancy and eliminating the iatrogenic and avoidable risks resulting from ovarian stimulation. Personalization of treatment in IVF should be based on the prediction of ovarian response for every individual. The starting point is to identify if a woman is likely to have a normal, poor or a hyper response and choose the ideal treatment protocol tailored to this prediction. The objective of this review is to summarize the predictive ability of ovarian reserve markers, such as antral follicle count (AFC) and anti-Mullerian hormone (AMH), and the therapeutic strategies that have been proposed in IVF after this prediction. A systematic review of the existing literature was performed by searching Medline, EMBASE, Cochrane library and Web of Science for publications in the English language related to AFC, AMH and their incorporation into controlled ovarian stimulation (COS) protocols in IVF. Literature available to May 2013 was included. The search generated 305 citations of which 41 and 25 studies, respectively, reporting the ability of AMH and AFC to predict response to COS were included in this review. The literature review demonstrated that AFC and AMH, the most sensitive markers of ovarian reserve identified to date, are ideal in planning personalized COS protocols. These sensitive markers permit prediction of the whole spectrum of ovarian response with reliable accuracy and clinicians may use either of the two markers as they can be considered interchangeable. Following the categorization of expected ovarian response to stimulation clinicians can adopt tailored therapeutic strategies for each patient. Current scientific trend suggests the elective use of the GnRH antagonist based regimen for hyper-responders, and probably also poor responders, as likely to be beneficial. The selection of the appropriate and individualized gonadotrophin dose is also of paramount importance for effective COS and subsequent IVF outcomes. Personalized IVF offers several benefits; it enables clinicians to give women more accurate information on their prognosis thus facilitating counselling especially in cases of extremes of ovarian response. The deployment of therapeutic strategies based on selective use of GnRH analogues and the fine tuning of the gonadotrophin dose on the basis of potential ovarian response in every single woman can allow for a safer and more effective IVF practice.

The impact of response measurement error on the analysis of designed experiments

DOE PAGES

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

2016-11-01

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

The impact of response measurement error on the analysis of designed experiments

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

Anderson-Cook, Christine Michaela; Hamada, Michael Scott; Burr, Thomas Lee

This study considers the analysis of designed experiments when there is measurement error in the true response or so-called response measurement error. We consider both additive and multiplicative response measurement errors. Through a simulation study, we investigate the impact of ignoring the response measurement error in the analysis, that is, by using a standard analysis based on t-tests. In addition, we examine the role of repeat measurements in improving the quality of estimation and prediction in the presence of response measurement error. We also study a Bayesian approach that accounts for the response measurement error directly through the specification ofmore » the model, and allows including additional information about variability in the analysis. We consider the impact on power, prediction, and optimization. Copyright © 2015 John Wiley & Sons, Ltd.« less

Effect of Surface Nonequilibrium Thermochemistry in Simulation of Carbon Based Ablators

NASA Technical Reports Server (NTRS)

Chen, Yih-Kang; Gokcen, Tahir

2012-01-01

This study demonstrates that coupling of a material thermal response code and a flow solver using finite-rate gas/surface interaction model provides time-accurate solutions for multidimensional ablation of carbon based charring ablators. The material thermal response code used in this study is the Two-dimensional Implicit Thermal Response and Ablation Program (TITAN), which predicts charring material thermal response and shape change on hypersonic space vehicles. Its governing equations include total energy balance, pyrolysis gas momentum conservation, and a three-component decomposition model. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation (DPLR) method. Loose coupling between material response and flow codes is performed by solving the surface mass balance in DPLR and the surface energy balance in TITAN. Thus, the material surface recession is predicted by finite-rate gas/surface interaction boundary conditions implemented in DPLR, and the surface temperature and pyrolysis gas injection rate are computed in TITAN. Two sets of gas/surface interaction chemistry between air and carbon surface developed by Park and Zhluktov, respectively, are studied. Coupled fluid-material response analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities are considered. The ablating material used in these arc-jet tests was a Phenolic Impregnated Carbon Ablator (PICA). Computational predictions of in-depth material thermal response and surface recession are compared with the experimental measurements for stagnation cold wall heat flux ranging from 107 to 1100 Watts per square centimeter.

Effect of Non-Equilibrium Surface Thermochemistry in Simulation of Carbon Based Ablators

NASA Technical Reports Server (NTRS)

Chen, Yih-Kanq; Gokcen, Tahir

2012-01-01

This study demonstrates that coupling of a material thermal response code and a flow solver using non-equilibrium gas/surface interaction model provides time-accurate solutions for the multidimensional ablation of carbon based charring ablators. The material thermal response code used in this study is the Two-dimensional Implicit Thermal-response and AblatioN Program (TITAN), which predicts charring material thermal response and shape change on hypersonic space vehicles. Its governing equations include total energy balance, pyrolysis gas mass conservation, and a three-component decomposition model. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation (DPLR) method. Loose coupling between the material response and flow codes is performed by solving the surface mass balance in DPLR and the surface energy balance in TITAN. Thus, the material surface recession is predicted by finite-rate gas/surface interaction boundary conditions implemented in DPLR, and the surface temperature and pyrolysis gas injection rate are computed in TITAN. Two sets of nonequilibrium gas/surface interaction chemistry between air and the carbon surface developed by Park and Zhluktov, respectively, are studied. Coupled fluid-material response analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities are considered. The ablating material used in these arc-jet tests was Phenolic Impregnated Carbon Ablator (PICA). Computational predictions of in-depth material thermal response and surface recession are compared with the experimental measurements for stagnation cold wall heat flux ranging from 107 to 1100 Watts per square centimeter.

Design guide for predicting nonlinear random response (including snap-through) of buckled plates

NASA Technical Reports Server (NTRS)

Ng, Chung Fai

1989-01-01

This design guide describes a method for predicting the random response of flat and curved plates which is based on theoretical analyses and experimental results. The plate curvature can be due to postbuckling, in-plane mechanical or thermal stresses. Based on a single mode formula, root mean square values of the strain response to broadband excitation are evaluated for different static buckled configurations using the equivalent linearization technique. The effects on the overall strain response due to instability motion of snap-through are included. Panel parameters include clamped and simply-supported boundaries, aspect ratio, thickness and length. Analytical results are compared with experimental results from tests with 12 in. x 15 in. aluminum plates under thermal loading in a progressive wave facility. Comparisons are also made with results from tests with a 2 in. x 15 in. x 0.032 in. aluminum beam under base mechanical excitation. The comparisons help to assess the accuracy of the theory and the conditions under which deviations from the theory due to effects of imperfection and higher modes are significant.

Predicting Reading Growth with Event-Related Potentials: Thinking Differently about Indexing “Responsiveness”

PubMed Central

Lemons, Christopher J.; Key, Alexandra P.F.; Fuchs, Douglas; Yoder, Paul J.; Fuchs, Lynn S.; Compton, Donald L.; Williams, Susan M.; Bouton, Bobette

2009-01-01

The purpose of this study was to determine if event-related potential (ERP) data collected during three reading-related tasks (Letter Sound Matching, Nonword Rhyming, and Nonword Reading) could be used to predict short-term reading growth on a curriculum-based measure of word identification fluency over 19 weeks in a sample of 29 first-grade children. Results indicate that ERP responses to the Letter Sound Matching task were predictive of reading change and remained so after controlling for two previously validated behavioral predictors of reading, Rapid Letter Naming and Segmenting. ERP data for the other tasks were not correlated with reading change. The potential for cognitive neuroscience to enhance current methods of indexing responsiveness in a response-to-intervention (RTI) model is discussed. PMID:20514353

The role of topic, interviewee and question in predicting rich interview data in the field of health research.

PubMed

Ogden, Jane; Cornwell, Danielle

2010-11-01

Although texts recommend the generation of rich data from interviews, no empirical evidence base exists for achieving this. This study aimed to operationalise richness and to assess which components of the interview (for example, topic, interviewee, question) were predictive. A total of 400 interview questions and their corresponding responses were selected from 10 qualitative studies in the area of health identified from university colleagues and the UK Data Archive database. The analysis used the text analysis program, Linguistic Inquiry and Word Count, and additional rating scales. Richness was operationalised along five dimensions. 'Length of response' was predicted by a personal, less specific or positive topic, not being a layperson, later questions, open or double questions; 'personal richness' was predicted by being a healthy participant and questions about the past and future; 'analytical responses' were predicted by a personal or less specific topic, not being a layperson, later questions, questions relating to insight and causation; 'action responses' were predicted by a less specific topic, not being a layperson, being healthy, later and open questions. The model for 'descriptive richness' was not significant. Overall, open questions, located later on and framed in the present or past tense, tended to be most predictive of richness. This could inform improvements in interview technique. © 2010 The Authors. Sociology of Health & Illness © 2010 Foundation for the Sociology of Health & Illness/Blackwell Publishing Ltd.

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Design of optimal hyperthermia protocols for prostate cancer by controlling HSP expression through computer modeling (Invited Paper)

NASA Astrophysics Data System (ADS)

Rylander, Marissa N.; Feng, Yusheng; Diller, Kenneth; Bass, J.

2005-04-01

Heat shock proteins (HSP) are critical components of a complex defense mechanism essential for preserving cell survival under adverse environmental conditions. It is inevitable that hyperthermia will enhance tumor tissue viability, due to HSP expression in regions where temperatures are insufficient to coagulate proteins, and would likely increase the probability of cancer recurrence. Although hyperthermia therapy is commonly used in conjunction with radiotherapy, chemotherapy, and gene therapy to increase therapeutic effectiveness, the efficacy of these therapies can be substantially hindered due to HSP expression when hyperthermia is applied prior to these procedures. Therefore, in planning hyperthermia protocols, prediction of the HSP response of the tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of overall tissue response. In this paper, we present a highly accurate, adaptive, finite element tumor model capable of predicting the HSP expression distribution and tissue damage region based on measured cellular data when hyperthermia protocols are specified. Cubic spline representations of HSP27 and HSP70, and Arrhenius damage models were integrated into the finite element model to enable prediction of the HSP expression and damage distribution in the tissue following laser heating. Application of the model can enable optimized treatment planning by controlling of the tissue response to therapy based on accurate prediction of the HSP expression and cell damage distribution.

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.

PubMed

Cook, Benjamin L; Progovac, Ana M; Chen, Pei; Mullin, Brian; Hou, Sherry; Baca-Garcia, Enrique

2016-01-01

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, "how do you feel today?" We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.

Cross-shift changes in FEV1 in relation to wood dust exposure: the implications of different exposure assessment methods

PubMed Central

Schlunssen, V; Sigsgaard, T; Schaumburg, I; Kromhout, H

2004-01-01

Background: Exposure-response analyses in occupational studies rely on the ability to distinguish workers with regard to exposures of interest. Aims: To evaluate different estimates of current average exposure in an exposure-response analysis on dust exposure and cross-shift decline in FEV1 among woodworkers. Methods: Personal dust samples (n = 2181) as well as data on lung function parameters were available for 1560 woodworkers from 54 furniture industries. The exposure to wood dust for each worker was calculated in eight different ways using individual measurements, group based exposure estimates, a weighted estimate of individual and group based exposure estimates, and predicted values from mixed models. Exposure-response relations on cross-shift changes in FEV1 and exposure estimates were explored. Results: A positive exposure-response relation between average dust exposure and cross-shift FEV1 was shown for non-smokers only and appeared to be most pronounced among pine workers. In general, the highest slope and standard error (SE) was revealed for grouping by a combination of task and factory size, the lowest slope and SE was revealed for estimates based on individual measurements, with the weighted estimate and the predicted values in between. Grouping by quintiles of average exposure for task and factory combinations revealed low slopes and high SE, despite a high contrast. Conclusion: For non-smokers, average dust exposure and cross-shift FEV1 were associated in an exposure dependent manner, especially among pine workers. This study confirms the consequences of using different exposure assessment strategies studying exposure-response relations. It is possible to optimise exposure assessment combining information from individual and group based exposure estimates, for instance by applying predicted values from mixed effects models. PMID:15377768

Test-Anchored Vibration Response Predictions for an Acoustically Energized Curved Orthogrid Panel with Mounted Components

NASA Technical Reports Server (NTRS)

Frady, Gregory P.; Duvall, Lowery D.; Fulcher, Clay W. G.; Laverde, Bruce T.; Hunt, Ronald A.

2011-01-01

rich body of vibroacoustic test data was recently generated at Marshall Space Flight Center for component-loaded curved orthogrid panels typical of launch vehicle skin structures. The test data were used to anchor computational predictions of a variety of spatially distributed responses including acceleration, strain and component interface force. Transfer functions relating the responses to the input pressure field were generated from finite element based modal solutions and test-derived damping estimates. A diffuse acoustic field model was applied to correlate the measured input sound pressures across the energized panel. This application quantifies the ability to quickly and accurately predict a variety of responses to acoustically energized skin panels with mounted components. Favorable comparisons between the measured and predicted responses were established. The validated models were used to examine vibration response sensitivities to relevant modeling parameters such as pressure patch density, mesh density, weight of the mounted component and model form. Convergence metrics include spectral densities and cumulative root-mean squared (RMS) functions for acceleration, velocity, displacement, strain and interface force. Minimum frequencies for response convergence were established as well as recommendations for modeling techniques, particularly in the early stages of a component design when accurate structural vibration requirements are needed relatively quickly. The results were compared with long-established guidelines for modeling accuracy of component-loaded panels. A theoretical basis for the Response/Pressure Transfer Function (RPTF) approach provides insight into trends observed in the response predictions and confirmed in the test data. The software developed for the RPTF method allows easy replacement of the diffuse acoustic field with other pressure fields such as a turbulent boundary layer (TBL) model suitable for vehicle ascent. Structural responses using a TBL model were demonstrated, and wind tunnel tests have been proposed to anchor the predictions and provide new insight into modeling approaches for this environment. Finally, design load factors were developed from the measured and predicted responses and compared with those derived from traditional techniques such as historical Mass Acceleration Curves and Barrett scaling methods for acreage and component-loaded panels.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Improvement of a predictive model in ovarian cancer patients submitted to platinum-based chemotherapy: implications of a GST activity profile.

PubMed

Pereira, Deolinda; Assis, Joana; Gomes, Mónica; Nogueira, Augusto; Medeiros, Rui

2016-05-01

The success of chemotherapy in ovarian cancer (OC) is directly associated with the broad variability in platinum response, with implications in patients survival. This heterogeneous response might result from inter-individual variations in the platinum-detoxification pathway due to the expression of glutathione-S-transferase (GST) enzymes. We hypothesized that GSTM1 and GSTT1 polymorphisms might have an impact as prognostic and predictive determinants for OC. We conducted a hospital-based study in a cohort of OC patients submitted to platinum-based chemotherapy. GSTM1 and GSTT1 genotypes were determined by multiplex PCR. GSTM1-null genotype patients presented a significantly longer 5-year survival and an improved time to progression when compared with GSTM1-wt genotype patients (log-rank test, P = 0.001 and P = 0.013, respectively). Multivariate Cox regression analysis indicates that the inclusion of genetic information regarding GSTM1 polymorphism increased the predictive ability of risk of death after OC platinum-based chemotherapy (c-index from 0.712 to 0.833). Namely, residual disease (HR, 4.90; P = 0.016) and GSTM1-wt genotype emerged as more important predictors of risk of death (HR, 2.29; P = 0.039; P = 0.036 after bootstrap). No similar effect on survival was observed regarding GSTT1 polymorphism, and there were no statistically significant differences between GSTM1 and GSTT1 genotypes and the assessed patients' clinical-pathological characteristics. GSTM1 polymorphism seems to have an impact in OC prognosis as it predicts a better response to platinum-based chemotherapy and hence an improved survival. The characterization of the GSTM1 genetic profile might be a useful molecular tool and a putative genetic marker for OC clinical outcome.

Can adaptive threshold-based metabolic tumor volume (MTV) and lean body mass corrected standard uptake value (SUL) predict prognosis in head and neck cancer patients treated with definitive radiotherapy/chemoradiotherapy?

PubMed

Akagunduz, Ozlem Ozkaya; Savas, Recep; Yalman, Deniz; Kocacelebi, Kenan; Esassolak, Mustafa

2015-11-01

To evaluate the predictive value of adaptive threshold-based metabolic tumor volume (MTV), maximum standardized uptake value (SUVmax) and maximum lean body mass corrected SUV (SULmax) measured on pretreatment positron emission tomography and computed tomography (PET/CT) imaging in head and neck cancer patients treated with definitive radiotherapy/chemoradiotherapy. Pretreatment PET/CT of the 62 patients with locally advanced head and neck cancer who were treated consecutively between May 2010 and February 2013 were reviewed retrospectively. The maximum FDG uptake of the primary tumor was defined according to SUVmax and SULmax. Multiple threshold levels between 60% and 10% of the SUVmax and SULmax were tested with intervals of 5% to 10% in order to define the most suitable threshold value for the metabolic activity of each patient's tumor (adaptive threshold). MTV was calculated according to this value. We evaluated the relationship of mean values of MTV, SUVmax and SULmax with treatment response, local recurrence, distant metastasis and disease-related death. Receiver-operating characteristic (ROC) curve analysis was done to obtain optimal predictive cut-off values for MTV and SULmax which were found to have a predictive value. Local recurrence-free (LRFS), disease-free (DFS) and overall survival (OS) were examined according to these cut-offs. Forty six patients had complete response, 15 had partial response, and 1 had stable disease 6 weeks after the completion of treatment. Median follow-up of the entire cohort was 18 months. Of 46 complete responders 10 had local recurrence, and of 16 partial or no responders 10 had local progression. Eighteen patients died. Adaptive threshold-based MTV had significant predictive value for treatment response (p=0.011), local recurrence/progression (p=0.050), and disease-related death (p=0.024). SULmax had a predictive value for local recurrence/progression (p=0.030). ROC curves analysis revealed a cut-off value of 14.00 mL for MTV and 10.15 for SULmax. Three-year LRFS and DFS rates were significantly lower in patients with MTV ≥ 14.00 mL (p=0.026, p=0.018 respectively), and SULmax≥10.15 (p=0.017, p=0.022 respectively). SULmax did not have a significant predictive value for OS whereas MTV had (p=0.025). Adaptive threshold-based MTV and SULmax could have a role in predicting local control and survival in head and neck cancer patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Harmonize input selection for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Rational Design and Tuning of Functional RNA Switch to Control an Allosteric Intermolecular Interaction.

PubMed

Endoh, Tamaki; Sugimoto, Naoki

2015-08-04

Conformational transitions of biomolecules in response to specific stimuli control many biological processes. In natural functional RNA switches, often called riboswitches, a particular RNA structure that has a suppressive or facilitative effect on gene expression transitions to an alternative structure with the opposite effect upon binding of a specific metabolite to the aptamer region. Stability of RNA secondary structure (-ΔG°) can be predicted based on thermodynamic parameters and is easily tuned by changes in nucleobases. We envisioned that tuning of a functional RNA switch that causes an allosteric interaction between an RNA and a peptide would be possible based on a predicted switching energy (ΔΔG°) that corresponds to the energy difference between the RNA secondary structure before (-ΔG°before) and after (-ΔG°after) the RNA conformational transition. We first selected functional RNA switches responsive to neomycin with predicted ΔΔG° values ranging from 5.6 to 12.2 kcal mol(-1). We then demonstrated a simple strategy to rationally convert the functional RNA switch to switches responsive to natural metabolites thiamine pyrophosphate, S-adenosyl methionine, and adenine based on the predicted ΔΔG° values. The ΔΔG° values of the designed RNA switches proportionally correlated with interaction energy (ΔG°interaction) between the RNA and peptide, and we were able to tune the sensitivity of the RNA switches for the trigger molecule. The strategy demonstrated here will be generally applicable for construction of functional RNA switches and biosensors in which mechanisms are based on conformational transition of nucleic acids.

Urbanization impacts on mammals across urban-forest edges and a predictive model of edge effects.

PubMed

Villaseñor, Nélida R; Driscoll, Don A; Escobar, Martín A H; Gibbons, Philip; Lindenmayer, David B

2014-01-01

With accelerating rates of urbanization worldwide, a better understanding of ecological processes at the wildland-urban interface is critical to conserve biodiversity. We explored the effects of high and low-density housing developments on forest-dwelling mammals. Based on habitat characteristics, we expected a gradual decline in species abundance across forest-urban edges and an increased decline rate in higher contrast edges. We surveyed arboreal mammals in sites of high and low housing density along 600 m transects that spanned urban areas and areas turn on adjacent native forest. We also surveyed forest controls to test whether edge effects extended beyond our edge transects. We fitted models describing richness, total abundance and individual species abundance. Low-density housing developments provided suitable habitat for most arboreal mammals. In contrast, high-density housing developments had lower species richness, total abundance and individual species abundance, but supported the highest abundances of an urban adapter (Trichosurus vulpecula). We did not find the predicted gradual decline in species abundance. Of four species analysed, three exhibited no response to the proximity of urban boundaries, but spilled over into adjacent urban habitat to differing extents. One species (Petaurus australis) had an extended negative response to urban boundaries, suggesting that urban development has impacts beyond 300 m into adjacent forest. Our empirical work demonstrates that high-density housing developments have negative effects on both community and species level responses, except for one urban adapter. We developed a new predictive model of edge effects based on our results and the literature. To predict animal responses across edges, our framework integrates for first time: (1) habitat quality/preference, (2) species response with the proximity to the adjacent habitat, and (3) spillover extent/sensitivity to adjacent habitat boundaries. This framework will allow scientists, managers and planners better understand and predict both species responses across edges and impacts of development in mosaic landscapes.

Urbanization Impacts on Mammals across Urban-Forest Edges and a Predictive Model of Edge Effects

PubMed Central

Villaseñor, Nélida R.; Driscoll, Don A.; Escobar, Martín A. H.; Gibbons, Philip; Lindenmayer, David B.

2014-01-01

With accelerating rates of urbanization worldwide, a better understanding of ecological processes at the wildland-urban interface is critical to conserve biodiversity. We explored the effects of high and low-density housing developments on forest-dwelling mammals. Based on habitat characteristics, we expected a gradual decline in species abundance across forest-urban edges and an increased decline rate in higher contrast edges. We surveyed arboreal mammals in sites of high and low housing density along 600 m transects that spanned urban areas and areas turn on adjacent native forest. We also surveyed forest controls to test whether edge effects extended beyond our edge transects. We fitted models describing richness, total abundance and individual species abundance. Low-density housing developments provided suitable habitat for most arboreal mammals. In contrast, high-density housing developments had lower species richness, total abundance and individual species abundance, but supported the highest abundances of an urban adapter (Trichosurus vulpecula). We did not find the predicted gradual decline in species abundance. Of four species analysed, three exhibited no response to the proximity of urban boundaries, but spilled over into adjacent urban habitat to differing extents. One species (Petaurus australis) had an extended negative response to urban boundaries, suggesting that urban development has impacts beyond 300 m into adjacent forest. Our empirical work demonstrates that high-density housing developments have negative effects on both community and species level responses, except for one urban adapter. We developed a new predictive model of edge effects based on our results and the literature. To predict animal responses across edges, our framework integrates for first time: (1) habitat quality/preference, (2) species response with the proximity to the adjacent habitat, and (3) spillover extent/sensitivity to adjacent habitat boundaries. This framework will allow scientists, managers and planners better understand and predict both species responses across edges and impacts of development in mosaic landscapes. PMID:24810286

Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

PubMed Central

Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

2014-01-01

Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of microbiota signatures for personalized nutrition. PMID:24603757

Intelligent seismic risk mitigation system on structure building

NASA Astrophysics Data System (ADS)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis.

PubMed

Baig, Sofia; Medlyn, Belinda E; Mercado, Lina M; Zaehle, Sönke

2015-12-01

The temperature dependence of the reaction kinetics of the Rubisco enzyme implies that, at the level of a chloroplast, the response of photosynthesis to rising atmospheric CO2 concentration (Ca ) will increase with increasing air temperature. Vegetation models incorporating this interaction predict that the response of net primary productivity (NPP) to elevated CO2 (eCa ) will increase with rising temperature and will be substantially larger in warm tropical forests than in cold boreal forests. We tested these model predictions against evidence from eCa experiments by carrying out two meta-analyses. Firstly, we tested for an interaction effect on growth responses in factorial eCa  × temperature experiments. This analysis showed a positive, but nonsignificant interaction effect (95% CI for above-ground biomass response = -0.8, 18.0%) between eCa and temperature. Secondly, we tested field-based eCa experiments on woody plants across the globe for a relationship between the eCa effect on plant biomass and mean annual temperature (MAT). This second analysis showed a positive but nonsignificant correlation between the eCa response and MAT. The magnitude of the interactions between CO2 and temperature found in both meta-analyses were consistent with model predictions, even though both analyses gave nonsignificant results. Thus, we conclude that it is not possible to distinguish between the competing hypotheses of no interaction vs. an interaction based on Rubisco kinetics from the available experimental database. Experiments in a wider range of temperature zones are required. Until such experimental data are available, model predictions should aim to incorporate uncertainty about this interaction. © 2015 John Wiley & Sons Ltd.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

PubMed Central

Arndt, Alice; Rubel, Julian; Berger, Thomas; Schröder, Johanna; Späth, Christina; Meyer, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen

2017-01-01

Background Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment. Objective The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects. Methods We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression. Results Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04). Conclusions These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources. PMID:28600278

The Predictive Validity of Interim Assessment Scores Based on the Full-Information Bifactor Model for the Prediction of End-of-Grade Test Performance

ERIC Educational Resources Information Center

Immekus, Jason C.; Atitya, Ben

2016-01-01

Interim tests are a central component of district-wide assessment systems, yet their technical quality to guide decisions (e.g., instructional) has been repeatedly questioned. In response, the study purpose was to investigate the validity of a series of English Language Arts (ELA) interim assessments in terms of dimensionality and prediction of…

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

PubMed

Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95.125% and an AUC of 0.987 on the HLA-DRB1*0101 allele of the Wang benchmark dataset. The results indicate that the proposed prediction technique "GAPES" is a promising technique that will help researchers and scientists to predict the protein structure and it will assist them in the intelligent design of new epitope-based vaccines. Copyright © 2017 Elsevier B.V. All rights reserved.

Genomic prediction of piglet response to infection with one of two porcine reproductive and respiratory syndrome virus isolates.

PubMed

Waide, Emily H; Tuggle, Christopher K; Serão, Nick V L; Schroyen, Martine; Hess, Andrew; Rowland, Raymond R R; Lunney, Joan K; Plastow, Graham; Dekkers, Jack C M

2018-02-01

Genomic prediction of the pig's response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0.23 for weight gain (WG) following experimental PRRSV challenge, which demonstrates that genomic selection could be used to improve response to PRRSV infection. Training on WG data during infection with a less virulent PRRSV, KS06, resulted in poor accuracy of prediction for WG during infection with a more virulent PRRSV, NVSL. Inclusion of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with a major quantitative trait locus (QTL) on chromosome 4 was vital for accurate prediction of VL. Overall, SNPs that were significantly associated with either trait in single SNP genome-wide association analysis were unable to predict the phenotypes with an accuracy as high as that obtained by using all genotyped SNPs across the genome. Inclusion of data from close relatives into the training population increased whole genome prediction accuracy by 33% for VL and by 37% for WG but did not affect the accuracy of prediction when using only SNPs in the major QTL region. Results show that genomic prediction of response to PRRSV infection is moderately accurate and, when using all SNPs on the porcine SNP60 Beadchip, is not very sensitive to differences in virulence of the PRRSV in training and validation populations. Including close relatives in the training population increased prediction accuracy when using the whole genome or SNPs other than those near a major QTL.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

XBeach-G: a tool for predicting gravel barrier response to extreme storm conditions

NASA Astrophysics Data System (ADS)

Masselink, Gerd; Poate, Tim; McCall, Robert; Roelvink, Dano; Russell, Paul; Davidson, Mark

2014-05-01

Gravel beaches protect low-lying back-barrier regions from flooding during storm events and their importance to society is widely acknowledged. Unfortunately, breaching and extensive storm damage has occurred at many gravel sites and this is likely to increase as a result of sea-level rise and enhanced storminess due to climate change. Limited scientific guidance is currently available to provide beach managers with operational management tools to predict the response of gravel beaches to storms. The New Understanding and Prediction of Storm Impacts on Gravel beaches (NUPSIG) project aims to improve our understanding of storm impacts on gravel coastal environments and to develop a predictive capability by modelling these impacts. The NUPSIG project uses a 5-pronged approach to address its aim: (1) analyse hydrodynamic data collected during a proto-type laboratory experiment on a gravel beach; (2) collect hydrodynamic field data on a gravel beach under a range of conditions, including storm waves with wave heights up to 3 m; (3) measure swash dynamics and beach response on 10 gravel beaches during extreme wave conditions with wave heights in excess of 3 m; (4) use the data collected under 1-3 to develop and validate a numerical model to model hydrodynamics and morphological response of gravel beaches under storm conditions; and (5) develop a tool for end-users, based on the model formulated under (4), for predicting storm response of gravel beaches and barriers. The aim of this presentation is to present the key results of the NUPSIG project and introduce the end-user tool for predicting storm response on gravel beaches. The model is based on the numerical model XBeach, and different forcing scenarios (wave and tides), barrier configurations (dimensions) and sediment characteristics are easily uploaded for model simulations using a Graphics User Interface (GUI). The model can be used to determine the vulnerability of gravel barriers to storm events, but can also be used to help optimise design criteria for gravel barriers to reduce their vulnerability and enhance their coastal protection ability.

Finite element thermal analysis of multispectral coatings for the ABL

NASA Astrophysics Data System (ADS)

Shah, Rashmi S.; Bettis, Jerry R.; Stewart, Alan F.; Bonsall, Lynn; Copland, James; Hughes, William; Echeverry, Juan C.

1999-04-01

The thermal response of a coated optical surface is an important consideration in the design of any high average power system. Finite element temperature distribution were calculated for both coating witness samples and calorimetry wafers and were compared to actual measured data under tightly controlled conditions. Coatings for ABL were deposited on various substrates including fused silica, ULE, Zerodur, and silicon. The witness samples were irradiate data high power levels at 1.315micrometers to evaluate laser damage thresholds and study absorption levels. Excellent agreement was obtained between temperature predictions and measured thermal response curves. When measured absorption values were not available, the code was used to predict coating absorption based on the measured temperature rise on the back surface. Using the finite element model, the damaging temperature rise can be predicted for a coating with known absorption based on run time, flux, and substrate material.

A prescribed wake rotor inflow and flow field prediction analysis, user's manual and technical approach

NASA Technical Reports Server (NTRS)

Egolf, T. A.; Landgrebe, A. J.

1982-01-01

A user's manual is provided which includes the technical approach for the Prescribed Wake Rotor Inflow and Flow Field Prediction Analysis. The analysis is used to provide the rotor wake induced velocities at the rotor blades for use in blade airloads and response analyses and to provide induced velocities at arbitrary field points such as at a tail surface. This analysis calculates the distribution of rotor wake induced velocities based on a prescribed wake model. Section operating conditions are prescribed from blade motion and controls determined by a separate blade response analysis. The analysis represents each blade by a segmented lifting line, and the rotor wake by discrete segmented trailing vortex filaments. Blade loading and circulation distributions are calculated based on blade element strip theory including the local induced velocity predicted by the numerical integration of the Biot-Savart Law applied to the vortex wake model.

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

Resolving Mixed Algal Species in Hyperspectral Images

PubMed Central

Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V.

2014-01-01

We investigated a lab-based hyperspectral imaging system's response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system's performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert's law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements. PMID:24451451

Validity, responsiveness, and minimal clinically important difference of EQ-5D-5L in stroke patients undergoing rehabilitation.

PubMed

Chen, Poyu; Lin, Keh-Chung; Liing, Rong-Jiuan; Wu, Ching-Yi; Chen, Chia-Ling; Chang, Ku-Chou

2016-06-01

To examine the criterion validity, responsiveness, and minimal clinically important difference (MCID) of the EuroQoL 5-Dimensions Questionnaire (EQ-5D-5L) and visual analog scale (EQ-VAS) in people receiving rehabilitation after stroke. The EQ-5D-5L, along with four criterion measures-the Medical Research Council scales for muscle strength, the Fugl-Meyer assessment, the functional independence measure, and the Stroke Impact Scale-was administered to 65 patients with stroke before and after 3- to 4-week therapy. Criterion validity was estimated using the Spearman correlation coefficient. Responsiveness was analyzed by the effect size, standardized response mean (SRM), and criterion responsiveness. The MCID was determined by anchor-based and distribution-based approaches. The percentage of patients exceeding the MCID was also reported. Concurrent validity of the EQ-Index was better compared with the EQ-VAS. The EQ-Index has better power for predicting the rehabilitation outcome in the activities of daily living than other motor-related outcome measures. The EQ-Index was moderately responsive to change (SRM = 0.63), whereas the EQ-VAS was only mildly responsive to change. The MCID estimation of the EQ-Index (the percentage of patients exceeding the MCID) was 0.10 (33.8 %) and 0.10 (33.8 %) based on the anchor-based and distribution-based approaches, respectively, and the estimation of EQ-VAS was 8.61 (41.5 %) and 10.82 (32.3 %). The EQ-Index has shown reasonable concurrent validity, limited predictive validity, and acceptable responsiveness for detecting the health-related quality of life in stroke patients undergoing rehabilitation, but not for EQ-VAS. Future research considering different recovery stages after stroke is warranted to validate these estimations.

Cell density dependence of Microcystis aeruginosa responses to copper algaecide concentrations: Implications for microcystin-LR release.

PubMed

Kinley, Ciera M; Iwinski, Kyla J; Hendrikse, Maas; Geer, Tyler D; Rodgers, John H

2017-11-01

Along with mechanistic models, predictions of exposure-response relationships for copper are often derived from laboratory toxicity experiments with standardized experimental exposures and conditions. For predictions of copper toxicity to algae, cell density is a critical factor often overlooked. For pulse exposures of copper-based algaecides in aquatic systems, cell density can significantly influence copper sorbed by the algal population, and consequent responses. A cyanobacterium, Microcystis aeruginosa, was exposed to a copper-based algaecide over a range of cell densities to model the density-dependence of exposures, and effects on microcystin-LR (MC-LR) release. Copper exposure concentrations were arrayed to result in a gradient of MC-LR release, and masses of copper sorbed to algal populations were measured following exposures. While copper exposure concentrations eliciting comparable MC-LR release ranged an order of magnitude (24-h EC50s 0.03-0.3mg Cu/L) among cell densities of 10 6 through 10 7 cells/mL, copper doses (mg Cu/mg algae) were similar (24-h EC50s 0.005-0.006mg Cu/mg algae). Comparisons of MC-LR release as a function of copper exposure concentrations and doses provided a metric of the density dependence of algal responses in the context of copper-based algaecide applications. Combined with estimates of other site-specific factors (e.g. water characteristics) and fate processes (e.g. dilution and dispersion, sorption to organic matter and sediments), measuring exposure-response relationships for specific cell densities can refine predictions for in situ exposures and algal responses. These measurements can in turn decrease the likelihood of amending unnecessary copper concentrations to aquatic systems, and minimize risks for non-target aquatic organisms. Copyright © 2017 Elsevier Inc. All rights reserved.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

PubMed

Maciukiewicz, Malgorzata; Marshe, Victoria S; Hauschild, Anne-Christin; Foster, Jane A; Rotzinger, Susan; Kennedy, James L; Kennedy, Sidney H; Müller, Daniel J; Geraci, Joseph

2018-04-01

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction. Copyright © 2017. Published by Elsevier Ltd.

Test-Anchored Vibration Response Predictions for an Acoustically Energized Curved Orthogrid Panel with Mounted Components

NASA Technical Reports Server (NTRS)

Frady, Gregory P.; Duvall, Lowery D.; Fulcher, Clay W. G.; Laverde, Bruce T.; Hunt, Ronald A.

2011-01-01

A rich body of vibroacoustic test data was recently generated at Marshall Space Flight Center for a curved orthogrid panel typical of launch vehicle skin structures. Several test article configurations were produced by adding component equipment of differing weights to the flight-like vehicle panel. The test data were used to anchor computational predictions of a variety of spatially distributed responses including acceleration, strain and component interface force. Transfer functions relating the responses to the input pressure field were generated from finite element based modal solutions and test-derived damping estimates. A diffuse acoustic field model was employed to describe the assumed correlation of phased input sound pressures across the energized panel. This application demonstrates the ability to quickly and accurately predict a variety of responses to acoustically energized skin panels with mounted components. Favorable comparisons between the measured and predicted responses were established. The validated models were used to examine vibration response sensitivities to relevant modeling parameters such as pressure patch density, mesh density, weight of the mounted component and model form. Convergence metrics include spectral densities and cumulative root-mean squared (RMS) functions for acceleration, velocity, displacement, strain and interface force. Minimum frequencies for response convergence were established as well as recommendations for modeling techniques, particularly in the early stages of a component design when accurate structural vibration requirements are needed relatively quickly. The results were compared with long-established guidelines for modeling accuracy of component-loaded panels. A theoretical basis for the Response/Pressure Transfer Function (RPTF) approach provides insight into trends observed in the response predictions and confirmed in the test data. The software modules developed for the RPTF method can be easily adapted for quick replacement of the diffuse acoustic field with other pressure field models; for example a turbulent boundary layer (TBL) model suitable for vehicle ascent. Wind tunnel tests have been proposed to anchor the predictions and provide new insight into modeling approaches for this type of environment. Finally, component vibration environments for design were developed from the measured and predicted responses and compared with those derived from traditional techniques such as Barrett scaling methods for unloaded and component-loaded panels.

Neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are predictive of chemotherapeutic response and prognosis in epithelial ovarian cancer patients treated with platinum-based chemotherapy.

PubMed

Miao, Yi; Yan, Qin; Li, Shuangdi; Li, Bilan; Feng, Youji

2016-06-07

The aim of present study was to investigate the role of preoperative neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) used as prognostic markers for predicting chemotherapeutic response and survival outcomes in patients with epithelial ovarian cancer (EOC) who are receiving platinum-based chemotherapy. A total of 344 patients diagnosed with EOC who are receiving platinum-based chemotherapy from 2005 to 2010 in the hospital were enrolled. NLR and PLR were calculated from complete blood cell count taken before operation. The patients were divided into platinum-resistant (P-R) group and platinum-sensitive (P-S) group according to chemotherapeutic response. Clinicopathologic variables and outcomes were retrospectively collected and compared among groups. We used receiver operating characteristic (ROC) curves to calculate optimal cut-off values for NLR and PLR to predict chemotherapeutic response and prognosis. The AUC, sensitivity, specificity of NLR > 3.02 to predict platinum resistance were 0.819, 75.0% and 81.45%, respectively. The corresponding values of PLR > 207 were 0.727, 60.42% and 85.48%, respectively. Patients with lower value of NLR (NLR < 3.02) or PLR (PLR < 207) had a longer progression-free survival (PFS) and overall survival (OS). In multivariate analysis, NLR and PLR showed a significant association with PFS (hazard ratio [HR], 1.733; 95%CI, 1.225-2.453, P = 0.002 and HR, 1.952; 95%CI, 1.430-2.662, P < 0.001) and OS (HR, 1.616; 95%CI, 1.138-2.297, P = 0.007, and HR, 2.167; 95%CI, 1.565-3.000, P < 0.001). These results suggest that the assessment of NLR and PLR could assist the identification of patients with poor prognosis and had potential clinical value in predicting platinum resistance in patients with EOC.

Responsibility/Threat Overestimation Moderates the Relationship Between Contamination-Based Disgust and Obsessive-Compulsive Concerns About Sexual Orientation.

PubMed

Ching, Terence H W; Williams, Monnica T; Siev, Jedidiah; Olatunji, Bunmi O

2018-05-01

Disgust has been shown to perform a "disease-avoidance" function in contamination fears. However, no studies have examined the relevance of disgust to obsessive-compulsive (OC) concerns about sexual orientation (e.g., fear of one's sexual orientation transforming against one's will, and compulsive avoidance of same-sex and/or gay or lesbian individuals to prevent that from happening). Therefore, we investigated whether the specific domain of contamination-based disgust (i.e., evoked by the perceived threat of transmission of essences between individuals) predicted OC concerns about sexual orientation, and whether this effect was moderated/amplified by obsessive beliefs, in evaluation of a "sexual orientation transformation-avoidance" function. We recruited 283 self-identified heterosexual college students (152 females, 131 males; mean age = 20.88 years, SD = 3.19) who completed three measures assessing disgust, obsessive beliefs, and OC concerns about sexual orientation. Results showed that contamination-based disgust (β = .17), responsibility/threat overestimation beliefs (β = .15), and their interaction (β = .17) each uniquely predicted OC concerns about sexual orientation, ts = 2.22, 2.50, and 2.90, ps < .05. Post hoc probing indicated that high contamination-based disgust accompanied by strong responsibility/threat overestimation beliefs predicted more severe OC concerns about sexual orientation, β = .48, t = 3.24, p < .001. The present study, therefore, provided preliminary evidence for a "sexual orientation transformation-avoidance" process underlying OC concerns about sexual orientation in heterosexual college students, which is facilitated by contamination-based disgust, and exacerbated by responsibility/threat overestimation beliefs. Treatment for OC concerns about sexual orientation should target such beliefs.

The diagnostic utility of the Rarely Missed Index of the Wechsler Memory Scale-Third Edition in detecting response bias in an adult male incarcerated setting.

PubMed

D'Amato, Christopher P; Denney, Robert L

2008-09-01

The purpose of this study was to utilize a known-group research design to evaluate the diagnostic utility of the Rarely Missed Index (RMI) of the Wechsler Memory Scale-Third Edition [Wechsler, D. (1997). Wechsler Adult Intelligence Test-3rd Edition. San Antonio, TX: The Psychological Corporation] in assessing response bias in an adult male incarcerated setting. Archival data from a sample of 60 adult male inmates who presented for neuropsychological testing were reviewed. Evaluees were assigned to one of two groups; probable malingerers (PM; n=30) and a group of valid test responders (n=30) (1999). Using the recommended cut-off score of 136 or less, the sensitivity of the RMI was extremely low at 33%. Its specificity was 83%. The positive predictive power of the RMI with the published base rate of 22.8 was 38%; with a negative predictive power of 81%. The positive predictive power of the RMI with a published base rate of 70.5 was 82%. The negative predictive power using a base rate of 70.5% was 34%. Results of the receiver operating characteristics (ROC) analysis indicated that the RMI score with a cut-off 136 or less performed only slightly better than chance in delineating probable malingerers from valid responders in this setting. Overall, the findings suggest that the RMI may not be a reliable index for detecting response bias in this setting and perhaps in similar settings.

Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment.

PubMed

Breska, Assaf; Deouell, Leon Y

2017-02-01

Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction.

Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment

PubMed Central

Deouell, Leon Y.

2017-01-01

Predicting the timing of upcoming events enables efficient resource allocation and action preparation. Rhythmic streams, such as music, speech, and biological motion, constitute a pervasive source for temporal predictions. Widely accepted entrainment theories postulate that rhythm-based predictions are mediated by synchronizing low-frequency neural oscillations to the rhythm, as indicated by increased phase concentration (PC) of low-frequency neural activity for rhythmic compared to random streams. However, we show here that PC enhancement in scalp recordings is not specific to rhythms but is observed to the same extent in less periodic streams if they enable memory-based prediction. This is inconsistent with the predictions of a computational entrainment model of stronger PC for rhythmic streams. Anticipatory change in alpha activity and facilitation of electroencephalogram (EEG) manifestations of response selection are also comparable between rhythm- and memory-based predictions. However, rhythmic sequences uniquely result in obligatory depression of preparation-related premotor brain activity when an on-beat event is omitted, even when it is strategically beneficial to maintain preparation, leading to larger behavioral costs for violation of prediction. Thus, while our findings undermine the validity of PC as a sign of rhythmic entrainment, they constitute the first electrophysiological dissociation, to our knowledge, between mechanisms of rhythmic predictions and of memory-based predictions: the former obligatorily lead to resonance-like preparation patterns (that are in line with entrainment), while the latter allow flexible resource allocation in time regardless of periodicity in the input. Taken together, they delineate the neural mechanisms of three distinct modes of preparation: continuous vigilance, interval-timing-based prediction and rhythm-based prediction. PMID:28187128

When more is less: Feedback effects in perceptual category learning ☆

PubMed Central

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

2008-01-01

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed. PMID:18455155

Test and Evaluation of TRUST: Tools for Recognizing Useful Signals of Trustworthiness

DTIC Science & Technology

2016-04-01

guaranteed, social exchange requires trust—the belief that others will follow through on their obligations. The model includes the beliefs that...current reflection could be measured based on properties of the skin, and (2) skin conductance response (SCR), where the fastest could be measured and...SEM prediction (H4d). The results of the LF HRV signals indicate the SEM model predicts distrust base on the experimental SS paradigm and SEM

Predictive factors in the evaluation of treatment response to neoadjuvant chemoradiotherapy in patients with advanced esophageal squamous cell cancer

PubMed Central

Wong, Claudia

2017-01-01

Neoadjuvant therapy before esophagectomy is evidence-based, and is a standard-of-care for locally advanced and operable esophageal cancer. However response to such treatment varies in individual patients, from no clinical response to pathological complete response. It has been consistently shown that a good pathological responses is of prognostic value, but perhaps in the expense of those who do not. It is important to identify suitable predictive factors for response, so that patients are not exposed to potentially harmful chemotherapy and/or radiotherapy without benefits. Alternative management strategies can be devised. Various clinical, radiological, serological and potential molecular markers have been studied. None has been shown to be sufficiently reliable to be used in daily practice. Certainly more understanding of the molecular basis for response to chemotherapy/radiotherapy is needed, so that patient treatment can be tailored and individualized. PMID:28815073

Organizational respect dampens the impact of group-based relative deprivation on willingness to protest pay cuts.

PubMed

Osborne, Danny; Huo, Yuen J; Smith, Heather J

2015-03-01

Although group-based relative deprivation predicts people's willingness to protest unfair outcomes, perceiving that one's subgroup is respected increases employees' support for organizations. An integration of these perspectives suggests that subgroup respect will dampen the impact of group-based relative deprivation on workers' responses to unfair organizational outcomes. We examined this hypothesis among university faculty (N = 804) who underwent a system-wide pay cut. As expected, group-based relative deprivation predicted protest intentions. This relationship was, however, muted among those who believed university administrators treated their area of expertise (i.e., their subgroup) with a high (vs. low) level of respect. Moderated mediation analyses confirmed that group-based relative deprivation had a conditional indirect effect on protest intentions via participants' (dis)identification with their university at low to moderate, but not high, levels of subgroup respect. Our finding that satisfying relational needs can attenuate responses to group-based relative deprivation demonstrates the benefits of integrating insights from distinct research traditions. © 2014 The British Psychological Society.

Empirical Model Development for Predicting Shock Response on Composite Materials Subjected to Pyroshock Loading. Volume 2, Part 1; Appendices

NASA Technical Reports Server (NTRS)

Gentz, Steven J.; Ordway, David O.; Parsons, David S.; Garrison, Craig M.; Rodgers, C. Steven; Collins, Brian W.

2015-01-01

The NASA Engineering and Safety Center (NESC) received a request to develop an analysis model based on both frequency response and wave propagation analyses for predicting shock response spectrum (SRS) on composite materials subjected to pyroshock loading. The model would account for near-field environment (approximately 9 inches from the source) dominated by direct wave propagation, mid-field environment (approximately 2 feet from the source) characterized by wave propagation and structural resonances, and far-field environment dominated by lower frequency bending waves in the structure. This document contains appendices to the Volume I report.

Using Mobile Phone Data to Predict the Spatial Spread of Cholera

PubMed Central

Bengtsson, Linus; Gaudart, Jean; Lu, Xin; Moore, Sandra; Wetter, Erik; Sallah, Kankoe; Rebaudet, Stanislas; Piarroux, Renaud

2015-01-01

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains. PMID:25747871

Using mobile phone data to predict the spatial spread of cholera.

PubMed

Bengtsson, Linus; Gaudart, Jean; Lu, Xin; Moore, Sandra; Wetter, Erik; Sallah, Kankoe; Rebaudet, Stanislas; Piarroux, Renaud

2015-03-09

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

Paracrine communication maximizes cellular response fidelity in wound signaling

PubMed Central

Handly, L Naomi; Pilko, Anna; Wollman, Roy

2015-01-01

Population averaging due to paracrine communication can arbitrarily reduce cellular response variability. Yet, variability is ubiquitously observed, suggesting limits to paracrine averaging. It remains unclear whether and how biological systems may be affected by such limits of paracrine signaling. To address this question, we quantify the signal and noise of Ca2+ and ERK spatial gradients in response to an in vitro wound within a novel microfluidics-based device. We find that while paracrine communication reduces gradient noise, it also reduces the gradient magnitude. Accordingly we predict the existence of a maximum gradient signal to noise ratio. Direct in vitro measurement of paracrine communication verifies these predictions and reveals that cells utilize optimal levels of paracrine signaling to maximize the accuracy of gradient-based positional information. Our results demonstrate the limits of population averaging and show the inherent tradeoff in utilizing paracrine communication to regulate cellular response fidelity. DOI: http://dx.doi.org/10.7554/eLife.09652.001 PMID:26448485

Bootstrapping agency: How control-relevant information affects motivation.

PubMed

Karsh, Noam; Eitam, Baruch; Mark, Ilya; Higgins, E Tory

2016-10-01

How does information about one's control over the environment (e.g., having an own-action effect) influence motivation? The control-based response selection framework was proposed to predict and explain such findings. Its key tenant is that control relevant information modulates both the frequency and speed of responses by determining whether a perceptual event is an outcome of one's actions or not. To test this framework empirically, the current study examines whether and how temporal and spatial contiguity/predictability-previously established as being important for one's sense of agency-modulate motivation from control. In 5 experiments, participants responded to a cue, potentially triggering a perceptual effect. Temporal (Experiments 1a-c) and spatial (Experiments 2a and b) contiguity/predictability between actions and their potential effects were experimentally manipulated. The influence of these control-relevant factors was measured, both indirectly (through their effect on explicit judgments of agency) and directly on response time and response frequency. The pattern of results was highly consistent with the control-based response selection framework in suggesting that control relevant information reliably modulates the impact of "having an effect" on different levels of action selection. We discuss the implications of this study for the notion of motivation from control and for the empirical work on the sense of agency. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

An efficient formulation of Krylov's prediction model for train induced vibrations based on the dynamic reciprocity theorem.

PubMed

Degrande, G; Lombaert, G

2001-09-01

In Krylov's analytical prediction model, the free field vibration response during the passage of a train is written as the superposition of the effect of all sleeper forces, using Lamb's approximate solution for the Green's function of a halfspace. When this formulation is extended with the Green's functions of a layered soil, considerable computational effort is required if these Green's functions are needed in a wide range of source-receiver distances and frequencies. It is demonstrated in this paper how the free field response can alternatively be computed, using the dynamic reciprocity theorem, applied to moving loads. The formulation is based on the response of the soil due to the moving load distribution for a single axle load. The equations are written in the wave-number-frequency domain, accounting for the invariance of the geometry in the direction of the track. The approach allows for a very efficient calculation of the free field vibration response, distinguishing the quasistatic contribution from the effect of the sleeper passage frequency and its higher harmonics. The methodology is validated by means of in situ vibration measurements during the passage of a Thalys high-speed train on the track between Brussels and Paris. It is shown that the model has good predictive capabilities in the near field at low and high frequencies, but underestimates the response in the midfrequency band.

Predicting community responses to perturbations in the face of imperfect knowledge and network complexity

USGS Publications Warehouse

Novak, M.; Wootton, J.T.; Doak, D.F.; Emmerson, M.; Estes, J.A.; Tinker, M.T.

2011-01-01

How best to predict the effects of perturbations to ecological communities has been a long-standing goal for both applied and basic ecology. This quest has recently been revived by new empirical data, new analysis methods, and increased computing speed, with the promise that ecologically important insights may be obtainable from a limited knowledge of community interactions. We use empirically based and simulated networks of varying size and connectance to assess two limitations to predicting perturbation responses in multispecies communities: (1) the inaccuracy by which species interaction strengths are empirically quantified and (2) the indeterminacy of species responses due to indirect effects associated with network size and structure. We find that even modest levels of species richness and connectance (??25 pairwise interactions) impose high requirements for interaction strength estimates because system indeterminacy rapidly overwhelms predictive insights. Nevertheless, even poorly estimated interaction strengths provide greater average predictive certainty than an approach that uses only the sign of each interaction. Our simulations provide guidance in dealing with the trade-offs involved in maximizing the utility of network approaches for predicting dynamics in multispecies communities. ?? 2011 by the Ecological Society of America.

Detecting a Clinically Meaningful Change in Tic Severity in Tourette Syndrome: A Comparison of Three Methods

PubMed Central

Jeon, Sangchoon; Walkup, John T; Woods, Douglas W.; Peterson, Alan; Piacentini, John; Wilhelm, Sabine; Katsovich, Lily; McGuire, Joseph F.; Dziura, James; Scahill, Lawrence

2014-01-01

Objective To compare three statistical strategies for classifying positive treatment response based on a dimensional measure (Yale Global Tic Severity Scale [YGTSS]) and a categorical measure (Clinical Global Impression-Improvement [CGI-I]). Method Subjects (N=232; 69.4% male; ages 9-69 years) with Tourette syndrome or chronic tic disorder participated in one of two 10-week, randomized controlled trials comparing behavioral treatment to supportive therapy. The YGTSS and CGI-I were rated by clinicians blind to treatment assignment. We examined the percent reduction in the YGTSS-Total Tic Score (TTS) against Much Improved or Very Much Improved on the CGI-I, computed a signal detection analysis (SDA) and built a mixture model to classify dimensional response based on the change in the YGTSS-TTS. Results A 25% decrease on the YGTSS-TTS predicted positive response on the CGI-I during the trial. The SDA showed that a 25% reduction in the YGTSS-TTS provided optimal sensitivity (87%) and specificity (84%) for predicting positive response. Using a mixture model without consideration of the CGI-I, the dimensional response was defined by 23% (or greater) reduction on the YGTSS-TTS. The odds ratio (OR) of positive response (OR=5.68, 95% CI=[2.99, 10.78]) on the CGI-I for behavioral intervention was greater than the dimensional response (OR=2.86, 95% CI=[1.65, 4.99]). Conclusion A twenty five percent reduction on the YGTSS-TTS is highly predictive of positive response by all three analytic methods. For trained raters, however, tic severity alone does not drive the classification of positive response. PMID:24001701

Predictive value of dorso-lateral prefrontal connectivity for rTMS response in treatment-resistant depression: A brain perfusion SPECT study.

PubMed

Richieri, Raphaëlle; Verger, Antoine; Boyer, Laurent; Boucekine, Mohamed; David, Anthony; Lançon, Christophe; Cermolacce, Michel; Guedj, Eric

2018-05-18

Previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect in patients with treatment resistant depression (TRD). However, results remain heterogeneous with many patients without effective response. The aim of this SPECT study was to determine before treatment the predictive value of the connectivity of the stimulated area on further rTMS response in patients with TRD. Fifty-eight TRD patients performed a brain perfusion SPECT before high frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). A voxel based-analysis was achieved to compare connectivity of the left DLPFC in responders and non-responders using inter-regional correlations (p < 0.005, corrected for cluster volume). A multiple logistic regression model was thereafter used with the goal of establishing a predictive score. Before rTMS, responders exhibited increased SPECT connectivity between the left DLPFC and the right cerebellum in comparison to non-responders, independently of age, gender, severity of depression, and severity of treatment resistance. The area under the curve for the combination of these two SPECT clusters to predict rTMS response was 0.756 (p < 0.005). SPECT connectivity of the left DLPFC predicts rTMS response before treatment. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

PubMed

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong-Ju; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung-Hwan; Kim, Youngsoo

2017-03-03

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Image quality prediction - An aid to the Viking lander imaging investigation on Mars

NASA Technical Reports Server (NTRS)

Huck, F. O.; Wall, S. D.

1976-01-01

Image quality criteria and image quality predictions are formulated for the multispectral panoramic cameras carried by the Viking Mars landers. Image quality predictions are based on expected camera performance, Mars surface radiance, and lighting and viewing geometry (fields of view, Mars lander shadows, solar day-night alternation), and are needed in diagnosis of camera performance, in arriving at a preflight imaging strategy, and revision of that strategy should the need arise. Landing considerations, camera control instructions, camera control logic, aspects of the imaging process (spectral response, spatial response, sensitivity), and likely problems are discussed. Major concerns include: degradation of camera response by isotope radiation, uncertainties in lighting and viewing geometry and in landing site local topography, contamination of camera window by dust abrasion, and initial errors in assigning camera dynamic ranges (gains and offsets).

The Effect of Basis Selection on Thermal-Acoustic Random Response Prediction Using Nonlinear Modal Simulation

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Przekop, Adam

2004-01-01

The goal of this investigation is to further develop nonlinear modal numerical simulation methods for prediction of geometrically nonlinear response due to combined thermal-acoustic loadings. As with any such method, the accuracy of the solution is dictated by the selection of the modal basis, through which the nonlinear modal stiffness is determined. In this study, a suite of available bases are considered including (i) bending modes only; (ii) coupled bending and companion modes; (iii) uncoupled bending and companion modes; and (iv) bending and membrane modes. Comparison of these solutions with numerical simulation in physical degrees-of-freedom indicates that inclusion of any membrane mode variants (ii - iv) in the basis affects the bending displacement and stress response predictions. The most significant effect is on the membrane displacement, where it is shown that only the type (iv) basis accurately predicts its behavior. Results are presented for beam and plate structures in the thermally pre-buckled regime.

Experimental and numerical study of physiological responses in hot environments.

PubMed

Yang, Jie; Weng, Wenguo; Zhang, Baoting

2014-10-01

This paper proposed a multi-node human thermal model to predict human thermal responses in hot environments. The model was extended based on the Tanabe's work by considering the effects of high temperature on heat production, blood flow rate, and heat exchange coefficients. Five healthy men dressed in shorts were exposed in thermal neutral (29 °C) and high temperature (45 °C) environments. The rectal temperatures and skin temperatures of seven human body segments were continuously measured during the experiment. Validation of this model was conducted with experimental data. The results showed that the current model could accurately predict the skin and core temperatures in terms of the tendency and absolute values. In the human body segments expect calf and trunk, the temperature differences between the experimental data and the predicted results in high temperature environment were smaller than those in the thermally neutral environment conditions. The extended model was proved to be capable of predicting accurately human physiological responses in hot environments. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dissociation between judgments and outcome-expectancy measures in covariation learning: a signal detection theory approach.

PubMed

Perales, José C; Catena, Andrés; Shanks, David R; González, José A

2005-09-01

A number of studies using trial-by-trial learning tasks have shown that judgments of covariation between a cue c and an outcome o deviate from normative metrics. Parameters based on trial-by-trial predictions were estimated from signal detection theory (SDT) in a standard causal learning task. Results showed that manipulations of P(c) when contingency (deltaP) was held constant did not affect participants' ability to predict the appearance of the outcome (d') but had a significant effect on response criterion (c) and numerical causal judgments. The association between criterion c and judgment was further demonstrated in 2 experiments in which the criterion was directly manipulated by linking payoffs to the predictive responses made by learners. In all cases, the more liberal the criterion c was, the higher judgments were. The results imply that the mechanisms underlying the elaboration of judgments and those involved in the elaboration of predictive responses are partially dissociable.

A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming.

PubMed

Cotto, Olivier; Wessely, Johannes; Georges, Damien; Klonner, Günther; Schmid, Max; Dullinger, Stefan; Thuiller, Wilfried; Guillaume, Frédéric

2017-05-05

Withstanding extinction while facing rapid climate change depends on a species' ability to track its ecological niche or to evolve a new one. Current methods that predict climate-driven species' range shifts use ecological modelling without eco-evolutionary dynamics. Here we present an eco-evolutionary forecasting framework that combines niche modelling with individual-based demographic and genetic simulations. Applying our approach to four endemic perennial plant species of the Austrian Alps, we show that accounting for eco-evolutionary dynamics when predicting species' responses to climate change is crucial. Perennial species persist in unsuitable habitats longer than predicted by niche modelling, causing delayed range losses; however, their evolutionary responses are constrained because long-lived adults produce increasingly maladapted offspring. Decreasing population size due to maladaptation occurs faster than the contraction of the species range, especially for the most abundant species. Monitoring of species' local abundance rather than their range may likely better inform on species' extinction risks under climate change.

Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas

PubMed Central

Lescroart, Mark D.; Stansbury, Dustin E.; Gallant, Jack L.

2015-01-01

Perception of natural visual scenes activates several functional areas in the human brain, including the Parahippocampal Place Area (PPA), Retrosplenial Complex (RSC), and the Occipital Place Area (OPA). It is currently unclear what specific scene-related features are represented in these areas. Previous studies have suggested that PPA, RSC, and/or OPA might represent at least three qualitatively different classes of features: (1) 2D features related to Fourier power; (2) 3D spatial features such as the distance to objects in a scene; or (3) abstract features such as the categories of objects in a scene. To determine which of these hypotheses best describes the visual representation in scene-selective areas, we applied voxel-wise modeling (VM) to BOLD fMRI responses elicited by a set of 1386 images of natural scenes. VM provides an efficient method for testing competing hypotheses by comparing predictions of brain activity based on encoding models that instantiate each hypothesis. Here we evaluated three different encoding models that instantiate each of the three hypotheses listed above. We used linear regression to fit each encoding model to the fMRI data recorded from each voxel, and we evaluated each fit model by estimating the amount of variance it predicted in a withheld portion of the data set. We found that voxel-wise models based on Fourier power or the subjective distance to objects in each scene predicted much of the variance predicted by a model based on object categories. Furthermore, the response variance explained by these three models is largely shared, and the individual models explain little unique variance in responses. Based on an evaluation of previous studies and the data we present here, we conclude that there is currently no good basis to favor any one of the three alternative hypotheses about visual representation in scene-selective areas. We offer suggestions for further studies that may help resolve this issue. PMID:26594164

Detecting failure of climate predictions

USGS Publications Warehouse

Runge, Michael C.; Stroeve, Julienne C.; Barrett, Andrew P.; McDonald-Madden, Eve

2016-01-01

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1, 2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.

PubMed

Alshurafa, Nabil; Sideris, Costas; Pourhomayoun, Mohammad; Kalantarian, Haik; Sarrafzadeh, Majid; Eastwood, Jo-Ann

2017-03-01

Remote health monitoring (RHM) systems are becoming more widely adopted by clinicians and hospitals to remotely monitor and communicate with patients while optimizing clinician time, decreasing hospital costs, and improving quality of care. In the Women's heart health study (WHHS), we developed Wanda-cardiovascular disease (CVD), where participants received healthy lifestyle education followed by six months of technology support and reinforcement. Wanda-CVD is a smartphone-based RHM system designed to assist participants in reducing identified CVD risk factors through wireless coaching using feedback and prompts as social support. Many participants benefitted from this RHM system. In response to the variance in participants' success, we developed a framework to identify classification schemes that predicted successful and unsuccessful participants. We analyzed both contextual baseline features and data from the first month of intervention such as activity, blood pressure, and questionnaire responses transmitted through the smartphone. A prediction tool can aid clinicians and scientists in identifying participants who may optimally benefit from the RHM system. Targeting therapies could potentially save healthcare costs, clinician, and participant time and resources. Our classification scheme yields RHM outcome success predictions with an F-measure of 91.9%, and identifies behaviors during the first month of intervention that help determine outcome success. We also show an improvement in prediction by using intervention-based smartphone data. Results from the WHHS study demonstrates that factors such as the variation in first month intervention response to the consumption of nuts, beans, and seeds in the diet help predict patient RHM protocol outcome success in a group of young Black women ages 25-45.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Masked priming effect reflects evidence accumulated by the prime.

PubMed

Kinoshita, Sachiko; Norris, Dennis

2010-01-01

In the same-different match task, masked priming is observed with the same responses but not different responses. Norris and Kinoshita's (2008) Bayesian reader account of masked priming explains this pattern based on the same principle as that explaining the absence of priming for nonwords in the lexical decision task. The pattern of priming follows from the way the model makes optimal decisions in the two tasks; priming does not depend on first activating the prime and then the target. An alternative explanation is in terms of a bias towards responding "same" that exactly counters the facilitatory effect of lexical access. The present study tested these two views by varying both the degree to which the prime predicts the response and the visibility of the prime. Unmasked primes produced effects expected from the view that priming is influenced by the degree to which the prime predicts the response. In contrast, with masked primes, the size of priming for the same response was completely unaffected by predictability. These results rule out response bias as an explanation of the absence of masked priming for different responses and, in turn, indicate that masked priming is not a consequence of automatic lexical access of the prime.

Application of Fracture Distribution Prediction Model in Xihu Depression of East China Sea

NASA Astrophysics Data System (ADS)

Yan, Weifeng; Duan, Feifei; Zhang, Le; Li, Ming

2018-02-01

There are different responses on each of logging data with the changes of formation characteristics and outliers caused by the existence of fractures. For this reason, the development of fractures in formation can be characterized by the fine analysis of logging curves. The well logs such as resistivity, sonic transit time, density, neutron porosity and gamma ray, which are classified as conventional well logs, are more sensitive to formation fractures. In view of traditional fracture prediction model, using the simple weighted average of different logging data to calculate the comprehensive fracture index, are more susceptible to subjective factors and exist a large deviation, a statistical method is introduced accordingly. Combining with responses of conventional logging data on the development of formation fracture, a prediction model based on membership function is established, and its essence is to analyse logging data with fuzzy mathematics theory. The fracture prediction results in a well formation in NX block of Xihu depression through two models are compared with that of imaging logging, which shows that the accuracy of fracture prediction model based on membership function is better than that of traditional model. Furthermore, the prediction results are highly consistent with imaging logs and can reflect the development of cracks much better. It can provide a reference for engineering practice.

How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration-exploitation trade-offs.

PubMed

Monk, Christopher T; Barbier, Matthieu; Romanczuk, Pawel; Watson, James R; Alós, Josep; Nakayama, Shinnosuke; Rubenstein, Daniel I; Levin, Simon A; Arlinghaus, Robert

2018-06-01

Understanding how humans and other animals behave in response to changes in their environments is vital for predicting population dynamics and the trajectory of coupled social-ecological systems. Here, we present a novel framework for identifying emergent social behaviours in foragers (including humans engaged in fishing or hunting) in predator-prey contexts based on the exploration difficulty and exploitation potential of a renewable natural resource. A qualitative framework is introduced that predicts when foragers should behave territorially, search collectively, act independently or switch among these states. To validate it, we derived quantitative predictions from two models of different structure: a generic mathematical model, and a lattice-based evolutionary model emphasising exploitation and exclusion costs. These models independently identified that the exploration difficulty and exploitation potential of the natural resource controls the social behaviour of resource exploiters. Our theoretical predictions were finally compared to a diverse set of empirical cases focusing on fisheries and aquatic organisms across a range of taxa, substantiating the framework's predictions. Understanding social behaviour for given social-ecological characteristics has important implications, particularly for the design of governance structures and regulations to move exploited systems, such as fisheries, towards sustainability. Our framework provides concrete steps in this direction. © 2018 John Wiley & Sons Ltd/CNRS.

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

PubMed

Månsson, K N T; Frick, A; Boraxbekk, C-J; Marquand, A F; Williams, S C R; Carlbring, P; Andersson, G; Furmark, T

2015-03-17

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

PubMed Central

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

Objective To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials and methods Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. Results The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. Discussion With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Conclusions Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC. PMID:23616206

GENOMIC PREDICTOR OF RESPONSE AND SURVIVAL FOLLOWING TAXANE-ANTHRACYCLINE CHEMOTHERAPY FOR INVASIVE BREAST CANCER

PubMed Central

Hatzis, Christos; Pusztai, Lajos; Valero, Vicente; Booser, Daniel J.; Esserman, Laura; Lluch, Ana; Vidaurre, Tatiana; Holmes, Frankie; Souchon, Eduardo; Martin, Miguel; Cotrina, José; Gomez, Henry; Hubbard, Rebekah; Chacón, J. Ignacio; Ferrer-Lozano, Jaime; Dyer, Richard; Buxton, Meredith; Gong, Yun; Wu, Yun; Ibrahim, Nuhad; Andreopoulou, Eleni; Ueno, Naoto T.; Hunt, Kelly; Yang, Wei; Nazario, Arlene; DeMichele, Angela; O’Shaughnessy, Joyce; Hortobagyi, Gabriel N.; Symmans, W. Fraser

2017-01-01

CONTEXT Accurate prediction of who will (or won’t) have high probability of survival benefit from standard treatments is fundamental for individualized cancer treatment strategies. OBJECTIVE To develop a predictor of response and survival from chemotherapy for newly diagnosed invasive breast cancer. DESIGN Development of different predictive signatures for resistance and response to neoadjuvant chemotherapy (stratified according to estrogen receptor (ER) status) from gene expression microarrays of newly diagnosed breast cancer (310 patients). Then prediction of breast cancer treatment-sensitivity using the combination of signatures for: 1) sensitivity to endocrine therapy, 2) chemo-resistance, and 3) chemo-sensitivity. Independent validation (198 patients) and comparison with other reported genomic predictors of chemotherapy response. SETTING Prospective multicenter study to develop and test genomic predictors for neoadjuvant chemotherapy. PATIENTS Newly diagnosed HER2-negative breast cancer treated with chemotherapy containing sequential taxane and anthracycline-based regimens then endocrine therapy (if hormone receptor-positive). MAIN OUTCOME MEASURES Distant relapse-free survival (DRFS) if predicted treatment-sensitive and absolute risk reduction (ARR, difference in DRFS of the two predicted groups) at median follow-up (3 years), and their 95% confidence intervals (CI). RESULTS Patients in the independent validation cohort (99% clinical Stage II–III) who were predicted to be treatment-sensitive (28% of total) had DRFS of 92% (CI 85–100) and survival benefit compared to others (absolute risk reduction (ARR) 18%; CI 6–28). Predictions were accurate if breast cancer was ER-positive (30% predicted sensitive, DRFS 97%, CI 91–100; ARR 11%, CI 0.1–21) or ER-negative (26% predicted sensitive, DRFS 83%, CI 68–100; ARR 26%, CI 4–28), and were significant in multivariate analysis after adjusting for relevant clinical-pathologic characteristics. Other genomic predictors showed paradoxically worse survival if predicted to be responsive to chemotherapy. CONCLUSION A genomic predictor combining ER status, predicted chemo-resistance, predicted chemo-sensitivity, and predicted endocrine sensitivity accurately identified patients with survival benefit following taxane-anthracycline chemotherapy. PMID:21558518

Correlation between MGMT promoter methylation and response to temozolomide-based therapy in neuroendocrine neoplasms: an observational retrospective multicenter study.

PubMed

Campana, Davide; Walter, Thomas; Pusceddu, Sara; Gelsomino, Fabio; Graillot, Emmanuelle; Prinzi, Natalie; Spallanzani, Andrea; Fiorentino, Michelangelo; Barritault, Marc; Dall'Olio, Filippo; Brighi, Nicole; Biasco, Guido

2018-06-01

Temozolomide (TEM) based therapy has been reported being effective in the treatment of metastatic neuroendocrine neoplasms (NEN), with response rates ranging from 30 to 70%. Among patients affected by advanced glioblastoma or melanoma and treated with TEM, loss of tumoral O6-methylguanine DNA methyltransferase (MGMT) is correlated with improved survival. In NEN patients, the role of MGMT deficiency in predicting clinical outcomes of TEM treatment is still under debate. In this study we evaluated 95 patients with advanced NENs undergoing treatment with TEM-based therapy. MGMT promoter methylation status was evaluated with two techniques: methylation specific-polymerase chain reaction or pyrosequencing. Treatment with TEM-based therapy was associated with an overall response rate of 27.4% according to RECIST criteria (51.8% of patients with and 17.7% without MGMT promoter methylation). Response to therapy, progression free survival and overall survival was correlated to MGMT status at univariate and multivariate analysis. Methylation of MGMT promoter could be a strong predictive factor of objective response and an important prognostic factor of a longer PFS and OS. According to our results, MGMT methylation status, evaluated with methylation specific-polymerase chain reaction or pyrosequencing, should have an important role in patients with metastatic NENs, in order to guide therapeutic options. These results need further confirmation with prospective studies.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

PubMed

Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

2016-12-01

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling

USGS Publications Warehouse

Wilcox, D.A.; Xie, Y.

2007-01-01

Integrated, GIS-based, wetland predictive models were constructed to assist in predicting the responses of wetland plant communities to proposed new water-level regulation plans for Lake Ontario. The modeling exercise consisted of four major components: 1) building individual site wetland geometric models; 2) constructing generalized wetland geometric models representing specific types of wetlands (rectangle model for drowned river mouth wetlands, half ring model for open embayment wetlands, half ellipse model for protected embayment wetlands, and ellipse model for barrier beach wetlands); 3) assigning wetland plant profiles to the generalized wetland geometric models that identify associations between past flooding / dewatering events and the regulated water-level changes of a proposed water-level-regulation plan; and 4) predicting relevant proportions of wetland plant communities and the time durations during which they would be affected under proposed regulation plans. Based on this conceptual foundation, the predictive models were constructed using bathymetric and topographic wetland models and technical procedures operating on the platform of ArcGIS. An example of the model processes and outputs for the drowned river mouth wetland model using a test regulation plan illustrates the four components and, when compared against other test regulation plans, provided results that met ecological expectations. The model results were also compared to independent data collected by photointerpretation. Although data collections were not directly comparable, the predicted extent of meadow marsh in years in which photographs were taken was significantly correlated with extent of mapped meadow marsh in all but barrier beach wetlands. The predictive model for wetland plant communities provided valuable input into International Joint Commission deliberations on new regulation plans and was also incorporated into faunal predictive models used for that purpose.

Influence of central set on anticipatory and triggered grip-force adjustments

NASA Technical Reports Server (NTRS)

Winstein, C. J.; Horak, F. B.; Fisher, B. E.; Peterson, B. W. (Principal Investigator)

2000-01-01

The effects of predictability of load magnitude on anticipatory and triggered grip-force adjustments were studied as nine normal subjects used a precision grip to lift, hold, and replace an instrumented test object. Experience with a predictable stimulus has been shown to enhance magnitude scaling of triggered postural responses to different amplitudes of perturbations. However, this phenomenon, known as a central-set effect, has not been tested systematically for grip-force responses in the hand. In our study, predictability was manipulated by applying load perturbations of different magnitudes to the test object under conditions in which the upcoming load magnitude was presented repeatedly or under conditions in which the load magnitudes were presented randomly, each with two different pre-load grip conditions (unconstrained and constrained). In constrained conditions, initial grip forces were maintained near the minimum level necessary to prevent pre-loaded object slippage, while in unconstrained conditions, no initial grip force restrictions were imposed. The effect of predictable (blocked) and unpredictable (random) load presentations on scaling of anticipatory and triggered grip responses was tested by comparing the slopes of linear regressions between the imposed load and grip response magnitude. Anticipatory and triggered grip force responses were scaled to load magnitude in all conditions. However, regardless of pre-load grip force constraint, the gains (slopes) of grip responses relative to load magnitudes were greater when the magnitude of the upcoming load was predictable than when the load increase was unpredictable. In addition, a central-set effect was evidenced by the fewer number of drop trials in the predictable relative to unpredictable load conditions. Pre-load grip forces showed the greatest set effects. However, grip responses showed larger set effects, based on prediction, when pre-load grip force was constrained to lower levels. These results suggest that anticipatory processes pertaining to load magnitude permit the response gain of both voluntary and triggered rapid grip force adjustments to be set, at least partially, prior to perturbation onset. Comparison of anticipatory set effects for reactive torque and lower extremity EMG postural responses triggered by surface translation perturbations suggests a more general rule governing anticipatory processes.

Applying Precision Medicine to Trial Design Using Physiology. Extracorporeal CO2 Removal for Acute Respiratory Distress Syndrome.

PubMed

Goligher, Ewan C; Amato, Marcelo B P; Slutsky, Arthur S

2017-09-01

In clinical trials of therapies for acute respiratory distress syndrome (ARDS), the average treatment effect in the study population may be attenuated because individual patient responses vary widely. This inflates sample size requirements and increases the cost and difficulty of conducting successful clinical trials. One solution is to enrich the study population with patients most likely to benefit, based on predicted patient response to treatment (predictive enrichment). In this perspective, we apply the precision medicine paradigm to the emerging use of extracorporeal CO 2 removal (ECCO 2 R) for ultraprotective ventilation in ARDS. ECCO 2 R enables reductions in tidal volume and driving pressure, key determinants of ventilator-induced lung injury. Using basic physiological concepts, we demonstrate that dead space and static compliance determine the effect of ECCO 2 R on driving pressure and mechanical power. This framework might enable prediction of individual treatment responses to ECCO 2 R. Enriching clinical trials by selectively enrolling patients with a significant predicted treatment response can increase treatment effect size and statistical power more efficiently than conventional enrichment strategies that restrict enrollment according to the baseline risk of death. To support this claim, we simulated the predicted effect of ECCO 2 R on driving pressure and mortality in a preexisting cohort of patients with ARDS. Our computations suggest that restricting enrollment to patients in whom ECCO 2 R allows driving pressure to be decreased by 5 cm H 2 O or more can reduce sample size requirement by more than 50% without increasing the total number of patients to be screened. We discuss potential implications for trial design based on this framework.

Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil

PubMed Central

Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

2017-01-01

Aim: Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Materials and methods: Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Results: Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Conclusion: Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. Clinical significance: To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil. Euroasian J Hepato-Gastroenterol 2017;7(1):27-33. PMID:29201768

Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil.

PubMed

V Picon, Rafael; Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

2017-01-01

Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil. Euroasian J Hepato-Gastroenterol 2017;7(1):27-33.

Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.

Modelling for Understanding AND for Prediction/Classification--The Power of Neural Networks in Research

ERIC Educational Resources Information Center

Cascallar, Eduardo; Musso, Mariel; Kyndt, Eva; Dochy, Filip

2014-01-01

Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current…

Examination of Factors Predicting Secondary Students' Interest in Tertiary STEM Education

ERIC Educational Resources Information Center

Chachashvili-Bolotin, Svetlana; Milner-Bolotin, Marina; Lissitsa, Sabina

2016-01-01

Based on the Social Cognitive Career Theory (SCCT), the study aims to investigate factors that predict students' interest in pursuing science, technology, engineering, and mathematics (STEM) fields in tertiary education both in general and in relation to their gender and socio-economic background. The results of the analysis of survey responses of…

Curved Saccade Trajectories Reveal Conflicting Predictions in Associative Learning

ERIC Educational Resources Information Center

Koenig, Stephan; Lachnit, Harald

2011-01-01

We report how the trajectories of saccadic eye movements are affected by memory interference acquired during associative learning. Human participants learned to perform saccadic choice responses based on the presentation of arbitrary central cues A, B, AC, BC, AX, BY, X, and Y that were trained to predict the appearance of a peripheral target…

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

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

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response

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

Jin, Xin; Baker, Kyri A; Isley, Steven C

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift.

PubMed

Fu, Cynthia H Y; Costafreda, Sergi G

2013-09-01

Neuroimaging research has substantiated the functional and structural abnormalities underlying psychiatric disorders but has, thus far, failed to have a significant impact on clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation. This review introduces the key concepts of this approach, including how the multivariate integration of patterns of brain abnormalities is a crucial component. We survey recent findings that have potential application for diagnosis, in particular early and differential diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response to treatment in depression. We discuss the specific clinical opportunities and the challenges for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We propose that longitudinal outcomes, such as early diagnosis and prediction of treatment response, offer definite opportunities for progress. We propose that efforts should be directed toward clinically challenging predictions in which neuroimaging may have added value, compared with the existing standard assessment. We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis.

Mixture effects in samples of multiple contaminants - An inter-laboratory study with manifold bioassays.

PubMed

Altenburger, Rolf; Scholze, Martin; Busch, Wibke; Escher, Beate I; Jakobs, Gianina; Krauss, Martin; Krüger, Janet; Neale, Peta A; Ait-Aissa, Selim; Almeida, Ana Catarina; Seiler, Thomas-Benjamin; Brion, François; Hilscherová, Klára; Hollert, Henner; Novák, Jiří; Schlichting, Rita; Serra, Hélène; Shao, Ying; Tindall, Andrew; Tolefsen, Knut-Erik; Umbuzeiro, Gisela; Williams, Tim D; Kortenkamp, Andreas

2018-05-01

Chemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findings support the ongoing development of bioanalytical tools for (i) compiling comprehensive effect-based batteries for water quality assessment, (ii) designing tailored surveillance methods to safeguard specific water uses, and (iii) devising strategies for effect-based diagnosis of complex contamination. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

A critical review and analysis of the use of exposure- and flux-based ozone indices for predicting vegetation effects

Treesearch

Robert C. Musselman; Allen S. Lefohn; William J. Massman; Robert L. Heath

2006-01-01

Early studies of plant response to ozone (O3) utilized concentration-based metrics, primarily by summarizing the commonly monitored hourly average data sets. Research with the O3 concentration parameter led to the recognition that both peak concentrations and cumulative effects are important when relating plant response to O3. The US and Canada currently use O3...

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

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

Luo, Y; McShan, D; Schipper, M

2014-06-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity tomore » different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.« less

Idiosyncratic species effects confound size-based predictions of responses to climate change.

PubMed

Twomey, Marion; Brodte, Eva; Jacob, Ute; Brose, Ulrich; Crowe, Tasman P; Emmerson, Mark C

2012-11-05

Understanding and predicting the consequences of warming for complex ecosystems and indeed individual species remains a major ecological challenge. Here, we investigated the effect of increased seawater temperatures on the metabolic and consumption rates of five distinct marine species. The experimental species reflected different trophic positions within a typical benthic East Atlantic food web, and included a herbivorous gastropod, a scavenging decapod, a predatory echinoderm, a decapod and a benthic-feeding fish. We examined the metabolism-body mass and consumption-body mass scaling for each species, and assessed changes in their consumption efficiencies. Our results indicate that body mass and temperature effects on metabolism were inconsistent across species and that some species were unable to meet metabolic demand at higher temperatures, thus highlighting the vulnerability of individual species to warming. While body size explains a large proportion of the variation in species' physiological responses to warming, it is clear that idiosyncratic species responses, irrespective of body size, complicate predictions of population and ecosystem level response to future scenarios of climate change.

Ocean Acidification Portends Acute Habitat Compression for Atlantic Cod (Gadus morhua) in a Physiologically-informed Metabolic Rate Model

NASA Astrophysics Data System (ADS)

Del Raye, G.; Weng, K.

2011-12-01

Ocean acidification affects organisms on a biochemical scale, yet its societal impacts manifest from changes that propagate through entire populations. Successful forecasting of the effects of ocean acidification therefore depends on at least two steps: (1) deducing systemic physiology based on subcellular stresses and (2) scaling individual physiology up to ecosystem processes. Predictions that are based on known biological processes (process-based models) may fare better than purely statistical models in both these steps because the latter are less robust to novel environmental conditions. Here we present a process-based model that uses temperature, pO2, and pCO2 to predict maximal aerobic scope in Atlantic cod. Using this model, we show that (i) experimentally-derived physiological parameters are sufficient to capture the response of cod aerobic scope to temperature and oxygen, and (ii) subcellular pH effects can be used to predict the systemic physiological response of cod to an acidified ocean. We predict that acute pH stress (on a scale of hours) could limit the mobility of Atlantic cod during diel vertical migration across a pCO2 gradient, promoting habitat compression. Finally, we use a global sensitivity analysis to identify opportunities for the improvement of model uncertainty as well as some physiological adaptations that could mitigate climate stresses on cod in the future.

Integration of Immune Cell Populations, mRNA-Seq, and CpG Methylation to Better Predict Humoral Immunity to Influenza Vaccination: Dependence of mRNA-Seq/CpG Methylation on Immune Cell Populations

PubMed Central

Zimmermann, Michael T.; Kennedy, Richard B.; Grill, Diane E.; Oberg, Ann L.; Goergen, Krista M.; Ovsyannikova, Inna G.; Haralambieva, Iana H.; Poland, Gregory A.

2017-01-01

The development of a humoral immune response to influenza vaccines occurs on a multisystems level. Due to the orchestration required for robust immune responses when multiple genes and their regulatory components across multiple cell types are involved, we examined an influenza vaccination cohort using multiple high-throughput technologies. In this study, we sought a more thorough understanding of how immune cell composition and gene expression relate to each other and contribute to interindividual variation in response to influenza vaccination. We first hypothesized that many of the differentially expressed (DE) genes observed after influenza vaccination result from changes in the composition of participants’ peripheral blood mononuclear cells (PBMCs), which were assessed using flow cytometry. We demonstrated that DE genes in our study are correlated with changes in PBMC composition. We gathered DE genes from 128 other publically available PBMC-based vaccine studies and identified that an average of 57% correlated with specific cell subset levels in our study (permutation used to control false discovery), suggesting that the associations we have identified are likely general features of PBMC-based transcriptomics. Second, we hypothesized that more robust models of vaccine response could be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. We employed machine learning to generate predictive models of B-cell ELISPOT response outcomes and hemagglutination inhibition (HAI) antibody titers. The top HAI and B-cell ELISPOT model achieved an area under the receiver operating curve (AUC) of 0.64 and 0.79, respectively, with linear model coefficients of determination of 0.08 and 0.28. For the B-cell ELISPOT outcomes, CpG methylation had the greatest predictive ability, highlighting potentially novel regulatory features important for immune response. B-cell ELISOT models using only PBMC composition had lower performance (AUC = 0.67), but highlighted well-known mechanisms. Our analysis demonstrated that each of the three data sets (cell composition, mRNA-Seq, and DNA methylation) may provide distinct information for the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and set the stage for a more thorough understanding of interindividual immune responses to influenza vaccination. PMID:28484452

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer.

PubMed

Dayde, Delphine; Tanaka, Ichidai; Jain, Rekha; Tai, Mei Chee; Taguchi, Ayumu

2017-03-07

The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue- and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer.

Investigation and suppression of high dynamic response encountered on an elastic supercritical wing

NASA Technical Reports Server (NTRS)

Seidel, David A.; Adams, William M., Jr.; Eckstrom, Clinton V.; Sandford, Maynard C.

1989-01-01

The DAST Aeroelastic Research Wing had been previously in the NASA Langley TDT and an unusual instability boundary was predicted based upon supercritical response data. Contrary to the predictions, no instability was found during the present test. Instead a region of high dynamic wing response was observed which reached a maximum value between Mach numbers 0.92 and 0.93. The amplitude of the dynamic response increased directly with dynamic pressure. The reponse appears to be related to chordwise shock movement in conjunction with flow separation and reattachment on the upper and lower wing surfaces. The onset of flow separation coincided with the occurrence of strong shocks on a surface. A controller was designed to suppress the wing response. The control law attenuated the response as compared with the uncontrolled case and added a small but significant amount of damping for the lower density condition.

Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer

PubMed Central

Tabchy, Adel; Valero, Vicente; Vidaurre, Tatiana; Lluch, Ana; Gomez, Henry; Martin, Miguel; Qi, Yuan; Barajas-Figueroa, Luis Javier; Souchon, Eduardo; Coutant, Charles; Doimi, Franco D; Ibrahim, Nuhad K; Gong, Yun; Hortobagyi, Gabriel N; Hess, Kenneth R; Symmans, W Fraser; Pusztai, Lajos

2010-01-01

Purpose We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FAC×6 preoperative chemotherapy. We also performed an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Experimental Design 273 patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n=138), or FAC × 6 (n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling and treatment response prediction. Results The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (p<0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% (95%CI:21–56%), the negative predictive value (NPV) 88% (CI:77–95%) and the AUC 0.711. In the FAC arm, the PPV was 9% (CI:1–29%) and the AUC 0.584. This suggests that the genomic predictor may have regimen-specificity. Its performance was similar to a clinical variable-based predictor nomogram. Conclusions Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next generation predictive markers will need to be developed separately for different molecular subsets of breast cancers. PMID:20829329

Development of Response Spectral Ground Motion Prediction Equations from Empirical Models for Fourier Spectra and Duration of Ground Motion

NASA Astrophysics Data System (ADS)

Bora, S. S.; Scherbaum, F.; Kuehn, N. M.; Stafford, P.; Edwards, B.

2014-12-01

In a probabilistic seismic hazard assessment (PSHA) framework, it still remains a challenge to adjust ground motion prediction equations (GMPEs) for application in different seismological environments. In this context, this study presents a complete framework for the development of a response spectral GMPE easily adjustable to different seismological conditions; and which does not suffer from the technical problems associated with the adjustment in response spectral domain. Essentially, the approach consists of an empirical FAS (Fourier Amplitude Spectrum) model and a duration model for ground motion which are combined within the random vibration theory (RVT) framework to obtain the full response spectral ordinates. Additionally, FAS corresponding to individual acceleration records are extrapolated beyond the frequency range defined by the data using the stochastic FAS model, obtained by inversion as described in Edwards & Faeh, (2013). To that end, an empirical model for a duration, which is tuned to optimize the fit between RVT based and observed response spectral ordinate, at each oscillator frequency is derived. Although, the main motive of the presented approach was to address the adjustability issues of response spectral GMPEs; comparison, of median predicted response spectra with the other regional models indicate that presented approach can also be used as a stand-alone model. Besides that, a significantly lower aleatory variability (σ<0.5 in log units) in comparison to other regional models, at shorter periods brands it to a potentially viable alternative to the classical regression (on response spectral ordinates) based GMPEs for seismic hazard studies in the near future. The dataset used for the presented analysis is a subset of the recently compiled database RESORCE-2012 across Europe, Middle East and the Mediterranean region.

Predicting mixture toxicity of seven phenolic compounds with similar and dissimilar action mechanisms to Vibrio qinghaiensis sp.nov.Q67.

PubMed

Huang, Wei Ying; Liu, Fei; Liu, Shu Shen; Ge, Hui Lin; Chen, Hong Han

2011-09-01

The predictions of mixture toxicity for chemicals are commonly based on two models: concentration addition (CA) and independent action (IA). Whether the CA and IA can predict mixture toxicity of phenolic compounds with similar and dissimilar action mechanisms was studied. The mixture toxicity was predicted on the basis of the concentration-response data of individual compounds. Test mixtures at different concentration ratios and concentration levels were designed using two methods. The results showed that the Weibull function fit well with the concentration-response data of all the components and their mixtures, with all relative coefficients (Rs) greater than 0.99 and root mean squared errors (RMSEs) less than 0.04. The predicted values from CA and IA models conformed to observed values of the mixtures. Therefore, it can be concluded that both CA and IA can predict reliable results for the mixture toxicity of the phenolic compounds with similar and dissimilar action mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Straightening Beta: Overdispersion of Lethal Chromosome Aberrations following Radiotherapeutic Doses Leads to Terminal Linearity in the Alpha–Beta Model

PubMed Central

Shuryak, Igor; Loucas, Bradford D.; Cornforth, Michael N.

2017-01-01

Recent technological advances allow precise radiation delivery to tumor targets. As opposed to more conventional radiotherapy—where multiple small fractions are given—in some cases, the preferred course of treatment may involve only a few (or even one) large dose(s) per fraction. Under these conditions, the choice of appropriate radiobiological model complicates the tasks of predicting radiotherapy outcomes and designing new treatment regimens. The most commonly used model for this purpose is the venerable linear-quadratic (LQ) formalism as it applies to cell survival. However, predictions based on the LQ model are frequently at odds with data following very high acute doses. In particular, although the LQ predicts a continuously bending dose–response relationship for the logarithm of cell survival, empirical evidence over the high-dose region suggests that the survival response is instead log-linear with dose. Here, we show that the distribution of lethal chromosomal lesions among individual human cells (lymphocytes and fibroblasts) exposed to gamma rays and X rays is somewhat overdispersed, compared with the Poisson distribution. Further, we show that such overdispersion affects the predicted dose response for cell survival (the fraction of cells with zero lethal lesions). This causes the dose response to approximate log-linear behavior at high doses, even when the mean number of lethal lesions per cell is well fitted by the continuously curving LQ model. Accounting for overdispersion of lethal lesions provides a novel, mechanistically based explanation for the observed shapes of cell survival dose responses that, in principle, may offer a tractable and clinically useful approach for modeling the effects of high doses per fraction. PMID:29312888

Exposure–response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development

PubMed Central

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure–response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks’ treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure–response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects’ sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure–response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs. PMID:26392753

Exposure-response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development.

PubMed

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure-response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks' treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure-response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects' sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure-response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs.

Predicting variation in subject thermal response during transcranial magnetic resonance guided focused ultrasound surgery: Comparison in seventeen subject datasets.

PubMed

Vyas, Urvi; Ghanouni, Pejman; Halpern, Casey H; Elias, Jeff; Pauly, Kim Butts

2016-09-01

In transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) treatments, the acoustic and spatial heterogeneity of the skull cause reflection, absorption, and scattering of the acoustic beams. These effects depend on skull-specific parameters and can lead to patient-specific thermal responses to the same transducer power. In this work, the authors develop a simulation tool to help predict these different experimental responses using 3D heterogeneous tissue models based on the subject CT images. The authors then validate and compare the predicted skull efficiencies to an experimental metric based on the subject thermal responses during tcMRgFUS treatments in a dataset of seventeen human subjects. Seventeen human head CT scans were used to create tissue acoustic models, simulating the effects of reflection, absorption, and scattering of the acoustic beam as it propagates through a heterogeneous skull. The hybrid angular spectrum technique was used to model the acoustic beam propagation of the InSightec ExAblate 4000 head transducer for each subject, yielding maps of the specific absorption rate (SAR). The simulation assumed the transducer was geometrically focused to the thalamus of each subject, and the focal SAR at the target was used as a measure of the simulated skull efficiency. Experimental skull efficiency for each subject was calculated using the thermal temperature maps from the tcMRgFUS treatments. Axial temperature images (with no artifacts) were reconstructed with a single baseline, corrected using a referenceless algorithm. The experimental skull efficiency was calculated by dividing the reconstructed temperature rise 8.8 s after sonication by the applied acoustic power. The simulated skull efficiency using individual-specific heterogeneous models predicts well (R(2) = 0.84) the experimental energy efficiency. This paper presents a simulation model to predict the variation in thermal responses measured in clinical ctMRGFYS treatments while being computationally feasible.

Predicting variation in subject thermal response during transcranial magnetic resonance guided focused ultrasound surgery: Comparison in seventeen subject datasets

PubMed Central

Vyas, Urvi; Ghanouni, Pejman; Halpern, Casey H.; Elias, Jeff; Pauly, Kim Butts

2016-01-01

Purpose: In transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) treatments, the acoustic and spatial heterogeneity of the skull cause reflection, absorption, and scattering of the acoustic beams. These effects depend on skull-specific parameters and can lead to patient-specific thermal responses to the same transducer power. In this work, the authors develop a simulation tool to help predict these different experimental responses using 3D heterogeneous tissue models based on the subject CT images. The authors then validate and compare the predicted skull efficiencies to an experimental metric based on the subject thermal responses during tcMRgFUS treatments in a dataset of seventeen human subjects. Methods: Seventeen human head CT scans were used to create tissue acoustic models, simulating the effects of reflection, absorption, and scattering of the acoustic beam as it propagates through a heterogeneous skull. The hybrid angular spectrum technique was used to model the acoustic beam propagation of the InSightec ExAblate 4000 head transducer for each subject, yielding maps of the specific absorption rate (SAR). The simulation assumed the transducer was geometrically focused to the thalamus of each subject, and the focal SAR at the target was used as a measure of the simulated skull efficiency. Experimental skull efficiency for each subject was calculated using the thermal temperature maps from the tcMRgFUS treatments. Axial temperature images (with no artifacts) were reconstructed with a single baseline, corrected using a referenceless algorithm. The experimental skull efficiency was calculated by dividing the reconstructed temperature rise 8.8 s after sonication by the applied acoustic power. Results: The simulated skull efficiency using individual-specific heterogeneous models predicts well (R2 = 0.84) the experimental energy efficiency. Conclusions: This paper presents a simulation model to predict the variation in thermal responses measured in clinical ctMRGFYS treatments while being computationally feasible. PMID:27587047

Predicting variation in subject thermal response during transcranial magnetic resonance guided focused ultrasound surgery: Comparison in seventeen subject datasets

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

Vyas, Urvi, E-mail: urvi.vyas@gmail.com; Ghanouni,

Purpose: In transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) treatments, the acoustic and spatial heterogeneity of the skull cause reflection, absorption, and scattering of the acoustic beams. These effects depend on skull-specific parameters and can lead to patient-specific thermal responses to the same transducer power. In this work, the authors develop a simulation tool to help predict these different experimental responses using 3D heterogeneous tissue models based on the subject CT images. The authors then validate and compare the predicted skull efficiencies to an experimental metric based on the subject thermal responses during tcMRgFUS treatments in a dataset of seventeen humanmore » subjects. Methods: Seventeen human head CT scans were used to create tissue acoustic models, simulating the effects of reflection, absorption, and scattering of the acoustic beam as it propagates through a heterogeneous skull. The hybrid angular spectrum technique was used to model the acoustic beam propagation of the InSightec ExAblate 4000 head transducer for each subject, yielding maps of the specific absorption rate (SAR). The simulation assumed the transducer was geometrically focused to the thalamus of each subject, and the focal SAR at the target was used as a measure of the simulated skull efficiency. Experimental skull efficiency for each subject was calculated using the thermal temperature maps from the tcMRgFUS treatments. Axial temperature images (with no artifacts) were reconstructed with a single baseline, corrected using a referenceless algorithm. The experimental skull efficiency was calculated by dividing the reconstructed temperature rise 8.8 s after sonication by the applied acoustic power. Results: The simulated skull efficiency using individual-specific heterogeneous models predicts well (R{sup 2} = 0.84) the experimental energy efficiency. Conclusions: This paper presents a simulation model to predict the variation in thermal responses measured in clinical ctMRGFYS treatments while being computationally feasible.« less

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

PubMed Central

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-01-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails. PMID:27645580

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

NASA Astrophysics Data System (ADS)

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion

PubMed Central

Choi, Hannah; Pasupathy, Anitha; Shea-Brown, Eric

2018-01-01

The primate visual system has an exquisite ability to discriminate partially occluded shapes. Recent electrophysiological recordings suggest that response dynamics in intermediate visual cortical area V4, shaped by feedback from prefrontal cortex (PFC), may play a key role. To probe the algorithms that may underlie these findings, we build and test a model of V4 and PFC interactions based on a hierarchical predictive coding framework. We propose that probabilistic inference occurs in two steps. Initially, V4 responses are driven solely by bottom-up sensory input and are thus strongly influenced by the level of occlusion. After a delay, V4 responses combine both feedforward input and feedback signals from the PFC; the latter reflect predictions made by PFC about the visual stimulus underlying V4 activity. We find that this model captures key features of V4 and PFC dynamics observed in experiments. Specifically, PFC responses are strongest for occluded stimuli and delayed responses in V4 are less sensitive to occlusion, supporting our hypothesis that the feedback signals from PFC underlie robust discrimination of occluded shapes. Thus, our study proposes that area V4 and PFC participate in hierarchical inference, with feedback signals encoding top-down predictions about occluded shapes. PMID:29566355

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties.

PubMed

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-20

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell 'A549_LUNG' and compound 'Topotecan'. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Responses to climate change in hot desert ecosystems: connecting local to global scales

USDA-ARS?s Scientific Manuscript database

The consequences of connectivity in resources, propagules, and information to the interplay between drivers and responses across scales can result in ecological dynamics that are not easily predicted based on local drivers. Three major classes of connectivity events link local ecological dynamics wi...

PREDICTING THE RISKS OF NEUROTOXIC VOLATILE ORGANIC COMPOUNDS BASED ON TARGET TISSUE DOSE.

EPA Science Inventory

Quantitative exposure-dose-response models relate the external exposure of a substance to the dose in the target tissue, and then relate the target tissue dose to production of adverse outcomes. We developed exposure-dose-response models to describe the affects of acute exposure...

Mechanisms governing the visco-elastic responses of living cells assessed by foam and tensegrity models.

PubMed

Cañadas, P; Laurent, V M; Chabrand, P; Isabey, D; Wendling-Mansuy, S

2003-11-01

The visco-elastic properties of living cells, measured to date by various authors, vary considerably, depending on the experimental methods and/or on the theoretical models used. In the present study, two mechanisms thought to be involved in cellular visco-elastic responses were analysed, based on the idea that the cytoskeleton plays a fundamental role in cellular mechanical responses. For this purpose, the predictions of an open unit-cell model and a 30-element visco-elastic tensegrity model were tested, taking into consideration similar properties of the constitutive F-actin. The quantitative predictions of the time constant and viscosity modulus obtained by both models were compared with previously published experimental data obtained from living cells. The small viscosity modulus values (10(0)-10(3) Pa x s) predicted by the tensegrity model may reflect the combined contributions of the spatially rearranged constitutive filaments and the internal tension to the overall cytoskeleton response to external loading. In contrast, the high viscosity modulus values (10(3)-10(5) Pa x s) predicted by the unit-cell model may rather reflect the mechanical response of the cytoskeleton to the bending of the constitutive filaments and/or to the deformation of internal components. The present results suggest the existence of a close link between the overall visco-elastic response of micromanipulated cells and the underlying architecture.

Relationships between response surfaces for tablet characteristics of placebo and API-containing tablets manufactured by direct compression method.

PubMed

Hayashi, Yoshihiro; Tsuji, Takahiro; Shirotori, Kaede; Oishi, Takuya; Kosugi, Atsushi; Kumada, Shungo; Hirai, Daijiro; Takayama, Kozo; Onuki, Yoshinori

2017-10-30

In this study, we evaluated the correlation between the response surfaces for the tablet characteristics of placebo and active pharmaceutical ingredient (API)-containing tablets. The quantities of lactose, cornstarch, and microcrystalline cellulose were chosen as the formulation factors. Ten tablet formulations were prepared. The tensile strength (TS) and disintegration time (DT) of tablets were measured as tablet characteristics. The response surfaces for TS and DT were estimated using a nonlinear response surface method incorporating multivariate spline interpolation, and were then compared with those of placebo tablets. A correlation was clearly observed for TS and DT of all APIs, although the value of the response surfaces for TS and DT was highly dependent on the type of API used. Based on this knowledge, the response surfaces for TS and DT of API-containing tablets were predicted from only two and four formulations using regression expression and placebo tablet data, respectively. The results from the evaluation of prediction accuracy showed that this method accurately predicted TS and DT, suggesting that it could construct a reliable response surface for TS and DT with a small number of samples. This technique assists in the effective estimation of the relationships between design variables and pharmaceutical responses during pharmaceutical development. Copyright © 2017 Elsevier B.V. All rights reserved.

Group-based guilt as a predictor of commitment to apology.

PubMed

McGarty, Craig; Pedersen, Anne; Leach, Colin Wayne; Mansell, Tamarra; Waller, Julie; Bliuc, Ana-Maria

2005-12-01

Whether the Australian government should officially apologize to Indigenous Australians for past wrongs is hotly debated in Australia. The predictors of support amongst non-Indigenous Australians for such an apology were examined in two studies. The first study (N=164) showed that group-based guilt was a good predictor of support for a government apology, as was the perception that non-Indigenous Australians were relatively advantaged. In the second study (N=116) it was found that group-based guilt was an excellent predictor of support for apology and was itself predicted by perceived non-Indigenous responsibility for harsh treatment of Indigenous people, and an absence of doubts about the legitimacy of group-based guilt. National identification was not a predictor of group-based guilt. The results of the two studies suggest that, just as individual emotions predict individual action tendencies, so group-based guilt predicts support for actions or decisions to be taken at the collective level.

Computational modeling of in vitro biological responses on polymethacrylate surfaces

PubMed Central

Ghosh, Jayeeta; Lewitus, Dan Y; Chandra, Prafulla; Joy, Abraham; Bushman, Jared; Knight, Doyle; Kohn, Joachim

2011-01-01

The objective of this research was to examine the capabilities of QSPR (Quantitative Structure Property Relationship) modeling to predict specific biological responses (fibrinogen adsorption, cell attachment and cell proliferation index) on thin films of different polymethacrylates. Using 33 commercially available monomers it is theoretically possible to construct a library of over 40,000 distinct polymer compositions. A subset of these polymers were synthesized and solvent cast surfaces were prepared in 96 well plates for the measurement of fibrinogen adsorption. NIH 3T3 cell attachment and proliferation index were measured on spin coated thin films of these polymers. Based on the experimental results of these polymers, separate models were built for homo-, co-, and terpolymers in the library with good correlation between experiment and predicted values. The ability to predict biological responses by simple QSPR models for large numbers of polymers has important implications in designing biomaterials for specific biological or medical applications. PMID:21779132

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

PubMed Central

Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

2015-01-01

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

Modeling the vestibulo-ocular reflex of the squirrel monkey during eccentric rotation and roll tilt

NASA Technical Reports Server (NTRS)

Merfeld, D. M.; Paloski, W. H. (Principal Investigator)

1995-01-01

Model simulations of the squirrel monkey vestibulo-ocular reflex (VOR) are presented for two motion paradigms: constant velocity eccentric rotation and roll tilt about a naso-occipital axis. The model represents the implementation of three hypotheses: the "internal model" hypothesis, the "gravito-inertial force (GIF) resolution" hypothesis, and the "compensatory VOR" hypothesis. The internal model hypothesis is based on the idea that the nervous system knows the dynamics of the sensory systems and implements this knowledge as an internal dynamic model. The GIF resolution hypothesis is based on the idea that the nervous system knows that gravity minus linear acceleration equals GIF and implements this knowledge by resolving the otolith measurement of GIF into central estimates of gravity and linear acceleration, such that the central estimate of gravity minus the central estimate of acceleration equals the otolith measurement of GIF. The compensatory VOR hypothesis is based on the idea that the VOR compensates for the central estimates of angular velocity and linear velocity, which sum in a near-linear manner. During constant velocity eccentric rotation, the model correctly predicts that: (1) the peak horizontal response is greater while "facing-motion" than with "back-to-motion"; (2) the axis of eye rotation shifts toward alignment with GIF; and (3) a continuous vertical response, slow phase downward, exists prior to deceleration. The model also correctly predicts that a torsional response during the roll rotation is the only velocity response observed during roll rotations about a naso-occipital axis. The success of this model in predicting the observed experimental responses suggests that the model captures the essence of the complex sensory interactions engendered by eccentric rotation and roll tilt.

Long-Term Viscoelastic Response of E-glass/Bismaleimide Composite in Seawater Environment

NASA Astrophysics Data System (ADS)

Yian, Zhao; Zhiying, Wang; Keey, Seah Leong; Boay, Chai Gin

2015-12-01

The effect of seawater absorption on the long-term viscoelastic response of E-glass/BMI composite is presented in this paper. The diffusion of seawater into the composite shows a two-stage behavior, dominated by Fickian diffusion initially and followed by polymeric relaxation. The Glass transition temperature (Tg) of the composite with seawater absorption is considerably lowered due to the plasticization effect. However the effect of water absorption at 50 °C is found to be reversible after drying process. The time-temperature superposition (TTS) was performed based on the results of Dynamic Mechanical Analysis to construct the master curve of storage modulus. The shift factors exhibit Arrhenius behavior when temperature is well below Tg and Vogel-Fulcher-Tammann (VFT) like behavior when temperature gets close to glass transition region. As a result, a semi-empirical formulation is proposed to account for the seawater absorption effect in predicting long-term viscoelastic response of BMI composites based on temperature dependent storage modulus and TTS. The predicted master curves show that the degradation of storage modulus accelerates with both seawater exposure and increasing temperature. The proposed formulation can be applied to predict the long-term durability of any thermorheologically simple composite materials in seawater environment.

High Resolution Flash Flood Forecasting Using a Wireless Sensor Network in the Dallas-Fort Worth Metroplex

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.

2017-12-01

In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

PubMed

Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

2018-01-01

Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

Neural activity to a partner's facial expression predicts self-regulation after conflict.

PubMed

Hooker, Christine I; Gyurak, Anett; Verosky, Sara C; Miyakawa, Asako; Ayduk, Ozlem

2010-03-01

Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to emotion regulation in response to laboratory-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk factor for mood and behavior problems after an interpersonal conflict. However, it remains unclear whether LPFC activity to a laboratory-based affective challenge predicts self-regulation in real life. We investigated whether LPFC activity to a laboratory-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During a functional magnetic resonance imaging scan, healthy, adult participants in committed relationships (n = 27) viewed positive, negative, and neutral facial expressions of their partners. In a three-week online daily diary, participants reported conflict occurrence, level of negative mood, rumination, and substance use. LPFC activity in response to the laboratory-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance use. Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Neural activity to a partner's facial expression predicts self-regulation after conflict

PubMed Central

Hooker, Christine I.; Gyurak, Anett; Verosky, Sara; Miyakawa, Asako; Ayduk, Özlem

2009-01-01

Introduction Failure to self-regulate after an interpersonal conflict can result in persistent negative mood and maladaptive behaviors. Research indicates that lateral prefrontal cortex (LPFC) activity is related to the regulation of emotional experience in response to lab-based affective challenges, such as viewing emotional pictures. This suggests that compromised LPFC function may be a risk-factor for mood and behavior problems after an interpersonal stressor. However, it remains unclear whether LPFC activity to a lab-based affective challenge predicts self-regulation in real-life. Method We investigated whether LPFC activity to a lab-based affective challenge (negative facial expressions of a partner) predicts self-regulation after a real-life affective challenge (interpersonal conflict). During an fMRI scan, healthy, adult participants in committed, dating relationships (N = 27) viewed positive, negative, and neutral facial expressions of their partners. In an online daily-diary, participants reported conflict occurrence, level of negative mood, rumination, and substance-use. Results LPFC activity in response to the lab-based affective challenge predicted self-regulation after an interpersonal conflict in daily life. When there was no interpersonal conflict, LPFC activity was not related to the change in mood or behavior the next day. However, when an interpersonal conflict did occur, ventral LPFC (VLPFC) activity predicted the change in mood and behavior the next day, such that lower VLPFC activity was related to higher levels of negative mood, rumination, and substance-use. Conclusions Low LPFC function may be a vulnerability and high LPFC function may be a protective factor for the development of mood and behavior problems after an interpersonal stressor. PMID:20004365

The dose-response relationship between the patch test and ROAT and the potential use for regulatory purposes.

PubMed

Fischer, Louise Arup; Voelund, Aage; Andersen, Klaus Ejner; Menné, Torkil; Johansen, Jeanne Duus

2009-10-01

Allergic contact dermatitis is common and can be prevented. The relationship between thresholds for patch tests and the repeated open application test (ROAT) is unclear. It would be desirable if patch test and ROAT data from already sensitized individuals could be used in prevention. The aim was to develop an equation that could predict the response to an allergen in a ROAT based on the dose-response curve derived by patch testing. Results from two human experimental elicitation studies with non-volatile allergens, nickel and the preservative methyldibromo glutaronitrile (MDBGN), were analysed by logistic dose-response statistics. The relation for volatile compounds was investigated using the results from experiments with the fragrance chemicals hydroxyisohexyl 3-cyclohexene carboxaldehyde and isoeugenol. For non-volatile compounds, the outcome of a ROAT can be estimated from the patch test by: ED(xx)(ROAT) = 0.0296 ED(xx)(patch test). For volatile compounds, the equation predicts that the response in the ROAT is more severe than the patch test response, but it overestimates the response. This equation may be used for non-volatile compounds other than nickel and MDBGN, after further validation. The relationship between the patch test and the ROAT can be used for prevention, to set safe levels of allergen exposure based on patch test data.

Chitosan based grey wastewater treatment--a statistical design approach.

PubMed

Thirugnanasambandham, K; Sivakumar, V; Prakash Maran, J; Kandasamy, S

2014-01-01

In this present study, grey wastewater was treated under different operating conditions such as agitation time (1-3 min), pH (2.5-5.5), chitosan dose (0.3-0.6g/l) and settling time (10-20 min) using response surface methodology (RSM). Four factors with three levels Box-Behnken response surface design (BBD) were employed to optimize and investigate the effect of process variables on the responses such as turbidity, BOD and COD removal. The results were analyzed by Pareto analysis of variance (ANOVA) and second order polynomial models were developed in order to predict the responses. Under the optimum conditions, experimental values such as turbidity (96%), BOD (91%) and COD (73%) removals are closely agreed with predicted values. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets.

PubMed

Kim, Tae-Hwan; Choi, Sung Jae; Lee, Young Ho; Song, Gwan Gyu; Ji, Jong Dae

2014-07-01

Anti-tumor necrosis factor (TNF) therapy is the treatment of choice for rheumatoid arthritis (RA) patients in whom standard disease-modifying anti-rheumatic drugs are ineffective. However, a substantial proportion of RA patients treated with anti-TNF agents do not show a significant clinical response. Therefore, biomarkers predicting response to anti-TNF agents are needed. Recently, gene expression profiling has been applied in research for developing such biomarkers. We compared gene expression profiles reported by previous studies dealing with the responsiveness of anti-TNF therapy in RA patients and attempted to identify differentially expressed genes (DEGs) that discriminated between responders and non-responders to anti-TNF therapy. We used microarray datasets available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). This analysis included 6 studies and 5 sets of microarray data that used peripheral blood samples for identification of DEGs predicting response to anti-TNF therapy. We found little overlap in the DEGs that were highly ranked in each study. Three DEGs including IL2RB, SH2D2A and G0S2 appeared in more than 1 study. In addition, a meta-analysis designed to increase statistical power found one DEG, G0S2 by the Fisher's method. Our finding suggests the possibility that G0S2 plays as a biomarker to predict response to anti-TNF therapy in patients with rheumatoid arthritis. Further investigations based on larger studies are therefore needed to confirm the significance of G0S2 in predicting response to anti-TNF therapy. Copyright © 2014 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

A clinical study of electrophysiological correlates of behavioural comfort levels in cochlear implantees.

PubMed

Raghunandhan, S; Ravikumar, A; Kameswaran, Mohan; Mandke, Kalyani; Ranjith, R

2014-05-01

Indications for cochlear implantation have expanded today to include very young children and those with syndromes/multiple handicaps. Programming the implant based on behavioural responses may be tedious for audiologists in such cases, wherein matching an effective Measurable Auditory Percept (MAP) and appropriate MAP becomes the key issue in the habilitation program. In 'Difficult to MAP' scenarios, objective measures become paramount to predict optimal current levels to be set in the MAP. We aimed to (a) study the trends in multi-modal electrophysiological tests and behavioural responses sequentially over the first year of implant use; (b) generate normative data from the above; (c) correlate the multi-modal electrophysiological thresholds levels with behavioural comfort levels; and (d) create predictive formulae for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis. This prospective study included 10 profoundly hearing impaired children aged between 2 and 7 years with normal inner ear anatomy and no additional handicaps. They received the Advanced Bionics HiRes 90 K Implant with Harmony Speech processor and used HiRes-P with Fidelity 120 strategy. They underwent, impedance telemetry, neural response imaging, electrically evoked stapedial response telemetry (ESRT), and electrically evoked auditory brainstem response (EABR) tests at 1, 4, 8, and 12 months of implant use, in conjunction with behavioural mapping. Trends in electrophysiological and behavioural responses were analyzed using paired t-test. By Karl Pearson's correlation method, electrode-wise correlations were derived for neural response imaging (NRI) thresholds versus most comfortable level (M-levels) and offset based (apical, mid-array, and basal array) correlations for EABR and ESRT thresholds versus M-levels were calculated over time. These were used to derive predictive formulae by linear and multiple regression analysis. Such statistically predicted M-levels were compared with the behaviourally recorded M-levels among the cohort, using Cronbach's alpha reliability test method for confirming the efficacy of this method. NRI, ESRT, and EABR thresholds showed statistically significant positive correlations with behavioural M-levels, which improved with implant use over time. These correlations were used to derive predicted M-levels using regression analysis. On an average, predicted M-levels were found to be statistically reliable and they were a fair match to the actual behavioural M-levels. When applied in clinical practice, the predicted values were found to be useful for programming members of the study group. However, individuals showed considerable deviations in behavioural M-levels, above and below the electrophysiologically predicted values, due to various factors. While the current method appears helpful as a reference to predict initial maps in 'difficult to Map' subjects, it is recommended that behavioural measures are mandatory to further optimize the maps for these individuals. The study explores the trends, correlations and individual variabilities that occur between electrophysiological tests and behavioural responses, recorded over time among a cohort of cochlear implantees. The statistical method shown may be used as a guideline to predict optimal behavioural levels in difficult situations among future implantees, bearing in mind that optimal M-levels for individuals can vary from predicted values. In 'Difficult to MAP' scenarios, following a protocol of sequential behavioural programming, in conjunction with electrophysiological correlates will provide the best outcomes.

Response prediction techniques and case studies of a path blocking system based on Global Transmissibility Direct Transmissibility method

NASA Astrophysics Data System (ADS)

Wang, Zengwei; Zhu, Ping; Zhao, Jianxuan

2017-02-01

In this paper, the prediction capabilities of the Global Transmissibility Direct Transmissibility (GTDT) method are further developed. Two path blocking techniques solely using the easily measured variables of the original system to predict the response of a path blocking system are generalized to finite element models of continuous systems. The proposed techniques are derived theoretically in a general form for the scenarios of setting the response of a subsystem to zero and of removing the link between two directly connected subsystems. The objective of this paper is to verify the reliability of the proposed techniques by finite element simulations. Two typical cases, the structural vibration transmission case and the structure-borne sound case, in two different configurations are employed to illustrate the validity of proposed techniques. The points of attention for each case have been discussed, and conclusions are given. It is shown that for the two cases of blocking a subsystem the proposed techniques are able to predict the new response using measured variables of the original system, even though operational forces are unknown. For the structural vibration transmission case of removing a connector between two components, the proposed techniques are available only when the rotational component responses of the connector are very small. The proposed techniques offer relative path measures and provide an alternative way to deal with NVH problems. The work in this paper provides guidance and reference for the engineering application of the GTDT prediction techniques.

Agent-Based Computational Modeling to Examine How Individual Cell Morphology Affects Dosimetry

EPA Science Inventory

Cell-based models utilizing high-content screening (HCS) data have applications for predictive toxicology. Evaluating concentration-dependent effects on cell fate and state response is a fundamental utilization of HCS data.Although HCS assays may capture quantitative readouts at ...

Radiation Hardened Electronics for Space Environments (RHESE)

NASA Technical Reports Server (NTRS)

Keys, Andrew S.; Adams, James H.; Frazier, Donald O.; Patrick, Marshall C.; Watson, Michael D.; Johnson, Michael A.; Cressler, John D.; Kolawa, Elizabeth A.

2007-01-01

Radiation Environmental Modeling is crucial to proper predictive modeling and electronic response to the radiation environment. When compared to on-orbit data, CREME96 has been shown to be inaccurate in predicting the radiation environment. The NEDD bases much of its radiation environment data on CREME96 output. Close coordination and partnership with DoD radiation-hardened efforts will result in leveraged - not duplicated or independently developed - technology capabilities of: a) Radiation-hardened, reconfigurable FPGA-based electronics; and b) High Performance Processors (NOT duplication or independent development).

Metabolic and anthropometric parameters contribute to ART-mediated CD4+ T cell recovery in HIV-1-infected individuals: an observational study.

PubMed

Azzoni, Livio; Foulkes, Andrea S; Firnhaber, Cynthia; Yin, Xiangfan; Crowther, Nigel J; Glencross, Deborah; Lawrie, Denise; Stevens, Wendy; Papasavvas, Emmanouil; Sanne, Ian; Montaner, Luis J

2011-07-29

The degree of immune reconstitution achieved in response to suppressive ART is associated with baseline individual characteristics, such as pre-treatment CD4 count, levels of viral replication, cellular activation, choice of treatment regimen and gender. However, the combined effect of these variables on long-term CD4 recovery remains elusive, and no single variable predicts treatment response. We sought to determine if adiposity and molecules associated with lipid metabolism may affect the response to ART and the degree of subsequent immune reconstitution, and to assess their ability to predict CD4 recovery. We studied a cohort of 69 (48 females and 21 males) HIV-infected, treatment-naïve South African subjects initiating antiretroviral treatment (d4T, 3Tc and lopinavir/ritonavir). We collected information at baseline and six months after viral suppression, assessing anthropometric parameters, dual energy X-ray absorptiometry and magnetic resonance imaging scans, serum-based clinical laboratory tests and whole blood-based flow cytometry, and determined their role in predicting the increase in CD4 count in response to ART. We present evidence that baseline CD4+ T cell count, viral load, CD8+ T cell activation (CD95 expression) and metabolic and anthropometric parameters linked to adiposity (LDL/HDL cholesterol ratio and waist/hip ratio) significantly contribute to variability in the extent of CD4 reconstitution (ΔCD4) after six months of continuous ART. Our final model accounts for 44% of the variability in CD4+ T cell recovery in virally suppressed individuals, representing a workable predictive model of immune reconstitution.

Recent advances in understanding idiopathic pulmonary fibrosis

PubMed Central

Daccord, Cécile; Maher, Toby M.

2016-01-01

Despite major research efforts leading to the recent approval of pirfenidone and nintedanib, the dismal prognosis of idiopathic pulmonary fibrosis (IPF) remains unchanged. The elaboration of international diagnostic criteria and disease stratification models based on clinical, physiological, radiological, and histopathological features has improved the accuracy of IPF diagnosis and prediction of mortality risk. Nevertheless, given the marked heterogeneity in clinical phenotype and the considerable overlap of IPF with other fibrotic interstitial lung diseases (ILDs), about 10% of cases of pulmonary fibrosis remain unclassifiable. Moreover, currently available tools fail to detect early IPF, predict the highly variable course of the disease, and assess response to antifibrotic drugs. Recent advances in understanding the multiple interrelated pathogenic pathways underlying IPF have identified various molecular phenotypes resulting from complex interactions among genetic, epigenetic, transcriptional, post-transcriptional, metabolic, and environmental factors. These different disease endotypes appear to confer variable susceptibility to the condition, differing risks of rapid progression, and, possibly, altered responses to therapy. The development and validation of diagnostic and prognostic biomarkers are necessary to enable a more precise and earlier diagnosis of IPF and to improve prediction of future disease behaviour. The availability of approved antifibrotic therapies together with potential new drugs currently under evaluation also highlights the need for biomarkers able to predict and assess treatment responsiveness, thereby allowing individualised treatment based on risk of progression and drug response. This approach of disease stratification and personalised medicine is already used in the routine management of many cancers and provides a potential road map for guiding clinical care in IPF. PMID:27303645

Predicting the sensitivity to ion therapy based on the response to photon irradiation--experimental evidence and mathematical modelling.

PubMed

Mohanty, Chitralekha; Zielinska-Chomej, Katarzyna; Edgren, Margareta; Hirayama, Ryoichi; Murakami, Takeshi; Lind, Bengt; Toma-Dasu, Iuliana

2014-06-01

The use of ion radiation therapy is growing due to the continuously increasing positive clinical experience obtained. Therefore, there is a high interest in radio-biological experiments comparing the relative efficiency in cell killing of ions and photons as photons are currently the main radiation modality used for cancer treatment. This comparison is particularly important since the treatment planning systems (TPSs) used at the main ion therapy Centers make use of parameters describing the cellular response to photons, respectively ions, determined in vitro. It was, therefore, the aim of this article to compare the effects of high linear energy transfer (LET) ion radiation with low LET photons and determine whether the cellular response to low LET could predict the response to high LET irradiation. Clonogenic cell survival data of five tumor cell lines irradiated with different ion beams of similar, clinically-relevant, LET were studied in relation to response to low LET photons. Two mathematical models were used to fit the data, the repairable-conditionally repairable damage (RCR) model and the linear quadratic (LQ) model. The results indicate that the relative biological efficiency of the high LET radiation assessed with the RCR model could be predicted based only on the response to the low LET irradiation. The particular features of the RCR model indicate that tumor cells showing a large capacity for repairing the damage will have the larger benefit from radiation therapy with ion beams. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Contingency Awareness Shapes Acquisition and Extinction of Emotional Responses in a Conditioning Model of Pain-Related Fear

PubMed Central

Labrenz, Franziska; Icenhour, Adriane; Benson, Sven; Elsenbruch, Sigrid

2015-01-01

As a fundamental learning process, fear conditioning promotes the formation of associations between predictive cues and biologically significant signals. In its application to pain, conditioning may provide important insight into mechanisms underlying pain-related fear, although knowledge especially in interoceptive pain paradigms remains scarce. Furthermore, while the influence of contingency awareness on excitatory learning is subject of ongoing debate, its role in pain-related acquisition is poorly understood and essentially unknown regarding extinction as inhibitory learning. Therefore, we addressed the impact of contingency awareness on learned emotional responses to pain- and safety-predictive cues in a combined dataset of two pain-related conditioning studies. In total, 75 healthy participants underwent differential fear acquisition, during which rectal distensions as interoceptive unconditioned stimuli (US) were repeatedly paired with a predictive visual cue (conditioned stimulus; CS+) while another cue (CS−) was presented unpaired. During extinction, both CS were presented without US. CS valence, indicating learned emotional responses, and CS-US contingencies were assessed on visual analog scales (VAS). Based on an integrative measure of contingency accuracy, a median-split was performed to compare groups with low vs. high contingency accuracy regarding learned emotional responses. To investigate predictive value of contingency accuracy, regression analyses were conducted. Highly accurate individuals revealed more pronounced negative emotional responses to CS+ and increased positive responses to CS− when compared to participants with low contingency accuracy. Following extinction, highly accurate individuals had fully extinguished pain-predictive cue properties, while exhibiting persistent positive emotional responses to safety signals. In contrast, individuals with low accuracy revealed equally positive emotional responses to both, CS+ and CS−. Contingency accuracy predicted variance in the formation of positive responses to safety cues while no predictive value was found for danger cues following acquisition and for neither cue following extinction. Our findings underscore specific roles of learned danger and safety in pain-related acquisition and extinction. Contingency accuracy appears to distinctly impact learned emotional responses to safety and danger cues, supporting aversive learning to occur independently from CS-US awareness. The interplay of cognitive and emotional factors in shaping excitatory and inhibitory pain-related learning may contribute to altered pain processing, underscoring its clinical relevance in chronic pain. PMID:26640433

Contingency Awareness Shapes Acquisition and Extinction of Emotional Responses in a Conditioning Model of Pain-Related Fear.

PubMed

Labrenz, Franziska; Icenhour, Adriane; Benson, Sven; Elsenbruch, Sigrid

2015-01-01

As a fundamental learning process, fear conditioning promotes the formation of associations between predictive cues and biologically significant signals. In its application to pain, conditioning may provide important insight into mechanisms underlying pain-related fear, although knowledge especially in interoceptive pain paradigms remains scarce. Furthermore, while the influence of contingency awareness on excitatory learning is subject of ongoing debate, its role in pain-related acquisition is poorly understood and essentially unknown regarding extinction as inhibitory learning. Therefore, we addressed the impact of contingency awareness on learned emotional responses to pain- and safety-predictive cues in a combined dataset of two pain-related conditioning studies. In total, 75 healthy participants underwent differential fear acquisition, during which rectal distensions as interoceptive unconditioned stimuli (US) were repeatedly paired with a predictive visual cue (conditioned stimulus; CS(+)) while another cue (CS(-)) was presented unpaired. During extinction, both CS were presented without US. CS valence, indicating learned emotional responses, and CS-US contingencies were assessed on visual analog scales (VAS). Based on an integrative measure of contingency accuracy, a median-split was performed to compare groups with low vs. high contingency accuracy regarding learned emotional responses. To investigate predictive value of contingency accuracy, regression analyses were conducted. Highly accurate individuals revealed more pronounced negative emotional responses to CS(+) and increased positive responses to CS(-) when compared to participants with low contingency accuracy. Following extinction, highly accurate individuals had fully extinguished pain-predictive cue properties, while exhibiting persistent positive emotional responses to safety signals. In contrast, individuals with low accuracy revealed equally positive emotional responses to both, CS(+) and CS(-). Contingency accuracy predicted variance in the formation of positive responses to safety cues while no predictive value was found for danger cues following acquisition and for neither cue following extinction. Our findings underscore specific roles of learned danger and safety in pain-related acquisition and extinction. Contingency accuracy appears to distinctly impact learned emotional responses to safety and danger cues, supporting aversive learning to occur independently from CS-US awareness. The interplay of cognitive and emotional factors in shaping excitatory and inhibitory pain-related learning may contribute to altered pain processing, underscoring its clinical relevance in chronic pain.

Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment outcome in virtual reality exposure therapy

PubMed Central

Norrholm, Seth Davin; Jovanovic, Tanja; Gerardi, Maryrose; Breazeale, Kathryn G.; Price, Matthew; Davis, Michael; Duncan, Erica; Ressler, Kerry J.; Bradley, Bekh; Rizzo, Albert; Tuerk, Peter W.; Rothbaum, Barbara O.

2017-01-01

Baseline cue-dependent physiological reactivity may serve as an objective measure of posttraumatic stress disorder (PTSD) symptoms. Additionally, prior animal model and psychological studies would suggest that subjects with greatest symptoms at baseline may have the greatest violation of expectancy to danger when undergoing exposure based psychotherapy; thus treatment approaches which enhanced the learning under these conditions would be optimal for those with maximal baseline cue-dependent reactivity. However methods to study this hypothesis objectively are lacking. Virtual reality (VR) methodologies have been successfully employed as an enhanced form of imaginal prolonged exposure therapy for the treatment of PTSD. Our goal was to examine the predictive nature of initial psychophysiological (e.g., startle, skin conductance, heart rate) and stress hormone responses (e.g., cortisol) during presentation of VR-based combat-related stimuli on PTSD treatment outcome. Combat veterans with PTSD underwent 6 weeks of VR exposure therapy combined with either D-cycloserine (DCS), alprazolam (ALP), or placebo (PBO). In the DCS group, startle response to VR scenes prior to initiation of treatment accounted for 76% of the variance in CAPS change scores, p < 0.001, in that higher responses predicted greater changes in symptom severity over time. Additionally, baseline cortisol reactivity was inversely associated with treatment response in the ALP group, p = 0.04. We propose that baseline cue-activated physiological measures will be sensitive to predicting patients’ level of response to exposure therapy, in particular in the presence of enhancement (e.g., DCS). PMID:27183343

Field Validation of a Transcriptional Assay for the Prediction of Age of Uncaged Aedes aegypti Mosquitoes in Northern Australia

PubMed Central

Hugo, Leon E.; Cook, Peter E.; Johnson, Petrina H.; Rapley, Luke P.; Kay, Brian H.; Ryan, Peter A.; Ritchie, Scott A.; O'Neill, Scott L.

2010-01-01

Background New strategies to eliminate dengue have been proposed that specifically target older Aedes aegypti mosquitoes, the proportion of the vector population that is potentially capable of transmitting dengue viruses. Evaluation of these strategies will require accurate and high-throughput methods of predicting mosquito age. We previously developed an age prediction assay for individual Ae. aegypti females based on the transcriptional profiles of a selection of age responsive genes. Here we conducted field testing of the method on Ae. aegypti that were entirely uncaged and free to engage in natural behavior. Methodology/Principal Findings We produced “free-range” test specimens by releasing 8007 adult Ae. aegypti inside and around an isolated homestead in north Queensland, Australia, and recapturing females at two day intervals. We applied a TaqMan probe-based assay design that enabled high-throughput quantitative RT-PCR of four transcripts from three age-responsive genes and a reference gene. An age prediction model was calibrated on mosquitoes maintained in small sentinel cages, in which 68.8% of the variance in gene transcription measures was explained by age. The model was then used to predict the ages of the free-range females. The relationship between the predicted and actual ages achieved an R2 value of 0.62 for predictions of females up to 29 days old. Transcriptional profiles and age predictions were not affected by physiological variation associated with the blood feeding/egg development cycle and we show that the age grading method could be applied to differentiate between two populations of mosquitoes having a two-fold difference in mean life expectancy. Conclusions/Significance The transcriptional profiles of age responsive genes facilitated age estimates of near-wild Ae. aegypti females. Our age prediction assay for Ae. aegypti provides a useful tool for the evaluation of mosquito control interventions against dengue where mosquito survivorship or lifespan reduction are crucial to their success. The approximate cost of the method was US$7.50 per mosquito and 60 mosquitoes could be processed in 3 days. The assay is based on conserved genes and modified versions are likely to support similar investigations of several important mosquito and other disease vectors. PMID:20186322

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

PubMed

Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary M; de Bruin, Hubert; Maccrimmon, Duncan J

2013-10-01

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Predicting Dynamic Postural Instability Using Center of Mass Time-to-Contact Information

PubMed Central

Hasson, Christopher J.; Van Emmerik, Richard E.A.; Caldwell, Graham E.

2008-01-01

Our purpose was to determine whether spatiotemporal measures of center of mass motion relative to the base of support boundary could predict stepping strategies after upper-body postural perturbations in humans. We expected that inclusion of center of mass acceleration in such time-to-contact (TtC) calculations would give better predictions and more advanced warning of perturbation severity. TtC measures were compared with traditional postural variables, which don’t consider support boundaries, and with an inverted pendulum model of dynamic stability developed by Hof et al. (2005). A pendulum was used to deliver sequentially increasing perturbations to 10 young adults, who were strapped to a wooden backboard that constrained motion to sagittal plane rotation about the ankle joint. Subjects were instructed to resist the perturbations, stepping only if necessary to prevent a fall. Peak center of mass and center of pressure velocity and acceleration demonstrated linear increases with postural challenge. In contrast, boundary relevant minimum TtC values decreased nonlinearly with postural challenge, enabling prediction of stepping responses using quadratic equations. When TtC calculations incorporated center of mass acceleration, the quadratic fits were better and gave more accurate predictions of the TtC values that would trigger stepping responses. In addition, TtC minima occurred earlier with acceleration inclusion, giving more advanced warning of perturbation severity. Our results were in agreement with TtC predictions based on Hof’s model, and suggest that TtC may function as a control parameter, influencing the postural control system’s decision to transition from a stationary base of support to a stepping strategy. PMID:18556003

Strategic modulation of cognitive control.

PubMed

Lungu, Ovidiu V; Liu, Tao; Waechter, Tobias; Willingham, Daniel T; Ashe, James

2007-08-01

The neural substrate of cognitive control is thought to comprise an evaluative component located in the anterior cingulate cortex (ACC) and an executive component in the prefrontal cortex (PFC). The control mechanism itself is mainly local, triggered by response conflict (monitored by the ACC) and involving the allocation of executive resources (recruited by the PFC) in a trial-to-trial fashion. However, another way to achieve control would be to use a strategic mechanism based on long-term prediction of upcoming events and on a chronic response strategy that ignores local features of the task. In the current study, we showed that such a strategic control mechanism was based on a functional dissociation or complementary relationship between the ACC and the PFC. When information in the environment was available to make predictions about upcoming stimuli, local task features (e.g., response conflict) were no longer used as a control signal. We suggest that having separate control mechanisms based on local or global task features allows humans to be persistent in pursuing their goals, yet flexible enough to adapt to changes in the environment.

Surprise beyond prediction error

PubMed Central

Chumbley, Justin R; Burke, Christopher J; Stephan, Klaas E; Friston, Karl J; Tobler, Philippe N; Fehr, Ernst

2014-01-01

Surprise drives learning. Various neural “prediction error” signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise. PMID:24700400

Scaling approach in predicting the seatbelt loading and kinematics of vulnerable occupants: How far can we go?

PubMed

Nie, Bingbing; Forman, Jason L; Joodaki, Hamed; Wu, Taotao; Kent, Richard W

2016-09-01

Occupants with extreme body size and shape, such as the small female or the obese, were reported to sustain high risk of injury in motor vehicle crashes (MVCs). Dimensional scaling approaches are widely used in injury biomechanics research based on the assumption of geometrical similarity. However, its application scope has not been quantified ever since. The objective of this study is to demonstrate the valid range of scaling approaches in predicting the impact response of the occupants with focus on the vulnerable populations. The present analysis was based on a data set consisting of 60 previously reported frontal crash tests in the same sled buck representing a typical mid-size passenger car. The tests included two categories of human surrogates: 9 postmortem human surrogates (PMHS) of different anthropometries (stature range: 147-189 cm; weight range: 27-151 kg) and 5 anthropomorphic test devices (ATDs). The impact response was considered including the restraint loads and the kinematics of multiple body segments. For each category of the human surrogates, a mid-size occupant was selected as a baseline and the impact response was scaled specifically to another subject based on either the body mass (body shape) or stature (the overall body size). To identify the valid range of the scaling approach, the scaled response was compared to the experimental results using assessment scores on the peak value, peak timing (the time when the peak value occurred), and the overall curve shape ranging from 0 (extremely poor) to 1 (perfect match). Scores of 0.7 to 0.8 and 0.8 to 1.0 indicate fair and acceptable prediction. For both ATDs and PMHS, the scaling factor derived from body mass proved an overall good predictor of the peak timing for the shoulder belt (0.868, 0.829) and the lap belt (0.858, 0.774) and for the peak value of the lap belt force (0.796, 0.869). Scaled kinematics based on body stature provided fair or acceptable prediction on the overall head/shoulder kinematics (0.741, 0.822 for the head; 0.817, 0.728 for the shoulder) regardless of the anthropometry. The scaling approach exhibited poor prediction capability on the curve shape for the restraint force (0.494 and 0.546 for the shoulder belt; 0.585 and 0.530 for the lap belt). It also cannot well predict the excursion of the pelvis and the knee. The results revealed that for the peak lap belt force and the forward motion of the head and shoulder, the underlying linear relationship with body size and shape is valid over a wide anthropometric range. The chaotic nature of the dynamic response cannot be fully recovered by the assumption of the whole-body geometrical similarity, especially for the curve shape. The valid range of the scaling approach established in this study can be reasonably referenced in predicting the impact response of a given specific population with expected deviation. Application of this knowledge also includes proposing strategies for restraint configuration and providing reference for ATD and/or human body model (HBM) development for vulnerable occupants.

A Mathematical Modeling Approach to Understanding the Effect of Anti‐Interleukin Therapy on Eosinophils

PubMed Central

Karelina, T; Voronova, V; Demin, O; Colice, G

2016-01-01

Emerging T‐helper type 2 (Th2) cytokine‐based asthma therapies, such as tralokinumab, lebrikizumab (anti‐interleukin (IL)‐13), and mepolizumab (anti‐IL‐5), have shown differences in their blood eosinophil (EOS) response. To better understand these effects, we developed a mathematical model of EOS dynamics. For the anti‐IL‐13 therapies, lebrikizumab and tralokinumab, the model predicted an increase of 30% and 10% in total and activated EOS in the blood, respectively, and a decrease in the total and activated EOS in the airways. The model predicted a rapid decrease in total and activated EOS levels in blood and airways for the anti‐IL‐5 therapy mepolizumab. All model‐based predictions were consistent with published clinical observations. The modeling approach provided insights into EOS response after treatment with Th2‐targeted therapies, and supports the hypothesis that an increase in blood EOS after anti‐IL‐13 therapy is part of the pharmacological action of these therapies. PMID:27885827

A PK-PD model-based assessment of sugammadex effects on coagulation parameters.

PubMed

Bosch, Rolien; van Lierop, Marie-José; de Kam, Pieter-Jan; Kruithof, Annelieke C; Burggraaf, Jacobus; de Greef, Rik; Visser, Sandra A G; Johnson-Levonas, Amy O; Kleijn, Huub-Jan

2016-03-10

Exposure-response analyses of sugammadex on activated partial thromboplastin time (APTT) and prothrombin time international normalized ratio (PT(INR)) were performed using data from two clinical trials in which subjects were co-treated with anti-coagulants, providing a framework to predict these responses in surgical patients on thromboprophylactic doses of low molecular weight or unfractionated heparin. Sugammadex-mediated increases in APTT and PT(INR) were described with a direct effect model, and this relationship was similar in the presence or absence of anti-coagulant therapy in either healthy volunteers or surgical patients. In surgical patients on thromboprophylactic therapy, model-based predictions showed 13.1% and 22.3% increases in respectively APTT and PT(INR) within 30min after administration of 16mg/kg sugammadex. These increases remain below thresholds seen following treatment with standard anti-coagulant therapy and were predicted to be short-lived paralleling the rapid decline in sugammadex plasma concentrations. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Online prediction of organileptic data for snack food using color images

NASA Astrophysics Data System (ADS)

Yu, Honglu; MacGregor, John F.

2004-11-01

In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

Effectiveness of evaluating tumor vascularization using 3D power Doppler ultrasound with high-definition flow technology in the prediction of the response to neoadjuvant chemotherapy for T2 breast cancer: a preliminary report

NASA Astrophysics Data System (ADS)

Shia, Wei-Chung; Chen, Dar-Ren; Huang, Yu-Len; Wu, Hwa-Koon; Kuo, Shou-Jen

2015-10-01

The aim of this study was to evaluate the effectiveness of advanced ultrasound (US) imaging of vascular flow and morphological features in the prediction of a pathologic complete response (pCR) and a partial response (PR) to neoadjuvant chemotherapy for T2 breast cancer. Twenty-nine consecutive patients with T2 breast cancer treated with six courses of anthracycline-based neoadjuvant chemotherapy were enrolled. Three-dimensional (3D) power Doppler US with high-definition flow (HDF) technology was used to investigate the blood flow in and morphological features of the tumors. Six vascularity quantization features, three morphological features, and two vascular direction features were selected and extracted from the US images. A support vector machine was used to evaluate the changes in vascularity after neoadjuvant chemotherapy, and pCR and PR were predicted on the basis of these changes. The most accurate prediction of pCR was achieved after the first chemotherapy cycle, with an accuracy of 93.1% and a specificity of 85.5%, while that of a PR was achieved after the second cycle, with an accuracy of 79.31% and a specificity of 72.22%. Vascularity data can be useful to predict the effects of neoadjuvant chemotherapy. Determination of changes in vascularity after neoadjuvant chemotherapy using 3D power Doppler US with HDF can generate accurate predictions of the patient response, facilitating early decision-making.

Lock-in amplifier error prediction and correction in frequency sweep measurements.

PubMed

Sonnaillon, Maximiliano Osvaldo; Bonetto, Fabian Jose

2007-01-01

This article proposes an analytical algorithm for predicting errors in lock-in amplifiers (LIAs) working with time-varying reference frequency. Furthermore, a simple method for correcting such errors is presented. The reference frequency can be swept in order to measure the frequency response of a system within a given spectrum. The continuous variation of the reference frequency produces a measurement error that depends on three factors: the sweep speed, the LIA low-pass filters, and the frequency response of the measured system. The proposed error prediction algorithm is based on the final value theorem of the Laplace transform. The correction method uses a double-sweep measurement. A mathematical analysis is presented and validated with computational simulations and experimental measurements.

Motion-based prediction explains the role of tracking in motion extrapolation.

PubMed

Khoei, Mina A; Masson, Guillaume S; Perrinet, Laurent U

2013-11-01

During normal viewing, the continuous stream of visual input is regularly interrupted, for instance by blinks of the eye. Despite these frequents blanks (that is the transient absence of a raw sensory source), the visual system is most often able to maintain a continuous representation of motion. For instance, it maintains the movement of the eye such as to stabilize the image of an object. This ability suggests the existence of a generic neural mechanism of motion extrapolation to deal with fragmented inputs. In this paper, we have modeled how the visual system may extrapolate the trajectory of an object during a blank using motion-based prediction. This implies that using a prior on the coherency of motion, the system may integrate previous motion information even in the absence of a stimulus. In order to compare with experimental results, we simulated tracking velocity responses. We found that the response of the motion integration process to a blanked trajectory pauses at the onset of the blank, but that it quickly recovers the information on the trajectory after reappearance. This is compatible with behavioral and neural observations on motion extrapolation. To understand these mechanisms, we have recorded the response of the model to a noisy stimulus. Crucially, we found that motion-based prediction acted at the global level as a gain control mechanism and that we could switch from a smooth regime to a binary tracking behavior where the dot is tracked or lost. Our results imply that a local prior implementing motion-based prediction is sufficient to explain a large range of neural and behavioral results at a more global level. We show that the tracking behavior deteriorates for sensory noise levels higher than a certain value, where motion coherency and predictability fail to hold longer. In particular, we found that motion-based prediction leads to the emergence of a tracking behavior only when enough information from the trajectory has been accumulated. Then, during tracking, trajectory estimation is robust to blanks even in the presence of relatively high levels of noise. Moreover, we found that tracking is necessary for motion extrapolation, this calls for further experimental work exploring the role of noise in motion extrapolation. Copyright © 2013 Elsevier Ltd. All rights reserved.

In monkeys making value-based decisions, amygdala neurons are sensitive to cue value as distinct from cue salience.

PubMed

Leathers, Marvin L; Olson, Carl R

2017-04-01

Neurons in the lateral intraparietal (LIP) area of macaque monkey parietal cortex respond to cues predicting rewards and penalties of variable size in a manner that depends on the motivational salience of the predicted outcome (strong for both large reward and large penalty) rather than on its value (positive for large reward and negative for large penalty). This finding suggests that LIP mediates the capture of attention by salient events and does not encode value in the service of value-based decision making. It leaves open the question whether neurons elsewhere in the brain encode value in the identical task. To resolve this issue, we recorded neuronal activity in the amygdala in the context of the task employed in the LIP study. We found that responses to reward-predicting cues were similar between areas, with the majority of reward-sensitive neurons responding more strongly to cues that predicted large reward than to those that predicted small reward. Responses to penalty-predicting cues were, however, markedly different. In the amygdala, unlike LIP, few neurons were sensitive to penalty size, few penalty-sensitive neurons favored large over small penalty, and the dependence of firing rate on penalty size was negatively correlated with its dependence on reward size. These results indicate that amygdala neurons encoded cue value under circumstances in which LIP neurons exhibited sensitivity to motivational salience. However, the representation of negative value, as reflected in sensitivity to penalty size, was weaker than the representation of positive value, as reflected in sensitivity to reward size. NEW & NOTEWORTHY This is the first study to characterize amygdala neuronal responses to cues predicting rewards and penalties of variable size in monkeys making value-based choices. Manipulating reward and penalty size allowed distinguishing activity dependent on motivational salience from activity dependent on value. This approach revealed in a previous study that neurons of the lateral intraparietal (LIP) area encode motivational salience. Here, it reveals that amygdala neurons encode value. The results establish a sharp functional distinction between the two areas. Copyright © 2017 the American Physiological Society.

The amphetamine response moderates the relationship between negative emotionality and alcohol use.

PubMed

Allen, Kenneth J D; Gabbay, Frances H

2013-02-01

Considerable evidence suggests that sensitivity to the stimulant effects of alcohol and other drugs is a risk marker for heavy or problematic use of those substances. A separate body of research implicates negative emotionality. The goal of the present study was to evaluate the independent and interactive effects of the stimulant response, assessed with an amphetamine challenge, and negative emotionality on alcohol and drug use. Healthy young women and men completed the Multidimensional Personality Questionnaire (MPQ) and an inventory assessing alcohol and other drug use. Subsequently, the effects of 10-mg d-amphetamine were determined in the laboratory using the Stimulant scale of the Biphasic Alcohol Effects Scale. Hierarchical regression analyses evaluated the effects of amphetamine response and the MPQ factor Negative Emotionality on measures of substance use. The amphetamine response moderated relationships between negative emotionality and alcohol use: in combination with a robust amphetamine response (i.e., enhanced stimulant effects as compared with baseline), negative emotionality predicted greater alcohol consumption, more episodes of binge drinking, and more frequent intoxication in regression models. A strong stimulant response independently predicted having used an illicit drug, and there was a trend for it to predict having used alcohol. Negative emotionality alone was not associated with any measure of alcohol or drug use. Consistent with the idea that emotion-based behavioral dysregulation promotes reward seeking, a high level of negative emotionality was associated with maladaptive alcohol use when it co-occurred with sensitivity to drug-based reward. The findings contribute to our understanding of how differences in personality may interact with those in drug response to affect alcohol use. Copyright © 2012 by the Research Society on Alcoholism.

The amphetamine response moderates the relationship between negative emotionality and alcohol use

PubMed Central

Allen, Kenneth J. D.; Gabbay, Frances H.

2012-01-01

Background Considerable evidence suggests that sensitivity to the stimulant effects of alcohol and other drugs is a risk marker for heavy or problematic use of those substances. A separate body of research implicates negative emotionality. The goal of the present study was to evaluate the independent and interactive effects of the stimulant response, assessed with an amphetamine challenge, and negative emotionality on alcohol and drug use. Methods Healthy young women and men completed the Multidimensional Personality Questionnaire (MPQ) and an inventory assessing alcohol and other drug use. Subsequently, the effects of 10 mg d-amphetamine were determined in the laboratory using the Stimulant scale of the Biphasic Alcohol Effects Scale. Hierarchical regression analyses evaluated the effects of amphetamine response and the MPQ factor Negative Emotionality on measures of substance use. Results The amphetamine response moderated relationships between negative emotionality and alcohol use: In combination with a robust amphetamine response (i.e., enhanced stimulant effects as compared to baseline), negative emotionality predicted greater alcohol consumption, more episodes of binge drinking, and more frequent intoxication in regression models. A strong stimulant response independently predicted having used an illicit drug, and there was a trend for it to predict having used alcohol. Negative emotionality alone was not associated with any measure of alcohol or drug use. Conclusions Consistent with the idea that emotion-based behavioral dysregulation promotes reward-seeking, a high level of negative emotionality was associated with maladaptive alcohol use when it co-occurred with sensitivity to drug-based reward. The findings contribute to our understanding of how differences in personality may interact with those in drug response to affect alcohol use. PMID:23240777

A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records.

PubMed

Ouyang, Liwen; Apley, Daniel W; Mehrotra, Sanjay

2016-04-01

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches. The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed. The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample. The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Linking non-culturable (qPCR) and culturable enterococci densities with hydrometeorological conditions

USGS Publications Warehouse

Byappanahalli, Muruleedhara N.; Whitman, Richard L.; Shively, Dawn A.; Nevers, Meredith B.

2010-01-01

Quantitative polymerase chain reaction (qPCR) measurement of enterococci has been proposed as a rapid technique for assessment of beach water quality, but the response of qPCR results to environmental conditions has not been fully explored. Culture-based E. coli and enterococci have been used in empirical predictive models to characterize their responses to environmental conditions and to increase monitoring frequency and efficiency. This approach has been attempted with qPCR results only in few studies. During the summer of 2006, water samples were collected from two southern Lake Michigan beaches and the nearby river outfall (Burns Ditch) and were analyzed for enterococci by culture-based and non-culture-based (i.e., qPCR) methods, as well as culture-based E. coli. Culturable enterococci densities (log CFU/100 ml) for the beaches were significantly correlated with enterococci qPCR cell equivalents (CE) (R = 0.650, P N = 32). Enterococci CE and CFU densities were highest in Burns Ditch relative to the beach sites; however, only CFUs were significantly higher (P R = 0.565, P N = 32). Culturable E. coli and enterococci densities were significantly correlated (R = 0.682, P N = 32). Regression analyses suggested that enterococci CFU could be predicted by lake turbidity, Burns Ditch discharge, and wind direction (adjusted R2 = 0.608); enterococci CE was best predicted by Burns Ditch discharge and log-transformed lake turbidity × wave height (adjusted R2 = 0.40). In summary, our results show that analytically, the qPCR method compares well to the non-culture-based method for measuring enterococci densities in beach water and that both these approaches can be predicted by hydrometeorological conditions. Selected predictors and model results highlight the differences between the environmental responses of the two method endpoints and the potentially high variance in qPCR results

Development of structural and material clavicle response corridors under axial compression and three point bending loading for clavicle finite element model validation.

PubMed

Zhang, Qi; Kindig, Matthew; Li, Zuoping; Crandall, Jeff R; Kerrigan, Jason R

2014-08-22

Clavicle injuries were frequently observed in automotive side and frontal crashes. Finite element (FE) models have been developed to understand the injury mechanism, although no clavicle loading response corridors yet exist in the literature to ensure the model response biofidelity. Moreover, the typically developed structural level (e.g., force-deflection) response corridors were shown to be insufficient for verifying the injury prediction capacity of FE model, which usually is based on strain related injury criteria. Therefore, the purpose of this study is to develop both the structural (force vs deflection) and material level (strain vs force) clavicle response corridors for validating FE models for injury risk modeling. 20 Clavicles were loaded to failure under loading conditions representative of side and frontal crashes respectively, half of which in axial compression, and the other half in three point bending. Both structural and material response corridors were developed for each loading condition. FE model that can accurately predict structural response and strain level provides a more useful tool in injury risk modeling and prediction. The corridor development method in this study could also be extended to develop corridors for other components of the human body. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

PubMed

Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

2018-02-06

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

Predicting photosynthesis and transpiration responses to ozone: decoupling modeled photosynthesis and stomatal conductance

NASA Astrophysics Data System (ADS)

Lombardozzi, D.; Levis, S.; Bonan, G.; Sparks, J. P.

2012-08-01

Plants exchange greenhouse gases carbon dioxide and water with the atmosphere through the processes of photosynthesis and transpiration, making them essential in climate regulation. Carbon dioxide and water exchange are typically coupled through the control of stomatal conductance, and the parameterization in many models often predict conductance based on photosynthesis values. Some environmental conditions, like exposure to high ozone (O3) concentrations, alter photosynthesis independent of stomatal conductance, so models that couple these processes cannot accurately predict both. The goals of this study were to test direct and indirect photosynthesis and stomatal conductance modifications based on O3 damage to tulip poplar (Liriodendron tulipifera) in a coupled Farquhar/Ball-Berry model. The same modifications were then tested in the Community Land Model (CLM) to determine the impacts on gross primary productivity (GPP) and transpiration at a constant O3 concentration of 100 parts per billion (ppb). Modifying the Vcmax parameter and directly modifying stomatal conductance best predicts photosynthesis and stomatal conductance responses to chronic O3 over a range of environmental conditions. On a global scale, directly modifying conductance reduces the effect of O3 on both transpiration and GPP compared to indirectly modifying conductance, particularly in the tropics. The results of this study suggest that independently modifying stomatal conductance can improve the ability of models to predict hydrologic cycling, and therefore improve future climate predictions.

A fresh look at the predictors of naming accuracy and errors in Alzheimer's disease.

PubMed

Cuetos, Fernando; Rodríguez-Ferreiro, Javier; Sage, Karen; Ellis, Andrew W

2012-09-01

In recent years, a considerable number of studies have tried to establish which characteristics of objects and their names predict the responses of patients with Alzheimer's disease (AD) in the picture-naming task. The frequency of use of words and their age of acquisition (AoA) have been implicated as two of the most influential variables, with naming being best preserved for objects with high-frequency, early-acquired names. The present study takes a fresh look at the predictors of naming success in Spanish and English AD patients using a range of measures of word frequency and AoA along with visual complexity, imageability, and word length as predictors. Analyses using generalized linear mixed modelling found that naming accuracy was better predicted by AoA ratings taken from older adults than conventional ratings from young adults. Older frequency measures based on written language samples predicted accuracy better than more modern measures based on the frequencies of words in film subtitles. Replacing adult frequency with an estimate of cumulative (lifespan) frequency did not reduce the impact of AoA. Semantic error rates were predicted by both written word frequency and senior AoA while null response errors were only predicted by frequency. Visual complexity, imageability, and word length did not predict naming accuracy or errors. ©2012 The British Psychological Society.

Predictive assessment in pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy

PubMed Central

Yuan, Zhengrong; Li, Jiao; Hu, Ruiqi; Jiao, Yang; Han, Yingying; Weng, Qiang

2015-01-01

Published data have shown inconsistent results about the pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy. This meta-analysis aimed to summarize published findings and provide more reliable association. A total of 53 eligible studies including 7433 patients were included. Patients bearing the favorable TrpTrp and TrpArg genotypes of Arg194Trp were more likely to better response rates to platinum-based chemotherapy compared to those with the unfavorable ArgArg genotype (TrpTrp+TrpArg vs. ArgArg: odds ratio (OR) = 2.02, 95% CI, 1.66–2.45). The GlnGln and GlnArg genotypes of Arg399Gln were significantly associated with the poorer response rates compared to those with the ArgArg genotype (GlnGln +GlnArg vs. ArgArg: OR = 0.68, 95% CI, 0.54–0.86). The GlnGln genotype might be more closely associated with shorter survival time and higher risks of death for patients (GlnGln vs. ArgArg: hazard ratio (HR) = 1.14, 95% CI, 0.75–1.75). Our cumulative meta-analyses indicated a distinct apparent trend toward a better response rate for Arg194Trp, but a poorer response rate in Arg399Gln. These findings indicate a predictive role of XRCC1 polymorphisms in clinical outcomes. The use of XRCC1 polymorphisms as predictive factor of clinical outcomes in personalized chemotherapy treatment requires further verification from large well-designed pharmacogenetics studies. PMID:26585370

Determinants of responsibility for health, spiritual health and interpersonal relationship based on theory of planned behavior in high school girl students.

PubMed

Rezazadeh, Afsaneh; Solhi, Mahnaz; Azam, Kamal

2015-01-01

Adolescence is a sensitive period of acquiring normal and abnormal habits for all oflife. The study investigates determinants of responsibility for health, spiritual health and interpersonal relations and predictive factors based on the theory of planned behavior in high school girl students in Tabriz. In this Cross-sectional study, 340 students were selected thorough multi-stage sampling. An author-made questionnaire based on standard questionnaires of Health Promotion and Lifestyle II (HPLPII), spiritual health standards (Palutzian & Ellison) and components of the theory of planned behavior (attitudes, subjective norms, perceived behavioral control, and behavioral intention) was used for data collection. The questionnaire was validated in a pilot study. Data were analyzed using SPSS v.15 and descriptive and analytical tests (Chi-square test, Pearson correlation co-efficient and liner regression test in backward method). Students' responsibility for health, spiritual health, interpersonal relationships, and concepts of theory of planned behavior was moderate. We found a significant positive correlation (p<0/001) among all concepts of theory of planned behavior. Attitude and perceived behavioral control predicted 35% of intention of behavioral change (p<0.001). Attitude, subjective norms, and perceived behavioral control predicted 74% of behavioral change in accountability for health (p<0.0001), 56% for behavioral change in spiritual health (p<0.0001) and 63% for behavioral change in interpersonal relationship (p<0.0001). Status of responsibility for health, spiritual health and interpersonal relationships of students was moderate. Hence, behavioral intention and its determinants such as perceived behavioral control should be noted in promoting intervention programs.

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

Zhang, P; Kuo, L; Yorke, E

Purpose: To develop a biological modeling strategy which incorporates the response observed on the mid-treatment PET/CT into a dose escalation design for adaptive radiotherapy of non-small-cell lung cancer. Method: FDG-PET/CT was acquired midway through standard fractionated treatment and registered to pre-treatment planning PET/CT to evaluate radiation response of lung cancer. Each mid-treatment PET voxel was assigned the median SUV inside a concentric 1cm-diameter sphere to account for registration and imaging uncertainties. For each voxel, the planned radiation dose, pre- and mid-treatment SUVs were used to parameterize the linear-quadratic model, which was then utilized to predict the SUV distribution after themore » full prescribed dose. Voxels with predicted post-treatment SUV≥2 were identified as the resistant target (response arm). An adaptive simultaneous integrated boost was designed to escalate dose to the resistant target as high as possible, while keeping prescription dose to the original target and lung toxicity intact. In contrast, an adaptive target volume was delineated based only on the intensity of mid-treatment PET/CT (intensity arm), and a similar adaptive boost plan was optimized. The dose escalation capability of the two approaches was compared. Result: Images of three patients were used in this planning study. For one patient, SUV prediction indicated complete response and no necessary dose escalation. For the other two, resistant targets defined in the response arm were multifocal, and on average accounted for 25% of the pre-treatment target, compared to 67% in the intensity arm. The smaller response arm targets led to a 6Gy higher mean target dose in the adaptive escalation design. Conclusion: This pilot study suggests that adaptive dose escalation to a biologically resistant target predicted from a pre- and mid-treatment PET/CT may be more effective than escalation based on the mid-treatment PET/CT alone. More plans and ultimately clinical protocols are needed to validate this approach. MSKCC has a research agreement with Varian Medical System.« less

New exposure-based metric approach for evaluating O3 risk to North American aspen forests

Treesearch

K.E. Percy; M. Nosal; W. Heilman; T. Dann; J. Sober; A.H. Legge; D.F. Karnosky

2007-01-01

The United States and Canada currently use exposure-based metrics to protect vegetation from O3. Using 5 years (1999-2003) of co-measured O3, meteorology and growth response, we have developed exposure-based regression models that predict Populus tremuloides growth change within the North American ambient...

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

PubMed

Yue, Zhenyu; Zhang, Wenna; Lu, Yongming; Yang, Qiaoyue; Ding, Qiuying; Xia, Junfeng; Chen, Yan

2015-01-01

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

PubMed

Xu, Yifang; Collins, Leslie M

2005-06-01

This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, Nucl. However, no specific rule for determining Nucl has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to Nucl at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly.

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Modelling the effects of climate change on the distribution and production of marine fishes: accounting for trophic interactions in a dynamic bioclimate envelope model.

PubMed

Fernandes, Jose A; Cheung, William W L; Jennings, Simon; Butenschön, Momme; de Mora, Lee; Frölicher, Thomas L; Barange, Manuel; Grant, Alastair

2013-08-01

Climate change has already altered the distribution of marine fishes. Future predictions of fish distributions and catches based on bioclimate envelope models are available, but to date they have not considered interspecific interactions. We address this by combining the species-based Dynamic Bioclimate Envelope Model (DBEM) with a size-based trophic model. The new approach provides spatially and temporally resolved predictions of changes in species' size, abundance and catch potential that account for the effects of ecological interactions. Predicted latitudinal shifts are, on average, reduced by 20% when species interactions are incorporated, compared to DBEM predictions, with pelagic species showing the greatest reductions. Goodness-of-fit of biomass data from fish stock assessments in the North Atlantic between 1991 and 2003 is improved slightly by including species interactions. The differences between predictions from the two models may be relatively modest because, at the North Atlantic basin scale, (i) predators and competitors may respond to climate change together; (ii) existing parameterization of the DBEM might implicitly incorporate trophic interactions; and/or (iii) trophic interactions might not be the main driver of responses to climate. Future analyses using ecologically explicit models and data will improve understanding of the effects of inter-specific interactions on responses to climate change, and better inform managers about plausible ecological and fishery consequences of a changing environment. © 2013 John Wiley & Sons Ltd.

The Economics of Root Distributions of Terrestrial Biomes in Response to Elevated CO2

NASA Astrophysics Data System (ADS)

Lu, M.; Hedin, L. O. O.

2017-12-01

Belowground root distributions of terrestrial biomes are central to understanding soil biogeochemical processes and land carbon sink. Yet models are thus far not able to predict root distributions across plant functional groups and major biomes, limiting our ability to predict the response of land systems to elevated CO2 concentration. Of particular concern is the apparent lack of stimulation of the aboveground carbon sink despite 30% increase of atmospheric CO2 over the past half-century, and despite the clear acceleration of the land carbon sink over the same period. This apparent discrepancy in land ecosystem response has led to the proposition that changes in belowground root dynamics might be responsible for the overlooked land sink. We here present a new modeling approach for predicting the response of root biomass and soil carbon storage to increased CO2. Our approach considers the first-principle mechanisms and tradeoffs by which plants and plant roots invest carbon to gain belowground resources, in collaboration with distinct root symbioses. We allow plants to locally compete for nutrients, with the ability to allocate biomass at different depths in the soil profile. We parameterized our model using an unprecedented global dataset of root traits, and validated our biome-level predictions with a recently updated global root biomass database. Our results support the idea that plants "dig deeper" when exposed to increased CO2, and we offer an economic-based mechanism for predicting the plant root response across soil conditions, plant functional groups and major biomes. Our model also recreates the observed responses across a range of free-air CO2 enrichment experiments, including a distinct response between plants associated with ectomycorrhizal and arbuscular mycorrhizal fungi. Most broadly, our findings suggest that roots may be increasingly important in the land carbon sink, and call for a greater effort to quantify belowground responses to elevated atmospheric CO2.

Predictive Ensemble Decoding of Acoustical Features Explains Context-Dependent Receptive Fields.

PubMed

Yildiz, Izzet B; Mesgarani, Nima; Deneve, Sophie

2016-12-07

A primary goal of auditory neuroscience is to identify the sound features extracted and represented by auditory neurons. Linear encoding models, which describe neural responses as a function of the stimulus, have been primarily used for this purpose. Here, we provide theoretical arguments and experimental evidence in support of an alternative approach, based on decoding the stimulus from the neural response. We used a Bayesian normative approach to predict the responses of neurons detecting relevant auditory features, despite ambiguities and noise. We compared the model predictions to recordings from the primary auditory cortex of ferrets and found that: (1) the decoding filters of auditory neurons resemble the filters learned from the statistics of speech sounds; (2) the decoding model captures the dynamics of responses better than a linear encoding model of similar complexity; and (3) the decoding model accounts for the accuracy with which the stimulus is represented in neural activity, whereas linear encoding model performs very poorly. Most importantly, our model predicts that neuronal responses are fundamentally shaped by "explaining away," a divisive competition between alternative interpretations of the auditory scene. Neural responses in the auditory cortex are dynamic, nonlinear, and hard to predict. Traditionally, encoding models have been used to describe neural responses as a function of the stimulus. However, in addition to external stimulation, neural activity is strongly modulated by the responses of other neurons in the network. We hypothesized that auditory neurons aim to collectively decode their stimulus. In particular, a stimulus feature that is decoded (or explained away) by one neuron is not explained by another. We demonstrated that this novel Bayesian decoding model is better at capturing the dynamic responses of cortical neurons in ferrets. Whereas the linear encoding model poorly reflects selectivity of neurons, the decoding model can account for the strong nonlinearities observed in neural data. Copyright © 2016 Yildiz et al.

Dynamic characteristics of oxygen consumption.

PubMed

Ye, Lin; Argha, Ahmadreza; Yu, Hairong; Celler, Branko G; Nguyen, Hung T; Su, Steven

2018-04-23

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO 2 -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

Accounting for Variability in Student Responses to Motion Questions

ERIC Educational Resources Information Center

Frank, Brian W.; Kanim, Stephen E.; Gomez, Luanna S.

2008-01-01

We describe the results of an experiment conducted to test predictions about student responses to questions about motion based on an explicit model of student thinking in terms of the cuing of a variety of different physical intuitions or conceptual resources. This particular model allows us to account for observed variations in patterns of…

Strategic Control over Extent and Timing of Distractor-Based Response Activation

ERIC Educational Resources Information Center

Jost, Kerstin; Wendt, Mike; Luna-Rodriguez, Aquiles; Löw, Andreas; Jacobsen, Thomas

2017-01-01

In choice reaction time (RT) tasks, performance is often influenced by the presence of nominally irrelevant stimuli, referred to as distractors. Recent research provided evidence that distractor processing can be adjusted to the utility of the distractors: Distractors predictive of the upcoming target/response were more attended to and also…

Evaluation of the 2006-2007 Students' Creative Response to Conflict Program

ERIC Educational Resources Information Center

Yungbluth, Stephen C.

2008-01-01

A quasi-experimental pre- and post-test design was used to evaluate the Students' Creative Response to Conflict (SCRC) program, which is based on the principles of conflict resolution education and social-emotional learning. It is predicted that SCRC will influence students to reduce their approval of aggression and associated problem behaviors…

Time and Associative Learning.

PubMed

Balsam, Peter D; Drew, Michael R; Gallistel, C R

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

Time and Associative Learning

PubMed Central

Balsam, Peter D; Drew, Michael R.; Gallistel, C.R.

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by “temporal pairing” and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities. PMID:21359131

A shock absorber model for structure-borne noise analyses

NASA Astrophysics Data System (ADS)

Benaziz, Marouane; Nacivet, Samuel; Thouverez, Fabrice

2015-08-01

Shock absorbers are often responsible for undesirable structure-borne noise in cars. The early numerical prediction of this noise in the automobile development process can save time and money and yet remains a challenge for industry. In this paper, a new approach to predicting shock absorber structure-borne noise is proposed; it consists in modelling the shock absorber and including the main nonlinear phenomena responsible for discontinuities in the response. The model set forth herein features: compressible fluid behaviour, nonlinear flow rate-pressure relations, valve mechanical equations and rubber mounts. The piston, base valve and complete shock absorber model are compared with experimental results. Sensitivity of the shock absorber response is evaluated and the most important parameters are classified. The response envelope is also computed. This shock absorber model is able to accurately reproduce local nonlinear phenomena and improves our state of knowledge on potential noise sources within the shock absorber.

USE OF WATERSHED CLASSIFICATION IN MONITORING FRAMEWORKS FOR THE WESTERN LAKE SUPERIOR BASIS

EPA Science Inventory

In this case study we predicted stream sensitivity to nonpoint source pollution based on the nonlinear responses of hydrologic regimes and associated loadings of nonpoint source pollutants to catchment properties. We assessed two hydrologically-based thresholds of impairment, on...

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

Rapid assessment of lamp spectrum to quantify ecological effects of light at night.

PubMed

Longcore, Travis; Rodríguez, Airam; Witherington, Blair; Penniman, Jay F; Herf, Lorna; Herf, Michael

2018-06-12

For many decades, the spectral composition of lighting was determined by the type of lamp, which also influenced potential effects of outdoor lights on species and ecosystems. Light-emitting diode (LED) lamps have dramatically increased the range of spectral profiles of light that is economically viable for outdoor lighting. Because of the array of choices, it is necessary to develop methods to predict the effects of different spectral profiles without conducting field studies, especially because older lighting systems are being replaced rapidly. We describe an approach to predict responses of exemplar organisms and groups to lamps of different spectral output by calculating an index based on action spectra from behavioral or visual characteristics of organisms and lamp spectral irradiance. We calculate relative response indices for a range of lamp types and light sources and develop an index that identifies lamps that minimize predicted effects as measured by ecological, physiological, and astronomical indices. Using these assessment metrics, filtered yellow-green and amber LEDs are predicted to have lower effects on wildlife than high pressure sodium lamps, while blue-rich lighting (e.g., K ≥ 2200) would have greater effects. The approach can be updated with new information about behavioral or visual responses of organisms and used to test new lighting products based on spectrum. Together with control of intensity, direction, and duration, the approach can be used to predict and then minimize the adverse effects of lighting and can be tailored to individual species or taxonomic groups. © 2018 Wiley Periodicals, Inc.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk.

PubMed

Cecchinato, A; De Marchi, M; Gallo, L; Bittante, G; Carnier, P

2009-10-01

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a(30), mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Prediction models for RCT and a(30) based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a(30), respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a(30) were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a(30) ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

Food-specific response inhibition, dietary restraint and snack intake in lean and overweight/obese adults: a moderated-mediation model

PubMed Central

Price, M; Lee, M; Higgs, S

2016-01-01

Background/Objectives: The relationship between response inhibition and obesity is currently unclear. This may be because of inconsistencies in methodology, design limitations and the use of narrow samples. In addition, dietary restraint has not been considered, yet restraint has been reported to moderate performance on behavioural tasks of response inhibition. The aim of this study was to investigate performance on both a food-based and a neutral stimuli go/no-go task, which addresses current design limitations, in lean and overweight/obese adults. The moderating role of dietary restraint in the relationship between body composition, response inhibition and snack intake was also measured. Subjects/Methods: Lean and overweight/obese, males and females (N=116) completed both a food-based and neutral category control go/no-go task, in a fully counterbalanced repeated-measures design. A bogus taste-test was then completed, followed by a self-report measure of dietary restraint. Results: PROCESS moderated-mediation analysis showed that overweight/obese, compared with lean, participants made more errors on the food-based (but not the neutral) go/no-go task, but only when they were low in dietary restraint. Performance on the food-based go/no-go task predicted snack intake across the sample. Increased intake in the overweight, low restrainers was fully mediated by increased errors on the food-based (but not the neutral) go/no-go task. Conclusions: Distinguishing between high and low restrained eaters in the overweight/obese population is crucial in future obesity research incorporating food-based go/no-go tasks. Poor response inhibition to food cues predicts overeating across weight groups, suggesting weight loss interventions and obesity prevention programmes should target behavioural inhibition training in such individuals. PMID:26592733

Food-specific response inhibition, dietary restraint and snack intake in lean and overweight/obese adults: a moderated-mediation model.

PubMed

Price, M; Lee, M; Higgs, S

2016-05-01

The relationship between response inhibition and obesity is currently unclear. This may be because of inconsistencies in methodology, design limitations and the use of narrow samples. In addition, dietary restraint has not been considered, yet restraint has been reported to moderate performance on behavioural tasks of response inhibition. The aim of this study was to investigate performance on both a food-based and a neutral stimuli go/no-go task, which addresses current design limitations, in lean and overweight/obese adults. The moderating role of dietary restraint in the relationship between body composition, response inhibition and snack intake was also measured. Lean and overweight/obese, males and females (N=116) completed both a food-based and neutral category control go/no-go task, in a fully counterbalanced repeated-measures design. A bogus taste-test was then completed, followed by a self-report measure of dietary restraint. PROCESS moderated-mediation analysis showed that overweight/obese, compared with lean, participants made more errors on the food-based (but not the neutral) go/no-go task, but only when they were low in dietary restraint. Performance on the food-based go/no-go task predicted snack intake across the sample. Increased intake in the overweight, low restrainers was fully mediated by increased errors on the food-based (but not the neutral) go/no-go task. Distinguishing between high and low restrained eaters in the overweight/obese population is crucial in future obesity research incorporating food-based go/no-go tasks. Poor response inhibition to food cues predicts overeating across weight groups, suggesting weight loss interventions and obesity prevention programmes should target behavioural inhibition training in such individuals.

Effect of response format on cognitive reflection: Validating a two- and four-option multiple choice question version of the Cognitive Reflection Test.

PubMed

Sirota, Miroslav; Juanchich, Marie

2018-03-27

The Cognitive Reflection Test, measuring intuition inhibition and cognitive reflection, has become extremely popular because it reliably predicts reasoning performance, decision-making, and beliefs. Across studies, the response format of CRT items sometimes differs, based on the assumed construct equivalence of tests with open-ended versus multiple-choice items (the equivalence hypothesis). Evidence and theoretical reasons, however, suggest that the cognitive processes measured by these response formats and their associated performances might differ (the nonequivalence hypothesis). We tested the two hypotheses experimentally by assessing the performance in tests with different response formats and by comparing their predictive and construct validity. In a between-subjects experiment (n = 452), participants answered stem-equivalent CRT items in an open-ended, a two-option, or a four-option response format and then completed tasks on belief bias, denominator neglect, and paranormal beliefs (benchmark indicators of predictive validity), as well as on actively open-minded thinking and numeracy (benchmark indicators of construct validity). We found no significant differences between the three response formats in the numbers of correct responses, the numbers of intuitive responses (with the exception of the two-option version, which had a higher number than the other tests), and the correlational patterns of the indicators of predictive and construct validity. All three test versions were similarly reliable, but the multiple-choice formats were completed more quickly. We speculate that the specific nature of the CRT items helps build construct equivalence among the different response formats. We recommend using the validated multiple-choice version of the CRT presented here, particularly the four-option CRT, for practical and methodological reasons. Supplementary materials and data are available at https://osf.io/mzhyc/ .

Predictive Multiple Model Switching Control with the Self-Organizing Map

NASA Technical Reports Server (NTRS)

Motter, Mark A.

2000-01-01

A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framework. Multiple SOMs collectively model the global response of a nonautonomous system to a finite set of representative prototype controls. Each SOM provides a codebook representation of the dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the global minimization of a similarity metric. The SOM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme that selects the best available model for the applied control. SOM based linear models are used to predict the response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

PubMed

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Gene expression levels of gamma-glutamyl hydrolase in tumor tissues may be a useful biomarker for the proper use of S-1 and tegafur-uracil/leucovorin in preoperative chemoradiotherapy for patients with rectal cancer.

PubMed

Sadahiro, Sotaro; Suzuki, T; Tanaka, A; Okada, K; Saito, G; Miyakita, H; Ogimi, T; Nagase, H

2017-06-01

Preoperative chemoradiotherapy (CRT) using 5-fluorouracil (5-FU)-based chemotherapy is the standard of care for rectal cancer. The effect of additional chemotherapy during the period between the completion of radiotherapy and surgery remains unclear. Predictive factors for CRT may differ between combination chemotherapy with S-1 and with tegafur-uracil/leucovorin (UFT/LV). The subjects were 54 patients with locally advanced rectal cancer who received preoperative CRT with S-1 or UFT/LV. The pathological tumor response was assessed according to the tumor regression grade (TRG). The expression levels of 18 CRT-related genes were determined using RT-PCR assay. A pathological response (TRG 1-2) was observed in 23 patients (42.6%). In a multivariate logistic regression analysis for pathological response, the overall expression levels of four genes, HIF1A, MTHFD1, GGH and TYMS, were significant, and the accuracy rate of the predictive model was 83.3%. The effects of the gene expression levels of GGH on the response differed significantly according to the treatment regimen. The total pathological response rate of both high-GGH patients in the S-1 group and low-GGH patients in the UFT/LV group was 58.3%. Additional treatment with 5-FU-based chemotherapy during the interval between radiotherapy and surgery is not beneficial in patients who have received 5-FU-based CRT. The expression levels of four genes, HIF1A, MTHFD1, GGH and TYMS, in tumor tissues can predict the response to preoperative CRT including either S-1 or UFT/LV. In particular, the gene expression level of GGH in tumor tissues may be a useful biomarker for the appropriate use of S-1 and UFT/LV in CRT.

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning.

PubMed

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

PubMed Central

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

Evaluation of predictive capacities of biomarkers based on research synthesis.

PubMed

Hattori, Satoshi; Zhou, Xiao-Hua

2016-11-10

The objective of diagnostic studies or prognostic studies is to evaluate and compare predictive capacities of biomarkers. Suppose we are interested in evaluation and comparison of predictive capacities of continuous biomarkers for a binary outcome based on research synthesis. In analysis of each study, subjects are often classified into two groups of the high-expression and low-expression groups according to a cut-off value, and statistical analysis is based on a 2 × 2 table defined by the response and the high expression or low expression of the biomarker. Because the cut-off is study specific, it is difficult to interpret a combined summary measure such as an odds ratio based on the standard meta-analysis techniques. The summary receiver operating characteristic curve is a useful method for meta-analysis of diagnostic studies in the presence of heterogeneity of cut-off values to examine discriminative capacities of biomarkers. We develop a method to estimate positive or negative predictive curves, which are alternative to the receiver operating characteristic curve based on information reported in published papers of each study. These predictive curves provide a useful graphical presentation of pairs of positive and negative predictive values and allow us to compare predictive capacities of biomarkers of different scales in the presence of heterogeneity in cut-off values among studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Challenges and Opportunities with Predicting in Vivo Phase II Metabolism via Glucuronidation from in Vitro Data

PubMed Central

Ge, Shufan; Tu, Yifan; Hu, Ming

2017-01-01

Glucuronidation is the most important phase II metabolic pathway which is responsible for the clearance of many endogenous and exogenous compounds. To better understand the elimination process for compounds undergoing glucuronidation and identify compounds with desirable in vivo pharmacokinetic properties, many efforts have been made to predict in vivo glucuronidation using in vitro data. In this article, we reviewed typical approaches used in previous predictions. The problems and challenges in prediction of glucuronidation were discussed. Besides that different incubation conditions can affect the prediction accuracy, other factors including efflux / uptake transporters, enterohepatic recycling, and deglucuronidation reactions also contribute to the disposition of glucuronides and make the prediction more difficult. PBPK modeling, which can describe more complicated process in vivo, is a promising prediction strategy which may greatly improve the prediction of glucuronidation and potential DDIs involving glucuronidation. Based on previous studies, we proposed a transport-glucuronidation classification system, which was built based on the kinetics of both glucuronidation and transport of the glucuronide. This system could be a very useful tool to achieve better in vivo predictions. PMID:28966903

Using a GIS model to assess terrestrial salamander response to alternative forest management plans

Treesearch

Eric J. Gustafson; Nathan L. Murphy; Thomas R. Crow

2001-01-01

A GIS model predicting the spatial distribution of terrestrial salamander abundance based on topography and forest age was developed using parameters derived from the literature. The model was tested by sampling salamander abundance across the full range of site conditions used in the model. A regression of the predictions of our GIS model against these sample data...

Computer prediction of insecticide efficacy for western spruce budworm and Douglas-fir tussock moth

Treesearch

Jacqueline L. Robertson; Molly W. Stock

1986-01-01

A generalized interactive computer model that simulates and predicts insecticide efficacy, over seasonal development of western spruce budworm and Douglas-fir tussock moth, is described. This model can be used for any insecticide for which the user has laboratory-based concentration-response data. The program has four options, is written in BASIC, and can be operated...

Normal response function method for mass and stiffness matrix updating using complex FRFs

NASA Astrophysics Data System (ADS)

Pradhan, S.; Modak, S. V.

2012-10-01

Quite often a structural dynamic finite element model is required to be updated so as to accurately predict the dynamic characteristics like natural frequencies and the mode shapes. Since in many situations undamped natural frequencies and mode shapes need to be predicted, it has generally been the practice in these situations to seek updating of only mass and stiffness matrix so as to obtain a reliable prediction model. Updating using frequency response functions (FRFs) has been one of the widely used approaches for updating, including updating of mass and stiffness matrices. However, the problem with FRF based methods, for updating mass and stiffness matrices, is that these methods are based on use of complex FRFs. Use of complex FRFs to update mass and stiffness matrices is not theoretically correct as complex FRFs are not only affected by these two matrices but also by the damping matrix. Therefore, in situations where updating of only mass and stiffness matrices using FRFs is required, the use of complex FRFs based updating formulation is not fully justified and would lead to inaccurate updated models. This paper addresses this difficulty and proposes an improved FRF based finite element model updating procedure using the concept of normal FRFs. The proposed method is a modified version of the existing response function method that is based on the complex FRFs. The effectiveness of the proposed method is validated through a numerical study of a simple but representative beam structure. The effect of coordinate incompleteness and robustness of method under presence of noise is investigated. The results of updating obtained by the improved method are compared with the existing response function method. The performance of the two approaches is compared for cases of light, medium and heavily damped structures. It is found that the proposed improved method is effective in updating of mass and stiffness matrices in all the cases of complete and incomplete data and with all levels and types of damping.

Mind wandering at the fingertips: automatic parsing of subjective states based on response time variability

PubMed Central

Bastian, Mikaël; Sackur, Jérôme

2013-01-01

Research from the last decade has successfully used two kinds of thought reports in order to assess whether the mind is wandering: random thought-probes and spontaneous reports. However, none of these two methods allows any assessment of the subjective state of the participant between two reports. In this paper, we present a step by step elaboration and testing of a continuous index, based on response time variability within Sustained Attention to Response Tasks (N = 106, for a total of 10 conditions). We first show that increased response time variability predicts mind wandering. We then compute a continuous index of response time variability throughout full experiments and show that the temporal position of a probe relative to the nearest local peak of the continuous index is predictive of mind wandering. This suggests that our index carries information about the subjective state of the subject even when he or she is not probed, and opens the way for on-line tracking of mind wandering. Finally we proceed a step further and infer the internal attentional states on the basis of the variability of response times. To this end we use the Hidden Markov Model framework, which allows us to estimate the durations of on-task and off-task episodes. PMID:24046753

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium.

PubMed

Rajeev, Lara; Luning, Eric G; Dehal, Paramvir S; Price, Morgan N; Arkin, Adam P; Mukhopadhyay, Aindrila

2011-10-12

Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

Static and free-vibrational response of semi-circular graphite-epoxy frames with thin-walled open sections

NASA Technical Reports Server (NTRS)

Collins, J. Scott; Johnson, Eric R.

1989-01-01

Experiments were conducted to measure the three-dimensional static and free vibrational response of two graphite-epoxy, thin-walled, open section frames. The frames are semi-circular with a radius of three feet, and one specimen has an I cross section and the other has a channel cross section. The flexibility influence coefficients were measured in static tests for loads applied at midspan with the ends of the specimens clamped. Natural frequencies and modes were determined from vibrational tests for free and clamped end conditions. The experimental data is used to evaluate a new finite element which was developed specifically for the analysis of curved, thin-walled structures. The formulation of the element is based on a Vlasov-type, thin-walled, curved beam theory. The predictions from the finite element program generally correlated well with the experimental data for the symmetric I-specimen. Discrepancies in some of the data were found to be due to flexibility in the clamped end conditions. With respect to the data for the channel specimen, the correlation was less satisfactory. The finite element analysis predicted the out-of-plane response of the channel specimen reasonably well, but large discrepancies occurred between the predicted in-plane response and the experimental data. The analysis predicted a much more compliant in-plane response than was observed in the experiments.

A simplified approach to quasi-linear viscoelastic modeling

PubMed Central

Nekouzadeh, Ali; Pryse, Kenneth M.; Elson, Elliot L.; Genin, Guy M.

2007-01-01

The fitting of quasi-linear viscoelastic (QLV) constitutive models to material data often involves somewhat cumbersome numerical convolution. A new approach to treating quasi-linearity in one dimension is described and applied to characterize the behavior of reconstituted collagen. This approach is based on a new principle for including nonlinearity and requires considerably less computation than other comparable models for both model calibration and response prediction, especially for smoothly applied stretching. Additionally, the approach allows relaxation to adapt with the strain history. The modeling approach is demonstrated through tests on pure reconstituted collagen. Sequences of “ramp-and-hold” stretching tests were applied to rectangular collagen specimens. The relaxation force data from the “hold” was used to calibrate a new “adaptive QLV model” and several models from literature, and the force data from the “ramp” was used to check the accuracy of model predictions. Additionally, the ability of the models to predict the force response on a reloading of the specimen was assessed. The “adaptive QLV model” based on this new approach predicts collagen behavior comparably to or better than existing models, with much less computation. PMID:17499254

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

PubMed

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

2013-01-01

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

Thematic and Spatial Resolutions Affect Model-Based Predictions of Tree Species Distribution

PubMed Central

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

2013-01-01

Subjective decisions of thematic and spatial resolutions in characterizing environmental heterogeneity may affect the characterizations of spatial pattern and the simulation of occurrence and rate of ecological processes, and in turn, model-based tree species distribution. Thus, this study quantified the importance of thematic and spatial resolutions, and their interaction in predictions of tree species distribution (quantified by species abundance). We investigated how model-predicted species abundances changed and whether tree species with different ecological traits (e.g., seed dispersal distance, competitive capacity) had different responses to varying thematic and spatial resolutions. We used the LANDIS forest landscape model to predict tree species distribution at the landscape scale and designed a series of scenarios with different thematic (different numbers of land types) and spatial resolutions combinations, and then statistically examined the differences of species abundance among these scenarios. Results showed that both thematic and spatial resolutions affected model-based predictions of species distribution, but thematic resolution had a greater effect. Species ecological traits affected the predictions. For species with moderate dispersal distance and relatively abundant seed sources, predicted abundance increased as thematic resolution increased. However, for species with long seeding distance or high shade tolerance, thematic resolution had an inverse effect on predicted abundance. When seed sources and dispersal distance were not limiting, the predicted species abundance increased with spatial resolution and vice versa. Results from this study may provide insights into the choice of thematic and spatial resolutions for model-based predictions of tree species distribution. PMID:23861828

Predictive effects of bilirubin on response of colorectal cancer to irinotecan-based chemotherapy.

PubMed

Yu, Qian-Qian; Qiu, Hong; Zhang, Ming-Sheng; Hu, Guang-Yuan; Liu, Bo; Huang, Liu; Liao, Xin; Li, Qian-Xia; Li, Zhi-Huan; Yuan, Xiang-Lin

2016-04-28

To examine the predictive effects of baseline serum bilirubin levels and UDP-glucuronosyltransferase (UGT) 1A1*28 polymorphism on response of colorectal cancer to irinotecan-based chemotherapy. The present study was based on a prospective multicenter longitudinal trial of Chinese metastatic colorectal cancer (mCRC) patients treated with irinotecan-based chemotherapy (NCT01282658). Baseline serum bilirubin levels, including total bilirubin (TBil) and unconjugated bilirubin (UBil), were measured, and genotyping of UGT1A1*28 polymorphism was performed. Receiver operating characteristic curve (ROC) analysis was used to determine cutoff values of TBil and UBil. The TBil values were categorized into > 13.0 or ≤ 13.0 groups; the UBil values were categorized into > 4.1 or ≤ 4.1 groups. Combining the cutoff values of TBil and UBil, which was recorded as CoBil, patients were classified into three groups. The classifier's performance of UGT1A1*28 and CoBil for predicting treatment response was evaluated by ROC analysis. Associations between response and CoBil or UGT1A1*28 polymorphism were estimated using simple and multiple logistic regression models. Among the 120 mCRC patients, the serum bilirubin level was significantly different between the UGT1A1*28 wild-type and mutant genotypes. Patients with the mutant genotype had an increased likelihood of a higher TBil (P = 0.018) and a higher UBil (P = 0.014) level compared with the wild-type genotype. Patients were stratified into three groups based on CoBil. Group 1 was patients with TBil > 13.0 and UBil > 4.1; Group 2 was patients with TBil ≤ 13.0 and UBil > 4.1; and Group 3 was patients with TBil ≤ 13.0 and UBil ≤ 4.1. Patients in Group 3 had more than a 10-fold higher likelihood of having a response in the simple (OR = 11.250; 95%CI: 2.286-55.367; P = 0.003) and multiple (OR = 16.001; 95%CI: 2.802 -91.371; P = 0.002) analyses compared with the Group 1 individuals. Patients carrying the UGT1A1*28 (TA)7 allele were 4-fold less likely to present with a response compared with the individuals harboring a homozygous (TA)6 genotype in the simple (OR = 0.267; 95%CI: 0.100-0.709; P = 0.008) and multiple (OR = 0.244; 95%CI: 0.088-0.678; P = 0.007) analyses. Classifier's performance of CoBil and UGT1A1*28 were comparable. CoBil and UGT1A1*28 are both independent biomarkers for predicting the treatment response of mCRC patients to irinotecan-based chemotherapy. After validation, CoBil, an easily determinable index in the clinic, might be helpful in facilitating stratification of mCRC patients for individualized treatment options.

Graduate Socialization in the Responsible Conduct of Research: A National Survey on the Research Ethics Training Experiences of Psychology Doctoral Students

PubMed Central

Fisher, Celia B.; Fried, Adam L.; Feldman, Lindsay G.

2013-01-01

Little is known about the mechanisms by which psychology graduate programs transmit responsible conduct of research (RCR) values. A national sample of 968 current students and recent graduates of mission-diverse doctoral psychology programs, completed a web-based survey on their research ethics challenges, perceptions of RCR mentoring and department climate, their ability to conduct research responsibility, and whether they believed psychology as a discipline promotes scientific integrity. Research experience, mentor RCR instruction and modeling, and department RCR policies predicted student RCR preparedness. Mentor RCR instruction, department RCR policies, and faculty modeling of RCR behaviors predicted confidence in the RCR integrity of the discipline. Implications for training are discussed. PMID:23641128

Plasticity models of material variability based on uncertainty quantification techniques

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

Jones, Reese E.; Rizzi, Francesco; Boyce, Brad

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQmore » techniques can be used in model selection and assessing the quality of calibrated physical parameters.« less

Genomancy: predicting tumour response to cancer therapy based on the oracle of genetics.

PubMed

Williams, P D; Lee, J K; Theodorescu, D

2009-01-01

Cells are complex systems that regulate a multitude of biologic pathways involving a diverse array of molecules. Cancer can develop when these pathways become deregulated as a result of mutations in the genes coding for these proteins or of epigenetic changes that affect gene expression, or both1,2. The diversity and interconnectedness of these pathways and their molecular components implies that a variety of mutations may lead to tumorigenic cellular deregulation3-6. This variety, combined with the requirement to overcome multiple anticancer defence mechanisms7, contributes to the heterogeneous nature of cancer. Consequently, tumours with similar histology may vary in their underlying molecular circuitry8-10, with resultant differences in biologic behaviour, manifested in proliferation rate, invasiveness, metastatic potential, and unfortunately, response to cytotoxic therapy. Thus, cancer can be thought of as a family of related tumour subtypes, highlighting the need for individualized prediction both of disease progression and of treatment response, based on the molecular characteristics of the tumour.

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China

PubMed Central

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-01-01

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation. PMID:28367963

Actual measurement, hygrothermal response experiment and growth prediction analysis of microbial contamination of central air conditioning system in Dalian, China.

PubMed

Lv, Yang; Hu, Guangyao; Wang, Chunyang; Yuan, Wenjie; Wei, Shanshan; Gao, Jiaoqi; Wang, Boyuan; Song, Fangchao

2017-04-03

The microbial contamination of central air conditioning system is one of the important factors that affect the indoor air quality. Actual measurement and analysis were carried out on microbial contamination in central air conditioning system at a venue in Dalian, China. Illumina miseq method was used and three fungal samples of two units were analysed by high throughput sequencing. Results showed that the predominant fungus in air conditioning unit A and B were Candida spp. and Cladosporium spp., and two fungus were further used in the hygrothermal response experiment. Based on the data of Cladosporium in hygrothermal response experiment, this paper used the logistic equation and the Gompertz equation to fit the growth predictive model of Cladosporium genera in different temperature and relative humidity conditions, and the square root model was fitted based on the two environmental factors. In addition, the models were carried on the analysis to verify the accuracy and feasibility of the established model equation.

Genetic and caregiving-based contributions to infant attachment: unique associations with distress reactivity and attachment security.

PubMed

Raby, K Lee; Cicchetti, Dante; Carlson, Elizabeth A; Cutuli, J J; Englund, Michelle M; Egeland, Byron

2012-09-01

In the longitudinal study reported here, we examined genetic and caregiving-based contributions to individual differences in infant attachment classifications. For 154 mother-infant pairs, we rated mothers' responsiveness to their 6-month-old infants during naturalistic interactions and classified infants' attachment organization at 12 and 18 months using the Strange Situation procedure. These infants were later genotyped with respect to the serotonin-transporter-linked polymorphic region (5-HTTLPR). Maternal responsiveness uniquely predicted infants' attachment security. Infants' 5-HTTLPR variation uniquely predicted their subtype of attachment security at 12 months and their subtype of attachment insecurity at 12 and 18 months. The short allele for 5-HTTLPR was associated with attachment classifications characterized by higher emotional distress. These findings suggest that 5-HTTLPR variation contributes to infants' emotional reactivity and that the degree to which caregivers are responsive influences how effectively infants use their caregivers for emotion regulation. Theoretical implications for the study of genetic and caregiving influences are discussed.

Nonlinear Visco-Elastic Response of Composites via Micro-Mechanical Models

NASA Technical Reports Server (NTRS)

Gates, Thomas S.; Sridharan, Srinivasan

2005-01-01

Micro-mechanical models for a study of nonlinear visco-elastic response of composite laminae are developed and their performance compared. A single integral constitutive law proposed by Schapery and subsequently generalized to multi-axial states of stress is utilized in the study for the matrix material. This is used in conjunction with a computationally facile scheme in which hereditary strains are computed using a recursive relation suggested by Henriksen. Composite response is studied using two competing micro-models, viz. a simplified Square Cell Model (SSCM) and a Finite Element based self-consistent Cylindrical Model (FECM). The algorithm is developed assuming that the material response computations are carried out in a module attached to a general purpose finite element program used for composite structural analysis. It is shown that the SSCM as used in investigations of material nonlinearity can involve significant errors in the prediction of transverse Young's modulus and shear modulus. The errors in the elastic strains thus predicted are of the same order of magnitude as the creep strains accruing due to visco-elasticity. The FECM on the other hand does appear to perform better both in the prediction of elastic constants and the study of creep response.

Long-term prediction test procedure for most ICs, based on linear response theory

NASA Technical Reports Server (NTRS)

Litovchenko, V.; Ivakhnenko, I.

1991-01-01

Experimentally, thermal annealing is known to be a factor which enables a number of different integrated circuits (IC's) to recover their operating characteristics after suffering radiation damage in the space radiation environment; thus, decreasing and limiting long term cumulative total-dose effects. This annealing is also known to be accelerated at elevated temperatures both during and after irradiation. Linear response theory (LRT) was applied, and a linear response function (LRF) to predict the radiation/annealing response of sensitive parameters of IC's for long term (several months or years) exposure to the space radiation environment were constructed. Compressing the annealing process from several years in orbit to just a few hours or days in the laboratory is achieved by subjecting the IC to elevated temperatures or by increasing the typical spaceflight dose rate by several orders of magnitude for simultaneous radiation/annealing only. The accomplishments are as follows: (1) the test procedure to make predictions of the radiation response was developed; (2) the calculation of the shift in the threshold potential due to the charge distribution in the oxide was written; (3) electron tunneling processes from the bulk Si to the oxide region in an MOS IC were estimated; (4) in order to connect the experimental annealing data to the theoretical model, constants of the model of the basic annealing process were established; (5) experimental data obtained at elevated temperatures were analyzed; (6) time compression and reliability of predictions for the long term region were shown; (7) a method to compress test time and to make predictions of response for the nonlinear region was proposed; and (8) nonlinearity of the LRF with respect to log(t) was calculated theoretically from a model.

Predicting streamflow response to fire-induced landcover change: implications of parameter uncertainty in the MIKE SHE model.

PubMed

McMichael, Christine E; Hope, Allen S

2007-08-01

Fire is a primary agent of landcover transformation in California semi-arid shrubland watersheds, however few studies have examined the impacts of fire and post-fire succession on streamflow dynamics in these basins. While it may seem intuitive that larger fires will have a greater impact on streamflow response than smaller fires in these watersheds, the nature of these relationships has not been determined. The effects of fire size on seasonal and annual streamflow responses were investigated for a medium-sized basin in central California using a modified version of the MIKE SHE model which had been previously calibrated and tested for this watershed using the Generalized Likelihood Uncertainty Estimation methodology. Model simulations were made for two contrasting periods, wet and dry, in order to assess whether fire size effects varied with weather regime. Results indicated that seasonal and annual streamflow response increased nearly linearly with fire size in a given year under both regimes. Annual flow response was generally higher in wetter years for both weather regimes, however a clear trend was confounded by the effect of stand age. These results expand our understanding of the effects of fire size on hydrologic response in chaparral watersheds, but it is important to note that the majority of model predictions were largely indistinguishable from the predictive uncertainty associated with the calibrated model - a key finding that highlights the importance of analyzing hydrologic predictions for altered landcover conditions in the context of model uncertainty. Future work is needed to examine how alternative decisions (e.g., different likelihood measures) may influence GLUE-based MIKE SHE streamflow predictions following different size fires, and how the effect of fire size on streamflow varies with other factors such as fire location.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

On the Use of Equivalent Linearization for High-Cycle Fatigue Analysis of Geometrically Nonlinear Structures

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.

2003-01-01

The use of stress predictions from equivalent linearization analyses in the computation of high-cycle fatigue life is examined. Stresses so obtained differ in behavior from the fully nonlinear analysis in both spectral shape and amplitude. Consequently, fatigue life predictions made using this data will be affected. Comparisons of fatigue life predictions based upon the stress response obtained from equivalent linear and numerical simulation analyses are made to determine the range over which the equivalent linear analysis is applicable.

Quantification of hazard prediction ability at hazard prediction training (Kiken-Yochi Training: KYT) by free-response receiver-operating characteristic (FROC) analysis.

PubMed

Hashida, Masahiro; Kamezaki, Ryousuke; Goto, Makoto; Shiraishi, Junji

2017-03-01

The ability to predict hazards in possible situations in a general X-ray examination room created for Kiken-Yochi training (KYT) is quantified by use of free-response receiver-operating characteristics (FROC) analysis for determining whether the total number of years of clinical experience, involvement in general X-ray examinations, occupation, and training each have an impact on the hazard prediction ability. Twenty-three radiological technologists (RTs) (years of experience: 2-28), four nurses (years of experience: 15-19), and six RT students observed 53 scenes of KYT: 26 scenes with hazardous points (hazardous points are those that might cause injury to patients) and 27 scenes without points. Based on the results of these observations, we calculated the alternative free-response receiver-operating characteristic (AFROC) curve and the figure of merit (FOM) to quantify the hazard prediction ability. The results showed that the total number of years of clinical experience did not have any impact on hazard prediction ability, whereas recent experience with general X-ray examinations greatly influenced this ability. In addition, the hazard prediction ability varied depending on the occupations of the observers while they were observing the same scenes in KYT. The hazard prediction ability of the radiologic technology students was improved after they had undergone patient safety training. This proposed method with FROC observer study enabled the quantification and evaluation of the hazard prediction capability, and the application of this approach to clinical practice may help to ensure the safety of examinations and treatment in the radiology department.

Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

PubMed

Fang, Lingzhao; Sahana, Goutam; Ma, Peipei; Su, Guosheng; Yu, Ying; Zhang, Shengli; Lund, Mogens Sandø; Sørensen, Peter

2017-05-12

A better understanding of the genetic architecture of complex traits can contribute to improve genomic prediction. We hypothesized that genomic variants associated with mastitis and milk production traits in dairy cattle are enriched in hepatic transcriptomic regions that are responsive to intra-mammary infection (IMI). Genomic markers [e.g. single nucleotide polymorphisms (SNPs)] from those regions, if included, may improve the predictive ability of a genomic model. We applied a genomic feature best linear unbiased prediction model (GFBLUP) to implement the above strategy by considering the hepatic transcriptomic regions responsive to IMI as genomic features. GFBLUP, an extension of GBLUP, includes a separate genomic effect of SNPs within a genomic feature, and allows differential weighting of the individual marker relationships in the prediction equation. Since GFBLUP is computationally intensive, we investigated whether a SNP set test could be a computationally fast way to preselect predictive genomic features. The SNP set test assesses the association between a genomic feature and a trait based on single-SNP genome-wide association studies. We applied these two approaches to mastitis and milk production traits (milk, fat and protein yield) in Holstein (HOL, n = 5056) and Jersey (JER, n = 1231) cattle. We observed that a majority of genomic features were enriched in genomic variants that were associated with mastitis and milk production traits. Compared to GBLUP, the accuracy of genomic prediction with GFBLUP was marginally improved (3.2 to 3.9%) in within-breed prediction. The highest increase (164.4%) in prediction accuracy was observed in across-breed prediction. The significance of genomic features based on the SNP set test were correlated with changes in prediction accuracy of GFBLUP (P < 0.05). GFBLUP provides a framework for integrating multiple layers of biological knowledge to provide novel insights into the biological basis of complex traits, and to improve the accuracy of genomic prediction. The SNP set test might be used as a first-step to improve GFBLUP models. Approaches like GFBLUP and SNP set test will become increasingly useful, as the functional annotations of genomes keep accumulating for a range of species and traits.

Comparisons of ground motions from five aftershocks of the 1999 Chi-Chi, Taiwan, earthquake with empirical predictions largely based on data from California

USGS Publications Warehouse

Wang, G.-Q.; Boore, D.M.; Igel, H.; Zhou, X.-Y.

2004-01-01

The observed ground motions from five large aftershocks of the 1999 Chi-Chi, Taiwan, earthquake are compared with predictions from four equations based primarily on data from California. The four equations for active tectonic regions are those developed by Abrahamson and Silva (1997), Boore et al. (1997), Campbell (1997, 2001), and Sadigh et al. (1997). Comparisons are made for horizontal-component peak ground accelerations and 5%-damped pseudoacceleration response spectra at periods between 0.02 sec and 5 sec. The observed motions are in reasonable agreement with the predictions, particularly for distances from 10 to 30 km. This is in marked contrast to the motions from the Chi-Chi mainshock, which are much lower than the predicted motions for periods less than about 1 sec. The results indicate that the low motions in the mainshock are not due to unusual, localized absorption of seismic energy, because waves from the mainshock and the aftershocks generally traverse the same section of the crust and are recorded at the same stations. The aftershock motions at distances of 30-60 km are somewhat lower than the predictions (but not nearly by as small a factor as those for the mainshock), suggesting that the ground motion attenuates more rapidly in this region of Taiwan than it does in the areas we compare with it. We provide equations for the regional attenuation of response spectra, which show increasing decay of motion with distance for decreasing oscillator periods. This observational study also demonstrates that ground motions have large earthquake-location-dependent variability for a specific site. This variability reduces the accuracy with which an earthquake-specific prediction of site response can be predicted. Online Material: PGAs and PSAs from the 1999 Chi-Chi earthquake and five aftershocks.

Using kinematic analysis of movement to predict the time occurrence of an evoked potential associated with a motor command.

PubMed

O'Reilly, Christian; Plamondon, Réjean; Landou, Mohamed K; Stemmer, Brigitte

2013-01-01

This article presents an exploratory study investigating the possibility of predicting the time occurrence of a motor event related potential (ERP) from a kinematic analysis of human movements. Although the response-locked motor potential may link the ERP components to the recorded response, to our knowledge no previous attempt has been made to predict a priori (i.e. before any contact with the electroencephalographic data) the time occurrence of an ERP component based only on the modeling of an overt response. The proposed analysis relies on the delta-lognormal modeling of velocity, as proposed by the kinematic theory of rapid human movement used in several studies of motor control. Although some methodological aspects of this technique still need to be fine-tuned, the initial results showed that the model-based kinematic analysis allowed the prediction of the time occurrence of a motor command ERP in most participants in the experiment. The average map of the motor command ERPs showed that this signal was stronger in electrodes close to the contra-lateral motor area (Fz, FCz, FC1, and FC3). These results seem to support the claims made by the kinematic theory that a motor command is emitted at time t(0), the time reference parameter of the model. This article proposes a new time marker directly associated with a cerebral event (i.e. the emission of a motor command) that can be used for the development of new data analysis methodologies and for the elaboration of new experimental protocols based on ERP. © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

An improved null model for assessing the net effects of multiple stressors on communities.

PubMed

Thompson, Patrick L; MacLennan, Megan M; Vinebrooke, Rolf D

2018-01-01

Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our compositional null model. © 2017 John Wiley & Sons Ltd.

Comparisons of ground motions from the 1999 Chi-Chi, earthquake with empirical predictions largely based on data from California

USGS Publications Warehouse

Boore, D.M.

2001-01-01

This article has the modest goal of comparing the ground motions recorded during the 1999 Chi-Chi, Taiwan, mainshock with predictions from four empirical-based equations commonly used for western North America; these empirical predictions are largely based on data from California. Comparisons are made for peak acceleration and 5%-damped response spectra at periods between 0.1 and 4 sec. The general finding is that the Chi-Chi ground motions are smaller than those predicted from the empirically based equations for periods less than about 1 sec by factors averaging about 0.4 but as small as 0.26 (depending on period, on which equation is used, and on whether the sites are assumed to be rock or soil). There is a trend for the observed motions to approach or even exceed the predicted motions for longer periods. Motions at similar distances (30-60 km) to the east and to the west of the fault differ dramatically at periods between about 2 and 20 sec: Long-duration wave trains are present on the motions to the west, and when normalized to similar amplitudes at short periods, the response spectra of the motions at the western stations are as much as five times larger than those of motions from eastern stations. The explanation for the difference is probably related to site and propagation effects; the western stations are on the Coastal Plain, whereas the eastern stations are at the foot of young and steep mountains, either in the relatively narrow Longitudinal Valley or along the eastern coast-the sediments underlying the eastern stations are probably shallower and have higher velocity than those under the western stations.

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

Wu, J; Gong, G; Cui, Y

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitativemore » Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.« less

Behavioral Impulsivity Does Not Predict Naturalistic Alcohol Consumption or Treatment Outcomes

PubMed Central

Mullen, Jillian; Mathias, Charles W.; Karns, Tara E.; Liang, Yuanyuan; Hill-Kapturczak, Nathalie; Roache, John D.; Lamb, Richard J.; Dougherty, Donald M.

2016-01-01

Objective The purpose of this study was to determine if behavioral impulsivity under multiple conditions (baseline, after alcohol consumption or after serotonin depletion) predicted naturalistic alcohol use or treatment outcomes from a moderation-based contingency management intervention. Method The current data analysis pulls information from three phases of a large study: 1) Phase 1 examined baseline and the effects of alcohol use and serotonin depletion on three types of behavioral impulsivity: response initiation (IMT task), response inhibition (GoStop task), and delay discounting (SKIP task); 2) Phase 2 involved 28 days of naturalistic drinking; and 3) Phase 3 involved 3 months of contingency management. During phases 2 and 3 alcohol use was measured objectively using transdermal alcohol monitors. The results of each individual phase has been previously published showing that at a group level the effects of alcohol consumption on impulsivity were dependent on the component of impulsivity being measured and the dose of alcohol consumed but serotonin depletion had no effect on impulsivity, and that a moderation-based contingency management intervention reduced heavy drinking. Results The current analysis combining data from those who completed all three phases (n = 67) showed that impulsivity measured at baseline, after alcohol consumption, or after serotonin depletion did not predict naturalistic drinking or treatment outcomes from a moderation-based CM treatment. Conclusions Contingency management interventions may prove to be an effective intervention for impulsive individuals, however, normal variations in measured impulsivity do not seem to relate to normal variations in drinking pattern or response to moderation-based contingency management. PMID:27746702

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

PubMed

Mehrotra, Nitin; Bhattaram, Atul; Earp, Justin C; Florian, Jeffry; Krudys, Kevin; Lee, Jee Eun; Lee, Joo Yeon; Liu, Jiang; Mulugeta, Yeruk; Yu, Jingyu; Zhao, Ping; Sinha, Vikram

2016-07-01

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy. Copyright © 2016 U.S. Government work not protected by U.S. copyright.

Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN

PubMed Central

Fakhri, Mansour

2014-01-01

Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation. PMID:24688400

Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN.

PubMed

Ghanizadeh, Ali Reza; Fakhri, Mansour

2014-01-01

Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000) four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) methods were then employed to predict the effective length (i.e., frequency) of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN) are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.

Photosensitizer fluorescence and singlet oxygen luminescence as dosimetric predictors of topical 5-aminolevulinic acid photodynamic therapy induced clinical erythema.

PubMed

Mallidi, Srivalleesha; Anbil, Sriram; Lee, Seonkyung; Manstein, Dieter; Elrington, Stefan; Kositratna, Garuna; Schoenfeld, David; Pogue, Brian; Davis, Steven J; Hasan, Tayyaba

2014-02-01

The need for patient-specific photodynamic therapy (PDT) in dermatologic and oncologic applications has triggered several studies that explore the utility of surrogate parameters as predictive reporters of treatment outcome. Although photosensitizer (PS) fluorescence, a widely used parameter, can be viewed as emission from several fluorescent states of the PS (e.g., minimally aggregated and monomeric), we suggest that singlet oxygen luminescence (SOL) indicates only the active PS component responsible for the PDT. Here, the ability of discrete PS fluorescence-based metrics (absolute and percent PS photobleaching and PS re-accumulation post-PDT) to predict the clinical phototoxic response (erythema) resulting from 5-aminolevulinic acid PDT was compared with discrete SOL (DSOL)-based metrics (DSOL counts pre-PDT and change in DSOL counts pre/post-PDT) in healthy human skin. Receiver operating characteristic curve (ROC) analyses demonstrated that absolute fluorescence photobleaching metric (AFPM) exhibited the highest area under the curve (AUC) of all tested parameters, including DSOL based metrics. The combination of dose-metrics did not yield better AUC than AFPM alone. Although sophisticated real-time SOL measurements may improve the clinical utility of SOL-based dosimetry, discrete PS fluorescence-based metrics are easy to implement, and our results suggest that AFPM may sufficiently predict the PDT outcomes and identify treatment nonresponders with high specificity in clinical contexts.

Prediction of Student's Mood during an Online Test Using Formula-based and Neural Network-based Method

ERIC Educational Resources Information Center

Moridis, Christos N.; Economides, Anastasios A.

2009-01-01

Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…

Process-based models are required to manage ecological systems in a changing world

Treesearch

K. Cuddington; M.-J. Fortin; L.R. Gerber; A. Hastings; A. Liebhold; M. OConnor; C. Ray

2013-01-01

Several modeling approaches can be used to guide management decisions. However, some approaches are better fitted than others to address the problem of prediction under global change. Process-based models, which are based on a theoretical understanding of relevant ecological processes, provide a useful framework to incorporate specific responses to altered...

Accounting for inter-annual and seasonal variability in regionalization of hydrologic response in the Great Lakes basin

NASA Astrophysics Data System (ADS)

Kult, J. M.; Fry, L. M.; Gronewold, A. D.

2012-12-01

Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response typically invoke the concept of regionalization, whereby knowledge pertaining to gauged catchments is transferred to ungauged catchments. In this study, we identify watershed physical characteristics acting as primary drivers of hydrologic response throughout the US portion of the Great Lakes basin. Relationships between watershed physical characteristics and hydrologic response are generated from 166 catchments spanning a variety of climate, soil, land cover, and land form regimes through regression tree analysis, leading to a grouping of watersheds exhibiting similar hydrologic response characteristics. These groupings are then used to predict response in ungauged watersheds in an uncertainty framework. Results from this method are assessed alongside one historical regionalization approach which, while simple, has served as a cornerstone of Great Lakes regional hydrologic research for several decades. Our approach expands upon previous research by considering multiple temporal characterizations of hydrologic response. Due to the substantial inter-annual and seasonal variability in hydrologic response observed over the Great Lakes basin, results from the regression tree analysis differ considerably depending on the level of temporal aggregation used to define the response. Specifically, higher levels of temporal aggregation for the response metric (for example, indices derived from long-term means of climate and streamflow observations) lead to improved watershed groupings with lower within-group variance. However, this perceived improvement in model skill occurs at the cost of understated uncertainty when applying the regression to time series simulations or as a basis for model calibration. In such cases, our results indicate that predictions based on long-term characterizations of hydrologic response can produce misleading conclusions when applied at shorter time steps. This study suggests that measures of hydrologic response quantified at these shorter time steps may provide a more robust basis for making predictions in applications of water resource management, model calibration and simulations, and human health and safety.

Ares I-X Ascent Base Environments

NASA Technical Reports Server (NTRS)

Mobley, B. L.; Bender, R. L.; Canabal, F.; Smith, Sheldon D.

2011-01-01

Plume induced base heating environments were measured during the flight of the NASA Constellation Ares I-X developmental launch vehicle, successfully flown on October 28, 2009. The Ares IX first stage is a four segment Space Shuttle derived booster with base consisting of a flared aft skirt, deceleration and tumble motors, and a thermal curtain surrounding the first stage 7.2 area ratio nozzle. Developmental Flight Instrumentation (DFI) consisted of radiometers, calorimeters, pressure transducers and gas temperature probes installed on the aft skirt and nozzle to measure the base environments. In addition, thermocouples were also installed between the layers of the flexible thermal curtain to provide insight into the curtain response to the base environments and to assist in understanding curtain failure during reentry. Plume radiation environment predictions were generated by the Reverse Monte Carlo (RMC) code and the convective base heating predictions utilized heritage MSFC empirical methods. These predictions were compared to the DFI data and results from the flight videography. Radiation predictions agreed with the flight measured data early in flight but gauge failures prevented high altitude comparisons. The convective environment comparisons demonstrated the need to improve the prediction methodology; particularly for low altitude, local plume recirculation. The convective comparisons showed relatively good agreement at altitudes greater than 50,000 feet.

Pragmatic hydraulic theory predicts stomatal responses to climatic water deficits.

PubMed

Sperry, John S; Wang, Yujie; Wolfe, Brett T; Mackay, D Scott; Anderegg, William R L; McDowell, Nate G; Pockman, William T

2016-11-01

Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (P canopy ) from soil water potential (P soil ) and vapor pressure deficit (D). Modeled responses to D and P soil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and P canopy across nine data sets from seasonally dry tropical forest and piñon-juniper woodland with < 26% mean error. Promising initial performance suggests the theory could be useful in improving ecosystem models. Better understanding of the variation in hydraulic properties along the root-stem-leaf continuum will simplify parameterization. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Examining the Predictive Validity of a Dynamic Assessment of Decoding to Forecast Response Tier 2 to Intervention

PubMed Central

Cho, Eunsoo; Compton, Donald L.; Fuchs, Doug; Fuchs, Lynn S.; Bouton, Bobette

2013-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small group tutoring in a response-to-intervention model. First-grade students (n=134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in reading for 14 weeks. Student responsiveness to Tier 2 was assessed weekly with word identification fluency (WIF). A series of conditional individual growth curve analyses were completed that modeled the correlates of WIF growth (final level of performance and growth). Its purpose was to examine the predictive validity of DA in the presence of 3 sets of variables: static decoding measures, Tier 1 responsiveness indicators, and pre-reading variables (phonemic awareness, rapid letter naming, oral vocabulary, and IQ). DA was a significant predictor of final level and growth, uniquely explaining 3% – 13% of the variance in Tier 2 responsiveness depending on the competing predictors in the model and WIF outcome (final level of performance or growth). Although the additional variances explained uniquely by DA were relatively small, results indicate the potential of DA in identifying Tier 2 nonresponders. PMID:23213050

Examining the predictive validity of a dynamic assessment of decoding to forecast response to tier 2 intervention.

PubMed

Cho, Eunsoo; Compton, Donald L; Fuchs, Douglas; Fuchs, Lynn S; Bouton, Bobette

2014-01-01

The purpose of this study was to examine the role of a dynamic assessment (DA) of decoding in predicting responsiveness to Tier 2 small-group tutoring in a response-to-intervention model. First grade students (n = 134) who did not show adequate progress in Tier 1 based on 6 weeks of progress monitoring received Tier 2 small-group tutoring in reading for 14 weeks. Student responsiveness to Tier 2 was assessed weekly with word identification fluency (WIF). A series of conditional individual growth curve analyses were completed that modeled the correlates of WIF growth (final level of performance and growth). Its purpose was to examine the predictive validity of DA in the presence of three sets of variables: static decoding measures, Tier 1 responsiveness indicators, and prereading variables (phonemic awareness, rapid letter naming, oral vocabulary, and IQ). DA was a significant predictor of final level and growth, uniquely explaining 3% to 13% of the variance in Tier 2 responsiveness depending on the competing predictors in the model and WIF outcome (final level of performance or growth). Although the additional variances explained uniquely by DA were relatively small, results indicate the potential of DA in identifying Tier 2 nonresponders. © Hammill Institute on Disabilities 2012.

Predicting water table response to rainfall events, central Florida.

PubMed

van Gaalen, J F; Kruse, S; Lafrenz, W B; Burroughs, S M

2013-01-01

A rise in water table in response to a rainfall event is a complex function of permeability, specific yield, antecedent soil-water conditions, water table level, evapotranspiration, vegetation, lateral groundwater flow, and rainfall volume and intensity. Predictions of water table response, however, commonly assume a linear relationship between response and rainfall based on cumulative analysis of water level and rainfall logs. By identifying individual rainfall events and responses, we examine how the response/rainfall ratio varies as a function of antecedent water table level (stage) and rainfall event size. For wells in wetlands and uplands in central Florida, incorporating stage and event size improves forecasting of water table rise by more than 30%, based on 10 years of data. At the 11 sites studied, the water table is generally least responsive to rainfall at smallest and largest rainfall event sizes and at lower stages. At most sites the minimum amount of rainfall required to induce a rise in water table is fairly uniform when the water table is within 50 to 100 cm of land surface. Below this depth, the minimum typically gradually increases with depth. These observations can be qualitatively explained by unsaturated zone flow processes. Overall, response/rainfall ratios are higher in wetlands and lower in uplands, presumably reflecting lower specific yields and greater lateral influx in wetland sites. Pronounced depth variations in rainfall/response ratios appear to correlate with soil layer boundaries, where corroborating data are available. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.

The Predictive Validity of a Gender-Responsive Needs Assessment: An Exploratory Study

ERIC Educational Resources Information Center

Salisbury, Emily J.; Van Voorhis, Patricia; Spiropoulos, Georgia V.

2009-01-01

Risk assessment and classification systems for women have been largely derived from male-based systems. As a result, many of the needs unique to women are not formally assessed or treated. Emerging research advocating a gender-responsive approach to the supervision and treatment of women offenders suggests that needs such as abuse, mental health,…

Relationship-Based Infant Care: Responsive, on Demand, and Predictable

ERIC Educational Resources Information Center

Petersen, Sandra; Wittmer, Donna

2008-01-01

Young babies are easily overwhelmed by the pain of hunger or gas. However, when an infant's day is filled with caregiving experiences characterized by quick responses to his cries and accurate interpretations of the meaning of his communication, the baby learns that he can count on being fed and comforted. He begins to develop trust in his teacher…

Predicting Response of ADHD Symptoms to Methylphenidate Treatment Based on Comorbid Anxiety

ERIC Educational Resources Information Center

Blouin, Brittany; Maddeaux, Cindy; Stanley Firestone, Jill; van Stralen, Judy

2010-01-01

Objective: In this small pilot study, the association of comorbid anxiety with the treatment of ADHD is studied. Methods: Eighteen volunteers from a pediatric clinic are tested for ADHD and anxiety and assessed for behavioral and cognitive ADHD symptomology. Response to methylphenidate as treatment for ADHD symptoms is measured 2 to 3 weeks, and…

Predictions of the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a data mining technique.

PubMed

Takada, M; Sugimoto, M; Ohno, S; Kuroi, K; Sato, N; Bando, H; Masuda, N; Iwata, H; Kondo, M; Sasano, H; Chow, L W C; Inamoto, T; Naito, Y; Tomita, M; Toi, M

2012-07-01

Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to develop a novel computational technique to predict the pathological complete response (pCR) to NAC in primary breast cancer patients. A mathematical model using alternating decision trees, an epigone of decision tree, was developed using 28 clinicopathological variables that were retrospectively collected from patients treated with NAC (n = 150), and validated using an independent dataset from a randomized controlled trial (n = 173). The model selected 15 variables to predict the pCR with yielding area under the receiver operating characteristics curve (AUC) values of 0.766 [95 % confidence interval (CI)], 0.671-0.861, P value < 0.0001) in cross-validation using training dataset and 0.787 (95 % CI 0.716-0.858, P value < 0.0001) in the validation dataset. Among three subtypes of breast cancer, the luminal subgroup showed the best discrimination (AUC = 0.779, 95 % CI 0.641-0.917, P value = 0.0059). The developed model (AUC = 0.805, 95 % CI 0.716-0.894, P value < 0.0001) outperformed multivariate logistic regression (AUC = 0.754, 95 % CI 0.651-0.858, P value = 0.00019) of validation datasets without missing values (n = 127). Several analyses, e.g. bootstrap analysis, revealed that the developed model was insensitive to missing values and also tolerant to distribution bias among the datasets. Our model based on clinicopathological variables showed high predictive ability for pCR. This model might improve the prediction of the response to NAC in primary breast cancer patients.

Identification of signaling components required for the prediction of cytokine release in RAW 264.7 macrophages

PubMed Central

Pradervand, Sylvain; Maurya, Mano R; Subramaniam, Shankar

2006-01-01

Background Release of immuno-regulatory cytokines and chemokines during inflammatory response is mediated by a complex signaling network. Multiple stimuli produce different signals that generate different cytokine responses. Current knowledge does not provide a complete picture of these signaling pathways. However, using specific markers of signaling pathways, such as signaling proteins, it is possible to develop a 'coarse-grained network' map that can help understand common regulatory modules for various cytokine responses and help differentiate between the causes of their release. Results Using a systematic profiling of signaling responses and cytokine release in RAW 264.7 macrophages made available by the Alliance for Cellular Signaling, an analysis strategy is presented that integrates principal component regression and exhaustive search-based model reduction to identify required signaling factors necessary and sufficient to predict the release of seven cytokines (G-CSF, IL-1α, IL-6, IL-10, MIP-1α, RANTES, and TNFα) in response to selected ligands. This study provides a model-based quantitative estimate of cytokine release and identifies ten signaling components involved in cytokine production. The models identified capture many of the known signaling pathways involved in cytokine release and predict potentially important novel signaling components, like p38 MAPK for G-CSF release, IFNγ- and IL-4-specific pathways for IL-1a release, and an M-CSF-specific pathway for TNFα release. Conclusion Using an integrative approach, we have identified the pathways responsible for the differential regulation of cytokine release in RAW 264.7 macrophages. Our results demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes. PMID:16507166

Superposition rheology.

PubMed

Dhont, J K; Wagner, N J

2001-02-01

The interpretation of superposition rheology data is still a matter of debate due to lack of understanding of viscoelastic superposition response on a microscopic level. So far, only phenomenological approaches have been described, which do not capture the shear induced microstructural deformation, which is responsible for the viscoelastic behavior to the superimposed flow. Experimentally there are indications that there is a fundamental difference between the viscoelastic response to an orthogonally and a parallel superimposed shear flow. We present theoretical predictions, based on microscopic considerations, for both orthogonal and parallel viscoelastic response functions for a colloidal system of attractive particles near their gas-liquid critical point. These predictions extend to values of the stationary shear rate where the system is nonlinearly perturbed, and are based on considerations on the colloidal particle level. The difference in response to orthogonal and parallel superimposed shear flow can be understood entirely in terms of microstructural distortion, where the anisotropy of the microstructure under shear flow conditions is essential. In accordance with experimental observations we find pronounced negative values for response functions in case of parallel superposition for an intermediate range of frequencies, provided that microstructure is nonlinearly perturbed by the stationary shear component. For the critical colloidal systems considered here, the Kramers-Kronig relations for the superimposed response functions are found to be valid. It is argued, however, that the Kramers-Kronig relations may be violated for systems where the stationary shear flow induces a considerable amount of new microstructure.

The effect of stimulus strength on the speed and accuracy of a perceptual decision.

PubMed

Palmer, John; Huk, Alexander C; Shadlen, Michael N

2005-05-02

Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piéron's Law, a power function dependence of response time on stimulus strength. The theory's analytic chronometric function allows one to extend theories of accuracy to response time.

Metabolic and anthropometric parameters contribute to ART-mediated CD4+ T cell recovery in HIV-1-infected individuals: an observational study

PubMed Central

2011-01-01

Background The degree of immune reconstitution achieved in response to suppressive ART is associated with baseline individual characteristics, such as pre-treatment CD4 count, levels of viral replication, cellular activation, choice of treatment regimen and gender. However, the combined effect of these variables on long-term CD4 recovery remains elusive, and no single variable predicts treatment response. We sought to determine if adiposity and molecules associated with lipid metabolism may affect the response to ART and the degree of subsequent immune reconstitution, and to assess their ability to predict CD4 recovery. Methods We studied a cohort of 69 (48 females and 21 males) HIV-infected, treatment-naïve South African subjects initiating antiretroviral treatment (d4T, 3Tc and lopinavir/ritonavir). We collected information at baseline and six months after viral suppression, assessing anthropometric parameters, dual energy X-ray absorptiometry and magnetic resonance imaging scans, serum-based clinical laboratory tests and whole blood-based flow cytometry, and determined their role in predicting the increase in CD4 count in response to ART. Results We present evidence that baseline CD4+ T cell count, viral load, CD8+ T cell activation (CD95 expression) and metabolic and anthropometric parameters linked to adiposity (LDL/HDL cholesterol ratio and waist/hip ratio) significantly contribute to variability in the extent of CD4 reconstitution (ΔCD4) after six months of continuous ART. Conclusions Our final model accounts for 44% of the variability in CD4+ T cell recovery in virally suppressed individuals, representing a workable predictive model of immune reconstitution. PMID:21801351

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Context-dependent plasticity in the subcortical encoding of linguistic pitch patterns

PubMed Central

Lau, Joseph C. Y.; Wong, Patrick C. M.

2016-01-01

We examined the mechanics of online experience-dependent auditory plasticity by assessing the influence of prior context on the frequency-following responses (FFRs), which reflect phase-locked responses from neural ensembles within the subcortical auditory system. FFRs were elicited to a Cantonese falling lexical pitch pattern from 24 native speakers of Cantonese in a variable context, wherein the falling pitch pattern randomly occurred in the context of two other linguistic pitch patterns; in a patterned context, wherein, the falling pitch pattern was presented in a predictable sequence along with two other pitch patterns, and in a repetitive context, wherein the falling pitch pattern was presented with 100% probability. We found that neural tracking of the stimulus pitch contour was most faithful and accurate when listening context was patterned and least faithful when the listening context was variable. The patterned context elicited more robust pitch tracking relative to the repetitive context, suggesting that context-dependent plasticity is most robust when the context is predictable but not repetitive. Our study demonstrates a robust influence of prior listening context that works to enhance online neural encoding of linguistic pitch patterns. We interpret these results as indicative of an interplay between contextual processes that are responsive to predictability as well as novelty in the presentation context. NEW & NOTEWORTHY Human auditory perception in dynamic listening environments requires fine-tuning of sensory signal based on behaviorally relevant regularities in listening context, i.e., online experience-dependent plasticity. Our finding suggests what partly underlie online experience-dependent plasticity are interplaying contextual processes in the subcortical auditory system that are responsive to predictability as well as novelty in listening context. These findings add to the literature that looks to establish the neurophysiological bases of auditory system plasticity, a central issue in auditory neuroscience. PMID:27832606

Context-dependent plasticity in the subcortical encoding of linguistic pitch patterns.

PubMed

Lau, Joseph C Y; Wong, Patrick C M; Chandrasekaran, Bharath

2017-02-01

We examined the mechanics of online experience-dependent auditory plasticity by assessing the influence of prior context on the frequency-following responses (FFRs), which reflect phase-locked responses from neural ensembles within the subcortical auditory system. FFRs were elicited to a Cantonese falling lexical pitch pattern from 24 native speakers of Cantonese in a variable context, wherein the falling pitch pattern randomly occurred in the context of two other linguistic pitch patterns; in a patterned context, wherein, the falling pitch pattern was presented in a predictable sequence along with two other pitch patterns, and in a repetitive context, wherein the falling pitch pattern was presented with 100% probability. We found that neural tracking of the stimulus pitch contour was most faithful and accurate when listening context was patterned and least faithful when the listening context was variable. The patterned context elicited more robust pitch tracking relative to the repetitive context, suggesting that context-dependent plasticity is most robust when the context is predictable but not repetitive. Our study demonstrates a robust influence of prior listening context that works to enhance online neural encoding of linguistic pitch patterns. We interpret these results as indicative of an interplay between contextual processes that are responsive to predictability as well as novelty in the presentation context. Human auditory perception in dynamic listening environments requires fine-tuning of sensory signal based on behaviorally relevant regularities in listening context, i.e., online experience-dependent plasticity. Our finding suggests what partly underlie online experience-dependent plasticity are interplaying contextual processes in the subcortical auditory system that are responsive to predictability as well as novelty in listening context. These findings add to the literature that looks to establish the neurophysiological bases of auditory system plasticity, a central issue in auditory neuroscience. Copyright © 2017 the American Physiological Society.

Phase-Amplitude Response Functions for Transient-State Stimuli

PubMed Central

2013-01-01

Abstract The phase response curve (PRC) is a powerful tool to study the effect of a perturbation on the phase of an oscillator, assuming that all the dynamics can be explained by the phase variable. However, factors like the rate of convergence to the oscillator, strong forcing or high stimulation frequency may invalidate the above assumption and raise the question of how is the phase variation away from an attractor. The concept of isochrons turns out to be crucial to answer this question; from it, we have built up Phase Response Functions (PRF) and, in the present paper, we complete the extension of advancement functions to the transient states by defining the Amplitude Response Function (ARF) to control changes in the transversal variables. Based on the knowledge of both the PRF and the ARF, we study the case of a pulse-train stimulus, and compare the predictions given by the PRC-approach (a 1D map) to those given by the PRF-ARF-approach (a 2D map); we observe differences up to two orders of magnitude in favor of the 2D predictions, especially when the stimulation frequency is high or the strength of the stimulus is large. We also explore the role of hyperbolicity of the limit cycle as well as geometric aspects of the isochrons. Summing up, we aim at enlightening the contribution of transient effects in predicting the phase response and showing the limits of the phase reduction approach to prevent from falling into wrong predictions in synchronization problems. List of Abbreviations PRC phase response curve, phase resetting curve. PRF phase response function. ARF amplitude response function. PMID:23945295

Do the conventional clinicopathologic parameters predict for response and survival in head and neck cancer patients undergoing neoadjuvant chemotherapy?

PubMed

Fonseca, E; Cruz, J J; Dueñas, A; Gómez, A; Sánchez, P; Martín, G; Nieto, A; Soria, P; Muñoz, A; Gómez, J L; Pardal, J L

1996-01-01

Neoadjuvant chemotherapy for head and neck carcinoma is still an important treatment modality. The prognostic value of patient and tumor parameters has been extensively evaluated in several trials, yielding mixed results. We report the prognostic factors emerging from a group of patients undergoing neoadjuvant chemotherapy. From April 1986 to June 1992, 149 consecutive patients received cisplatin-5-fluorouracil-based neoadjuvant chemotherapy. After four courses of chemotherapy, patients underwent local-regional treatment with surgery, radiation or both. A variety of patient and tumor characteristics were evaluated as predictors for response to chemotherapy and survival. The complete response, partial response and no response rates to NAC were 52%, 33% and 15%, respectively. No parameters predicted response to chemotherapy. At a maximum follow-up of 87 months, overall survival was 39% and disease-free survival was 49%. Variables shown to be predictors of survival in univariate analyses were age, performance status, histology, site, T, N, stage, and response to chemotherapy. Using the Cox regression analysis, only complete response to induction chemotherapy (P = 0.0006), performance status (P = 0.03), stage (P = 0.01), age (P = 0.03) and primary tumor site (P = 0.04) emerged as independent prognostic factors for survival. Complete response to chemotherapy was confirmed as the strongest prognostic factor influencing survival. However, conventional clinicopathologic factors did not predict response, hence, potential prognostic biologic and molecular factors for response must be sought. At present, much effort must be made for the improvement of the complete response rate, which seems to be a requisite to prolong survival.

Improvement of Varioptic's liquid lens based on electrowetting: how to obtain a short response time and its application in the design of a high resolution iris biometric system

NASA Astrophysics Data System (ADS)

Burger, Benjamin; Meimon, Serge C.; Petit, Cyril; Nguyen, Minh Chau

2015-02-01

This communication presents the results obtained for decreasing the response time of electrowetting-based real time focus correctors (liquid lenses). In order to provide a compact iris biometric system demonstrator, we have achieved a response time at 90% of 7.5 ms for a change in focalization from 0 diopter to 10 diopter with a liquid lens having an aperture of 1.9 mm. We have used a hydrodynamic fluid reorganization model to predict the features of these fast liquid lenses and evaluated the sensivity of the response time to the different conception parameters.

Predicting operator workload during system design

NASA Technical Reports Server (NTRS)

Aldrich, Theodore B.; Szabo, Sandra M.

1988-01-01

A workload prediction methodology was developed in response to the need to measure workloads associated with operation of advanced aircraft. The application of the methodology will involve: (1) conducting mission/task analyses of critical mission segments and assigning estimates of workload for the sensory, cognitive, and psychomotor workload components of each task identified; (2) developing computer-based workload prediction models using the task analysis data; and (3) exercising the computer models to produce predictions of crew workload under varying automation and/or crew configurations. Critical issues include reliability and validity of workload predictors and selection of appropriate criterion measures.

SAbPred: a structure-based antibody prediction server

PubMed Central

Dunbar, James; Krawczyk, Konrad; Leem, Jinwoo; Marks, Claire; Nowak, Jaroslaw; Regep, Cristian; Georges, Guy; Kelm, Sebastian; Popovic, Bojana; Deane, Charlotte M.

2016-01-01

SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred. PMID:27131379

Prediction of the cause, effects, and prevention of drug-nutrient interactions using attributes and attribute values.

PubMed

Roe, D A

1985-01-01

Drug-nutrient interactions and their adverse outcomes have previously been identified by observation, investigation, and literature reports. Knowing the attributes of the drugs, availability of knowledge base management systems for microcomputer use can facilitate prediction of the mechanism and the effects of drug-nutrient interactions. Examples used to illustrate this approach are prediction of lactose intolerance in drug-induced malabsorption, and prediction of the mechanism responsible for drug-induced flush reactions. In the future we see that there may be many opportunities to use this system further in the investigation of complex drug-nutrient interactions.

Predicting the Probability for Faculty Adopting an Audience Response System in Higher Education

ERIC Educational Resources Information Center

Chan, Tan Fung Ivan; Borja, Marianne; Welch, Brett; Batiuk, Mary Ellen

2016-01-01

Instructional technologies can be effective tools to foster student engagement, but university faculty may be reluctant to integrate innovative and evidence-based modern learning technologies into instruction. Based on Rogers' diffusion of innovation theory, this quantitative, nonexperimental, one-shot cross-sectional survey determined what…

Analysis on mechanics response of long-life asphalt pavement at moist hot heavy loading area

NASA Astrophysics Data System (ADS)

Xu, Xinquan; Li, Hao; Wu, Chuanhai; Li, Shanqiang

2018-04-01

Based on the durability of semi-rigid base asphalt pavement test road in Guangdong Yunluo expressway, by comparing the mechanics response of modified semi-rigid base, RCC base and inverted semi-rigid base with the state of continuous, using four unit five parameter model to evaluate rut depth of asphalt pavement structure, and through commonly used fatigue life prediction model to evaluate fatigue performance of three types of asphalt pavement structure. Theoretical calculation and four years tracking observation results of test road show that rut depth of modified semi-rigid base asphalt pavement is the minimum, the road performance is the best, and the fatigue performance is the optimal.

Watching the clock

PubMed Central

Fetterman, J. Gregor; Killeen, Peter R.; Hall, Scott

2008-01-01

Four rats and four pigeons were monitored while performing retrospective timing tasks. All animals displayed collateral behaviors which could have mediated their temporal judgements. Statistical analysis made a good case for such mediation in the case of two pigeons performing on a spatially-differentiated response, but not for the two responding on a color-differentiated response. For the rats, all of which performed on a spatially-differentiated task, prediction of their temporal judgements was always better if based on collateral activity than if based on the passage of time. PMID:19701487

Graphene-based perfect optical absorbers harnessing guided mode resonances.

PubMed

Grande, M; Vincenti, M A; Stomeo, T; Bianco, G V; de Ceglia, D; Aközbek, N; Petruzzelli, V; Bruno, G; De Vittorio, M; Scalora, M; D'Orazio, A

2015-08-10

We investigate graphene-based optical absorbers that exploit guided mode resonances (GMRs) attaining theoretically perfect absorption over a bandwidth of few nanometers (over the visible and near-infrared ranges) with a 40-fold increase of the monolayer graphene absorption. We analyze the influence of the geometrical parameters on the absorption rate and the angular response for oblique incidence. Finally, we experimentally verify the theoretical predictions in a one-dimensional, dielectric grating by placing it near either a metallic or a dielectric mirror, thus achieving very good agreement between numerical predictions and experimental results.

Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment outcome in virtual reality exposure therapy.

PubMed

Norrholm, Seth Davin; Jovanovic, Tanja; Gerardi, Maryrose; Breazeale, Kathryn G; Price, Matthew; Davis, Michael; Duncan, Erica; Ressler, Kerry J; Bradley, Bekh; Rizzo, Albert; Tuerk, Peter W; Rothbaum, Barbara O

2016-07-01

Baseline cue-dependent physiological reactivity may serve as an objective measure of posttraumatic stress disorder (PTSD) symptoms. Additionally, prior animal model and psychological studies would suggest that subjects with greatest symptoms at baseline may have the greatest violation of expectancy to danger when undergoing exposure based psychotherapy; thus treatment approaches which enhanced the learning under these conditions would be optimal for those with maximal baseline cue-dependent reactivity. However methods to study this hypothesis objectively are lacking. Virtual reality (VR) methodologies have been successfully employed as an enhanced form of imaginal prolonged exposure therapy for the treatment of PTSD. Our goal was to examine the predictive nature of initial psychophysiological (e.g., startle, skin conductance, heart rate) and stress hormone responses (e.g., cortisol) during presentation of VR-based combat-related stimuli on PTSD treatment outcome. Combat veterans with PTSD underwent 6 weeks of VR exposure therapy combined with either d-cycloserine (DCS), alprazolam (ALP), or placebo (PBO). In the DCS group, startle response to VR scenes prior to initiation of treatment accounted for 76% of the variance in CAPS change scores, p < 0.001, in that higher responses predicted greater changes in symptom severity over time. Additionally, baseline cortisol reactivity was inversely associated with treatment response in the ALP group, p = 0.04. We propose that baseline cue-activated physiological measures will be sensitive to predicting patients' level of response to exposure therapy, in particular in the presence of enhancement (e.g., DCS). Published by Elsevier Ltd.

Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD

NASA Astrophysics Data System (ADS)

Kim, H. S.

2015-02-01

The main objective of this study was to assess the predictive uncertainty from the rainfall-runoff model structure coupling a conceptual module (non-linear module) with a metric transfer function module (linear module). The methodology was primarily based on the comparison between the outputs of the rainfall-runoff model and those from an alternative model approach. An alternative model approach was used to minimise uncertainties arising from data and the model structure. A baseflow filter was adopted to better understand deficiencies in the forms of the rainfall-runoff model by avoiding the uncertainties related to data and the model structure. The predictive uncertainty from the model structure was investigated for representative groups of catchments having similar hydrological response characteristics in the upper Murrumbidgee Catchment. In the assessment of model structure suitability, the consistency (or variability) of catchment response over time and space in model performance and parameter values has been investigated to detect problems related to the temporal and spatial variability of the model accuracy. The predictive error caused by model uncertainty was evaluated through analysis of the variability of the model performance and parameters. A graphical comparison of model residuals, effective rainfall estimates and hydrographs was used to determine a model's ability related to systematic model deviation between simulated and observed behaviours and general behavioural differences in the timing and magnitude of peak flows. The model's predictability was very sensitive to catchment response characteristics. The linear module performs reasonably well in the wetter catchments but has considerable difficulties when applied to the drier catchments where a hydrologic response is dominated by quick flow. The non-linear module has a potential limitation in its capacity to capture non-linear processes for converting observed rainfall into effective rainfall in both the wetter and drier catchments. The comparative study based on a better quantification of the accuracy and precision of hydrological modelling predictions yields a better understanding for the potential improvement of model deficiencies.

Versatility of Cooperative Transcriptional Activation: A Thermodynamical Modeling Analysis for Greater-Than-Additive and Less-Than-Additive Effects

PubMed Central

Frank, Till D.; Carmody, Aimée M.; Kholodenko, Boris N.

2012-01-01

We derive a statistical model of transcriptional activation using equilibrium thermodynamics of chemical reactions. We examine to what extent this statistical model predicts synergy effects of cooperative activation of gene expression. We determine parameter domains in which greater-than-additive and less-than-additive effects are predicted for cooperative regulation by two activators. We show that the statistical approach can be used to identify different causes of synergistic greater-than-additive effects: nonlinearities of the thermostatistical transcriptional machinery and three-body interactions between RNA polymerase and two activators. In particular, our model-based analysis suggests that at low transcription factor concentrations cooperative activation cannot yield synergistic greater-than-additive effects, i.e., DNA transcription can only exhibit less-than-additive effects. Accordingly, transcriptional activity turns from synergistic greater-than-additive responses at relatively high transcription factor concentrations into less-than-additive responses at relatively low concentrations. In addition, two types of re-entrant phenomena are predicted. First, our analysis predicts that under particular circumstances transcriptional activity will feature a sequence of less-than-additive, greater-than-additive, and eventually less-than-additive effects when for fixed activator concentrations the regulatory impact of activators on the binding of RNA polymerase to the promoter increases from weak, to moderate, to strong. Second, for appropriate promoter conditions when activator concentrations are increased then the aforementioned re-entrant sequence of less-than-additive, greater-than-additive, and less-than-additive effects is predicted as well. Finally, our model-based analysis suggests that even for weak activators that individually induce only negligible increases in promoter activity, promoter activity can exhibit greater-than-additive responses when transcription factors and RNA polymerase interact by means of three-body interactions. Overall, we show that versatility of transcriptional activation is brought about by nonlinearities of transcriptional response functions and interactions between transcription factors, RNA polymerase and DNA. PMID:22506020

DPYD*2A and MTHFR C677T predict toxicity and efficacy, respectively, in patients on chemotherapy with 5-fluorouracil for colorectal cancer.

PubMed

Nahid, Noor Ahmed; Apu, Mohd Nazmul Hasan; Islam, Md Reazul; Shabnaz, Samia; Chowdhury, Surid Mohammad; Ahmed, Maizbha Uddin; Nahar, Zabun; Islam, Md Siddiqul; Islam, Mohammad Safiqul; Hasnat, Abul

2018-01-01

Significant inter-individual variation in the sensitivity to 5-fluorouracil (5-FU) represents a major therapeutic hindrance either by impairing drug response or inducing adverse drug reactions (ADRs). This study aimed at exploring the cause behind this inter-individual alterations in consequences of 5-fluorouracil-based chemotherapy by investigating the effects of DPYD*2A and MTHFR C677T polymorphisms on toxicity and response of 5-FU in Bangladeshi colorectal cancer patients. Colorectal cancer patients (n = 161) receiving 5-FU-based chemotherapy were prospectively enrolled. DPYD and MTHFR polymorphisms were assessed in peripheral leukocytes. Multivariate analyses were applied to evaluate which variables could predict chemotherapy-induced toxicity and efficacy. Multivariate analyses showed that DPYD*2A polymorphism was a predictive factor (P = 0.023) for grade 3 and grade 4 5-fluorouracil-related toxicities. Although MTHFR C677T polymorphism might act as forecasters for grade 3 or grade 4 neutropenia, diarrhea, and mucositis, this polymorphism was found to increase significantly (P = 0.006) the response of 5-FU. DPYD*2A and MTHFR C677T polymorphisms could explain 5-FU toxicity or clinical outcome in Bangladeshi colorectal patients.

Making smart social judgments takes time: infants' recruitment of goal information when generating action predictions.

PubMed

Krogh-Jespersen, Sheila; Woodward, Amanda L

2014-01-01

Previous research has shown that young infants perceive others' actions as structured by goals. One open question is whether the recruitment of this understanding when predicting others' actions imposes a cognitive challenge for young infants. The current study explored infants' ability to utilize their knowledge of others' goals to rapidly predict future behavior in complex social environments and distinguish goal-directed actions from other kinds of movements. Fifteen-month-olds (N = 40) viewed videos of an actor engaged in either a goal-directed (grasping) or an ambiguous (brushing the back of her hand) action on a Tobii eye-tracker. At test, critical elements of the scene were changed and infants' predictive fixations were examined to determine whether they relied on goal information to anticipate the actor's future behavior. Results revealed that infants reliably generated goal-based visual predictions for the grasping action, but not for the back-of-hand behavior. Moreover, response latencies were longer for goal-based predictions than for location-based predictions, suggesting that goal-based predictions are cognitively taxing. Analyses of areas of interest indicated that heightened attention to the overall scene, as opposed to specific patterns of attention, was the critical indicator of successful judgments regarding an actor's future goal-directed behavior. These findings shed light on the processes that support "smart" social behavior in infants, as it may be a challenge for young infants to use information about others' intentions to inform rapid predictions.

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

PubMed Central

Howard, Réka; Carriquiry, Alicia L.; Beavis, William D.

2017-01-01

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one. PMID:28720710

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

PubMed

Abajian, Aaron; Murali, Nikitha; Savic, Lynn Jeanette; Laage-Gaupp, Fabian Max; Nezami, Nariman; Duncan, James S; Schlachter, Todd; Lin, MingDe; Geschwind, Jean-François; Chapiro, Julius

2018-06-01

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques. This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation. Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0). Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques. Copyright © 2018 SIR. Published by Elsevier Inc. All rights reserved.

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

PubMed Central

2011-01-01

Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

An automated construction of error models for uncertainty quantification and model calibration

NASA Astrophysics Data System (ADS)

Josset, L.; Lunati, I.

2015-12-01

To reduce the computational cost of stochastic predictions, it is common practice to rely on approximate flow solvers (or «proxy»), which provide an inexact, but computationally inexpensive response [1,2]. Error models can be constructed to correct the proxy response: based on a learning set of realizations for which both exact and proxy simulations are performed, a transformation is sought to map proxy into exact responses. Once the error model is constructed a prediction of the exact response is obtained at the cost of a proxy simulation for any new realization. Despite its effectiveness [2,3], the methodology relies on several user-defined parameters, which impact the accuracy of the predictions. To achieve a fully automated construction, we propose a novel methodology based on an iterative scheme: we first initialize the error model with a small training set of realizations; then, at each iteration, we add a new realization both to improve the model and to evaluate its performance. More specifically, at each iteration we use the responses predicted by the updated model to identify the realizations that need to be considered to compute the quantity of interest. Another user-defined parameter is the number of dimensions of the response spaces between which the mapping is sought. To identify the space dimensions that optimally balance mapping accuracy and risk of overfitting, we follow a Leave-One-Out Cross Validation. Also, the definition of a stopping criterion is central to an automated construction. We use a stability measure based on bootstrap techniques to stop the iterative procedure when the iterative model has converged. The methodology is illustrated with two test cases in which an inverse problem has to be solved and assess the performance of the method. We show that an iterative scheme is crucial to increase the applicability of the approach. [1] Josset, L., and I. Lunati, Local and global error models for improving uncertainty quantification, Math.ematical Geosciences, 2013 [2] Josset, L., D. Ginsbourger, and I. Lunati, Functional Error Modeling for uncertainty quantification in hydrogeology, Water Resources Research, 2015 [3] Josset, L., V. Demyanov, A.H. Elsheikhb, and I. Lunati, Accelerating Monte Carlo Markov chains with proxy and error models, Computer & Geosciences, 2015 (In press)

A Bayesian predictive two-stage design for phase II clinical trials.

PubMed

Sambucini, Valeria

2008-04-15

In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.

Survey of computer programs for prediction of crash response and of its experimental validation

NASA Technical Reports Server (NTRS)

Kamat, M. P.

1976-01-01

The author seeks to critically assess the potentialities of the mathematical and hybrid simulators which predict post-impact response of transportation vehicles. A strict rigorous numerical analysis of a complex phenomenon like crash may leave a lot to be desired with regard to the fidelity of mathematical simulation. Hybrid simulations on the other hand which exploit experimentally observed features of deformations appear to hold a lot of promise. MARC, ANSYS, NONSAP, DYCAST, ACTION, WHAM II and KRASH are among some of the simulators examined for their capabilities with regard to prediction of post impact response of vehicles. A review of these simulators reveals that much more by way of an analysis capability may be desirable than what is currently available. NASA's crashworthiness testing program in conjunction with similar programs of various other agencies, besides generating a large data base, will be equally useful in the validation of new mathematical concepts of nonlinear analysis and in the successful extension of other techniques in crashworthiness.

Prediction and verification of creep behavior in metallic materials and components for the space shuttle thermal protection system

NASA Technical Reports Server (NTRS)

Davis, J. W.; Cramer, B. A.

1976-01-01

A method of analysis was developed for predicting permanent cyclic creep deflections in stiffened panel structures. This method uses creep equations based on cyclic tensile creep tests and a computer program to predict panel deflections as a function of mission cycle. Four materials were investigated - a titanium alloy (Ti-6Al-4V), a cobalt alloy (L605), and two nickel alloys (Rene'41 and TDNiCr). Steady-state and cyclic creep response data were obtained by testing tensile specimens fabricated from thin gage sheet (0.025 and 0.63 cm nominal). Steady-state and cyclic creep equations were developed which describe creep as a function of time, temperature and load. Tests were also performed on subsize (6.35 x 30.5 cm) rib and corrugation stiffened panels. These tests were used to correlate creep responses between elemental specimens and panels. The panel response was analyzed by use of a specially written computer program.

Toward a Predictive Understanding of Earth’s Microbiomes to Address 21st Century Challenges

PubMed Central

Blaser, Martin J.; Cardon, Zoe G.; Cho, Mildred K.; Dangl, Jeffrey L.; Green, Jessica L.; Knight, Rob; Maxon, Mary E.; Northen, Trent R.; Pollard, Katherine S.

2016-01-01

ABSTRACT Microorganisms have shaped our planet and its inhabitants for over 3.5 billion years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we propose that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function. From the parallelization of gene function testing to precision manipulation of genes, communities, and model ecosystems and development of novel analytical and simulation approaches, we outline strategies to move microbiome research into an era of causality. These efforts will improve prediction of ecosystem response and enable the development of new, responsible, microbiome-based solutions to significant challenges of our time. PMID:27178263

Toward a Predictive Understanding of Earth's Microbiomes to Address 21st Century Challenges.

PubMed

Blaser, Martin J; Cardon, Zoe G; Cho, Mildred K; Dangl, Jeffrey L; Donohue, Timothy J; Green, Jessica L; Knight, Rob; Maxon, Mary E; Northen, Trent R; Pollard, Katherine S; Brodie, Eoin L

2016-05-13

Microorganisms have shaped our planet and its inhabitants for over 3.5 billion years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we propose that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function. From the parallelization of gene function testing to precision manipulation of genes, communities, and model ecosystems and development of novel analytical and simulation approaches, we outline strategies to move microbiome research into an era of causality. These efforts will improve prediction of ecosystem response and enable the development of new, responsible, microbiome-based solutions to significant challenges of our time. Copyright © 2016 Blaser et al.

Comparison of UWCC MOX fuel measurements to MCNP-REN calculations

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

Abhold, M.; Baker, M.; Jie, R.

1998-12-31

The development of neutron coincidence counting has greatly improved the accuracy and versatility of neutron-based techniques to assay fissile materials. Today, the shift register analyzer connected to either a passive or active neutron detector is widely used by both domestic and international safeguards organizations. The continued development of these techniques and detectors makes extensive use of the predictions of detector response through the use of Monte Carlo techniques in conjunction with the point reactor model. Unfortunately, the point reactor model, as it is currently used, fails to accurately predict detector response in highly multiplying mediums such as mixed-oxide (MOX) lightmore » water reactor fuel assemblies. For this reason, efforts have been made to modify the currently used Monte Carlo codes and to develop new analytical methods so that this model is not required to predict detector response. The authors describe their efforts to modify a widely used Monte Carlo code for this purpose and also compare calculational results with experimental measurements.« less

Predicting Improvement in Writer's Cramp Symptoms following Botulinum Neurotoxin Injection Therapy.

PubMed

Jackman, Mallory; Delrobaei, Mehdi; Rahimi, Fariborz; Atashzar, S Farokh; Shahbazi, Mahya; Patel, Rajni; Jog, Mandar

2016-01-01

Writer's cramp is a specific focal hand dystonia causing abnormal posturing and tremor in the upper limb. The most popular medical intervention, botulinum neurotoxin type A (BoNT-A) therapy, is variably effective for 50-70% of patients. BoNT-A non-responders undergo ineffective treatment and may experience significant side effects. Various assessments have been used to determine response prediction to BoNT-A, but not in the same population of patients. A comprehensive assessment was employed to measure various symptom aspects. Clinical scales, full upper-limb kinematic measures, self-report, and task performance measures were assessed for nine writer's cramp patients at baseline. Patients received two BoNT-A injections then were classified as responders or non-responders based on a quantified self-report measure. Baseline scores were compared between groups, across all measures, to determine which scores predicted a positive BoNT-A response. Five of nine patients were responders. No kinematic measures were predictably different between groups. Analyses revealed three features that predicted a favorable response and separated the two groups: higher than average cramp severity and cramp frequency, and below average cramp latency. Non-kinematic measures appear to be superior in making such predictions. Specifically, measures of cramp severity, frequency, and latency during performance of a specific set of writing and drawing tasks were predictive factors. Since kinematic was not used to determine the injection pattern and the injections were visually guided, it may still be possible to use individual patient kinematics for better outcomes.

Predicting tumor responses to mitomycin C on the basis of DT-diaphorase activity or drug metabolism by tumor homogenates: implications for enzyme-directed bioreductive drug development.

PubMed

Phillips, R M; Burger, A M; Loadman, P M; Jarrett, C M; Swaine, D J; Fiebig, H H

2000-11-15

Mitomycin C (MMC) is a clinically used anticancer drug that is reduced to cytotoxic metabolites by cellular reductases via a process known as bioreductive drug activation. The identification of key enzymes responsible for drug activation has been investigated extensively with the ultimate aim of tailoring drug administration to patients whose tumors possess the biochemical machinery required for drug activation. In the case of MMC, considerable interest has been centered upon the enzyme DT-diaphorase (DTD) although conflicting reports of good and poor correlations between enzyme activity and response in vitro and in vivo have been published. The principle aim of this study was to provide a definitive answer to the question of whether tumor response to MMC could be predicted on the basis of DTD activity in a large panel of human tumor xenografts. DTD levels were measured in 45 human tumor xenografts that had been characterized previously in terms of their sensitivity to MMC in vitro and in vivo (the in vivo response profile to MMC was taken from work published previously). A poor correlation between DTD activity and antitumor activity in vitro as well as in vivo was obtained. This study also assessed the predictive value of an alternative approach based upon the ability of tumor homogenates to metabolize MMC. This approach is based on the premise that the overall rate of MMC metabolism may provide a better indicator of response than single enzyme measurements. MMC metabolism was evaluated in tumor homogenates (clarified by centrifugation at 1000 x g for 1 min) by measuring the disappearance of the parent compound by HPLC. In responsive [T/C <10% (T/C defined as the relative size of treated and control tumors)] and resistant (T/C >50%) tumors, the mean half life of MMC was 75+/-48.3 and 280+/-129.6 min, respectively. The difference between the two groups was statistically significant (P < 0.005). In conclusion, these results unequivocally demonstrate that response to MMC in vivo cannot be predicted on the basis of DTD activity. Measurement of MMC metabolism by tumor homogenates on the other hand may provide a better indicator of tumor response, and further studies are required to determine whether this approach has real clinical potential in terms of individualizing patient chemotherapy.

Predicting Long-term Temperature Increase for Time-Dependent SAR Levels with a Single Short-term Temperature Response

PubMed Central

Carluccio, Giuseppe; Bruno, Mary; Collins, Christopher M.

2015-01-01

Purpose Present a novel method for rapid prediction of temperature in vivo for a series of pulse sequences with differing levels and distributions of specific energy absorption rate (SAR). Methods After the temperature response to a brief period of heating is characterized, a rapid estimate of temperature during a series of periods at different heating levels is made using a linear heat equation and Impulse-Response (IR) concepts. Here the initial characterization and long-term prediction for a complete spine exam are made with the Pennes’ bioheat equation where, at first, core body temperature is allowed to increase and local perfusion is not. Then corrections through time allowing variation in local perfusion are introduced. Results The fast IR-based method predicted maximum temperature increase within 1% of that with a full finite difference simulation, but required less than 3.5% of the computation time. Even higher accelerations are possible depending on the time step size chosen, with loss in temporal resolution. Correction for temperature-dependent perfusion requires negligible additional time, and can be adjusted to be more or less conservative than the corresponding finite difference simulation. Conclusion With appropriate methods, it is possible to rapidly predict temperature increase throughout the body for actual MR examinations. (200/200 words) PMID:26096947

Predicting long-term temperature increase for time-dependent SAR levels with a single short-term temperature response.

PubMed

Carluccio, Giuseppe; Bruno, Mary; Collins, Christopher M

2016-05-01

Present a novel method for rapid prediction of temperature in vivo for a series of pulse sequences with differing levels and distributions of specific energy absorption rate (SAR). After the temperature response to a brief period of heating is characterized, a rapid estimate of temperature during a series of periods at different heating levels is made using a linear heat equation and impulse-response (IR) concepts. Here the initial characterization and long-term prediction for a complete spine exam are made with the Pennes' bioheat equation where, at first, core body temperature is allowed to increase and local perfusion is not. Then corrections through time allowing variation in local perfusion are introduced. The fast IR-based method predicted maximum temperature increase within 1% of that with a full finite difference simulation, but required less than 3.5% of the computation time. Even higher accelerations are possible depending on the time step size chosen, with loss in temporal resolution. Correction for temperature-dependent perfusion requires negligible additional time and can be adjusted to be more or less conservative than the corresponding finite difference simulation. With appropriate methods, it is possible to rapidly predict temperature increase throughout the body for actual MR examinations. © 2015 Wiley Periodicals, Inc.

Pretreatment evaluation of fluorescence resonance energy transfer-based drug sensitivity test for patients with chronic myelogenous leukemia treated with dasatinib.

PubMed

Kondo, Takeshi; Fujioka, Mari; Tsuda, Masumi; Murai, Kazunori; Yamaguchi, Kohei; Miyagishima, Takuto; Shindo, Motohiro; Nagashima, Takahiro; Wakasa, Kentaro; Fujimoto, Nozomu; Yamamoto, Satoshi; Yonezumi, Masakatsu; Saito, Souichi; Sato, Shinji; Ogawa, Kazuei; Chou, Takaaki; Watanabe, Reiko; Kato, Yuichi; Takahashi, Shuichiro; Okano, Yoshiaki; Yamamoto, Joji; Ohta, Masatsugu; Iijima, Hiroaki; Oba, Koji; Kishino, Satoshi; Sakamoto, Junichi; Ishida, Yoji; Ohba, Yusuke; Teshima, Takanori

2018-05-02

Tyrosine kinase inhibitors (TKI) are used for primary therapy in patients with newly diagnosed CML. However, a reliable method for optimal selection of a TKI from the viewpoint of drug sensitivity of CML cells has not been established. We have developed a FRET-based drug sensitivity test in which a CrkL-derived fluorescent biosensor efficiently quantifies the kinase activity of BCR-ABL of living cells and sensitively evaluates the inhibitory activity of a TKI against BCR-ABL. Here, we validated the utility of the FRET-based drug sensitivity test carried out at diagnosis for predicting the molecular efficacy. Sixty-two patients with newly diagnosed chronic phase CML were enrolled in this study and treated with dasatinib. Bone marrow cells at diagnosis were subjected to FRET analysis. The ΔFRET value was calculated by subtraction of FRET efficiency in the presence of dasatinib from that in the absence of dasatinib. Treatment response was evaluated every 3 months by the BCR-ABL1 International Scale. Based on the ΔFRET value and molecular response, a threshold of the ΔFRET value in the top 10% of FRET efficiency was set to 0.31. Patients with ΔFRET value ≥0.31 had significantly superior molecular responses (MMR at 6 and 9 months and both MR4 and MR4.5 at 6, 9, and 12 months) compared with the responses in patients with ΔFRET value <0.31. These results suggest that the FRET-based drug sensitivity test at diagnosis can predict early and deep molecular responses. This study is registered with UMIN Clinical Trials Registry (UMIN000006358). © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Northward migration under a changing climate: a case study of blackgum (Nyssa Sylvatica)

Treesearch

Johanna Desprez; Basil V. Iannone III; Peilin Yang; Christopher M. Oswalt; Songlin Fei

2014-01-01

Species are predicted to shift their distribution ranges in response to climate change. Region-wide, empirically-based studies, however, are still limited to support these predictions. We used a model tree species, blackgum (Nyssa sylvatica), to study climate-induced range shift. Data collected from two separate sampling periods (1980s and 2007) by the USDA’s Forestry...

Life-history traits predict perennial species response to fire in a desert ecosystem

USGS Publications Warehouse

Shryock, Daniel F.; DeFalco, Lesley A.; Esque, Todd C.

2014-01-01

The Mojave Desert of North America has become fire-prone in recent decades due to invasive annual grasses that fuel wildfires following years of high rainfall. Perennial species are poorly adapted to fire in this system, and post-fire shifts in species composition have been substantial but variable across community types. To generalize across a range of conditions, we investigated whether simple life-history traits could predict how species responded to fire. Further, we classified species into plant functional types (PFTs) based on combinations of life-history traits and evaluated whether these groups exhibited a consistent fire-response. Six life-history traits varied significantly between burned and unburned areas in short (up to 4 years) or long-term (up to 52 years) post-fire datasets, including growth form, lifespan, seed size, seed dispersal, height, and leaf longevity. Forbs and grasses consistently increased in abundance after fire, while cacti were reduced and woody species exhibited a variable response. Woody species were classified into three PFTs based on combinations of life-history traits. Species in Group 1 increased in abundance after fire and were characterized by short lifespans, small, wind-dispersed seeds, low height, and deciduous leaves. Species in Group 2 were reduced by fire and distinguished from Group 1 by longer lifespans and evergreen leaves. Group 3 species, which also decreased after fire, were characterized by long lifespans, large non-wind dispersed seeds, and taller heights. Our results show that PFTs based on life-history traits can reliably predict the responses of most species to fire in the Mojave Desert. Dominant, long-lived species of this region possess a combination of traits limiting their ability to recover, presenting a clear example of how a novel disturbance regime may shift selective environmental pressures to favor alternative life-history strategies.

Competition-interaction landscapes for the joint response of forests to climate change.

PubMed

Clark, James S; Bell, David M; Kwit, Matthew C; Zhu, Kai

2014-06-01

The recent global increase in forest mortality episodes could not have been predicted from current vegetation models that are calibrated to regional climate data. Physiological studies show that mortality results from interactions between climate and competition at the individual scale. Models of forest response to climate do not include interactions because they are hard to estimate and require long-term observations on individual trees obtained at frequent (annual) intervals. Interactions involve multiple tree responses that can only be quantified if these responses are estimated as a joint distribution. A new approach provides estimates of climate–competition interactions in two critical ways, (i) among individuals, as a joint distribution of responses to combinations of inputs, such as resources and climate, and (ii) within individuals, due to allocation requirements that control outputs, such as demographic rates. Application to 20 years of data from climate and competition gradients shows that interactions control forest responses, and their omission from models leads to inaccurate predictions. Species most vulnerable to increasing aridity are not those that show the largest growth response to precipitation, but rather depend on interactions with the local resource environment. This first assessment of regional species vulnerability that is based on the scale at which climate operates, individual trees competing for carbon and water, supports predictions of potential savannification in the southeastern US.

Optimization of Nd: YAG Laser Marking of Alumina Ceramic Using RSM And ANN

NASA Astrophysics Data System (ADS)

Peter, Josephine; Doloi, B.; Bhattacharyya, B.

2011-01-01

The present research papers deals with the artificial neural network (ANN) and the response surface methodology (RSM) based mathematical modeling and also an optimization analysis on marking characteristics on alumina ceramic. The experiments have been planned and carried out based on Design of Experiment (DOE). It also analyses the influence of the major laser marking process parameters and the optimal combination of laser marking process parametric setting has been obtained. The output of the RSM optimal data is validated through experimentation and ANN predictive model. A good agreement is observed between the results based on ANN predictive model and actual experimental observations.

Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

NASA Astrophysics Data System (ADS)

Jahani, Nariman; Cohen, Eric; Hsieh, Meng-Kang; Weinstein, Susan P.; Pantalone, Lauren; Davatzikos, Christos; Kontos, Despina

2018-02-01

We examined the ability of DCE-MRI longitudinal features to give early prediction of recurrence-free survival (RFS) in women undergoing neoadjuvant chemotherapy for breast cancer, in a retrospective analysis of 106 women from the ISPY 1 cohort. These features were based on the voxel-wise changes seen in registered images taken before treatment and after the first round of chemotherapy. We computed the transformation field using a robust deformable image registration technique to match breast images from these two visits. Using the deformation field, parametric response maps (PRM) — a voxel-based feature analysis of longitudinal changes in images between visits — was computed for maps of four kinetic features (signal enhancement ratio, peak enhancement, and wash-in/wash-out slopes). A two-level discrete wavelet transform was applied to these PRMs to extract heterogeneity information about tumor change between visits. To estimate survival, a Cox proportional hazard model was applied with the C statistic as the measure of success in predicting RFS. The best PRM feature (as determined by C statistic in univariable analysis) was determined for each of the four kinetic features. The baseline model, incorporating functional tumor volume, age, race, and hormone response status, had a C statistic of 0.70 in predicting RFS. The model augmented with the four PRM features had a C statistic of 0.76. Thus, our results suggest that adding information on the texture of voxel-level changes in tumor kinetic response between registered images of first and second visits could improve early RFS prediction in breast cancer after neoadjuvant chemotherapy.

Risk avoidance in sympatric large carnivores: reactive or predictive?

PubMed

Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

2013-09-01

1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration.

PubMed

Iturria-Medina, Yasser; Carbonell, Félix M; Evans, Alan C

2018-06-14

Personalized Medicine (PM) seeks to assist the patients according to their specific treatment needs and potential intervention responses. However, in the neurological context, this approach is limited by crucial methodological challenges, such as the requirement for an understanding of the causal disease mechanisms and the inability to predict the brain's response to therapeutic interventions. Here, we introduce and validate the concept of the personalized Therapeutic Intervention Fingerprint (pTIF), which predicts the effectiveness of potential interventions for controlling a patient's disease evolution. Each subject's pTIF can be inferred from multimodal longitudinal imaging (e.g. amyloid-β, metabolic and tau PET; vascular, functional and structural MRI). We studied an aging population (N = 331) comprising cognitively normal and neurodegenerative patients, longitudinally scanned using six different neuroimaging modalities. We found that the resulting pTIF vastly outperforms cognitive and clinical evaluations on predicting individual variability in gene expression (GE) profiles. Furthermore, after regrouping the patients according to their predicted primary single-target interventions, we observed that these pTIF-based subgroups present distinctively altered molecular pathway signatures, supporting the across-population identification of dissimilar pathological stages, in active correspondence with different therapeutic needs. The results further evidence the imprecision of using broad clinical categories for understanding individual molecular alterations and selecting appropriate therapeutic needs. To our knowledge, this is the first study highlighting the direct link between multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level. Inspired by the principles of PM, the proposed pTIF framework is a promising step towards biomarker-driven assisted therapeutic interventions, with additional important implications for selective enrollment of patients in clinical trials. Copyright © 2018 Elsevier Inc. All rights reserved.