Rostral anterior cingulate cortex activity and early symptom improvement during treatment for major depressive disorder

PubMed Central

Korb, Alexander S.; Hunter, Aimee M.; Cook, Ian A.; Leuchter, Andrew F.

2011-01-01

In treatment trials for Major Depressive Disorder (MDD), early symptom improvement is predictive of eventual clinical response. Clinical response may also be predicted by elevated pretreatment theta (4-7 Hz) current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC). We investigated the relationship between pretreatment EEG and early improvement in predicting clinical outcome in 72 MDD subjects across three placebo-controlled treatment trials. Subjects were randomized to receive fluoxetine, venlafaxine, or placebo. Theta current density in the rACC and mOFC was computed with Low-Resolution Brain Electromagnetic Tomography (LORETA). An ANCOVA, examining week 8 Hamilton Depression Rating Scale (HamD) percent change, showed a significant effect of week-2-HamD-percent-change, and a significant three-way interaction of week-2-HamD-percent-change × Treatment × rACC. Medication subjects with robust early improvement showed almost no relationship between rACC theta current density and final clinical outcome. However, in subjects with little early improvement, rACC activity showed a strong relationship with clinical outcome. The model examining mOFC showed a trend in the three-way interaction. A combination of pretreatment rACC activity and early symptom improvement may be useful for predicting treatment response. PMID:21546222

Does an understanding of ecosystems responses to rainfall pulses improve predictions of responses of drylands to climate change?

USDA-ARS?s Scientific Manuscript database

Drylands will experience more intense and frequent droughts and floods. Ten-year field experiments manipulating the amount and variability of precipitation suggest that we cannot predict responses of drylands to climate change based on pulse experimentation. Long-term drought experiments showed no e...

An Empirical Correction Method for Improving off-Axes Response Prediction in Component Type Flight Mechanics Helicopter Models

NASA Technical Reports Server (NTRS)

Mansur, M. Hossein; Tischler, Mark B.

1997-01-01

Historically, component-type flight mechanics simulation models of helicopters have been unable to satisfactorily predict the roll response to pitch stick input and the pitch response to roll stick input off-axes responses. In the study presented here, simple first-order low-pass filtering of the elemental lift and drag forces was considered as a means of improving the correlation. The method was applied to a blade-element model of the AH-64 APache, and responses of the modified model were compared with flight data in hover and forward flight. Results indicate that significant improvement in the off-axes responses can be achieved in hover. In forward flight, however, the best correlation in the longitudinal and lateral off-axes responses required different values of the filter time constant for each axis. A compromise value was selected and was shown to result in good overall improvement in the off-axes responses. The paper describes both the method and the model used for its implementation, and presents results obtained at hover and in forward flight.

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Computational Modeling in Concert with Laboratory Studies: Application to B Cell Differentiation

EPA Science Inventory

Remediation is expensive, so accurate prediction of dose-response is important to help control costs. Dose response is a function of biological mechanisms. Computational models of these mechanisms improve the efficiency of research and provide the capability for prediction.

Early improvements in anxiety, depression, and anger/hostility symptoms and response to antidepressant treatment.

PubMed

Farabaugh, Amy; Sonawalla, Shamsah; Johnson, Daniel P; Witte, Janet; Papakostas, George I; Goodness, Tracie; Clain, Alisabet; Baer, Lee; Mischoulon, David; Fava, Maurizio; Harley, Rebecca

2010-08-01

The purpose of this study was to examine whether treatment response to fluoxetine by depressed outpatients was predicted by early improvement on any of 3 subscales (Anxiety, Depression, and Anger/Hostility) of the Symptom Questionnaire (SQ). We evaluated 169 depressed outpatients (52.6% female) between ages 18 and 65 (mean age, 40.3 +/- 10.6 years) meeting DSM-IIIR criteria for major depressive disorder (MDD). All patients completed the SQ at baseline (week 0) and at weeks 2, 4, and 8 of treatment with fluoxetine 20 mg/d. We defined treatment response as a > or= 50% reduction in score on the 17-item Hamilton Rating Scale for Depression, and early improvement on 3 SQ subscales (Anxiety, Depression, and Anger/Hostility) as a >30% reduction in score by week 2. The percentage of patients with significant early improvement in anger was significantly greater than the percentage of those with early improvements in anxiety or depression. When early improvement on the Anxiety, Depression, and Anger/Hostility subscales of the SQ were assessed independently by logistic regression, all 3 subscales were predictors of response to treatment. Early improvement in anger, anxiety, and depressive symptoms may predict response to antidepressant treatment among outpatients with MDD.

Plant water potential improves prediction of empirical stomatal models.

PubMed

Anderegg, William R L; Wolf, Adam; Arango-Velez, Adriana; Choat, Brendan; Chmura, Daniel J; Jansen, Steven; Kolb, Thomas; Li, Shan; Meinzer, Frederick; Pita, Pilar; Resco de Dios, Víctor; Sperry, John S; Wolfe, Brett T; Pacala, Stephen

2017-01-01

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

Early improvement as a resilience signal predicting later remission to antidepressant treatment in patients with Major Depressive Disorder: Systematic review and meta-analysis.

PubMed

Wagner, Stefanie; Engel, Alice; Engelmann, Jan; Herzog, David; Dreimüller, Nadine; Müller, Marianne B; Tadić, André; Lieb, Klaus

2017-11-01

Early improvement of depressive symptoms during the first two weeks of antidepressant treatment has been discussed to be a resilience signal predicting a later positive treatment outcome in patients with Major Depressive Disorder (MDD). However, the predictive value of early improvement varies between studies, and the use of different antidepressants may explain heterogeneous results. The objective of this review was to assess the predictive value of early improvement on later response and remission and to identify antidepressants with the highest chance of early improvement. We included 17 randomized controlled trials investigating early improvement in 14,779 adult patients with MDD comparing monotherapy with an antidepressant against placebo or another antidepressant drug. 62% (range: 35-85%) of patients treated with an antidepressant and 47% (range: 21-69%) with placebo were early improver, defined as a >20%/25% symptom reduction after two weeks of treatment. Early improvement predicted response and remission after 5-12 weeks of treatment with high sensitivity (85%; 95%-CI: 84.3 to 85.7) and low to moderate specificity (54%; 95%-CI: 53.1 to 54.9). Early improver had a 8.37 fold (6.97-10.05) higher likelihood to become responder and a 6.38 fold (5.07-8.02) higher likelihood to be remitter at endpoint than non-improver. The highest early improver rates were achieved in patients treated with mirtazapine or a tricyclic antidepressant. This finding of a high predictive value of early improvement on treatment outcome may be important for treatment decisions in the early course of antidepressant treatment. Further studies should test the efficacy of such early treatment decisions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Early prediction of blonanserin response in Japanese patients with schizophrenia.

PubMed

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Our results suggest that the blonanserin response at week 4 could predict the later response at week 8.

Early prediction of blonanserin response in Japanese patients with schizophrenia

PubMed Central

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Background Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. Methods An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Results Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Conclusion Our results suggest that the blonanserin response at week 4 could predict the later response at week 8. PMID:25285009

Development of an improved capability for predicting the response of highway bridges : final report.

DOT National Transportation Integrated Search

1986-01-01

This study compared experimental and analytical stress and deflection response of a simply-supported highway bridge as measured from a field test and as predicted from a finite-element analysis. The field test was conducted on one span of a six-span ...

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.

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.

Effect of MR Imaging Contrast Thresholds on Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes: A Subgroup Analysis of the ACRIN 6657/I-SPY 1 TRIAL

PubMed Central

Li, Wen; Arasu, Vignesh; Newitt, David C.; Jones, Ella F.; Wilmes, Lisa; Gibbs, Jessica; Kornak, John; Joe, Bonnie N.; Esserman, Laura J.; Hylton, Nola M.

2016-01-01

Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (ΔFTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery. Early percent enhancement threshold (PEt) and signal enhancement ratio threshold (SERt) were varied. The predictive performance of resulting FTV predictors was evaluated using the area under the receiver operating characteristic curve. A total number of 116 patients were studied both as a full cohort and in the following groups defined by hormone receptor (HR) and HER2 receptor subtype: 45 HR+/HER2−, 39 HER2+, and 30 triple negatives. High AUCs were found at different ranges of PEt and SERt levels in different subtypes. Findings from this study suggest that the predictive performance to treatment response by MRI varies by contrast thresholds, and that pathologic complete response prediction may be improved through subtype-specific contrast enhancement thresholds. A validation study is underway with a larger patient population. PMID:28066808

Baseline neuropsychological profile and cognitive response to cerebrospinal fluid shunting for idiopathic normal pressure hydrocephalus.

PubMed

Thomas, George; McGirt, Matthew J; Woodworth, Graeme; Heidler, Jennifer; Rigamonti, Daniele; Hillis, Argye E; Williams, Michael A

2005-01-01

To evaluate neurocognitive changes and predict neurocognitive outcome after ventriculoperitoneal shunting for idiopathic normal pressure hydrocephalus (INPH). Reports of neurocognitive response to shunting have been variable and studies that predict cognitive outcomes after shunting are limited. We reviewed our experience with cognitive outcomes for INPH patients who were selected for shunting based on abnormal cerebrospinal fluid (CSF) pressure monitoring and positive response in any of the NPH symptoms following large volume CSF drainage. Forty-two INPH patients underwent neurocognitive testing and Folstein Mini-Mental State Examination (MMSE) prior to shunting. Neurocognitive testing or MMSEwere performed at least 3 months after shunt insertion. Significant improvement in a neurocognitive subtest was defined as improvement by one standard deviation (1 SD) for the patient's age, sex and education level. Significant improvement in overall neurocognitive outcome was defined as a 4-point improvement in MMSE or improvement by 1 SD in 50% of the administered neurocognitive subtests. Nonparametric tests were used to assess changes. Predictors of outcome were assessed via logistic regression analysis. Twenty-two patients (52.3%) showed overall neurocognitive improvement, and significant improvement was seen in tests of verbal memory and psychomotor speed. Predictive analysis showed that patients scoring more than 1 SD below mean at baseline on verbal memory immediate recall were fourfold less likely to show overall cognitive improvement, and sixfold less likely if also associated with visuoconstructional deficit or executive dysfunction. Verbal memory scores at baseline were higher in patients who showed overall cognitive improvement. Shunting INPH patients on the basis of CSF pressure monitoring and drainage response shows a significant rate of cognitive improvement, and baseline neurocognitive test scores may distinguish patients likely to respond to shunt surgery from those who will not. Copyright (c) 2005 S. Karger AG, Basel.

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Review of Nearshore Morphologic Prediction

NASA Astrophysics Data System (ADS)

Plant, N. G.; Dalyander, S.; Long, J.

2014-12-01

The evolution of the world's erodible coastlines will determine the balance between the benefits and costs associated with human and ecological utilization of shores, beaches, dunes, barrier islands, wetlands, and estuaries. So, we would like to predict coastal evolution to guide management and planning of human and ecological response to coastal changes. After decades of research investment in data collection, theoretical and statistical analysis, and model development we have a number of empirical, statistical, and deterministic models that can predict the evolution of the shoreline, beaches, dunes, and wetlands over time scales of hours to decades, and even predict the evolution of geologic strata over the course of millennia. Comparisons of predictions to data have demonstrated that these models can have meaningful predictive skill. But these comparisons also highlight the deficiencies in fundamental understanding, formulations, or data that are responsible for prediction errors and uncertainty. Here, we review a subset of predictive models of the nearshore to illustrate tradeoffs in complexity, predictive skill, and sensitivity to input data and parameterization errors. We identify where future improvement in prediction skill will result from improved theoretical understanding, and data collection, and model-data assimilation.

Short and long term improvements in quality of chronic care delivery predict program sustainability.

PubMed

Cramm, Jane Murray; Nieboer, Anna Petra

2014-01-01

Empirical evidence on sustainability of programs that improve the quality of care delivery over time is lacking. Therefore, this study aims to identify the predictive role of short and long term improvements in quality of chronic care delivery on program sustainability. In this longitudinal study, professionals [2010 (T0): n=218, 55% response rate; 2011 (T1): n=300, 68% response rate; 2012 (T2): n=265, 63% response rate] from 22 Dutch disease-management programs completed surveys assessing quality of care and program sustainability. Our study findings indicated that quality of chronic care delivery improved significantly in the first 2 years after implementation of the disease-management programs. At T1, overall quality, self-management support, delivery system design, and integration of chronic care components, as well as health care delivery and clinical information systems and decision support, had improved. At T2, overall quality again improved significantly, as did community linkages, delivery system design, clinical information systems, decision support and integration of chronic care components, and self-management support. Multilevel regression analysis revealed that quality of chronic care delivery at T0 (p<0.001) and quality changes in the first (p<0.001) and second (p<0.01) years predicted program sustainability. In conclusion this study showed that disease-management programs based on the chronic care model improved the quality of chronic care delivery over time and that short and long term changes in the quality of chronic care delivery predicted the sustainability of the projects. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Improving Upon an Empirical Procedure for Characterizing Magnetospheric States

NASA Astrophysics Data System (ADS)

Fung, S. F.; Neufeld, J.; Shao, X.

2012-12-01

Work is being performed to improve upon an empirical procedure for describing and predicting the states of the magnetosphere [Fung and Shao, 2008]. We showed in our previous paper that the state of the magnetosphere can be described by a quantity called the magnetospheric state vector (MS vector) consisting of a concatenation of a set of driver-state and a set of response-state parameters. The response state parameters are time-shifted individually to account for their nominal response times so that time does not appear as an explicit parameter in the MS prescription. The MS vector is thus conceptually analogous to the set of vital signs for describing the state of health of a human body. In that previous study, we further demonstrated that since response states are results of driver states, then there should be a correspondence between driver and response states. Such correspondence can be used to predict the subsequent response state from any known driver state with a few hours' lead time. In this paper, we investigate a few possible ways to improve the magnetospheric state descriptions and prediction efficiency by including additional driver state parameters, such as solar activity, IMF-Bx and -By, and optimizing parameter bin sizes. Fung, S. F. and X. Shao, Specification of multiple geomagnetic responses to variable solar wind and IMF input, Ann. Geophys., 26, 639-652, 2008.

FES-Rowing versus Zoledronic Acid to Improve Bone Health in SCI

DTIC Science & Technology

2014-10-01

may FES rowing improves tibial stress distribution. This image demonstrates the change in stress distribution in response to the same axial force...kN/mm). This indicates improved bone strength and better stress distribution. improve fracture risk prediction and detection of response to...osteoporosis-related bone fracture . This study aims to learn if the severe osteoporosis in lower extremities caused by spinal cord injuries can be slowed or

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.

Validation of Shoulder Response of Human Body Finite-Element Model (GHBMC) Under Whole Body Lateral Impact Condition.

PubMed

Park, Gwansik; Kim, Taewung; Panzer, Matthew B; Crandall, Jeff R

2016-08-01

In previous shoulder impact studies, the 50th-percentile male GHBMC human body finite-element model was shown to have good biofidelity regarding impact force, but under-predicted shoulder deflection by 80% compared to those observed in the experiment. The goal of this study was to validate the response of the GHBMC M50 model by focusing on three-dimensional shoulder kinematics under a whole-body lateral impact condition. Five modifications, focused on material properties and modeling techniques, were introduced into the model and a supplementary sensitivity analysis was done to determine the influence of each modification to the biomechanical response of the body. The modified model predicted substantially improved shoulder response and peak shoulder deflection within 10% of the observed experimental data, and showed good correlation in the scapula kinematics on sagittal and transverse planes. The improvement in the biofidelity of the shoulder region was mainly due to the modifications of material properties of muscle, the acromioclavicular joint, and the attachment region between the pectoralis major and ribs. Predictions of rib fracture and chest deflection were also improved because of these modifications.

Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/. Copyright © 2016 Elsevier B.V. All rights reserved.

Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

PubMed

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p < .001). Elevated caudate activation was associated with robust improvement for methylphenidate and little improvement for atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p < .001). Enhanced caudate activation for response inhibition may be a candidate biomarker of superior response to methylphenidate over atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

PubMed Central

Noren, David P.; Long, Byron L.; Norel, Raquel; Rrhissorrakrai, Kahn; Hess, Kenneth; Hu, Chenyue Wendy; Bisberg, Alex J.; Schultz, Andre; Engquist, Erik; Liu, Li; Lin, Xihui; Chen, Gregory M.; Xie, Honglei; Hunter, Geoffrey A. M.; Norman, Thea; Friend, Stephen H.; Stolovitzky, Gustavo; Kornblau, Steven; Qutub, Amina A.

2016-01-01

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response. PMID:27351836

Microbial models with data-driven parameters predict stronger soil carbon responses to climate change.

PubMed

Hararuk, Oleksandra; Smith, Matthew J; Luo, Yiqi

2015-06-01

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values. © 2014 John Wiley & Sons Ltd.

Predicting outcome of Internet-based treatment for depressive symptoms.

PubMed

Warmerdam, Lisanne; Van Straten, Annemieke; Twisk, Jos; Cuijpers, Pim

2013-01-01

In this study we explored predictors and moderators of response to Internet-based cognitive behavioral therapy (CBT) and Internet-based problem-solving therapy (PST) for depressive symptoms. The sample consisted of 263 participants with moderate to severe depressive symptoms. Of those, 88 were randomized to CBT, 88 to PST and 87 to a waiting list control condition. Outcomes were improvement and clinically significant change in depressive symptoms after 8 weeks. Higher baseline depression and higher education predicted improvement, while higher education, less avoidance behavior and decreased rational problem-solving skills predicted clinically significant change across all groups. No variables were found that differentially predicted outcome between Internet-based CBT and Internet-based PST. More research is needed with sufficient power to investigate predictors and moderators of response to reveal for whom Internet-based therapy is best suited.

Predictors of response to Systems Training for Emotional Predictability and Problem Solving (STEPPS) for borderline personality disorder: an exploratory study.

PubMed

Black, D W; Allen, J; St John, D; Pfohl, B; McCormick, B; Blum, N

2009-07-01

Few predictors of treatment outcome or early discontinuation have been identified in persons with borderline personality disorder (BPD). The aim of the study was to examine the relationship between baseline clinical variables and treatment response and early discontinuation in a randomized controlled trial of System Training for Emotional Predictability and Problem Solving, a new cognitive group treatment. Improvement was rated using the Zanarini Rating Scale for BPD, the Clinical Global Impression Scale, the Global Assessment Scale and the Beck Depression Inventory. Subjects were assessed during the 20 week trial and a 1-year follow-up. Higher baseline severity was associated with greater improvement in global functioning and BPD-related symptoms. Higher impulsivity was predictive of early discontinuation. Optimal improvement was associated with attending > or = 15 sessions. Subjects likely to improve have the more severe BPD symptoms at baseline, while high levels of impulsivity are associated with early discontinuation.

Transcranial Direct Current Stimulation in Post-stroke Chronic Aphasia: The Impact of Baseline Severity and Task Specificity in a Pilot Sample

PubMed Central

Norise, Catherine; Sacchetti, Daniela; Hamilton, Roy

2017-01-01

Emerging evidence suggests that transcranial direct current stimulation (tDCS) can improve aspects of language production in persons with chronic non-fluent aphasia due to left hemisphere stroke. However, to date, studies exploring factors that predict response to tDCS in this or any patient population remain sparse, as are studies that investigate the specific aspects of language performance that are most responsive to stimulation. The current study explored factors that could predict recovery of language fluency and which aspects of language fluency could be expected to improve with the identified factor(s). We report nine patients who demonstrated deficits in fluency as assessed using the Cookie Theft picture description task of the Boston Diagnostic Aphasia Examination. In the treatment condition, subjects received a 2.0 mA current through 5 cm × 5 cm electrodes for 20 min at a site previously shown to elicit a patient-dependent optimal response to tDCS. They were then tested 2-weeks and 2-months after treatment. In the sham condition, a subset of these subjects were tested on the same protocol with sham instead of real tDCS. The current study assessed language fluency improvements in measures of production at the word-level and sentence level, grammatical accuracy, and lexical selection as a function of baseline aphasia severity. A more severe baseline language profile was associated with larger improvements in fluency at the word-level after real tDCS but not sham stimulation. These improvements were maintained at the 2-week follow-up. The results suggest that for at least some outcome measures, baseline severity may be an important factor in predicting the response to tDCS in patients with chronic non-fluent aphasia. Moving forward, the ability to identify patient factors that can predict response could help refine strategies for the administration of therapeutic tDCS, focusing attention on those patients most likely to benefit from stimulation. PMID:28611609

Improved predictions of saturated and unsaturated zone drawdowns in a heterogeneous unconfined aquifer via transient hydraulic tomography: Laboratory sandbox experiments

NASA Astrophysics Data System (ADS)

Berg, Steven J.; Illman, Walter A.

2012-11-01

SummaryInterpretation of pumping tests in unconfined aquifers has largely been based on analytical solutions that disregard aquifer heterogeneity. In this study, we investigate whether the prediction of drawdown responses in a heterogeneous unconfined aquifer and the unsaturated zone above it with a variably saturated groundwater flow model can be improved by including information on hydraulic conductivity (K) and specific storage (Ss) from transient hydraulic tomography (THT). We also investigate whether these predictions are affected by the use of unsaturated flow parameters estimated through laboratory hanging column experiments or calibration of in situ drainage curves. To investigate these issues, we designed and conducted laboratory sandbox experiments to characterize the saturated and unsaturated properties of a heterogeneous unconfined aquifer. Specifically, we conducted pumping tests under fully saturated conditions and interpreted the drawdown responses by treating the medium to be either homogeneous or heterogeneous. We then conducted another pumping test and allowed the water table to drop, similar to a pumping test in an unconfined aquifer. Simulations conducted using a variably saturated flow model revealed: (1) homogeneous parameters in the saturated and unsaturated zones have a difficult time predicting the responses of the heterogeneous unconfined aquifer; (2) heterogeneous saturated hydraulic parameter distributions obtained via THT yielded significantly improved drawdown predictions in the saturated zone of the unconfined aquifer; and (3) considering heterogeneity of unsaturated zone parameters produced a minor improvement in predictions in the unsaturated zone, but not the saturated zone. These results seem to support the finding by Mao et al. (2011) that spatial variability in the unsaturated zone plays a minor role in the formation of the S-shape drawdown-time curve observed during pumping in an unconfined aquifer.

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

Rheumatoid arthritis patient perceptions on the value of predictive testing for treatments: a qualitative study.

PubMed

Kumar, Kanta; Peters, Sarah; Barton, Anne

2016-11-08

Rheumatoid arthritis (RA) is a long term condition that requires early treatment to control symptoms and improve long-term outcomes. Lack of response to RA treatments is not only a waste of healthcare resources, but also causes disability and distress to patients. Identifying biomarkers predictive of treatment response offers an opportunity to improve clinical decisions about which treatment to recommend in patients and could ultimately lead to better patient outcomes. The aim of this study was to explore the understanding of and factors affecting Rheumatoid Arthritis (RA) patients' decisions around predictive treatment testing. A qualitative study was conducted with a purposive sample of 16 patients with RA from three major UK cities. Four focus groups explored patient perceptions of the use of biomarker tests to predict response to treatments. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis by three researchers. Data were organised within three interlinking themes: [1] Perceptions of predictive tests and patient preference of tests; [2] Utility of the test to manage expectations; [3] The influence of the disease duration on take up of predictive testing. During consultations for predictive testing, patients felt they would need, first, careful explanations detailing the consequences of untreated RA and delayed treatment response and, second, support to balance the risks of tests, which might be invasive and/or only moderately accurate, with the potential benefits of better management of symptoms. This study provides important insights into predictive testing. Besides supporting clinical decision making, the development of predictive testing in RA is largely supported by patients. Developing strategies which communicate risk information about predictive testing effectively while reducing the psychological burden associated with this information will be essential to maximise uptake.

Probabilistic Polling And Voting In The 2008 Presidential Election

PubMed Central

Delavande, Adeline; Manski, Charles F.

2010-01-01

This article reports new empirical evidence on probabilistic polling, which asks persons to state in percent-chance terms the likelihood that they will vote and for whom. Before the 2008 presidential election, seven waves of probabilistic questions were administered biweekly to participants in the American Life Panel (ALP). Actual voting behavior was reported after the election. We find that responses to the verbal and probabilistic questions are well-aligned ordinally. Moreover, the probabilistic responses predict voting behavior beyond what is possible using verbal responses alone. The probabilistic responses have more predictive power in early August, and the verbal responses have more power in late October. However, throughout the sample period, one can predict voting behavior better using both types of responses than either one alone. Studying the longitudinal pattern of responses, we segment respondents into those who are consistently pro-Obama, consistently anti-Obama, and undecided/vacillators. Membership in the consistently pro- or anti-Obama group is an almost perfect predictor of actual voting behavior, while the undecided/vacillators group has more nuanced voting behavior. We find that treating the ALP as a panel improves predictive power: current and previous polling responses together provide more predictive power than do current responses alone. PMID:24683275

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

PubMed Central

Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

2016-01-01

Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016. © 2016 Wiley Periodicals, Inc. PMID:26757216

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.

Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

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 of 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 vaccine prediction. 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. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of nomographs for analysis and prediction of receiver spurious response EMI

NASA Astrophysics Data System (ADS)

Heather, F. W.

1985-07-01

Spurious response EMI for the front end of a superheterodyne receiver follows a simple mathematic formula; however, the application of the formula to predict test frequencies produces more data than can be evaluated. An analysis technique has been developed to graphically depict all receiver spurious responses usig a nomograph and to permit selection of optimum test frequencies. The discussion includes the math model used to simulate a superheterodyne receiver, the implementation of the model in the computer program, the approach to test frequency selection, interpretation of the nomographs, analysis and prediction of receiver spurious response EMI from the nomographs, and application of the nomographs. In addition, figures are provided of sample applications. This EMI analysis and prediction technique greatly improves the Electromagnetic Compatibility (EMC) test engineer's ability to visualize the scope of receiver spurious response EMI testing and optimize test frequency selection.

Optimization of a novel improver gel formulation for Barbari flat bread using response surface methodology.

PubMed

Pourfarzad, Amir; Haddad Khodaparast, Mohammad Hossein; Karimi, Mehdi; Mortazavi, Seyed Ali

2014-10-01

Nowadays, the use of bread improvers has become an essential part of improving the production methods and quality of bakery products. In the present study, the Response Surface Methodology (RSM) was used to determine the optimum improver gel formulation which gave the best quality, shelf life, sensory and image properties for Barbari flat bread. Sodium stearoyl-2-lactylate (SSL), diacetyl tartaric acid esters of monoglyceride (DATEM) and propylene glycol (PG) were constituents of the gel and considered in this study. A second-order polynomial model was fitted to each response and the regression coefficients were determined using least square method. The optimum gel formulation was found to be 0.49 % of SSL, 0.36 % of DATEM and 0.5 % of PG when desirability function method was applied. There was a good agreement between the experimental data and their predicted counterparts. Results showed that the RSM, image processing and texture analysis are useful tools to investigate, approximate and predict a large number of bread properties.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

PubMed Central

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T.; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J.

2017-01-01

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival. PMID:28401902

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

PubMed

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

2017-04-12

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis

PubMed Central

Hueber, Wolfgang; Tomooka, Beren H; Batliwalla, Franak; Li, Wentian; Monach, Paul A; Tibshirani, Robert J; Van Vollenhoven, Ronald F; Lampa, Jon; Saito, Kazuyoshi; Tanaka, Yoshiya; Genovese, Mark C; Klareskog, Lars; Gregersen, Peter K; Robinson, William H

2009-01-01

Introduction Anti-TNF therapies have revolutionized the treatment of rheumatoid arthritis (RA), a common systemic autoimmune disease involving destruction of the synovial joints. However, in the practice of rheumatology approximately one-third of patients demonstrate no clinical improvement in response to treatment with anti-TNF therapies, while another third demonstrate a partial response, and one-third an excellent and sustained response. Since no clinical or laboratory tests are available to predict response to anti-TNF therapies, great need exists for predictive biomarkers. Methods Here we present a multi-step proteomics approach using arthritis antigen arrays, a multiplex cytokine assay, and conventional ELISA, with the objective to identify a biomarker signature in three ethnically diverse cohorts of RA patients treated with the anti-TNF therapy etanercept. Results We identified a 24-biomarker signature that enabled prediction of a positive clinical response to etanercept in all three cohorts (positive predictive values 58 to 72%; negative predictive values 63 to 78%). Conclusions We identified a multi-parameter protein biomarker that enables pretreatment classification and prediction of etanercept responders, and tested this biomarker using three independent cohorts of RA patients. Although further validation in prospective and larger cohorts is needed, our observations demonstrate that multiplex characterization of autoantibodies and cytokines provides clinical utility for predicting response to the anti-TNF therapy etanercept in RA patients. PMID:19460157

Neural predictors of individual differences in response to math tutoring in primary-grade school children

PubMed Central

Supekar, Kaustubh; Swigart, Anna G.; Tenison, Caitlin; Jolles, Dietsje D.; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-01-01

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8–9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures. PMID:23630286

Neural predictors of individual differences in response to math tutoring in primary-grade school children.

PubMed

Supekar, Kaustubh; Swigart, Anna G; Tenison, Caitlin; Jolles, Dietsje D; Rosenberg-Lee, Miriam; Fuchs, Lynn; Menon, Vinod

2013-05-14

Now, more than ever, the ability to acquire mathematical skills efficiently is critical for academic and professional success, yet little is known about the behavioral and neural mechanisms that drive some children to acquire these skills faster than others. Here we investigate the behavioral and neural predictors of individual differences in arithmetic skill acquisition in response to 8-wk of one-to-one math tutoring. Twenty-four children in grade 3 (ages 8-9 y), a critical period for acquisition of basic mathematical skills, underwent structural and resting-state functional MRI scans pretutoring. A significant shift in arithmetic problem-solving strategies from counting to fact retrieval was observed with tutoring. Notably, the speed and accuracy of arithmetic problem solving increased with tutoring, with some children improving significantly more than others. Next, we examined whether pretutoring behavioral and brain measures could predict individual differences in arithmetic performance improvements with tutoring. No behavioral measures, including intelligence quotient, working memory, or mathematical abilities, predicted performance improvements. In contrast, pretutoring hippocampal volume predicted performance improvements. Furthermore, pretutoring intrinsic functional connectivity of the hippocampus with dorsolateral and ventrolateral prefrontal cortices and the basal ganglia also predicted performance improvements. Our findings provide evidence that individual differences in morphometry and connectivity of brain regions associated with learning and memory, and not regions typically involved in arithmetic processing, are strong predictors of responsiveness to math tutoring in children. More generally, our study suggests that quantitative measures of brain structure and intrinsic brain organization can provide a more sensitive marker of skill acquisition than behavioral measures.

Sex differences in the prediction of the effectiveness of paroxetine for patients with major depressive disorder identified using a receiver operating characteristic curve analysis for early response.

PubMed

Tomita, Tetsu; Yasui-Furukori, Norio; Norio, Yasui-Furukori; Sato, Yasushi; Nakagami, Taku; Tsuchimine, Shoko; Kaneda, Ayako; Kaneko, Sunao

2014-01-01

We investigated cutoff values for the early response of patients with major depressive disorder to paroxetine and their sex differences by using a receiver operating characteristic (ROC) curve analysis to predict the effectiveness of paroxetine. In total, 120 patients with major depressive disorder were enrolled and treated with 10-40 mg/day paroxetine for 6 weeks; 89 patients completed the protocol. A clinical evaluation using the Montgomery-Asberg Depression Rating Scale (MADRS) was performed at weeks 0, 1, 2, 4, and 6. In male subjects, the cutoff values for MADRS improvement rating in week 1, week 2, and week 4 were 20.9%, 34.9%, and 33.3%, respectively. The sensitivities and the specificities were 83.3% and 80.0%, 83.3% and 80.0%, and 100% and 90%, respectively. The areas under the curve (AUC) were 0.908, 0.821, and 0.979, respectively. In female subjects, the cutoff values for the MADRS improvement rating in week 1, week 2, and week 4 were 21.4%, 35.7%, and 32.3%, respectively. The sensitivities and the specificities were 71.4% and 84.6%, 73.8% and 76.9%, and 90.5% and 76.9%, respectively. The AUCs were 0.781, 0.735, and 0.904, respectively. Early improvement with paroxetine may predict the long-term response. The accuracy of the prediction for the response is higher in male subjects.

Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments

Treesearch

S. Vicca; M. Bahn; M. Estiarte; E. E. van Loon; R. Vargas; G. Alberti; P. Ambus; M. A. Arain; C. Beier; L. P. Bentley; W. Borken; N. Buchmann; S. L. Collins; G. de Dato; J. S. Dukes; C. Escolar; P. Fay; G. Guidolotti; P. J. Hanson; A. Kahmen; G. Kröel-Dulay; T. Ladreiter-Knauss; K. S. Larsen; E. Lellei-Kovacs; E. Lebrija-Trejos; F. T. Maestre; S. Marhan; M. Marshall; P. Meir; Y. Miao; J. Muhr; P. A. Niklaus; R. Ogaya; J. Peñuelas; C. Poll; L. E. Rustad; K. Savage; A. Schindlbacher; I. K. Schmidt; A. R. Smith; E. D. Sotta; V. Suseela; A. Tietema; N. van Gestel; O. van Straaten; S. Wan; U. Weber; I. A. Janssens

2014-01-01

As a key component of the carbon cycle, soil CO2 efflux (SCE) is being increasingly studied to improve our mechanistic understanding of this important carbon flux. Predicting ecosystem responses to climate change often depends an extrapolation of current relationships between ecosystem processes and their climatic drivers to conditions not yet experienced by the...

The costs and benefits of temporal predictability: impaired inhibition of prepotent responses accompanies increased activation of task-relevant responses.

PubMed

Korolczuk, Inga; Burle, Boris; Coull, Jennifer T

2018-06-20

While the benefit of temporal predictability on sensorimotor processing is well established, it is still unknown whether this is due to efficient execution of an appropriate response and/or inhibition of an inappropriate one. To answer this question, we examined the effects of temporal predictability in tasks that required selective (Simon task) or global (Stop-signal task) inhibitory control of prepotent responses. We manipulated temporal expectation by presenting cues that either predicted (temporal cues) or not (neutral cues) when the target would appear. In the Simon task, performance was better when target location (left/right) was compatible with the hand of response and performance was improved further still if targets were temporally cued. However, Conditional Accuracy Functions revealed that temporal predictability selectively increased the number of fast, impulsive errors. Temporal cueing had no effect on selective response inhibition, as measured by the dynamics of the interference effect (delta plots) in the Simon task. By contrast, in the Stop-signal task, Stop-signal reaction time, a covert measure of a more global form of response inhibition, was significantly longer in temporally predictive trials. Therefore, when the time of target onset could be predicted in advance, it was harder to stop the impulse to respond to the target. Collectively, our results indicate that temporal cueing compounded the interfering effects of a prepotent response on task performance. We suggest that although temporal predictability enhances activation of task-relevant responses, it impairs inhibition of prepotent responses. Copyright © 2018 Elsevier B.V. All rights reserved.

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

The VACS index accurately predicts mortality and treatment response among multi-drug resistant HIV infected patients participating in the options in management with antiretrovirals (OPTIMA) study.

PubMed

Brown, Sheldon T; Tate, Janet P; Kyriakides, Tassos C; Kirkwood, Katherine A; Holodniy, Mark; Goulet, Joseph L; Angus, Brian J; Cameron, D William; Justice, Amy C

2014-01-01

The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel's C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14-0.49) and 0.39(95% CI 0.22-0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27-3.38) and 1.51 (95%CI 0.90-2.53) for the 25% least improved scores. The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research.

Predictors of response to intra-articular steroid injections in patients with osteoarthritis of the knee joint.

PubMed

Fatimah, Nibah; Salim, Babur; Raja, Ejaz-Ul-Haq; Nasim, Amjad

2016-10-01

This study aimed to determine the factors associated with response to intra-articular steroid injection (IASI) in patients with knee joint osteoarthritis. One hundred seventy-four female patients, age ranging from 30 to 80 years, diagnosed to have osteoarthritis of the knee joint, were given IASI. Response to IASI was assessed by using WOMAC and VAS at 2 weeks, 4 weeks and 3 months. At 3 months, the subjects were categorized as responders, partial responders and non-responders to treatment by IASI. Various factors were narrowed down to see their effect on response, namely age, BMI, smoking habits, comorbidities, presence of clinical effusion, radiographic score, local knee tenderness, range of movement and socioeconomic status. One hundred twenty-four patients completed the study. 16.1 % showed 50 % or more improvement in WOMAC score at 3 months post IASI therapy, whereas 38.7 % of OA patients had more than 50 % improvement in VAS score. Out of all factors, range of movement, local knee tenderness and radiographic score of the affected joint are the three parameters which can predict the improvement in WOMAC score after 3 months of IASI therapy (P = 0.013, P = 0.045 and P = 0.000, respectively). Age of the patient can predict improvement in VAS at 3 months post IASI (P = 0.027). We conclude that age, range of movement, local knee tenderness and radiographic score of the affected joint can predict response to IASI after 3 months of IASI therapy.

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

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

Sun, W; Jiang, M; Yin, F

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, amore » Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results demonstrate that the ASMLP respiratory prediction method is more accurate than MLP method and can improve the respiration forecast accuracy.« less

Systematic review of FDG-PET prediction of complete pathological response and survival in rectal cancer.

PubMed

Memon, Sameer; Lynch, A Craig; Akhurst, Timothy; Ngan, Samuel Y; Warrier, Satish K; Michael, Michael; Heriot, Alexander G

2014-10-01

Advances in the management of rectal cancer have resulted in an increased application of multimodal therapy with the aim of tailoring therapy to individual patients. Complete pathological response (pCR) is associated with improved survival and may be potentially managed without radical surgical resection. Over the last decade, there has been increasing interest in the ability of functional imaging to predict complete response to treatment. The aim of this review was to assess the role of (18)F-flurordeoxyglucose positron emission tomography (FDG-PET) in prediction of pCR and prognosis in resectable locally advanced rectal cancer. A search of the MEDLINE and Embase databases was conducted, and a systematic review of the literature investigating positron emission tomography (PET) in the prediction of pCR and survival in rectal cancer was performed. Seventeen series assessing PET prediction of pCR were included in the review. Seven series assessed postchemoradiation SUVmax, which was significantly different between response groups in all six studies that assessed this. Nine series assessed the response index (RI) for SUVmax, which was significantly different between response groups in seven series. Thirteen studies investigated PET response for prediction of survival. Metabolic complete response assessed by SUV2max or visual response and RISUVmax showed strong associations with disease-free survival (DFS) and overall survival (OS). SUV2max and RISUVmax appear to be useful FDG-PET markers for prediction of pCR and these parameters also show strong associations with DFS and OS. FDG-PET may have a role in outcome prediction in patients with advanced rectal cancer.

Probabilistic Polling And Voting In The 2008 Presidential Election: Evidence From The American Life Panel.

PubMed

Delavande, Adeline; Manski, Charles F

2010-01-01

This article reports new empirical evidence on probabilistic polling , which asks persons to state in percent-chance terms the likelihood that they will vote and for whom. Before the 2008 presidential election, seven waves of probabilistic questions were administered biweekly to participants in the American Life Panel (ALP). Actual voting behavior was reported after the election. We find that responses to the verbal and probabilistic questions are well-aligned ordinally. Moreover, the probabilistic responses predict voting behavior beyond what is possible using verbal responses alone. The probabilistic responses have more predictive power in early August, and the verbal responses have more power in late October. However, throughout the sample period, one can predict voting behavior better using both types of responses than either one alone. Studying the longitudinal pattern of responses, we segment respondents into those who are consistently pro-Obama , consistently anti-Obama , and undecided/vacillators . Membership in the consistently pro- or anti-Obama group is an almost perfect predictor of actual voting behavior, while the undecided/vacillators group has more nuanced voting behavior. We find that treating the ALP as a panel improves predictive power: current and previous polling responses together provide more predictive power than do current responses alone.

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.

Effect of wake structure on blade-vortex interaction phenomena: Acoustic prediction and validation

NASA Technical Reports Server (NTRS)

Gallman, Judith M.; Tung, Chee; Schultz, Klaus J.; Splettstoesser, Wolf; Buchholz, Heino

1995-01-01

During the Higher Harmonic Control Aeroacoustic Rotor Test, extensive measurements of the rotor aerodynamics, the far-field acoustics, the wake geometry, and the blade motion for powered, descent, flight conditions were made. These measurements have been used to validate and improve the prediction of blade-vortex interaction (BVI) noise. The improvements made to the BVI modeling after the evaluation of the test data are discussed. The effects of these improvements on the acoustic-pressure predictions are shown. These improvements include restructuring the wake, modifying the core size, incorporating the measured blade motion into the calculations, and attempting to improve the dynamic blade response. A comparison of four different implementations of the Ffowcs Williams and Hawkings equation is presented. A common set of aerodynamic input has been used for this comparison.

A clinician's perspective on memory reconsolidation as the primary basis for psychotherapeutic change in posttraumatic stress disorder (PTSD).

PubMed

Kimbrel, Nathan A; Meyer, Eric C; Beckham, Jean C

2015-01-01

Lane et al.'s proposal that psychotherapeutic change comes about through memory reconsolidation is compelling; however, the model would be strengthened by the inclusion of predictions regarding additional factors that might influence treatment response, predictions for improving outcomes for non-responsive patients, and a discussion of how the proposed model might explain individual differences in vulnerability for mental health problems.

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.

Harnessing quantitative genetics and genomics for understanding and improving complex traits in crops

USDA-ARS?s Scientific Manuscript database

Classical quantitative genetics aids crop improvement by providing the means to estimate heritability, genetic correlations, and predicted responses to various selection schemes. Genomics has the potential to aid quantitative genetics and applied crop improvement programs via large-scale, high-thro...

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

Numerical convergence and validation of the DIMP inverse particle transport model

DOE PAGES

Nelson, Noel; Azmy, Yousry

2017-09-01

The data integration with modeled predictions (DIMP) model is a promising inverse radiation transport method for solving the special nuclear material (SNM) holdup problem. Unlike previous methods, DIMP is a completely passive nondestructive assay technique that requires no initial assumptions regarding the source distribution or active measurement time. DIMP predicts the most probable source location and distribution through Bayesian inference and quasi-Newtonian optimization of predicted detector re-sponses (using the adjoint transport solution) with measured responses. DIMP performs well with for-ward hemispherical collimation and unshielded measurements, but several considerations are required when using narrow-view collimated detectors. DIMP converged well to themore » correct source distribution as the number of synthetic responses increased. DIMP also performed well for the first experimental validation exercise after applying a collimation factor, and sufficiently reducing the source search vol-ume's extent to prevent the optimizer from getting stuck in local minima. DIMP's simple point detector response function (DRF) is being improved to address coplanar false positive/negative responses, and an angular DRF is being considered for integration with the next version of DIMP to account for highly collimated responses. Overall, DIMP shows promise for solving the SNM holdup inverse problem, especially once an improved optimization algorithm is implemented.« less

Spouses’ Attachment Pairings Predict Neuroendocrine, Behavioral, and Psychological Responses to Marital Conflict

PubMed Central

Beck, Lindsey A.; Pietromonaco, Paula R.; DeBuse, Casey J.; Powers, Sally I.; Sayer, Aline G.

2014-01-01

This research investigated how spouses’ attachment styles jointly contributed to their stress responses. Newlywed couples discussed relationship conflicts. Salivary cortisol indexed physiological stress; observer-rated behaviors indexed behavioral stress; self-reported distress indexed psychological stress. Multilevel modeling tested predictions that couples including one anxious and one avoidant partner or two anxious partners would show distinctive stress responses. As predicted, couples with anxious wives and avoidant husbands showed physiological reactivity in anticipation of conflict: Both spouses showed sharp increases in cortisol, followed by rapid declines. These couples also showed distinctive behaviors during conflict: Anxious wives had difficulty recognizing avoidant husbands’ distress, and avoidant husbands had difficulty approaching anxious wives for support. Contrary to predictions, couples including two anxious partners did not show distinctive stress responses. Findings suggest that the fit between partners’ attachment styles can improve understanding of relationships by specifying conditions under which partners’ attachment characteristics jointly influence individual and relationship outcomes. PMID:23773048

A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

PubMed Central

Carvalho, Benilton S.; Bilevicius, Elizabeth; Alvim, Marina K. M.; Lopes-Cendes, Iscia

2017-01-01

Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner. PMID:28052106

Validation and Use of a Predictive Modeling Tool: Employing Scientific Findings to Improve Responsible Conduct of Research Education.

PubMed

Mulhearn, Tyler J; Watts, Logan L; Todd, E Michelle; Medeiros, Kelsey E; Connelly, Shane; Mumford, Michael D

2017-01-01

Although recent evidence suggests ethics education can be effective, the nature of specific training programs, and their effectiveness, varies considerably. Building on a recent path modeling effort, the present study developed and validated a predictive modeling tool for responsible conduct of research education. The predictive modeling tool allows users to enter ratings in relation to a given ethics training program and receive instantaneous evaluative information for course refinement. Validation work suggests the tool's predicted outcomes correlate strongly (r = 0.46) with objective course outcomes. Implications for training program development and refinement are discussed.

A Combined Pharmacokinetic and Radiologic Assessment of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Predicts Response to Chemoradiation in Locally Advanced Cervical Cancer

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

Semple, Scott; Harry, Vanessa N. MRCOG.; Parkin, David E.

2009-10-01

Purpose: To investigate the combination of pharmacokinetic and radiologic assessment of dynamic contrast-enhanced magnetic resonance imaging (MRI) as an early response indicator in women receiving chemoradiation for advanced cervical cancer. Methods and Materials: Twenty women with locally advanced cervical cancer were included in a prospective cohort study. Dynamic contrast-enhanced MRI was carried out before chemoradiation, after 2 weeks of therapy, and at the conclusion of therapy using a 1.5-T MRI scanner. Radiologic assessment of uptake parameters was obtained from resultant intensity curves. Pharmacokinetic analysis using a multicompartment model was also performed. General linear modeling was used to combine radiologic andmore » pharmacokinetic parameters and correlated with eventual response as determined by change in MRI tumor size and conventional clinical response. A subgroup of 11 women underwent repeat pretherapy MRI to test pharmacokinetic reproducibility. Results: Pretherapy radiologic parameters and pharmacokinetic K{sup trans} correlated with response (p < 0.01). General linear modeling demonstrated that a combination of radiologic and pharmacokinetic assessments before therapy was able to predict more than 88% of variance of response. Reproducibility of pharmacokinetic modeling was confirmed. Conclusions: A combination of radiologic assessment with pharmacokinetic modeling applied to dynamic MRI before the start of chemoradiation improves the predictive power of either by more than 20%. The potential improvements in therapy response prediction using this type of combined analysis of dynamic contrast-enhanced MRI may aid in the development of more individualized, effective therapy regimens for this patient group.« less

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%).

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Light transport feature for SCINFUL.

PubMed

Etaati, G R; Ghal-Eh, N

2008-03-01

An extended version of the scintillator response function prediction code SCINFUL has been developed by incorporating PHOTRACK, a Monte Carlo light transport code. Comparisons of calculated and experimental results for organic scintillators exposed to neutrons show that the extended code improves the predictive capability of SCINFUL.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2014-11-01

Accurately predicting the response of Amazonia to climate change is important for predicting changes across the globe. However, changes in multiple climatic factors simultaneously may result in complex non-linear responses, which are difficult to predict using vegetation models. Using leaf and canopy scale observations, this study evaluated the capability of five vegetation models (CLM3.5, ED2, JULES, SiB3, and SPA) to simulate the responses of canopy and leaf scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation. There was greater model-data consistency in the response of net ecosystem exchange to changes in temperature, than in the response to temperature of leaf area index (LAI), net photosynthesis (An) and stomatal conductance (gs). Modelled canopy scale fluxes are calculated by scaling leaf scale fluxes to LAI, and therefore in this study similarities in modelled ecosystem scale responses to drought and temperature were the result of inconsistent leaf scale and LAI responses among models. Across the models, the response of An to temperature was more closely linked to stomatal behaviour than biochemical processes. Consequently all the models predicted that GPP would be higher if tropical forests were 5 °C colder, closer to the model optima for gs. There was however no model consistency in the response of the An-gs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the An-gs relationships amongst models were caused by to non-linear model responses induced by simultaneous drought and temperature change. To improve the reliability of simulations of the response of Amazonian rainforest to climate change the mechanistic underpinnings of vegetation models need more complete validation to improve accuracy and consistency in the scaling of processes from leaf to canopy.

Negative affective spillover from daily events predicts early response to cognitive therapy for depression.

PubMed

Cohen, Lawrence H; Gunthert, Kathleen C; Butler, Andrew C; Parrish, Brendt P; Wenze, Susan J; Beck, Judith S

2008-12-01

This study evaluated the predictive role of depressed outpatients' (N = 62) affective reactivity to daily stressors in their rates of improvement in cognitive therapy (CT). For 1 week before treatment, patients completed nightly electronic diaries that assessed daily stressors and negative affect (NA). The authors used multilevel modeling to compute each patient's within-day relationship between daily stressors and daily NA (within-day reactivity), as well as the relationship between daily stressors and next-day NA (next-day reactivity; affective spillover). In growth model analyses, the authors evaluated the predictive role of patients' NA reactivity in their early (Sessions 1-4) and late (Sessions 5-12) response to CT. Within-day NA reactivity did not predict early or late response to CT. However, next-day reactivity predicted early response to CT, such that patients who had greater NA spillover in response to negative events had a slower rate of symptom change during the first 4 sessions. Affective spillover did not influence later response to CT. The findings suggest that depressed patients who have difficulty bouncing back the next day from their NA reactions to a relative increase in daily negative events will respond less quickly to the early sessions of CT.

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.

Hyperspectral Remote Sensing of New England Coastal Waters to Predict Seagrass Distribution

EPA Science Inventory

The U.S. Environmental Protection Agency is working to improve its ability to quantify and predict aquatic (freshwater, estuarine, marine) ecosystem response and recovery to changing nutrient loads. The objective of this research is to quantify the relationship of nutrients with...

Finite element analysis of large transient elastic-plastic deformations of simple structures, with application to the engine rotor fragment containment/deflection problem

NASA Technical Reports Server (NTRS)

Wu, R. W.; Witmer, E. A.

1972-01-01

Assumed-displacement versions of the finite-element method are developed to predict large-deformation elastic-plastic transient deformations of structures. Both the conventional and a new improved finite-element variational formulation are derived. These formulations are then developed in detail for straight-beam and curved-beam elements undergoing (1) Bernoulli-Euler-Kirchhoff or (2) Timoshenko deformation behavior, in one plane. For each of these categories, several types of assumed-displacement finite elements are developed, and transient response predictions are compared with available exact solutions for small-deflection, linear-elastic transient responses. The present finite-element predictions for large-deflection elastic-plastic transient responses are evaluated via several beam and ring examples for which experimental measurements of transient strains and large transient deformations and independent finite-difference predictions are available.

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

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements.

PubMed

Lan, Hui; Carson, Rachel; Provart, Nicholas J; Bonner, Anthony J

2007-09-21

Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress. Using in house and publicly available data, we assembled a large set of gene expression measurements for A. thaliana. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC50 and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl. Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions - in this case, predictions of genes involved in stress response in plants - and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in A. thaliana that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.

The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer.

PubMed

van Rossum, Peter S N; Fried, David V; Zhang, Lifei; Hofstetter, Wayne L; van Vulpen, Marco; Meijer, Gert J; Court, Laurence E; Lin, Steven H

2016-05-01

A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (18)F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors. This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation (18)F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of (18)F-FDG PET, conventional quantitative metabolic features, and comprehensive (18)F-FDG PET texture/geometry features, respectively. The clinical benefit of (18)F-FDG PET was determined using decision-curve analysis. A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding (18)F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive (18)F-FDG PET texture/geometry features (corrected c-index, 0.77). However, at a decision threshold of 0.9 or higher, representing a clinically relevant predictive value for pathCR at which one may be willing to omit surgery, there was no clear incremental value. Subjective and quantitative assessment of (18)F-FDG PET provides statistical incremental value for predicting pathCR after preoperative chemoradiotherapy in esophageal cancer. However, the discriminatory improvement beyond clinical predictors does not translate into a clinically relevant benefit that could change decision making. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

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

Extreme-Scale Computing Project Aims to Advance Precision Oncology | Poster

Cancer.gov

Two government agencies and five national laboratories are collaborating to develop extremely high-performance computing capabilities that will analyze mountains of research and clinical data to improve scientific understanding of cancer, predict drug response, and improve treatments for patients.

The prediction of swimming performance in competition from behavioral information.

PubMed

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.

Exercise testing after beta-blockade: improved specificity and predictive value in detecting coronary heart disease.

PubMed Central

Marcomichelakis, J; Donaldson, R; Green, J; Joseph, S; Kelly, H B; Taggart, P; Somerville, W

1980-01-01

The value of exercise testing in detecting myocardial ischaemia resulting from coronary atheroma remains controversial. In order to increase the reliability of exercise testing, all its components (asymptomatic, haemodynamic, and electrocardiographic) have been scrutinised. In this study, concerned only with the electrocardiographic response to exercise, the incorporation of beta-blockade into the standard exercise procedure has improved specificity and predictive value without affecting sensitivity. Fifty patients with anginal pain and 50 asymptomatic subjects with an abnormal electrocardiogram were investigated by exercise testing before and after beta-blockade (oxprenolol). All subjects had coronary arteriograms and left ventriculograms, and the results of exercise testing were related to the presence or absence of obstructive coronary artery disease. Possible causes of false positive exercise tests were eliminated by echocardiography. Though beta-blockade was unreliable in distinguishing ischaemic from non-ischaemic resting electrocardiograms, it eliminated all the false positive electrocardiographic responses to exercise in both groups and did not abolish any of the true positive electrocardiographic responses. Thus, specificity and predictive value were improved without reduction in sensitivity. This technique may not necessarily be applicable to other groups of patients or to a random population, but the results of this study suggest it will be a useful additional routine procedure in the investigation of coronary heart disease. PMID:7437172

Pulse pressure variation and prediction of fluid responsiveness in patients ventilated with low tidal volumes.

PubMed

Oliveira-Costa, Clarice Daniele Alves de; Friedman, Gilberto; Vieira, Sílvia Regina Rios; Fialkow, Léa

2012-07-01

To determine the utility of pulse pressure variation (ΔRESP PP) in predicting fluid responsiveness in patients ventilated with low tidal volumes (V T) and to investigate whether a lower ΔRESP PP cut-off value should be used when patients are ventilated with low tidal volumes. This cross-sectional observational study included 37 critically ill patients with acute circulatory failure who required fluid challenge. The patients were sedated and mechanically ventilated with a V T of 6-7 ml/kg ideal body weight, which was monitored with a pulmonary artery catheter and an arterial line. The mechanical ventilation and hemodynamic parameters, including ΔRESP PP, were measured before and after fluid challenge with 1,000 ml crystalloids or 500 ml colloids. Fluid responsiveness was defined as an increase in the cardiac index of at least 15%. ClinicalTrial.gov: NCT01569308. A total of 17 patients were classified as responders. Analysis of the area under the ROC curve (AUC) showed that the optimal cut-off point for ΔRESP PP to predict fluid responsiveness was 10% (AUC = 0.74). Adjustment of the ΔRESP PP to account for driving pressure did not improve the accuracy (AUC = 0.76). A ΔRESP PP ≥ 10% was a better predictor of fluid responsiveness than central venous pressure (AUC = 0.57) or pulmonary wedge pressure (AUC = 051). Of the 37 patients, 25 were in septic shock. The AUC for ΔRESP PP ≥ 10% to predict responsiveness in patients with septic shock was 0.484 (sensitivity, 78%; specificity, 93%). The parameter D RESP PP has limited value in predicting fluid responsiveness in patients who are ventilated with low tidal volumes, but a ΔRESP PP>10% is a significant improvement over static parameters. A ΔRESP PP ≥ 10% may be particularly useful for identifying responders in patients with septic shock.

Clinical utility of C-reactive protein to predict treatment response during cystic fibrosis pulmonary exacerbations.

PubMed

Sharma, Ashutosh; Kirkpatrick, Gordon; Chen, Virginia; Skolnik, Kate; Hollander, Zsuzsanna; Wilcox, Pearce; Quon, Bradley S

2017-01-01

C-reactive protein (CRP) is a systemic marker of inflammation that correlates with disease status in cystic fibrosis (CF). The clinical utility of CRP measurement to guide pulmonary exacerbation (PEx) treatment decisions remains uncertain. To determine whether monitoring CRP during PEx treatment can be used to predict treatment response. We hypothesized that early changes in CRP can be used to predict treatment response. We reviewed all PEx events requiring hospitalization for intravenous (IV) antibiotics over 2 years at our institution. 83 PEx events met our eligibility criteria. CRP levels from admission to day 5 were evaluated to predict treatment non-response, using a modified version of a prior published composite definition. CRP was also evaluated to predict time until next exacerbation (TUNE). 53% of 83 PEx events were classified as treatment non-response. Paradoxically, 24% of PEx events were characterized by a ≥ 50% increase in CRP levels within the first five days of treatment. Absolute change in CRP from admission to day 5 was not associated with treatment non-response (p = 0.58). Adjusted for FEV1% predicted, admission log10 CRP was associated with treatment non-response (OR: 2.39; 95% CI: 1.14 to 5.91; p = 0.03) and shorter TUNE (HR: 1.60; 95% CI: 1.13 to 2.27; p = 0.008). The area under the receiver operating characteristics (ROC) curve of admission CRP to predict treatment non-response was 0.72 (95% CI 0.61-0.83; p<0.001). 23% of PEx events were characterized by an admission CRP of > 75 mg/L with a specificity of 90% for treatment non-response. Admission CRP predicts treatment non-response and time until next exacerbation. A very elevated admission CRP (>75mg/L) is highly specific for treatment non-response and might be used to target high-risk patients for future interventional studies aimed at improving exacerbation outcomes.

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.

Effects of Age and Disease Severity on Systemic Corticosteroid Responses in Asthma.

PubMed

Phipatanakul, Wanda; Mauger, David T; Sorkness, Ronald L; Gaffin, Jonathan M; Holguin, Fernando; Woodruff, Prescott G; Ly, Ngoc P; Bacharier, Leonard B; Bhakta, Nirav R; Moore, Wendy C; Bleecker, Eugene R; Hastie, Annette T; Meyers, Deborah A; Castro, Mario; Fahy, John V; Fitzpatrick, Anne M; Gaston, Benjamin M; Jarjour, Nizar N; Levy, Bruce D; Peters, Stephen P; Teague, W Gerald; Fajt, Merritt; Wenzel, Sally E; Erzurum, Serpil C; Israel, Elliot

2017-06-01

Phenotypic distinctions between severe asthma (SA) and nonsevere asthma (NONSA) may be confounded by differential adherence or incorrect use of corticosteroids. To determine if there are persistent phenotypic distinctions between SA (as defined by 2014 American Thoracic Society/European Respiratory Society guidelines) and NONSA after intramuscular triamcinolone acetonide (TA), and to identify predictors of a corticosteroid response in these populations. A total of 526 adults age 18 years and older (315 SA) and 188 children age 6 to less than 18 years (107 SA) in the NHLBI Severe Asthma Research Program III were characterized before and 3 weeks after TA. The primary outcome for corticosteroid response was defined as greater than or equal to 10-point improvement in percent predicted FEV 1 . Adult asthma groups exhibited a small but significant mean FEV 1 % predicted improvement after TA (SA group mean difference, 3.4%; 95% confidence interval, 2.2-4.7%; P = 0.001), whereas children did not. Adult SA continued to manifest lower FEV 1 and worse asthma control as compared with NONSA after TA. In children, after TA only prebronchodilator FEV 1 distinguished SA from NONSA. A total of 21% of adults with SA and 20% of children with SA achieved greater than or equal to 10% improvement after TA. Baseline bronchodilator response and fractional exhaled nitric oxide had good sensitivity and specificity for predicting response in all groups except children with NONSA. One in five patients with SA exhibit greater than or equal to 10% improvement in FEV 1 with parenteral corticosteroid. Those likely to respond had greater bronchodilator responsiveness and fractional exhaled nitric oxide levels. In adults, differences in airflow obstruction and symptoms between SA and NONSA persist after parenteral corticosteroids, suggesting a component of corticosteroid nonresponsive pathobiology in adults with SA that may differ in children. Clinical trial registered with www.clinicaltrials.gov (NCT 01606826).

Clinical utility of therapeutic drug monitoring in biological disease modifying anti-rheumatic drug treatment of rheumatic disorders: a systematic narrative review.

PubMed

Van Herwaarden, Noortje; Van Den Bemt, Bart J F; Wientjes, Maike H M; Kramers, Cornelis; Den Broeder, Alfons A

2017-08-01

Biological Disease Modifying Anti-Rheumatic Drugs (bDMARDs) have improved the treatment outcomes of inflammatory rheumatic diseases including Rheumatoid Arthritis and spondyloarthropathies. Inter-individual variation exists in (maintenance of) response to bDMARDs. Therapeutic Drug Monitoring (TDM) of bDMARDs could potentially help in optimizing treatment for the individual patient. Areas covered: Evidence of clinical utility of TDM in bDMARD treatment is reviewed. Different clinical scenarios will be discussed, including: prediction of response after start of treatment, prediction of response to a next bDMARD in case of treatment failure of the first, prediction of successful dose reduction or discontinuation in case of low disease activity, prediction of response to dose-escalation in case of active disease and prediction of response to bDMARD in case of flare in disease activity. Expert opinion: The limited available evidence does often not report important outcomes for diagnostic studies, such as sensitivity and specificity. In most clinical relevant scenarios, predictive value of serum (anti-) drug levels is absent, therefore the use of TDM of bDMARDs cannot be advocated. Well-designed prospective studies should be done to further investigate the promising scenarios to determine the place of TDM in clinical practice.

Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes.

PubMed

Li, Jiang; Yoshikawa, Akane; Brennan, Mark D; Ramsey, Timothy L; Meltzer, Herbert Y

2018-02-01

Biomarkers which predict response to atypical antipsychotic drugs (AAPDs) increases their benefit/risk ratio. We sought to identify common variants in genes which predict response to lurasidone, an AAPD, by associating genome-wide association study (GWAS) data and changes (Δ) in Positive And Negative Syndrome Scale (PANSS) scores from two 6-week randomized, placebo-controlled trials of lurasidone in schizophrenia (SCZ) patients. We also included SCZ risk SNPs identified by the Psychiatric Genomics Consortium using a polygenic risk analysis. The top genomic loci, with uncorrected p<10 -4 , include: 1) synaptic adhesion (PTPRD, LRRC4C, NRXN1, ILIRAPL1, SLITRK1) and scaffolding (MAGI1, MAGI2, NBEA) genes, both essential for synaptic function; 2) other synaptic plasticity-related genes (NRG1/3 and KALRN); 3) the neuron-specific RNA splicing regulator, RBFOX1; and 4) ion channel genes, e.g. KCNA10, KCNAB1, KCNK9 and CACNA2D3). Some genes predicted response for patients with both European and African Ancestries. We replicated some SNPs reported to predict response to other atypical APDs in other GWAS. Although none of the biomarkers reached genome-wide significance, many of the genes and associated pathways have previously been linked to SCZ. Two polygenic modeling approaches, GCTA-GREML and PLINK-Polygenic Risk Score, demonstrated that some risk genes related to neurodevelopment, synaptic biology, immune response, and histones, also contributed to prediction of response. The top hits predicting response to lurasidone did not predict improvement with placebo. This is the first evidence from clinical trials that SCZ risk SNPs are related to clinical response to an AAPD. These results need to be replicated in an independent sample. Copyright © 2017. Published by Elsevier B.V.

Baseline MRA predicts the treatment response to vasodilator udenafil in patients with secondary Raynaud's phenomenon.

PubMed

Park, J K; Park, E-A; Lee, W; Kim, Y K; Lee, E Y; Song, Y W; Lee, E B

2014-01-01

High-resolution MR angiography (HR-MRA) demonstrates blood flow in the digital arteries, which correlates with the severity of Raynaud's phenomenon (RP). This study investigates whether baseline HR-MRA of the hand can predict the treatment response to udenafil, a new PDE5-inhibitor, in patients with secondary RP. Baseline MRA and Doppler ultrasound were obtained in 12 patients with secondary RP. The patients were treated with udenafil 100 mg/day for 4 weeks and changes in blood flow were measured. Blood flow on MRA was scored on a 4-point scale: 0, no visible flow; 1, visible flow to the proximal phalanx; 2, to the middle phalanx; and 3, to the distal phalanx. Peak systolic velocity (PSV) was measured to determine blood flow. Paired t-test and ANOVA were used to determine the treatment response of the different MRA scores. On baseline MRA, 53.3% of digital arteries had an MRA score of 0, 25.8% MRA score of 1, 9.2% MRA score of 2, and 11.6% MRA score of 3. Overall, 4-week udenafil treatment improved digital flow (p<0.05) in all MRA scores. Digital arteries with MRA score 2 showed the best response with improvement in PSV by 14.5 mm/sec (p<0.01), whereas improvement in arteries of MRA scores 1 and 3 were not better than an MRA score of 0 (all, p>0.05). Digital arteries with moderate blood flow observed on MRA respond best to treatment with udenalfil. Therefore, baseline MRA may help predict treatment response in patients with secondary RP.

Parent Prediction of Child Mood and Emotional Resilience: The Role of Parental Responsiveness and Psychological Control

PubMed Central

Boughton, Kristy L.; Lumley, Margaret N.

2011-01-01

Research consistently shows low to moderate agreement between parent and child reports of child mood, suggesting that parents are not always the best predictors of child emotional functioning. This study examines parental responsiveness and psychological control for improving prediction of early adolescent mood and emotional resilience beyond parent report of child emotional functioning. Participants were 268 early adolescents administered measures of depression symptoms, emotional resilience, and perceptions of parenting. Parents of participating youth completed measures of youth emotional functioning. Parental responsiveness and psychological control each emerged as family variables that may be of value for predicting child emotional functioning beyond parent reports. Specifically, responsiveness explained significant variance in child depression and resilience after accounting for parent reports, while parental psychological control increased prediction of child mood alone. Results generally suggest that parenting behaviours may be an important consideration when children and parents provide discrepant reports of child emotional well-being. Conceptual and clinical implications of these results are discussed. PMID:22110912

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.

Molecular dynamics simulation of potentiometric sensor response: the effect of biomolecules, surface morphology and surface charge.

PubMed

Lowe, B M; Skylaris, C-K; Green, N G; Shibuta, Y; Sakata, T

2018-05-10

The silica-water interface is critical to many modern technologies in chemical engineering and biosensing. One technology used commonly in biosensors, the potentiometric sensor, operates by measuring the changes in electric potential due to changes in the interfacial electric field. Predictive modelling of this response caused by surface binding of biomolecules remains highly challenging. In this work, through the most extensive molecular dynamics simulation of the silica-water interfacial potential and electric field to date, we report a novel prediction and explanation of the effects of nano-morphology on sensor response. Amorphous silica demonstrated a larger potentiometric response than an equivalent crystalline silica model due to increased sodium adsorption, in agreement with experiments showing improved sensor response with nano-texturing. We provide proof-of-concept that molecular dynamics can be used as a complementary tool for potentiometric biosensor response prediction. Effects that are conventionally neglected, such as surface morphology, water polarisation, biomolecule dynamics and finite-size effects, are explicitly modelled.

Dealing with feeling: Specific emotion regulation skills predict responses to stress in psychosis.

PubMed

Lincoln, Tania M; Hartmann, Maike; Köther, Ulf; Moritz, Steffen

2015-08-15

Elevated negative affect is an established link between minor stressors and psychotic symptoms. Less clear is why people with psychosis fail to regulate distressing emotions effectively. This study tests whether subjective, psychophysiological and symptomatic responses to stress can be predicted by specific emotion regulation (ER) difficulties. Participants with psychotic disorders (n=35) and healthy controls (n=28) were assessed for ER-skills at baseline. They were then exposed to a noise versus no stressor on different days, during which self-reported stress responses, state paranoia and skin conductance levels (SCL) were assessed. Participants with psychosis showed a stronger increase in self-reported stress and SCL in response to the stressor than healthy controls. Stronger increases in self-reported stress were predicted by a reduced ability to be aware of and tolerate distressing emotions, whereas increases in SCL were predicted by a reduced ability to be aware of, tolerate, accept and modify them. Although paranoid symptoms were not significantly affected by the stressors, individual variation in paranoid responses was also predicted by a reduced ability to be aware of and tolerate emotions. Differences in stress responses in the samples were no longer significant after controlling for ER skills. Thus, interventions that improve ER-skills could reduce stress-sensitivity in psychosis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Predictors of response to cognitive remediation in service recipients with severe mental illness.

PubMed

Lindenmayer, Jean-Pierre; Ozog, Veronica Anna; Khan, Anzalee; Ljuri, Isidora; Fregenti, Samantha; McGurk, Susan R

2017-03-01

Cognitive challenges are prominent features of individuals diagnosed with schizophrenia, impairing occupational, social, and economic functioning. These challenges are predictive of social and work outcomes. Cognitive remediation has been shown to be effective in improving both cognitive and social functions. However, cognitive remediation does not produce improvement in all participants. We investigated demographic, neurocognitive, and psychopathological predictors associated with improvement following cognitive remediation interventions in service recipients with severe mental illnesses. One hundred thirty-seven adult participants with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.) were enrolled in 12-week cognitive remediation programs. Assessments of demographic and illness variables, together with baseline and end point assessment of psychopathology (Positive and Negative Syndrome Scale [PANSS]), neurocognition (Measurement and Treatment Research to Improve Cognition in Schizophrenia [MATRICS] Consensus Cognitive Battery [MCBB]), and social functions (Personal and Social Performance Scale [PSP]) were conducted. Change in cognitive domains was calculated using the reliable change index. Logistic regression analysis was used to assess predictors of cognitive improvement after the intervention. Sixty-two percent of participants improved on at least 1 of the MCCB domains. Higher baseline speed of processing, attention or vigilance, and working memory predicted a positive response to cognitive remediation. Younger age, higher education level, shorter length of stay, and lower PANSS Negative and Disorganized factors were additional predictors. Our results indicate the clinical usefulness of cognitive remediation and identified a pattern of clinical and cognitive predictors of good response to the intervention. Identification of these predictive factors by clinicians may enhance the outcome and aid in the development of individualized rehabilitative cognitive remediation treatment plans. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Reduced Short Interval Cortical Inhibition Correlates with Atomoxetine Response in Children with ADHD

PubMed Central

Chen, Tina H.; Wu, Steve W.; Welge, Jeffrey A.; Dixon, Stephan; Shahana, Nasrin; Huddleston, David A.; Sarvis, Adam R.; Sallee, Floyd R.; Gilbert, Donald L.

2014-01-01

Clinical trials in children with Attention Deficit Hyperactivity Disorder (ADHD) show variability in behavioral responses to the selective norepinephrine reuptake inhibitor atomoxetine (ATX). The objective of this study was to determine whether Transcranial Magnetic Stimulation (TMS)-evoked Short Interval Cortical Inhibition (SICI) might be a biomarker predicting, or correlating with, clinical ATX response. At baseline and after 4 weeks of ATX treatment in 7–12 year old children with ADHD, TMS-SICI was measured, blinded to clinical improvement. Primary analysis was by multivariate ANCOVA. Baseline SICI did not predict clinical responses. However, paradoxically, after 4 weeks of ATX, mean SICI was reduced 31.9% in responders and increased 6.1% in non-responders (ANCOVA t41=2.88; p = .0063). Percent reductions in SICI correlated with reductions in ADHD-Rating Scale (ADHDRS) (r = .50; p = .0005). In children ages 7–12 years with ADHD treated with ATX, improvements in clinical symptoms are correlated with reductions in motor cortex SICI. PMID:24413361

Asthma treatment outcome in children is associated with vascular endothelial growth factor A (VEGFA) polymorphisms.

PubMed

Balantic, Mateja; Rijavec, Matija; Skerbinjek Kavalar, Maja; Suskovic, Stanislav; Silar, Mira; Kosnik, Mitja; Korosec, Peter

2012-06-01

Asthma is a common chronic disease characterized by airway inflammation and structural remodeling. Vascular endothelial growth factor (VEGF), a major regulator of angiogenesis, is elevated in asthma patients. VEGF contributes to airway responsiveness and remodeling. It has been shown that treatment of asthma patients decreases VEGF levels, and inhibition of VEGF diminishes asthma symptoms in mice. Therefore, polymorphisms in the vascular endothelial growth factor A (VEGFA) gene might be associated with asthma treatment response. This study enrolled 131 children with asthma treated with different therapies - specifically, the inhaled corticosteroid (ICS) fluticasone propionate or the leukotriene receptor antagonist (LTRA) montelukast. We performed an association analysis between improvement of lung function - assessed by measurement of the percentage of the predicted forced expiratory volume in 1 second (%predicted FEV(1)), the ratio between the FEV(1) and the forced vital capacity (FEV(1)/FVC) after 6 and 12 months of treatment, and asthma control after 12 months of treatment - and two polymorphisms, rs2146323 and rs833058, in the VEGFA gene. Polymorphism rs2146323 A>C in VEGFA was associated with response to ICS therapy. Asthma patients with the AA genotype had a greater improvement in the %predicted FEV(1) than those with the AC or CC genotype (p = 0.018). Conversely, the AA genotype in rs2146323 was associated with uncontrolled asthma in patients regularly receiving LTRA therapy (p = 0.020) and a worse FEV(1)/FVC ratio in patients who episodically used LTRA therapy (p = 0.044). Furthermore, polymorphism rs833058 C>T was associated with treatment response to episodically used LTRA therapy. A subgroup of patients with the TT genotype had an improvement in the %predicted FEV(1), compared with no improvement in patients with the CT or CC genotype (p = 0.029). Our results showed that treatment response to commonly used asthma therapies (ICS or LTRA) is associated with polymorphisms rs2146323 and rs833058 in VEGFA. With additional replication of this preliminary study, our findings could contribute to the development of individualized asthma therapy.

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.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

PubMed Central

Biehl, Michael; Sadowski, Peter; Bhanot, Gyan; Bilal, Erhan; Dayarian, Adel; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Zeller, Michael D.; Hormoz, Sahand

2015-01-01

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. Contact: meikelbiehl@gmail.com PMID:24994890

MiRNA Expression Analysis of Pretreatment Biopsies Predicts the Pathological Response of Esophageal Squamous Cell Carcinomas to Neoadjuvant Chemoradiotherapy.

PubMed

Wen, Jing; Luo, Kongjia; Liu, Hui; Liu, Shiliang; Lin, Guangrong; Hu, Yi; Zhang, Xu; Wang, Geng; Chen, Yuping; Chen, Zhijian; Li, Yi; Lin, Ting; Xie, Xiuying; Liu, Mengzhong; Wang, Huiyun; Yang, Hong; Fu, Jianhua

2016-05-01

To identify miRNA markers useful for esophageal squamous cell carcinoma (ESCC) neoadjuvant chemoradiotherapy (neo-CRT) response prediction. Neo-CRT followed by surgery improves ESCC patients' survival compared with surgery alone. However, CRT outcomes are heterogeneous, and no current methods can predict CRT responses. Differentially expressed miRNAs between ESCC pathological responders and nonresponders after neo-CRT were identified by miRNA profiling and verified by real-time quantitative polymerase chain reaction (qPCR) of 27 ESCCs in the training set. Several class prediction algorithms were used to build the response-classifying models with the qPCR data. Predictive powers of the models were further assessed with a second set of 79 ESCCs. Ten miRNAs with greater than a 1.5-fold change between pathological responders and nonresponders were identified and verified, respectively. A support vector machine (SVM) prediction model, composed of 4 miRNAs (miR-145-5p, miR-152, miR-193b-3p, and miR-376a-3p), were developed. It provided overall accuracies of 100% and 87.3% for discriminating pathological responders and nonresponders in the training and external validation sets, respectively. In multivariate analysis, the subgroup determined by the SVM model was the only independent factor significantly associated with neo-CRT response in the external validation sets. Combined qPCR of the 4 miRNAs provides the possibility of ESCC neo-CRT response prediction, which may facilitate individualized ESCC treatment. Further prospective validation in larger independent cohorts is necessary to fully assess its predictive power.

Effect of shaping sensor data on pilot response

NASA Technical Reports Server (NTRS)

Bailey, Roger M.

1990-01-01

The pilot of a modern jet aircraft is subjected to varying workloads while being responsible for multiple, ongoing tasks. The ability to associate the pilot's responses with the task/situation, by modifying the way information is presented relative to the task, could provide a means of reducing workload. To examine the feasibility of this concept, a real time simulation study was undertaken to determine whether preprocessing of sensor data would affect pilot response. Results indicated that preprocessing could be an effective way to tailor the pilot's response to displayed data. The effects of three transformations or shaping functions were evaluated with respect to the pilot's ability to predict and detect out-of-tolerance conditions while monitoring an electronic engine display. Two nonlinear transformations, on being the inverse of the other, were compared to a linear transformation. Results indicate that a nonlinear transformation that increases the rate-or-change of output relative to input tends to advance the prediction response and improve the detection response, while a nonlinear transformation that decreases the rate-of-change of output relative to input tends to lengthen the prediction response and make detection more difficult.

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

2017-03-01

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Variability of annoyance response due to aircraft noise

NASA Technical Reports Server (NTRS)

Dempsey, T. K.; Cawthorn, J. M.

1979-01-01

An investigation was conducted to study the variability in the response of subjects participating in noise experiments. This paper presents a description of a model developed to include this variability which incorporates an aircraft-noise adaptation level or an annoyance calibration for each individual. The results indicate that the use of an aircraft-noise adaption level improved prediction accuracy of annoyance responses (and simultaneously reduced response variation).

Speech entrainment compensates for Broca's area damage.

PubMed

Fridriksson, Julius; Basilakos, Alexandra; Hickok, Gregory; Bonilha, Leonardo; Rorden, Chris

2015-08-01

Speech entrainment (SE), the online mimicking of an audiovisual speech model, has been shown to increase speech fluency in patients with Broca's aphasia. However, not all individuals with aphasia benefit from SE. The purpose of this study was to identify patterns of cortical damage that predict a positive response SE's fluency-inducing effects. Forty-four chronic patients with left hemisphere stroke (15 female) were included in this study. Participants completed two tasks: 1) spontaneous speech production, and 2) audiovisual SE. Number of different words per minute was calculated as a speech output measure for each task, with the difference between SE and spontaneous speech conditions yielding a measure of fluency improvement. Voxel-wise lesion-symptom mapping (VLSM) was used to relate the number of different words per minute for spontaneous speech, SE, and SE-related improvement to patterns of brain damage in order to predict lesion locations associated with the fluency-inducing response to SE. Individuals with Broca's aphasia demonstrated a significant increase in different words per minute during SE versus spontaneous speech. A similar pattern of improvement was not seen in patients with other types of aphasia. VLSM analysis revealed damage to the inferior frontal gyrus predicted this response. Results suggest that SE exerts its fluency-inducing effects by providing a surrogate target for speech production via internal monitoring processes. Clinically, these results add further support for the use of SE to improve speech production and may help select patients for SE treatment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Speech Entrainment Compensates for Broca's Area Damage

PubMed Central

Fridriksson, Julius; Basilakos, Alexandra; Hickok, Gregory; Bonilha, Leonardo; Rorden, Chris

2015-01-01

Speech entrainment (SE), the online mimicking of an audiovisual speech model, has been shown to increase speech fluency in patients with Broca's aphasia. However, not all individuals with aphasia benefit from SE. The purpose of this study was to identify patterns of cortical damage that predict a positive response SE's fluency-inducing effects. Forty-four chronic patients with left hemisphere stroke (15 female) were included in this study. Participants completed two tasks: 1) spontaneous speech production, and 2) audiovisual SE. Number of different words per minute was calculated as a speech output measure for each task, with the difference between SE and spontaneous speech conditions yielding a measure of fluency improvement. Voxel-wise lesion-symptom mapping (VLSM) was used to relate the number of different words per minute for spontaneous speech, SE, and SE-related improvement to patterns of brain damage in order to predict lesion locations associated with the fluency-inducing response to speech entrainment. Individuals with Broca's aphasia demonstrated a significant increase in different words per minute during speech entrainment versus spontaneous speech. A similar pattern of improvement was not seen in patients with other types of aphasia. VLSM analysis revealed damage to the inferior frontal gyrus predicted this response. Results suggest that SE exerts its fluency-inducing effects by providing a surrogate target for speech production via internal monitoring processes. Clinically, these results add further support for the use of speech entrainment to improve speech production and may help select patients for speech entrainment treatment. PMID:25989443

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.

Refining success of cardiac resynchronization therapy using a simple score predicting the amount of reverse ventricular remodelling: results from the Markers and Response to CRT (MARC) study.

PubMed

Maass, Alexander H; Vernooy, Kevin; Wijers, Sofieke C; van 't Sant, Jetske; Cramer, Maarten J; Meine, Mathias; Allaart, Cornelis P; De Lange, Frederik J; Prinzen, Frits W; Gerritse, Bart; Erdtsieck, Erna; Scheerder, Coert O S; Hill, Michael R S; Scholten, Marcoen; Kloosterman, Mariëlle; Ter Horst, Iris A H; Voors, Adriaan A; Vos, Marc A; Rienstra, Michiel; Van Gelder, Isabelle C

2018-02-01

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in systolic heart failure patients with ventricular conduction delay. Variability of individual response to CRT warrants improved patient selection. The Markers and Response to CRT (MARC) study was designed to investigate markers related to response to CRT. We prospectively studied the ability of 11 clinical, 11 electrocardiographic, 4 echocardiographic, and 16 blood biomarkers to predict CRT response in 240 patients. Response was measured by the reduction of indexed left ventricular end-systolic volume (LVESVi) at 6 months follow-up. Biomarkers were related to LVESVi change using log-linear regression on continuous scale. Covariates that were significant univariately were included in a multivariable model. The final model was utilized to compose a response score. Age was 67 ± 10 years, 63% were male, 46% had ischaemic aetiology, LV ejection fraction was 26 ± 8%, LVESVi was 75 ± 31 mL/m2, and QRS was 178 ± 23 ms. At 6 months LVESVi was reduced to 58 ± 31 mL/m2 (relative reduction of 22 ± 24%), 130 patients (61%) showed ≥ 15% LVESVi reduction. In univariate analysis 17 parameters were significantly associated with LVESVi change. In the final model age, QRSAREA (using vectorcardiography) and two echocardiographic markers (interventricular mechanical delay and apical rocking) remained significantly associated with the amount of reverse ventricular remodelling. This CAVIAR (CRT-Age-Vectorcardiographic QRSAREA -Interventricular Mechanical delay-Apical Rocking) response score also predicted clinical outcome assessed by heart failure hospitalizations and all-cause mortality. The CAVIAR response score predicts the amount of reverse remodelling after CRT and may be used to improve patient selection. Clinical Trials: NCT01519908. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

[Arterial pressure curve and fluid status].

PubMed

Pestel, G; Fukui, K

2009-04-01

Fluid optimization is a major contributor to improved outcome in patients. Unfortunately, anesthesiologists are often in doubt whether an additional fluid bolus will improve the hemodynamics of the patient or not as excess fluid may even jeopardize the condition. This article discusses physiological concepts of liberal versus restrictive fluid management followed by a discussion on the respective capabilities of various monitors to predict fluid responsiveness. The parameter difference in pulse pressure (dPP), derived from heart-lung interaction in mechanically ventilated patients is discussed in detail. The dPP cutoff value of 13% to predict fluid responsiveness is presented together with several assessment techniques of dPP. Finally, confounding variables on dPP measurements, such as ventilation parameters, pneumoperitoneum and use of norepinephrine are also mentioned.

Annoyance due to simulated blade-slap noise

NASA Technical Reports Server (NTRS)

Powell, C. A.

1978-01-01

The effects of several characteristics of blade slap noise on annoyance response were studied. These characteristics or parameters were the sound pressure level of the continuous noise used to simulate helicopter broadband noise, the ratio of impulse peak to broadband noise or crest factor, the number of pressure excursions comprising an impulse event, the rise and fall time of the individual impulses, and the repetition frequency of the impulses. Analyses were conducted to determine the correlation between subjective response and various physical measures for the range of parameters studied. A small but significant improvement in the predictive ability of PNL was provided by an A-weighted crest factor correlation. No significant improvement in predictive ability was provided by a rate correction.

An Improved Dynamic Model for the Respiratory Response to Exercise

PubMed Central

Serna, Leidy Y.; Mañanas, Miguel A.; Hernández, Alher M.; Rabinovich, Roberto A.

2018-01-01

Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model). Secondly, to propose changes in the models' structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency. PMID:29467674

The renal protective effect of angiotensin receptor blockers depends on intra-individual response variation in multiple risk markers.

PubMed

Schievink, Bauke; de Zeeuw, Dick; Parving, Hans-Henrik; Rossing, Peter; Lambers Heerspink, Hiddo Jan

2015-10-01

Angiotensin receptor blockers (ARBs) are renoprotective and targeted to blood pressure. However, ARBs have multiple other (off-target) effects which may affect renal outcome. It is unknown whether on-target and off-target effects are congruent within individuals. If not, this variation in short term effects may have important implications for the prediction of individual long term renal outcomes. Our aim was to assess intra-individual variability in multiple parameters in response to ARBs in type 2 diabetes. Changes in systolic blood pressure (SBP), albuminuria, potassium, haemoglobin, cholesterol and uric acid after 6 months of losartan treatment were assessed in the RENAAL database. Improvement in predictive performance of renal outcomes (ESRD or doubling serum creatinine) for each individual using ARB-induced changes in all risk markers was assessed by the relative integrative discrimination index (RIDI). SBP response showed high variability (mean -5.7 mmHg, 5(th) to 95(th) percentile -36.5 to +24.0 mmHg) between individuals. Changes in off-target parameters also showed high variability between individuals. No congruency was observed between responses to losartan in multiple parameters within individuals. Using individual responses in all risk markers significantly improved renal risk prediction (RIDI 30.4%, P < 0.01) compared with using only SBP changes. Results were successfully replicated in two independent trials with irbesartan, IDNT and IRMA-2. In this post hoc analysis we showed that ARBs have multiple off-target effects which vary between and within individuals. Combining all ARB-induced responses beyond SBP provides a more accurate prediction of who will benefit from ARB therapy. Prospective trials are required to validate these findings. © 2015 The British Pharmacological Society.

Nanofluidic Digital PCR and Extended Genotyping of RAS and BRAF for Improved Selection of Metastatic Colorectal Cancer Patients for Anti-EGFR Therapies.

PubMed

Azuara, Daniel; Santos, Cristina; Lopez-Doriga, Adriana; Grasselli, Julieta; Nadal, Marga; Sanjuan, Xavier; Marin, Fátima; Vidal, Joana; Montal, Robert; Moreno, Victor; Bellosillo, Beatriz; Argiles, Guillem; Elez, Elena; Dienstmann, Rodrigo; Montagut, Clara; Tabernero, Josep; Capellá, Gabriel; Salazar, Ramon

2016-05-01

The clinical significance of low-frequent RAS pathway-mutated alleles and the optimal sensitivity cutoff value in the prediction of response to anti-EGFR therapy in metastatic colorectal cancer (mCRC) patients remains controversial. We aimed to evaluate the added value of genotyping an extended RAS panel using a robust nanofluidic digital PCR (dPCR) approach. A panel of 34 hotspots, including RAS (KRAS and NRAS exons 2/3/4) and BRAF (V600E), was analyzed in tumor FFPE samples from 102 mCRC patients treated with anti-EGFR therapy. dPCR was compared with conventional quantitative PCR (qPCR). Response rates, progression-free survival (PFS), and overall survival (OS) were correlated to the mutational status and the mutated allele fraction. Tumor response evaluations were not available in 9 patients and were excluded for response rate analysis. Twenty-two percent of patients were positive for one mutation with qPCR (mutated alleles ranged from 2.1% to 66.6%). Analysis by dPCR increased the number of positive patients to 47%. Mutated alleles for patients only detected by dPCR ranged from 0.04% to 10.8%. An inverse correlation between the fraction of mutated alleles and radiologic response was observed. ROC analysis showed that a fraction of 1% or higher of any mutated alleles offered the best predictive value for all combinations of RAS and BRAF analysis. In addition, this threshold also optimized prediction both PFS and OS. We conclude that mutation testing using an extended gene panel, including RAS and BRAF with a threshold of 1% improved prediction of response to anti-EGFR therapy. Mol Cancer Ther; 15(5); 1106-12. ©2016 AACR. ©2016 American Association for Cancer Research.

The role of adipokines in the rapid antidepressant effects of ketamine.

PubMed

Machado-Vieira, R; Gold, P W; Luckenbaugh, D A; Ballard, E D; Richards, E M; Henter, I D; De Sousa, R T; Niciu, M J; Yuan, P; Zarate, C A

2017-01-01

We previously found that body mass index (BMI) strongly predicted response to ketamine. Adipokines have a key role in metabolism (including BMI). They directly regulate inflammation and neuroplasticity pathways and also influence insulin sensitivity, bone metabolism and sympathetic outflow; all of these have been implicated in mood disorders. Here, we sought to examine the role of three key adipokines-adiponectin, resistin and leptin-as potential predictors of response to ketamine or as possible transducers of its therapeutic effects. Eighty treatment-resistant subjects who met DSM-IV criteria for either major depressive disorder (MDD) or bipolar disorder I/II and who were currently experiencing a major depressive episode received a single ketamine infusion (0.5 mg kg -1 for 40 min). Plasma adipokine levels were measured at three time points (pre-infusion baseline, 230 min post infusion and day 1 post infusion). Overall improvement and response were assessed using percent change from baseline on the Montgomery-Asberg Depression Rating Scale and the Hamilton Depression Rating Scale. Lower baseline levels of adiponectin significantly predicted ketamine's antidepressant efficacy, suggesting an adverse metabolic state. Because adiponectin significantly improves insulin sensitivity and has potent anti-inflammatory effects, this finding suggests that specific systemic abnormalities might predict positive response to ketamine. A ketamine-induced decrease in resistin was also observed; because resistin is a potent pro-inflammatory compound, this decrease suggests that ketamine's anti-inflammatory effects may be transduced, in part, by its impact on resistin. Overall, the findings suggest that adipokines may either predict response to ketamine or have a role in its possible therapeutic effects.

Improving hydrologic disaster forecasting and response for transportation by assimilating and fusing NASA and other data sets : final report.

DOT National Transportation Integrated Search

2017-04-15

In this 3-year project, the research team developed the Hydrologic Disaster Forecast and Response (HDFR) system, a set of integrated software tools for end users that streamlines hydrologic prediction workflows involving automated retrieval of hetero...

Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

USGS Publications Warehouse

Francy, Donna S.; Brady, Amie M.G.; Carvin, Rebecca B.; Corsi, Steven R.; Fuller, Lori M.; Harrison, John H.; Hayhurst, Brett A.; Lant, Jeremiah; Nevers, Meredith B.; Terrio, Paul J.; Zimmerman, Tammy M.

2013-01-01

Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.). Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

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

JPL's Role in Advancing Earth System Science to Meet the Challenges of Climate and Environmental Change

NASA Technical Reports Server (NTRS)

Evans, Diane

2012-01-01

Objective 2.1.1: Improve understanding of and improve the predictive capability for changes in the ozone layer, climate forcing, and air quality associated with changes in atmospheric composition. Objective 2.1.2: Enable improved predictive capability for weather and extreme weather events. Objective 2.1.3: Quantify, understand, and predict changes in Earth s ecosystems and biogeochemical cycles, including the global carbon cycle, land cover, and biodiversity. Objective 2.1.4: Quantify the key reservoirs and fluxes in the global water cycle and assess water cycle change and water quality. Objective 2.1.5: Improve understanding of the roles of the ocean, atmosphere, land and ice in the climate system and improve predictive capability for its future evolution. Objective 2.1.6: Characterize the dynamics of Earth s surface and interior and form the scientific basis for the assessment and mitigation of natural hazards and response to rare and extreme events. Objective 2.1.7: Enable the broad use of Earth system science observations and results in decision-making activities for societal benefits.

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.

Negative Affective Spillover from Daily Events Predicts Early Response to Cognitive Therapy for Depression

ERIC Educational Resources Information Center

Cohen, Lawrence H.; Gunthert, Kathleen C.; Butler, Andrew C.; Parrish, Brendt P.; Wenze, Susan J.; Beck, Judith S.

2008-01-01

This study evaluated the predictive role of depressed outpatients' (N = 62) affective reactivity to daily stressors in their rates of improvement in cognitive therapy (CT). For 1 week before treatment, patients completed nightly electronic diaries that assessed daily stressors and negative affect (NA). The authors used multilevel modeling to…

Novel enzymatic assay predicts minoxidil response in the treatment of androgenetic alopecia.

PubMed

Goren, Andy; Castano, Juan Antonio; McCoy, John; Bermudez, Fernando; Lotti, Torello

2014-01-01

Topical minoxidil is the most common drug used for the treatment of androgenetic alopecia (AGA) in men and women. Although topical minoxidil exhibits a good safety profile, the efficacy in the overall population remains relatively low at 30-40%. To observe significant improvement in hair growth, minoxidil is typically used daily for a period of at least 3-4 months. Due to the significant time commitment and low response rate, a biomarker for predicting patient response prior to therapy would be advantageous. Minoxidil is converted in the scalp to its active form, minoxidil sulfate, by the sulfotransferase enzyme SULT1A1. We hypothesized that SULT1A1 enzyme activity in the hair follicle correlates with minoxidil response for the treatment of AGA. Our preliminary retrospective study of a SULT1A1 activity assay demonstrates 95% sensitivity and 73% specificity in predicting minoxidil treatment response for AGA. A larger prospective study is now under way to further validate this novel assay. © 2013 Wiley Periodicals, Inc.

Hydrothermal germination models: Improving experimental efficiency by limiting data collection to the relevant hydrothermal range

USDA-ARS?s Scientific Manuscript database

Hydrothermal models used to predict germination response in the field are usually parameterized with data from laboratory experiments that examine the full range of germination response to temperature and water potential. Inclusion of low water potential and high and low-temperature treatments, how...

Rapid response predicts 12-month post-treatment outcomes in binge-eating disorder: theoretical and clinical implications

PubMed Central

Grilo, C. M.; White, M. A.; Wilson, G. T.; Gueorguieva, R.; Masheb, R. M.

2011-01-01

Background We examined rapid response in obese patients with binge-eating disorder (BED) in a clinical trial testing cognitive behavioral therapy (CBT) and behavioral weight loss (BWL). Method Altogether, 90 participants were randomly assigned to CBT or BWL. Assessments were performed at baseline, throughout and post-treatment and at 6- and 12-month follow-ups. Rapid response, defined as ≥70% reduction in binge eating by week four, was determined by receiver operating characteristic curves and used to predict outcomes. Results Rapid response characterized 57% of participants (67% of CBT, 47% of BWL) and was unrelated to most baseline variables. Rapid response predicted greater improvements across outcomes but had different prognostic significance and distinct time courses for CBT versus BWL. Patients receiving CBT did comparably well regardless of rapid response in terms of reduced binge eating and eating disorder psychopathology but did not achieve weight loss. Among patients receiving BWL, those without rapid response failed to improve further. However, those with rapid response were significantly more likely to achieve binge-eating remission (62% v. 13%) and greater reductions in binge-eating frequency, eating disorder psychopathology and weight loss. Conclusions Rapid response to treatment in BED has prognostic significance through 12-month follow-up, provides evidence for treatment specificity and has clinical implications for stepped-care treatment models for BED. Rapid responders who receive BWL benefit in terms of both binge eating and short-term weight loss. Collectively, these findings suggest that BWL might be a candidate for initial intervention in stepped-care models with an evaluation of progress after 1 month to identify non-rapid responders who could be advised to consider a switch to a specialized treatment. PMID:21923964

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.

Impact of modellers' decisions on hydrological a priori predictions

NASA Astrophysics Data System (ADS)

Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

2013-07-01

The purpose of this paper is to stimulate a re-thinking of how we, the catchment hydrologists, could become reliable forecasters. A group of catchment modellers predicted the hydrological response of a man-made 6 ha catchment in its initial phase (Chicken Creek) without having access to the observed records. They used conceptually different model families. Their modelling experience differed largely. The prediction exercise was organized in three steps: (1) for the 1st prediction modellers received a basic data set describing the internal structure of the catchment (somewhat more complete than usually available to a priori predictions in ungauged catchments). They did not obtain time series of stream flow, soil moisture or groundwater response. (2) Before the 2nd improved prediction they inspected the catchment on-site and attended a workshop where the modellers presented and discussed their first attempts. (3) For their improved 3rd prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step 1. Here, we detail the modeller's decisions in accounting for the various processes based on what they learned during the field visit (step 2) and add the final outcome of step 3 when the modellers made use of additional data. We document the prediction progress as well as the learning process resulting from the availability of added information. For the 2nd and 3rd step, the progress in prediction quality could be evaluated in relation to individual modelling experience and costs of added information. We learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.

Improving Reading Comprehension by Predicting, Monitoring Comprehension, Remediation, and Personal Response Strategies.

ERIC Educational Resources Information Center

Jacobucci, Leanne; Richert, Judy; Ronan, Susan; Tanis, Ariana

This report describes a program for improving inconsistent reading comprehension. The targeted population consisted of first, third, and fifth grade classrooms in a diverse middle class community located in Illinois. The problems of low academic achievement were documented through teacher observation, reading comprehension test scores, and low…

A coupled aero-structural model of a HAWT blade for dynamic load and response prediction in time-domain for health monitoring applications

NASA Astrophysics Data System (ADS)

Sauder, Heather Scot

To reach the high standards set for renewable energy production in the US and around the globe, wind turbines with taller towers and longer blades are being designed for onshore and offshore wind developments to capture more energy from higher winds aloft and a larger rotor diameter. However, amongst all the wind turbine components wind turbine blades are still the most prone to damage. Given that wind turbine blades experience dynamic loads from multiple sources, there is a need to be able to predict the real-time load, stress distribution and response of the blade in a given wind environment for damage, flutter and fatigue life predictions. Current methods of wind-induced response analysis for wind turbine blades use approximations that are not suitable for wind turbine blade airfoils which are thick, and therefore lead to inaccurate life predictions. Additionally, a time-domain formulation can prove to be especially advantageous for predicting aerodynamic loads on wind turbine blades since they operate in a turbulent atmospheric boundary layer. This will help to analyze the blades on wind turbines that operate individually or in a farm setting where they experience high turbulence in the wake of another wind turbine. A time-domain formulation is also useful for examining the effects of gusty winds that are transient in nature like in gust fronts, thunderstorms or extreme events such as hurricanes, microbursts, and tornadoes. Time-domain methods present the opportunity for real-time health monitoring strategies that can easily be used with finite element methods for prediction of fatigue life or onset of flutter instability. The purpose of the proposed work is to develop a robust computational model to predict the loads, stresses and response of a wind turbine blade in operating and extreme wind conditions. The model can be used to inform health monitoring strategies for preventative maintenance and provide a realistic number of stress cycles that the blade will experience for fatigue life prediction procedures. To fill in the gaps in the existing knowledge and meet the overall goal of the proposed research, the following objectives were accomplished: (a) improve the existing aeroelastic (motion- and turbulence-induced) load models to predict the response of wind turbine blade airfoils to understand its behavior in turbulent wind, (b) understand, model and predict the response of wind turbine blades in transient or gusty wind, boundary-layer wind and incoherent wind over the span of the blade, (c) understand the effects of aero-structural coupling between the along-wind, cross-wind and torsional vibrations, and finally (d) develop a computational tool using the improved time-domain load model to predict the real-time load, stress distribution and response of a given wind turbine blade during operating and parked conditions subject to a specific wind environment both in a short and long term for damage, flutter and fatigue life predictions.

Daily FOUR score assessment provides accurate prognosis of long-term outcome in out-of-hospital cardiac arrest.

PubMed

Weiss, N; Venot, M; Verdonk, F; Chardon, A; Le Guennec, L; Llerena, M C; Raimbourg, Q; Taldir, G; Luque, Y; Fagon, J-Y; Guerot, E; Diehl, J-L

2015-05-01

The accurate prediction of outcome after out-of-hospital cardiac arrest (OHCA) is of major importance. The recently described Full Outline of UnResponsiveness (FOUR) is well adapted to mechanically ventilated patients and does not depend on verbal response. To evaluate the ability of FOUR assessed by intensivists to accurately predict outcome in OHCA. We prospectively identified patients admitted for OHCA with a Glasgow Coma Scale below 8. Neurological assessment was performed daily. Outcome was evaluated at 6 months using Glasgow-Pittsburgh Cerebral Performance Categories (GP-CPC). Eighty-five patients were included. At 6 months, 19 patients (22%) had a favorable outcome, GP-CPC 1-2, and 66 (78%) had an unfavorable outcome, GP-CPC 3-5. Compared to both brainstem responses at day 3 and evolution of Glasgow Coma Scale, evolution of FOUR score over the three first days was able to predict unfavorable outcome more precisely. Thus, absence of improvement or worsening from day 1 to day 3 of FOUR had 0.88 (0.79-0.97) specificity, 0.71 (0.66-0.76) sensitivity, 0.94 (0.84-1.00) PPV and 0.54 (0.49-0.59) NPV to predict unfavorable outcome. Similarly, the brainstem response of FOUR score at 0 evaluated at day 3 had 0.94 (0.89-0.99) specificity, 0.60 (0.50-0.70) sensitivity, 0.96 (0.92-1.00) PPV and 0.47 (0.37-0.57) NPV to predict unfavorable outcome. The absence of improvement or worsening from day 1 to day 3 of FOUR evaluated by intensivists provides an accurate prognosis of poor neurological outcome in OHCA. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

California Drought and the 2015-2016 El Niño

NASA Astrophysics Data System (ADS)

Cash, B.

2017-12-01

California winter rainfall is examined in observations and data from the North American Multi-Model Ensemble (NMME) and Project Metis, a new suite of seasonal integrations made using the operational European Centre for Medium-Range Weather Forecasts model. We focus on the 2015-2016 season, and the non-canonical response to the major El Niño event that occurred. We show that the Metis ensemble mean is capable of distinguishing between the response to the 1997/98 and 2015/16 events, while the two events are more similar in the NMME. We also show that unpredicted variations in the atmospheric circulation in the north Pacific significantly affect southern California rainfall totals. Improving prediction of these variations is thus a key target for improving seasonal rainfall predictions for this region.

Pilot study of pyridostigmine in constipated patients with autonomic neuropathy.

PubMed

Bharucha, Adil E; Low, Phillip A; Camilleri, Michael; Burton, Duane; Gehrking, Tonette L; Zinsmeister, Alan R

2008-08-01

The effects of cholinesterase inhibitors, which increase colonic motility in health, on chronic constipation are unknown. Our aims were to evaluate the efficacy of cholinesterase inhibitors for dysautonomia and chronic constipation and to assess whether acute effects could predict the long term response. In this single-blind study, 10 patients with autonomic neuropathy and constipation were treated with placebo (2 weeks), followed by an escalating dose of pyridostigmine to the maximum tolerated dose (i.e., 180-540 mg daily) for 6 weeks. Symptoms and gastrointestinal transit were assessed at 2 and 8 weeks. The acute effects of neostigmine on colonic transit and motility were also assessed. At baseline, 4, 6, and 3 patients had delayed gastric, small intestinal, and colonic transit respectively. Pyridostigmine was well tolerated in most patients, improved symptoms in 4 patients, and accelerated the geometric center for colonic transit at 24 h by > or =0.7 unit in 3 patients. The effects of i.v. neostigmine on colonic transit and compliance predicted (P < 0.05) the effects of pyridostigmine on colonic transit. Pyridostigmine improves colonic transit and symptoms in some patients with autonomic neuropathy and constipation. The motor response to neostigmine predicted the response to oral pyridostigmine.

Moderators, mediators, and other predictors of risperidone response in children with autistic disorder and irritability.

PubMed

Arnold, L Eugene; Farmer, Cristan; Kraemer, Helena Chmura; Davies, Mark; Witwer, Andrea; Chuang, Shirley; DiSilvestro, Robert; McDougle, Christopher J; McCracken, James; Vitiello, Benedetto; Aman, Michael G; Scahill, Lawrence; Posey, David J; Swiezy, Naomi B

2010-04-01

The National Institute of Mental Health (NIMH) Research Units on Pediatric Psychopharmacology (RUPP) Autism Network found an effect size of d = 1.2 in favor of risperidone on the main outcome measure in an 8-week double-blind, placebo-controlled trial for irritability in autistic disorder. This paper explores moderators and mediators of this effect. Intention-to-treat (ITT) analyses were conducted with suspected moderators and mediators entered into the regression equations. MacArthur Foundation Network subgroup guidelines were followed in the evaluation of the results. Only baseline severity moderated treatment response: Higher severity showed greater improvement for risperidone but not for placebo. Weight gain mediated treatment response negatively: those who gained more weight improved less with risperidone and more with placebo. Compliance correlated with outcome for risperidone but not placebo. Higher dose correlated with worse outcome for placebo, but not risperidone. Of nonspecific predictors, parent education, family income, and low baseline prolactin positively predicted outcome; anxiety, bipolar symptoms, oppositional-defiant symptoms, stereotypy, and hyperactivity negatively predicted outcome. Risperidone moderated the effect of change in 5'-nucleotidase, a marker of zinc status, for which decrease was associated with improvement only with risperidone, not with placebo. The benefit-risk ratio of risperidone is better with greater symptom severity. Risperidone can be individually titrated to optimal dosage for excellent response in the majority of children. Weight gain is not necessary for risperidone benefit and may even detract from it. Socioeconomic advantage, low prolactin, and absence of co-morbid problems nonspecifically predict better outcome. Mineral interactions with risperidone deserve further study.

Photopolymer holographic recording material

NASA Astrophysics Data System (ADS)

Lawrence, J. R.; O'Neill, F. T.; Sheridan, J. T.

Photopolymers are promising materials for use in holography. They have many advantages, such as ease of preparation, and are capable of efficiencies of up to 100%. A disadvantage of these materials is their inability to record high spatial frequency gratings when compared to other materials such as dichromated gelatin and silver halide photographic emulsion. Until recently, the drop off at high spatial frequencies of the material response was not predicted by any of the diffusion based models available. It has recently been proposed that this effect is due to polymer chains growing away from their initiation point and causing a smeared profile to be recorded. This is termed a non-local material response. Simple analytic expressions have been derived using this model and fits to experimental data have allowed values to be estimated for material parameters such as the diffusion coefficient of monomer, the ratio of polymerisation rate to diffusion rate and the distance that the polymer chains spread during holographic recording. The model predicts that the spatial frequency response might be improved by decreasing the mean polymer chain lengths and/or by increasing the mobility of the molecules used in the material. The experimental work carried out to investigate these predictions is reported here. This work involved (a) the changing of the molecular weights of chemical components within the material (dyes and binders) and (b) the addition of a chemical retarder in order to shorten the polymer chains, thereby decreasing the extent of the non-local effect. Although no significant improvement in spatial frequency response was observed the model appears to offer an improved understanding of the operation of the material.

Precision medicine and precision therapeutics: hedgehog signaling pathway, basal cell carcinoma and beyond.

PubMed

Mohan, Shalini V; Chang, Anne Lynn S

2014-06-01

Precision medicine and precision therapeutics is currently in its infancy with tremendous potential to improve patient care by better identifying individuals at risk for skin cancer and predict tumor responses to treatment. This review focuses on the Hedgehog signaling pathway, its critical role in the pathogenesis of basal cell carcinoma, and the emergence of targeted treatments for advanced basal cell carcinoma. Opportunities to utilize precision medicine are outlined, such as molecular profiling to predict basal cell carcinoma response to targeted therapy and to inform therapeutic decisions.

Can current moisture responses predict soil CO2 efflux under altered precipitation regimes? A synthesis of manipulation experiments

USDA-ARS?s Scientific Manuscript database

As a key component of the carbon cycle, soil respiration (Rsoil) is being excessively studied with the aim of improving our understanding as well as our ability to predict Rsoil when climate changes. Many manipulation experiments have been performed to test how Rsoil and other carbon fluxes and ecos...

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.

Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

PubMed

Dang-Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O'Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean-Philippe

2017-11-01

While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling the effect of shroud contact and friction dampers on the mistuned response of turbopumps

NASA Technical Reports Server (NTRS)

Griffin, Jerry H.; Yang, M.-T.

1994-01-01

The contract has been revised. Under the revised scope of work a reduced order model has been developed that can be used to predict the steady-state response of mistuned bladed disks. The approach has been implemented in a computer code, LMCC. It is concluded that: the reduced order model displays structural fidelity comparable to that of a finite element model of an entire bladed disk system with significantly improved computational efficiency; and, when the disk is stiff, both the finite element model and LMCC predict significantly more amplitude variation than was predicted by earlier models. This second result may have important practical ramifications, especially in the case of integrally bladed disks.

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Jedlovec, Gary J.

2012-01-01

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Assessment of prediction skill in equatorial Pacific Ocean in high resolution model of CFS

NASA Astrophysics Data System (ADS)

Arora, Anika; Rao, Suryachandra A.; Pillai, Prasanth; Dhakate, Ashish; Salunke, Kiran; Srivastava, Ankur

2018-01-01

The effect of increasing atmospheric resolution on prediction skill of El Niño southern oscillation phenomenon in climate forecast system model is explored in this paper. Improvement in prediction skill for sea surface temperature (SST) and winds at all leads compared to low resolution model in the tropical Indo-Pacific basin is observed. High resolution model is able to capture extreme events reasonably well. As a result, the signal to noise ratio is improved in the high resolution model. However, spring predictability barrier (SPB) for summer months in Nino 3 and Nino 3.4 region is stronger in high resolution model, in spite of improvement in overall prediction skill and dynamics everywhere else. Anomaly correlation coefficient of SST in high resolution model with observations in Nino 3.4 region targeting boreal summer months when predicted at lead times of 3-8 months in advance decreased compared its lower resolution counterpart. It is noted that higher variance of winds predicted in spring season over central equatorial Pacific compared to observed variance of winds results in stronger than normal response on subsurface ocean, hence increases SPB for boreal summer months in high resolution model.

Interpersonal maladjustment as predictor of mothers' response to a relational parenting intervention.

PubMed

Suchman, Nancy E; McMahon, Thomas J; Luthar, Suniya S

2004-09-01

In previous work, Luthar and Suchman (2000, Development & Psychopathology, 12, 235) reported results of a randomized clinical trial testing the efficacy of the Relational Psychotherapy Mothers' Group (RPMG) for methadone-maintained mothers. In this extension, we examined maternal interpersonal maladjustment as a predictor of differential response to RPMG versus standard drug counseling (DC). We predicted that RPMG mothers with high levels of interpersonal maladjustment would improve on parent-child relationship indices, whereas DC mothers with high levels of interpersonal maladjustment would show no improvement. Fifty-two mothers enrolled in the study completed baseline, post-treatment and 6-month followup assessments and a subset of 24 "target" children between the ages of 7 and 16 completed measures on mothers' parenting. As predicted, results of hierarchical regression analyses indicated moderate interpersonal maladjustment x treatment interaction effects for all parenting outcomes at post-treatment and for a subset of outcomes at followup. Plotted interactions confirmed predictions that, as maternal interpersonal maladjustment increased, parenting problems improved for RPMG mothers and remained the same or worsened for DC mothers. Results indicate the potential value of interpersonally oriented interventions for substance-abusing mothers and their children.

Putting mechanisms into crop production models.

PubMed

Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

2013-09-01

Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

Global precipitation measurement (GPM) preliminary design

NASA Astrophysics Data System (ADS)

Neeck, Steven P.; Kakar, Ramesh K.; Azarbarzin, Ardeshir A.; Hou, Arthur Y.

2008-10-01

The overarching Earth science mission objective of the Global Precipitation Measurement (GPM) mission is to develop a scientific understanding of the Earth system and its response to natural and human-induced changes. This will enable improved prediction of climate, weather, and natural hazards for present and future generations. The specific scientific objectives of GPM are advancing: Precipitation Measurement through combined use of active and passive remote-sensing techniques, Water/Energy Cycle Variability through improved knowledge of the global water/energy cycle and fresh water availability, Climate Prediction through better understanding of surface water fluxes, soil moisture storage, cloud/precipitation microphysics and latent heat release, Weather Prediction through improved numerical weather prediction (NWP) skills from more accurate and frequent measurements of instantaneous rain rates with better error characterizations and improved assimilation methods, Hydrometeorological Prediction through better temporal sampling and spatial coverage of highresolution precipitation measurements and innovative hydro-meteorological modeling. GPM is a joint initiative with the Japan Aerospace Exploration Agency (JAXA) and other international partners and is the backbone of the Committee on Earth Observation Satellites (CEOS) Precipitation Constellation. It will unify and improve global precipitation measurements from a constellation of dedicated and operational active/passive microwave sensors. GPM is completing the Preliminary Design Phase and is advancing towards launch in 2013 and 2014.

Use of the Temperament and Character Inventory to Predict Response to Repetitive Transcranial Magnetic Stimulation for Major Depression

PubMed Central

Siddiqi, Shan H.; Chockalingam, Ravikumar; Cloninger, C. Robert; Lenze, Eric J.; Cristancho, Pilar

2016-01-01

Objective . The goal of this study was to investigate the utility of the Temperament and Character Inventory (TCI) in predicting antidepressant response to repetitive transcranial magnetic stimulation (rTMS). Background Although rTMS of the dorsolateral prefrontal cortex (DLPFC) is an established antidepressant treatment, little is known about predictors of response. The TCI measures multiple personality dimensions (harm avoidance, novelty seeking, reward dependence, persistence, self-directedness, self-transcendence, and cooperativeness), some of which have predicted response to pharmacotherapy and cognitive-behavioral therapy. A previous study suggested a possible association between self-directedness and response to rTMS in melancholic depression, although this was limited by the fact that melancholic depression is associated with a limited range of TCI profiles. Methods . Nineteen patients with a major depressive episode completed the TCI prior to a clinical course of rTMS over the DLPFC. Treatment response was defined as ≥50% decrease in scores on the Hamilton Rating Scale for Depression (HAM-D). Baseline scores on each TCI dimension were compared between responders and non-responders via analysis of variance. Pearson correlations were also calculated for temperament/character scores in comparison with percentage improvement in HAM-D scores. Results Eleven of the 19 patients responded to rTMS. T-scores for persistence were significantly higher in responders than in non-responders (P=0.022). Linear regression revealed a correlation between persistence scores and percentage improvement in HAM-D scores. Conclusions Higher persistence scores predicted antidepressant response to rTMS. This may be explained by rTMS-induced enhancement of cortical excitability, which has been found to be decreased in patients with high persistence. Personality assessment that includes measurement of TCI persistence may be a useful component of precision medicine initiatives in rTMS for depression. PMID:27123799

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Synthesising empirical results to improve predictions of post-wildfire runoff and erosion response

Treesearch

Richard A. Shakesby; John A. Moody; Deborah A. Martin; Pete Robichaud

2016-01-01

Advances in research into wildfire impacts on runoff and erosion have demonstrated increasing complexity of controlling factors and responses, which, combined with changing fire frequency, present challenges for modellers. We convened a conference attended by experts and practitioners in post-wildfire impacts, meteorology and related research, including...

Control of the induced microgravity environment of the Man Tended Free Flyer (MTFF)

NASA Technical Reports Server (NTRS)

Schlund, Juergen

1988-01-01

Induced disturbance sources have been identified on board the Man Tended Free Flyer (MTFF). Vibration responses at sensitive payload/spacecraft interfaces have been predicted by the application of an empirically found spacecraft dynamic transfer function. Vibrations from fluid loops (Freon, water) and of reaction wheels are assessed to be the main contributors to the induced microgravity environment. The expected payload acceleration response amplitudes presented here are more than one hundred times higher than the admissible values given by the MTFF system requirement, not considering the structural striction-friction effects which could be avoided by appropriate design. Real responses will be significantly lower because the derivation of excitation and transmission functions are based on worst case assumptions. The results indicate that future activities must be concentrated on equipment design improvement and the implementation of vibration reduction along the disturbance transmission path. The activities must be accompanied by early equipment and assembly development tests and transmissibility measurements with the integrated spacecraft engineering and structural models in order to improve the accuracy of payload response predictions.

A Dynamic Calibration Method for Experimental and Analytical Hub Load Comparison

NASA Technical Reports Server (NTRS)

Kreshock, Andrew R.; Thornburgh, Robert P.; Wilbur, Matthew L.

2017-01-01

This paper presents the results from an ongoing effort to produce improved correlation between analytical hub force and moment prediction and those measured during wind-tunnel testing on the Aeroelastic Rotor Experimental System (ARES), a conventional rotor testbed commonly used at the Langley Transonic Dynamics Tunnel (TDT). A frequency-dependent transformation between loads at the rotor hub and outputs of the testbed balance is produced from frequency response functions measured during vibration testing of the system. The resulting transformation is used as a dynamic calibration of the balance to transform hub loads predicted by comprehensive analysis into predicted balance outputs. In addition to detailing the transformation process, this paper also presents a set of wind-tunnel test cases, with comparisons between the measured balance outputs and transformed predictions from the comprehensive analysis code CAMRAD II. The modal response of the testbed is discussed and compared to a detailed finite-element model. Results reveal that the modal response of the testbed exhibits a number of characteristics that make accurate dynamic balance predictions challenging, even with the use of the balance transformation.

Multiple network-constrained regressions expand insights into influenza vaccination responses.

PubMed

Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

2017-07-15

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer . steven.kleinstein@yale.edu or stefan.avey@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively.

PubMed

Lahner, D; Kabon, B; Marschalek, C; Chiari, A; Pestel, G; Kaider, A; Fleischmann, E; Hetz, H

2009-09-01

Fluid management guided by oesophageal Doppler monitor has been reported to improve perioperative outcome. Stroke volume variation (SVV) is considered a reliable clinical predictor of fluid responsiveness. Consequently, the aim of the present trial was to evaluate the accuracy of SVV determined by arterial pulse contour (APCO) analysis, using the FloTrac/Vigileo system, to predict fluid responsiveness as measured by the oesophageal Doppler. Patients undergoing major abdominal surgery received intraoperative fluid management guided by oesophageal Doppler monitoring. Fluid boluses of 250 ml each were administered in case of a decrease in corrected flow time (FTc) to <350 ms. Patients were connected to a monitoring device, obtaining SVV by APCO. Haemodynamic variables were recorded before and after fluid bolus application. Fluid responsiveness was defined as an increase in stroke volume index >10%. The ability of SVV to predict fluid responsiveness was assessed by calculation of the area under the receiver operating characteristic (ROC) curve. Twenty patients received 67 fluid boluses. Fifty-two of the 67 fluid boluses administered resulted in fluid responsiveness. SVV achieved an area under the ROC curve of 0.512 [confidence interval (CI) 0.32-0.70]. A cut-off point for fluid responsiveness was found for SVV > or =8.5% (sensitivity: 77%; specificity: 43%; positive predictive value: 84%; and negative predictive value: 33%). This prospective, interventional observer-blinded study demonstrates that SVV obtained by APCO, using the FloTrac/Vigileo system, is not a reliable predictor of fluid responsiveness in the setting of major abdominal surgery.

Multi-response optimization of T300/epoxy prepreg tape-wound cylinder by grey relational analysis coupled with the response surface method

NASA Astrophysics Data System (ADS)

Kang, Chao; Shi, Yaoyao; He, Xiaodong; Yu, Tao; Deng, Bo; Zhang, Hongji; Sun, Pengcheng; Zhang, Wenbin

2017-09-01

This study investigates the multi-objective optimization of quality characteristics for a T300/epoxy prepreg tape-wound cylinder. The method integrates the Taguchi method, grey relational analysis (GRA) and response surface methodology, and is adopted to improve tensile strength and reduce residual stress. In the winding process, the main process parameters involving winding tension, pressure, temperature and speed are selected to evaluate the parametric influences on tensile strength and residual stress. Experiments are conducted using the Box-Behnken design. Based on principal component analysis, the grey relational grades are properly established to convert multi-responses into an individual objective problem. Then the response surface method is used to build a second-order model of grey relational grade and predict the optimum parameters. The predictive accuracy of the developed model is proved by two test experiments with a low prediction error of less than 7%. The following process parameters, namely winding tension 124.29 N, pressure 2000 N, temperature 40 °C and speed 10.65 rpm, have the highest grey relational grade and give better quality characteristics in terms of tensile strength and residual stress. The confirmation experiment shows that better results are obtained with GRA improved by the proposed method than with ordinary GRA. The proposed method is proved to be feasible and can be applied to optimize the multi-objective problem in the filament winding process.

Head losses prediction and analysis in a bulb turbine draft tube under different operating conditions using unsteady simulations

NASA Astrophysics Data System (ADS)

Wilhelm, S.; Balarac, G.; Métais, O.; Ségoufin, C.

2016-11-01

Flow prediction in a bulb turbine draft tube is conducted for two operating points using Unsteady RANS (URANS) simulations and Large Eddy Simulations (LES). The inlet boundary condition of the draft tube calculation is a rotating two dimensional velocity profile exported from a RANS guide vane- runner calculation. Numerical results are compared with experimental data in order to validate the flow field and head losses prediction. Velocity profiles prediction is improved with LES in the center of the draft tube compared to URANS results. Moreover, more complex flow structures are obtained with LES. A local analysis of the predicted flow field using the energy balance in the draft tube is then introduced in order to detect the hydrodynamic instabilities responsible for head losses in the draft tube. In particular, the production of turbulent kinetic energy next to the draft tube wall and in the central vortex structure is found to be responsible for a large part of the mean kinetic energy dissipation in the draft tube and thus for head losses. This analysis is used in order to understand the differences in head losses for different operating points. The numerical methodology could then be improved thanks to an in-depth understanding of the local flow topology.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-03-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed Central

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-01-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

Improving Communicative Competence with "Clickers": Acceptance/Attitudes among Nigerian Primary School Teachers

ERIC Educational Resources Information Center

Agbatogun, Alaba Olaoluwakotansibe

2014-01-01

This study examined the predictive power of teachers' perceived usefulness (PU), perceived ease of use (PEU), behavioural intention (BI) to use personal response system (PRS) and computer experience (CE) on teachers' acceptance and attitude towards using PRS in improving communicative competence in the classroom where English is taught as a second…

Validity and responsiveness of the pediatric quality of life inventory (PedsQL) 4.0 generic core scales in the pediatric inpatient setting.

PubMed

Desai, Arti D; Zhou, Chuan; Stanford, Susan; Haaland, Wren; Varni, James W; Mangione-Smith, Rita M

2014-12-01

Validated patient-reported outcomes responsive to clinical change are needed to evaluate the effectiveness of quality improvement interventions. To evaluate responsiveness, construct validity, and predictive validity of the Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales in the pediatric inpatient setting. Prospective, cohort study of parents and caregivers of patients 1 month to 18 years old (n = 4637) and patients 13 to 18 years old (n = 359) admitted to Seattle Children's Hospital between October 1, 2011, and December 31, 2013. Of 7184 eligible participants invited to complete the survey, 4637 (64.5%) completed the PedsQL on admission, and of these 2694 (58.1%) completed the follow-up survey 2 to 8 weeks after discharge. Responsiveness was assessed by calculating improvement scores (difference between follow-up and admission scores). Construct validity was examined by comparing the mean improvement scores for known groups differing by medical complexity. Predictive validity was assessed using Poisson regression to examine associations among admission scores, prolonged length of stay (≥3 days), and 30-day readmissions or emergency department (ED) return visits. Similar models examined the association between improvement scores and risk for 30-day readmissions or ED return visits. The mean (SD) PedsQL improvement scores (scale, 0-100) were 22.1 (22.7) for total, 29.4 (32.4) for physical, and 17.1 (21.0) for psychosocial. The mean PedsQL total improvement scores were lower for patients with medically complex conditions compared with patients without chronic conditions (13.7 [95% CI, 11.6-15.8] vs. 24.1 [95% CI, 22.4-25.7], P < .001). A 10-point decrement in the PedsQL total admission score below the established community-based mean was associated with an increase in risk for prolonged length of stay (15% [95% CI, 13%-17%]), 30-day readmissions (8% [95% CI, 3%-14%]), and ED return visits (13% [95% CI, 6%-20%]). A 5-point decrement in the PedsQL total improvement score below the study sample mean improvement score was associated with an increase in risk for 30-day readmissions or ED return visits (9% [95% CI, -1% to 19%]). The PedsQL demonstrated responsiveness, construct validity, and predictive validity in hospitalized pediatric patients. The PedsQL may be a useful patient-reported outcome for hospital-based clinical effectiveness research.

Predictors of response to a behavioral treatment in patients with chronic gastric motility disorders

NASA Technical Reports Server (NTRS)

Rashed, Hani; Cutts, Teresa; Abell, Thomas; Cowings, Patricia; Toscano, William; El-Gammal, Ahmed; Adl, Dima

2002-01-01

Chronic gastric motility disorders have proven intractable to most traditional therapies. Twenty-six patients with chronic nausea and vomiting were treated with a behavioral technique, autonomic training (AT) with directed imagery (verbal instructions), to help facilitate physiological control. After treatment, gastrointestinal symptoms decreased by >30% in 58% of the treated patients. We compared those improved patients to the 43% who did not improve significantly. No significant differences existed in baseline symptoms and autonomic measures between both groups. However, baseline measures of gastric emptying and autonomic function predicted treatment outcome. Patients who improved manifested mild to moderate delay in baseline gastric emptying measures. The percent of liquid gastric emptying at 60 mins and the sympathetic adrenergic measure of percent of change in the foot cutaneous blood flow in response to cold stress test predicted improvement in AT outcome, with clinical diagnostic values of 77% and 71%, respectively. We conclude that AT treatment can be efficacious in some patients with impaired gastric emptying and adrenergic dysfunction. More work is warranted to compare biofeedback therapy with gastric motility patients and controls in population-based studies.

ASXL1 and BIM germ line variants predict response and identify CML patients with the greatest risk of imatinib failure

PubMed Central

Marum, Justine E.; Yeung, David T.; Purins, Leanne; Reynolds, John; Parker, Wendy T.; Stangl, Doris; Wang, Paul P. S.; Price, David J.; Tuke, Jonathan; Schreiber, Andreas W.; Scott, Hamish S.; Hughes, Timothy P.

2017-01-01

Scoring systems used at diagnosis of chronic myeloid leukemia (CML), such as Sokal risk, provide important response prediction for patients treated with imatinib. However, the sensitivity and specificity of scoring systems could be enhanced for improved identification of patients with the highest risk. We aimed to identify genomic predictive biomarkers of imatinib response at diagnosis to aid selection of first-line therapy. Targeted amplicon sequencing was performed to determine the germ line variant profile in 517 and 79 patients treated with first-line imatinib and nilotinib, respectively. The Sokal score and ASXL1 rs4911231 and BIM rs686952 variants were independent predictors of early molecular response (MR), major MR, deep MRs (MR4 and MR4.5), and failure-free survival (FFS) with imatinib treatment. In contrast, the ASXL1 and BIM variants did not consistently predict MR or FFS with nilotinib treatment. In the imatinib-treated cohort, neither Sokal or the ASXL1 and BIM variants predicted overall survival (OS) or progression to accelerated phase or blast crisis (AP/BC). The Sokal risk score was combined with the ASXL1 and BIM variants in a classification tree model to predict imatinib response. The model distinguished an ultra-high-risk group, representing 10% of patients, that predicted inferior OS (88% vs 97%; P = .041), progression to AP/BC (12% vs 1%; P = .034), FFS (P < .001), and MRs (P < .001). The ultra-high-risk patients may be candidates for more potent or combination first-line therapy. These data suggest that germ line genetic variation contributes to the heterogeneity of response to imatinib and may contribute to a prognostic risk score that allows early optimization of therapy. PMID:29296778

Improved darunavir genotypic mutation score predicting treatment response for patients infected with HIV-1 subtype B and non-subtype B receiving a salvage regimen.

PubMed

De Luca, Andrea; Flandre, Philippe; Dunn, David; Zazzi, Maurizio; Wensing, Annemarie; Santoro, Maria Mercedes; Günthard, Huldrych F; Wittkop, Linda; Kordossis, Theodoros; Garcia, Federico; Castagna, Antonella; Cozzi-Lepri, Alessandro; Churchill, Duncan; De Wit, Stéphane; Brockmeyer, Norbert H; Imaz, Arkaitz; Mussini, Cristina; Obel, Niels; Perno, Carlo Federico; Roca, Bernardino; Reiss, Peter; Schülter, Eugen; Torti, Carlo; van Sighem, Ard; Zangerle, Robert; Descamps, Diane

2016-05-01

The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir. Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV-1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0). TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27% and raltegravir or maraviroc or enfuvirtide in 53%. The prediction model included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R(2) = 0.47 [average squared error (ASE) = 0.67, P < 10(-6)]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our final model outperformed models with existing interpretation systems in both training and validation sets. A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

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

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score.

PubMed

Cuppen, Bvj; Fritsch-Stork, Rde; Eekhout, I; de Jager, W; Marijnissen, A C; Bijlsma, Jwj; Custers, M; van Laar, J M; Lafeber, Fpjg; Welsing, Pmj

2018-01-01

In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value. In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI). For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11). No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

Plaque Structural Stress Estimations Improve Prediction of Future Major Adverse Cardiovascular Events After Intracoronary Imaging.

PubMed

Brown, Adam J; Teng, Zhongzhao; Calvert, Patrick A; Rajani, Nikil K; Hennessy, Orla; Nerlekar, Nitesh; Obaid, Daniel R; Costopoulos, Charis; Huang, Yuan; Hoole, Stephen P; Goddard, Martin; West, Nick E J; Gillard, Jonathan H; Bennett, Martin R

2016-06-01

Although plaque rupture is responsible for most myocardial infarctions, few high-risk plaques identified by intracoronary imaging actually result in future major adverse cardiovascular events (MACE). Nonimaging markers of individual plaque behavior are therefore required. Rupture occurs when plaque structural stress (PSS) exceeds material strength. We therefore assessed whether PSS could predict future MACE in high-risk nonculprit lesions identified on virtual-histology intravascular ultrasound. Baseline nonculprit lesion features associated with MACE during long-term follow-up (median: 1115 days) were determined in 170 patients undergoing 3-vessel virtual-histology intravascular ultrasound. MACE was associated with plaque burden ≥70% (hazard ratio: 8.6; 95% confidence interval, 2.5-30.6; P<0.001) and minimal luminal area ≤4 mm(2) (hazard ratio: 6.6; 95% confidence interval, 2.1-20.1; P=0.036), although absolute event rates for high-risk lesions remained <10%. PSS derived from virtual-histology intravascular ultrasound was subsequently estimated in nonculprit lesions responsible for MACE (n=22) versus matched control lesions (n=22). PSS showed marked heterogeneity across and between similar lesions but was significantly increased in MACE lesions at high-risk regions, including plaque burden ≥70% (13.9±11.5 versus 10.2±4.7; P<0.001) and thin-cap fibroatheroma (14.0±8.9 versus 11.6±4.5; P=0.02). Furthermore, PSS improved the ability of virtual-histology intravascular ultrasound to predict MACE in plaques with plaque burden ≥70% (adjusted log-rank, P=0.003) and minimal luminal area ≤4 mm(2) (P=0.002). Plaques responsible for MACE had larger superficial calcium inclusions, which acted to increase PSS (P<0.05). Baseline PSS is increased in plaques responsible for MACE and improves the ability of intracoronary imaging to predict events. Biomechanical modeling may complement plaque imaging for risk stratification of coronary nonculprit lesions. © 2016 American Heart Association, Inc.

Neoadjuvant therapy in the treatment of breast cancer

PubMed Central

Teshome, Mediget; Hunt, Kelly K.

2014-01-01

Synopsis Neoadjuvant systemic therapy in the treatment of breast cancer was initially employed for patients with inoperable disease. Over the past several decades this treatment approach has proved beneficial in many other patients including those with early-stage, operable breast cancer. Several randomized prospective studies have shown comparable survival rates when compared with adjuvant systemic therapy. Additionally, neoadjuvant chemotherapy can decrease the tumor burden facilitating breast conservation in selected patients without significant increases in local recurrence. Response to therapy has proven to be a strong predictor of outcome, with patients achieving pathologic complete response (pCR) demonstrating improved survival compared with those achieving less than a pCR. Furthermore, molecular subtype analysis has shown improved response following neoadjuvant chemotherapy in certain tumor types providing patients with the most aggressive subtypes a chance at cure with targeted therapies. In particular, targeting the HER2-positive subtype with trastuzumab and other HER2-directed therapies has markedly improved the outcome in these patients. Conversely, the early recognition of poor responders is important in limiting the toxicity of ineffective therapy and altering management. Neoadjuvant endocrine therapy in postmenopausal women with hormone receptor-positive tumors consistently decreases tumor size improving rates of breast conservation. Aromatase inhibitors have demonstrated superiority to tamoxifen with improved response and favorable toxicity profiles. Imaging modalities have shown promise in predicting patients with pCR, however they have not yet eliminated the need for surgical intervention. Less invasive surgical strategies such as breast conserving surgery and sentinel lymph node dissection have been shown to be safe following neoadjuvant chemotherapy in selected patients. A multidisciplinary approach with primary systemic therapy when indicated, improves the likelihood for breast conservation, provides a window into tumor biology and predicts patient outcomes. PMID:24882348

Modelling of individual subject ozone exposure response kinetics.

PubMed

Schelegle, Edward S; Adams, William C; Walby, William F; Marion, M Susan

2012-06-01

A better understanding of individual subject ozone (O(3)) exposure response kinetics will provide insight into how to improve models used in the risk assessment of ambient ozone exposure. To develop a simple two compartment exposure-response model that describes individual subject decrements in forced expiratory volume in one second (FEV(1)) induced by the acute inhalation of O(3) lasting up to 8 h. FEV(1) measurements of 220 subjects who participated in 14 previously completed studies were fit to the model using both particle swarm and nonlinear least squares optimization techniques to identify three subject-specific coefficients producing minimum "global" and local errors, respectively. Observed and predicted decrements in FEV(1) of the 220 subjects were used for validation of the model. Further validation was provided by comparing the observed O(3)-induced FEV(1) decrements in an additional eight studies with predicted values obtained using model coefficients estimated from the 220 subjects used in cross validation. Overall the individual subject measured and modeled FEV(1) decrements were highly correlated (mean R(2) of 0.69 ± 0.24). In addition, it was shown that a matrix of individual subject model coefficients can be used to predict the mean and variance of group decrements in FEV(1). This modeling approach provides insight into individual subject O(3) exposure response kinetics and provides a potential starting point for improving the risk assessment of environmental O(3) exposure.

Incorporating redox processes improves prediction of carbon and nutrient cycling and greenhouse gas emission

NASA Astrophysics Data System (ADS)

Tang, Guoping; Zheng, Jianqiu; Yang, Ziming; Graham, David; Gu, Baohua; Mayes, Melanie; Painter, Scott; Thornton, Peter

2016-04-01

Among the coupled thermal, hydrological, geochemical, and biological processes, redox processes play major roles in carbon and nutrient cycling and greenhouse gas (GHG) emission. Increasingly, mechanistic representation of redox processes is acknowledged as necessary for accurate prediction of GHG emission in the assessment of land-atmosphere interactions. Simple organic substrates, Fe reduction, microbial reactions, and the Windermere Humic Aqueous Model (WHAM) were added to a reaction network used in the land component of an Earth system model. In conjunction with this amended reaction network, various temperature response functions used in ecosystem models were assessed for their ability to describe experimental observations from incubation tests with arctic soils. Incorporation of Fe reduction reactions improves the prediction of the lag time between CO2 and CH4 accumulation. The inclusion of the WHAM model enables us to approximately simulate the initial pH drop due to organic acid accumulation and then a pH increase due to Fe reduction without parameter adjustment. The CLM4.0, CENTURY, and Ratkowsky temperature response functions better described the observations than the Q10 method, Arrhenius equation, and ROTH-C. As electron acceptors between O2 and CO2 (e.g., Fe(III), SO42-) are often involved, our results support inclusion of these redox reactions for accurate prediction of CH4 production and consumption. Ongoing work includes improving the parameterization of organic matter decomposition to produce simple organic substrates, examining the influence of redox potential on methanogenesis under thermodynamically favorable conditions, and refining temperature response representation near the freezing point by additional model-experiment iterations. We will use the model to describe observed GHG emission at arctic and tropical sites.

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.

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.

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.

Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

PubMed

Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

2015-07-01

Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

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

Spacecraft Charging and Auroral Boundary Predictions in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Minow, Joseph I.

2016-01-01

Auroral charging of spacecraft is an important class of space weather impacts on technological systems in low Earth orbit. In order for space weather models to accurately specify auroral charging environments, they must provide the appropriate plasma environment characteristics responsible for charging. Improvements in operational space weather prediction capabilities relevant to charging must be tested against charging observations.

Future of Earth Orientation Predictions

DTIC Science & Technology

2010-01-01

introduced into the prediction process will increase . Potential drivers for change are discussed and possible directions for change are outlined. Keywords...is increasing as data latency has been reduced. However, all of these have been natural progressions; straightforward responses to improvements in...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Naval Observatory,3450 Massachusetts Ave NW,Washington,DC,20392 8. PERFORMING ORGANIZATION

Intolerance of uncertainty and transdiagnostic group cognitive behavioral therapy for anxiety.

PubMed

Talkovsky, Alexander M; Norton, Peter J

2016-06-01

Recent evidence suggests intolerance of uncertainty (IU) is a transdiagnostic variable elevated across anxiety disorders. No studies have investigated IU's response to transdiagnostic group CBT for anxiety (TGCBT). This study evaluated IU outcomes following TGCBT across anxiety disorders. 151 treatment-seekers with primary diagnoses of social anxiety disorder, panic disorder, or GAD were evaluated before and after 12 weeks of TGCBT and completed self-report questionnaires at pre-, mid-, and post-treatment. IU decreased significantly following treatment. Decreases in IU predicted improvements in clinical presentation across diagnoses. IU interacted with time to predict improvement in clinical presentation irrespective of primary diagnosis. IU also interacted with time to predict improvement in clinical presentation although interactions of time with diagnosis-specific measures did not. IUS interacted with time to predict reduction in anxiety and fear symptoms, and inhibitory IU interacted with time to predicted reductions in anxiety symptoms but prospective IU did not. IU appears to be an important transdiagnostic variable in CBT implicated in both initial presentation and treatment change. Further implications are discussed. Published by Elsevier Ltd.

Consumer factors predicting level of treatment response to illness management and recovery.

PubMed

White, Dominique A; McGuire, Alan B; Luther, Lauren; Anderson, Adrienne I; Phalen, Peter; McGrew, John H

2017-12-01

This study aims to identify consumer-level predictors of level of treatment response to illness management and recovery (IMR) to target the appropriate consumers and aid psychiatric rehabilitation settings in developing intervention adaptations. Secondary analyses from a multisite study of IMR were conducted. Self-report data from consumer participants of the parent study (n = 236) were analyzed for the current study. Consumers completed prepost surveys assessing illness management, coping, goal-related hope, social support, medication adherence, and working alliance. Correlations and multiple regression analyses were run to identify self-report variables that predicted level of treatment response to IMR. Analyses revealed that goal-related hope significantly predicted level of improved illness self-management, F(1, 164) = 10.93, p < .001, R2 = .248, R2 change = .05. Additionally, we found that higher levels of maladaptive coping at baseline were predictive of higher levels of adaptive coping at follow-up, F(2, 180) = 5.29, p < .02, R2 = .38, R2 change = .02. Evidence did not support additional predictors. Previously, consumer-level predictors of level of treatment response have not been explored for IMR. Although 2 significant predictors were identified, study findings suggest more work is needed. Future research is needed to identify additional consumer-level factors predictive of IMR treatment response in order to identify who would benefit most from this treatment program. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Prediction of the space adaptation syndrome

NASA Technical Reports Server (NTRS)

Reschke, M. F.; Homick, J. L.; Ryan, P.; Moseley, E. C.

1984-01-01

The univariate and multivariate relationships of provocative measures used to produce motion sickness symptoms were described. Normative subjects were used to develop and cross-validate sets of linear equations that optimally predict motion sickness in parabolic flights. The possibility of reducing the number of measurements required for prediction was assessed. After describing the variables verbally and statistically for 159 subjects, a factor analysis of 27 variables was completed to improve understanding of the relationships between variables and to reduce the number of measures for prediction purposes. The results of this analysis show that none of variables are significantly related to the responses to parabolic flights. A set of variables was selected to predict responses to KC-135 flights. A series of discriminant analyses were completed. Results indicate that low, moderate, or severe susceptibility could be correctly predicted 64 percent and 53 percent of the time on original and cross-validation samples, respectively. Both the factor analysis and the discriminant analysis provided no basis for reducing the number of tests.

Genotype-phenotype associations in French patients with phenylketonuria and importance of genotype for full assessment of tetrahydrobiopterin responsiveness.

PubMed

Jeannesson-Thivisol, Elise; Feillet, François; Chéry, Céline; Perrin, Pascal; Battaglia-Hsu, Shyue-Fang; Herbeth, Bernard; Cano, Aline; Barth, Magalie; Fouilhoux, Alain; Mention, Karine; Labarthe, François; Arnoux, Jean-Baptiste; Maillot, François; Lenaerts, Catherine; Dumesnil, Cécile; Wagner, Kathy; Terral, Daniel; Broué, Pierre; de Parscau, Loïc; Gay, Claire; Kuster, Alice; Bédu, Antoine; Besson, Gérard; Lamireau, Delphine; Odent, Sylvie; Masurel, Alice; Guéant, Jean-Louis; Namour, Fares

2015-12-15

Mutations in Phenylalanine Hydroxylase (PAH) gene cause phenylketonuria. Sapropterin (BH4), the enzyme cofactor, is an important therapeutical strategy in phenylketonuria. However, PAH is a highly polymorphic gene and it is difficult to identify BH4-responsive genotypes. We seek here to improve prediction of BH4-responsiveness through comparison of genotypes, BH4-loading test, predictions of responsiveness according to the literature and types and locations of mutations. A total of 364 French patients among which, 9 % had mild hyperphenylalaninemia, 17.7 % mild phenylketonuria and 73.1 % classical phenylketonuria, benefited from a 24-hour BH4-loading test and had the PAH gene sequenced and analyzed by Multiplex Ligation Probe Amplification. Overall, 31.6 % of patients were BH4-responsive. The number of different mutations found was 127, including 26 new mutations. The mutations c.434A > T, c.500A > T, c.529G > C, c.1045 T > G and c.1196 T > C were newly classified as being BH4-responsive. We identified 261 genotypes, among which 46 were newly recognized as being BH4-responsive. Even though patients carry 2 responsive alleles, BH4-responsiveness cannot be predicted with certainty unless they present mild hyperphenylalaninemia. BH4-responsiveness cannot be predicted in patients carrying one responsive mutation only. In general, the milder the phenotype is, the stronger the BH4-response is. Almost exclusively missense mutations, particularly in exons 12, 11 and 8, are associated with BH4-responsiveness and any other type of mutation predicts a negative response. This study is the first of its kind, in a French population, to identify the phenotype associated with several combinations of PAH mutations. As others, it highlights the necessity of performing simultaneously BH4 loading test and molecular analysis in monitoring phenylketonuria patients.

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.

Modeling Rabbit Responses to Single and Multiple Aerosol ...

EPA Pesticide Factsheets

Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

California Drought and the 2015-2016 El Niño: Implications for Seasonal Forecasts

NASA Astrophysics Data System (ADS)

Cash, B.

2017-12-01

California winter rainfall is examined in observations and data from the North American Multi-Model Ensemble (NMME) and Project Metis, a new suite of seasonal integrations made using the operational European Centre for Medium-Range Weather Forecasts model. We focus on the 2015-2016 season, and the non-canonical response to the major El Niño event that occurred. We show that the Metis ensemble mean is capable of distinguishing between the response to the 1997/98 and 2015/16 events, while the two events are more similar in the NMME. We also show that unpredicted variations in the atmospheric circulation in the north Pacific significantly affect southern California rainfall totals. Improving prediction of these variations is thus a key target for improving seasonal rainfall predictions for this region.

Auditory and visual P300 evoked potentials do not predict response to valproate treatment of aggression in patients with borderline and antisocial personality disorders.

PubMed

Reeves, Roy R; Struve, Frederick A; Patrick, Gloria

2005-01-01

In this study of patients with borderline personality disorder (BPD) or antisocial personality disorder (ASPD) hospitalized because of aggressive behavior, auditory and visual P300 evoked potentials were obtained prior to treatment with valproate. Eight ASPD patients (8 males, 0 females) and 11 BPD patients (2 males, 9 females) showed improvement, while in 7 patients with ASPD (7 males, 0 females) and 10 patients with BPD (2 males, 8 females), aggression was not improved. Differences in auditory and visual P300 latencies and amplitudes were not significant for either diagnosis, or for both diagnoses combined. These findings suggest that auditory or visual P300 evoked potentials may not be useful for predicting response of aggressive behavior to valproate treatment in patients with BPD or ASPD.

Kinetic microplate bioassays for relative potency of antibiotics improved by partial Least Square (PLS) regression.

PubMed

Francisco, Fabiane Lacerda; Saviano, Alessandro Morais; Almeida, Túlia de Souza Botelho; Lourenço, Felipe Rebello

2016-05-01

Microbiological assays are widely used to estimate the relative potencies of antibiotics in order to guarantee the efficacy, safety, and quality of drug products. Despite of the advantages of turbidimetric bioassays when compared to other methods, it has limitations concerning the linearity and range of the dose-response curve determination. Here, we proposed to use partial least squares (PLS) regression to solve these limitations and to improve the prediction of relative potencies of antibiotics. Kinetic-reading microplate turbidimetric bioassays for apramacyin and vancomycin were performed using Escherichia coli (ATCC 8739) and Bacillus subtilis (ATCC 6633), respectively. Microbial growths were measured as absorbance up to 180 and 300min for apramycin and vancomycin turbidimetric bioassays, respectively. Conventional dose-response curves (absorbances or area under the microbial growth curve vs. log of antibiotic concentration) showed significant regression, however there were significant deviation of linearity. Thus, they could not be used for relative potency estimations. PLS regression allowed us to construct a predictive model for estimating the relative potencies of apramycin and vancomycin without over-fitting and it improved the linear range of turbidimetric bioassay. In addition, PLS regression provided predictions of relative potencies equivalent to those obtained from agar diffusion official methods. Therefore, we conclude that PLS regression may be used to estimate the relative potencies of antibiotics with significant advantages when compared to conventional dose-response curve determination. Copyright © 2016 Elsevier B.V. All rights reserved.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers*

PubMed Central

Hyung, Seok-Won; Lee, Min Young; Yu, Jong-Han; Shin, Byunghee; Jung, Hee-Jung; Park, Jong-Moon; Han, Wonshik; Lee, Kyung-Min; Moon, Hyeong-Gon; Zhang, Hui; Aebersold, Ruedi; Hwang, Daehee; Lee, Sang-Won; Yu, Myeong-Hee; Noh, Dong-Young

2011-01-01

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies. PMID:21799047

Predicting responses to climate change requires all life-history stages.

PubMed

Zeigler, Sara

2013-01-01

In Focus: Radchuk, V., Turlure, C. & Schtickzelle, N. (2013) Each life stage matters: the importance of assessing response to climate change over the complete life cycle in butterflies. Journal of Animal Ecology, 82, 275-285. Population-level responses to climate change depend on many factors, including unexpected interactions among life history attributes; however, few studies examine climate change impacts over complete life cycles of focal species. Radchuk, Turlure & Schtickzelle () used experimental and modelling approaches to predict population dynamics for the bog fritillary butterfly under warming scenarios. Although they found that warming improved fertility and survival of all stages with one exception, populations were predicted to decline because overwintering larvae, whose survival declined with warming, were disproportionately important contributors to population growth. This underscores the importance of considering all life history stages in analyses of climate change's effects on population dynamics. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

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.

SU-E-J-275: Review - Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

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

Lu, W; Wang, J; Zhang, H

Purpose: To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy. Methods: We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation. Results: Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss ormore » gain, and motion. Many semi-automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi-automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi-modality segmentation methods have been shown to improve accuracy compared to single-modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response. Conclusion: This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

The 5-HTTLPR and BDNF polymorphisms moderate the association between uncinate fasciculus connectivity and antidepressants treatment response in major depression.

PubMed

Tatham, Erica L; Hall, Geoff B C; Clark, Darren; Foster, Jane; Ramasubbu, Rajamannar

2017-03-01

Symptom improvement in depression due to antidepressant treatment is highly variable and clinically unpredictable. Linking neuronal connectivity and genetic risk factors in predicting antidepressant response has clinical implications. Our investigation assessed whether indices of white matter integrity, serotonin transporter-linked polymorphism (5-HTTLPR) and brain-derived neurotrophic factor (BDNF) val66met polymorphism predicted magnitude of depression symptom change following antidepressant treatment. Fractional anisotropy (FA) was used as an indicator of white matter integrity and was assessed in the uncinate fasciculus and superior longitudinal fasciculus using tract-based spatial statistics (TBSS) and probabilistic tractography. Forty-six medication-free patients with major depressive disorder participated in a diffusion tensor imaging scan prior to completing an 8-week treatment regime with citalopram or quetiapine XR. Indexed improvements in Hamilton Depression Rating Scale score from baseline to 8-week endpoint were used as an indicator of depression improvement. Carriers of the BDNF met allele exhibited lower FA values in the left uncinate fasciculus relative to val/val individuals [F(1, 40) = 7.314, p = 0.009]. Probabilistic tractography identified that higher FA in the left uncinate fasciculus predicted percent change in depression severity, with BDNF moderating this association [F(3, 30) = 3.923, p = 0.018]. An interaction between FA in the right uncinate fasciculus and 5-HTTLPR also predicted percent change in depression severity [F(5, 25) = 5.315, p = 0.002]. Uncorrected TBSS results revealed significantly higher FA in hippocampal portions of the cingulum bundle in responders compared to non-responders (p = 0.016). The predictive value of prefrontal and amygdala/hippocampal WM connectivity on antidepressant treatment response may be influenced by 5-HTTLPR and BDNF polymorphisms in MDD.

Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapeutics.

PubMed

Strug, Lisa J; Gonska, Tanja; He, Gengming; Keenan, Katherine; Ip, Wan; Boëlle, Pierre-Yves; Lin, Fan; Panjwani, Naim; Gong, Jiafen; Li, Weili; Soave, David; Xiao, Bowei; Tullis, Elizabeth; Rabin, Harvey; Parkins, Michael D; Price, April; Zuberbuhler, Peter C; Corvol, Harriet; Ratjen, Felix; Sun, Lei; Bear, Christine E; Rommens, Johanna M

2016-10-15

Cystic fibrosis is realizing the promise of personalized medicine. Recent advances in drug development that target the causal CFTR directly result in lung function improvement, but variability in response is demanding better prediction of outcomes to improve management decisions. The genetic modifier SLC26A9 contributes to disease severity in the CF pancreas and intestine at birth and here we assess its relationship with disease severity and therapeutic response in the airways. SLC26A9 association with lung disease was assessed in individuals from the Canadian and French CF Gene Modifier consortia with CFTR-gating mutations and in those homozygous for the common Phe508del mutation. Variability in response to a CFTR-directed therapy attributed to SLC26A9 genotype was assessed in Canadian patients with gating mutations. A primary airway model system determined if SLC26A9 shows modification of Phe508del CFTR function upon treatment with a CFTR corrector. In those with gating mutations that retain cell surface-localized CFTR we show that SLC26A9 modifies lung function while this is not the case in individuals homozygous for Phe508del where cell surface expression is lacking. Treatment response to ivacaftor, which aims to improve CFTR-channel opening probability in patients with gating mutations, shows substantial variability in response, 28% of which can be explained by rs7512462 in SLC26A9 (P = 0.0006). When homozygous Phe508del primary bronchial cells are treated to restore surface CFTR, SLC26A9 likewise modifies treatment response (P = 0.02). Our findings indicate that SLC26A9 airway modification requires CFTR at the cell surface, and that a common variant in SLC26A9 may predict response to CFTR-directed therapeutics.

Identifying and Predicting Classes of Response to Explicit Phonological Spelling Instruction during Independent Composing

ERIC Educational Resources Information Center

Amtmann, Dagmar; Abbott, Robert D.; Berninger, Virginia W.

2008-01-01

After explicit spelling instruction, low achieving second grade spellers increased the number of correctly spelled words during composing but differed in response trajectories. Class 1 (low initial and slow growth) had the lowest initial performance and improved at a relatively slow rate. Class 2 (high initial and fast growth) started higher than…

TThe role of nitrogen availability in land-atmosphere interactions: a systematic evaluation of carbon-nitrogen coupling in a global land surface model using plot-level nitrogen fertilization experiments

NASA Astrophysics Data System (ADS)

Thomas, R. Q.; Goodale, C. L.; Bonan, G. B.; Mahowald, N. M.; Ricciuto, D. M.; Thornton, P. E.

2010-12-01

Recent research from global land surface models emphasizes the important role of nitrogen cycling on global climate, via its control on the terrestrial carbon balance. Despite the implications of nitrogen cycling on global climate predictions, the research community has not performed a systematic evaluation of nitrogen cycling in global models. Here, we present such an evaluation for one global land model, CLM-CN. In the evaluation we simulated 45 plot-scale nitrogen-fertilization experiments distributed across 33 temperate and boreal forest sites. Model predictions were evaluated against field observations by comparing the vegetation and soil carbon responses to the additional nitrogen. Aggregated across all experiments, the model predicted a larger vegetation carbon response and a smaller soil carbon response than observed; the responses partially offset each other, leading to a slightly larger total ecosystem carbon response than observed. However, the model-observation agreement improved for vegetation carbon when the sites with observed negative carbon responses to nitrogen were excluded, which may be because the model lacks mechanisms whereby nitrogen additions increase tree mortality. Among experiments, younger forests and boreal forests’ vegetation carbon responses were less than predicted and mature forests (> 40 years old) were greater than predicted. Specific to the CLM-CN, this study used a systematic evaluation to identify key areas to focus model development, especially soil carbon- nitrogen interactions and boreal forest nitrogen cycling. Applicable to the modeling community, this study demonstrates a standardized protocol for comparing carbon-nitrogen interactions among global land models.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

USGS Publications Warehouse

Vitousek, Sean; Barnard, Patrick; Limber, Patrick W.; Erikson, Li; Cole, Blake

2017-01-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

Tailor-made heart simulation predicts the effect of cardiac resynchronization therapy in a canine model of heart failure.

PubMed

Panthee, Nirmal; Okada, Jun-ichi; Washio, Takumi; Mochizuki, Youhei; Suzuki, Ryohei; Koyama, Hidekazu; Ono, Minoru; Hisada, Toshiaki; Sugiura, Seiryo

2016-07-01

Despite extensive studies on clinical indices for the selection of patient candidates for cardiac resynchronization therapy (CRT), approximately 30% of selected patients do not respond to this therapy. Herein, we examined whether CRT simulations based on individualized realistic three-dimensional heart models can predict the therapeutic effect of CRT in a canine model of heart failure with left bundle branch block. In four canine models of failing heart with dyssynchrony, individualized three-dimensional heart models reproducing the electromechanical activity of each animal were created based on the computer tomographic images. CRT simulations were performed for 25 patterns of three ventricular pacing lead positions. Lead positions producing the best and the worst therapeutic effects were selected in each model. The validity of predictions was tested in acute experiments in which hearts were paced from the sites identified by simulations. We found significant correlations between the experimentally observed improvement in ejection fraction (EF) and the predicted improvements in ejection fraction (P<0.01) or the maximum value of the derivative of left ventricular pressure (P<0.01). The optimal lead positions produced better outcomes compared with the worst positioning in all dogs studied, although there were significant variations in responses. Variations in ventricular wall thickness among the dogs may have contributed to these responses. Thus CRT simulations using the individualized three-dimensional heart models can predict acute hemodynamic improvement, and help determine the optimal positions of the pacing lead. Copyright © 2016 Elsevier B.V. All rights reserved.

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

NASA Astrophysics Data System (ADS)

Vitousek, Sean; Barnard, Patrick L.; Limber, Patrick; Erikson, Li; Cole, Blake

2017-04-01

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea level rise scenarios of 0.93 to 2.0 m.

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

PubMed

Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

2013-12-23

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

Integrating environmental and genetic effects to predict responses of tree populations to climate.

PubMed

Wang, Tongli; O'Neill, Gregory A; Aitken, Sally N

2010-01-01

Climate is a major environmental factor affecting the phenotype of trees and is also a critical agent of natural selection that has molded among-population genetic variation. Population response functions describe the environmental effect of planting site climates on the performance of a single population, whereas transfer functions describe among-population genetic variation molded by natural selection for climate. Although these approaches are widely used to predict the responses of trees to climate change, both have limitations. We present a novel approach that integrates both genetic and environmental effects into a single "universal response function" (URF) to better predict the influence of climate on phenotypes. Using a large lodgepole pine (Pinus contorta Dougl. ex Loud.) field transplant experiment composed of 140 populations planted on 62 sites to demonstrate the methodology, we show that the URF makes full use of data from provenance trials to: (1) improve predictions of climate change impacts on phenotypes; (2) reduce the size and cost of future provenance trials without compromising predictive power; (3) more fully exploit existing, less comprehensive provenance tests; (4) quantify and compare environmental and genetic effects of climate on population performance; and (5) predict the performance of any population growing in any climate. Finally, we discuss how the last attribute allows the URF to be used as a mechanistic model to predict population and species ranges for the future and to guide assisted migration of seed for reforestation, restoration, or afforestation and genetic conservation in a changing climate.

AREDS simplified severity scale as a predictive factor for response to aflibercept therapy for typical neovascular age-related macular degeneration.

PubMed

Sakurada, Yoichi; Kikushima, Wataru; Sugiyama, Atsushi; Yoneyama, Seigo; Tanabe, Naohiko; Matsubara, Mio; Iijima, Hiroyuki

2018-01-01

To investigate whether the severity of the condition in the untreated fellow eye is a predictive factor for the response to intravitreal aflibercept injection (IAI) for exudative age-related macular degeneration (AMD). A retrospective medical chart review was conducted for 88 patients with treatment-naïve neovascular AMD, who were initially treated with three monthly IAIs, followed by monthly monitoring and re-injection as needed for at least 12 months. Subjects were classified into three groups according to the severity of the condition in their untreated eye, based on the severity scale in the Age-Related Eye Disease Study (AREDS): group 0, AREDS severity level 1 (no drusen); group 1, AREDS severity level 2 or 3 (any drusen); group 2, AREDS severity level 4 (advanced AMD). Genotyping was performed in all cases for ARMS2 A69S and CFH I62V. Fellow-eye severity was associated with age and the risk variant of ARMS2 A69S (P = 0.005 and 0.001, respectively). Although best-corrected visual acuity (BCVA) had improved significantly after 12 months in all groups, this improvement was significantly greater in group 0 than in the other groups (P = 0.008). The retreatment-free period was also significantly longer for group 0 than for the other groups (P = 0.016), and the number of additional injections was significantly associated with fellow-eye severity (P = 0.007). Fellow-eye severity was associated with treatment response in terms of visual improvement and retreatment and may be a predictive factor for response to IAI for neovascular AMD.

Advances and trends in computational structural mechanics

NASA Technical Reports Server (NTRS)

Noor, A. K.

1986-01-01

Recent developments in computational structural mechanics are reviewed with reference to computational needs for future structures technology, advances in computational models for material behavior, discrete element technology, assessment and control of numerical simulations of structural response, hybrid analysis, and techniques for large-scale optimization. Research areas in computational structural mechanics which have high potential for meeting future technological needs are identified. These include prediction and analysis of the failure of structural components made of new materials, development of computational strategies and solution methodologies for large-scale structural calculations, and assessment of reliability and adaptive improvement of response predictions.

A Population Genetics Model of Marker-Assisted Selection

PubMed Central

Luo, Z. W.; Thompson, R.; Woolliams, J. A.

1997-01-01

A deterministic two-loci model was developed to predict genetic response to marker-assisted selection (MAS) in one generation and in multiple generations. Formulas were derived to relate linkage disequilibrium in a population to the proportion of additive genetic variance used by MAS, and in turn to an extra improvement in genetic response over phenotypic selection. Predictions of the response were compared to those predicted by using an infinite-loci model and the factors affecting efficiency of MAS were examined. Theoretical analyses of the present study revealed the nonlinearity between the selection intensity and genetic response in MAS. In addition to the heritability of the trait and the proportion of the marker-associated genetic variance, the frequencies of the selectively favorable alleles at the two loci, one marker and one quantitative trait locus, were found to play an important role in determining both the short- and long-term efficiencies of MAS. The evolution of linkage disequilibrium and thus the genetic response over several generations were predicted theoretically and examined by simulation. MAS dissipated the disequilibrium more quickly than drift alone. In some cases studied, the rate of dissipation was as large as that to be expected in the circumstance where the true recombination fraction was increased by three times and selection was absent. PMID:9215918

Improving rice models for more reliable prediction of responses of rice yield to CO2 and temperature elevaton

USDA-ARS?s Scientific Manuscript database

Materials and Methods The simulation exercise and model improvement were implemented in phase-wise. In the first modelling activities, the model sensitivities were evaluated to given CO2 concentrations varying from 360 to 720 'mol mol-1 at an interval of 90 'mol mol-1 and air temperature increments...

Sensitivity of predicted bioaerosol exposure from open windrow composting facilities to ADMS dispersion model parameters.

PubMed

Douglas, P; Tyrrel, S F; Kinnersley, R P; Whelan, M; Longhurst, P J; Walsh, K; Pollard, S J T; Drew, G H

2016-12-15

Bioaerosols are released in elevated quantities from composting facilities and are associated with negative health effects, although dose-response relationships are not well understood, and require improved exposure classification. Dispersion modelling has great potential to improve exposure classification, but has not yet been extensively used or validated in this context. We present a sensitivity analysis of the ADMS dispersion model specific to input parameter ranges relevant to bioaerosol emissions from open windrow composting. This analysis provides an aid for model calibration by prioritising parameter adjustment and targeting independent parameter estimation. Results showed that predicted exposure was most sensitive to the wet and dry deposition modules and the majority of parameters relating to emission source characteristics, including pollutant emission velocity, source geometry and source height. This research improves understanding of the accuracy of model input data required to provide more reliable exposure predictions. Copyright © 2016. Published by Elsevier Ltd.

Personality traits predict treatment outcome with an antidepressant in patients with functional gastrointestinal disorder.

PubMed

Tanum, L; Malt, U F

2000-09-01

We investigated the relationship between personality traits and response to treatment with the tetracyclic antidepressant mianserin or placebo in patients with functional gastrointestinal disorder (FGD) without psychopathology. Forty-eight patients completed the Buss-Durkee Hostility Inventory, Neuroticism Extroversion Openness -Personality Inventory (NEO-PI), and Eysenck Personality Questionnaire (EPQ), neuroticism + lie subscales, before they were consecutively allocated to a 7-week double-blind treatment study with mianserin or placebo. Treatment response to pain and target symptoms were recorded daily with the Visual Analogue Scale and Clinical Global Improvement Scale at every visit. A low level of neuroticism and little concealed aggressiveness predicted treatment outcome with the antidepressant drug mianserin in non-psychiatric patients with FGD. Inversely, moderate to high neuroticism and marked concealed aggressiveness predicted poor response to treatment. These findings were most prominent in women. Personality traits were better predictors of treatment outcome than serotonergic sensitivity assessed with the fenfluramine test. Assessment of the personality traits negativism, irritability, aggression, and neuroticism may predict response to drug treatment of FGD even when serotonergic sensitivity is controlled for. If confirmed in future studies, the findings point towards a more differential psychopharmacologic treatment of FGD.

Characterization of structural connections using free and forced response test data

NASA Technical Reports Server (NTRS)

Lawrence, Charles; Huckelbridge, Arthur A.

1989-01-01

The accurate prediction of system dynamic response often has been limited by deficiencies in existing capabilities to characterize connections adequately. Connections between structural components often are complex mechanically, and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic preditions. A procedure for identifying physical connection properties from free and forced response test data is developed, then verified utilizing a system having both a linear and nonlinear connection. Connection properties are computed in terms of physical parameters so that the physical characteristics of the connections can better be understood, in addition to providing improved input for the system model. The identification procedure is applicable to multi-degree of freedom systems, and does not require that the test data be measured directly at the connection locations.

Incorporation of Dynamic SSI Effects in the Design Response Spectra

NASA Astrophysics Data System (ADS)

Manjula, N. K.; Pillai, T. M. Madhavan; Nagarajan, Praveen; Reshma, K. K.

2018-05-01

Many studies in the past on dynamic soil-structure interactions have revealed the detrimental and advantageous effects of soil flexibility. Based on such studies, the design response spectra of international seismic codes are being improved worldwide. The improvements required for the short period range of the design response spectra in the Indian seismic code (IS 1893:2002) are presented in this paper. As the recent code revisions has not incorporated the short period amplifications, proposals given in this paper are equally applicable for the latest code also (IS 1893:2016). Analyses of single degree of freedom systems are performed to predict the required improvements. The proposed modifications to the constant acceleration portion of the spectra are evaluated with respect to the current design spectra in Eurocode 8.

Pilot study of pyridostigmine in constipated patients with autonomic neuropathy

PubMed Central

Bharucha, Adil E.; Camilleri, Michael; Burton, Duane; Low, Phillip A.; Gehrking, Tonette L.; Zinsmeister, Alan R.

2008-01-01

Background The effects of cholinesterase inhibitors, which increase colonic motility in health, on chronic constipation are unknown. Our aims were to evaluate the efficacy of cholinesterase inhibitors for dysautonomia and chronic constipation and to assess whether acute effects could predict the long term response. Methods In this single-blind study, 10 patients with autonomic neuropathy and constipation were treated with placebo (2 weeks), followed by an escalating dose of pyridostigmine to the maximum tolerated dose (i.e., 180–540 mg daily) for 6 weeks. Symptoms and gastrointestinal transit were assessed at 2 and 8 weeks. The acute effects of neostigmine on colonic transit and motility were also assessed. Results At baseline, 4, 6, and 3 patients had delayed gastric, small intestinal, and colonic transit respectively. Pyridostigmine was well tolerated in most patients, improved symptoms in 4 patients, and accelerated the geometric center for colonic transit at 24 h by ≥0.7 unit in 3 patients. The effects of i.v. neostigmine on colonic transit and compliance predicted (P < 0.05) the effects of pyridostigmine on colonic transit. Conclusions Pyridostigmine improves colonic transit and symptoms in some patients with autonomic neuropathy and constipation. The motor response to neostigmine predicted the response to oral pyridostigmine. PMID:18622640

Behavior and neuroimaging at baseline predict individual response to combined mathematical and working memory training in children.

PubMed

Nemmi, Federico; Helander, Elin; Helenius, Ola; Almeida, Rita; Hassler, Martin; Räsänen, Pekka; Klingberg, Torkel

2016-08-01

Mathematical performance is highly correlated with several general cognitive abilities, including working memory (WM) capacity. Here we investigated the effect of numerical training using a number-line (NLT), WM training (WMT), or the combination of the two on a composite score of mathematical ability. The aim was to investigate if the combination contributed to the outcome, and determine if baseline performance or neuroimaging predict the magnitude of improvement. We randomly assigned 308, 6-year-old children to WMT, NLT, WMT+NLT or a control intervention. Overall, there was a significant effect of NLT but not WMT. The WMT+NLT was the only group that improved significantly more than the controls, although the interaction NLTxWM was non-significant. Higher WM and maths performance predicted larger benefits for WMT and NLT, respectively. Neuroimaging at baseline also contributed significant information about training gain. Different individuals showed as much as a three-fold difference in their responses to the same intervention. These results show that the impact of an intervention is highly dependent on individual characteristics of the child. If differences in responses could be used to optimize the intervention for each child, future interventions could be substantially more effective. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Extended charge banking model of dual path shocks for implantable cardioverter defibrillators

PubMed Central

Dosdall, Derek J; Sweeney, James D

2008-01-01

Background Single path defibrillation shock methods have been improved through the use of the Charge Banking Model of defibrillation, which predicts the response of the heart to shocks as a simple resistor-capacitor (RC) circuit. While dual path defibrillation configurations have significantly reduced defibrillation thresholds, improvements to dual path defibrillation techniques have been limited to experimental observations without a practical model to aid in improving dual path defibrillation techniques. Methods The Charge Banking Model has been extended into a new Extended Charge Banking Model of defibrillation that represents small sections of the heart as separate RC circuits, uses a weighting factor based on published defibrillation shock field gradient measures, and implements a critical mass criteria to predict the relative efficacy of single and dual path defibrillation shocks. Results The new model reproduced the results from several published experimental protocols that demonstrated the relative efficacy of dual path defibrillation shocks. The model predicts that time between phases or pulses of dual path defibrillation shock configurations should be minimized to maximize shock efficacy. Discussion Through this approach the Extended Charge Banking Model predictions may be used to improve dual path and multi-pulse defibrillation techniques, which have been shown experimentally to lower defibrillation thresholds substantially. The new model may be a useful tool to help in further improving dual path and multiple pulse defibrillation techniques by predicting optimal pulse durations and shock timing parameters. PMID:18673561

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.

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

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.

Centralized and Decentralized Control for Demand Response

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

Lu, Shuai; Samaan, Nader A.; Diao, Ruisheng

2011-04-29

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generation resources are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their relative advantages and disadvantages in terms of delay time, predictability, complexity,more » and reliability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the predictability and simplicity of centralized control to achieve the best performance of the smart grid.« less

Impediments to predicting site response: Seismic property estimation and modeling simplifications

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Guzina, B.B.

2009-01-01

We compare estimates of the empirical transfer function (ETF) to the plane SH-wave theoretical transfer function (TTF) within a laterally constant medium for invasive and noninvasive estimates of the seismic shear-wave slownesses at 13 Kiban-Kyoshin network stations throughout Japan. The difference between the ETF and either of the TTFs is substantially larger than the difference between the two TTFs computed from different estimates of the seismic properties. We show that the plane SH-wave TTF through a laterally homogeneous medium at vertical incidence inadequately models observed amplifications at most sites for both slowness estimates, obtained via downhole measurements and the spectral analysis of surface waves. Strategies to improve the predictions can be separated into two broad categories: improving the measurement of soil properties and improving the theory that maps the 1D soil profile onto spectral amplification. Using an example site where the 1D plane SH-wave formulation poorly predicts the ETF, we find a more satisfactory fit to the ETF by modeling the full wavefield and incorporating spatially correlated variability of the seismic properties. We conclude that our ability to model the observed site response transfer function is limited largely by the assumptions of the theoretical formulation rather than the uncertainty of the soil property estimates.

Does CSF outflow resistance predict the response to shunting in patients with normal pressure hydrocephalus?

PubMed

Boon, A J; Tans, J T; Delwel, E J; Egeler-Peerdeman, S M; Hanlo, P W; Wurzer, J A; Avezaat, C J; de Jong, D A; Gooskens, R H; Hermans, J

1998-01-01

The value of the measurements of CSF outflow resistance (Rcsf) relative to predicting outcome after shunting was studied. In a group of 101 patients with mainly idiopathic normal pressure hydrocephalus (NPH) Rcsf was obtained by lumbar constant flow infusion. Gait disturbance and dementia were quantified using an NPH scale (NPHS) and disability by the Modified Rankin scale (MRS). Patients were assessed before and at 1, 3, 6, 9 and 12 months after surgery. Outcome measures were differences between the preoperative and last NPHS and MRS scores. Improvement was defined as a change of > or = 15% in NPHS and > or = 1 grade in MRS. Intention-to-treat analysis of all patients at one year yielded improvement of 57% in NPHS and 59% in MRS. Efficacy analysis, excluding comorbidity unrelated to NPH, revealed positive predictive values of around 80% at Rcsf < 18, and between 90% and 100% at Rcsf > or = 18 mm Hg/ml/min. For Rcsf > or = 18, the likelihood ratios were also higher. We conclude that the best predictor of the response to shunting is an Rcsf > or = 18 mm Hg/ml/min. Since two-thirds of the patients with Rcsf < 18 showed improvement as well, these patients should not be denied shunting.

Motivation and placebos: do different mechanisms occur in different contexts?

PubMed

Hyland, Michael E

2011-06-27

This paper challenges the common assumption that the mechanisms underlying short-term placebo paradigms (where there is no motivation for health improvement) and long-term placebo paradigms (where patients value improvement in their health) are the same. Three types of motivational theory are reviewed: (i) classical placebo motivation theory that the placebo response results from the desire for therapeutic improvement; (ii) goal activation model that expectancy-driven placebo responses are enhanced when the placebo response satisfies an activated goal; and (iii) motivational concordance model that the placebo response is the consequence of concordance between the placebo ritual and significant intrinsic motives. It is suggested that current data are consistent with the following theory: response expectancy, conditioning and goal activation are responsible for short-term placebo effects but long-term therapeutic change is achieved through the effects of goal satisfaction and affect on the inflammatory response system and hypothalamic-pituitary-adrenal axis. Empirical predictions of this new theory are outlined, including ways in which placebo effects can be combined with other psychologically mediated effects on short-term and long-term psychological and physiological state.

Motivation and placebos: do different mechanisms occur in different contexts?

PubMed Central

Hyland, Michael E.

2011-01-01

This paper challenges the common assumption that the mechanisms underlying short-term placebo paradigms (where there is no motivation for health improvement) and long-term placebo paradigms (where patients value improvement in their health) are the same. Three types of motivational theory are reviewed: (i) classical placebo motivation theory that the placebo response results from the desire for therapeutic improvement; (ii) goal activation model that expectancy-driven placebo responses are enhanced when the placebo response satisfies an activated goal; and (iii) motivational concordance model that the placebo response is the consequence of concordance between the placebo ritual and significant intrinsic motives. It is suggested that current data are consistent with the following theory: response expectancy, conditioning and goal activation are responsible for short-term placebo effects but long-term therapeutic change is achieved through the effects of goal satisfaction and affect on the inflammatory response system and hypothalamic–pituitary–adrenal axis. Empirical predictions of this new theory are outlined, including ways in which placebo effects can be combined with other psychologically mediated effects on short-term and long-term psychological and physiological state. PMID:21576140

Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach-A Case Study for the City of Wuhan in China.

PubMed

Gao, Yuan; Zhang, Chuanrong; He, Qingsong; Liu, Yaolin

2017-06-15

Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study-simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan.

Response to Laser Treatment of Café au Lait Macules Based on Morphologic Features.

PubMed

Belkin, Daniel A; Neckman, Julia P; Jeon, Hana; Friedman, Paul; Geronemus, Roy G

2017-11-01

Response to laser treatment for café au lait macules (CALMs) is inconsistent and difficult to predict. To test the hypothesis that irregularly bordered CALMs of the "coast of Maine" subtype respond better to treatment than those of the smooth-bordered "coast of California" subtype. This retrospective case series included patients from 2 multiple-clinician US practices treated from 2005 through 2016. All patients had a clinical diagnosis of CALM and were treated with a Q-switched or picosecond laser. A total of 51 consecutive patients were eligible, 6 of whom were excluded owing to ambiguous lesion subtype. Observers were blinded to final patient groupings. Treatment with 755-nm alexandrite picosecond laser, Q-switched ruby laser, Q-switched alexandrite laser, or Q-switched 1064-nm Nd:YAG laser. Main outcome was grade in a visual analog scale (VAS) consisting of 4 levels of treatment response: poor (grade 1, 0%-25% improvement), fair (grade 2, 26%-50% improvement), good (grade 3, 51%-75% improvement), and excellent (grade 4, 76%-100% improvement). Forty-five patients were included in the series, 19 with smooth-bordered lesions and 26 with irregularly bordered lesions. Thirty-four (76%) of the participants were female; 33 (73%) were white; and the mean age at the time of laser treatment was 14.5 years (range, 0-44 years). Smooth-bordered lesions received a mean VAS score of 1.76, corresponding to a fair response on average (26%-50% pigmentary clearance). Irregularly bordered lesions received a mean VAS score of 3.67, corresponding to an excellent response on average (76%-100% clearance) (P < .001). CALMs with jagged or ill-defined borders of the coast of Maine subtype tend to respond well to laser treatment, whereas those with smooth and well-defined borders of the coast of California subtype tend to have poor response. Clinicians using Q-switched or picosecond lasers to treat CALMs can use morphologic characteristics to help predict response and more effectively manage patient expectations.

Prognostic Role of Right Ventricular Function in Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy.

PubMed

Rapacciuolo, Antonio; Maffè, Stefano; Palmisano, Pietro; Ferraro, Anna; Cecchetto, Antonella; D'Onofrio, Antonio; Solimene, Francesco; Musatti, Paola; Paffoni, Paola; Esposito, Francesca; Parravicini, Umberto; Agresta, Alessia; Botto, Giovanni Luca; Malacrida, Maurizio; Stabile, Giuseppe

2016-11-01

Because 20% to 40% of patients undergoing cardiac resynchronization therapy (CRT) do not respond to it, identification of potential factors predicting response is a relevant research topic. There is a possible association between right ventricular function and response to CRT. We analyzed 227 patients from the Cardiac Resynchronization Therapy Modular Registry (CRT-MORE) who received CRT according to current guidelines from March to December 2013. Response to therapy was defined as a decrease of ≥15% in left ventricular end-systolic volume (LVESV) at 6 months. The tricuspid annular plane systolic excursion (TAPSE) value that best predicted improvement in LVESV (sensitivity 68%, specificity 54%) was 17 mm. Stratifying patients according to TAPSE, LVESV decreased ≥15% in 78% of patients with TAPSE >17 mm (vs 59% in patients with TAPSE ≤17 mm; P = 0.006). At multivariate analysis, TAPSE >17 mm was independently associated with LVESV improvement (odds ratio: 1.97, 95% confidence interval: 1.03-3.80, P < 0.05), together with ischemic etiology (odds ratio: 0.39, 95% confidence interval: 0.20-0.75, P < 0.01). These results were confirmed for New York Heart Association class III to IV patients (79% echocardiographic response rate in patients with TAPSE >17 mm vs 55% in patients with TAPSE <17 mm; P = 0.012). Baseline signs of right ventricular dysfunction suggest possible remodeling after CRT. A TAPSE value of 17 mm was identified as a good cutoff for predicting a better response to CRT in patients with both mildly symptomatic and severe heart failure. © 2016 Wiley Periodicals, Inc.

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

Cytogenetic and molecular predictors of response in patients with myeloid malignancies without del[5q] treated with lenalidomide

PubMed Central

2012-01-01

Background While lenalidomide (LEN) shows high efficacy in myelodysplastic syndromes (MDS) with del[5q], responses can be also seen in patients presenting without del[5q]. We hypothesized that improved detection of chromosomal abnormalities with new karyotyping tools may better predict response to LEN. Design and methods We have studied clinical, molecular and cytogenetic features of 42 patients with MDS, myeloproliferative neoplasms (MPN), MDS/MPN overlap syndromes and secondary acute myeloid leukemia (sAML) without del[5q] by metaphase cytogenetics (MC) who underwent therapy with LEN. Results Fluorescence in situ hybridization (FISH) or single nucleotide polymorphism array (SNP-A)-based karyotyping marginally increased the diagnostic yield over MC, detecting 2/42 (4.8%) additional cases with del[5q], one of whom were responded to LEN. Responses were more often observed in patients with a normal karyotype by MC (60% vs abnormal MC; 17%, p = .08) and those with gain of chromosome 8 material by either of all 3 karyotyping methods (83% vs all other chromosomal abnormalities; 44% p = .11). However, 5 out of those 6 patients received combined LEN/AZA therapy and it may also suggest those with gain of chromosome 8 material respond well to AZA. The addition of FISH or SNP-A did not improve the predictive value of normal cytogenetics by MC. Mutational analysis of TET2, UTX, CBL, EZH2, ASXL1, TP53, RAS, IDH1/2, and DNMT-3A was performed on 21 of 41 patients, and revealed 13 mutations in 11 patients, but did not show any molecular markers of responsiveness to LEN. Conclusions Normal karyotype and gain of chromosome 8 material was predictive of response to LEN in non-del[5q] patients with myeloid malignancies. PMID:22390313

Nottingham Clinico-Pathological Response Index (NPRI) after neoadjuvant chemotherapy (Neo-ACT) accurately predicts clinical outcome in locally advanced breast cancer.

PubMed

Abdel-Fatah, Tarek M; Ball, Graham; Lee, Andrew H S; Pinder, Sarah; MacMilan, R Douglas; Cornford, Eleanor; Moseley, Paul M; Silverman, Rafael; Price, James; Latham, Bruce; Palmer, David; Chan, Arlene; Ellis, Ian O; Chan, Stephen Y T

2015-03-01

There is a need to identify more sensitive clinicopathologic criteria to assess the response to neoadjuvant chemotherapy (Neo-ACT) and guide subsequent adjuvant therapy. We performed a clinicopathologic assessment of 426 patients who had completed Neo-ACT for locally advanced breast cancer (LABC) with a median follow-up of 70 months. Patients were divided into a training set treated with anthracycline combination chemotherapy (n = 172); an internal validation set treated with anthracycline and taxane (n = 129); and an external validation set treated with anthracycline with or without taxane (n = 125). A multivariate Cox regression model demonstrated the absence of fibrosis, presence of lymphovascular invasion, increasing number of lymph node metastases, and administration of hormone therapy were significantly associated with short breast cancer-specific survival (BCSS) and disease-free survival (DFS); Ps < 0.01, while reduction of tumor size was associated with DFS (P = 0.022). Nottingham Clinico-Pathological Response Indexes (NPRI) were calculated, and four prognostic groups (NPRI-PG) were identified. Patients in prognostic group 2 (NPRI-PG2) for BCSS (66 of 172; 38.4%) have the same prognosis as those who achieved pathologic complete response (pCR; NPRI-PG1; 15%). Receiver-operating characteristic (ROC) curves indicated that the NPRI outperformed the currently used prognostic factors and adding the NPRI improved their performance as a predictor for both BCSS (area under the curve [AUC], 0.88) and DFS (AUC, 0.87). The NPRI predicts BCSS and DFS, with a higher sensitivity than pCR. The NPRI can also improve the sensitivity and specificity of clinicopathologic response as a study endpoint, for assessing response to Neo-ACT, and can serve as a valuable tool for the discovery of future predictive molecular markers. ©2014 American Association for Cancer Research.

Does Classification of Chronic Musculoskeletal Disorder Patients Into Psychosocial Subgroups Predict Differential Treatment Responsiveness and 1-Year Outcomes After a Functional Restoration Program?

PubMed

Asih, Sali; Mayer, Tom G; Williams, Mark; Choi, Yun Hee; Gatchel, Robert J

2015-12-01

The objectives of this study: (1) to assess whether Multidimensional Pain Inventory (MPI) profiles predicted differential responses to a functional restoration program (FRP) in chronic disabling occupational musculoskeletal disorder (CDOMD) patients; (2) to examine whether coping style improves following FRP; and (3) to determine whether discharge MPI profiles predict discharge psychosocial and 1-year socioeconomic outcomes. Consecutive CDOMD patients (N=716) were classified into Adaptive Coper (AC, n=209), Interpersonally Distressed (ID, n=154), Dysfunctional (DYS, n=310), and Anomalous (n=43) using the MPI, and reclassified at discharge. Profiles were compared on psychosocial measures and 1-year socioeconomic outcomes. An intent-to-treat sample analyzed the effect of drop-outs on treatment responsiveness. The MPI classification significantly predicted program completion (P=0.001), although the intent-to-treat analyses found no significant effects of drop-out on treatment responsiveness. There was a significant increase in the number of patients who became AC or Anomalous at FRP discharge and a decrease in those who were ID or DYS. Patients who changed or remained as DYS at FRP discharge reported the highest levels of pain, disability, and depression. No significant interaction effect was found between MPI group and time for pain intensity or disability. All groups improved on psychosocial measures at discharge. DYS patients had decreased work retention and a greater health care utilization at 1 year. An FRP was clinically effective for CDOMD patients regardless of initial MPI profiles. The FRP modified profiles, with patients changing from negative to positive profiles. Discharge DYS were more likely to have poor 1-year outcomes. Those classified as Anomalous had a good prognosis for functional recovery similar to ACs.

Neurophysiologic predictors of response to atomoxetine in young adults with attention deficit hyperactivity disorder: a pilot project.

PubMed

Leuchter, Andrew F; McGough, James J; Korb, Alexander S; Hunter, Aimee M; Glaser, Paul E A; Deldar, Ahmed; Durell, Todd M; Cook, Ian A

2014-07-01

Atomoxetine is a non-stimulant medication with sustained benefit throughout the day, and is a useful pharmacologic treatment option for young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). It is difficult to determine, however, those patients for whom atomoxetine will be both effective and advantageous. Patients may need to take the medication for several weeks before therapeutic benefit is apparent, so a biomarker that could predict atomoxetine effectiveness early in the course of treatment could be clinically useful. There has been increased interest in the study of thalamocortical oscillatory activity using quantitative electroencephalography (qEEG) as a biomarker in ADHD. In this study, we investigated qEEG absolute power, relative power, and cordance, which have been shown to predict response to reuptake inhibitor antidepressants in Major Depressive Disorder (MDD), as potential predictors of response to atomoxetine. Forty-four young adults with ADHD (ages 18-30) enrolled in a multi-site, double-blind placebo-controlled study of the effectiveness of atomoxetine and underwent serial qEEG recordings at pretreatment baseline and one week after the start of medication. qEEG measures were calculated from a subset of the sample (N = 29) that provided useable qEEG recordings. Left temporoparietal cordance in the theta frequency band after one week of treatment was associated with ADHD symptom improvement and quality of life measured at 12 weeks in atomoxetine-treated subjects, but not in those treated with placebo. Neither absolute nor relative power measures selectively predicted improvement in medication-treated subjects. Measuring theta cordance after one week of treatment could be useful in predicting atomoxetine treatment response in adult ADHD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Do soil tests help forecast nitrogen response in first-year corn following alfalfa on fine-textured soils?

USDA-ARS?s Scientific Manuscript database

Improved methods of predicting grain yield response to fertilizer N for first-year corn (Zea mays L.) following alfalfa (Medicago sativa L.) on fine-textured soils are needed. Data from 21 site-years in the North Central Region were used to (i) determine how Illinois soil nitrogen test (ISNT) and pr...

Frontal lesions predict response to prism adaptation treatment in spatial neglect: A randomised controlled study.

PubMed

Goedert, Kelly M; Chen, Peii; Foundas, Anne L; Barrett, A M

2018-03-20

Spatial neglect commonly follows right hemisphere stroke. It is defined as impaired contralesional stimulus detection, response, or action, causing functional disability. While prism adaptation treatment is highly promising to promote functional recovery of spatial neglect, not all individuals respond. Consistent with a primary effect of prism adaptation on spatial movements, we previously demonstrated that functional improvement after prism adaptation treatment is linked to frontal lobe lesions. However, that study was a treatment-only study with no randomised control group. The current study randomised individuals with spatial neglect to receive 10 days of prism adaptation treatment or to receive only standard care (control group). Replicating our earlier results, we found that the presence of frontal lesions moderated response to prism adaptation treatment: among prism-treated patients, only those with frontal lesions demonstrated functional improvements in their neglect symptoms. Conversely, among individuals in the standard care control group, the presence of frontal lesions did not modify recovery. These results suggest that further research is needed on how frontal lesions may predict response to prism adaptation treatment. Additionally, the results help elucidate the neural network involved in spatial movement and could be used to aid decisions about treatment.

Reduced short interval cortical inhibition correlates with atomoxetine response in children with attention-deficit hyperactivity disorder (ADHD).

PubMed

Chen, Tina H; Wu, Steve W; Welge, Jeffrey A; Dixon, Stephan G; Shahana, Nasrin; Huddleston, David A; Sarvis, Adam R; Sallee, Floyd R; Gilbert, Donald L

2014-12-01

Clinical trials in children with attention-deficit hyperactivity disorder (ADHD) show variability in behavioral responses to the selective norepinephrine reuptake inhibitor atomoxetine. The objective of this study was to determine whether transcranial magnetic stimulation-evoked short interval cortical inhibition might be a biomarker predicting, or correlating with, clinical atomoxetine response. At baseline and after 4 weeks of atomoxetine treatment in 7- to 12-year-old children with ADHD, transcranial magnetic stimulation short interval cortical inhibition was measured, blinded to clinical improvement. Primary analysis was by multivariate analysis of covariance. Baseline short interval cortical inhibition did not predict clinical responses. However, paradoxically, after 4 weeks of atomoxetine, mean short interval cortical inhibition was reduced 31.9% in responders and increased 6.1% in nonresponders (analysis of covariance t 41 = 2.88; P = .0063). Percentage reductions in short interval cortical inhibition correlated with reductions in the ADHD Rating Scale (r = 0.50; P = .0005). In children ages 7 to 12 years with ADHD treated with atomoxetine, improvements in clinical symptoms are correlated with reductions in motor cortex short interval cortical inhibition. © The Author(s) 2014.

Subjective field study of response to impulsive helicopter noise

NASA Technical Reports Server (NTRS)

Powell, C. A.

1981-01-01

Subjects, located outdoors and indoors, judged the noisiness and other subjective noise characteristics of flyovers of two helicopters and a propeller driven airplane as part of a study of the effects of impulsiveness on the subjective response to helicopter noise. In the first experiment, the impulsive characteristics of one helicopter was controlled by varying the main rotor speed while maintaining a constant airspeed in level flight. The second experiment which utilized only the helicopters, included descent and level flight operations. The more impulsive helicopter was consistently judged less noisy than the less impulsive helicopter at equal effective perceived noise levels (EPNL). The ability of EPNL to predict noisiness was not improved by the addition of either of two proposed impulse corrections. A subjective measure of impulsiveness, however, which was not significantly related to the proposed impulse corrections, was found to improve the predictive ability of EPNL.

Applications of genetic data to improve management and conservation of river fishes and their habitats

USGS Publications Warehouse

Scribner, Kim T.; Lowe, Winsor H.; Landguth, Erin L.; Luikart, Gordon; Infante, Dana M.; Whelan, Gary; Muhlfeld, Clint C.

2015-01-01

Environmental variation and landscape features affect ecological processes in fluvial systems; however, assessing effects at management-relevant temporal and spatial scales is challenging. Genetic data can be used with landscape models and traditional ecological assessment data to identify biodiversity hotspots, predict ecosystem responses to anthropogenic effects, and detect impairments to underlying processes. We show that by combining taxonomic, demographic, and genetic data of species in complex riverscapes, managers can better understand the spatial and temporal scales over which environmental processes and disturbance influence biodiversity. We describe how population genetic models using empirical or simulated genetic data quantify effects of environmental processes affecting species diversity and distribution. Our summary shows that aquatic assessment initiatives that use standardized data sets to direct management actions can benefit from integration of genetic data to improve the predictability of disturbance–response relationships of river fishes and their habitats over a broad range of spatial and temporal scales.

Preliminary evaluation of hybrid titanium composite laminates

NASA Technical Reports Server (NTRS)

Miller, J. L.; Progar, D. J.; Johnson, W. S.; St.clair, T. L.

1994-01-01

In this study, the mechanical response of hybrid titanium composite laminates (HTCL) was evaluated at room and elevated temperatures. Also, the use of an elastic-plastic laminate analysis program for predicting the tensile response from constituent properties was verified. The improvement in mechanical properties achieved by the laminates was assessed by comparing the results of static strength and constant amplitude fatigue tests to those for monolithic titanium sheet. Two HTCL were fabricated with different fiber volume fractions, resin layer thicknesses, and resins. One panel was thicker and was more poorly bonded in comparison to other. Consequently, the former had a lower tensile strength, while fewer cracks grew in this panel and at a slower rate. Both panels showed an improvement in fatigue life of almost two orders of magnitude. The model predictions were also in good agreement with the experimental results for both HTCL panels.

Structure-based energetics of protein interfaces guide Foot-and-Mouth Disease virus vaccine design

PubMed Central

Scott, Katherine; Burman, Alison; Loureiro, Silvia; Ren, Jingshan; Porta, Claudine; Ginn, Helen M.; Jackson, Terry; Perez-Martin, Eva; Siebert, C. Alistair; Paul, Guntram; Huiskonen, Juha T.; Jones, Ian M.; Esnouf, Robert M.; Fry, Elizabeth E.; Maree, Francois F.; Charleston, Bryan; Stuart, David I.

2018-01-01

Summary Virus capsids are primed for disassembly yet capsid integrity is key to generating a protective immune response. Here we devise a computational method to assess relative stability of protein-protein interfaces and use it to design improved candidate vaccines for two of the least stable, but globally important, serotypes of Foot-and-Mouth Disease virus (FMDV), O and SAT2. FMDV capsids comprise identical pentameric protein subunits held together by tenuous non-covalent interactions, and are often unstable. Chemically inactivated or recombinant empty capsids, which could form the basis of future vaccines, are even less stable than live virus. We use a novel restrained molecular dynamics strategy, to rank mutations predicted to strengthen the pentamer interfaces to produce stabilized capsids. Structural analyses and stability assays confirmed the predictions, and vaccinated animals generated improved neutralising antibody responses to stabilised particles over parental viruses and wild-type capsids. PMID:26389739

Pharmacogenetics predictive of response and toxicity in acute lymphoblastic leukemia therapy.

PubMed

Mei, Lin; Ontiveros, Evelena P; Griffiths, Elizabeth A; Thompson, James E; Wang, Eunice S; Wetzler, Meir

2015-07-01

Acute lymphoblastic leukemia (ALL) is a relatively rare disease in adults accounting for no more than 20% of all cases of acute leukemia. By contrast with the pediatric population, in whom significant improvements in long term survival and even cure have been achieved over the last 30years, adult ALL remains a significant challenge. Overall survival in this group remains a relatively poor 20-40%. Modern research has focused on improved pharmacokinetics, novel pharmacogenetics and personalized principles to optimize the efficacy of the treatment while reducing toxicity. Here we review the pharmacogenetics of medications used in the management of patients with ALL, including l-asparaginase, glucocorticoids, 6-mercaptopurine, methotrexate, vincristine and tyrosine kinase inhibitors. Incorporating recent pharmacogenetic data, mainly from pediatric ALL, will provide novel perspective of predicting response and toxicity in both pediatric and adult ALL therapies. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Application of Satellite-Derived, High-Resolution Land Use/Land Cover Data to Improve Urban Air Quality Model Forecasts

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.; Lapenta, W. M.; Crosson, W. L.; Estes, M. G., Jr.; Limaye, A.; Kahn, M.

2006-01-01

Local and state agencies are responsible for developing state implementation plans to meet National Ambient Air Quality Standards. Numerical models used for this purpose simulate the transport and transformation of criteria pollutants and their precursors. The specification of land use/land cover (LULC) plays an important role in controlling modeled surface meteorology and emissions. NASA researchers have worked with partners and Atlanta stakeholders to incorporate an improved high-resolution LULC dataset for the Atlanta area within their modeling system and to assess meteorological and air quality impacts of Urban Heat Island (UHI) mitigation strategies. The new LULC dataset provides a more accurate representation of land use, has the potential to improve model accuracy, and facilitates prediction of LULC changes. Use of the new LULC dataset for two summertime episodes improved meteorological forecasts, with an existing daytime cold bias of approx. equal to 3 C reduced by 30%. Model performance for ozone prediction did not show improvement. In addition, LULC changes due to Atlanta area urbanization were predicted through 2030, for which model simulations predict higher urban air temperatures. The incorporation of UHI mitigation strategies partially offset this warming trend. The data and modeling methods used are generally applicable to other U.S. cities.

The predictive value of external continuous lumbar drainage, with cerebrospinal fluid outflow controlled by medium pressure valve, in normal pressure hydrocephalus.

PubMed

Panagiotopoulos, V; Konstantinou, D; Kalogeropoulos, A; Maraziotis, T

2005-09-01

Although sporadic studies have described temporary external cerebrospinal fluid (CSF) lumbar drainage as a highly accurate test for predicting the outcome after ventricular shunting in normal pressure hydrocephalus (NPH) patients, a more recent study reports that the positive predictive value of external lumbar drainage (ELD) is high but the negative predictive value is deceptively low. Therefore, we conducted a prospective study in order to evaluate the predictive value of a continuous ELD, with CSF outflow controlled by medium pressure valve, in NPH patients. Twenty-seven patients with presumed NPH were admitted to our department and CSF drainage was carried out by a temporary (ELD) with CSF outflow controlled by a medium pressure valve for five days. All patients received a ventriculoperitoneal shunt using a medium pressure valve based upon preoperative clinical and radiographic criteria of NPH, regardless of ELD outcome. Clinical evaluation of gait disturbances, urinary incontinence and mental status, and radiological evaluation with brain CT was performed prior to and after ELD test, as well as three months after shunting. Twenty-two patients were finally shunted and included in this study. In a three-month follow-up, using a previously validated score system, overall improvement after permanent shunting correlated well to improvement after ELD test (Spearman's rho = 0.462, p = 0.03). When considering any degree of improvement as a positive response, ELD test yielded high positive predictive values for all individual parameters (gait disturbances 94%, 95% CI 71%-100%, urinary incontinence 100%, 95% CI 66%-100%, and mental status 100%, 95% CI 66%-100%) but negative predictive values were low (< 50%) except for cognitive impairment (85%, 95% CI 55%-98%). This study suggests that a positive ELD-valve system test should be considered a reliable criterion for preoperative selection of shunt-responsive NPH patients. In case of a negative ELD-valve system test, further investigation of the presumed NPH patients with additional tests should be performed.

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.

Mathematical modeling improves EC50 estimations from classical dose-response curves.

PubMed

Nyman, Elin; Lindgren, Isa; Lövfors, William; Lundengård, Karin; Cervin, Ida; Sjöström, Theresia Arbring; Altimiras, Jordi; Cedersund, Gunnar

2015-03-01

The β-adrenergic response is impaired in failing hearts. When studying β-adrenergic function in vitro, the half-maximal effective concentration (EC50 ) is an important measure of ligand response. We previously measured the in vitro contraction force response of chicken heart tissue to increasing concentrations of adrenaline, and observed a decreasing response at high concentrations. The classical interpretation of such data is to assume a maximal response before the decrease, and to fit a sigmoid curve to the remaining data to determine EC50 . Instead, we have applied a mathematical modeling approach to interpret the full dose-response curve in a new way. The developed model predicts a non-steady-state caused by a short resting time between increased concentrations of agonist, which affect the dose-response characterization. Therefore, an improved estimate of EC50 may be calculated using steady-state simulations of the model. The model-based estimation of EC50 is further refined using additional time-resolved data to decrease the uncertainty of the prediction. The resulting model-based EC50 (180-525 nm) is higher than the classically interpreted EC50 (46-191 nm). Mathematical modeling thus makes it possible to re-interpret previously obtained datasets, and to make accurate estimates of EC50 even when steady-state measurements are not experimentally feasible. The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database, and may be accessed at http://jjj.bio.vu.nl/database/nyman. © 2015 FEBS.

Study on model current predictive control method of PV grid- connected inverters systems with voltage sag

NASA Astrophysics Data System (ADS)

Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.

2016-08-01

According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.

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.

Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study.

PubMed

Schmaal, Lianne; Marquand, Andre F; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G; van der Wee, Nic J A; Veltman, Dick J; Penninx, Brenda W J H

2015-08-15

A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Predicting climate-driven regime shifts versus rebound potential in coral reefs.

PubMed

Graham, Nicholas A J; Jennings, Simon; MacNeil, M Aaron; Mouillot, David; Wilson, Shaun K

2015-02-05

Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PubMed

Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

2017-01-01

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

Paradoxical effect of dopamine medication on cognition in Parkinson's disease: relationship to side of motor onset.

PubMed

Hanna-Pladdy, Brenda; Pahwa, Rajesh; Lyons, Kelly E

2015-04-01

Parkinson's disease (PD) is characterized by asymmetric motor symptom onset attributed to greater degeneration of dopamine neurons contralateral to the affected side. However, whether motor asymmetries predict cognitive profiles in PD, and to what extent dopamine influences cognition remains controversial. This study evaluated cognitive variability in PD by measuring differential response to dopamine replacement therapy (DRT) based on hemispheric asymmetries. The influence of DRT on cognition was evaluated in mild PD patients (n = 36) with left or right motor onset symptoms. All subjects were evaluated on neuropsychological measures on and off DRT and compared to controls (n = 42). PD patients were impaired in executive, memory and motor domains irrespective of side of motor onset, although patients with left hemisphere deficit displayed greater cognitive impairment. Patients with right hemisphere deficit responded to DRT with significant improvement in sensorimotor deficits, and with corresponding improvement in attention and verbal memory functions. Conversely, patients with greater left hemisphere dopamine deficiency did not improve in attentional functions and declined in verbal memory recall following DRT. These findings support the presence of extensive mild cognitive deficits in early PD not fully explained by dopamine depletion alone. The paradoxical effects of levodopa on verbal memory were predicted by extent of fine motor impairment and sensorimotor response to levodopa, which reflects extent of dopamine depletion. The findings are discussed with respect to factors influencing variable cognitive profiles in early PD, including hemispheric asymmetries and differential response to levodopa based on dopamine levels predicting amelioration or overdosing.

Establishing Esri ArcGIS Enterprise Platform Capabilities to Support Response Activities of the NASA Earth Science Disasters Program

NASA Astrophysics Data System (ADS)

Molthan, A.; Seepersad, J.; Shute, J.; Carriere, L.; Duffy, D.; Tisdale, B.; Kirschbaum, D.; Green, D. S.; Schwizer, L.

2017-12-01

NASA's Earth Science Disasters Program promotes the use of Earth observations to improve the prediction of, preparation for, response to, and recovery from natural and technological disasters. NASA Earth observations and those of domestic and international partners are combined with in situ observations and models by NASA scientists and partners to develop products supporting disaster mitigation, response, and recovery activities among several end-user partners. These products are accompanied by training to ensure proper integration and use of these materials in their organizations. Many products are integrated along with other observations available from other sources in GIS-capable formats to improve situational awareness and response efforts before, during and after a disaster. Large volumes of NASA observations support the generation of disaster response products by NASA field center scientists, partners in academia, and other institutions. For example, a prediction of high streamflows and inundation from a NASA-supported model may provide spatial detail of flood extent that can be combined with GIS information on population density, infrastructure, and land value to facilitate a prediction of who will be affected, and the economic impact. To facilitate the sharing of these outputs in a common framework that can be easily ingested by downstream partners, the NASA Earth Science Disasters Program partnered with Esri and the NASA Center for Climate Simulation (NCCS) to establish a suite of Esri/ArcGIS services to support the dissemination of routine and event-specific products to end users. This capability has been demonstrated to key partners including the Federal Emergency Management Agency using a case-study example of Hurricane Matthew, and will also help to support future domestic and international disaster events. The Earth Science Disasters Program has also established a longer-term vision to leverage scientists' expertise in the development and delivery of end-user training, increase public awareness of NASA's Disasters Program, and facilitate new partnerships with disaster response organizations. Future research and development will foster generation of products that leverage NASA's Earth observations for disaster prediction, preparation and mitigation, response, and recovery.

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

Did I Do That? Expectancy Effects of Brain Stimulation on Error-related Negativity and Sense of Agency.

PubMed

Hoogeveen, Suzanne; Schjoedt, Uffe; van Elk, Michiel

2018-06-19

This study examines the effects of expected transcranial stimulation on the error(-related) negativity (Ne or ERN) and the sense of agency in participants who perform a cognitive control task. Placebo transcranial direct current stimulation was used to elicit expectations of transcranially induced cognitive improvement or impairment. The improvement/impairment manipulation affected both the Ne/ERN and the sense of agency (i.e., whether participants attributed errors to oneself or the brain stimulation device): Expected improvement increased the ERN in response to errors compared with both impairment and control conditions. Expected impairment made participants falsely attribute errors to the transcranial stimulation. This decrease in sense of agency was correlated with a reduced ERN amplitude. These results show that expectations about transcranial stimulation impact users' neural response to self-generated errors and the attribution of responsibility-especially when actions lead to negative outcomes. We discuss our findings in relation to predictive processing theory according to which the effect of prior expectations on the ERN reflects the brain's attempt to generate predictive models of incoming information. By demonstrating that induced expectations about transcranial stimulation can have effects at a neural level, that is, beyond mere demand characteristics, our findings highlight the potential for placebo brain stimulation as a promising tool for research.

Tests of the Dynamic Field Theory and the Spatial Precision Hypothesis: Capturing a Qualitative Developmental Transition in Spatial Working Memory

PubMed Central

Schutte, Anne R.; Spencer, John P.

2009-01-01

This study tested a dynamic field theory (DFT) of spatial working memory and an associated spatial precision hypothesis (SPH). Between three and six years of age there is a qualitative shift in how children use reference axes to remember locations: 3-year-olds’ spatial recall responses are biased toward reference axes after short memory delays, whereas 6-year-olds’ responses are biased away from reference axes. According to the DFT and the SPH, quantitative improvements over development in the precision of excitatory and inhibitory working memory processes lead to this qualitative shift. Simulations of the DFT in Experiment 1 predict that improvements in precision should cause the spatial range of targets attracted toward a reference axis to narrow gradually over development with repulsion emerging and gradually increasing until responses to most targets show biases away from the axis. Results from Experiment 2 with 3- to 5-year-olds support these predictions. Simulations of the DFT in Experiment 3 quantitatively fit the empirical results and offer insights into the neural processes underlying this developmental change. PMID:19968430

Book Review: Regional Hydrological Response to Climate Change

NASA Technical Reports Server (NTRS)

Koster, Randal

1998-01-01

The book being reviewed, Regional Hydrological Response to Climate Change, addresses the effects of global climate change, particularly global warming induced by greenhouse gas emissions, on hydrological budgets at the regional scale. As noted in its preface, the book consists of peer-reviewed papers delivered at scientific meetings held by the International Geographical Union Working Group on Regional Hydrological Response to Climate Change and Global Warming, supplemented with some additional chapters that round out coverage of the topic. The editors hope that this book will serve as "not only a record of current achievements, but also a stimulus to further hydrological research as the detail and spatial resolution of Global Climate Models improves". The reviewer found the background material on regional climatology to be valuable and the methodologies presented to be of interest. The value of the book is significantly diminished, however by the dated nature of some of the material and by large uncertainties in the predictions of regional precipitation change. The book would have been improved by a much more extensive documentation of the uncertainty associated with each step of the prediction process.

Modeling of zero gravity venting: Studies of two-phase heat transfer under reduced gravity

NASA Technical Reports Server (NTRS)

Merte, H., Jr.

1986-01-01

The objective is to predict the pressure response of a saturated liquid-vapor system when undergoing a venting or depressurization process in zero gravity at low vent rates. An experimental investigation of the venting of cylindrical containers partially filled with initially saturated liquids was previously conducted under zero-gravity conditions and compared with an analytical model which incorporated the effect of interfacial mass transfer on the ullage pressure response during venting. A new model is presented to improve the estimation of the interfacial mass transfer. Duhammel's superposition integral is incorporated to approximate the transient temperature response of the interface, treating the liquid as a semi-infinite solid with conduction heat transfer. Account is also taken of the condensation taking place within the bulk of a saturated vapor as isentropic expansion takes place. Computational results are presented for the venting of R-11 from a given vessel and initial state for five different venting rates over a period of three seconds, and compared to prior NASA experiments. An improvement in the prediction of the final pressure takes place, but is still considerably below the measurements.

Departure Queue Prediction for Strategic and Tactical Surface Scheduler Integration

NASA Technical Reports Server (NTRS)

Zelinski, Shannon; Windhorst, Robert

2016-01-01

A departure metering concept to be demonstrated at Charlotte Douglas International Airport (CLT) will integrate strategic and tactical surface scheduling components to enable the respective collaborative decision making and improved efficiency benefits these two methods of scheduling provide. This study analyzes the effect of tactical scheduling on strategic scheduler predictability. Strategic queue predictions and target gate pushback times to achieve a desired queue length are compared between fast time simulations of CLT surface operations with and without tactical scheduling. The use of variable departure rates as a strategic scheduler input was shown to substantially improve queue predictions over static departure rates. With target queue length calibration, the strategic scheduler can be tuned to produce average delays within one minute of the tactical scheduler. However, root mean square differences between strategic and tactical delays were between 12 and 15 minutes due to the different methods the strategic and tactical schedulers use to predict takeoff times and generate gate pushback clearances. This demonstrates how difficult it is for the strategic scheduler to predict tactical scheduler assigned gate delays on an individual flight basis as the tactical scheduler adjusts departure sequence to accommodate arrival interactions. Strategic/tactical scheduler compatibility may be improved by providing more arrival information to the strategic scheduler and stabilizing tactical scheduler changes to runway sequence in response to arrivals.

Results of an integrated structure/control law design sensitivity analysis

NASA Technical Reports Server (NTRS)

Gilbert, Michael G.

1989-01-01

A design sensitivity analysis method for Linear Quadratic Cost, Gaussian (LQG) optimal control laws, which predicts change in the optimal control law due to changes in fixed problem parameters using analytical sensitivity equations is discussed. Numerical results of a design sensitivity analysis for a realistic aeroservoelastic aircraft example are presented. In this example, the sensitivity of the optimally controlled aircraft's response to various problem formulation and physical aircraft parameters is determined. These results are used to predict the aircraft's new optimally controlled response if the parameter was to have some other nominal value during the control law design process. The sensitivity results are validated by recomputing the optimal control law for discrete variations in parameters, computing the new actual aircraft response, and comparing with the predicted response. These results show an improvement in sensitivity accuracy for integrated design purposes over methods which do not include changes in the optimal control law. Use of the analytical LQG sensitivity expressions is also shown to be more efficient than finite difference methods for the computation of the equivalent sensitivity information.

Frailty and its prediction of disability and health care utilization: the added value of interviews and physical measures following a self-report questionnaire.

PubMed

Gobbens, Robbert J J; van Assen, Marcel A L M

2012-01-01

To establish whether the prediction of the adverse outcomes disability and six indicators of health care utilization one and two years later by the three frailty domains (physical, psychological, social) of the Tilburg Frailty Indicator (TFI) is improved by adding interview and physical measures of frailty. A representative sample of 245 Dutch community-dwelling persons aged 75 years and older (response rate 53%) participated in 2008, one year later in 2009 (n=179, 73%) and again two years later in 2010 (n=141, 58%). Frailty was assessed with the TFI, an easy to administer self-report measure. Disability was measured using the Groningen Activity Restriction Scale (GARS). Indicators of health care utilization were: visit to a general practitioner (gp), contacts with health care professionals (hcps), hospital admission, receiving personal care, receiving nursing care, and receiving informal care. After controlling for background characteristics, the TFI predicted disability and the indicators of health care utilization. Interviews and physical measures of frailty improved the prediction of disability. The Hospital Anxiety and Depression Scale (HADS-A) improved the prediction of contacts with hcps, but the interview and physical measures of frailty did not improve the predictions of the other indicators of health care utilization. Assessment by the self-report TFI is sufficient for predicting six indicators of health care utilization, but for predicting disability the use of both the TFI and the Timed Up & Go (TUG) test is recommended. It is advisable assessing all three frailty domains when examining frailty and its prediction of adverse outcomes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Study of improved modeling and solution procedures for nonlinear analysis. [aircraft-like structures

NASA Technical Reports Server (NTRS)

Kamat, M. P.

1979-01-01

An evaluation of the ACTION computer code on an aircraft like structure is presented. This computer program proved adequate in predicting gross response parameters in structures which undergo severe localized cross sectional deformations.

A microstructure sensitive study of rolling contact fatigue in bearing steels: A numerical and experimental approach

NASA Astrophysics Data System (ADS)

Pandkar, Anup Surendra

Bearings are an integral part of machine components that transmit rotary power such as cars, motors, engines etc. Safe bearing operation is essential to avoid serious failures and accidents, which necessitates their timely replacement. This calls for an accurate bearing life prediction methods. Based on the Lundberg-Palmgen (LP) model, current life models consistently under predict bearings lives. Improvement in life prediction requires understanding of the bearing failure mechanism i.e. Rolling Contact Fatigue (RCF). The goal of this research is to develop a mechanistic framework required for an improved bearing life prediction model. Such model should account for metal plasticity, influence of microstructural features and cyclically evolving stressstrain fields induced during RCF. To achieve this, elastic-plastic finite element (FE) study is undertaken to investigate the response of M50-NiL bearing steel during RCF. Specifically, a microstructure sensitive study of the influence of non-metallic inclusions on RCF response of bearings is presented. M50-NiL microstructure consists of carbides which are orders of magnitude smaller than bearing dimensions. To account for this size difference, a multi-scale FE modeling approach is employed. The FE results reveal that hard carbide particles act as local stress risers, alter surrounding stressstrain fields and cause micro-scale yielding of steel matrix. Moreover, they introduce a shear stress cycle with non-zero mean stress, which promotes micro-plastic strain accumulation via ratcheting mechanism. Localized ratcheting is primarily responsible for cyclic hardening within the RCF affected region. Such evolution of subsurface hardness can be used to quantify RCF induced damage. To investigate this further, cyclic hardening response of the RCF affected region is simulated. The results show good agreement with the experimental observations. The cyclic stress-strain fields obtained from these simulations and the knowledge of hardness evolution can prove useful for future improvements to life models. The material parameters required for FE simulations are not available for many bearing steels. A novel method is presented to estimate these parameters for M50-NiL using the experimental results. Based on logical assumptions, this method provides meaningful estimates of material parameters. Modeling techniques and conclusions drawn from this research are helpful for improvements in life models.

Review of current nuclear fallout codes.

PubMed

Auxier, Jerrad P; Auxier, John D; Hall, Howard L

2017-05-01

The importance of developing a robust nuclear forensics program to combat the illicit use of nuclear material that may be used as an improvised nuclear device is widely accepted. In order to decrease the threat to public safety and improve governmental response, government agencies have developed fallout-analysis codes to predict the fallout particle size, dose, and dispersion and dispersion following a detonation. This paper will review the different codes that have been developed for predicting fallout from both chemical and nuclear weapons. This will decrease the response time required for the government to respond to the event. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation.

PubMed

Baumgärtel, Regina M; Krawczyk-Becker, Martin; Marquardt, Daniel; Völker, Christoph; Hu, Hongmei; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Ernst, Stephan M A; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-12-30

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise. Three instrumental measures were employed to assess predicted speech intelligibility and predicted sound quality: the intelligibility-weighted signal-to-noise ratio, the short-time objective intelligibility measure, and the perceptual evaluation of speech quality. The results show substantial improvements in predicted speech intelligibility as well as sound quality for the proposed algorithms. The evaluated coherence-based noise reduction algorithm was able to provide improvements in predicted audio signal quality. For the tested single-channel noise reduction algorithm, improvements in intelligibility-weighted signal-to-noise ratio were observed in all but the nonstationary cafeteria ambient noise scenario. Binaural minimum variance distortionless response beamforming algorithms performed particularly well in all noise scenarios. © The Author(s) 2015.

Comparing Binaural Pre-processing Strategies I

PubMed Central

Krawczyk-Becker, Martin; Marquardt, Daniel; Völker, Christoph; Hu, Hongmei; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Ernst, Stephan M. A.; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-01-01

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise. Three instrumental measures were employed to assess predicted speech intelligibility and predicted sound quality: the intelligibility-weighted signal-to-noise ratio, the short-time objective intelligibility measure, and the perceptual evaluation of speech quality. The results show substantial improvements in predicted speech intelligibility as well as sound quality for the proposed algorithms. The evaluated coherence-based noise reduction algorithm was able to provide improvements in predicted audio signal quality. For the tested single-channel noise reduction algorithm, improvements in intelligibility-weighted signal-to-noise ratio were observed in all but the nonstationary cafeteria ambient noise scenario. Binaural minimum variance distortionless response beamforming algorithms performed particularly well in all noise scenarios. PMID:26721920

New Paradigm for Translational Modeling to Predict Long‐term Tuberculosis Treatment Response

PubMed Central

Bartelink, IH; Zhang, N; Keizer, RJ; Strydom, N; Converse, PJ; Dooley, KE; Nuermberger, EL

2017-01-01

Abstract Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials. PMID:28561946

NEOadjuvant therapy monitoring with PET and CT in Esophageal Cancer (NEOPEC-trial)

PubMed Central

2008-01-01

Background Surgical resection is the preferred treatment of potentially curable esophageal cancer. To improve long term patient outcome, many institutes apply neoadjuvant chemoradiotherapy. In a large proportion of patients no response to chemoradiotherapy is achieved. These patients suffer from toxic and ineffective neoadjuvant treatment, while appropriate surgical therapy is delayed. For this reason a diagnostic test that allows for accurate prediction of tumor response early during chemoradiotherapy is of crucial importance. CT-scan and endoscopic ultrasound have limited accuracy in predicting histopathologic tumor response. Data suggest that metabolic changes in tumor tissue as measured by FDG-PET predict response better. This study aims to compare FDG-PET and CT-scan for the early prediction of non-response to preoperative chemoradiotherapy in patients with potentially curable esophageal cancer. Methods/design Prognostic accuracy study, embedded in a randomized multicenter Dutch trial comparing neoadjuvant chemoradiotherapy for 5 weeks followed by surgery versus surgery alone for esophageal cancer. This prognostic accuracy study is performed only in the neoadjuvant arm of the randomized trial. In 6 centers, 150 consecutive patients will be included over a 3 year period. FDG-PET and CT-scan will be performed before and 2 weeks after the start of the chemoradiotherapy. All patients complete the 5 weeks regimen of neoadjuvant chemoradiotherapy, regardless the test results. Pathological examination of the surgical resection specimen will be used as reference standard. Responders are defined as patients with < 10% viable residual tumor cells (Mandard-score). Difference in accuracy (area under ROC curve) and negative predictive value between FDG-PET and CT-scan are primary endpoints. Furthermore, an economic evaluation will be performed, comparing survival and costs associated with the use of FDG-PET (or CT-scan) to predict tumor response with survival and costs of neoadjuvant chemoradiotherapy without prediction of response (reference strategy). Discussion The NEOPEC-trial could be the first sufficiently powered study that helps justify implementation of FDG-PET for response-monitoring in patients with esophageal cancer in clinical practice. Trial registration ISRCTN45750457 PMID:18671847

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Rapid response systems.

PubMed

Lyons, Patrick G; Edelson, Dana P; Churpek, Matthew M

2018-07-01

Rapid response systems are commonly employed by hospitals to identify and respond to deteriorating patients outside of the intensive care unit. Controversy exists about the benefits of rapid response systems. We aimed to review the current state of the rapid response literature, including evolving aspects of afferent (risk detection) and efferent (intervention) arms, outcome measurement, process improvement, and implementation. Articles written in English and published in PubMed. Rapid response systems are heterogeneous, with important differences among afferent and efferent arms. Clinically meaningful outcomes may include unexpected mortality, in-hospital cardiac arrest, length of stay, cost, and processes of care at end of life. Both positive and negative interventional studies have been published, although the two largest randomized trials involving rapid response systems - the Medical Early Response and Intervention Trial (MERIT) and the Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients (EPOCH) trial - did not find a mortality benefit with these systems, albeit with important limitations. Advances in monitoring technologies, risk assessment strategies, and behavioral ergonomics may offer opportunities for improvement. Rapid responses may improve some meaningful outcomes, although these findings remain controversial. These systems may also improve care for patients at the end of life. Rapid response systems are expected to continue evolving with novel developments in monitoring technologies, risk prediction informatics, and work in human factors. Copyright © 2018 Elsevier B.V. All rights reserved.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2015-04-01

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model - CLM3.5-DGVM; Ecosystem Demography model version 2 - ED2; the Joint UK Land Environment Simulator version 2.1 - JULES; Simple Biosphere model version 3 - SiB3; and the soil-plant-atmosphere model - SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model-data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.

Adding Postal Follow-Up to a Web-Based Survey of Primary Care and Gastroenterology Clinic Physician Chiefs Improved Response Rates but not Response Quality or Representativeness.

PubMed

Partin, Melissa R; Powell, Adam A; Burgess, Diana J; Haggstrom, David A; Gravely, Amy A; Halek, Krysten; Bangerter, Ann; Shaukat, Aasma; Nelson, David B

2015-09-01

This study assessed whether postal follow-up to a web-based physician survey improves response rates, response quality, and representativeness. We recruited primary care and gastroenterology chiefs at 125 Veterans Affairs medical facilities to complete a 10-min web-based survey on colorectal cancer screening and diagnostic practices in 2010. We compared response rates, response errors, and representativeness in the primary care and gastroenterology samples before and after adding postal follow-up. Adding postal follow-up increased response rates by 20-25 percentage points; markedly greater increases than predicted from a third e-mail reminder. In the gastroenterology sample, the mean number of response errors made by web responders (0.25) was significantly smaller than the mean number made by postal responders (2.18), and web responders provided significantly longer responses to open-ended questions. There were no significant differences in these outcomes in the primary care sample. Adequate representativeness was achieved before postal follow-up in both samples, as indicated by the lack of significant differences between web responders and the recruitment population on facility characteristics. We conclude adding postal follow-up to this web-based physician leader survey improved response rates but not response quality or representativeness. © The Author(s) 2013.

Time course and predictors of health-related quality of life improvement and medication satisfaction in children diagnosed with attention-deficit/hyperactivity disorder treated with the methylphenidate transdermal system.

PubMed

Frazier, Thomas W; Weiss, Margaret; Hodgkins, Paul; Manos, Michael J; Landgraf, Jeanne M; Gibbins, Christopher

2010-10-01

The aim of this study was to evaluate the time course and predictors of improvement in health-related quality of life (HRQL) and medication satisfaction in children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and treated with the methylphenidate transdermal system (MTS). Temporal relationships between ADHD symptoms, medication satisfaction, and HRQL measures were examined via latent growth curve, structural path, and growth mixture models. Higher levels of medication satisfaction at the end of titration predicted greater increases in family HRQL (p=0.004) and, to a lesser extent, child HRQL (p=0.068) throughout the study. At 4 of 6 (p<0.05) and 5 of 6 (p<0.10) contemporaneous time points, ADHD symptoms predicted child HRQL. At 2 of 6 (p<0.05) and 3 of 6 (p<0.10) contemporaneous time points, ADHD symptoms predicted family HRQL. ADHD did not predict child or family HRQL improvements at subsequent time points. A uniform pattern of change for child HRQL was noted, with most HRQL change following the pattern of symptom change during titration. Three distinct patterns of change were noted for family HRQL. In most cases, medication satisfaction, ADHD symptoms, and HRQL improved simultaneously, suggesting that HRQL was not a delayed response to improvement in symptoms. Children showed a uniform pattern of improvement in HRQL that followed symptom change; three distinct patterns of change were found for improvement in family HRQL.

Causes of variation among rice models in yield response to CO2 examined with Free-Air CO2 Enrichment and growth chamber experiments.

PubMed

Hasegawa, Toshihiro; Li, Tao; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Baker, Jeffrey; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fugice, Job; Fumoto, Tamon; Gaydon, Donald; Kumar, Soora Naresh; Lafarge, Tanguy; Marcaida Iii, Manuel; Masutomi, Yuji; Nakagawa, Hiroshi; Oriol, Philippe; Ruget, Françoise; Singh, Upendra; Tang, Liang; Tao, Fulu; Wakatsuki, Hitomi; Wallach, Daniel; Wang, Yulong; Wilson, Lloyd Ted; Yang, Lianxin; Yang, Yubin; Yoshida, Hiroe; Zhang, Zhao; Zhu, Jianguo

2017-11-01

The CO 2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO 2 ] (E-[CO 2 ]) by comparison to free-air CO 2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO 2 ] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO 2 ] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO 2 ] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO 2 ] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO 2 ] × N interactions is necessary to better evaluate management practices under climate change.

Improved high-dimensional prediction with Random Forests by the use of co-data.

PubMed

Te Beest, Dennis E; Mes, Steven W; Wilting, Saskia M; Brakenhoff, Ruud H; van de Wiel, Mark A

2017-12-28

Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary 'co-data' can be used to improve the performance of a Random Forest in such a setting. Co-data are incorporated in the Random Forest by replacing the uniform sampling probabilities that are used to draw candidate variables by co-data moderated sampling probabilities. Co-data here are defined as any type information that is available on the variables of the primary data, but does not use its response labels. These moderated sampling probabilities are, inspired by empirical Bayes, learned from the data at hand. We demonstrate the co-data moderated Random Forest (CoRF) with two examples. In the first example we aim to predict the presence of a lymph node metastasis with gene expression data. We demonstrate how a set of external p-values, a gene signature, and the correlation between gene expression and DNA copy number can improve the predictive performance. In the second example we demonstrate how the prediction of cervical (pre-)cancer with methylation data can be improved by including the location of the probe relative to the known CpG islands, the number of CpG sites targeted by a probe, and a set of p-values from a related study. The proposed method is able to utilize auxiliary co-data to improve the performance of a Random Forest.

Neural Markers of Attention to Aversive Pictures Predict Response to Cognitive Behavioral Therapy in Anxiety and Depression

PubMed Central

Stange, Jonathan P.; MacNamara, Annmarie; Barnas, Olga; Kennedy, Amy E.; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2016-01-01

Excessive attention toward aversive information may be a core mechanism underlying emotional disorders, but little is known about whether this is predictive of response to treatments. We evaluated whether enhanced attention toward aversive stimuli, as indexed by an event-related potential component, the late positive potential (LPP), would predict response to cognitive behavioral therapy (CBT) in patients with social anxiety disorder and/or major depressive disorder. Thirty-two patients receiving 12 weeks of CBT responded to briefly-presented pairs of aversive and neutral pictures that served as targets or distracters while electroencephaolography was recorded. Patients with larger pre-treatment LPPs to aversive relative to neutral distracters (when targets were aversive) were more likely to respond to CBT, and demonstrated larger reductions in symptoms of depression and anxiety following treatment. Increased attention toward irrelevant aversive stimuli may signal attenuated top-down control, so treatments like CBT that improve this control could be beneficial for these individuals. PMID:27784617

Neural markers of attention to aversive pictures predict response to cognitive behavioral therapy in anxiety and depression.

PubMed

Stange, Jonathan P; MacNamara, Annmarie; Barnas, Olga; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-02-01

Excessive attention toward aversive information may be a core mechanism underlying emotional disorders, but little is known about whether this is predictive of response to treatments. We evaluated whether enhanced attention toward aversive stimuli, as indexed by an event-related potential component, the late positive potential (LPP), would predict response to cognitive behavioral therapy (CBT) in patients with social anxiety disorder and/or major depressive disorder. Thirty-two patients receiving 12 weeks of CBT responded to briefly-presented pairs of aversive and neutral pictures that served as targets or distracters while electroencephaolography was recorded. Patients with larger pre-treatment LPPs to aversive relative to neutral distracters (when targets were aversive) were more likely to respond to CBT, and demonstrated larger reductions in symptoms of depression and anxiety following treatment. Increased attention toward irrelevant aversive stimuli may signal attenuated top-down control, so treatments like CBT that improve this control could be beneficial for these individuals. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Responsiveness to montelukast is associated with bronchial hyperresponsiveness and total immunoglobulin E but not polymorphisms in the leukotriene C4 synthase and cysteinyl leukotriene receptor 1 genes in Korean children with exercise-induced asthma (EIA).

PubMed

Lee, S-Y; Kim, H-B; Kim, J-H; Kim, B-S; Kang, M-J; Jang, S-O; Seo, H-J; Hong, S-J

2007-10-01

As previous studies have shown that cysteinyl leukotrienes are important mediators in exercise-induced bronchoconstriction (EIB), and leukotriene receptor antagonists (LTRAs) such as montelukast have been shown to improve post-exercise bronchoconstrictor responses, we herein investigated whether clinical responsiveness to montelukast was associated with polymorphisms in the genes encoding leukotriene C4 synthase (LTC4S) and cysteinyl leukotriene receptor 1 (CysLTR1) and/or clinical parameters in Korean asthmatic children with EIB. The study population consisted of 100 asthmatic children with EIB. The individuals studied were given exercise challenge tests before and after receiving montelukast (5 mg/day) for 8 weeks. Responders were defined as children showing>10% post-treatment improvement in forced expiratory volume in 1 s (FEV1). The LTC4S A(-444)C and CysLTR1 T(+927)C polymorphisms were genotyped by PCR-restriction fragment length polymorphism analysis. Of 100 enrolled children, 68 were classified as responders and 32 were classified as non-responders. No significant association was observed between montelukast responsiveness and LTC4S or CysLTR1 genotype, either alone or in combination. In contrast, montelukast-induced improvement in FEV(1) after exercise was correlated with higher pre-treatment PC20 (methacholine) values (r=0.210, P=0.036) and lower total IgE levels (r=-0.216, P=0.031). The LTC4S A(-444)C and CysLTR1 T(+927)C genotypes do not appear to be useful for predicting clinical responsiveness to montelukast, whereas bronchial hyperresponsiveness and total IgE appear to predict the degree of montelukast responsiveness in Korean asthmatic children with EIB.

Iodine-131 metaiodobenzylguanidine treatment for metastatic carcinoid. Results in 98 patients.

PubMed

Safford, Shawn D; Coleman, R Edward; Gockerman, Jon P; Moore, Joseph; Feldman, Jerome; Onaitis, Mark W; Tyler, Douglas S; Olson, John A

2004-11-01

Iodine-131 metaiodobenzylguanidine (131I-MIBG) is useful for imaging carcinoid tumors and recently has been applied to the palliative treatment of metastatic carcinoid in small studies. The authors now report their results on the therapeutic utility of high-dose 131I-MIBG treatment in a large group of patients with metastatic carcinoid tumors. The authors performed a retrospective review of 98 patients with metastatic carcinoid who were treated at their institution with 131I-MIBG over a 15-year period. Endpoints examined included the World Health Organization criteria for treatment response: symptoms, hormone (5-hydroxyindoleacetic acid [5-HIAA]) production, and clinical tumor response. Patients received a median dose of 401 +/- 202 millicuries (mCi) 131I-MIBG. The median survival after treatment was 2.3 years. Patients who experienced a symptomatic response had improved survival (5.76 years vs. 2.09 years; P < 0.01). For the 56 patients who had 5-HIAA levels monitored, the mean urine 5-HIAA levels decreased significantly after 131I-MIBG treatment (126 +/- 122 ng/mL vs. 91 +/- 125 ng/mL; P < 0.01); however, the patients with reduced 5-HIAA levels did not experience improved survival (4.11 years vs. 3.42 years; P = 0.2). Patients who received an initial 131I-MIBG dose > 400 mCi lived longer than patients who received < 400 mCi (4.69 years vs. 1.86 years; P = 0.05). Radiographic tumor response did not predict survival. Toxicity included pancytopenia, thrombocytopenia, nausea, and emesis. The current data support 131I-MIBG treatment in select patients with metastatic carcinoid who progress despite optimal medical management. Improved survival was predicted best by symptomatic response to 131I-MIBG treatment, but not by hormone or radiographic response.

Sea surface temperature predictions using a multi-ocean analysis ensemble scheme

NASA Astrophysics Data System (ADS)

Zhang, Ying; Zhu, Jieshun; Li, Zhongxian; Chen, Haishan; Zeng, Gang

2017-08-01

This study examined the global sea surface temperature (SST) predictions by a so-called multiple-ocean analysis ensemble (MAE) initialization method which was applied in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). Different from most operational climate prediction practices which are initialized by a specific ocean analysis system, the MAE method is based on multiple ocean analyses. In the paper, the MAE method was first justified by analyzing the ocean temperature variability in four ocean analyses which all are/were applied for operational climate predictions either at the European Centre for Medium-range Weather Forecasts or at NCEP. It was found that these systems exhibit substantial uncertainties in estimating the ocean states, especially at the deep layers. Further, a set of MAE hindcasts was conducted based on the four ocean analyses with CFSv2, starting from each April during 1982-2007. The MAE hindcasts were verified against a subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) Project. Comparisons suggested that MAE shows better SST predictions than CFSRR over most regions where ocean dynamics plays a vital role in SST evolutions, such as the El Niño and Atlantic Niño regions. Furthermore, significant improvements were also found in summer precipitation predictions over the equatorial eastern Pacific and Atlantic oceans, for which the local SST prediction improvements should be responsible. The prediction improvements by MAE imply a problem for most current climate predictions which are based on a specific ocean analysis system. That is, their predictions would drift towards states biased by errors inherent in their ocean initialization system, and thus have large prediction errors. In contrast, MAE arguably has an advantage by sampling such structural uncertainties, and could efficiently cancel these errors out in their predictions.

Early prediction of extreme stratospheric polar vortex states based on causal precursors

NASA Astrophysics Data System (ADS)

Kretschmer, Marlene; Runge, Jakob; Coumou, Dim

2017-08-01

Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1-15 (16-30) days with false-alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions.

Tropical forests and global change: filling knowledge gaps.

PubMed

Zuidema, Pieter A; Baker, Patrick J; Groenendijk, Peter; Schippers, Peter; van der Sleen, Peter; Vlam, Mart; Sterck, Frank

2013-08-01

Tropical forests will experience major changes in environmental conditions this century. Understanding their responses to such changes is crucial to predicting global carbon cycling. Important knowledge gaps exist: the causes of recent changes in tropical forest dynamics remain unclear and the responses of entire tropical trees to environmental changes are poorly understood. In this Opinion article, we argue that filling these knowledge gaps requires a new research strategy, one that focuses on trees instead of leaves or communities, on long-term instead of short-term changes, and on understanding mechanisms instead of documenting changes. We propose the use of tree-ring analyses, stable-isotope analyses, manipulative field experiments, and well-validated simulation models to improve predictions of forest responses to global change. Copyright © 2013 Elsevier Ltd. All rights reserved.

Watchful Waiting for Cases of Pediatric Otitis Media: Modeling Parental Response to Physician Advice.

PubMed

MacGeorge, Erina L; Smith, Rachel A; Caldes, Emily P; Hackman, Nicole M

2016-08-01

Watchful waiting (WW) can reduce unnecessary antibiotic use in the treatment of pediatric otitis media (ear infection), but its utility is impaired by underutilization and noncompliance. Guided by advice response theory, the current study proposes advantage and capacity as factors that predict how caregivers evaluate and respond affectively to WW. Parents (N = 373) of at least 1 child age 5 years or younger completed questionnaires that assessed responses to hypothetical WW advice for their youngest child. Perceptions of advantage from WW and the capacity to monitor and manage symptoms predicted advice quality, physician trust, and future compliance both directly and indirectly through negative affect. The findings suggest the elaboration of advice response theory to include more aspects of advice content evaluation (e.g., advantage) and the influence of negative affect. The study also provides practical guidance for physicians seeking to improve caregiver reception of WW advice.

Evaluation of a Singular Value Decomposition Approach for Impact Dynamic Data Correlation

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Lyle, Karen H.; Lessard, Wendy B.

2003-01-01

Impact dynamic tests are used in the automobile and aircraft industries to assess survivability of occupants during crash, to assert adequacy of the design, and to gain federal certification. Although there is no substitute for experimental tests, analytical models are often developed and used to study alternate test conditions, to conduct trade-off studies, and to improve designs. To validate results from analytical predictions, test and analysis results must be compared to determine the model adequacy. The mathematical approach evaluated in this paper decomposes observed time responses into dominant deformation shapes and their corresponding contribution to the measured response. To correlate results, orthogonality of test and analysis shapes is used as a criterion. Data from an impact test of a composite fuselage is used and compared to finite element predictions. In this example, the impact response was decomposed into multiple shapes but only two dominant shapes explained over 85% of the measured response

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

Pinilla, Maria Isabel

This report seeks to study and benchmark code predictions against experimental data; determine parameters to match MCNP-simulated detector response functions to experimental stilbene measurements; add stilbene processing capabilities to DRiFT; and improve NEUANCE detector array modeling and analysis using new MCNP6 and DRiFT features.

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.

Utilizing multiple scale models to improve predictions of extra-axial hemorrhage in the immature piglet.

PubMed

Scott, Gregory G; Margulies, Susan S; Coats, Brittany

2016-10-01

Traumatic brain injury (TBI) is a leading cause of death and disability in the USA. To help understand and better predict TBI, researchers have developed complex finite element (FE) models of the head which incorporate many biological structures such as scalp, skull, meninges, brain (with gray/white matter differentiation), and vasculature. However, most models drastically simplify the membranes and substructures between the pia and arachnoid membranes. We hypothesize that substructures in the pia-arachnoid complex (PAC) contribute substantially to brain deformation following head rotation, and that when included in FE models accuracy of extra-axial hemorrhage prediction improves. To test these hypotheses, microscale FE models of the PAC were developed to span the variability of PAC substructure anatomy and regional density. The constitutive response of these models were then integrated into an existing macroscale FE model of the immature piglet brain to identify changes in cortical stress distribution and predictions of extra-axial hemorrhage (EAH). Incorporating regional variability of PAC substructures substantially altered the distribution of principal stress on the cortical surface of the brain compared to a uniform representation of the PAC. Simulations of 24 non-impact rapid head rotations in an immature piglet animal model resulted in improved accuracy of EAH prediction (to 94 % sensitivity, 100 % specificity), as well as a high accuracy in regional hemorrhage prediction (to 82-100 % sensitivity, 100 % specificity). We conclude that including a biofidelic PAC substructure variability in FE models of the head is essential for improved predictions of hemorrhage at the brain/skull interface.

Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach—A Case Study for the City of Wuhan in China

PubMed Central

Gao, Yuan; Zhang, Chuanrong; He, Qingsong; Liu, Yaolin

2017-01-01

Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study—simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan. PMID:28617348

Chemotherapy Response Assessment by FDG-PET-CT in Early-stage Classical Hodgkin Lymphoma: Moving Beyond the Five-Point Deauville Score

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

Milgrom, Sarah A., E-mail: samilgrom@mdanderson.org; Dong, Wenli; Akhtari, Mani

Purpose: In early-stage classical Hodgkin lymphoma, fluorodeoxyglucose positron emission tomography (PET)-computed tomography (CT) scans are performed routinely after chemotherapy, and the 5-point Deauville score is used to report the disease response. We hypothesized that other PET-CT parameters, considered in combination with Deauville score, would improve risk stratification. Methods and Materials: Patients treated for stage I to II Hodgkin lymphoma from 2003 to 2013, who were aged ≥18 years and had analyzable PET-CT scans performed before and after chemotherapy, were eligible. The soft tissue volume (STV), maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis were recorded from the PET-CTmore » scans before and after chemotherapy. Reductions were defined as 1 − (final PET-CT value)/(corresponding initial PET-CT value). The primary endpoint was freedom from progression (FFP). Results: For 202 patients treated with chemotherapy with or without radiation therapy, the 5-year FFP was 89% (95% confidence interval 85%-93%). All PET-CT parameters were strongly associated with the Deauville score (P<.001) and FFP (P<.0001) on univariate analysis. The Deauville score was highly predictive of FFP (C-index 0.89) but was less discriminating in the Deauville 1 to 4 subset (C-index 0.67). Therefore, we aimed to identify PET-CT parameters that would improve risk stratification for this subgroup (n=187). STV reduction was predictive of outcome (C-index 0.71) and was dichotomized with an optimal cutoff of 0.65 (65% reduction in STV). A model incorporating the Deauville score and STV reduction predicted FFP more accurately than either measurement alone in the Deauville 1 to 4 subset (C-index 0.83). The improvement in predictive accuracy of this composite measure compared with the Deauville score alone met statistical significance (P=.045). Conclusions: The relative reduction in tumor size is an independent predictor of outcome. Combined with the Deauville score, it might improve risk stratification and contribute to response-adapted individualization of therapy.« less

Validation of an individualised model of human thermoregulation for predicting responses to cold air

NASA Astrophysics Data System (ADS)

van Marken Lichtenbelt, Wouter D.; Frijns, Arjan J. H.; van Ooijen, Marieke J.; Fiala, Dusan; Kester, Arnold M.; van Steenhoven, Anton A.

2007-01-01

Most computer models of human thermoregulation are population based. Here, we individualised the Fiala model [Fiala et al. (2001) Int J Biometeorol 45:143 159] with respect to anthropometrics, body fat, and metabolic rate. The predictions of the adapted multisegmental thermoregulatory model were compared with measured skin temperatures of individuals. Data from two experiments, in which reclining subjects were suddenly exposed to mild to moderate cold environmental conditions, were used to study the effect on dynamic skin temperature responses. Body fat was measured by the three-compartment method combining underwater weighing and deuterium dilution. Metabolic rate was determined by indirect calorimetry. In experiment 1, the bias (mean difference) between predicted and measured mean skin temperature decreased from 1.8°C to -0.15°C during cold exposure. The standard deviation of the mean difference remained of the same magnitude (from 0.7°C to 0.9°C). In experiment 2 the bias of the skin temperature changed from 2.0±1.09°C using the standard model to 1.3±0.93°C using individual characteristics in the model. The inclusion of individual characteristics thus improved the predictions for an individual and led to a significantly smaller systematic error. However, a large part of the discrepancies in individual response to cold remained unexplained. Possible further improvements to the model accomplished by inclusion of more subject characteristics (i.e. body fat distribution, body shape) and model refinements on the level of (skin) blood perfusion, and control functions, are discussed.

Dead or Alive? Using Membrane Failure and Chlorophyll a Fluorescence to Predict Plant Mortality from Drought1[OPEN

PubMed Central

Speckman, Heather N.; Huhn, Bridger J.; Strawn, Rachel N.; Weinig, Cynthia

2017-01-01

Climate models predict widespread increases in both drought intensity and duration in the next decades. Although water deficiency is a significant determinant of plant survival, limited understanding of plant responses to extreme drought impedes forecasts of both forest and crop productivity under increasing aridity. Drought induces a suite of physiological responses; however, we lack an accurate mechanistic description of plant response to lethal drought that would improve predictive understanding of mortality under altered climate conditions. Here, proxies for leaf cellular damage, chlorophyll a fluorescence, and electrolyte leakage were directly associated with failure to recover from drought upon rewatering in Brassica rapa (genotype R500) and thus define the exact timing of drought-induced death. We validated our results using a second genotype (imb211) that differs substantially in life history traits. Our study demonstrates that whereas changes in carbon dynamics and water transport are critical indicators of drought stress, they can be unrelated to visible metrics of mortality, i.e. lack of meristematic activity and regrowth. In contrast, membrane failure at the cellular scale is the most proximate cause of death. This hypothesis was corroborated in two gymnosperms (Picea engelmannii and Pinus contorta) that experienced lethal water stress in the field and in laboratory conditions. We suggest that measurement of chlorophyll a fluorescence can be used to operationally define plant death arising from drought, and improved plant characterization can enhance surface model predictions of drought mortality and its consequences to ecosystem services at a global scale. PMID:28710130

Dead or Alive? Using Membrane Failure and Chlorophyll a Fluorescence to Predict Plant Mortality from Drought.

PubMed

Guadagno, Carmela R; Ewers, Brent E; Speckman, Heather N; Aston, Timothy Llewellyn; Huhn, Bridger J; DeVore, Stanley B; Ladwig, Joshua T; Strawn, Rachel N; Weinig, Cynthia

2017-09-01

Climate models predict widespread increases in both drought intensity and duration in the next decades. Although water deficiency is a significant determinant of plant survival, limited understanding of plant responses to extreme drought impedes forecasts of both forest and crop productivity under increasing aridity. Drought induces a suite of physiological responses; however, we lack an accurate mechanistic description of plant response to lethal drought that would improve predictive understanding of mortality under altered climate conditions. Here, proxies for leaf cellular damage, chlorophyll a fluorescence, and electrolyte leakage were directly associated with failure to recover from drought upon rewatering in Brassica rapa (genotype R500) and thus define the exact timing of drought-induced death. We validated our results using a second genotype (imb211) that differs substantially in life history traits. Our study demonstrates that whereas changes in carbon dynamics and water transport are critical indicators of drought stress, they can be unrelated to visible metrics of mortality, i.e. lack of meristematic activity and regrowth. In contrast, membrane failure at the cellular scale is the most proximate cause of death. This hypothesis was corroborated in two gymnosperms ( Picea engelmannii and Pinus contorta ) that experienced lethal water stress in the field and in laboratory conditions. We suggest that measurement of chlorophyll a fluorescence can be used to operationally define plant death arising from drought, and improved plant characterization can enhance surface model predictions of drought mortality and its consequences to ecosystem services at a global scale. © 2017 American Society of Plant Biologists. All Rights Reserved.

Promoting healthy eating and active playtime by connecting to nature families with preschool children: evaluation of pilot study "Play&Grow".

PubMed

Sobko, Tanja; Jia, Zhenzhen; Kaplan, Matthew; Lee, Alfred; Tseng, Chia-Huei

2017-04-01

This pilot project aimed to evaluate the "Play&Grow" program which promotes age-appropriate dietary habits and playtime healthy routines through "connectedness to nature" experiences in Hong Kong families with young children. Thirty-eight preschoolers (aged 33.97 ± 9.38 mo), mothers, and their domestic workers were recruited. The families attended one workshop/week for a 4-mo period, consisting of: (i) health topic; (ii) food games; (iii) nature-related outdoor activities. Feeding practices, particularly Promoting and Encouragement to eat (PE) and Instrumental Feeding (IF) improved after the intervention (P = 0.008 and P = 0.016, respectively). Mother's BMI, responsibility for child's meal, child's birth weight had a bearing on the improvement of PE, r 2 = 0.243, F(3,33) = 3.54, P = 0.025. Domestic helper's responsibility for child's cooking and her IF practices could predict child's picky eating (r 2 = 0.203, F(2,34) = 4.322, P = 0.021). Mother's responsibility for child and helper's responsibility for cooking could predict child's consumption of salty foods (r 2 = 0.252, F(2,34) = 5.737, P = 0.007). Physical activity of caregivers improved after the intervention. The pilot confirmed the design, protocols, evaluation instruments, and logistics of the study. Modified "Play&Grow" intervention will be conducted in a more rigorous randomized controlled trial to determine the long-term impact on obesity prevention in Hong Kong.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

We-Language and Sustained Reductions in Drinking in Couple-Based Treatment for Alcohol Use Disorders.

PubMed

Hallgren, Kevin A; McCrady, Barbara S

2016-03-01

Couple-based treatments for alcohol use disorders (AUDs) produce higher rates of abstinence than individual-based treatments and posit that active involvement of both identified patients (IPs) and significant others (SOs) is partly responsible for these improvements. Separate research on couples' communication has suggested that pronoun usage can indicate a communal approach to coping with health-related problems. The present study tested whether communal coping, indicated by use of more first-person plural pronouns ("we" language), fewer second-person pronouns ("you" language), and fewer first-person singular pronouns ("I" language), predicted improvements in abstinence in couple-based AUD treatment. Pronoun use was measured in first- and mid-treatment sessions for 188 heterosexual couples in four clinical trials of alcohol behavioral couple therapy (ABCT). Percentages of days abstinent were assessed during treatment and over a 6-month follow-up period. Greater IP and SO "we" language during both sessions was correlated with greater improvement in abstinent days during treatment. Greater SO "we" language during first- and mid-treatment sessions was correlated with greater improvement in abstinence at follow-up. Greater use of IP and SO "you" and "I" language had mixed correlations with abstinence, typically being unrelated to or predicting less improvement in abstinence. When all pronoun variables were entered into regression models, only greater IP "we" langue and lower IP "you" language predicted improvements in abstinence during treatment, and only SO "we" language predicted improvements during follow-up. Most pronoun categories had little or no association with baseline relationship distress. Results suggest that communal coping predicts better abstinence outcomes in couple-based AUD treatment. © 2015 Family Process Institute.

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

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

Ramuhalli, Pradeep; Coble, Jamie B.; Shumaker, Brent

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or moremore » sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

Plasma microRNA profile as a predictor of early virological response to interferon treatment in chronic hepatitis B patients.

PubMed

Zhang, Xiaonan; Chen, Cuncun; Wu, Min; Chen, Liang; Zhang, Jiming; Zhang, Xinxin; Zhang, Zhanqin; Wu, Jingdi; Wang, Jiefei; Chen, Xiaorong; Huang, Tao; Chen, Lixiang; Yuan, Zhenghong

2012-01-01

Interferon (IFN) and pegylated interferon (PEG-IFN) treatment of chronic hepatitis B leads to a sustained virological response in a limited proportion of patients and has considerable side effects. To find novel markers associated with prognosis of IFN therapy, we investigated whether a pretreatment plasma microRNA profile could be used to predict early virological response to IFN. We performed microRNA microarray analysis of plasma samples from 94 patients with chronic hepatitis B who received IFN therapy. The microRNA profiles from 13 liver biopsy samples were also measured. The OneR feature ranking and incremental feature selection method were used to rank and optimize the number of features in the model. Support vector machine prediction engine and jack-knife cross-validation were used to generate and evaluate the prediction model. The optimized model consisting of 11 microRNAs yielded a 74.2% overall accuracy in the training group and was independently confirmed in the test group (71.4% accuracy). Univariate and multivariate logistic regression analyses confirmed its independent association with early virological response (OR=7.35; P=2.12×10(-5)). Combining the microRNA profile with the alanine aminotransferase level improved the overall accuracy from 73.4% to 77.3%. Co-transfection of an HBV replicative construct with microRNA mimics revealed that let-7f, miR-939 and miR-638 were functionally associated with the HBV life cycle. The 11 microRNA signatures in plasma, together with basic clinical variables, might provide an accurate method to assist in medication decisions and improve the overall sustained response to IFN treatment.

Idiopathic normal pressure hydrocephalus: the CSF tap-test may predict the clinical response to shunting.

PubMed

Sand, T; Bovim, G; Grimse, R; Myhr, G; Helde, G; Cappelen, J

1994-05-01

A follow-up study was performed in nine patients with idiopathic normal pressure hydrocephalus (NPH) 37 months (mean) after shunting and 10 non-operated controls with comparable degrees of ventricular enlargement, gait disorder, and dementia. Five operated patients vs. no controls reported sustained general improvement (p < 0.02). Objectively improved gait at follow-up (compared with preoperative status) was found in five of the six tested NPH-patients vs. none of the controls (p < 0.005). Improved gait and/or psychometric function was found in four of six NPH vs. none of eight control patients (p < 0.02) after drainage of 40 ml cerebrospinal fluid (CSF tap-test). Improved gait during the CSF tap-test predicted continued improvement at follow-up. Temporal horn size was the only radiological variable which showed a (moderate) positive correlation with resistance to CSF absorption and rate of pressure increase. The size of the third ventricle diminished in parallel with clinical improvement.

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.

NASA/FAA general aviation crash dynamics program

NASA Technical Reports Server (NTRS)

Thomson, R. G.; Hayduk, R. J.; Carden, H. D.

1981-01-01

The program involves controlled full scale crash testing, nonlinear structural analyses to predict large deflection elastoplastic response, and load attenuating concepts for use in improved seat and subfloor structure. Both analytical and experimental methods are used to develop expertise in these areas. Analyses include simplified procedures for estimating energy dissipating capabilities and comprehensive computerized procedures for predicting airframe response. These analyses are developed to provide designers with methods for predicting accelerations, loads, and displacements on collapsing structure. Tests on typical full scale aircraft and on full and subscale structural components are performed to verify the analyses and to demonstrate load attenuating concepts. A special apparatus was built to test emergency locator transmitters when attached to representative aircraft structure. The apparatus is shown to provide a good simulation of the longitudinal crash pulse observed in full scale aircraft crash tests.

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.

Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) as a Cognitive Evaluation Tool for Patients with Normal Pressure Hydrocephalus.

PubMed

Nakatsu, Daisuke; Fukuhara, Toru; Chaytor, Naomi S; Phatak, Vaishali S; Avellino, Anthony M

2016-01-01

External lumbar drainage (ELD) is recognized as a screening method for ventriculo-peritoneal shunting (VPS) candidacy for possible normal pressure hydrocephalus (NPH). This study focused on the ELD predictability of the cognitive outcome after VPS for NPH. In addition, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was examined in ELD cognition screening. ELD results were considered positive with any improvement in gait and/or cognition. Among 36 patients examined for possible NPH, 26 underwent VPS because of positive ELD. Cognitive outcome after VPS was assessed at 6-month follow-up. The RBANS scores, examined pre- and post-ELD, were evaluated statistically to identify consistency with the neuropsychologist judgment and the predictability of cognitive outcome after VPS. Among 26 shunted patients, gait was improved in 24. Cognitive improvement was rated in 19, and there were 9 false negative and 5 false positive in ELD cognition screening. The neuropsychologist judgment in ELD cognition screening is most consistent with the RBANS score in delayed memory. The patients rated as improved in cognition after VPS had significantly lower RBANS scores pre-ELD in immediate memory and delayed memory. If both scores at pre-ELD were ≤ 80 (13 patients), all were rated as improved in cognition after VPS. ELD screening was highly predictive of clinical gait improvement but not of cognitive improvement after VPS for possible NPH. Particularly among patients with a positive ELD gait response, pre-ELD low RBANS scores in memory predicted cognitive improvement after VPS. RBANS seems effective in evaluating cognition for NPH.

Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) as a Cognitive Evaluation Tool for Patients with Normal Pressure Hydrocephalus

PubMed Central

NAKATSU, Daisuke; FUKUHARA, Toru; CHAYTOR, Naomi S.; PHATAK, Vaishali S.; AVELLINO, Anthony M.

2016-01-01

External lumbar drainage (ELD) is recognized as a screening method for ventriculo-peritoneal shunting (VPS) candidacy for possible normal pressure hydrocephalus (NPH). This study focused on the ELD predictability of the cognitive outcome after VPS for NPH. In addition, Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was examined in ELD cognition screening. ELD results were considered positive with any improvement in gait and/or cognition. Among 36 patients examined for possible NPH, 26 underwent VPS because of positive ELD. Cognitive outcome after VPS was assessed at 6-month follow-up. The RBANS scores, examined pre- and post-ELD, were evaluated statistically to identify consistency with the neuropsychologist judgment and the predictability of cognitive outcome after VPS. Among 26 shunted patients, gait was improved in 24. Cognitive improvement was rated in 19, and there were 9 false negative and 5 false positive in ELD cognition screening. The neuropsychologist judgment in ELD cognition screening is most consistent with the RBANS score in delayed memory. The patients rated as improved in cognition after VPS had significantly lower RBANS scores pre-ELD in immediate memory and delayed memory. If both scores at pre-ELD were ≤ 80 (13 patients), all were rated as improved in cognition after VPS. ELD screening was highly predictive of clinical gait improvement but not of cognitive improvement after VPS for possible NPH. Particularly among patients with a positive ELD gait response, pre-ELD low RBANS scores in memory predicted cognitive improvement after VPS. RBANS seems effective in evaluating cognition for NPH. PMID:26369720

Predicting Structure-Function Relations and Survival following Surgical and Bronchoscopic Lung Volume Reduction Treatment of Emphysema.

PubMed

Mondoñedo, Jarred R; Suki, Béla

2017-02-01

Lung volume reduction surgery (LVRS) and bronchoscopic lung volume reduction (bLVR) are palliative treatments aimed at reducing hyperinflation in advanced emphysema. Previous work has evaluated functional improvements and survival advantage for these techniques, although their effects on the micromechanical environment in the lung have yet to be determined. Here, we introduce a computational model to simulate a force-based destruction of elastic networks representing emphysema progression, which we use to track the response to lung volume reduction via LVRS and bLVR. We find that (1) LVRS efficacy can be predicted based on pre-surgical network structure; (2) macroscopic functional improvements following bLVR are related to microscopic changes in mechanical force heterogeneity; and (3) both techniques improve aspects of survival and quality of life influenced by lung compliance, albeit while accelerating disease progression. Our model predictions yield unique insights into the microscopic origins underlying emphysema progression before and after lung volume reduction.

Predicting Structure-Function Relations and Survival following Surgical and Bronchoscopic Lung Volume Reduction Treatment of Emphysema

PubMed Central

Mondoñedo, Jarred R.

2017-01-01

Lung volume reduction surgery (LVRS) and bronchoscopic lung volume reduction (bLVR) are palliative treatments aimed at reducing hyperinflation in advanced emphysema. Previous work has evaluated functional improvements and survival advantage for these techniques, although their effects on the micromechanical environment in the lung have yet to be determined. Here, we introduce a computational model to simulate a force-based destruction of elastic networks representing emphysema progression, which we use to track the response to lung volume reduction via LVRS and bLVR. We find that (1) LVRS efficacy can be predicted based on pre-surgical network structure; (2) macroscopic functional improvements following bLVR are related to microscopic changes in mechanical force heterogeneity; and (3) both techniques improve aspects of survival and quality of life influenced by lung compliance, albeit while accelerating disease progression. Our model predictions yield unique insights into the microscopic origins underlying emphysema progression before and after lung volume reduction. PMID:28182686

Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response.

PubMed

Jacobsen, Kathryn H; Aguirre, A Alonso; Bailey, Charles L; Baranova, Ancha V; Crooks, Andrew T; Croitoru, Arie; Delamater, Paul L; Gupta, Jhumka; Kehn-Hall, Kylene; Narayanan, Aarthi; Pierobon, Mariaelena; Rowan, Katherine E; Schwebach, J Reid; Seshaiyer, Padmanabhan; Sklarew, Dann M; Stefanidis, Anthony; Agouris, Peggy

2016-03-01

As the Ebola outbreak in West Africa wanes, it is time for the international scientific community to reflect on how to improve the detection of and coordinated response to future epidemics. Our interdisciplinary team identified key lessons learned from the Ebola outbreak that can be clustered into three areas: environmental conditions related to early warning systems, host characteristics related to public health, and agent issues that can be addressed through the laboratory sciences. In particular, we need to increase zoonotic surveillance activities, implement more effective ecological health interventions, expand prediction modeling, support medical and public health systems in order to improve local and international responses to epidemics, improve risk communication, better understand the role of social media in outbreak awareness and response, produce better diagnostic tools, create better therapeutic medications, and design better vaccines. This list highlights research priorities and policy actions the global community can take now to be better prepared for future emerging infectious disease outbreaks that threaten global public health and security.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

PubMed

Torres, Leigh G; Read, Andrew J; Halpin, Patrick

2008-10-01

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

What plant hydraulics can tell us about responses to climate-change droughts.

PubMed

Sperry, John S; Love, David M

2015-07-01

Climate change exposes vegetation to unusual drought, causing declines in productivity and increased mortality. Drought responses are hard to anticipate because canopy transpiration and diffusive conductance (G) respond to drying soil and vapor pressure deficit (D) in complex ways. A growing database of hydraulic traits, combined with a parsimonious theory of tree water transport and its regulation, may improve predictions of at-risk vegetation. The theory uses the physics of flow through soil and xylem to quantify how canopy water supply declines with drought and ceases by hydraulic failure. This transpiration 'supply function' is used to predict a water 'loss function' by assuming that stomatal regulation exploits transport capacity while avoiding failure. Supply-loss theory incorporates root distribution, hydraulic redistribution, cavitation vulnerability, and cavitation reversal. The theory efficiently defines stomatal responses to D, drying soil, and hydraulic vulnerability. Driving the theory with climate predicts drought-induced loss of plant hydraulic conductance (k), canopy G, carbon assimilation, and productivity. Data lead to the 'chronic stress hypothesis' wherein > 60% loss of k increases mortality by multiple mechanisms. Supply-loss theory predicts the climatic conditions that push vegetation over this risk threshold. The theory's simplicity and predictive power encourage testing and application in large-scale modeling. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Structural Dynamic Behavior of Wind Turbines

NASA Technical Reports Server (NTRS)

Thresher, Robert W.; Mirandy, Louis P.; Carne, Thomas G.; Lobitz, Donald W.; James, George H. III

2009-01-01

The structural dynamicist s areas of responsibility require interaction with most other members of the wind turbine project team. These responsibilities are to predict structural loads and deflections that will occur over the lifetime of the machine, ensure favorable dynamic responses through appropriate design and operational procedures, evaluate potential design improvements for their impact on dynamic loads and stability, and correlate load and control test data with design predictions. Load prediction has been a major concern in wind turbine designs to date, and it is perhaps the single most important task faced by the structural dynamics engineer. However, even if we were able to predict all loads perfectly, this in itself would not lead to an economic system. Reduction of dynamic loads, not merely a "design to loads" policy, is required to achieve a cost-effective design. The two processes of load prediction and structural design are highly interactive: loads and deflections must be known before designers and stress analysts can perform structural sizing, which in turn influences the loads through changes in stiffness and mass. Structural design identifies "hot spots" (local areas of high stress) that would benefit most from dynamic load alleviation. Convergence of this cycle leads to a turbine structure that is neither under-designed (which may result in structural failure), nor over-designed (which will lead to excessive weight and cost).

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.Above results are discussed in a peer-review paper just being accepted for publication on Climate Dynamics (Alessandri et al., 2017; doi:10.1007/s00382-017-3766-y).

Glutathione-s-transferase-pi expression in early breast cancer: association with outcome and response to chemotherapy.

PubMed

Arun, Banu K; Granville, Laura A; Yin, Guosheng; Middleton, Lavinia P; Dawood, Shaheenah; Kau, Shu-Wan; Kamal, Arif; Hsu, Limin; Hortobagyi, Gabriel N; Sahin, Aysegul A

2010-06-01

Glutathione-S-transferase-pi (GST-pi) is a detoxification enzyme expressed in breast cancer; however its involvement in chemotherapy sensitivity and prognosis is not well understood. We evaluated the expression of GSTpi and its predictive role of chemotherapy response. Breast tumor samples from 166 patients at stage I/II of the disease were immunostained for GST-pi, and the expression was 96 %. There was a trend toward improved disease-free survival with high GST-pi expression (p =.09). There was a statistically non-significant association between high GST-pi expression and improved outcome with adjuvant chemotherapy (p =.055). Further studies should evaluate the role of GST-pi expression in relation to response to different chemotherapies.

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.

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.

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.

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.

Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery.

PubMed

Khor, Sara; Lavallee, Danielle; Cizik, Amy M; Bellabarba, Carlo; Chapman, Jens R; Howe, Christopher R; Lu, Dawei; Mohit, A Alex; Oskouian, Rod J; Roh, Jeffrey R; Shonnard, Neal; Dagal, Armagan; Flum, David R

2018-03-07

Functional impairment and pain are common indications for the initiation of lumbar spine surgery, but information about expected improvement in these patient-reported outcome (PRO) domains is not readily available to most patients and clinicians considering this type of surgery. To assess population-level PRO response after lumbar spine surgery, and develop/validate a prediction tool for PRO improvement. This statewide multicenter cohort was based at 15 Washington state hospitals representing approximately 75% of the state's spine fusion procedures. The Spine Surgical Care and Outcomes Assessment Program and the survey center at the Comparative Effectiveness Translational Network prospectively collected clinical and PRO data from adult candidates for lumbar surgery, preoperatively and postoperatively, between 2012 and 2016. Prediction models were derived for PRO improvement 1 year after lumbar fusion surgeries on a random sample of 85% of the data and were validated in the remaining 15%. Surgical candidates from 2012 through 2015 were included; follow-up surveying continued until December 31, 2016, and data analysis was completed from July 2016 to April 2017. Functional improvement, defined as a reduction in Oswestry Disability Index score of 15 points or more; and back pain and leg pain improvement, defined a reduction in Numeric Rating Scale score of 2 points or more. A total of 1965 adult lumbar surgical candidates (mean [SD] age, 61.3 [12.5] years; 944 [59.6%] female) completed baseline surveys before surgery and at least 1 postoperative follow-up survey within 3 years. Of these, 1583 (80.6%) underwent elective lumbar fusion procedures; 1223 (77.3%) had stenosis, and 1033 (65.3%) had spondylolisthesis. Twelve-month follow-up participation rates for each outcome were between 66% and 70%. Improvements were reported in function, back pain, and leg pain at 12 months by 306 of 528 surgical patients (58.0%), 616 of 899 patients (68.5%), and 355 of 464 patients (76.5%), respectively, whose baseline scores indicated moderate to severe symptoms. Among nonoperative patients, 35 (43.8%), 47 (53.4%), and 53 (63.9%) reported improvements in function, back pain, and leg pain, respectively. Demographic and clinical characteristics included in the final prediction models were age, sex, race, insurance status, American Society of Anesthesiologists score, smoking status, diagnoses, prior surgery, prescription opioid use, asthma, and baseline PRO scores. The models had good predictive performance in the validation cohort (concordance statistic, 0.66-0.79) and were incorporated into a patient-facing, web-based interactive tool (https://becertain.shinyapps.io/lumbar_fusion_calculator). The PRO response prediction tool, informed by population-level data, explained most of the variability in pain reduction and functional improvement after surgery. Giving patients accurate information about their likelihood of outcomes may be a helpful component in surgery decision making.

Perception-action coupling and anticipatory performance in baseball batting.

PubMed

Ranganathan, Rajiv; Carlton, Les G

2007-09-01

The authors examined 10 expert and 10 novice baseball batters' ability to distinguish between a fastball and a change-up in a virtual environment. They used 2 different response modes: (a) an uncoupled response in which the batters verbally predicted the type of pitch and (b) a coupled response in which the batters swung a baseball bat to try and hit the virtual ball. The authors manipulated visual information from the pitcher and ball in 6 visual conditions. The batters were more accurate in predicting the type of pitch when the response was uncoupled. In coupled responses, experts were better able to use the first 100 ms of ball flight independently of the pitcher's kinematics. In addition, the skilled batters' stepping patterns were related to the pitcher's kinematics, whereas their swing time was related to ball speed. Those findings suggest that specific task requirements determine whether a highly coupled perception-action environment improves anticipatory performance. The authors also highlight the need for research on interceptive actions to be conducted in the performer's natural environment.

Ultrasound disease activity of bilateral wrist and finger joints at three months reflects the clinical response at six months of patients with rheumatoid arthritis treated with biologic disease-modifying anti-rheumatic drugs.

PubMed

Kawashiri, Shin-Ya; Nishino, Ayako; Shimizu, Toshimasa; Umeda, Masataka; Fukui, Shoichi; Nakashima, Yoshikazu; Suzuki, Takahisa; Koga, Tomohiro; Iwamoto, Naoki; Ichinose, Kunihiro; Tamai, Mami; Nakamura, Hideki; Origuchi, Tomoki; Aoyagi, Kiyoshi; Kawakami, Atsushi

2017-03-01

We evaluated whether the early responsiveness of ultrasound synovitis can predict the clinical response in rheumatoid arthritis (RA) patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs). Articular synovitis was assessed by ultrasound at 22 bilateral wrist and finger joints in 39 RA patients treated with bDMARDs. Each joint was assigned a gray-scale (GS) and power Doppler (PD) score from 0 to 3, and the sum of the GS or PD scores was considered to represent the ultrasound disease activity. We investigated the correlation of the change in ultrasound disease activity at three months with the EULAR response criteria at six months. GS and PD scores were significantly decreased at three months (p < 0.0001). The % changes of the GS and PD scores at three months were significantly higher at six months in moderate and good responders compared with non-responders (p < 0.05). These tendencies were numerically more prominent if clinical response was set as good responder or Disease Activity Score 28 remission. Poor improvement of ultrasound synovitis scores had good predictive value for non-responders at six months. The responsiveness of ultrasound disease activity is considered to predict further clinical response in RA patients treated with bDMARDs.

A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique.

PubMed

García Nieto, P J; Alonso Fernández, J R; Sánchez Lasheras, F; de Cos Juez, F J; Díaz Muñiz, C

2012-07-15

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in drinking and recreational water uses. The aim of this study is to improve our previous and successful work about cyanotoxins prediction from some experimental cyanobacteria concentrations in the Trasona reservoir (Asturias, Northern Spain) using the multivariate adaptive regression splines (MARS) technique at a local scale. In fact, this new improvement consists of using not only biological variables, but also the physical-chemical ones. As a result, the coefficient of determination has improved from 0.84 to 0.94, that is to say, more accurate predictive calculations and a better approximation to the real problem were obtained. Finally the agreement of the MARS model with experimental data confirmed the good performance. Copyright © 2012 Elsevier B.V. All rights reserved.

Predictive control of intersegmental tarsal movements in an insect.

PubMed

Costalago-Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

2017-08-01

In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.

Buckling Behavior of Compression-Loaded Composite Cylindrical Shells with Reinforced Cutouts

NASA Technical Reports Server (NTRS)

Hilburger, Mark W.; Starnes, James H., Jr.

2002-01-01

Results from a numerical study of the response of thin-wall compression-loaded quasi-isotropic laminated composite cylindrical shells with reinforced and unreinforced square cutouts are presented. The effects of cutout reinforcement orthotropy, size, and thickness on the nonlinear response of the shells are described. A high-fidelity nonlinear analysis procedure has been used to predict the nonlinear response of the shells. The analysis procedure includes a nonlinear static analysis that predicts stable response characteristics of the shells and a nonlinear transient analysis that predicts unstable dynamic buckling response characteristics. The results illustrate how a compression-loaded shell with an unreinforced cutout can exhibit a complex nonlinear response. In particular, a local buckling response occurs in the shell near the cutout and is caused by a complex nonlinear coupling between local shell-wall deformations and in-plane destabilizing compression stresses near the cutout. In general, the addition of reinforcement around a cutout in a compression-loaded shell can retard or eliminate the local buckling response near the cutout and increase the buckling load of the shell, as expected. However, results are presented that show how certain reinforcement configurations can actually cause an unexpected increase in the magnitude of local deformations and stresses in the shell and cause a reduction in the buckling load. Specific cases are presented that suggest that the orthotropy, thickness, and size of a cutout reinforcement in a shell can be tailored to achieve improved response characteristics.

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.

Birth weight predicted baseline muscular efficiency, but not response of energy expenditure to calorie restriction: An empirical test of the predictive adaptive response hypothesis.

PubMed

Workman, Megan; Baker, Jack; Lancaster, Jane B; Mermier, Christine; Alcock, Joe

2016-07-01

Aiming to test the evolutionary significance of relationships linking prenatal growth conditions to adult phenotypes, this study examined whether birth size predicts energetic savings during fasting. We specifically tested a Predictive Adaptive Response (PAR) model that predicts greater energetic saving among adults who were born small. Data were collected from a convenience sample of young adults living in Albuquerque, NM (n = 34). Indirect calorimetry quantified changes in resting energy expenditure (REE) and active muscular efficiency that occurred in response to a 29-h fast. Multiple regression analyses linked birth weight to baseline and postfast metabolic values while controlling for appropriate confounders (e.g., sex, body mass). Birth weight did not moderate the relationship between body size and energy expenditure, nor did it predict the magnitude change in REE or muscular efficiency observed from baseline to after fasting. Alternative indicators of birth size were also examined (e.g., low v. normal birth weight, comparison of tertiles), with no effects found. However, baseline muscular efficiency improved by 1.1% per 725 g (S.D.) increase in birth weight (P = 0.037). Birth size did not influence the sensitivity of metabolic demands to fasting-neither at rest nor during activity. Moreover, small birth size predicted a reduction in the efficiency with which muscles convert energy expended into work accomplished. These results do not support the ascription of adaptive function to phenotypes associated with small birth size. © 2015 Wiley Periodicals, Inc. Am. J. Hum. Biol. 28:484-492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

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.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Treesearch

Xiaohui Feng; María Uriarte; Grizelle González; Sasha Reed; Jill Thompson; Jess K. Zimmerman; Lora Murphy

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very...

Comment on “Mycorrhizal association as a primary control of the CO 2 fertilization effect”

DOE PAGES

Norby, R. J.; De Kauwe, M. G.; Walker, A. P.; ...

2017-01-26

Terrer et al. (Reports, 1 July 2016, p. 72) used meta-analysis of CO 2 enrichment experiments as evidence of an interaction between mycorrhizal symbiosis and soil nitrogen availability. The comment presented here challenges their database and biomass as the response metric, and hence their recommendation that incorporation of mycorrhizae in models will improve predictions of terrestrial ecosystem responses to increasing atmospheric CO 2.

Comment on “Mycorrhizal association as a primary control of the CO 2 fertilization effect”

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

Norby, R. J.; De Kauwe, M. G.; Walker, A. P.

Terrer et al. (Reports, 1 July 2016, p. 72) used meta-analysis of CO 2 enrichment experiments as evidence of an interaction between mycorrhizal symbiosis and soil nitrogen availability. The comment presented here challenges their database and biomass as the response metric, and hence their recommendation that incorporation of mycorrhizae in models will improve predictions of terrestrial ecosystem responses to increasing atmospheric CO 2.

Predictors of In-Hospital Mortality After Rapid Response Team Calls in a 274 Hospital Nationwide Sample.

PubMed

Shappell, Claire; Snyder, Ashley; Edelson, Dana P; Churpek, Matthew M

2018-07-01

Despite wide adoption of rapid response teams across the United States, predictors of in-hospital mortality for patients receiving rapid response team calls are poorly characterized. Identification of patients at high risk of death during hospitalization could improve triage to intensive care units and prompt timely reevaluations of goals of care. We sought to identify predictors of in-hospital mortality in patients who are subjects of rapid response team calls and to develop and validate a predictive model for death after rapid response team call. Analysis of data from the national Get with the Guidelines-Medical Emergency Team event registry. Two-hundred seventy four hospitals participating in Get with the Guidelines-Medical Emergency Team from June 2005 to February 2015. 282,710 hospitalized adults on surgical or medical wards who were subjects of a rapid response team call. None. The primary outcome was death during hospitalization; candidate predictors included patient demographic- and event-level characteristics. Patients who died after rapid response team were older (median age 72 vs 66 yr), were more likely to be admitted for noncardiac medical illness (70% vs 58%), and had greater median length of stay prior to rapid response team (81 vs 47 hr) (p < 0.001 for all comparisons). The prediction model had an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.78-0.79), with systolic blood pressure, time since admission, and respiratory rate being the most important variables. Patients who die following rapid response team calls differ significantly from surviving peers. Recognition of these factors could improve postrapid response team triage decisions and prompt timely goals of care discussions.

A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt.

PubMed

Li, Xiaochuan; Bai, Xuedong; Wu, Yaohong; Ruan, Dike

2016-03-15

To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD). From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different admission times to hospital. Outcome was assessed according to the Japanese Orthopedic Association (JOA) recovery rate as excellent, good, fair, and poor. The first two results were considered as effective clinical outcome (ECO). Baseline patient and clinical characteristics were considered as secondary variables. A multivariate logistic regression model was used to construct a model with the ECO as a dependent variable and other factors as explanatory variables. The odds ratios (ORs) of each risk factor were adjusted and transformed into a scoring system. Area under the curve (AUC) was calculated and validated in both internal and external samples. Moreover, calibration plot and predictive ability of this scoring system were also tested for further validation. Patients with DD with ECOs in both construction and validation models were around 76 % (76.4 and 75.5 % respectively). more preoperative visual analog pain scale (VAS) score (OR = 1.56, p < 0.01), stenosis levels of L4/5 or L5/S1 (OR = 1.44, p = 0.04), stenosis locations with neuroforamen (OR = 1.95, p = 0.01), neurological deficit (OR = 1.62, p = 0.01), and more VAS improvement of selective nerve route block (SNRB) (OR = 3.42, p = 0.02). the internal area under the curve (AUC) was 0.85, and the external AUC was 0.72, with a good calibration plot of prediction accuracy. Besides, the predictive ability of ECOs was not different from the actual results (p = 0.532). We have constructed and validated a predictive model for confirming responsible nerve roots in patients with DD. The associated risk factors were preoperative VAS score, stenosis levels of L4/5 or L5/S1, stenosis locations with neuroforamen, neurological deficit, and VAS improvement of SNRB. A tool such as this is beneficial in the preoperative counseling of patients, shared surgical decision making, and ultimately improving safety in spine surgery.

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.

An improved thermoregulatory model for cooling garment applications with transient metabolic rates

NASA Astrophysics Data System (ADS)

Westin, Johan K.

Current state-of-the-art thermoregulatory models do not predict body temperatures with the accuracies that are required for the development of automatic cooling control in liquid cooling garment (LCG) systems. Automatic cooling control would be beneficial in a variety of space, aviation, military, and industrial environments for optimizing cooling efficiency, for making LCGs as portable and practical as possible, for alleviating the individual from manual cooling control, and for improving thermal comfort and cognitive performance. In this study, we adopt the Fiala thermoregulatory model, which has previously demonstrated state-of-the-art predictive abilities in air environments, for use in LCG environments. We validate the numerical formulation with analytical solutions to the bioheat equation, and find our model to be accurate and stable with a variety of different grid configurations. We then compare the thermoregulatory model's tissue temperature predictions with experimental data where individuals, equipped with an LCG, exercise according to a 700 W rectangular type activity schedule. The root mean square (RMS) deviation between the model response and the mean experimental group response is 0.16°C for the rectal temperature and 0.70°C for the mean skin temperature, which is within state-of-the-art variations. However, with a mean absolute body heat storage error 3¯ BHS of 9.7 W˙h, the model fails to satisfy the +/-6.5 W˙h accuracy that is required for the automatic LCG cooling control development. In order to improve model predictions, we modify the blood flow dynamics of the thermoregulatory model. Instead of using step responses to changing requirements, we introduce exponential responses to the muscle blood flow and the vasoconstriction command. We find that such modifications have an insignificant effect on temperature predictions. However, a new vasoconstriction dependency, i.e. the rate of change of hypothalamus temperature weighted by the hypothalamus error signal (DeltaThy˙ dThy/dt), proves to be an important signal that governs the thermoregulatory response during conditions of simultaneously increasing core and decreasing skin temperatures, which is a common scenario in LCG environments. With the new ?DeltaThy˙dThy /dt dependency in the vasoconstriction command, the 3¯ BHS for the exercise period is reduced by 59% (from 12.9 W˙h to 5.2 W˙h). Even though the new 3¯ BHS of 5.8 W˙h for the total activity schedule is within the target accuracy of +/-6.5 W˙h, 3¯ BHS fails to stay within the target accuracy during the entire activity schedule. With additional improvements to the central blood pool formulation, the LCG boundary condition, and the agreement between model set-points and actual experimental initial conditions, it seems possible to achieve the strict accuracy that is needed for automatic cooling control development.

Early circulating tumor DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer.

PubMed

O'Leary, Ben; Hrebien, Sarah; Morden, James P; Beaney, Matthew; Fribbens, Charlotte; Huang, Xin; Liu, Yuan; Bartlett, Cynthia Huang; Koehler, Maria; Cristofanilli, Massimo; Garcia-Murillas, Isaac; Bliss, Judith M; Turner, Nicholas C

2018-03-01

CDK4/6 inhibition substantially improves progression-free survival (PFS) for women with advanced estrogen receptor-positive breast cancer, although there are no predictive biomarkers. Early changes in circulating tumor DNA (ctDNA) level may provide early response prediction, but the impact of tumor heterogeneity is unknown. Here we use plasma samples from patients in the randomized phase III PALOMA-3 study of CDK4/6 inhibitor palbociclib and fulvestrant for women with advanced breast cancer and show that relative change in PIK3CA ctDNA level after 15 days treatment strongly predicts PFS on palbociclib and fulvestrant (hazard ratio 3.94, log-rank p = 0.0013). ESR1 mutations selected by prior hormone therapy are shown to be frequently sub clonal, with ESR1 ctDNA dynamics offering limited prediction of clinical outcome. These results suggest that early ctDNA dynamics may provide a robust biomarker for CDK4/6 inhibitors, with early ctDNA dynamics demonstrating divergent response of tumor sub clones to treatment.

Use of cerebrospinal fluid flow rates measured by phase-contrast MR to predict outcome of ventriculoperitoneal shunting for idiopathic normal-pressure hydrocephalus.

PubMed

Dixon, Geoffrey R; Friedman, Jonathan A; Luetmer, Patrick H; Quast, Lynn M; McClelland, Robyn L; Petersen, Ronald C; Maher, Cormac O; Ebersold, Michael J

2002-06-01

To determine whether favorable clinical response and magnitude of improvement are associated with increased aqueductal cerebrospinal fluid (CSF) flow rates in patients who undergo ventriculoperitoneal shunting (VPS) for idiopathic normal-pressure hydrocephalus (NPH). Between January 1995 and June 2000, 49 patients (14 men and 35 women; mean age, 72.9 years; range, 54-88 years) underwent magnetic resonance quantification of aqueductal CSF flow followed by VPS for presumed idiopathic NPH at the Mayo Clinic, Rochester, Minn. Logistic regression models for the odds of any improvement in score as a function of aqueductal CSF flow and separate models for any improvement in gait, incontinence, cognition, and total score were constructed. Forty-two patients (86%) had improvement in gait at postoperative follow-up (mean, 10 months). Of the 32 patients with incontinence, 27 (69%) improved. Of the 36 patients with cognitive impairment, 16 (44%) improved. In univariate and fully adjusted models, increased CSF flow through the aqueduct was not significantly associated with improvement or the magnitude of improvement in gait, cognition, or incontinence. Thirty-six patients underwent high-volume lumbar puncture preoperatively, of whom 5 (14%) had no response. The aqueductal CSF flow rates of these 5 patients were significantly higher than those of the patients who improved after lumbar puncture. Postoperative complications occurred in 15 patients. The aqueductal CSF flow rates in these 15 patients were not significantly different from those of patients who experienced no complications. Among patients who underwent VPS for the treatment of NPH, measurement of CSF flow through the cerebral aqueduct did not reliably predict which patients would improve after shunting or the magnitude of improvement.

A pilot validation of a modified Illness Perceptions Questionnaire designed to predict response to cognitive therapy for psychosis.

PubMed

Marcus, Elena; Garety, Philippa; Weinman, John; Emsley, Richard; Dunn, Graham; Bebbington, Paul; Freeman, Daniel; Kuipers, Elizabeth; Fowler, David; Hardy, Amy; Waller, Helen; Jolley, Suzanne

2014-12-01

Clinical responsiveness to cognitive behavioural therapy for psychosis (CBTp) varies. Recent research has demonstrated that illness perceptions predict active engagement in therapy, and, thereby, better outcomes. In this study, we aimed to investigate the psychometric properties of a modification of the Illness Perceptions Questionnaire (M-IPQ) designed to predict response following CBTp. Fifty-six participants with persistent, distressing delusions completed the M-IPQ; forty before a brief CBT intervention targeting persecutory ideation and sixteen before and after a control condition. Additional predictors of outcome (delusional conviction, symptom severity and belief inflexibility) were assessed at baseline. Outcomes were assessed at baseline and at follow-up four to eight weeks later. The M-IPQ comprised two factors measuring problem duration and therapy-specific perceptions of Cure/Control. Associated subscales, formed by summing the relevant items for each factor, were reliable in their structure. The Cure/Control subscale was also reliable over time; showed convergent validity with other predictors of outcome; predicted therapy outcomes; and differentially predicted treatment effects. We measured outcome without an associated measure of engagement, in a small sample. Findings are consistent with hypothesis and existing research, but require replication in a larger, purposively recruited sample. The Cure/Control subscale of the M-IPQ shows promise as a predictor of response to therapy. Specifically targeting these illness perceptions in the early stages of cognitive behavioural therapy may improve engagement and, consequently, outcomes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

PubMed Central

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Sorger, Peter K.; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo

2010-01-01

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. PMID:20186320

An inflammation-related nomogram for predicting the survival of patients with non-small cell lung cancer after pulmonary lobectomy.

PubMed

Wang, Ying; Qu, Xiao; Kam, Ngar-Woon; Wang, Kai; Shen, Hongchang; Liu, Qi; Du, Jiajun

2018-06-26

Emerging inflammatory response biomarkers are developed to predict the survival of patients with cancer, the aim of our study is to establish an inflammation-related nomogram based on the classical predictive biomarkers to predict the survivals of patients with non-small cell lung cancer (NSCLC). Nine hundred and fifty-two NSCLC patients with lung cancer surgery performed were enrolled into this study. The cutoffs of inflammatory response biomarkers were determined by Receiver operating curve (ROC). Univariate and multivariate analysis were conducted to select independent prognostic factors to develop the nomogram. The median follow-up time was 40.0 months (range, 1 to 92 months). The neutrophil to lymphocyte ratio (cut-off: 3.10, HR:1.648, P = 0.045) was selected to establish the nomogram which could predict the 5-year OS probability. The C-index of nomogram was 0.72 and the 5-year OS calibration curve displayed an optimal agreement between the actual observed outcomes and the predictive results. Neutrophil to lymphocyte ratio was shown to be a valuable biomarker for predicting survival of patients with NSCLC. The addition of neutrophil to lymphocyte ratio could improve the accuracy and predictability of the nomogram in order to provide reference for clinicians to assess patient outcomes.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

PubMed Central

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; Calaza, Manuel; Elmarakeby, Haitham; Heath, Lenwood S.; Long, Quan; Moore, Jonathan D.; Opiyo, Stephen Obol; Savage, Richard S.; Zhu, Jun; 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 Jr., 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.

2016-01-01

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 (h2=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. PMID:27549343

Blood eosinophil levels as a biomarker in COPD.

PubMed

Brusselle, Guy; Pavord, Ian D; Landis, Sarah; Pascoe, Steven; Lettis, Sally; Morjaria, Nikhil; Barnes, Neil; Hilton, Emma

2018-05-01

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disorder and patients respond differently to treatment. Blood eosinophils are a potential biomarker to stratify patient subsets for COPD therapy. We reviewed the value of blood eosinophils in predicting exacerbation risk and response to corticosteroid treatment in the available literature (PubMed articles in English; keywords: "COPD" and "eosinophil"; published prior to May 2017). Overall, clinical data suggest that in patients with a history of COPD exacerbations, a higher blood eosinophil count predicts an increased risk of future exacerbations and is associated with improved response to treatment with inhaled corticosteroids (in combination with long-acting bronchodilator[s]). Blood eosinophils are therefore a promising biomarker for phenotyping patients with COPD, although prospective studies are needed to assess blood eosinophils as a biomarker of corticosteroid response for this. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Drugs in the treatment of tinnitus.

PubMed

Goodey, R J

1981-01-01

Many drugs and some foods can cause or aggravate tinnitus in some patients. These substances should be identified and withdrawn. Tinnitus may be improved by the treatment of associated conditions, infections, or hearing loss with appropriate drugs--hypotensives, antibiotics, vasodilators, fluoride or thyroxine. Intravenous lignocaine can temporarily reduce or abolish tinnitus in many patients but can aggravate existing tinnitus in some and may have no effect on others. Analogy with pain of central origin suggests that the beneficial effects of lignocaine (lidocaine) may be due to its anticonvulsant action. Lignocaine is used as a test to distinguish between different mechanisms of tinnitus and to predict responses to oral anticonvulsants. Dramatic responses with lignocaine are usually associated with cochlear hearing loss and often with comparable though less marked responses to oral anticonvulsants. Patients who do not benefit from lignocaine do not respond to oral anticonvulsants. The action of anticonvulsants is often potentiated by tricyclic antidepressants. The majority of patients who respond to lignocaine can also have their tinnitus effectively masked, as predicted, on a tinnitus synthesizer. A small proportion respond to masking and not to lignocaine and a small proportion to lignocaine and not to masking. Beneficial effects of masking and anticonvulsants are cumulative. Anticonvulsants may also produce subjective improvement in clarity, improved tolerance of hearing aids and increased masking benefit when a hearing aid is worn.

Maize transgenes containing zein promoters are regulated by opaque2

USDA-ARS?s Scientific Manuscript database

Transgenes have great potential in crop improvement, but relatively little is known about the epistatic interaction of transgenes with the native genes in the genome. Understanding these interactions is critical for predicting the response of transgenes to different genetic backgrounds and environm...

Early Prediction of Acute Antidepressant Treatment Response and Remission in Pediatric Major Depressive Disorder

ERIC Educational Resources Information Center

Tao, Rongrong; Emslie, Graham; Mayes, Taryn; Nakonezny, Paul; Kennard, Betsy; Hughes, Carroll

2009-01-01

The rate of symptom improvement during the early weeks of acute fluoxetine treatment is a good indicator of remission. This finding was made after evaluating the outcome of the fluoxetine treatment on 168 children and adults with depression.

Prospective study of serial 18F-FDG PET and 18F-fluoride (18F-NaF) PET to predict time to skeletal related events, time-to-progression, and survival in patients with bone-dominant metastatic breast cancer.

PubMed

Peterson, Lanell M; O'Sullivan, Janet; Wu, Qian Vicky; Novakova-Jiresova, Alena; Jenkins, Isaac; Lee, Jean H; Shields, Andrew; Montgomery, Susan; Linden, Hannah M; Gralow, Julie R; Gadi, Vijayakrishna K; Muzi, Mark; Kinahan, Paul E; Mankoff, David A; Specht, Jennifer M

2018-05-10

Assessing therapy response of breast cancer bone metastases is challenging. In retrospective studies, serial 18 F-FDG PET was predictive of time to skeletal related events (tSRE) and time-to-progression (TTP). 18 F-NaF PET improves bone metastasis detection compared to bone scans. We prospectively tested 18 F-FDG PET and 18 F-NaF PET to predict tSRE, TTP, and overall survival (OS) in patients with bone-dominant metastatic breast cancer (BD MBC). Methods: Patients with BD MBC were imaged with 18 F-FDG PET and 18 F-NaF PET prior to starting new therapy (scan1) and again at a range of times centered around approximately 4 months later (scan2). SUV max and SULpeak were recorded for a single index lesion and up to 5 most dominant lesions for each scan. tSRE, TTP, and OS were assessed exclusive of the PET images. Univariate Cox regression was performed to test the association between clinical endpoints and 18 F-FDG PET and 18 F-NaF PET measures. mPERCIST (Modified PET Response Criteria in Solid Tumors) criteria were also applied. Survival curves for mPERCIST compared response categories of Complete Response+Partial Response+Stable Disease versus Progressive Disease (CR+PR+SD vs PD) for tSRE, TTP, and OS. Results: Twenty-eight patients were evaluated. Higher FDG SULpeak at scan2 predicted shorter time to tSRE ( P = <0.001) and TTP ( P = 0.044). Higher FDG SUV max at scan2 predicted a shorter time to tSRE ( P = <0.001). A multivariable model using FDG SUV max of the index lesion at scan1 plus the difference in SUV max of up to 5 lesions between scans was predictive for tSRE and TTP. Among 24 patients evaluable by 18 F-FDG PET mPERCIST, tSRE and TTP were longer in responders (CR, PR, or stable) compared to non-responders (PD) ( P = 0.007, 0.028 respectively), with a trend toward improved survival ( P = 0.1). An increase in the uptake between scans of up to 5 lesions by 18 F-NaF PET was associated with longer OS ( P = 0.027). Conclusion: Changes in 18 F-FDG PET parameters during therapy are predictive of tSRE and TTP, but not OS. mPERCIST evaluation in bone lesions may be useful in assessing response to therapy and is worthy of evaluation in multicenter, prospective trials. Serial 18 F-NaF PET was associated with OS, but was not useful for predicting TTP or tSRE in BD MBC. Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Landing Gear Noise Prediction and Analysis for Tube-and-Wing and Hybrid-Wing-Body Aircraft

NASA Technical Reports Server (NTRS)

Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

2016-01-01

Improvements and extensions to landing gear noise prediction methods are developed. New features include installation effects such as reflection from the aircraft, gear truck angle effect, local flow calculation at the landing gear locations, gear size effect, and directivity for various gear designs. These new features have not only significantly improved the accuracy and robustness of the prediction tools, but also have enabled applications to unconventional aircraft designs and installations. Systematic validations of the improved prediction capability are then presented, including parametric validations in functional trends as well as validations in absolute amplitudes, covering a wide variety of landing gear designs, sizes, and testing conditions. The new method is then applied to selected concept aircraft configurations in the portfolio of the NASA Environmentally Responsible Aviation Project envisioned for the timeframe of 2025. The landing gear noise levels are on the order of 2 to 4 dB higher than previously reported predictions due to increased fidelity in accounting for installation effects and gear design details. With the new method, it is now possible to reveal and assess the unique noise characteristics of landing gear systems for each type of aircraft. To address the inevitable uncertainties in predictions of landing gear noise models for future aircraft, an uncertainty analysis is given, using the method of Monte Carlo simulation. The standard deviation of the uncertainty in predicting the absolute level of landing gear noise is quantified and determined to be 1.4 EPNL dB.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Progress of Aircraft System Noise Assessment with Uncertainty Quantification for the Environmentally Responsible Aviation Project

NASA Technical Reports Server (NTRS)

Thomas, Russell H.; Burley, Casey L.; Guo, Yueping

2016-01-01

Aircraft system noise predictions have been performed for NASA modeled hybrid wing body aircraft advanced concepts with 2025 entry-into-service technology assumptions. The system noise predictions developed over a period from 2009 to 2016 as a result of improved modeling of the aircraft concepts, design changes, technology development, flight path modeling, and the use of extensive integrated system level experimental data. In addition, the system noise prediction models and process have been improved in many ways. An additional process is developed here for quantifying the uncertainty with a 95% confidence level. This uncertainty applies only to the aircraft system noise prediction process. For three points in time during this period, the vehicle designs, technologies, and noise prediction process are documented. For each of the three predictions, and with the information available at each of those points in time, the uncertainty is quantified using the direct Monte Carlo method with 10,000 simulations. For the prediction of cumulative noise of an advanced aircraft at the conceptual level of design, the total uncertainty band has been reduced from 12.2 to 9.6 EPNL dB. A value of 3.6 EPNL dB is proposed as the lower limit of uncertainty possible for the cumulative system noise prediction of an advanced aircraft concept.

Predictive cues for auditory stream formation in humans and monkeys.

PubMed

Aggelopoulos, Nikolaos C; Deike, Susann; Selezneva, Elena; Scheich, Henning; Brechmann, André; Brosch, Michael

2017-12-18

Auditory perception is improved when stimuli are predictable, and this effect is evident in a modulation of the activity of neurons in the auditory cortex as shown previously. Human listeners can better predict the presence of duration deviants embedded in stimulus streams with fixed interonset interval (isochrony) and repeated duration pattern (regularity), and neurons in the auditory cortex of macaque monkeys have stronger sustained responses in the 60-140 ms post-stimulus time window under these conditions. Subsequently, the question has arisen whether isochrony or regularity in the sensory input contributed to the enhancement of the neuronal and behavioural responses. Therefore, we varied the two factors isochrony and regularity independently and measured the ability of human subjects to detect deviants embedded in these sequences as well as measuring the responses of neurons the primary auditory cortex of macaque monkeys during presentations of the sequences. The performance of humans in detecting deviants was significantly increased by regularity. Isochrony enhanced detection only in the presence of the regularity cue. In monkeys, regularity increased the sustained component of neuronal tone responses in auditory cortex while isochrony had no consistent effect. Although both regularity and isochrony can be considered as parameters that would make a sequence of sounds more predictable, our results from the human and monkey experiments converge in that regularity has a greater influence on behavioural performance and neuronal responses. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

PubMed Central

Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

2011-01-01

Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

Improving sneaky-sex in a low oxygen environment: reproductive and physiological responses of male mosquito fish to chronic hypoxia.

PubMed

Carter, Alecia J; Wilson, Robbie S

2006-12-01

Few studies have examined the adaptive significance of reversible acclimation responses. The aerobic performance and mating behaviour of the sexually coercive male eastern mosquito fish (Gambusia holbrooki) offers an excellent model system for testing the benefits of reversible acclimation responses to mating success. We exposed male mosquito fish to normoxic or hypoxic conditions for 4 weeks and tested their maximum sustained swimming performance and their ability to obtain coercive matings under both normoxic and hypoxic conditions. We predicted that hypoxia-acclimated males would possess greater swimming and mating performance in hypoxic conditions than normoxic-acclimated males, and vice versa when tested in normoxia. Supporting our predictions, we found the sustained swimming performance of male mosquito fish was greater in a hypoxic environment following long-term exposure to low partial pressures of oxygen. However, the benefits of acclimation responses to mating performance were dependent on whether they were tested in the presence or absence of male-male competition. In a non-competitive environment, male mosquito fish acclimated to hypoxic conditions spent a greater amount of time following females and obtained more copulations than normoxic-acclimated males when tested in low partial pressures of oxygen. When males were competed against each other for copulations, we found no influence of long-term exposure to different partial pressures of oxygen on mating behaviour. Thus, despite improvements in the aerobic capacity of male mosquito fish following long-term acclimation to hypoxic conditions, these benefits did not always manifest themselves in improved mating performance. This study represents one of the first experimental tests of the benefits of reversible acclimation responses, and indicates that the ecological significance of physiological plasticity may be more complicated than previously imagined.

Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy

PubMed Central

Johnson, Douglas B.; Estrada, Monica V.; Salgado, Roberto; Sanchez, Violeta; Doxie, Deon B.; Opalenik, Susan R.; Vilgelm, Anna E.; Feld, Emily; Johnson, Adam S.; Greenplate, Allison R.; Sanders, Melinda E.; Lovly, Christine M.; Frederick, Dennie T.; Kelley, Mark C.; Richmond, Ann; Irish, Jonathan M.; Shyr, Yu; Sullivan, Ryan J.; Puzanov, Igor; Sosman, Jeffrey A.; Balko, Justin M.

2016-01-01

Anti-PD-1 therapy yields objective clinical responses in 30–40% of advanced melanoma patients. Since most patients do not respond, predictive biomarkers to guide treatment selection are needed. We hypothesize that MHC-I/II expression is required for tumour antigen presentation and may predict anti-PD-1 therapy response. In this study, across 60 melanoma cell lines, we find bimodal expression patterns of MHC-II, while MHC-I expression was ubiquitous. A unique subset of melanomas are capable of expressing MHC-II under basal or IFNγ-stimulated conditions. Using pathway analysis, we show that MHC-II(+) cell lines demonstrate signatures of ‘PD-1 signalling', ‘allograft rejection' and ‘T-cell receptor signalling', among others. In two independent cohorts of anti-PD-1-treated melanoma patients, MHC-II positivity on tumour cells is associated with therapeutic response, progression-free and overall survival, as well as CD4+ and CD8+ tumour infiltrate. MHC-II+ tumours can be identified by melanoma-specific immunohistochemistry using commercially available antibodies for HLA-DR to improve anti-PD-1 patient selection. PMID:26822383

The impact of warming on greenhouse gas fluxes: an experimental comparison which reveals the varied response of ecosystems to climate change.

NASA Astrophysics Data System (ADS)

Stockdale, James; Ineson, Philip

2016-04-01

Modelled predictions of the response of terrestrial systems to climate change are highly variable, yet the response of net ecosystem exchange (NEE) is a vital ecosystem behaviour to understand due to its inherent feedback to the carbon cycle. The establishment and subsequent monitoring of replicated experimental manipulations are a direct method to reveal these responses, yet are difficult to achieve as they typically resource-heavy and labour intensive. We actively manipulated the temperature at three agricultural grasslands in southern England and deployed novel 'SkyLine' systems, recently developed at the University of York, to continuously monitor GHG fluxes. Each 'SkyLine' is a low-cost and fully autonomous technology yet produces fluxes at a near-continuous temporal frequency and across a wide spatial area. The results produced by 'SkyLine' enable the detail response of each system to increased temperature over diurnal and seasonal timescales. Unexpected differences in NEE are shown between superficially similar ecosystems which, upon investigation, suggest that interactions between a variety of environmental variables are key and that knowledge of pre-existing environmental conditions help to predict a systems response to future climate. For example, the prevailing hydrological conditions at each site appear to affect its response to changing temperature. The high-frequency data shown here, combined with the fully-replicated experimental design reveal complex interactions which must be understood to improve predictions of ecosystem response to a changing climate.

Incorporating Scale-Dependent Fracture Stiffness for Improved Reservoir Performance Prediction

NASA Astrophysics Data System (ADS)

Crawford, B. R.; Tsenn, M. C.; Homburg, J. M.; Stehle, R. C.; Freysteinson, J. A.; Reese, W. C.

2017-12-01

We present a novel technique for predicting dynamic fracture network response to production-driven changes in effective stress, with the potential for optimizing depletion planning and improving recovery prediction in stress-sensitive naturally fractured reservoirs. A key component of the method involves laboratory geomechanics testing of single fractures in order to develop a unique scaling relationship between fracture normal stiffness and initial mechanical aperture. Details of the workflow are as follows: tensile, opening mode fractures are created in a variety of low matrix permeability rocks with initial, unstressed apertures in the micrometer to millimeter range, as determined from image analyses of X-ray CT scans; subsequent hydrostatic compression of these fractured samples with synchronous radial strain and flow measurement indicates that both mechanical and hydraulic aperture reduction varies linearly with the natural logarithm of effective normal stress; these stress-sensitive single-fracture laboratory observations are then upscaled to networks with fracture populations displaying frequency-length and length-aperture scaling laws commonly exhibited by natural fracture arrays; functional relationships between reservoir pressure reduction and fracture network porosity, compressibility and directional permeabilities as generated by such discrete fracture network modeling are then exported to the reservoir simulator for improved naturally fractured reservoir performance prediction.

Imaging Predictors of Improvement From a Motor Learning-Based Intervention for Children With Unilateral Cerebral Palsy.

PubMed

Schertz, Mitchell; Shiran, Shelly I; Myers, Vicki; Weinstein, Maya; Fattal-Valevski, Aviva; Artzi, Moran; Ben Bashat, Dafna; Gordon, Andrew M; Green, Dido

2016-08-01

Background Motor-learning interventions may improve hand function in children with unilateral cerebral palsy (UCP) but with inconsistent outcomes across participants. Objective To examine if pre-intervention brain imaging predicts benefit from bimanual intervention. Method Twenty children with UCP with Manual Ability Classification System levels I to III, aged 7-16 years, participated in an intensive bimanual intervention. Assessments included the Assisting Hand Assessment (AHA), Jebsen Taylor Test of Hand Function (JTTHF) and Children's Hand Experience Questionnaire (CHEQ) at baseline (T1), completion (T2) and 8-10 weeks post-intervention (T3). Imaging at baseline included conventional structural (radiological score), functional (fMRI) and diffusion tensor imaging (DTI). Results Improvements were seen across assessments; AHA (P = 0.04), JTTHF (P < .001) and CHEQ (P < 0.001). Radiological score significantly correlated with improvement at T2; AHA (r = .475) and CHEQ (r = .632), but negatively with improvement on unimanual measures at T3 (JTTFH r = -.514). fMRI showed negative correlations between contralesional brain activation when moving the affected hand and AHA improvements (T2: r = -.562, T3: r = -0.479). Fractional Anisotropy in the affected posterior limb of the internal capsule correlated negatively with increased bimanual use on CHEQ at T2 (r = -547) and AHA at T3 (r = -.656). Conclusions Children with greater structural, functional and connective brain damage showed enhanced responses to bimanual intervention. Baseline imaging may identify parameters predicting response to intervention in children with UCP. © The Author(s) 2015.

Experience with decision support system and comfort with topic predict clinicians' responses to alerts and reminders.

PubMed

Bauer, Nerissa S; Carroll, Aaron E; Saha, Chandan; Downs, Stephen M

2016-04-01

Clinicians at our institution typically respond to about half of the prompts they are given by the clinic's computer decision support system (CDSS). We sought to examine factors associated with clinician response to CDSS prompts as part of a larger, ongoing quality improvement effort to optimize CDSS use. We examined patient, prompt, and clinician characteristics associated with clinician response to decision support prompts from the Child Health Improvement through Computer Automation (CHICA) system. We asked pediatricians who were nonusers of CHICA to rate decision support topics as "easy" or "not easy" to discuss with patients and their guardians. We analyzed these ratings and data, from July 1, 2009 to January 29, 2013, utilizing a hierarchical regression model, to determine whether factors such as comfort with the prompt topic and the length of the user's experience with CHICA contribute to user response rates. We examined 414 653 prompts from 22 260 patients. The length of time a clinician had been using CHICA was associated with an increase in their prompt response rate. Clinicians were more likely to respond to topics rated as "easy" to discuss. The position of the prompt on the page, clinician gender, and the patient's age, race/ethnicity, and preferred language were also predictive of prompt response rate. This study highlights several factors associated with clinician prompt response rates that could be generalized to other health information technology applications, including the clinician's length of exposure to the CDSS, the prompt's position on the page, and the clinician's comfort with the prompt topic. Incorporating continuous quality improvement efforts when designing and implementing health information technology may ensure that its use is optimized. © 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.

Test and Analysis of a Buckling-Critical Large-Scale Sandwich Composite Cylinder

NASA Technical Reports Server (NTRS)

Schultz, Marc R.; Sleight, David W.; Gardner, Nathaniel W.; Rudd, Michelle T.; Hilburger, Mark W.; Palm, Tod E.; Oldfield, Nathan J.

2018-01-01

Structural stability is an important design consideration for launch-vehicle shell structures and it is well known that the buckling response of such shell structures can be very sensitive to small geometric imperfections. As part of an effort to develop new buckling design guidelines for sandwich composite cylindrical shells, an 8-ft-diameter honeycomb-core sandwich composite cylinder was tested under pure axial compression to failure. The results from this test are compared with finite-element-analysis predictions and overall agreement was very good. In particular, the predicted buckling load was within 1% of the test and the character of the response matched well. However, it was found that the agreement could be improved by including composite material nonlinearity in the analysis, and that the predicted buckling initiation site was sensitive to the addition of small bending loads to the primary axial load in analyses.

Optimum surface roughness prediction for titanium alloy by adopting response surface methodology

NASA Astrophysics Data System (ADS)

Yang, Aimin; Han, Yang; Pan, Yuhang; Xing, Hongwei; Li, Jinze

Titanium alloy has been widely applied in industrial engineering products due to its advantages of great corrosion resistance and high specific strength. This paper investigated the processing parameters for finish turning of titanium alloy TC11. Firstly, a three-factor central composite design of experiment, considering the cutting speed, feed rate and depth of cut, are conducted in titanium alloy TC11 and the corresponding surface roughness are obtained. Then a mathematic model is constructed by the response surface methodology to fit the relationship between the process parameters and the surface roughness. The prediction accuracy was verified by the one-way ANOVA. Finally, the contour line of the surface roughness under different combination of process parameters are obtained and used for the optimum surface roughness prediction. Verification experimental results demonstrated that material removal rate (MRR) at the obtained optimum can be significantly improved without sacrificing the surface roughness.

Investigation of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

NASA Technical Reports Server (NTRS)

Davis, Brian A.

2005-01-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical model. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. Excellent agreement is achieved between the predicted and measured results, thereby quantitatively validating the numerical tool.

Reconstructing disturbances and their biogeochemical consequences over multiple timescales

USGS Publications Warehouse

McLauchlan, Kendra K.; Higuera, Philip E.; Gavin, Daniel G.; Perakis, Steven S.; Mack, Michelle C.; Alexander, Heather; Battles, John; Biondi, Franco; Buma, Brian; Colombaroli, Daniele; Enders, Sara K.; Engstrom, Daniel R.; Hu, Feng Sheng; Marlon, Jennifer R.; Marshall, John; McGlone, Matt; Morris, Jesse L.; Nave, Lucas E.; Shuman, Bryan; Smithwick, Erica A.H.; Urrego, Dunia H.; Wardle, David A.; Williams, Christopher J.; Williams, Joseph J.

2014-01-01

Ongoing changes in disturbance regimes are predicted to cause acute changes in ecosystem structure and function in the coming decades, but many aspects of these predictions are uncertain. A key challenge is to improve the predictability of postdisturbance biogeochemical trajectories at the ecosystem level. Ecosystem ecologists and paleoecologists have generated complementary data sets about disturbance (type, severity, frequency) and ecosystem response (net primary productivity, nutrient cycling) spanning decadal to millennial timescales. Here, we take the first steps toward a full integration of these data sets by reviewing how disturbances are reconstructed using dendrochronological and sedimentary archives and by summarizing the conceptual frameworks for carbon, nitrogen, and hydrologic responses to disturbances. Key research priorities include further development of paleoecological techniques that reconstruct both disturbances and terrestrial ecosystem dynamics. In addition, mechanistic detail from disturbance experiments, long-term observations, and chronosequences can help increase the understanding of ecosystem resilience.

Idiopathic Chronic Parotitis: Imaging Findings and Sialendoscopic Response.

PubMed

Heineman, Thomas E; Kacker, Ashutosh; Kutler, David I

2015-01-01

The purpose of this study was to correlate imaging and sialendoscopic findings to therapeutic response in patients with idiopathic chronic parotitis. We retrospectively reviewed 122 consecutive sialendoscopies performed in an academic medical center by two surgeons between 2008 and 2013. Forty-one (34%) and 54 (44%) patients were excluded on the basis of having parotid or submandibular sialolith, respectively. Nineteen cases were included in the study with idiopathic chronic parotitis. There was a median follow-up of 5 months. Computed tomography (CT) imaging had a sensitivity and specificity of 80.0 and 71.4%, respectively, for predicting abnormal findings on sialendoscopy, while magnetic resonance imaging (MRI) had 100% accuracy in a small set of cases. In glands with noticeable pathology present on preoperative imaging or sialendoscopy, 11 out of 12 glands (92%) treated experienced symptomatic improvement, while 3 out of 7 glands (43%) without pathology on imaging or endoscopy experienced symptomatic improvement (p = 0.038). Sialendoscopy for the treatment of idiopathic chronic parotid disease can improve pain and swelling with a higher frequency of success in patients with abnormalities noted on endoscopy. CT and MRI have a moderate degree of accuracy in predicting which patients will benefit from therapeutic sialendoscopy. © 2015 S. Karger AG, Basel.

Improved HPLC method with the aid of chemometric strategy: determination of loxoprofen in pharmaceutical formulation.

PubMed

Venkatesan, P; Janardhanan, V Sree; Muralidharan, C; Valliappan, K

2012-06-01

Loxoprofen belongs to a class of Nonsteroidal anti-inflammatory drug acts by inhibiting isoforms of cyclo-oxygenase 1 and 2. In this study an improved RP-HPLC method was developed for the quantification of loxoprofen in pharmaceutical dosage form. For that purpose an experimental design approach was employed. Factors-independent variables (organic modifier, pH of the mobile phase and flow rate) were extracted from the preliminary study and as dependent variables three responses (loxoprofen retention factor, resolution between loxoprofen probenecid and retention time of probenecid) were selected. For the improvement of method development and optimization step, Derringer's desirability function was applied to simultaneously optimize the chosen three responses. The procedure allowed deduction of optimal conditions and the predicted optimum was acetonitrile: water (53:47, v/v), pH of the mobile phase adjusted at to 2.9 with ortho phosphoric acid. The separation was achieved in less than 4minutes. The method was applied in the quality control of commercial tablets. The method showed good agreement between the experimental data and predictive value throughout the studied parameter space. The optimized assay condition was validated according to International conference on harmonisation guidelines to confirm specificity, linearity, accuracy and precision.

Expression of tolerance associated gene-1, a mitochondrial protein inhibiting T cell activation, can be used to predict response to immune modulating therapies.

PubMed

Keeren, Kathrin; Friedrich, Markus; Gebuhr, Inga; Philipp, Sandra; Sabat, Robert; Sterry, Wolfram; Brandt, Christine; Meisel, Christian; Grütz, Gerald; Volk, Hans-Dieter; Sawitzki, Birgit

2009-09-15

Immune modulating therapies gain increasing importance in treatment of patients with autoimmune diseases such as psoriasis. None of the currently applied biologics achieves significant clinical improvement in all treated patients. Because the therapy with biologics is cost intensive and sometimes associated with side effects, noninvasive diagnostic tools for early prediction of responders are of major interest. We studied the effects of Alefacept (LFA3Ig), an approved drug for treatment of psoriasis, on leukocytes in vitro and in vivo to identify gene markers predictive for treatment response and to further investigate its molecular mechanisms of action. In an open-label study, 20 psoriasis patients were treated weekly with 15 mg Alefacept over 12 wk. We demonstrate that transcription of the tolerance-associated gene (TOAG-1) is significantly up-regulated whereas receptor for hyaluronic acid mediated migration (RHAMM) transcription is down-regulated in PBMCs of responding patients before clinical improvement. TOAG-1 is exclusively localized within mitochondria. Overexpression of TOAG-1 in murine T cells leads to increased susceptibility to apoptosis. Addition of Alefacept to stimulated human T cells in vitro resulted in reduced frequencies of activated CD137(+) cells, increased TOAG-1 but reduced RHAMM expression. This was accompanied by reduced proliferation and enhanced apoptosis. Inhibition of proliferation was dependent on enhanced PDL1 expression of APCs. Thus, peripheral changes of TOAG-1 and RHAMM expression can be used to predict clinical response to Alefacept treatment in psoriasis patients. In the presence of APCs Alefacept can inhibit T cell activation and survival by increasing expression of TOAG-1 on T cells and PDL1 on APCs.

Locally advanced rectal cancer: post-chemoradiotherapy ADC histogram analysis for predicting a complete response.

PubMed

Cho, Seung Hyun; Kim, Gab Chul; Jang, Yun-Jin; Ryeom, Hunkyu; Kim, Hye Jung; Shin, Kyung-Min; Park, Jun Seok; Choi, Gyu-Seog; Kim, See Hyung

2015-09-01

The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC. © The Foundation Acta Radiologica 2014.

CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva.

PubMed

van Dijk, Lisanne V; Brouwer, Charlotte L; van der Schaaf, Arjen; Burgerhof, Johannes G M; Beukinga, Roelof J; Langendijk, Johannes A; Sijtsema, Nanna M; Steenbakkers, Roel J H M

2017-02-01

Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER 12m ) and sticky saliva (STIC 12m ) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XER base ) or sticky saliva (STIC base ) scores. The purpose is to improve prediction of XER 12m and STIC 12m with patient-specific characteristics, based on CT image biomarkers (IBMs). Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancer patients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping. The prediction of XER 12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XER base . For STIC 12m , the IBM maximum CT intensity of the submandibular gland was selected in addition to STIC base and mean dose to submandibular glands. Prediction of XER 12m and STIC 12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting the response to CTLA-4 blockade by longitudinal noninvasive monitoring of CD8 T cells

PubMed Central

Whang, Katherine A.; LeGall, Camille; Cragnolini, Juan J.; Bierie, Brian; Gostissa, Monica; Grotenbreg, Gijsbert M.; Bhan, Atul; Weinberg, Robert A.

2017-01-01

Immunotherapy using checkpoint-blocking antibodies against targets such as CTLA-4 and PD-1 can cure melanoma and non–small cell lung cancer in a subset of patients. The presence of CD8 T cells in the tumor correlates with improved survival. We show that immuno–positron emission tomography (immuno-PET) can visualize tumors by detecting infiltrating lymphocytes and, through longitudinal observation of individual animals, distinguish responding tumors from those that do not respond to therapy. We used 89Zr-labeled PEGylated single-domain antibody fragments (VHHs) specific for CD8 to track the presence of intratumoral CD8+ T cells in the immunotherapy-susceptible B16 melanoma model in response to checkpoint blockade. A 89Zr-labeled PEGylated anti-CD8 VHH detected thymus and secondary lymphoid structures as well as intratumoral CD8 T cells. Animals that responded to CTLA-4 therapy showed a homogeneous distribution of the anti-CD8 PET signal throughout the tumor, whereas more heterogeneous infiltration of CD8 T cells correlated with faster tumor growth and worse responses. To support the validity of these observations, we used two different transplantable breast cancer models, yielding results that conformed with predictions based on the antimelanoma response. It may thus be possible to use immuno-PET and monitor antitumor immune responses as a prognostic tool to predict patient responses to checkpoint therapies. PMID:28666979

Effects of Predictability of Load Magnitude on the Response of the Flexor Digitorum Superficialis to a Sudden Fingers Extension

PubMed Central

Aimola, Ettore; Valle, Maria Stella; Casabona, Antonino

2014-01-01

Muscle reflexes, evoked by opposing a sudden joint displacement, may be modulated by several factors associated with the features of the mechanical perturbation. We investigated the variations of muscle reflex response in relation to the predictability of load magnitude during a reactive grasping task. Subjects were instructed to flex the fingers 2–5 very quickly after a stretching was exerted by a handle pulled by loads of 750 or 1250 g. Two blocks of trials, one for each load (predictable condition), and one block of trials with a randomized distribution of the loads (unpredictable condition) were performed. Kinematic data were collected by an electrogoniometer attached to the middle phalanx of the digit III while the electromyography of the Flexor Digitorum Superficialis muscle was recorded by surface electrodes. For each trial we measured the kinematics of the finger angular rotation, the latency of muscle response and the level of muscle activation recorded below 50 ms (short-latency reflex), between 50 and 100 ms (long-latency reflex) and between 100 and 140 ms (initial portion of voluntary response) from the movement onset. We found that the latency of the muscle response lengthened from predictable (35.5±1.3 ms for 750 g and 35.5±2.5 ms for 1250 g) to unpredictable condition (43.6±1.3 ms for 750 g and 40.9±2.1 ms for 1250 g) and the level of muscle activation increased with load magnitude. The parallel increasing of muscle activation and load magnitude occurred within the window of the long-latency reflex during the predictable condition, and later, at the earliest portion of the voluntary response, in the unpredictable condition. Therefore, these results indicate that when the amount of an upcoming perturbation is known in advance, the muscle response improves, shortening the latency and modulating the muscle activity in relation to the mechanical demand. PMID:25271638

Variation in both IL28B and KIR2DS3 genes influence pegylated interferon and ribavirin hepatitis C treatment outcome in HIV-1 co-infection.

PubMed

Keane, Ciara; O'Shea, Daire; Reiberger, Thomas; Peck-Radosavljevic, Markus; Farrell, Gillian; Bergin, Colm; Gardiner, Clair M

2013-01-01

Pegylated-IFN and ribavirin remains the current treatment for chronic HCV infection in patients co-infected with HIV-1, but this regimen has low efficacy rates, particularly for HCV genotype 1/4 infection, has severe side effects and is extremely costly. Therefore, accurate prediction of treatment response is urgently required. We have recently shown that the NK cell gene, KIR2DS3 and a SNP associated with the IL28B gene synergise to increase the risk of chronic infection in primary HCV mono-infected patients. Identification of SNPs associated with the IL28B gene has also proven very powerful for predicting patient response to treatment. Patients co-infected with HIV-1 are of particular concern given they respond less well to HCV treatment, have more side effects and suffer a more rapid liver disease progression. In this study, we examined both IL28B and KIR2DS3 for their ability to predict treatment response in a cohort of HIV-1/HCV co-infected patients attending two treatment centres in Europe. We found that variation in both host genetic risk factors, IL28B and KIR2DS3, was strongly associated with sustained virological response (SVR) to treatment in our co-infected cohort (n = 149). The majority of patients who achieved a rapid virological response (RVR) achieved a SVR. However, it is currently impossible to predict treatment outcome in patients who fail to achieve an RVR. In our cohort, the presence of host genetic risk factors, IL28B-T and KIR2DS3 alleles, resulted in increased odds of treatment failure in these RVR negative patients (n = 88). Our data suggests that testing for host genetic factors will improve predicting treatment responsiveness in the clinical management of co-infected patients, and provides further evidence of the importance of the innate immune system in the immune response to HCV.

Some aspects of optical feedback with cadmium sulfide and related photoconductors. [for extended frequency response

NASA Technical Reports Server (NTRS)

Katzberg, S. J.

1974-01-01

A primary limitation of many solid state photoconductors used in electro-optical systems is their slow response in converting varying light intensities into electrical signals. An optical feedback technique is presented which can extend the frequency response of systems that use these detectors by orders of magnitude without adversely affecting overall signal-to-noise ratio performance. The technique is analyzed to predict the improvement possible and a system is implemented using cadmium sulfide to demonstrate the effectiveness of the technique and the validity of the analysis.

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment

NASA Astrophysics Data System (ADS)

De Kauwe, M. G.; Medlyn, B.; Walker, A.; Zaehle, S.; Pendall, E.; Norby, R. J.

2017-12-01

Multifactor experiments are often advocated as important for advancing models, yet to date, such models have only been tested against single-factor experiments. We applied 10 models to the multifactor Prairie Heating and CO2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor experiments can be used to constrain models and to identify a road map for model improvement. We found models performed poorly in ambient conditions: comparison with data highlighted model failures particularly with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single-factors treatments was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO2-induced water savings to extend the growing season length. Observed interactive (CO2 × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. We outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

Brain potentials measured during a Go/NoGo task predict completion of substance abuse treatment.

PubMed

Steele, Vaughn R; Fink, Brandi C; Maurer, J Michael; Arbabshirani, Mohammad R; Wilber, Charles H; Jaffe, Adam J; Sidz, Anna; Pearlson, Godfrey D; Calhoun, Vince D; Clark, Vincent P; Kiehl, Kent A

2014-07-01

U.S. nationwide estimates indicate that 50% to 80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. We tested whether pretreatment neural measures of a response inhibition (Go/NoGo) task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Adult incarcerated participants (n = 89; women n = 55) who volunteered for substance abuse treatment performed a Go/NoGo task while event-related potentials (ERPs) were recorded. Stimulus- and response-locked ERPs were compared between participants who completed (n = 68; women = 45) and discontinued (n = 21; women = 10) treatment. As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, depression, anxiety, motivation for change, and years of drug abuse). Participants who discontinued treatment exhibited deficiencies in sensory gating, as indexed by smaller P2; error-monitoring, as indexed by smaller ERN/Ne; and adjusting response strategy posterror, as indexed by larger Pe. The combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

Predicting the threshold of pulse-train electrical stimuli using a stochastic auditory nerve model: the effects of stimulus noise.

PubMed

Xu, Yifang; Collins, Leslie M

2004-04-01

The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in some human subjects (Zeng et al., 2000). In this paper, thresholds for noise-modulated pulse-train stimuli are predicted utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. The neural refractory effect is described using a Markov model for a noise-free pulse-train stimulus and a closed-form solution for the steady-state neural response is provided. For noise-modulated pulse-train stimuli, a recursive method using the conditional probability is utilized to track the neural responses to each successive pulse. A neural spike count rule has been presented for both threshold and intensity discrimination under the assumption that auditory perception occurs via integration over a relatively long time period (Bruce et al., 1999). An alternative approach originates from the hypothesis of the multilook model (Viemeister and Wakefield, 1991), which argues that auditory perception is based on several shorter time integrations and may suggest an NofM model for prediction of pulse-train threshold. This motivates analyzing the neural response to each individual pulse within a pulse train, which is considered to be the brief look. A logarithmic rule is hypothesized for pulse-train threshold. Predictions from the multilook model are shown to match trends in psychophysical data for noise-free stimuli that are not always matched by the long-time integration rule. Theoretical predictions indicate that threshold decreases as noise variance increases. Theoretical models of the neural response to pulse-train stimuli not only reduce calculational overhead but also facilitate utilization of signal detection theory and are easily extended to multichannel psychophysical tasks.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

PubMed

Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

2017-06-23

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd. All rights reserved.

Brain Potentials Measured During a Go/NoGo Task Predict Completion of Substance Abuse Treatment

PubMed Central

Steele, Vaughn R.; Fink, Brandi C.; Maurer, J. Michael; Arbabshirani, Mohammad R.; Wilber, Charles H.; Jaffe, Adam J.; Sidz, Anna; Pearlson, Godfrey D.; Calhoun, Vince D.; Clark, Vincent P.; Kiehl, Kent A.

2014-01-01

Background US nationwide estimates indicate 50–80% of prisoners have a history of substance abuse or dependence. Tailoring substance abuse treatment to specific needs of incarcerated individuals could improve effectiveness of treating substance dependence and preventing drug abuse relapse. The purpose of the present study was to test the hypothesis that pre-treatment neural measures of a Go/NoGo task would predict which individuals would or would not complete a 12-week cognitive behavioral substance abuse treatment program. Methods Adult incarcerated participants (N=89; Females=55) who volunteered for substance abuse treatment performed a response inhibition (Go/NoGo) task while event-related potentials (ERP) were recorded. Stimulus- and response-locked ERPs were compared between individuals who completed (N=68; Females=45) and discontinued (N=21; Females=10) treatment. Results As predicted, stimulus-locked P2, response-locked error-related negativity (ERN/Ne), and response-locked error positivity (Pe), measured with windowed time-domain and principal component analysis, differed between groups. Using logistic regression and support-vector machine (i.e., pattern classifiers) models, P2 and Pe predicted treatment completion above and beyond other measures (i.e., N2, P300, ERN/Ne, age, sex, IQ, impulsivity, and self-reported depression, anxiety, motivation for change, and years of drug abuse). Conclusions We conclude individuals who discontinue treatment exhibited deficiencies in sensory gating, as indexed by smaller P2, error-monitoring, as indexed by smaller ERN/Ne, and adjusting response strategy post-error, as indexed by larger Pe. However, the combination of P2 and Pe reliably predicted 83.33% of individuals who discontinued treatment. These results may help in the development of individualized therapies, which could lead to more favorable, long-term outcomes. PMID:24238783

Progress in Space Weather Modeling and Observations Needed to Improve the Operational NAIRAS Model Aircraft Radiation Exposure Predictions

NASA Astrophysics Data System (ADS)

Mertens, C. J.; Kress, B. T.; Wiltberger, M. J.; Tobiska, W.; Xu, X.

2011-12-01

The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) is a prototype operational model for predicting commercial aircraft radiation exposure from galactic and solar cosmic rays. NAIRAS predictions are currently streaming live from the project's public website, and the exposure rate nowcast is also available on the SpaceWx smartphone app for iPhone, IPad, and Android. Cosmic rays are the primary source of human exposure to high linear energy transfer radiation at aircraft altitudes, which increases the risk of cancer and other adverse health effects. Thus, the NAIRAS model addresses an important national need with broad societal, public health and economic benefits. The processes responsible for the variability in the solar wind, interplanetary magnetic field, solar energetic particle spectrum, and the dynamical response of the magnetosphere to these space environment inputs, strongly influence the composition and energy distribution of the atmospheric ionizing radiation field. During the development of the NAIRAS model, new science questions were identified that must be addressed in order to obtain a more reliable and robust operational model of atmospheric radiation exposure. Addressing these science questions require improvements in both space weather modeling and observations. The focus of this talk is to present these science questions, the proposed methodologies for addressing these science questions, and the anticipated improvements to the operational predictions of atmospheric radiation exposure. The overarching goal of this work is to provide a decision support tool for the aviation industry that will enable an optimal balance to be achieved between minimizing health risks to passengers and aircrew while simultaneously minimizing costs to the airline companies.

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

PubMed

Lee, Bernard Kok Bang; Tiong, Kai Hung; Chang, Jit Kang; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Tan, Aik Choon; Khang, Tsung Fei; Cheong, Sok Ching

2017-01-25

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC 50 ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC 50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

Testosterone Treatment and Sexual Function in Older Men With Low Testosterone Levels.

PubMed

Cunningham, Glenn R; Stephens-Shields, Alisa J; Rosen, Raymond C; Wang, Christina; Bhasin, Shalender; Matsumoto, Alvin M; Parsons, J Kellogg; Gill, Thomas M; Molitch, Mark E; Farrar, John T; Cella, David; Barrett-Connor, Elizabeth; Cauley, Jane A; Cifelli, Denise; Crandall, Jill P; Ensrud, Kristine E; Gallagher, Laura; Zeldow, Bret; Lewis, Cora E; Pahor, Marco; Swerdloff, Ronald S; Hou, Xiaoling; Anton, Stephen; Basaria, Shehzad; Diem, Susan J; Tabatabaie, Vafa; Ellenberg, Susan S; Snyder, Peter J

2016-08-01

The Testosterone Trials are a coordinated set of seven trials to determine the efficacy of T in symptomatic men ≥65 years old with unequivocally low T levels. Initial results of the Sexual Function Trial showed that T improved sexual activity, sexual desire, and erectile function. To assess the responsiveness of specific sexual activities to T treatment; to relate hormone changes to changes in sexual function; and to determine predictive baseline characteristics and T threshold for sexual outcomes. A placebo-controlled trial. Twelve academic medical centers in the United States. A total of 470 men ≥65 years of age with low libido, average T <275 ng/dL, and a partner willing to have sexual intercourse at least twice a month. Men were assigned to take T gel or placebo for 1 year. Sexual function was assessed by three questionnaires every 3 months: the Psychosexual Daily Questionnaire, the Derogatis Interview for Sexual Function, and the International Index of Erectile Function. Compared with placebo, T administration significantly improved 10 of 12 measures of sexual activity. Incremental increases in total and free T and estradiol levels were associated with improvements in sexual activity and desire, but not erectile function. No threshold T level was observed for any outcome, and none of the 27 baseline characteristics predicted responsiveness to T. In older men with low libido and low T levels, improvements in sexual desire and activity in response to T treatment were related to the magnitude of increases in T and estradiol levels, but there was no clear evidence of a threshold effect.

DNA Adducts from Anticancer Drugs as Candidate Predictive Markers for Precision Medicine

PubMed Central

2016-01-01

Biomarker-driven drug selection plays a central role in cancer drug discovery and development, and in diagnostic strategies to improve the use of traditional chemotherapeutic drugs. DNA-modifying anticancer drugs are still used as first line medication, but drawbacks such as resistance and side effects remain an issue. Monitoring the formation and level of DNA modifications induced by anticancer drugs is a potential strategy for stratifying patients and predicting drug efficacy. In this perspective, preclinical and clinical data concerning the relationship between drug-induced DNA adducts and biological response for platinum drugs and combination therapies, nitrogen mustards and half-mustards, hypoxia-activated drugs, reductase-activated drugs, and minor groove binding agents are presented and discussed. Aspects including measurement strategies, identification of adducts, and biological factors that influence the predictive relationship between DNA modification and biological response are addressed. A positive correlation between DNA adduct levels and response was observed for the majority of the studies, demonstrating the high potential of using DNA adducts from anticancer drugs as mechanism-based biomarkers of susceptibility, especially as bioanalysis approaches with higher sensitivity and throughput emerge. PMID:27936622

Full-Scale Turbofan-Engine Turbine-Transfer Function Determination Using Three Internal Sensors

NASA Technical Reports Server (NTRS)

Hultgren, Lennart S.

2012-01-01

Noise-source separation techniques, using three engine-internal sensors, are applied to existing static-engine test data to determine the turbine transfer function for the currently subdominant combustion noise. The results are used to assess the combustion-noise prediction capability of the Aircraft Noise Prediction Program (ANOPP) and an improvement to the combustion-noise module GECOR is suggested. The work was carried out in response to the NASA Fundamental Aeronautics Subsonic Fixed Wing Program s Reduced-Perceived-Noise Technical Challenge.

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

Smith, Kandler A; Santhanagopalan, Shriram; Yang, Chuanbo

Computer models are helping to accelerate the design and validation of next generation batteries and provide valuable insights not possible through experimental testing alone. Validated 3-D physics-based models exist for predicting electrochemical performance, thermal and mechanical response of cells and packs under normal and abuse scenarios. The talk describes present efforts to make the models better suited for engineering design, including improving their computation speed, developing faster processes for model parameter identification including under aging, and predicting the performance of a proposed electrode material recipe a priori using microstructure models.

Pyrolysis Model Development for a Multilayer Floor Covering

PubMed Central

McKinnon, Mark B.; Stoliarov, Stanislav I.

2015-01-01

Comprehensive pyrolysis models that are integral to computational fire codes have improved significantly over the past decade as the demand for improved predictive capabilities has increased. High fidelity pyrolysis models may improve the design of engineered materials for better fire response, the design of the built environment, and may be used in forensic investigations of fire events. A major limitation to widespread use of comprehensive pyrolysis models is the large number of parameters required to fully define a material and the lack of effective methodologies for measurement of these parameters, especially for complex materials. The work presented here details a methodology used to characterize the pyrolysis of a low-pile carpet tile, an engineered composite material that is common in commercial and institutional occupancies. The studied material includes three distinct layers of varying composition and physical structure. The methodology utilized a comprehensive pyrolysis model (ThermaKin) to conduct inverse analyses on data collected through several experimental techniques. Each layer of the composite was individually parameterized to identify its contribution to the overall response of the composite. The set of properties measured to define the carpet composite were validated against mass loss rate curves collected at conditions outside the range of calibration conditions to demonstrate the predictive capabilities of the model. The mean error between the predicted curve and the mean experimental mass loss rate curve was calculated as approximately 20% on average for heat fluxes ranging from 30 to 70 kW·m−2, which is within the mean experimental uncertainty. PMID:28793556

Digital PCR quantification of MGMT methylation refines prediction of clinical benefit from alkylating agents in glioblastoma and metastatic colorectal cancer.

PubMed

Barault, L; Amatu, A; Bleeker, F E; Moutinho, C; Falcomatà, C; Fiano, V; Cassingena, A; Siravegna, G; Milione, M; Cassoni, P; De Braud, F; Rudà, R; Soffietti, R; Venesio, T; Bardelli, A; Wesseling, P; de Witt Hamer, P; Pietrantonio, F; Siena, S; Esteller, M; Sartore-Bianchi, A; Di Nicolantonio, F

2015-09-01

O(6)-methyl-guanine-methyl-transferase (MGMT) silencing by promoter methylation may identify cancer patients responding to the alkylating agents dacarbazine or temozolomide. We evaluated the prognostic and predictive value of MGMT methylation testing both in tumor and cell-free circulating DNA (cfDNA) from plasma samples using an ultra-sensitive two-step digital PCR technique (methyl-BEAMing). Results were compared with two established techniques, methylation-specific PCR (MSP) and Bs-pyrosequencing. Thresholds for MGMT methylated status for each technique were established in a training set of 98 glioblastoma (GBM) patients. The prognostic and the predictive value of MGMT methylated status was validated in a second cohort of 66 GBM patients treated with temozolomide in which methyl-BEAMing displayed a better specificity than the other techniques. Cutoff values of MGMT methylation specific for metastatic colorectal cancer (mCRC) tissue samples were established in a cohort of 60 patients treated with dacarbazine. In mCRC, both quantitative assays methyl-BEAMing and Bs-pyrosequencing outperformed MSP, providing better prediction of treatment response and improvement in progression-free survival (PFS) (P < 0.001). Ability of methyl-BEAMing to identify responding patients was validated in a cohort of 23 mCRC patients treated with temozolomide and preselected for MGMT methylated status according to MSP. In mCRC patients treated with dacarbazine, exploratory analysis of cfDNA by methyl-BEAMing showed that MGMT methylation was associated with better response and improved median PFS (P = 0.008). Methyl-BEAMing showed high reproducibility, specificity and sensitivity and was applicable to formalin-fixed paraffin-embedded tissues and cfDNA. This study supports the quantitative assessment of MGMT methylation for clinical purposes since it could refine prediction of response to alkylating agents. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

FDG-PET Response Prediction in Pediatric Hodgkin's Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value.

PubMed

Hussien, Amr Elsayed M; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

2015-01-28

In pediatric Hodgkin's lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

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

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Tan, Maxine; McMeekin, Scott; Thai, Theresa; Moore, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to identify and apply quantitative image biomarkers for early prediction of the tumor response to the chemotherapy among the ovarian cancer patients participated in the clinical trials of testing new drugs. In the experiment, we retrospectively selected 30 cases from the patients who participated in Phase I clinical trials of new drug or drug agents for ovarian cancer treatment. Each case is composed of two sets of CT images acquired pre- and post-treatment (4-6 weeks after starting treatment). A computer-aided detection (CAD) scheme was developed to extract and analyze the quantitative image features of the metastatic tumors previously tracked by the radiologists using the standard Response Evaluation Criteria in Solid Tumors (RECIST) guideline. The CAD scheme first segmented 3-D tumor volumes from the background using a hybrid tumor segmentation scheme. Then, for each segmented tumor, CAD computed three quantitative image features including the change of tumor volume, tumor CT number (density) and density variance. The feature changes were calculated between the matched tumors tracked on the CT images acquired pre- and post-treatments. Finally, CAD predicted patient's 6-month progression-free survival (PFS) using a decision-tree based classifier. The performance of the CAD scheme was compared with the RECIST category. The result shows that the CAD scheme achieved a prediction accuracy of 76.7% (23/30 cases) with a Kappa coefficient of 0.493, which is significantly higher than the performance of RECIST prediction with a prediction accuracy and Kappa coefficient of 60% (17/30) and 0.062, respectively. This study demonstrated the feasibility of analyzing quantitative image features to improve the early predicting accuracy of the tumor response to the new testing drugs or therapeutic methods for the ovarian cancer patients.

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.

Quality of life measured with EuroQol-five dimensions questionnaire predicts long-term mortality, response, and reverse remodelling in cardiac resynchronization therapy patients.

PubMed

Nagy, Klaudia Vivien; Széplaki, Gábor; Perge, Péter; Boros, András Mihály; Kosztin, Annamária; Apor, Astrid; Molnár, Levente; Szilágyi, Szabolcs; Tahin, Tamás; Zima, Endre; Kutyifa, Valentina; Gellér, László; Merkely, Béla

2017-11-22

There are previous studies on quality of life (QoL) in cardiac resynchronization therapy (CRT) patients; however, there are no data with the short EuroQol-five dimensions (EQ-5D) questionnaire predicting outcomes. We aimed to assess the predictive role of baseline QoL and QoL change at 6 months after CRT with EQ-5D on 5-year mortality and response. In our prospective follow-up study, 130 heart failure (HF) patients undergoing CRT were enrolled. Clinical evaluation, echocardiography, and EQ-5D were performed at baseline and at 6 months of follow-up, continued to 5 years. Primary endpoint was all-cause mortality at 5 years. Secondary endpoints were (i) clinical response with at least one class improvement in New York Heart Association without HF hospitalization and (ii) reverse remodelling with 15% reduction in left ventricular end-systolic volume at 6 months. Fifty-four (41.5%) patients died during 5 years, 85 (65.3%) clinical responders were identified, and 63 patients (48.5%) had reverse remodelling. Baseline issues with mobility were associated with lower response [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.16-0.84; P = 0.018]. Lack of reverse remodelling correlated with self-care issues at baseline (OR 0.10, 95% CI 0.01-0.94; P = 0.04). Furthermore, self-care difficulties [hazard ratio (HR) 2.39, 95% CI 1.17-4.86; P = 0.01) or more anxiety (HR 1.51, 95% CI 1.00-2.26; P = 0.04) predicted worse long-term survival. At 6 months, mobility (HR 3.95, 95% CI 1.89-8.20; P < 0.001), self-care (HR 7.69, 95% CI 2.23-25.9; P = 0.001), or ≥ 10% visual analogue scale (VAS) (HR 2.24, 95% CI 1.27-3.94; P = 0.005) improvement anticipated better survival at 5 years. EuroQol-five dimension is a simple method assessing QoL in CRT population. Mobility issues at baseline are associated with lower clinical response, whereas self-care issues predict lack of reverse remodelling. Problems with mobility or anxiety before CRT and persistent issues with mobility, self-care, and VAS scale at 6 months predict adverse outcome. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Cardiology

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

PubMed Central

McCoy, Andrea

2017-01-01

Introduction Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach. Methods In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow. Results Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%. Conclusion The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC. PMID:29450295

Combined ursodeoxycholic acid (UDCA) and fenofibrate in primary biliary cholangitis patients with incomplete UDCA response may improve outcomes.

PubMed

Cheung, A C; Lapointe-Shaw, L; Kowgier, M; Meza-Cardona, J; Hirschfield, G M; Janssen, H L A; Feld, J J

2016-01-01

Fibrates appear to improve biochemistry in patients with primary biliary cholangitis (PBC), but it is unclear which factors predict response and whether treatment improves transplant-free survival. To evaluate biochemical profiles, liver-related outcomes and adverse events following fenofibrate therapy in PBC patients with incomplete response to ursodeoxycholic acid (UDCA). A retrospective cohort study was performed at a tertiary centre. Cox regression was used to compare outcomes between patients treated with fibrates and UDCA (FF) or UDCA alone, adjusted for a propensity score to account for treatment selection bias. A total of 120 patients were included (FF group n = 46, UDCA group n = 74, median fenofibrate treatment 11 months); 41% vs. 7% met the Toronto criteria for biochemical response [alkaline phosphatase ≤1.67 times the upper limit of normal] in the FF and UDCA groups, respectively (P = 0.0001). Fenofibrate was also associated with improved decompensation-free and transplant-free survival [hazard ratio (HR) 0.09, 95% CI 0.03-0.32, P = 0.0002]. However, only fenofibrate use, not biochemical response, was independently associated with improved outcomes on multivariable analysis (HR 0.40, 95% CI 0.17-0.93, P = 0.03). Twenty-two percent discontinued fenofibrate due to adverse events (most common: abdominal pain and myalgias). In cirrhotic patients, bilirubin increased more rapidly in the FF group (P = 0.005). Fenofibrate therapy is associated with significant improvement in alkaline phosphatase, decompensation-free and transplant-free survival in PBC patients with incomplete UDCA response. However, fenofibrate should be used cautiously in cirrhosis, with close monitoring for clinical/biochemical decompensation. Additional studies are required to assess the validity of alkaline phosphatase as an appropriate response criteria for fibrate therapy. © 2015 John Wiley & Sons Ltd.

Pharmacogenetic algorithm for individualized controlled ovarian stimulation (iCOS) in assisted reproductive technology cycles.

PubMed

Roque, Matheus; Bianco, Bianca; Christofolini, Denise M; Cordts, Emerson B; Vilarino, Fabia L; Carvalho, Waldemar; Valle, Marcello; Sampaio, Marcos; Geber, Selmo; Esteves, Sandro C; Barbosa, Caio P

2018-06-14

Controlled ovarian stimulation (COS) is crucial for optimizing in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) success. Multiple factors influence the ovarian response to COS, making predictions about oocyte yields not so straightforward. As a result, the ovarian response may be poor or suboptimal, or even excessive, all of which have negative consequences for the affected patient. There is a group of patients that present with a suboptimal response to COS despite normal biomarkers of ovarian reserve, such as AFC and AMH. These patients have a lower number of retrieved oocytes than what was expected based on their ovarian reserve, thus showing the inadequacy of using only the traditional ovarian reserve biomarkers to predict the ovarian response. Suboptimal response to COS might be related to ovarian sensitivity to exogenous gonadotropins modulated by genetic factors. The understanding of the gene polymorphisms related to reproductive function can help to improve the clinical management of this patient population and to explain some of the individual patient variability in response to COS. The development of a pharmacogenetic approach concerning COS in the context of assisted reproduction seems attractive as it might help to understand the relationship between genetic variants and ovarian response to exogenous gonadotropins. The patient ́s genetic profile could be used to select the most appropriate gonadotropin type, predict the optimal dosage for each drug, develop a cost-effective treatment plan, maximize the success rates, and lastly, decrease the time-to-pregnancy.

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

PubMed Central

Hervé, Mylène; Bergon, Aurélie; Le Guisquet, Anne-Marie; Leman, Samuel; Consoloni, Julia-Lou; Fernandez-Nunez, Nicolas; Lefebvre, Marie-Noëlle; El-Hage, Wissam; Belzeaux, Raoul; Belzung, Catherine; Ibrahim, El Chérif

2017-01-01

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD. PMID:28848385

Limits on the prediction of helicopter rotor noise using thickness and loading sources: Validation of helicopter noise prediction techniques

NASA Technical Reports Server (NTRS)

Succi, G. P.

1983-01-01

The techniques of helicopter rotor noise prediction attempt to describe precisely the details of the noise field and remove the empiricisms and restrictions inherent in previous methods. These techniques require detailed inputs of the rotor geometry, operating conditions, and blade surface pressure distribution. The Farassat noise prediction techniques was studied, and high speed helicopter noise prediction using more detailed representations of the thickness and loading noise sources was investigated. These predictions were based on the measured blade surface pressures on an AH-1G rotor and compared to the measured sound field. Although refinements in the representation of the thickness and loading noise sources improve the calculation, there are still discrepancies between the measured and predicted sound field. Analysis of the blade surface pressure data indicates shocks on the blades, which are probably responsible for these discrepancies.

Information processing bias and pharmacotherapy outcome in older adults with generalized anxiety disorder.

PubMed

Steiner, Amanda R W; Petkus, Andrew J; Nguyen, Hoang; Wetherell, Julie Loebach

2013-08-01

Information processing bias was evaluated in a sample of 25 older adults with generalized anxiety disorder (GAD) over the course of 12 weeks of escitalopram pharmacotherapy. Using the CANTAB Affective Go/No Go test, treatment response (as measured by the Hamilton Anxiety Rating Scale, Penn State Worry Questionnaire, and Generalized Anxiety Disorder Severity Scale) was predicted from a bias score (i.e., difference score between response latencies for negative and positive words) using mixed-models regression. A more positive bias score across time predicted better response to treatment. Faster responses to positive words relative to negative words were associated with greater symptomatic improvement over time as reflected by scores on the GADSS. There was a trend toward significance for PSWQ scores and no significant effects related to HAMA outcomes. These preliminary findings offer further insights into the role of biased cognitive processing of emotional material in the manifestation of late-life anxiety symptoms. Published by Elsevier Ltd.

Performance Variability as a Predictor of Response to Aphasia Treatment.

PubMed

Duncan, E Susan; Schmah, Tanya; Small, Steven L

2016-10-01

Performance variability in individuals with aphasia is typically regarded as a nuisance factor complicating assessment and treatment. We present the alternative hypothesis that intraindividual variability represents a fundamental characteristic of an individual's functioning and an important biomarker for therapeutic selection and prognosis. A total of 19 individuals with chronic aphasia participated in a 6-week trial of imitation-based speech therapy. We assessed improvement both on overall language functioning and repetition ability. Furthermore, we determined which pretreatment variables best predicted improvement on the repetition test. Significant gains were made on the Western Aphasia Battery-Revised (WAB) Aphasia Quotient, Cortical Quotient, and 2 subtests as well as on a separate repetition test. Using stepwise regression, we found that pretreatment intraindividual variability was the only predictor of improvement in performance on the repetition test, with greater pretreatment variability predicting greater improvement. Furthermore, the degree of reduction in this variability over the course of treatment was positively correlated with the degree of improvement. Intraindividual variability may be indicative of potential for improvement on a given task, with more uniform performance suggesting functioning at or near peak potential. © The Author(s) 2016.

Detecting causal drivers and empirical prediction of the Indian Summer Monsoon

NASA Astrophysics Data System (ADS)

Di Capua, G.; Vellore, R.; Raghavan, K.; Coumou, D.

2017-12-01

The Indian summer monsoon (ISM) is crucial for the economy, society and natural ecosystems on the Indian peninsula. Predict the total seasonal rainfall at several months lead time would help to plan effective water management strategies, improve flood or drought protection programs and prevent humanitarian crisis. However, the complexity and strong internal variability of the ISM circulation system make skillful seasonal forecasting challenging. Moreover, to adequately identify the low-frequency, and far-away processes which influence ISM behavior novel tools are needed. We applied a Response-Guided Causal Precursor Detection (RGCPD) scheme, which is a novel empirical prediction method which unites a response-guided community detection scheme with a causal discovery algorithm (CEN). These tool allow us to assess causal pathways between different components of the ISM circulation system and with far-away regions in the tropics, mid-latitudes or Arctic. The scheme has successfully been used to identify causal precursors of the Stratospheric polar vortex enabling skillful predictions at (sub) seasonal timescales (Kretschmer et al. 2016, J.Clim., Kretschmer et al. 2017, GRL). We analyze observed ISM monthly rainfall over the monsoon trough region. Applying causal discovery techniques, we identify several causal precursor communities in the fields of 2m-temperature, sea level pressure and snow depth over Eurasia. Specifically, our results suggest that surface temperature conditions in both tropical and Arctic regions contribute to ISM variability. A linear regression prediction model based on the identified set of communities has good hindcasting skills with 4-5 months lead times. Further we separate El Nino, La Nina and ENSO-neutral years from each other and find that the causal precursors are different dependent on ENSO state. The ENSO-state dependent causal precursors give even higher skill, especially for La Nina years when the ISM is relatively strong. These findings are promising results that might ultimately contribute to both improved understanding of the ISM circulation system and help improving seasonal ISM forecasts.

Theory of mind, inhibitory control, and preschool-age children's suggestibility in different interviewing contexts.

PubMed

Scullin, Matthew H; Bonner, Karri

2006-02-01

The current study examined the relations among 3- to 5-year-olds' theory of mind, inhibitory control, and three measures of suggestibility: yielding to suggestive questions (yield), shifting answers in response to negative feedback (shift), and accuracy in response to misleading questions during a pressured interview about a live event. Theory of mind aided in the prediction of suggestibility about the live event, and inhibitory control was a moderator variable affecting the consistency of children's sensitivity to social pressure across situations. The findings indicate that theory of mind and inhibitory control predict children's suggestibility about a live event above and beyond yield, shift, and age and that the construct validity of shift may improve as children's inhibitory control develops.

Deep learning in pharmacogenomics: from gene regulation to patient stratification.

PubMed

Kalinin, Alexandr A; Higgins, Gerald A; Reamaroon, Narathip; Soroushmehr, Sayedmohammadreza; Allyn-Feuer, Ari; Dinov, Ivo D; Najarian, Kayvan; Athey, Brian D

2018-05-01

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.

An Extended Damage Plasticity Model for Shotcrete: Formulation and Comparison with Other Shotcrete Models

PubMed Central

Neuner, Matthias; Gamnitzer, Peter; Hofstetter, Günter

2017-01-01

The aims of the present paper are (i) to briefly review single-field and multi-field shotcrete models proposed in the literature; (ii) to propose the extension of a damage-plasticity model for concrete to shotcrete; and (iii) to evaluate the capabilities of the proposed extended damage-plasticity model for shotcrete by comparing the predicted response with experimental data for shotcrete and with the response predicted by shotcrete models, available in the literature. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated shotcrete models and they will serve as a basis for the design of a new lab test program, complementing the existing ones. PMID:28772445

Increasing sugar transport to improve soybean response to elevated [CO2

USDA-ARS?s Scientific Manuscript database

Elevated atmospheric [CO2] causes a direct increase in instantaneous photosynthesis and sugar production in C3 plants, leading to a yield increase which is promising to meet future food demand. However, previous studies have shown that soybean yield does not increase as much as predicted under eleva...

Thinking Fast and Slow about Causality: Response to Palinkas

ERIC Educational Resources Information Center

Marsh, Jeanne C.

2014-01-01

Larry Palinkas advances the developing science of social work by providing an explanation of how social science research methods, both qualitative and quantitative, can improve our capacity to draw casual inferences. Understanding causal relations and making causal inferences--with the promise of being able to predict and control outcomes--is…

Molecular analysis of transplant rejection: marching onward

PubMed Central

Lakkis, Fadi G.

2013-01-01

Transcriptional profiling of organ transplants is increasingly defining the biological pathways responsible for graft rejection at the molecular level and identifying gene transcripts that diagnose or predict rejection. These advances hold significant promise for the treatment of organ rejection and for improving clinical outcomes after transplantation, but hurdles remain. PMID:24145950

Characteristics of Placebo Responders in Pediatric Clinical Trials of Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Newcorn, Jeffrey H.; Sutton, Virginia K.; Zhang, Shuyu; Wilens, Timothy; Kratochvil, Christopher; Emslie, Graham J.; D'Souza, Deborah N.; Schuh, Leslie M.; Allen, Albert J.

2009-01-01

Objective: Understanding placebo response is a prerequisite to improving clinical trial methodology. Data from placebo-controlled trials of atomoxetine in the treatment of children and adolescents with attention-deficit/hyperactivity disorder (ADHD) were analyzed to identify demographic and clinical characteristics that might predict placebo…

Methodological Considerations in the Study of Earthworms in Forest Ecosystems

Treesearch

Dylan Rhea-Fournier; Grizelle Gonzalez

2017-01-01

Decades of studies have shown that soil macrofauna, especially earthworms, play dominant engineering roles in soils, affecting physical, chemical, and biological components of ecosystems. Quantifying these effects would allow crucial improvement in biogeochemical budgets and modeling, predicting response of land use and disturbance, and could be applied to...

Response Latency as a Predictor of the Accuracy of Children's Reports

ERIC Educational Resources Information Center

Ackerman, Rakefet; Koriat, Asher

2011-01-01

Researchers have explored various diagnostic cues to the accuracy of information provided by child eyewitnesses. Previous studies indicated that children's confidence in their reports predicts the relative accuracy of these reports, and that the confidence-accuracy relationship generally improves as children grow older. In this study, we examined…

Plant functional traits improve diversity-based predictions of temporal stability of grassland productivity

USDA-ARS?s Scientific Manuscript database

Aboveground net primary productivity (ANPP) varies in response to temporal fluctuations in weather. Temporal stability (mean/standard deviation) of community ANPP may be increased, on average, by increasing plant species richness, but stability also may differ widely at a given richness level imply...

Differential Diagnosis of Specific Learning Disability within a Response to Intervention Framework

ERIC Educational Resources Information Center

Boneshefski, Michael J.

2017-01-01

The purpose of this study was to determine to what extent two major specific learning disability (SLD) criteria, including a student's level of academic achievement and rate of improvement (ROI), predict multidisciplinary evaluation teams' decision-making regarding referral for special education evaluation and special education eligibility.…

Improving SWAT model prediction using an upgraded denitrification scheme and constrained auto calibration

USDA-ARS?s Scientific Manuscript database

The reliability of common calibration practices for process based water quality models has recently been questioned. A so-called “adequately calibrated model” may contain input errors not readily identifiable by model users, or may not realistically represent intra-watershed responses. These short...

On the Identification of Associations between Five World Health Organization Water, Sanitation and Hygiene Phenotypes and Six Predictors in Low and Middle-Income Countries.

PubMed

Ellis, Hugh; Schoenberger, Erica

2017-01-01

According to the most recent estimates, 842,000 deaths in low- to middle-income countries were attributable to inadequate water, sanitation and hygiene in 2012. Despite billions of dollars and decades of effort, we still lack a sound understanding of which kinds of WASH interventions are most effective in improving public health outcomes, and an important corollary-whether the right things are being measured. The World Health Organization (WHO) has made a concerted effort to compile comprehensive data on drinking water quality and sanitation in the developing world. A recent 2014 report provides information on three phenotypes (responses): Unsafe Water Deaths, Unsafe Sanitation Deaths, Unsafe Hygiene Deaths; two grouped phenotypes: Unsafe Water and Sanitation Deaths and Unsafe Water, Sanitation and Hygiene Deaths; and six explanatory variables (predictors): Improved Sanitation, Unimproved Water Source, Piped Water To Premises, Other Improved Water Source, Filtered and Bottled Water in the Household and Handwashing. Regression analyses were performed to identify statistically significant associations between these mortality responses and predictors. Good fitted-model performance required: (1) the use of population-normalized death fractions as opposed to number of deaths; (2) transformed response (logit or power); and (3) square-root predictor transformation. Given the complexity and heterogeneity of the relationships and countries being studied, these models exhibited remarkable performance and explained, for example, about 85% of the observed variance in population-normalized Unsafe Sanitation Death fraction, with a high F-statistic and highly statistically significant predictor p-values. Similar performance was found for all other responses, which was an unexpected result (the expected associations between responses and predictors-i.e., water-related with water-related, etc. did not occur). The set of statistically significant predictors remains the same across all responses. That is, Unsafe Water Source (UWS), Improved Sanitation (IS) and Filtered and Bottled Water in the Household (FBH) were the only statistically significant predictors whether the response was Unsafe Sanitation Death Fraction, Unsafe Hygiene Death Fraction or Unsafe Water Death Fraction. Moreover, the fraction of variance explained for all fitted models remained relatively high (adjusted R2 ranges from 0.7605 to 0.8533). We find that two of the statistically significant predictors-Improved Sanitation and Unimproved Water Sources-are particularly influential. We also find that some predictors (Piped Water to Premises, Other Improved Water Sources) have very little explanatory power for predicting mortality and one (Other Improved Water Sources) has a counterintuitive effect on response (Unsafe Sanitary Death Fraction increases with increases in OIWS) and one predictor (Hand Washing) to have essentially no explanatory usefulness. Our results suggest that a higher priority may need to be given to improved sanitation than has been the case. Nevertheless, while our focus in this paper is mortality, morbidity is a staggering consequence of inadequate water, sanitation and hygiene, and lower impact on mortality may not mean a similarly low impact on morbidity. More specifically, those predictors that we found uninfluential for predicting mortality-related responses may indeed be important when morbidity is the response.

Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

PubMed

Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

2012-01-01

In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

Addition of mushroom powder to pasta enhances the antioxidant content and modulates the predictive glycaemic response of pasta.

PubMed

Lu, Xikun; Brennan, Margaret A; Serventi, Luca; Liu, Jianfu; Guan, Wenqiang; Brennan, Charles S

2018-10-30

This study reports the effects of addition of mushroom powder on the nutritional properties, predictive in vitro glycaemic response and antioxidant potential of durum wheat pasta. Addition of the mushroom powder enriched the pasta as a source of protein, and soluble and insoluble dietary fibre compared with durum wheat semolina. Incorporation of mushroom powder significantly decreased the extent of starch degradation and the area under the curve (AUC) of reducing sugars released during digestion, while the total phenolic content and antioxidant capacities of samples increased. A mutual inhibition system between the degree of starch gelatinisation and antioxidant capacity of the pasta samples was observed. These results suggest that mushroom powder could be incorporated into fresh semolina pasta, conferring healthier characteristics, namely lowering the potential glycaemic response and improving antioxidant capacity of the pasta. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Flutter and Forced Response Analyses of Cascades using a Two-Dimensional Linearized Euler Solver

NASA Technical Reports Server (NTRS)

Reddy, T. S. R.; Srivastava, R.; Mehmed, O.

1999-01-01

Flutter and forced response analyses for a cascade of blades in subsonic and transonic flow is presented. The structural model for each blade is a typical section with bending and torsion degrees of freedom. The unsteady aerodynamic forces due to bending and torsion motions. and due to a vortical gust disturbance are obtained by solving unsteady linearized Euler equations. The unsteady linearized equations are obtained by linearizing the unsteady nonlinear equations about the steady flow. The predicted unsteady aerodynamic forces include the effect of steady aerodynamic loading due to airfoil shape, thickness and angle of attack. The aeroelastic equations are solved in the frequency domain by coupling the un- steady aerodynamic forces to the aeroelastic solver MISER. The present unsteady aerodynamic solver showed good correlation with published results for both flutter and forced response predictions. Further improvements are required to use the unsteady aerodynamic solver in a design cycle.

Impact of modellers' decisions on hydrological a priori predictions

NASA Astrophysics Data System (ADS)

Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

2014-06-01

In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information. In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.

Divergent responses to spring and winter warming drive community level flowering trends

PubMed Central

Cook, Benjamin I.; Wolkovich, Elizabeth M.; Parmesan, Camille

2012-01-01

Analyses of datasets throughout the temperate midlatitude regions show a widespread tendency for species to advance their springtime phenology, consistent with warming trends over the past 20–50 y. Within these general trends toward earlier spring, however, are species that either have insignificant trends or have delayed their timing. Various explanations have been offered to explain this apparent nonresponsiveness to warming, including the influence of other abiotic cues (e.g., photoperiod) or reductions in fall/winter chilling (vernalization). Few studies, however, have explicitly attributed the historical trends of nonresponding species to any specific factor. Here, we analyzed long-term data on phenology and seasonal temperatures from 490 species on two continents and demonstrate that (i) apparent nonresponders are indeed responding to warming, but their responses to fall/winter and spring warming are opposite in sign and of similar magnitude; (ii) observed trends in first flowering date depend strongly on the magnitude of a given species’ response to fall/winter vs. spring warming; and (iii) inclusion of fall/winter temperature cues strongly improves hindcast model predictions of long-term flowering trends compared with models with spring warming only. With a few notable exceptions, climate change research has focused on the overall mean trend toward phenological advance, minimizing discussion of apparently nonresponding species. Our results illuminate an understudied source of complexity in wild species responses and support the need for models incorporating diverse environmental cues to improve predictability of community level responses to anthropogenic climate change. PMID:22615406

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.

PubMed

Sasaki, Satoshi; Comber, Alexis J; Suzuki, Hiroshi; Brunsdon, Chris

2010-01-28

Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations. Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared. The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

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.

Whole genome prediction and heritability of childhood asthma phenotypes.

PubMed

McGeachie, Michael J; Clemmer, George L; Croteau-Chonka, Damien C; Castaldi, Peter J; Cho, Michael H; Sordillo, Joanne E; Lasky-Su, Jessica A; Raby, Benjamin A; Tantisira, Kelan G; Weiss, Scott T

2016-12-01

While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma-related phenotypes. We applied several WGP methods to a well-phenotyped cohort of 832 children with mild-to-moderate asthma from CAMP. We assessed narrow-sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre- and post-bronchodilator forced expiratory volume in 1 sec (FEV 1 ), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort. We found that longitudinal lung function phenotypes demonstrated significant narrow-sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4-8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts. Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP-prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma-related heritable traits.

A crossbred reference population can improve the response to genomic selection for crossbred performance.

PubMed

Esfandyari, Hadi; Sørensen, Anders Christian; Bijma, Piter

2015-09-29

Breeding goals in a crossbreeding system should be defined at the commercial crossbred level. However, selection is often performed to improve purebred performance. A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance. Optimization of the GS model raises the question of whether marker effects should be estimated from data on the pure lines or crossbreds. Therefore, the first objective of this study was to compare response to selection of crossbreds by simulating a two-way crossbreeding program with either a purebred or a crossbred training population. We assumed a trait of interest that was controlled by loci with additive and dominance effects. Animals were selected on estimated breeding values for crossbred performance. There was no genotype by environment interaction. Linkage phase and strength of linkage disequilibrium between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs) can differ between breeds, which causes apparent effects of SNPs to be line-dependent. Thus, our second objective was to compare response to GS based on crossbred phenotypes when the line origin of alleles was taken into account or not in the estimation of breeding values. Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population. Response to selection in crossbreds was larger if both phenotypes and genotypes were collected on crossbreds than if phenotypes were only recorded on crossbreds and genotypes on their parents. If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles. Response to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improves genomic prediction.

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

PubMed

Kramer, Kirsten E; Small, Gary W

2009-02-01

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy.

PubMed

Chen, Yong-Zi; Kim, Youngchul; Soliman, Hatem H; Ying, GuoGuang; Lee, Jae K

2018-06-01

ER-negative breast cancer includes most aggressive subtypes of breast cancer such as triple negative (TN) breast cancer. Excluded from hormonal and targeted therapies effectively used for other subtypes of breast cancer, standard chemotherapy is one of the primary treatment options for these patients. However, as ER- patients have shown highly heterogeneous responses to different chemotherapies, it has been difficult to select most beneficial chemotherapy treatments for them. In this study, we have simultaneously developed single drug biomarker models for four standard chemotherapy agents: paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) to predict responses and survival of ER- breast cancer patients treated with combination chemotherapies. We then flexibly combined these individual drug biomarkers for predicting patient outcomes of two independent cohorts of ER- breast cancer patients who were treated with different drug combinations of neoadjuvant chemotherapy. These individual and combined drug biomarker models significantly predicted chemotherapy response for 197 ER- patients in the Hatzis cohort (AUC = 0.637, P  = 0.002) and 69 ER- patients in the Hess cohort (AUC = 0.635, P  = 0.056). The prediction was also significant for the TN subgroup of both cohorts (AUC = 0.60, 0.72, P  = 0.043, 0.009). In survival analysis, our predicted responder patients showed significantly improved survival with a >17 months longer median PFS than the predicted non-responder patients for both ER- and TN subgroups (log-rank test P -value = 0.018 and 0.044). This flexible prediction capability based on single drug biomarkers may allow us to even select new drug combinations most beneficial to individual patients with ER- breast cancer. © 2018 The authors.

Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology.

PubMed

Lei, Chon Lok; Wang, Ken; Clerx, Michael; Johnstone, Ross H; Hortigon-Vinagre, Maria P; Zamora, Victor; Allan, Andrew; Smith, Godfrey L; Gavaghan, David J; Mirams, Gary R; Polonchuk, Liudmila

2017-01-01

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

Ecological and soil hydraulic implications of microbial responses to stress - A modeling analysis

NASA Astrophysics Data System (ADS)

Brangarí, Albert C.; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier; Manzoni, Stefano

2018-06-01

A better understanding of microbial dynamics in porous media may lead to improvements in the design and management of a number of technological applications, ranging from the degradation of contaminants to the optimization of agricultural systems. To this aim, there is a recognized need for predicting the proliferation of soil microbial biomass (often organized in biofilms) under different environments and stresses. We present a general multi-compartment model to account for physiological responses that have been extensively reported in the literature. The model is used as an explorative tool to elucidate the ecological and soil hydraulic consequences of microbial responses, including the production of extracellular polymeric substances (EPS), the induction of cells into dormancy, and the allocation and reuse of resources between biofilm compartments. The mechanistic model is equipped with indicators allowing the microorganisms to monitor environmental and biological factors and react according to the current stress pressures. The feedbacks of biofilm accumulation on the soil water retention are also described. Model runs simulating different degrees of substrate and water shortage show that adaptive responses to the intensity and type of stress provide a clear benefit to microbial colonies. Results also demonstrate that the model may effectively predict qualitative patterns in microbial dynamics supported by empirical evidence, thereby improving our understanding of the effects of pore-scale physiological mechanisms on the soil macroscale phenomena.

Does d-Cycloserine Augmentation of CBT Improve Therapeutic Homework Compliance for Pediatric Obsessive-Compulsive Disorder?

PubMed

Park, Jennifer M; Small, Brent J; Geller, Daniel A; Murphy, Tanya K; Lewin, Adam B; Storch, Eric A

2014-07-01

Clinical studies in adults and children with obsessive-compulsive disorder (OCD) have shown that d-cycloserine (DCS) can improve treatment response by enhancing fear extinction learning during exposure-based psychotherapy. Some have hypothesized that improved treatment response is a function of increased compliance and engagement in therapeutic homework tasks, a core component of behavioral treatment. The present study examined the relationship between DCS augmented cognitive-behavioral therapy (CBT) and homework compliance in a double-blind, placebo controlled trial with 30 youth with OCD. All children received 10 CBT sessions, the last seven of which included exposure and response prevention paired with DCS or placebo dosed 1 h before the session started. Results suggested that DCS augmented CBT did not predict improved homework compliance over the course of treatment, relative to the placebo augmented CBT group. However, when groups were collapsed, homework compliance was directly associated with treatment outcome. These findings suggest that while DCS may not increase homework compliance over time, more generally, homework compliance is an integral part of pediatric OCD treatment outcome.

Does d-Cycloserine Augmentation of CBT Improve Therapeutic Homework Compliance for Pediatric Obsessive–Compulsive Disorder?

PubMed Central

Park, Jennifer M.; Small, Brent J.; Geller, Daniel A.; Murphy, Tanya K.; Lewin, Adam B.; Storch, Eric A.

2014-01-01

Clinical studies in adults and children with obsessive–compulsive disorder (OCD) have shown that d-cycloserine (DCS) can improve treatment response by enhancing fear extinction learning during exposure-based psychotherapy. Some have hypothesized that improved treatment response is a function of increased compliance and engagement in therapeutic homework tasks, a core component of behavioral treatment. The present study examined the relationship between DCS augmented cognitive-behavioral therapy (CBT) and homework compliance in a double-blind, placebo controlled trial with 30 youth with OCD. All children received 10 CBT sessions, the last seven of which included exposure and response prevention paired with DCS or placebo dosed 1 h before the session started. Results suggested that DCS augmented CBT did not predict improved homework compliance over the course of treatment, relative to the placebo augmented CBT group. However, when groups were collapsed, homework compliance was directly associated with treatment outcome. These findings suggest that while DCS may not increase homework compliance over time, more generally, homework compliance is an integral part of pediatric OCD treatment outcome. PMID:24999301

Quantum descriptors for predictive toxicology of halogenated aliphatic hydrocarbons.

PubMed

Trohalaki, S; Pachter, R

2003-04-01

In order to improve Quantitative Structure-Activity Relationships (QSARs) for halogenated aliphatics (HA) and to better understand the biophysical mechanism of toxic response to these ubiquitous chemicals, we employ improved quantum-mechanical descriptors to account for HA electrophilicity. We demonstrate that, unlike the lowest unoccupied molecular orbital energy, ELUMO, which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory. We also show that employing the reciprocal of ELUMO, which is more consistent with frontier molecular orbital (FMO) theory, improves the correlations with in vitro toxicity data. We offer explanations based on FMO theory for a result from our previous work, in which the LUMO energies of HA anions correlated surprisingly well with in vitro toxicity data. Additional descriptors are also suggested and interpreted in terms of the accepted biophysical mechanism of toxic response to HAs and new QSARs are derived for various chemical categories that compose the data set employed. These alternate descriptors provide important insight and could benefit other classes of compounds where the biophysical mechanism of toxic response involves dissociative attachment.

Serial headache drawings by children with migraine: correlation with clinical headache status.

PubMed

Stafstrom, Carl E; Goldenholz, Shira R; Dulli, Douglas A

2005-10-01

Children's artistic self-depictions of headache provide valuable insights into their experience of pain and aid in the diagnostic differentiation of headache types. In a previous study, we compared the clinical diagnosis (gold standard) and artistic diagnosis of headaches in 226 children. In approximately 90% of cases, the drawing predicted the clinical diagnosis of migraine versus nonmigraine headache correctly. In the present study, we explored whether headache drawings correlate with clinical improvement after treatment in children with migraine headaches followed longitudinally. Children seen in the Pediatric Neurology Clinic with the chief complaint of headache were asked to draw a picture of what their headache feels like. On subsequent clinic visits, children with the clinical diagnosis of migraine were asked to draw another picture depicting their current headache. The two drawings were compared to assess whether there was improvement; this "artistic response" was then correlated with the child's clinical status (ie, whether the headaches were improved clinically). One hundred eleven children (66 girls, 45 boys) participated in the study, with a mean interval of 5.3 +/- 2.3 (standard error of the mean) months between the first and second visits. The mean age at the first visit was 11.6 +/- 3.1 years. The raters agreed that serial drawings were both improved or both not improved in 99 of the 111 cases (89%; interrater reliability kappa score of 0.767). Fifty-three children had improvements in their headaches and drawings, 3 children had an improved drawing but no clinical headache improvement, 32 children had no improvement in either their drawing or clinical headaches, and 11 children had improved headaches but no improvement in their drawing. The sensitivity of the drawings for clinical improvement was 0.83, and the specificity was 0.91. The predictive value of an improved headache drawing for an improved clinical response was 0.946. There was no correlation between specific treatment modality and artistic response. We concluded that children's headache drawings are a useful adjunct for the diagnosis of headache type and provide valuable insights into their experience of pain. The present data show that headache drawings can be used longitudinally to provide additional information about the clinical course. The technique is simple, inexpensive, and enjoyable for children and can be applied in a variety of clinical settings.

Strain dependency of the effects of nicotine and mecamylamine in a rat model of attention.

PubMed

Hahn, Britta; Riegger, Katelyn E; Elmer, Greg I

2016-04-01

Processes of attention have a heritable component, suggesting that genetic predispositions may predict variability in the response to attention-enhancing drugs. Among lead compounds with attention-enhancing properties are nicotinic acetylcholine receptor (nAChR) agonists. This study aims to test, by comparing three rat strains, whether genotype may influence the sensitivity to nicotine in the 5-choice serial reaction time task (5-CSRTT), a rodent model of attention. Strains tested were Long Evans (LE), Sprague Dawley (SD), and Wistar rats. The 5-CSRTT requires responses to light stimuli presented randomly in one of five locations. The effect of interest was an increased percentage of responses in the correct location (accuracy), the strongest indicator of improved attention. Nicotine (0.05-0.2 mg/kg s.c.) reduced omission errors and response latency and increased anticipatory responding in all strains. In contrast, nicotine dose-dependently increased accuracy in Wistar rats only. The nAChR antagonist mecamylamine (0.75-3 mg/kg s.c.) increased omissions, slowed responses, and reduced anticipatory responding in all strains. There were no effects on accuracy, which was surprising giving the clear improvement with nicotine in the Wistar group. The findings suggest strain differences in the attention-enhancing effects of nicotine, which would indicate that genetic predispositions predict variability in the efficacy of nAChR compounds for enhancing attention. The absence of effect of mecamylamine on response accuracy may suggest a contribution of nAChR desensitization to the attention-enhancing effects of nicotine.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling.

PubMed

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha; Thompson, Jill; Zimmerman, Jess K; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate. © 2017 John Wiley & Sons Ltd.

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

USGS Publications Warehouse

Feng, Xiaohui; Uriarte, María; González, Grizelle; Reed, Sasha C.; Thompson, Jill; Zimmerman, Jess K.; Murphy, Lora

2018-01-01

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

PubMed

Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

2016-08-01

Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is <0.5 from the observed response. The effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value for patients undergoing procedures with varying degrees of stimulation.

Subjective response as a consideration in the pharmacogenetics of alcoholism treatment.

PubMed

Roche, Daniel Jo; Ray, Lara A

2015-01-01

Currently available pharmacological treatments for alcoholism have modest efficacy and high individual variability in treatment outcomes, both of which have been partially attributed to genetic factors. One path to reducing the variability and improving the efficacy associated with these pharmacotherapies may be to identify overlapping genetic contributions to individual differences in both subjective responses to alcohol and alcoholism pharmacotherapy outcomes. As acute subjective response to alcohol is highly predictive of future alcohol related problems, identifying such shared genetic mechanisms may inform the development of personalized treatments that can effectively target converging pathophysiological mechanisms that convey risk for alcoholism. The focus of this review is to revisit the association between subjective response to alcohol and the etiology of alcoholism while also describing genetic contributions to this relationship, discuss potential pharmacogenetic approaches to target subjective response to alcohol in order to improve the treatment of alcoholism and examine conceptual and methodological issues associated with these topics, and outline future approaches to overcome these challenges.

Exploring the Use of Molecular Biomarkers for Precision Medicine in Age-Related Macular Degeneration.

PubMed

Lorés-Motta, Laura; de Jong, Eiko K; den Hollander, Anneke I

2018-06-01

Precision medicine aims to improve patient care by adjusting medication to each patient's individual needs. Age-related macular degeneration (AMD) is a heterogeneous eye disease in which several pathways are involved, and the risk factors driving the disease differ per patient. As a consequence, precision medicine holds promise for improved management of this disease, which is nowadays a main cause of vision loss in the elderly. In this review, we provide an overview of the studies that have evaluated the use of molecular biomarkers to predict response to treatment in AMD. We predominantly focus on genetic biomarkers, but also include studies that examined circulating or eye fluid biomarkers in treatment response. This involves studies on treatment response to dietary supplements, response to anti-vascular endothelial growth factor, and response to complement inhibitors. In addition, we highlight promising new therapies that have been or are currently being tested in clinical trials and discuss the molecular studies that can help identify the most suitable patients for these upcoming therapeutic approaches.

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

Thomas, Minta; De Brabanter, Kris; Suykens, Johan A K; De Moor, Bart

2014-12-31

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Improving urban wind flow predictions through data assimilation

NASA Astrophysics Data System (ADS)

Sousa, Jorge; Gorle, Catherine

2017-11-01

Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

Predicting elastic properties of β-HMX from first-principles calculations.

PubMed

Peng, Qing; Rahul; Wang, Guangyu; Liu, Gui-Rong; Grimme, Stefan; De, Suvranu

2015-05-07

We investigate the performance of van der Waals (vdW) functions in predicting the elastic constants of β cyclotetramethylene tetranitramine (HMX) energetic molecular crystals using density functional theory (DFT) calculations. We confirm that the accuracy of the elastic constants is significantly improved using the vdW corrections with environment-dependent C6 together with PBE and revised PBE exchange-correlation functionals. The elastic constants obtained using PBE-D3(0) calculations yield the most accurate mechanical response of β-HMX when compared with experimental stress-strain data. Our results suggest that PBE-D3 calculations are reliable in predicting the elastic constants of this material.

Regional analysis of drought and heat impacts on forests: current and future science directions.

PubMed

Law, Beverly E

2014-12-01

Accurate assessments of forest response to current and future climate and human actions are needed at regional scales. Predicting future impacts on forests will require improved analysis of species-level adaptation, resilience, and vulnerability to mortality. Land system models can be enhanced by creating trait-based groupings of species that better represent climate sensitivity, such as risk of hydraulic failure from drought. This emphasizes the need for more coordinated in situ and remote sensing observations to track changes in ecosystem function, and to improve model inputs, spatio-temporal diagnosis, and predictions of future conditions, including implications of actions to mitigate climate change. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Rational Design of Mouse Models for Cancer Research.

PubMed

Landgraf, Marietta; McGovern, Jacqui A; Friedl, Peter; Hutmacher, Dietmar W

2018-03-01

The laboratory mouse is widely considered as a valid and affordable model organism to study human disease. Attempts to improve the relevance of murine models for the investigation of human pathologies led to the development of various genetically engineered, xenograft and humanized mouse models. Nevertheless, most preclinical studies in mice suffer from insufficient predictive value when compared with cancer biology and therapy response of human patients. We propose an innovative strategy to improve the predictive power of preclinical cancer models. Combining (i) genomic, tissue engineering and regenerative medicine approaches for rational design of mouse models with (ii) rapid prototyping and computational benchmarking against human clinical data will enable fast and nonbiased validation of newly generated models. Copyright © 2017 Elsevier Ltd. All rights reserved.

The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

NASA Technical Reports Server (NTRS)

House, P. R.; Lapenta, W.; Schiffer, R.

2008-01-01

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).

Understanding reduced rotavirus vaccine efficacy in low socio-economic settings.

PubMed

Lopman, Benjamin A; Pitzer, Virginia E; Sarkar, Rajiv; Gladstone, Beryl; Patel, Manish; Glasser, John; Gambhir, Manoj; Atchison, Christina; Grenfell, Bryan T; Edmunds, W John; Kang, Gagandeep; Parashar, Umesh D

2012-01-01

Rotavirus vaccine efficacy ranges from >90% in high socio-economic settings (SES) to 50% in low SES. With the imminent introduction of rotavirus vaccine in low SES countries, understanding reasons for reduced efficacy in these settings could identify strategies to improve vaccine performance. We developed a mathematical model to predict rotavirus vaccine efficacy in high, middle and low SES based on data specific for each setting on incidence, protection conferred by natural infection and immune response to vaccination. We then examined factors affecting efficacy. Vaccination was predicted to prevent 93%, 86% and 51% of severe rotavirus gastroenteritis in high, middle and low SES, respectively. Also predicted was that vaccines are most effective against severe disease and efficacy declines with age in low but not high SES. Reduced immunogenicity of vaccination and reduced protection conferred by natural infection are the main factors that compromise efficacy in low SES. The continued risk of severe disease in non-primary natural infections in low SES is a key factor underpinning reduced efficacy of rotavirus vaccines. Predicted efficacy was remarkably consistent with observed clinical trial results from different SES, validating the model. The phenomenon of reduced vaccine efficacy can be predicted by intrinsic immunological and epidemiological factors of low SES populations. Modifying aspects of the vaccine (e.g. improving immunogenicity in low SES) and vaccination program (e.g. additional doses) may bring improvements.

Prediction of cognitive outcome based on the progression of auditory discrimination during coma.

PubMed

Juan, Elsa; De Lucia, Marzia; Tzovara, Athina; Beaud, Valérie; Oddo, Mauro; Clarke, Stephanie; Rossetti, Andrea O

2016-09-01

To date, no clinical test is able to predict cognitive and functional outcome of cardiac arrest survivors. Improvement of auditory discrimination in acute coma indicates survival with high specificity. Whether the degree of this improvement is indicative of recovery remains unknown. Here we investigated if progression of auditory discrimination can predict cognitive and functional outcome. We prospectively recorded electroencephalography responses to auditory stimuli of post-anoxic comatose patients on the first and second day after admission. For each recording, auditory discrimination was quantified and its evolution over the two recordings was used to classify survivors as "predicted" when it increased vs. "other" if not. Cognitive functions were tested on awakening and functional outcome was assessed at 3 months using the Cerebral Performance Categories (CPC) scale. Thirty-two patients were included, 14 "predicted survivors" and 18 "other survivors". "Predicted survivors" were more likely to recover basic cognitive functions shortly after awakening (ability to follow a standardized neuropsychological battery: 86% vs. 44%; p=0.03 (Fisher)) and to show a very good functional outcome at 3 months (CPC 1: 86% vs. 33%; p=0.004 (Fisher)). Moreover, progression of auditory discrimination during coma was strongly correlated with cognitive performance on awakening (phonemic verbal fluency: rs=0.48; p=0.009 (Spearman)). Progression of auditory discrimination during coma provides early indication of future recovery of cognitive functions. The degree of improvement is informative of the degree of functional impairment. If confirmed in a larger cohort, this test would be the first to predict detailed outcome at the single-patient level. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis.

PubMed

Haagen, Joris F G; Ter Heide, F Jackie June; Mooren, Trudy M; Knipscheer, Jeroen W; Kleber, Rolf J

2017-03-01

Given the recent peak in refugee numbers and refugees' high odds of developing post-traumatic stress disorder (PTSD), finding ways to alleviate PTSD in refugees is of vital importance. However, there are major differences in PTSD treatment response between refugees, the determinants of which are largely unknown. This study aimed at improving PTSD treatment for adult refugees by identifying PTSD treatment response predictors. A prospective longitudinal multilevel modelling design was used to predict PTSD severity scores over time. We analysed data from a randomized controlled trial with pre-, post-, and follow-up measurements of the safety and efficacy of eye movement desensitization and reprocessing and stabilization in asylum seekers and refugees suffering from PTSD. Lack of refugee status, comorbid depression, demographic, trauma-related and treatment-related variables were analysed as potential predictors of PTSD treatment outcome. Treatment outcome data from 72 participants were used. The presence (B = 6.5, p = .03) and severity (B = 6.3, p < .01) of a pre-treatment depressive disorder predicted poor treatment response and explained 39% of the variance between individuals. Refugee patients who suffer from PTSD and severe comorbid depression benefit less from treatment aimed at alleviating PTSD. Results highlight the need for treatment adaptations for PTSD and comorbid severe depression in traumatized refugees, including testing whether initial targeting of severe depressive symptoms increases PTSD treatment effectiveness. There are differences in post-traumatic stress disorder (PTSD) treatment response between traumatized refugees. Comorbid depressive disorder and depression severity predict poor PTSD response. Refugees with PTSD and severe depression may not benefit from PTSD treatment. Targeting comorbid severe depression before PTSD treatment is warranted. This study did not correct for multiple hypothesis testing. Comorbid depression may differentially impact alternative PTSD treatments. © 2016 The British Psychological Society.

Tumor response ratio predicts overall survival in breast cancer patients treated with neoadjuvant chemotherapy.

PubMed

Miller, Marian; Ottesen, Rebecca A; Niland, Joyce C; Kruper, Laura; Chen, Steven L; Vito, Courtney

2014-10-01

Neoadjuvant chemotherapy (NAC) is commonly used to treat locally advanced breast cancer. Pathologic complete response (pCR) predicts improved overall survival (OS); however, prognosis of patients with partial response remains unclear. We evaluated whether tumor response ratio (TRR) is a better predictor of OS than current staging methods. Using the National Comprehensive Cancer Network Breast Cancer Outcomes Database, we identified patients with stage I-III breast cancer who had NAC and pretreatment imaging at City of Hope (1997-2010). Patient demographics, tumor characteristics, and OS were analyzed. TRR was calculated as residual in-breast disease divided by size on pre-NAC imaging. Four TRR groups were stratified; TRR 0 (pCR), TRR > 0-0.4 (strong partial response, SPR), TRR > 0.4-1.0 (weak partial response, WPR), or TRR > 1.0 (tumor growth, TG). OS was estimated by the Kaplan-Meier method and tested by the log-rank test. Cox regression was performed to evaluate associations between OS and TRR in a multivariable analysis while controlling for potential confounders. There were 218 eligible patients identified; 59 (27 %) had pCR, 61 (28 %) SPR, 72 (33 %) WPR, and 26 (12 %) TG. Five-year OS decreased continuously with increasing TRR:pCR (90 %), SPR (79 %), WPR (66 %), and TG (60 %). TRR was the only measure that significantly predicted OS (p = 0.0035); pathologic stage (p = 0.23) and pre-NAC clinical tumor stage (cT) (p = 0.87) were not significant. TRR continued to be statistically significant by multivariable analysis (p = 0.016). TRR takes into account both pretreatment and residual disease and more accurately predicts OS than pathologic stage and pre-NAC cT. TRR may be useful to more accurately assess prognosis and OS in breast cancer patients undergoing NAC.

Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information.

PubMed Central

Lamers, L M

1999-01-01

OBJECTIVE: To evaluate the predictive accuracy of the Diagnostic Cost Group (DCG) model using health survey information. DATA SOURCES/STUDY SETTING: Longitudinal data collected for a sample of members of a Dutch sickness fund. In the Netherlands the sickness funds provide compulsory health insurance coverage for the 60 percent of the population in the lowest income brackets. STUDY DESIGN: A demographic model and DCG capitation models are estimated by means of ordinary least squares, with an individual's annual healthcare expenditures in 1994 as the dependent variable. For subgroups based on health survey information, costs predicted by the models are compared with actual costs. Using stepwise regression procedures a subset of relevant survey variables that could improve the predictive accuracy of the three-year DCG model was identified. Capitation models were extended with these variables. DATA COLLECTION/EXTRACTION METHODS: For the empirical analysis, panel data of sickness fund members were used that contained demographic information, annual healthcare expenditures, and diagnostic information from hospitalizations for each member. In 1993, a mailed health survey was conducted among a random sample of 15,000 persons in the panel data set, with a 70 percent response rate. PRINCIPAL FINDINGS: The predictive accuracy of the demographic model improves when it is extended with diagnostic information from prior hospitalizations (DCGs). A subset of survey variables further improves the predictive accuracy of the DCG capitation models. The predictable profits and losses based on survey information for the DCG models are smaller than for the demographic model. Most persons with predictable losses based on health survey information were not hospitalized in the preceding year. CONCLUSIONS: The use of diagnostic information from prior hospitalizations is a promising option for improving the demographic capitation payment formula. This study suggests that diagnostic information from outpatient utilization is complementary to DCGs in predicting future costs. PMID:10029506

The effectiveness of research-based physics learning module with predict-observe-explain strategies to improve the student’s competence

NASA Astrophysics Data System (ADS)

Usmeldi

2018-05-01

The preliminary study shows that many students are difficult to master the concept of physics. There are still many students who have not mastery learning physics. Teachers and students still use textbooks. Students rarely do experiments in the laboratory. One model of learning that can improve students’ competence is a research-based learning with Predict- Observe-Explain (POE) strategies. To implement this learning, research-based physics learning modules with POE strategy are used. The research aims to find out the effectiveness of implementation of research-based physics learning modules with POE strategy to improving the students’ competence. The research used a quasi-experimental with pretest-posttest group control design. Data were collected using observation sheets, achievement test, skill assessment sheets, questionnaire of attitude and student responses to learning implementation. The results of research showed that research-based physics learning modules with POE strategy was effective to improve the students’ competence, in the case of (1) mastery learning of physics has been achieved by majority of students, (2) improving the students competency of experimental class including high category, (3) there is a significant difference between the average score of students’ competence of experimental class and the control class, (4) the average score of the students competency of experimental class is higher than the control class, (5) the average score of the students’ responses to the learning implementation is very good category, this means that most students can implement research-based learning with POE strategies.

Clinical Decision Support Alert Appropriateness: A Review and Proposal for Improvement

PubMed Central

McCoy, Allison B.; Thomas, Eric J.; Krousel-Wood, Marie; Sittig, Dean F.

2014-01-01

Background Many healthcare providers are adopting clinical decision support (CDS) systems to improve patient safety and meet meaningful use requirements. Computerized alerts that prompt clinicians about drug-allergy, drug-drug, and drug-disease warnings or provide dosing guidance are most commonly implemented. Alert overrides, which occur when clinicians do not follow the guidance presented by the alert, can hinder improved patient outcomes. Methods We present a review of CDS alerts and describe a proposal to develop novel methods for evaluating and improving CDS alerts that builds upon traditional informatics approaches. Our proposal incorporates previously described models for predicting alert overrides that utilize retrospective chart review to determine which alerts are clinically relevant and which overrides are justifiable. Results Despite increasing implementations of CDS alerts, detailed evaluations rarely occur because of the extensive labor involved in manual chart reviews to determine alert and response appropriateness. Further, most studies have solely evaluated alert overrides that are appropriate or justifiable. Our proposal expands the use of web-based monitoring tools with an interactive dashboard for evaluating CDS alert and response appropriateness that incorporates the predictive models. The dashboard provides 2 views, an alert detail view and a patient detail view, to provide a full history of alerts and help put the patient's events in context. Conclusion The proposed research introduces several innovations to address the challenges and gaps in alert evaluations. This research can transform alert evaluation processes across healthcare settings, leading to improved CDS, reduced alert fatigue, and increased patient safety. PMID:24940129

Let's Walk Outdoors! Self-Paced Walking Outdoors Improves Future Intention to Exercise in Women With Obesity.

PubMed

Krinski, Kleverton; Machado, Daniel G S; Lirani, Luciana S; DaSilva, Sergio G; Costa, Eduardo C; Hardcastle, Sarah J; Elsangedy, Hassan M

2017-04-01

In order to examine whether environmental settings influence psychological and physiological responses of women with obesity during self-paced walking, 38 women performed two exercise sessions (treadmill and outdoors) for 30 min, where oxygen uptake, heart rate, ratings of perceived exertion, affect, attentional focus, enjoyment, and future intentions to walk were analyzed. Physiological responses were similar during both sessions. However, during outdoor exercise, participants displayed higher externally focused attention, positive affect, and lower ratings of perceived exertion, followed by greater enjoyment and future intention to participate in outdoor walking. The more externally focused attention predicted greater future intentions to participate in walking. Therefore, women with obesity self-selected an appropriate exercise intensity to improve fitness and health in both environmental settings. Also, self-paced outdoor walking presented improved psychological responses. Health care professionals should consider promoting outdoor forms of exercise to maximize psychological benefits and promote long-term adherence to a physically active lifestyle.

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.

Nonlinear engine model for idle speed control

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

Livshiz, M.; Sanvido, D.J.; Stiles, S.D.

1994-12-31

This paper describes a nonlinear model of an engine used for the design of idle speed control and prediction in a broad range of idle speeds and operational conditions. Idle speed control systems make use of both spark advance and the idle air actuator to control engine speed for improved response relative to variations in the target idle speed due to load disturbances. The control system at idle can be presented by a multiple input multiple output (MIMO) nonlinear model. Information of nonlinearities helps to improve performance of the system over the whole range of engine speeds. A proposed simplemore » nonlinear model of the engine at idle was applied for design of optimal controllers and predictors for improved steady state, load rejection and transition from and to idle. This paper describes vehicle results of engine speed prediction based on the described model.« less

Measuring populations to improve vaccination coverage

NASA Astrophysics Data System (ADS)

Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

2016-10-01

In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.

Measuring populations to improve vaccination coverage

PubMed Central

Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.

2016-01-01

In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes. PMID:27703191

Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling

PubMed Central

Traina, Tiffany A.; Dugan, Ute; Higgins, Brian; Kolinsky, Kenneth; Theodoulou, Maria; Hudis, Clifford A.; Norton, Larry

2011-01-01

Background To hasten and improve anticancer drug development, we created a novel approach to generating and analyzing preclinical dose-scheduling data so as to optimize benefit-to-toxicity ratios. Methods We applied mathematical methods based upon Norton-Simon growth kinetic modeling to tumor-volume data from breast cancer xenografts treated with capecitabine (Xeloda®, Roche) at the conventional schedule of 14 days of treatment followed by a 7-day rest (14 - 7). Results The model predicted that 7 days of treatment followed by a 7-day rest (7 - 7) would be superior. Subsequent preclinical studies demonstrated that this biweekly capecitabine schedule allowed for safe delivery of higher daily doses, improved tumor response, and prolonged animal survival. Conclusions We demonstrated that the application of Norton-Simon modeling to the design and analysis of preclinical data predicts an improved capecitabine dosing schedule in xenograft models. This method warrants further investigation and application in clinical drug development. PMID:20519801

Wall Thickness, Pulmonary Hypertension, and Diastolic Filling Abnormalities Predict Response to Postoperative Biventricular Pacing

PubMed Central

Brusen, Robin M.; Hahn, Rebecca; Cabreriza, Santos E.; Cheng, Bin; Wang, Daniel Y.; Truong, Wanda; Spotnitz, Henry M.

2017-01-01

Objective Post-cardiopulmonary bypass biventricular pacing improves hemodynamics but without clearly defined predictors of response. Based on preclinical studies and prior observations, it was suspected that diastolic dysfunction or pulmonary hypertension is predictive of hemodynamic benefit. Design Randomized controlled study of temporary biventricular pacing after cardiopulmonary bypass. Setting Single-center study at university-affiliated tertiary care hospital. Interventions Patients who underwent bypass with pre-operative ejection fraction ≤40% and QRS duration ≥100 ms or double-valve surgery were enrolled. At 3 time points between separation from bypass and postoperative day 1, pacing delays were varied to optimize hemodynamics. Participants Data from 43 patients were analyzed. Measurements and Main Results Cardiac output and arterial pressure were measured under no pacing, atrial pacing, and biventricular pacing. Preoperative echocardiograms and pulmonary artery catheterizations were reviewed, and measures of both systolic and diastolic function were compared to hemodynamic response. Early after separation, improvement in cardiac output was positively correlated with pulmonary vascular resistance (R2 = 0.97, p < 0.001), ventricle wall thickness (R2 = 0.72, p = 0.002)), and E/e′, a measure of abnormal diastolic ventricular filling velocity (R2 = 0.56, p = 0.04). Similar trends were seen with mean arterial pressure. QRS duration and ejection fraction did not correlate significantly with improvements in hemodynamics. Conclusions There may be an effect of biventricular pacing related to amelioration of abnormal diastolic filling patterns rather than electrical resynchronization in the postoperative state. PMID:25998068

Improving the detection of tectonic transients in Japan by accounting for Earth's deformation response to surface mass loading

NASA Astrophysics Data System (ADS)

Martens, H. R.; Simons, M.; Moore, A. W.; Owen, S. E.; Rivera, L. A.

2016-12-01

We explore the contributions of oceanic, atmospheric, and hydrologic mass loading to Global Navigation Satellite System (GNSS)-inferred observations of surface displacements in Japan. Surface mass loading (SML) generates mm- to cm-level deformation of the solid Earth on time scales of hours to years, which exceeds the measurement uncertainties of most GNSS position estimates. By improving the efficiency and accuracy of the prediction and empirical estimation of SML response, we aim to reduce the variance of GNSS time series and therefore enhance the ability to resolve subtle tectonic signals, such as aseismic transients associated with subduction zone processes. Using the GIPSY software in precise point positioning mode, we estimate time series of sub-daily receiver positions for the GNSS Earth Observation Network System (GEONET) in Japan. We also model the Earth's elastic deformation response to a variety of surface mass loads, including loads of atmospheric (e.g., ECMWF) and oceanic (e.g., TPXO8-Atlas, ECCO2) origin. We extract periodic signals, such as the ocean tides and seasonal variations in hydrological loading, using harmonic analysis. Deformation caused by non-periodic loads, such as non-tidal oceanic and atmospheric loads, can be predicted and removed to further reduce the variance. We seek to streamline the workflow for estimating SML-induced surface displacements from a variety of sources in order to account for loading signals in routine GNSS data processing, thereby improving the ability to assess the mechanics of plate boundaries.

Learning Temporal Statistics for Sensory Predictions in Aging.

PubMed

Luft, Caroline Di Bernardi; Baker, Rosalind; Goldstone, Aimee; Zhang, Yang; Kourtzi, Zoe

2016-03-01

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Does Response to Induction Chemotherapy Predict Survival for Locally Advanced Non-Small-Cell Lung Cancer? Secondary Analysis of RTOG 8804/8808

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

McAleer, Mary Frances, E-mail: mfmcalee@mdanderson.or; Moughan, Jennifer M.S.; Byhardt, Roger W.

2010-03-01

Purpose: Induction chemotherapy (ICT) improves survival compared with radiotherapy (RT) alone in locally advanced non-small-cell lung cancer (LANSCLC) patients with good prognostic factors. Concurrent chemoradiotherapy (CCRT) is superior to ICT followed by RT. The question arises whether ICT response predicts the outcome of patients subsequently treated with CCRT or RT. Methods and Materials: Between 1988 and 1992, 194 LANSCLC patients were treated prospectively with ICT (two cycles of vinblastine and cisplatin) and then CCRT (cisplatin plus 63 Gy for 7 weeks) in the Radiation Therapy Oncology Group 8804 trial (n = 30) or ICT and then RT (60 Gy/6 wk)more » on Radiation Therapy Oncology Group 8808 trial (n = 164). Of the 194 patients, 183 were evaluable and 141 had undergone a postinduction assessment. The overall survival (OS) of those with complete remission (CR) or partial remission (PR) was compared with that of patients with stable disease (SD) or progressive disease (PD) after ICT. Results: Of the 141 patients, 6, 30, 99, and 6 had CR, PR, SD, and PD, respectively. The log-rank test showed a significant difference (p <0.0001) in OS when the response groups were compared (CR/PR vs. SD/PD). On univariate and multivariate analyses, a trend was seen toward a response to ICT with OS (p = 0.097 and p = 0.06, respectively). A squamous histologic type was associated with worse OS on univariate and multivariate analyses (p = 0.031 and p = 0.018, respectively). SD/PD plus a squamous histologic type had a hazard ratio of 2.25 vs. CR/PR plus a nonsquamous histologic type (p = 0.007) on covariate analysis. Conclusion: The response to ICT was associated with a significant survival difference when the response groups were compared. A response to ICT showed a trend toward, but was not predictive of, improved OS in LANSCLC patients. Patients with SD/PD after ICT and a squamous histologic type had the poorest OS. These data suggest that patients with squamous LANSCLC might benefit from immediate RT or CCRT.« less

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)

Predictors of response to anti-tumor necrosis factor therapy in ulcerative colitis.

PubMed

Zampeli, Evanthia; Gizis, Michalis; Siakavellas, Spyros I; Bamias, Giorgos

2014-08-15

Ulcerative colitis (UC) is an immune-mediated, chronic inflammatory disease of the large intestine. Its course is characterized by flares of acute inflammation and periods of low-grade chronic inflammatory activity or remission. Monoclonal antibodies against tumor necrosis factor (anti-TNF) are part of the therapeutic armamentarium and are used in cases of moderate to severe UC that is refractory to conventional treatment with corticosteroids and/or immunosuppressants. Therapeutic response to these agents is not uniform and a large percentage of patients either fail to improve (primary non-response) or lose response after a period of improvement (secondary non-response/loss of response). In addition, the use of anti-TNF agents has been related to uncommon but potentially serious adverse effects that preclude their administration or lead to their discontinuation. Finally, use of these medications is associated with a considerable cost for the health system. The identification of parameters that may predict response to anti-TNF drugs in UC would help to better select for patients with a high probability to respond and minimize risk and costs for those who will not respond. Analysis of the major clinical trials and the accumulated experience with the use of anti-TNF drugs in UC has resulted to the report of such prognostic factors. Included are clinical and epidemiological characteristics, laboratory markers, endoscopic indicators and molecular (immunological/genetic) signatures. Such predictive parameters of long-term outcomes may either be present at the commencement of treatment or determined during the early period of therapy. Validation of these prognostic markers in large cohorts of patients with variable characteristics will facilitate their introduction into clinical practice and the best selection of UC patients who will benefit from anti-TNF therapy.

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

Temporal plasticity in auditory cortex improves neural discrimination of speech sounds

PubMed Central

Engineer, Crystal T.; Shetake, Jai A.; Engineer, Navzer D.; Vrana, Will A.; Wolf, Jordan T.; Kilgard, Michael P.

2017-01-01

Background Many individuals with language learning impairments exhibit temporal processing deficits and degraded neural responses to speech sounds. Auditory training can improve both the neural and behavioral deficits, though significant deficits remain. Recent evidence suggests that vagus nerve stimulation (VNS) paired with rehabilitative therapies enhances both cortical plasticity and recovery of normal function. Objective/Hypothesis We predicted that pairing VNS with rapid tone trains would enhance the primary auditory cortex (A1) response to unpaired novel speech sounds. Methods VNS was paired with tone trains 300 times per day for 20 days in adult rats. Responses to isolated speech sounds, compressed speech sounds, word sequences, and compressed word sequences were recorded in A1 following the completion of VNS-tone train pairing. Results Pairing VNS with rapid tone trains resulted in stronger, faster, and more discriminable A1 responses to speech sounds presented at conversational rates. Conclusion This study extends previous findings by documenting that VNS paired with rapid tone trains altered the neural response to novel unpaired speech sounds. Future studies are necessary to determine whether pairing VNS with appropriate auditory stimuli could potentially be used to improve both neural responses to speech sounds and speech perception in individuals with receptive language disorders. PMID:28131520

Modulation of Limbic and Prefrontal Connectivity by Electroconvulsive Therapy in Treatment-resistant Depression: A Preliminary Study.

PubMed

Cano, Marta; Cardoner, Narcís; Urretavizcaya, Mikel; Martínez-Zalacaín, Ignacio; Goldberg, Ximena; Via, Esther; Contreras-Rodríguez, Oren; Camprodon, Joan; de Arriba-Arnau, Aida; Hernández-Ribas, Rosa; Pujol, Jesús; Soriano-Mas, Carles; Menchón, José M

2016-01-01

Although current models of depression suggest that a sequential modulation of limbic and prefrontal connectivity is needed for illness recovery, neuroimaging studies of electroconvulsive therapy (ECT) have focused on assessing functional connectivity (FC) before and after an ECT course, without characterizing functional changes occurring at early treatment phases. To assess sequential changes in limbic and prefrontal FC during the course of ECT and their impact on clinical response. Longitudinal intralimbic and limbic-prefrontal networks connectivity study. We assessed 15 patients with treatment-resistant depression at four different time-points throughout the entire course of an ECT protocol and 10 healthy participants at two functional neuroimaging examinations. Furthermore, a path analysis to test direct and indirect predictive effects of limbic and prefrontal FC changes on clinical response measured with the Hamilton Rating Scale for Depression was also performed. An early significant intralimbic FC decrease significantly predicted a later increase in limbic-prefrontal FC, which in turn significantly predicted clinical improvement at the end of an ECT course. Our data support that treatment response involves sequential changes in FC within regions of the intralimbic and limbic-prefrontal networks. This approach may help in identifying potential early biomarkers of treatment response. Copyright © 2015 Elsevier Inc. All rights reserved.

Do we have biomarkers to predict response to neoadjuvant and adjuvant chemotherapy and immunotherapy in bladder cancer?

PubMed Central

Wezel, Felix; Vallo, Stefan

2017-01-01

Radical cystectomy (RC) is the standard of care treatment of localized muscle-invasive bladder cancer (BC). However, about 50% of patients develop metastases within 2 years after cystectomy. Neoadjuvant cisplatin-based chemotherapy before cystectomy improves the overall survival (OS) in patients with muscle-invasive BC. Pathological response to neoadjuvant treatment is a strong predictor of better disease-specific survival. Nevertheless, some patients do not benefit from chemotherapy. The identification of reliable biomarkers enabling clinicians to identify patients who might benefit from chemotherapy is a very important clinical task. An identification tool could lead to individualized therapy, optimizing response rates. In addition, unnecessary treatment with chemotherapy which potentially leads to a loss of quality of life and which might also might cause a delay of cystectomy in a neoadjuvant setting could be avoided. The present review aims to summarize and discuss the current literature on biomarkers for the prediction of response to systemic therapy in muscle-invasive BC. Tremendous efforts in genetic and molecular characterization have led to the identification of predictive candidate biomarkers in urothelial carcinoma (UC), although prospective validation is pending. Ongoing clinical trials examining the benefit of individual therapies in UC of the bladder (UCB) by molecular patient selection hold promise to shed light on this question. PMID:29354494

Pharmacokinetic optimization of class-selective histone deacetylase inhibitors and identification of associated candidate predictive biomarkers of hepatocellular carcinoma tumor response.

PubMed

Wong, Jason C; Tang, Guozhi; Wu, Xihan; Liang, Chungen; Zhang, Zhenshan; Guo, Lei; Peng, Zhenghong; Zhang, Weixing; Lin, Xianfeng; Wang, Zhanguo; Mei, Jianghua; Chen, Junli; Pan, Song; Zhang, Nan; Liu, Yongfu; Zhou, Mingwei; Feng, Lichun; Zhao, Weili; Li, Shijie; Zhang, Chao; Zhang, Meifang; Rong, Yiping; Jin, Tai-Guang; Zhang, Xiongwen; Ren, Shuang; Ji, Ying; Zhao, Rong; She, Jin; Ren, Yi; Xu, Chunping; Chen, Dawei; Cai, Jie; Shan, Song; Pan, Desi; Ning, Zhiqiang; Lu, Xianping; Chen, Taiping; He, Yun; Chen, Li

2012-10-25

Herein, we describe the pharmacokinetic optimization of a series of class-selective histone deacetylase (HDAC) inhibitors and the subsequent identification of candidate predictive biomarkers of hepatocellular carcinoma (HCC) tumor response for our clinical lead using patient-derived HCC tumor xenograft models. Through a combination of conformational constraint and scaffold hopping, we lowered the in vivo clearance (CL) and significantly improved the bioavailability (F) and exposure (AUC) of our HDAC inhibitors while maintaining selectivity toward the class I HDAC family with particular potency against HDAC1, resulting in clinical lead 5 (HDAC1 IC₅₀ = 60 nM, mouse CL = 39 mL/min/kg, mouse F = 100%, mouse AUC after single oral dose at 10 mg/kg = 6316 h·ng/mL). We then evaluated 5 in a biomarker discovery pilot study using patient-derived tumor xenograft models, wherein two out of the three models responded to treatment. By comparing tumor response status to compound tumor exposure, induction of acetylated histone H3, candidate gene expression changes, and promoter DNA methylation status from all three models at various time points, we identified preliminary candidate response prediction biomarkers that warrant further validation in a larger cohort of patient-derived tumor models and through confirmatory functional studies.

Local air gap thickness and contact area models for realistic simulation of human thermo-physiological response

NASA Astrophysics Data System (ADS)

Psikuta, Agnes; Mert, Emel; Annaheim, Simon; Rossi, René M.

2018-02-01

To evaluate the quality of new energy-saving and performance-supporting building and urban settings, the thermal sensation and comfort models are often used. The accuracy of these models is related to accurate prediction of the human thermo-physiological response that, in turn, is highly sensitive to the local effect of clothing. This study aimed at the development of an empirical regression model of the air gap thickness and the contact area in clothing to accurately simulate human thermal and perceptual response. The statistical model predicted reliably both parameters for 14 body regions based on the clothing ease allowances. The effect of the standard error in air gap prediction on the thermo-physiological response was lower than the differences between healthy humans. It was demonstrated that currently used assumptions and methods for determination of the air gap thickness can produce a substantial error for all global, mean, and local physiological parameters, and hence, lead to false estimation of the resultant physiological state of the human body, thermal sensation, and comfort. Thus, this model may help researchers to strive for improvement of human thermal comfort, health, productivity, safety, and overall sense of well-being with simultaneous reduction of energy consumption and costs in built environment.

Tidal Response of Preliminary Jupiter Model

NASA Astrophysics Data System (ADS)

Wahl, Sean M.; Hubbard, William B.; Militzer, Burkhard

2016-11-01

In anticipation of improved observational data for Jupiter’s gravitational field, from the Juno spacecraft, we predict the static tidal response for a variety of Jupiter interior models based on ab initio computer simulations of hydrogen-helium mixtures. We calculate hydrostatic-equilibrium gravity terms, using the non-perturbative concentric Maclaurin Spheroid method that eliminates lengthy expansions used in the theory of figures. Our method captures terms arising from the coupled tidal and rotational perturbations, which we find to be important for a rapidly rotating planet like Jupiter. Our predicted static tidal Love number, {k}2=0.5900, is ˜10% larger than previous estimates. The value is, as expected, highly correlated with the zonal harmonic coefficient J 2, and is thus nearly constant when plausible changes are made to the interior structure while holding J 2 fixed at the observed value. We note that the predicted static k 2 might change, due to Jupiter’s dynamical response to the Galilean moons, and find reasons to argue that the change may be detectable—although we do not present here a theory of dynamical tides for highly oblate Jovian planets. An accurate model of Jupiter’s tidal response will be essential for interpreting Juno observations and identifying tidal signals from effects of other interior dynamics of Jupiter’s gravitational field.

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.

Advanced Subsonic Technology (AST) Area of Interest (AOI) 6: Develop and Validate Aeroelastic Codes for Turbomachinery

NASA Technical Reports Server (NTRS)

Gardner, Kevin D.; Liu, Jong-Shang; Murthy, Durbha V.; Kruse, Marlin J.; James, Darrell

1999-01-01

AlliedSignal Engines, in cooperation with NASA GRC (National Aeronautics and Space Administration Glenn Research Center), completed an evaluation of recently-developed aeroelastic computer codes using test cases from the AlliedSignal Engines fan blisk and turbine databases. Test data included strain gage, performance, and steady-state pressure information obtained for conditions where synchronous or flutter vibratory conditions were found to occur. Aeroelastic codes evaluated included quasi 3-D UNSFLO (MIT Developed/AE Modified, Quasi 3-D Aeroelastic Computer Code), 2-D FREPS (NASA-Developed Forced Response Prediction System Aeroelastic Computer Code), and 3-D TURBO-AE (NASA/Mississippi State University Developed 3-D Aeroelastic Computer Code). Unsteady pressure predictions for the turbine test case were used to evaluate the forced response prediction capabilities of each of the three aeroelastic codes. Additionally, one of the fan flutter cases was evaluated using TURBO-AE. The UNSFLO and FREPS evaluation predictions showed good agreement with the experimental test data trends, but quantitative improvements are needed. UNSFLO over-predicted turbine blade response reductions, while FREPS under-predicted them. The inviscid TURBO-AE turbine analysis predicted no discernible blade response reduction, indicating the necessity of including viscous effects for this test case. For the TURBO-AE fan blisk test case, significant effort was expended getting the viscous version of the code to give converged steady flow solutions for the transonic flow conditions. Once converged, the steady solutions provided an excellent match with test data and the calibrated DAWES (AlliedSignal 3-D Viscous Steady Flow CFD Solver). However, efforts expended establishing quality steady-state solutions prevented exercising the unsteady portion of the TURBO-AE code during the present program. AlliedSignal recommends that unsteady pressure measurement data be obtained for both test cases examined for use in aeroelastic code validation.

A community effort to assess and improve drug sensitivity prediction algorithms

PubMed Central

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2015-01-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods. PMID:24880487

A community effort to assess and improve drug sensitivity prediction algorithms.

PubMed

Costello, James C; Heiser, Laura M; Georgii, Elisabeth; Gönen, Mehmet; Menden, Michael P; Wang, Nicholas J; Bansal, Mukesh; Ammad-ud-din, Muhammad; Hintsanen, Petteri; Khan, Suleiman A; Mpindi, John-Patrick; Kallioniemi, Olli; Honkela, Antti; Aittokallio, Tero; Wennerberg, Krister; Collins, James J; Gallahan, Dan; Singer, Dinah; Saez-Rodriguez, Julio; Kaski, Samuel; Gray, Joe W; Stolovitzky, Gustavo

2014-12-01

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Measurement and prediction of the thermomechanical response of shape memory alloy hybrid composite beams

NASA Astrophysics Data System (ADS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-05-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

NASA Technical Reports Server (NTRS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-01-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change.

PubMed

Garrard, Georgia E; McCarthy, Michael A; Vesk, Peter A; Radford, James Q; Bennett, Andrew F

2012-01-01

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature. 2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable. 3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation. 4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation. 5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon. 6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model. © 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

On Winning the Race for Predicting the Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Goswami, Bhupendra

2013-03-01

Skillful prediction of Indian summer monsoon rainfall (ISMR) one season in advance remains a ``grand challenge'' for the climate science community even though such forecasts have tremendous socio-economic implications over the region. Continued poor skill of the ocean-atmosphere coupled models in predicting ISMR is an enigma in the backdrop when these models have high skill in predicting seasonal mean rainfall over the rest of the Tropics. Here, I provide an overview of the fundamental processes responsible for limited skill of climate models and outline a framework for achieving the limit on potential predictability within a reasonable time frame. I also show that monsoon intra-seasonal oscillations (MISO) act as building blocks of the Asian monsoon and provide a bridge between the two problems, the potential predictability limit and the simulation of seasonal mean climate. The correlation between observed ISMR and ensemble mean of predicted ISMR (R) can still be used as a metric for forecast verification. Estimate of potential limit of predictability of Asian monsoon indicates that the highest achievable R is about 0.75. Improvements in climate models and data assimilation over the past one decade has slowly improved R from near zero a decade ago to about 0.4 currently. The race for achieving useful prediction can be won, if we can push this skill up to about 0.7. It requires focused research in improving simulations of MISO, monsoon seasonal cycle and ENSO-monsoon relationship by the climate models. In order to achieve this goal by 2015-16 timeframe, IITM is leading a Program called Monsoon Mission supported by the Ministry of Earth Sciences, Govt. of India (MoES). As improvement in skill of forecasts can come only if R & D is carried out on an operational modeling system, the Climate Forecast System of National Centre for Environmental Prediction (NCEP) of NOAA, U.S.A has been selected as our base system. The Mission envisages building partnership between operational forecasting agency and National and International R & D Organizations to work on improving modeling system. MoES has provided substantial funding to the Mission to fund proposals from International R & D Organizations to work with Indian Organizations in this Mission to achieve this goal. The conceptual framework and the roadmap for the Mission will be highlighted. Indian Institute of Tropical Meteorology is funded by Ministry of Earth Sciences, Govt. of India.

Inter-model Diversity of ENSO simulation and its relation to basic states

NASA Astrophysics Data System (ADS)

Kug, J. S.; Ham, Y. G.

2016-12-01

In this study, a new methodology is developed to improve the climate simulation of state-of-the-art coupledglobal climate models (GCMs), by a postprocessing based on the intermodel diversity. Based on the closeconnection between the interannual variability and climatological states, the distinctive relation between theintermodel diversity of the interannual variability and that of the basic state is found. Based on this relation,the simulated interannual variabilities can be improved, by correcting their climatological bias. To test thismethodology, the dominant intermodel difference in precipitation responses during El Niño-SouthernOscillation (ENSO) is investigated, and its relationship with climatological state. It is found that the dominantintermodel diversity of the ENSO precipitation in phase 5 of the Coupled Model Intercomparison Project(CMIP5) is associated with the zonal shift of the positive precipitation center during El Niño. This dominantintermodel difference is significantly correlated with the basic states. The models with wetter (dryer) climatologythan the climatology of the multimodel ensemble (MME) over the central Pacific tend to shift positiveENSO precipitation anomalies to the east (west). Based on the model's systematic errors in atmosphericENSO response and bias, the models with better climatological state tend to simulate more realistic atmosphericENSO responses.Therefore, the statistical method to correct the ENSO response mostly improves the ENSO response. Afterthe statistical correction, simulating quality of theMMEENSO precipitation is distinctively improved. Theseresults provide a possibility that the present methodology can be also applied to improving climate projectionand seasonal climate prediction.

Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models

NASA Astrophysics Data System (ADS)

Qi, D.; Majda, A.

2017-12-01

A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with distinct statistical structures.

Association of blood eosinophils and plasma periostin with FEV1 response after 3-month inhaled corticosteroid and long-acting beta2-agonist treatment in stable COPD patients

PubMed Central

Park, Hye Yun; Lee, Hyun; Koh, Won-Jung; Kim, Seonwoo; Jeong, Ina; Koo, Hyeon-Kyoung; Kim, Tae-Hyung; Kim, Jin Woo; Kim, Woo Jin; Oh, Yeon-Mok; Sin, Don D; Lim, Seong Yong; Lee, Sang-Do

2016-01-01

Background COPD patients with increased airway eosinophilic inflammation show a favorable response to inhaled corticosteroids (ICS) in combination with a long-acting bronchodilator. Recent studies have demonstrated a significant correlation of sputum eosinophilia with blood eosinophils and periostin. We investigated whether high blood eosinophils and plasma periostin were associated with an improvement in forced expiratory volume in 1 second (FEV1) after 3-month treatment with ICS/long-acting beta2-agonist (LABA) in stable COPD patients. Patients and methods Blood eosinophils and plasma periostin levels were measured in 130 stable COPD subjects selected from the Korean Obstructive Lung Disease cohort. Subjects began a 3-month ICS/LABA treatment after washout period. Results High blood eosinophils (>260/µL, adjusted odds ratio =3.52, P=0.009) and high plasma periostin (>23 ng/mL, adjusted odds ratio =3.52, P=0.013) were significantly associated with FEV1 responders (>12% and 200 mL increase in FEV1 from baseline after treatment). Moreover, the addition of high blood eosinophils to age, baseline positive bronchodilator response, and FEV1 <50% of the predicted value significantly increased the area under the curve for prediction of FEV1 responders (from 0.700 to 0.771; P=0.045). Conclusion High blood eosinophils and high plasma periostin were associated with improved lung function after 3-month ICS/LABA treatment. In particular, high blood eosinophils, in combination with age and baseline lung function parameters, might be a possible biomarker for identification of COPD patients with favorable FEV1 improvement in response to ICS/LABA treatment. PMID:26730185

Association of blood eosinophils and plasma periostin with FEV1 response after 3-month inhaled corticosteroid and long-acting beta2-agonist treatment in stable COPD patients.

PubMed

Park, Hye Yun; Lee, Hyun; Koh, Won-Jung; Kim, Seonwoo; Jeong, Ina; Koo, Hyeon-Kyoung; Kim, Tae-Hyung; Kim, Jin Woo; Kim, Woo Jin; Oh, Yeon-Mok; Sin, Don D; Lim, Seong Yong; Lee, Sang-Do

2016-01-01

COPD patients with increased airway eosinophilic inflammation show a favorable response to inhaled corticosteroids (ICS) in combination with a long-acting bronchodilator. Recent studies have demonstrated a significant correlation of sputum eosinophilia with blood eosinophils and periostin. We investigated whether high blood eosinophils and plasma periostin were associated with an improvement in forced expiratory volume in 1 second (FEV1) after 3-month treatment with ICS/long-acting beta2-agonist (LABA) in stable COPD patients. Blood eosinophils and plasma periostin levels were measured in 130 stable COPD subjects selected from the Korean Obstructive Lung Disease cohort. Subjects began a 3-month ICS/LABA treatment after washout period. High blood eosinophils (>260/µL, adjusted odds ratio =3.52, P=0.009) and high plasma periostin (>23 ng/mL, adjusted odds ratio =3.52, P=0.013) were significantly associated with FEV1 responders (>12% and 200 mL increase in FEV1 from baseline after treatment). Moreover, the addition of high blood eosinophils to age, baseline positive bronchodilator response, and FEV1 <50% of the predicted value significantly increased the area under the curve for prediction of FEV1 responders (from 0.700 to 0.771; P=0.045). High blood eosinophils and high plasma periostin were associated with improved lung function after 3-month ICS/LABA treatment. In particular, high blood eosinophils, in combination with age and baseline lung function parameters, might be a possible biomarker for identification of COPD patients with favorable FEV1 improvement in response to ICS/LABA treatment.

Harassment of adults by immatures in bonobos (Pan paniscus): testing the Exploratory Aggression and Rank Improvement hypotheses.

PubMed

Boose, Klaree; White, Frances

2017-10-01

The immatures of many primate species frequently pester adult group members with aggressive behaviors referred to as a type of harassment. Although these behaviors are characteristic of immatures as they develop from infancy through adolescence, there have been few studies that specifically address the adaptive significance of harassment. Two functional hypotheses have been generated from observations of the behavior in chimpanzees. The Exploratory Aggression hypothesis describes harassment as a mechanism used by immatures to learn about the parameters of aggression and dominance behavior and to acquire information about novel, complex, or unpredictable relationships. The Rank Improvement hypothesis describes harassment as a mechanism of dominance acquisition used by immatures to outrank adults. This study investigated harassment of adults by immatures in a group of bonobos housed at the Columbus Zoo and compared the results to the predictions outlined by the Exploratory Aggression and Rank Improvement hypotheses. Although all immature bonobos in this group harassed adults, adolescents performed the behavior more frequently than did infants or juveniles and low-ranking adults were targeted more frequently than high-ranking. Targets responded more with agonistic behaviors than with neutral behaviors and the amount of harassment an individual received was significantly correlated with the amount of agonistic responses given. Furthermore, bouts of harassment were found to continue significantly more frequently when responses were agonistic than when they were neutral. Adolescents elicited mostly agonistic responses from targets whereas infants and juveniles received mostly neutral responses. These results support predictions from each hypothesis where harassment functions both as a mechanism of social exploration and as a tool to establish dominance rank.

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.

Earth System Model Needs for Including the Interactive Representation of Nitrogen Deposition and Drought Effects on Forested Ecosystems

DOE PAGES

Drewniak, Beth; Gonzalez-Meler, Miquel

2017-07-27

One of the biggest uncertainties of climate change is determining the response of vegetation to many co-occurring stressors. In particular, many forests are experiencing increased nitrogen deposition and are expected to suffer in the future from increased drought frequency and intensity. Interactions between drought and nitrogen deposition are antagonistic and non-additive, which makes predictions of vegetation response dependent on multiple factors. The tools we use (Earth system models) to evaluate the impact of climate change on the carbon cycle are ill equipped to capture the physiological feedbacks and dynamic responses of ecosystems to these types of stressors. In this manuscript,more » we review the observed effects of nitrogen deposition and drought on vegetation as they relate to productivity, particularly focusing on carbon uptake and partitioning. We conclude there are several areas of model development that can improve the predicted carbon uptake under increasing nitrogen deposition and drought. This includes a more flexible framework for carbon and nitrogen partitioning, dynamic carbon allocation, better representation of root form and function, age and succession dynamics, competition, and plant modeling using trait-based approaches. These areas of model development have the potential to improve the forecasting ability and reduce the uncertainty of climate models.« less

Earth System Model Needs for Including the Interactive Representation of Nitrogen Deposition and Drought Effects on Forested Ecosystems

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

Drewniak, Beth; Gonzalez-Meler, Miquel

One of the biggest uncertainties of climate change is determining the response of vegetation to many co-occurring stressors. In particular, many forests are experiencing increased nitrogen deposition and are expected to suffer in the future from increased drought frequency and intensity. Interactions between drought and nitrogen deposition are antagonistic and non-additive, which makes predictions of vegetation response dependent on multiple factors. The tools we use (Earth system models) to evaluate the impact of climate change on the carbon cycle are ill equipped to capture the physiological feedbacks and dynamic responses of ecosystems to these types of stressors. In this manuscript,more » we review the observed effects of nitrogen deposition and drought on vegetation as they relate to productivity, particularly focusing on carbon uptake and partitioning. We conclude there are several areas of model development that can improve the predicted carbon uptake under increasing nitrogen deposition and drought. This includes a more flexible framework for carbon and nitrogen partitioning, dynamic carbon allocation, better representation of root form and function, age and succession dynamics, competition, and plant modeling using trait-based approaches. These areas of model development have the potential to improve the forecasting ability and reduce the uncertainty of climate models.« less

Predictors of Response and Mechanisms of Change in an Organizational Skills Intervention for Students with ADHD

PubMed Central

Becker, Stephen P.; Epstein, Jeffery N.; Vaughn, Aaron J.; Girio-Herrera, Erin

2013-01-01

The purpose of the study was to evaluate predictors of response and mechanisms of change for the Homework, Organization, and Planning Skills (HOPS) intervention for middle school students with Attention-Deficit/Hyperactivity Disorder (ADHD). Twenty-three middle school students with ADHD (grades 6–8) received the HOPS intervention implemented by school mental health providers and made significant improvements in parent-rated materials organization and planning skills, impairment due to organizational skills problems, and homework problems. Predictors of response examined included demographic and child characteristics, such as gender, ethnicity, intelligence, ADHD and ODD symptom severity, and ADHD medication use. Mechanisms of change examined included the therapeutic alliance and adoption of the organization and planning skills taught during the HOPS intervention. Participant implementation of the HOPS binder materials organization system and the therapeutic alliance as rated by the student significantly predicted post-intervention outcomes after controlling for pre-intervention severity. Adoption of the binder materials organization system predicted parent-rated improvements in organization, planning, and homework problems above and beyond the impact of the therapeutic alliance. These findings demonstrate the importance of teaching students with ADHD to use a structured binder organization system for organizing and filing homework and classwork materials and for transferring work to and from school. PMID:24319323

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments*

PubMed Central

Searle, Brian C.; Egertson, Jarrett D.; Bollinger, James G.; Stergachis, Andrew B.; MacCoss, Michael J.

2015-01-01

Targeted mass spectrometry is an essential tool for detecting quantitative changes in low abundant proteins throughout the proteome. Although selected reaction monitoring (SRM) is the preferred method for quantifying peptides in complex samples, the process of designing SRM assays is laborious. Peptides have widely varying signal responses dictated by sequence-specific physiochemical properties; one major challenge is in selecting representative peptides to target as a proxy for protein abundance. Here we present PREGO, a software tool that predicts high-responding peptides for SRM experiments. PREGO predicts peptide responses with an artificial neural network trained using 11 minimally redundant, maximally relevant properties. Crucial to its success, PREGO is trained using fragment ion intensities of equimolar synthetic peptides extracted from data independent acquisition experiments. Because of similarities in instrumentation and the nature of data collection, relative peptide responses from data independent acquisition experiments are a suitable substitute for SRM experiments because they both make quantitative measurements from integrated fragment ion chromatograms. Using an SRM experiment containing 12,973 peptides from 724 synthetic proteins, PREGO exhibits a 40–85% improvement over previously published approaches at selecting high-responding peptides. These results also represent a dramatic improvement over the rules-based peptide selection approaches commonly used in the literature. PMID:26100116

Improved integrating-sphere throughput with a lens and nonimaging concentrator.

PubMed

Chenault, D B; Snail, K A; Hanssen, L M

1995-12-01

A reflectometer design utilizing an integrating sphere with a lens and nonimaging concentrator is described. Compared with previous designs where a collimator was used to restrict the detector field of view, the concentrator-lens combination significantly increases the throughput of the reflectometer. A procedure for designing lens-concentrators is given along with the results of parametric studies. The measured angular response of a lens-concentrator system is compared with ray-trace predictions and with the response of an ideal system.

Computational Model of the Fathead Minnow Hypothalamic-Pituitary-Gonadal Axis: Incorporating Protein Synthesis in Improving Predictability of Responses to Endocrine Active Chemicals

EPA Science Inventory

There is international concern about chemicals that alter endocrine system function in humans and/or wildlife and subsequently cause adverse effects. We previously developed a mechanistic computational model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minno...

DNA-Guided Precision Medicine for Cancer: A Case of Irrational Exuberance?

PubMed

Voest, Emile E; Bernards, Rene

2016-02-01

Precision treatment with targeted cancer drugs requires the selection of patients who are most likely to benefit from a given therapy. We argue here that the use of a combination of both DNA and transcriptome analyses will significantly improve drug response prediction. ©2016 American Association for Cancer Research.

Predicting Differential Response to EMG Biofeedback and Relaxation Training: The Role of Cognitive Structure.

ERIC Educational Resources Information Center

Hart, James D.

1984-01-01

Analyzed treatment outcome data for 102 headache patients who had been assigned randomly to receive either EMG biofeedback (N=70) or relaxation training (N=32). Analysis demonstrated that relaxation training was significantly more effective than biofeedback and that mixed headache patients improved significantly less than either migraine or…

Management filters and species traits: Weed community assembly in long-term organic and conventional systems

USDA-ARS?s Scientific Manuscript database

Community assembly theory provides a useful framework to assess the response of weed communities to agricultural management systems and to improve the predictive power of weed science. Under this framework, weed community assembly is constrained by abiotic and biotic "filters" that act on species tr...

An eco-hydrological approach to predicting regional vegetation and groundwater response to ecological water convergence in dryland riparian ecosystems

USDA-ARS?s Scientific Manuscript database

To improve the management strategy of riparian restoration, better understanding of the dynamic of eco-hydrological system and its feedback between hydrological and ecological components are needed. The fully distributed eco-hydrological model coupled with a hydrology component was developed based o...