A comparison of modified versions of the Static-99 and the Sex Offender Risk Appraisal Guide.

PubMed

Nunes, Kevin L; Firestone, Philip; Bradford, John M; Greenberg, David M; Broom, Ian

2002-07-01

The predictive validity of 2 risk assessment instruments for sex offenders, modified versions of the Static-99 and the Sex Offender Risk Appraisal Guide, was examined and compared in a sample of 258 adult male sex offenders. In addition, the independent contributions to the prediction of recidivism made by each instrument and by various phallometric indices were explored. Both instruments demonstrated moderate levels of predictive accuracy for sexual and violent (including sexual) recidivism. They were not significantly different in terms of their predictive accuracy for sexual or violent recidivism, nor did they contribute independently to the prediction of sexual or violent recidivism. Of the phallometric indices examined, only the pedophile index added significantly to the prediction of sexual recidivism, but not violent recidivism, above the Static-99 alone.

Changes in need satisfaction and motivation orientation as predictors of psychological and behavioural outcomes in exercise referral.

PubMed

Rahman, Rachel Jane; Thogersen-Ntoumani, Cecilie; Thatcher, Joanne; Doust, Jonathan

2011-11-01

Employing Self-Determination Theory (Deci & Ryan, 1985) as a theoretical framework, this study examined psychological need satisfaction and motivational regulations as predictors of psychological and behavioural outcomes in exercise referral (ER). ER patients (N = 293; mean age 54.49) completed the measures of motivational regulations, psychological need satisfaction, health-related quality of life, life satisfaction, anxiety, depression and physical activity at entry, exit and 6 months following the end of a supervised exercise programme. Change in (Δ) intrinsic motivation during the scheme significantly predicted adherence and Δ habitual physical activity. Δ psychological need satisfaction from entry to exit significantly predicted Δ habitual physical activity from exit to 6-month follow-up. Δ psychological need satisfaction significantly predicted Δ motivational regulation and Δ psychological outcomes. Contrary to expectations, Δ self-determined regulation did not significantly predict Δ psychological outcomes during the structured part of the scheme, however, it did significantly predict Δ in psychological outcomes from exit to 6-month follow-up. These findings expand on cross-sectional research to demonstrate that psychological need satisfaction during supervised ER longitudinally predicts motivational regulation and psychological outcomes up to 6 months after a structured programme.

Final Scientific/Technical Report for Subseasonal to Seasonal Prediction of Extratropical Storm Track Activity over the U.S. using NMME data

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

Chang, Edmund Kar-Man

The goals of the project are: 1) To develop and assess subseasonal to seasonal prediction products for storm track activity derived from NMME data; 2) Assess how much of the predictable signal can be associated with ENSO and other modes of large scale low frequency atmosphere-ocean variability; and 3) Further explore the link between storm track variations and extreme weather statistics. Significant findings of this project include the followings: 1) Our assessment of NMME reforecasts of storm track variability has demonstrated that NMME models have substantial skill in predicting storm track activity in the vicinity of North America - Subseasonalmore » skill is high only for leads of less than 1 month. However, seasonal (winter) prediction skill near North America is high even out to 4 to 5 months lead - Much of the skill for leads of 1 month or longer is related to the influence of ENSO - Nevertheless, lead 0 NMME predictions are significantly more skillful than those based on ENSO influence 2) Our results have demonstrated that storm track variations highly modulate the frequency of occurrence of weather extremes - Extreme cold, high wind, and extreme precipitation events in winter - Extreme heat events in summer - These results suggest that NMME storm track predictions can be developed to serve as a useful guidance to assist the formulation of monthly/seasonal outlooks« less

On the Validity of Validity Scales: The Importance of Defensive Responding in the Prediction of Institutional Misconduct

ERIC Educational Resources Information Center

Edens, John F.; Ruiz, Mark A.

2006-01-01

This study examined the effects of defensive responding on the prediction of institutional misconduct among male inmates (N = 349) who completed the Personality Assessment Inventory (L. C. Morey, 1991). Hierarchical logistic regression analyses demonstrated significant main effects for the Antisocial Features (ANT) scale as well as main effects…

Low Data Drug Discovery with One-Shot Learning

PubMed Central

2017-01-01

Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. Model.2015, 55, 263–27425635324). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016). PMID:28470045

Associative cueing of attention through implicit feature-location binding.

PubMed

Girardi, Giovanna; Nico, Daniele

2017-09-01

In order to assess associative learning between two task-irrelevant features in cueing spatial attention, we devised a task in which participants have to make an identity comparison between two sequential visual stimuli. Unbeknownst to them, location of the second stimulus could be predicted by the colour of the first or a concurrent sound. Albeit unnecessary to perform the identity-matching judgment the predictive features thus provided an arbitrary association favouring the spatial anticipation of the second stimulus. A significant advantage was found with faster responses at predicted compared to non-predicted locations. Results clearly demonstrated an associative cueing of attention via a second-order arbitrary feature/location association but with a substantial discrepancy depending on the sensory modality of the predictive feature. With colour as predictive feature, significant advantages emerged only after the completion of three blocks of trials. On the contrary, sound affected responses from the first block of trials and significant advantages were manifest from the beginning of the second. The possible mechanisms underlying the associative cueing of attention in both conditions are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of general movements and heart rate variability in prediction of neurodevelopmental outcome in preterm infants.

PubMed

Dimitrijević, Lidija; Bjelaković, Bojko; Čolović, Hristina; Mikov, Aleksandra; Živković, Vesna; Kocić, Mirjana; Lukić, Stevo

2016-08-01

Adverse neurologic outcome in preterm infants could be associated with abnormal heart rate (HR) characteristics as well as with abnormal general movements (GMs) in the 1st month of life. To demonstrate to what extent GMs assessment can predict neurological outcome in preterm infants in our clinical setting; and to assess the clinical usefulness of time-domain indices of heart rate variability (HRV) in improving predictive value of poor repertoire (PR) GMs in writhing period. Qualitative assessment of GMs at 1 and 3 months corrected age; 24h electrocardiography (ECG) recordings and analyzing HRV at 1 month corrected age. Seventy nine premature infants at risk of neurodevelopmental impairments were included prospectively. Neurodevelopmental outcome was assessed at the age of 2 years corrected. Children were classified as having normal neurodevelopmental status, minor neurologic dysfunction (MND), or cerebral palsy (CP). We found that GMs in writhing period (1 month corrected age) predicted CP at 2 years with sensitivity of 100%, and specificity of 72.1%. Our results demonstrated the excellent predictive value of cramped synchronized (CS) GMs, but not of PR pattern. Analyzing separately a group of infants with PR GMs we found significantly lower values of HRV parameters in infants who later developed CP or MND vs. infants with PR GMs who had normal outcome. The quality of GMs was predictive for neurodevelopmental outcome at 2 years. Prediction of PR GMs was significantly enhanced with analyzing HRV parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Leg pain and psychological variables predict outcome 2-3 years after lumbar fusion surgery.

PubMed

Abbott, Allan D; Tyni-Lenné, Raija; Hedlund, Rune

2011-10-01

Prediction studies testing a thorough range of psychological variables in addition to demographic, work-related and clinical variables are lacking in lumbar fusion surgery research. This prospective cohort study aimed at examining predictions of functional disability, back pain and health-related quality of life (HRQOL) 2-3 years after lumbar fusion by regressing nonlinear relations in a multivariate predictive model of pre-surgical variables. Before and 2-3 years after lumbar fusion surgery, patients completed measures investigating demographics, work-related variables, clinical variables, functional self-efficacy, outcome expectancy, fear of movement/(re)injury, mental health and pain coping. Categorical regression with optimal scaling transformation, elastic net regularization and bootstrapping were used to investigate predictor variables and address predictive model validity. The most parsimonious and stable subset of pre-surgical predictor variables explained 41.6, 36.0 and 25.6% of the variance in functional disability, back pain intensity and HRQOL 2-3 years after lumbar fusion. Pre-surgical control over pain significantly predicted functional disability and HRQOL. Pre-surgical catastrophizing and leg pain intensity significantly predicted functional disability and back pain while the pre-surgical straight leg raise significantly predicted back pain. Post-operative psychomotor therapy also significantly predicted functional disability while pre-surgical outcome expectations significantly predicted HRQOL. For the median dichotomised classification of functional disability, back pain intensity and HRQOL levels 2-3 years post-surgery, the discriminative ability of the prediction models was of good quality. The results demonstrate the importance of pre-surgical psychological factors, leg pain intensity, straight leg raise and post-operative psychomotor therapy in the predictions of functional disability, back pain and HRQOL-related outcomes.

Latino Adolescents' Ethnic Identity: Is There a Developmental Progression and Does Growth in Ethnic Identity Predict Growth in Self-Esteem?

ERIC Educational Resources Information Center

Umana-Taylor, Adriana J.; Gonzales-Backen, Melinda A.; Guimond, Amy B.

2009-01-01

The current longitudinal study of 323 Latino adolescents (50.5% male; M age = 15.31 years) examined whether ethnic identity exploration, resolution, and affirmation demonstrated significant growth over a 4-year period and whether growth in ethnic identity predicted growth in self-esteem. Findings from multiple-group latent growth curve models…

Modeling strength loss in wood by chemical composition. Part I, An individual component model for southern pine

Treesearch

J. E. Winandy; P. K. Lebow

2001-01-01

In this study, we develop models for predicting loss in bending strength of clear, straight-grained pine from changes in chemical composition. Although significant work needs to be done before truly universal predictive models are developed, a quantitative fundamental relationship between changes in chemical composition and strength loss for pine was demonstrated. In...

Predicting when biliary excretion of parent drug is a major route of elimination in humans.

PubMed

Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

2014-09-01

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

Theory of mind reasoning in schizophrenia patients and non-psychotic relatives.

PubMed

Cassetta, Briana; Goghari, Vina

2014-08-15

Research consistently demonstrates that schizophrenia patients have theory of mind (ToM) impairments. Additionally, there is some evidence that family members of schizophrenia patients also demonstrate impairments in ToM, suggesting a genetic vulnerability for the disorder. This study assessed ToM abilities (i.e., sarcasm comprehension) in schizophrenia patients and their first-degree biological relatives during video-taped social interactions, to be representative of real-world interactions and to assess for disease-specific and/or genetic liability effects. Additionally, we assessed whether ToM abilities predicted social and global functioning in schizophrenia patients, and whether symptoms were associated with ToM deficits. Schizophrenia patients demonstrated impairments in sarcasm comprehension compared to controls and relatives, whereas relatives showed intact comprehension. Symptoms of schizophrenia significantly predicted worse ToM abilities. Furthermore, in schizophrenia patients, impaired ToM reasoning predicted worse social and global functioning. Given schizophrenia patients demonstrated impairments in ToM reasoning in a task that resembles real-life interactions, this might be a key area for remediation. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Ironic and Reinvestment Effects in Baseball Pitching: How Information About an Opponent Can Influence Performance Under Pressure.

PubMed

Gray, Rob; Orn, Anders; Woodman, Tim

2017-02-01

Are pressure-induced performance errors in experts associated with novice-like skill execution (as predicted by reinvestment/conscious processing theories) or expert execution toward a result that the performer typically intends to avoid (as predicted by ironic processes theory)? The present study directly compared these predictions using a baseball pitching task with two groups of experienced pitchers. One group was shown only their target, while the other group was shown the target and an ironic (avoid) zone. Both groups demonstrated significantly fewer target hits under pressure. For the target-only group, this was accompanied by significant changes in expertise-related kinematic variables. In the ironic group, the number of pitches thrown in the ironic zone was significantly higher under pressure, and there were no significant changes in kinematics. These results suggest that information about an opponent can influence the mechanisms underlying pressure-induced performance errors.

Predictive contribution of neutrophil/lymphocyte ratio in diagnosis of brucellosis.

PubMed

Olt, Serdar; Ergenç, Hasan; Açıkgöz, Seyyid Bilal

2015-01-01

Here we wanted to investigate predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in the diagnosis of brucellosis. Thirty-two brucellosis patients diagnosed with positive serum agglutination test and thirty-two randomized healthy subjects were enrolled in this study retrospectively. Result with ROC analyzes the baseline NLR and hemoglobin values were found to be significantly associated with brucellosis (P = 0.01, P = 0.01, resp.). Herein we demonstrated for the first time that NLR values were significantly associated with brucellosis. This situation can help clinicians during diagnosis of brucellosis.

Phenome-driven disease genetics prediction toward drug discovery

PubMed Central

Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong

2015-01-01

Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e−4) and 81.3% (P < e−12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. Availability and implementation: nlp.case.edu/public/data/DMN Contact: rxx@case.edu PMID:26072493

Lynch syndrome-associated colorectal carcinoma: frequent involvement of the left colon and rectum and late-onset presentation supports a universal screening approach.

PubMed

Hartman, Douglas J; Brand, Randall E; Hu, Huankai; Bahary, Nathan; Dudley, Beth; Chiosea, Simon I; Nikiforova, Marina N; Pai, Reetesh K

2013-11-01

The optimal strategy for screening patients with colorectal carcinoma for Lynch syndrome (LS) is a subject of continued debate in the literature with some advocating universal screening while others arguing for selective screening. We evaluated 1292 colorectal carcinomas for DNA mismatch repair protein abnormalities and identified 150 (11.6%) tumors demonstrating high-levels of microsatellite instability (MSI-H). MSI-H colorectal carcinomas were divided into sporadic (112/1292, 8.7%) and LS/probable LS-associated (38/1292, 2.9%) groups based on BRAF V600E mutation, MLH1 promoter hypermethylation, cancer history, and germline mismatch repair gene mutation. All MSI-H colorectal carcinomas were analyzed for grade, location, and tumor histology. The utility of the revised Bethesda guidelines and published predictive pathology models for MSI-H colorectal carcinomas (PREDICT and MSPath) were evaluated. Left-sided MSI-H colorectal carcinomas were more frequently associated with LS compared with right-sided MSI-H colorectal carcinomas (12/21, 57% versus 26/129, 20%, P = .0008). There was no significant difference in histology between sporadic MSI-H and LS/probable LS-associated colorectal carcinomas except for a slightly higher proportion of sporadic MSI-H tumors demonstrating tumor-infiltrating lymphocytes (81% versus 61%, P = .015). Neither pathology predictive model identified all LS-associated colorectal carcinomas (PREDICT: 33/38, 87%; MSPath: 35/38, 92%). 12/117 (10%) MSI-H colorectal carcinomas identified in patients >60 years were LS/probable LS-associated. Our results demonstrate that models of predicting MSI-H fail to identify LS-associated colorectal carcinoma given their reliance on right-sided location. A significant proportion (32%) of LS-associated colorectal carcinoma is identified in patients >60 years. Finally, our results demonstrate similar morphologic features between LS-associated and sporadic MSI-H colorectal carcinomas. © 2013.

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…

Pre-operative labs: Wasted dollars or predictors of post-operative cardiac and septic events in orthopaedic trauma patients?

PubMed

Lakomkin, Nikita; Sathiyakumar, Vasanth; Dodd, Ashley C; Jahangir, A Alex; Whiting, Paul S; Obremskey, William T; Sethi, Manish K

2016-06-01

As US healthcare expenditures continue to rise, there is significant pressure to reduce the cost of inpatient medical services. Studies have estimated that over 70% of routine labs may not yield clinical benefits while adding over $300 in costs per day for every inpatient. Although orthopaedic trauma patients tend to have longer inpatient stays and hip fractures have been associated with significant morbidity, there is a dearth of data examining pre-operative labs in predicting post-operative adverse events in these populations. The purpose of this study was to assess whether pre-operative labs significantly predict post-operative cardiac and septic complications in orthopaedic trauma and hip fracture patients. Between 2006 and 2013, 56,336 (15.6%) orthopaedic trauma patients were identified and 27,441 patients (7.6%) were diagnosed with hip fractures. Pre-operative labs included sodium, BUN, creatinine, albumin, bilirubin, SGOT, alkaline phosphatase, white count, hematocrit, platelet count, prothrombin time, INR, and partial thromboplastin time. For each of these labs, patients were deemed to have normal or abnormal values. Patients were noted to have developed cardiac or septic complications if they sustained (1) myocardial infarction (MI), (2) cardiac arrest, or (3) septic shock within 30 days after surgery. Separate regressions incorporating over 40 patient characteristics including age, gender, pre-operative comorbidities, and labs were performed for orthopaedic trauma patients in order to determine whether pre-operative labs predicted adverse cardiac or septic outcomes. 749 (1.3%) orthopaedic trauma patients developed cardiac complications and 311 (0.6%) developed septic shock. Multivariate regression demonstrated that abnormal pre-operative platelet values were significantly predictive of post-operative cardiac arrest (OR: 11.107, p=0.036), and abnormal bilirubin levels were predictive (OR: 8.487, p=0.008) of the development of septic shock in trauma patients. In the hip fracture cohort, abnormal partial thromboplastin time was significantly associated with post-operative myocardial infarction (OR: 15.083, p=0.046), and abnormal bilirubin (OR: 58.674, p=0.002) significantly predicted the onset of septic shock. This is the first study to demonstrate the utility of pre-operative labs in predicting perioperative cardiac and septic adverse events in orthopaedic trauma and hip fracture patients. Particular attention should be paid to haematologic/coagulation labs (platelets, PTT) and bilirubin values. Prognostic Level II. Copyright © 2016 Elsevier Ltd. All rights reserved.

The lepidoptera as predictable communities of herbivores: a test of niche assembly using the moth communities of Morgan-Monroe State Forest

Treesearch

Keith S. Summerville; Michael R. Saunders; Jamie L. Lane

2013-01-01

The response of forest insect communities to disturbances such as timber harvest likely will depend on the underlying ecological assembly rules that affect community structure. Two competing hypotheses are niche assembly, which seeks to demonstrate significant species-environment correlations, and dispersal-assembly, which seeks to demonstrate spatial autocorrelation...

Small artery elasticity predicts future cardiovascular events in chinese patients with angiographic coronary artery disease.

PubMed

Wan, Zhaofei; Liu, Xiaojun; Wang, Xinhong; Liu, Fuqiang; Liu, Weimin; Wu, Yue; Pei, Leilei; Yuan, Zuyi

2014-04-01

Arterial elasticity has been shown to predict cardiovascular disease (CVD) in apparently healthy populations. The present study aimed to explore whether arterial elasticity could predict CVD events in Chinese patients with angiographic coronary artery disease (CAD). Arterial elasticity of 365 patients with angiographic CAD was measured. During follow-up (48 months; range 6-65), 140 CVD events occurred (including 34 deaths). Univariate Cox analysis demonstrated that both large arterial elasticity and small arterial elasticity were significant predictors of CVD events. Multivariate Cox analysis indicated that small arterial elasticity remained significant. Kaplan-Meier analysis showed that the probability of having a CVD event/CVD death increased with a decrease of small arterial elasticity (P < .001, respectively). Decreased small arterial elasticity independently predicts the risk of CVD events in Chinese patients with angiographic CAD.

Fermentation of Saccharomyces cerevisiae - Combining kinetic modeling and optimization techniques points out avenues to effective process design.

PubMed

Scheiblauer, Johannes; Scheiner, Stefan; Joksch, Martin; Kavsek, Barbara

2018-09-14

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations. Copyright © 2018 Elsevier Ltd. All rights reserved.

A comparison between the patella and the calcaneus using ultrasound velocity and attenuation as predictors of bone mineral density

NASA Astrophysics Data System (ADS)

Han, S. M.; Davis, J.

1997-10-01

The bone mineral density (BMD), ultrasound velocity (UV) and attenuation were examined in sixteen matched sets of human patellae and calcanei. For the sixteen calcanei, BMD was strongly correlated with all ultrasound parameters. Calcaneal UV appeared to be inferior to attenuation in the ability to predict BMD. For the sixteen patellae, the average UV was found to be greater in the superior/inferior direction than in the anterior/posterior and medial/lateral directions. It was found that patella BMD was significantly correlated with each of three directional ultrasound velocities. The relationship between BMD and ultrasound attenuation parameters was not significant in the patella. A comparative study of the two different bone sets demonstrated that the BMDs of the patella and calcaneus were significantly correlated with each other. Ultrasound velocity of calcaneus, measured in the medial/lateral direction, was not significantly associated with any of three directional ultrasound velocities in the patella. Similarly, ultrasound attenuation parameters of calcaneus were not significantly correlated with those of patella. The present study also demonstrated evidence that when predicting BMDs at their respective sites using ultrasound, the calcaneus appeared to be superior to the patella.

NASA's Evolutionary Xenon Thruster (NEXT) Project Qualification Propellant Throughput Milestone: Performance, Erosion, and Thruster Service Life Prediction After 450 kg

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 28,500 hr of operation and processed 466 kg of xenon throughput--more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.

Investigation of energy transport in DIII-D high- β P EAST-demonstration discharges with the TGLF turbulent and NEO neoclassical transport models [Investigation of energy transport in DIII-D high- β P EAST-demonstration discharges with turbulent and neoclassical transport models

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

Pan, Chengkang; Staebler, Gary M.; Lao, Lang L.

Here, energy transport analyses of DIII-D high-β P EAST-demonstration discharges have been performed using the TGYRO transport package with TGLF turbulent and NEO neoclassical transport models under the OMFIT integrated modeling framework. Ion energy transport is shown to be dominated by neoclassical transport and ion temperature profiles predicted by TGYRO agree closely with the experimental measured profiles for these high-β P discharges. Ion energy transport is largely insensitive to reductions in the E × B flow shear stabilization. The Shafranov shift is shown to play a role in the suppression of the ion turbulent energy transport below the neoclassical level.more » Electron turbulent energy transport is under-predicted by TGLF and a significant shortfall in the electron energy transport over the whole core plasma is found with TGLF predictions for these high-β P discharges. TGYRO can successfully predict the experimental ion and electron temperature profiles by artificially increasing the saturated turbulence level for ETG driven modes used in TGLF.« less

Investigation of energy transport in DIII-D high- β P EAST-demonstration discharges with the TGLF turbulent and NEO neoclassical transport models [Investigation of energy transport in DIII-D high- β P EAST-demonstration discharges with turbulent and neoclassical transport models

DOE PAGES

Pan, Chengkang; Staebler, Gary M.; Lao, Lang L.; ...

2017-01-11

Here, energy transport analyses of DIII-D high-β P EAST-demonstration discharges have been performed using the TGYRO transport package with TGLF turbulent and NEO neoclassical transport models under the OMFIT integrated modeling framework. Ion energy transport is shown to be dominated by neoclassical transport and ion temperature profiles predicted by TGYRO agree closely with the experimental measured profiles for these high-β P discharges. Ion energy transport is largely insensitive to reductions in the E × B flow shear stabilization. The Shafranov shift is shown to play a role in the suppression of the ion turbulent energy transport below the neoclassical level.more » Electron turbulent energy transport is under-predicted by TGLF and a significant shortfall in the electron energy transport over the whole core plasma is found with TGLF predictions for these high-β P discharges. TGYRO can successfully predict the experimental ion and electron temperature profiles by artificially increasing the saturated turbulence level for ETG driven modes used in TGLF.« less

Prediction of Kinematic and Kinetic Performance in a Drop Vertical Jump with Individual Anthropometric Factors in Adolescent Female Athletes: Implications for Cadaveric Investigations

PubMed Central

Bates, Nathaniel A.; Myer, Gregory D.; Hewett, Timothy E.

2014-01-01

Anterior cruciate ligament injuries are common, expensive to repair, and often debilitate athletic careers. Robotic manipulators have evaluated knee ligament biomechanics in cadaveric specimens, but face limitations such as accounting for variation in bony geometry between specimens that may influence dynamic motion pathways. This study examined individual anthropometric measures for significant linear relationships with in vivo kinematic and kinetic performance and determined their implications for robotic studies. Anthropometrics and 3D motion during a 31 cm drop vertical jump task were collected in high school female basketball players. Anthropometric measures demonstrated differential statistical significance in linear regression models relative to kinematic variables (P-range < 0.01-0.95). However, none of the anthropometric relationships accounted for clinical variance or provided substantive univariate accuracy needed for clinical prediction algorithms (r2 < 0.20). Mass and BMI demonstrated models that were significant (P < 0.05) and predictive (r2 > 0.20) relative to peak flexion moment, peak adduction moment, flexion moment range, abduction moment range, and internal rotation moment range. The current findings indicate that anthropometric measures are less associated with kinematics than with kinetics. Relative to the robotic manipulation of cadaveric limbs, the results do not support the need to normalize kinematic rotations relative to specimen dimensions. PMID:25266933

The prediction of nozzle performance and heat transfer in hydrogen/oxygen rocket engines with transpiration cooling, film cooling, and high area ratios

NASA Technical Reports Server (NTRS)

Kacynski, Kenneth J.; Hoffman, Joe D.

1993-01-01

An advanced engineering computational model has been developed to aid in the analysis and design of hydrogen/oxygen chemical rocket engines. The complete multi-species, chemically reacting and diffusing Navier-Stokes equations are modelled, finite difference approach that is tailored to be conservative in an axisymmetric coordinate system for both the inviscid and viscous terms. Demonstration cases are presented for a 1030:1 area ratio nozzle, a 25 lbf film cooled nozzle, and transpiration cooled plug-and-spool rocket engine. The results indicate that the thrust coefficient predictions of the 1030:1 nozzle and the film cooled nozzle are within 0.2 to 0.5 percent, respectively, of experimental measurements when all of the chemical reaction and diffusion terms are considered. Further, the model's predictions agree very well with the heat transfer measurements made in all of the nozzle test cases. The Soret thermal diffusion term is demonstrated to have a significant effect on the predicted mass fraction of hydrogen along the wall of the nozzle in both the laminar flow 1030:1 nozzle and the turbulent plug-and-spool rocket engine analysis cases performed. Further, the Soret term was shown to represent a significant fraction of the diffusion fluxes occurring in the transpiration cooled rocket engine.

Diatomic gasdynamic lasers.

NASA Technical Reports Server (NTRS)

Mckenzie, R. L.

1972-01-01

Predictions from a numerical model of the vibrational relaxation of anharmonic diatomic oscillators in supersonic expansions are used to show the extent to which the small anharmonicity of gases like CO can cause significant overpopulations of upper vibrational states. When mixtures of CO and N2 are considered, radiative gain on many of the vibration-rotation transitions of CO is predicted. Experiments are described that qualitatively verify the predictions by demonstrating laser oscillation in CO-N2 expansions. The resulting CO-N2 gasdynamic laser displays performance characteristics that equal or exceed those of similar CO2 lasers.

Diatomic gasdynamic lasers

NASA Technical Reports Server (NTRS)

Mckenzie, R. L.

1971-01-01

Predictions from a numerical model of the vibrational relaxation of anharmonic diatomic oscillators in supersonic expansions are used to show the extent to which the small anharmonicity of gases like CO can cause significant overpopulations of upper vibrational states. When mixtures of CO and N2 are considered, radiative gain on many of the vibration-rotation transitions of CO is predicted. Experiments are described that qualitatively verify the predictions by demonstrating laser oscillation in CO-N2 expansions. The resulting CO-N2 gasdynamic laser displays performance characteristics that equal or exceed those of similar CO2 lasers.

Predictive Contribution of Neutrophil/Lymphocyte Ratio in Diagnosis of Brucellosis

PubMed Central

Olt, Serdar; Ergenç, Hasan; Açıkgöz, Seyyid Bilal

2015-01-01

Here we wanted to investigate predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) in the diagnosis of brucellosis. Thirty-two brucellosis patients diagnosed with positive serum agglutination test and thirty-two randomized healthy subjects were enrolled in this study retrospectively. Result with ROC analyzes the baseline NLR and hemoglobin values were found to be significantly associated with brucellosis (P = 0.01, P = 0.01, resp.). Herein we demonstrated for the first time that NLR values were significantly associated with brucellosis. This situation can help clinicians during diagnosis of brucellosis. PMID:25722970

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.

PubMed

Nielsen, Morten; Andreatta, Massimo

2016-03-30

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .

Downregulation of miR‑135a predicts poor prognosis in acute myeloid leukemia and regulates leukemia progression via modulating HOXA10 expression.

PubMed

Xu, Hongwei; Wen, Quan

2018-05-23

MicroRNA‑135a (miR‑135a) has been shown to exert important roles in various human cancer types, such as glioblastoma, thyroid carcinoma and renal carcinoma. However, the function of miR‑135a in acute myeloid leukemia (AML) remains largely unknown. In the present study, it was demonstrated that miR‑135a expression was significantly downregulated in AML cells compared with normal control cells. Furthermore, the downregulation of miR‑135a in patients with AML predicted poor prognosis. Through functional experiments, overexpression of miR‑135a was demonstrated to significantly inhibit the proliferation and cell cycle of AML cells, while it promoted cellular apoptosis. miR‑135a directly targeted HOXA10 in AML cells. miR‑135a overexpression significantly suppressed the mRNA and protein levels of HOXA10 in AML cells. Moreover, there was an inverse association between miR‑135a expression and HOXA10 level in AML samples. Additionally, by ectopic expression of HOXA10, restoration of HOXA10 significantly abolished the effects of miR‑135a overexpression on AML cell proliferation, cell cycle and apoptosis. In conclusion, the present study demonstrated that miR‑135a serves as a tumor suppressor in AML by targeting HOXA10, and miR‑135a may be a promising prognostic biomarker for AML patients.

Status of the NASA's Evolutionary Xenon Thruster (NEXT) Long-Duration Test After 30,352 Hours of Operation

NASA Technical Reports Server (NTRS)

Herman, Daniel A.

2010-01-01

The NASA s Evolutionary Xenon Thruster (NEXT) program is tasked with significantly improving and extending the capabilities of current state-of-the-art NSTAR thruster. The service life capability of the NEXT ion thruster is being assessed by thruster wear test and life-modeling of critical thruster components, such as the ion optics and cathodes. The NEXT Long-Duration Test (LDT) was initiated to validate and qualify the NEXT thruster propellant throughput capability. The NEXT thruster completed the primary goal of the LDT; namely to demonstrate the project qualification throughput of 450 kg by the end of calendar year 2009. The NEXT LDT has demonstrated 30,352 hr of operation and processed 490 kg of xenon throughput--surpassing the NSTAR Extended Life Test hours demonstrated and more than double the throughput demonstrated by the NSTAR flight-spare. Thruster performance changes have been consistent with a priori predictions. Thruster erosion has been minimal and consistent with the thruster service life assessment, which predicts the first failure mode at greater than 750 kg throughput. The life-limiting failure mode for NEXT is predicted to be loss of structural integrity of the accelerator grid due to erosion by charge-exchange ions.

Endogenous Opiate System and Systematic Desensitization.

ERIC Educational Resources Information Center

Egan, Kelly J.; And Others

1988-01-01

Administered intravenous infusions to phobic patients prior to systematic desensitization. Saline-infused subjects significantly demonstrated the predicted symptom decrease in response to systematic desensitization, whereas naloxone-infused subjects showed no change. Subject reports and psychophysiological measures of arousal indicated no…

Inspiratory muscular weakness is most evident in chronic stroke survivors with lower walking speeds.

PubMed

Pinheiro, M B; Polese, J C; Faria, C D; Machado, G C; Parreira, V F; Britto, R R; Teixeira-Salmela, L F

2014-06-01

Respiratory muscular weakness and associated changes in thoracoabdominal motion have been poorly studied in stroke subjects, since the individuals' functional levels were not previously considered in the investigations. To investigate the breathing patterns, thoracoabdominal motion, and respiratory muscular strength in chronic stroke subjects, who were stratified into two groups, according to their walking speeds. Cross-sectional, observational study. University laboratory. Eighty-nine community-dwelling chronic stroke subjects The subjects, according to their gait speeds, were stratified into community (gait speed ≥0.8 m/s) and non-community ambulators (gait speed <0.8 m/s). Variables related to pulmonary function, breathing patterns, and thoracoabdominal motions were assessed. Measures of maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP) were obtained and were compared with the reference values for the Brazilian population. The MIP and MEP values were expressed as percentages of the predicted values. Mann-Whitney-U or independent Student t-tests were employed to compare the differences between the two groups for the selected variables. No significant between-group differences were found for the variables related to the breathing patterns and thoracoabdominal motions (0.01 < z/t < 1.51; 0.14

Detection of colorectal neoplasia: Combination of eight blood-based, cancer-associated protein biomarkers.

PubMed

Wilhelmsen, Michael; Christensen, Ib J; Rasmussen, Louise; Jørgensen, Lars N; Madsen, Mogens R; Vilandt, Jesper; Hillig, Thore; Klaerke, Michael; Nielsen, Knud T; Laurberg, Søren; Brünner, Nils; Gawel, Susan; Yang, Xiaoqing; Davis, Gerard; Heijboer, Annemieke; Martens, Frans; Nielsen, Hans J

2017-03-15

Serological biomarkers may be an option for early detection of colorectal cancer (CRC). The present study assessed eight cancer-associated protein biomarkers in plasma from subjects undergoing first time ever colonoscopy due to symptoms attributable to colorectal neoplasia. Plasma AFP, CA19-9, CEA, hs-CRP, CyFra21-1, Ferritin, Galectin-3 and TIMP-1 were determined in EDTA-plasma using the Abbott ARCHITECT® automated immunoassay platform. Primary endpoints were detection of (i) CRC and high-risk adenoma and (ii) CRC. Logistic regression was performed. Final reduced models were constructed selecting the four biomarkers with the highest likelihood scores. Subjects (N = 4,698) were consecutively included during 2010-2012. Colonoscopy detected 512 CRC patients, 319 colonic cancer and 193 rectal cancer. Extra colonic malignancies were detected in 177 patients, 689 had adenomas of which 399 were high-risk, 1,342 had nonneoplastic bowell disease and 1,978 subjects had 'clean' colorectum. Univariable analysis demonstrated that all biomarkers were statistically significant. Multivariate logistic regression demonstrated that the blood-based biomarkers in combination significantly predicted the endpoints. The reduced model resulted in the selection of CEA, hs-CRP, CyFra21-1 and Ferritin for the two endpoints; AUCs were 0.76 and 0.84, respectively. The postive predictive value at 90% sensitivity was 25% for endpoint 1 and the negative predictive value was 93%. For endpoint 2, the postive predictive value was 18% and the negative predictive value was 97%. Combinations of serological protein biomarkers provided a significant identification of subjects with high risk of the presence of colorectal neoplasia. The present set of biomarkers could become important adjunct in early detection of CRC. © 2016 UICC.

Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods

NASA Astrophysics Data System (ADS)

Mahynski, Nathan A.; Blanco, Marco A.; Errington, Jeffrey R.; Shen, Vincent K.

2017-02-01

We present a method for predicting the free energy landscape of fluids at low temperatures from flat-histogram grand canonical Monte Carlo simulations performed at higher ones. We illustrate our approach for both pure and multicomponent systems using two different sampling methods as a demonstration. This allows us to predict the thermodynamic behavior of systems which undergo both first order and continuous phase transitions upon cooling using simulations performed only at higher temperatures. After surveying a variety of different systems, we identify a range of temperature differences over which the extrapolation of high temperature simulations tends to quantitatively predict the thermodynamic properties of fluids at lower ones. Beyond this range, extrapolation still provides a reasonably well-informed estimate of the free energy landscape; this prediction then requires less computational effort to refine with an additional simulation at the desired temperature than reconstruction of the surface without any initial estimate. In either case, this method significantly increases the computational efficiency of these flat-histogram methods when investigating thermodynamic properties of fluids over a wide range of temperatures. For example, we demonstrate how a binary fluid phase diagram may be quantitatively predicted for many temperatures using only information obtained from a single supercritical state.

Interaction of CD38 Variant and Chronic Interpersonal Stress Prospectively Predicts Social Anxiety and Depression Symptoms Over Six Years

PubMed Central

Tabak, Benjamin A.; Vrshek-Schallhorn, Suzanne; Zinbarg, Richard E.; Prenoveau, Jason M.; Mineka, Susan; Redei, Eva E.; Adam, Emma K.; Craske, Michelle G.

2015-01-01

Variation in the CD38 gene, which regulates secretion of the neuropeptide oxytocin, has been associated with several social phenotypes. Specifically, rs3796863 A allele carriers have demonstrated increased social sensitivity. In 400 older adolescents, we used trait-state-occasion modeling to investigate how rs3796863 genotype, baseline ratings of chronic interpersonal stress, and their gene-environment (GxE) interaction predicted trait social anxiety and depression symptoms over six years. We found significant GxE effects for CD38 A-carrier genotypes and chronic interpersonal stress at baseline predicting greater social anxiety and depression symptoms. A significant GxE effect of smaller magnitude was also found for C/C genotype and chronic interpersonal stress predicting greater depression; however, this effect was small compared to the main effect of chronic interpersonal stress. Thus, in the context of chronic interpersonal stress, heightened social sensitivity associated with the rs3796863 A allele may prospectively predict risk for social anxiety and (to a lesser extent) depression. PMID:26958455

Voidage correction algorithm for unresolved Euler-Lagrange simulations

NASA Astrophysics Data System (ADS)

Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan

2018-04-01

The effect of grid coarsening on the predicted total drag force and heat exchange rate in dense gas-particle flows is investigated using Euler-Lagrange (EL) approach. We demonstrate that grid coarsening may reduce the predicted total drag force and exchange rate. Surprisingly, exchange coefficients predicted by the EL approach deviate more significantly from the exact value compared to results of Euler-Euler (EE)-based calculations. The voidage gradient is identified as the root cause of this peculiar behavior. Consequently, we propose a correction algorithm based on a sigmoidal function to predict the voidage experienced by individual particles. Our correction algorithm can significantly improve the prediction of exchange coefficients in EL models, which is tested for simulations involving Euler grid cell sizes between 2d_p and 12d_p . It is most relevant in simulations of dense polydisperse particle suspensions featuring steep voidage profiles. For these suspensions, classical approaches may result in an error of the total exchange rate of up to 30%.

Predictors of early growth in academic achievement: the head-toes-knees-shoulders task

PubMed Central

McClelland, Megan M.; Cameron, Claire E.; Duncan, Robert; Bowles, Ryan P.; Acock, Alan C.; Miao, Alicia; Pratt, Megan E.

2014-01-01

Children's behavioral self-regulation and executive function (EF; including attentional or cognitive flexibility, working memory, and inhibitory control) are strong predictors of academic achievement. The present study examined the psychometric properties of a measure of behavioral self-regulation called the Head-Toes-Knees-Shoulders (HTKS) by assessing construct validity, including relations to EF measures, and predictive validity to academic achievement growth between prekindergarten and kindergarten. In the fall and spring of prekindergarten and kindergarten, 208 children (51% enrolled in Head Start) were assessed on the HTKS, measures of cognitive flexibility, working memory (WM), and inhibitory control, and measures of emergent literacy, mathematics, and vocabulary. For construct validity, the HTKS was significantly related to cognitive flexibility, working memory, and inhibitory control in prekindergarten and kindergarten. For predictive validity in prekindergarten, a random effects model indicated that the HTKS significantly predicted growth in mathematics, whereas a cognitive flexibility task significantly predicted growth in mathematics and vocabulary. In kindergarten, the HTKS was the only measure to significantly predict growth in all academic outcomes. An alternative conservative analytical approach, a fixed effects analysis (FEA) model, also indicated that growth in both the HTKS and measures of EF significantly predicted growth in mathematics over four time points between prekindergarten and kindergarten. Results demonstrate that the HTKS involves cognitive flexibility, working memory, and inhibitory control, and is substantively implicated in early achievement, with the strongest relations found for growth in achievement during kindergarten and associations with emergent mathematics. PMID:25071619

Ring-enhancement pattern on contrast-enhanced CT predicts adenosquamous carcinoma of the pancreas: a matched case-control study.

PubMed

Imaoka, Hiroshi; Shimizu, Yasuhiro; Mizuno, Nobumasa; Hara, Kazuo; Hijioka, Susumu; Tajika, Masahiro; Tanaka, Tsutomu; Ishihara, Makoto; Ogura, Takeshi; Obayashi, Tomohiko; Shinagawa, Akihide; Sakaguchi, Masafumi; Yamaura, Hidekazu; Kato, Mina; Niwa, Yasumasa; Yamao, Kenji

2014-01-01

Adenosquamous carcinoma of the pancreas (ASC) is a rare malignant neoplasm of the pancreas, exhibiting both glandular and squamous differentiation. However, little is known about its imaging features. This study examined the imaging features of pancreatic ASC. We evaluated images of contrast-enhanced computed tomography (CT) and endoscopic ultrasonography (EUS). As controls, solid pancreatic neoplasms matched in a 2:1 ratio to ASC cases for age, sex and tumor location were also evaluated. Twenty-three ASC cases were examined, and 46 solid pancreatic neoplasms (43 pancreatic ductal adenocarcinomas, two pancreatic neuroendocrine tumors and one acinar cell carcinoma) were matched as controls. Univariate analysis demonstrated significant differences in the outline and vascularity of tumors on contrast-enhanced CT in the ASC and control groups (P < 0.001 and P < 0.001, respectively). A smooth outline, cystic changes, and the ring-enhancement pattern on contrast-enhanced CT were seen to have significant predictive powers by stepwise forward logistic regression analysis (P = 0.044, P = 0.010, and P = 0.001, respectively). Of the three, the ring-enhancement pattern was the most useful, and its predictive diagnostic sensitivity, specificity, positive predictive value and negative predictive value for diagnosis of ASC were 65.2%, 89.6%, 75.0% and 84.3%, respectively. These results demonstrate that presence of the ring-enhancement pattern on contrast-enhanced CT is the most useful predictive factor for ASC. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

UK Environmental Prediction - integration and evaluation at the convective scale

NASA Astrophysics Data System (ADS)

Lewis, Huw; Brunet, Gilbert; Harris, Chris; Best, Martin; Saulter, Andrew; Holt, Jason; Bricheno, Lucy; Brerton, Ashley; Reynard, Nick; Blyth, Eleanor; Martinez de la Torre, Alberto

2015-04-01

It has long been understood that accurate prediction and warning of the impacts of severe weather requires an integrated approach to forecasting. This was well demonstrated in the UK throughout winter 2013/14 when an exceptional run of severe winter storms, often with damaging high winds and intense rainfall led to significant damage from the large waves and storm surge along coastlines, and from saturated soils, high river flows and significant flooding inland. The substantial impacts on individuals, businesses and infrastructure indicate a pressing need to understand better the value that might be delivered through more integrated environmental prediction. To address this need, the Met Office, Centre for Ecology & Hydrology and National Oceanography Centre have begun to develop the foundations of a coupled high resolution probabilistic forecast system for the UK at km-scale. This links together existing model components of the atmosphere, coastal ocean, land surface and hydrology. Our initial focus on a 2-year Prototype project will demonstrate the UK coupled prediction concept in research mode, including an analysis of the winter 2013/14 storms and its impacts. By linking science development to operational collaborations such as the UK Natural Hazards Partnership, we can ensure that science priorities are rooted in user requirements. This presentation will provide an overview of UK environmental prediction activities and an update on progress during the first year of the Prototype project. We will present initial results from the coupled model development and discuss the challenges to realise the potential of integrated regional coupled forecasting for improving predictions and applications.

Helicopter rotor wake geometry and its influence in forward flight. Volume 1: Generalized wake geometry and wake effect on rotor airloads and performance

NASA Technical Reports Server (NTRS)

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

1983-01-01

An analytic investigation to generalize wake geometry of a helicopter rotor in steady level forward flight and to demonstrate the influence of wake deformation in the prediction of rotor airloads and performance is described. Volume 1 presents a first level generalized wake model based on theoretically predicted tip vortex geometries for a selected representative blade design. The tip vortex distortions are generalized in equation form as displacements from the classical undistorted tip vortex geometry in terms of vortex age, blade azimuth, rotor advance ratio, thrust coefficient, and number of blades. These equations were programmed to provide distorted wake coordinates at very low cost for use in rotor airflow and airloads prediction analyses. The sensitivity of predicted rotor airloads, performance, and blade bending moments to the modeling of the tip vortex distortion are demonstrated for low to moderately high advance ratios for a representative rotor and the H-34 rotor. Comparisons with H-34 rotor test data demonstrate the effects of the classical, predicted distorted, and the newly developed generalized wake models on airloads and blade bending moments. Use of distorted wake models results in the occurrence of numerous blade-vortex interactions on the forward and lateral sides of the rotor disk. The significance of these interactions is related to the number and degree of proximity to the blades of the tip vortices. The correlation obtained with the distorted wake models (generalized and predicted) is encouraging.

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study.

PubMed

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-03-27

Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1-7.2. Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1-7.2 is most promising.

View of God as benevolent and forgiving or punishing and judgmental predicts HIV disease progression.

PubMed

Ironson, Gail; Stuetzle, Rick; Ironson, Dale; Balbin, Elizabeth; Kremer, Heidemarie; George, Annie; Schneiderman, Neil; Fletcher, Mary Ann

2011-12-01

This study assessed the predictive relationship between View of God beliefs and change in CD4-cell and Viral Load (VL) in HIV positive people over an extended period. A diverse sample of HIVseropositive participants (N = 101) undergoing comprehensive psychological assessment and blood draws over the course of 4 years completed the View of God Inventory with subscales measuring Positive View (benevolent/forgiving) and Negative View of God (harsh/judgmental/punishing). Adjusting for initial disease status, age, gender, ethnicity, education, and antiretroviral medication (at every 6-month visit), a Positive View of God predicted significantly slower disease-progression (better preservation of CD4-cells, better control of VL), whereas a Negative View of God predicted faster disease-progression over 4 years. Effect sizes were greater than those previously demonstrated for psychosocial variables known to predict HIV-disease-progression, such as depression and coping. Results remained significant even after adjusting for church attendance and psychosocial variables (health behaviors, mood, and coping). These results provide good initial evidence that spiritual beliefs may predict health outcomes.

Preschool Inhibitory Control Predicts ADHD Group Status and Inhibitory Weakness in School.

PubMed

Jacobson, Lisa A; Schneider, Heather; Mahone, E Mark

2017-12-26

Discriminative utility of performance measures of inhibitory control was examined in preschool children with and without ADHD to determine whether performance measures added to diagnostic prediction and to prediction of informant-rated day-to-day executive function. Children ages 4-5 years (N = 105, 61% boys; 54 ADHD, medication-naïve) were assessed using performance measures (Auditory Continuous Performance Test for Preschoolers-Commission errors, Conflicting Motor Response Test, NEPSY Statue) and caregiver (parent, teacher) ratings of inhibition (Behavior Rating Inventory of Executive Function-Preschool version). Performance measures and parent and teacher reports of inhibitory control significantly and uniquely predicted ADHD group status; however, performance measures did not add to prediction of group status beyond parent reports. Performance measures did significantly predict classroom inhibitory control (teacher ratings), over and above parent reports of inhibitory control. Performance measures of inhibitory control may be adequate predictors of ADHD status and good predictors of young children's classroom inhibitory control, demonstrating utility as components of clinical assessments. © The Author(s) 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auralization Architectures for NASA?s Next Generation Aircraft Noise Prediction Program

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Lopes, Leonard V.; Burley, Casey L.; Aumann, Aric R.

2013-01-01

Aircraft community noise is a significant concern due to continued growth in air traffic, increasingly stringent environmental goals, and operational limitations imposed by airport authorities. The assessment of human response to noise from future aircraft can only be afforded through laboratory testing using simulated flyover noise. Recent work by the authors demonstrated the ability to auralize predicted flyover noise for a state-of-the-art reference aircraft and a future hybrid wing body aircraft concept. This auralization used source noise predictions from NASA's Aircraft NOise Prediction Program (ANOPP) as input. The results from this process demonstrated that auralization based upon system noise predictions is consistent with, and complementary to, system noise predictions alone. To further develop and validate the auralization process, improvements to the interfaces between the synthesis capability and the system noise tools are required. This paper describes the key elements required for accurate noise synthesis and introduces auralization architectures for use with the next-generation ANOPP (ANOPP2). The architectures are built around a new auralization library and its associated Application Programming Interface (API) that utilize ANOPP2 APIs to access data required for auralization. The architectures are designed to make the process of auralizing flyover noise a common element of system noise prediction.

An Exploratory Application of Neural Networks to the Sortie Generation Forecasting Problem

DTIC Science & Technology

1991-09-01

modifiable--a configuration clearly inspired by neurophysiology . Rosenblatt and others effectively demonstrated that in using such an architectural...different perspectives (i.e., longitudinally and cross-sectionally), significance testing between prediction results may promote more enlightened network

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Double Dissociation in the Anatomy of Socioemotional Disinhibition and Executive Functioning in Dementia

PubMed Central

Krueger, Casey E.; Laluz, Victor; Rosen, Howard J.; Neuhaus, John M.; Miller, Bruce L.; Kramer, Joel H.

2010-01-01

Objective To determine if socioemotional disinhibition and executive dysfunction are related to dissociable patterns of brain atrophy in neurodegenerative disease. Previous studies have indicated that behavioral and cognitive dysfunction in neurodegenerative disease are linked to atrophy in different parts of the frontal lobe, but these prior studies did not establish that these relationships were specific, which would best be demonstrated by a double dissociation. Method Subjects included 157 patients with neurodegenerative disease. A semi-automated parcellation program (Freesurfer) was used to generate regional cortical volumes from structural MRI scans. Regions of interest (ROIs) included anterior cingulate cortex (ACC), orbitofrontal cortex (OFC), middle frontal gyrus (MFG) and inferior frontal gyrus (IFG). Socioemotional disinhibition was measured using the Neuropsychiatric Inventory. Principal component analysis including three tasks of executive function (EF; verbal fluency, Stroop Interference, modified Trails) was used to generate a single factor score to represent EF. Results Partial correlations between ROIs, disinhibition, and EF were computed after controlling for total intracranial volume, MMSE, diagnosis, age, and education. Brain regions significantly correlated with disinhibition (ACC, OFC, IFG, and temporal lobes) and EF (MFG) were entered into separate hierarchical regressions to determine which brain regions predicted disinhibition and EF. OFC was the only brain region to significantly predict disinhibition and MFG significantly predicted executive functioning performance. A multivariate general linear model demonstrated a significant interaction between ROIs and cognitive-behavioral functions. Conclusions These results support a specific association between orbitofrontal areas and behavioral management as compared to dorsolateral areas and EF. PMID:21381829

Value of intracochlear electrically evoked auditory brainstem response after cochlear implantation in patients with narrow internal auditory canal.

PubMed

Song, Mee Hyun; Bae, Mi Ran; Kim, Hee Nam; Lee, Won-Sang; Yang, Won Sun; Choi, Jae Young

2010-08-01

Cochlear implantation in patients with narrow internal auditory canal (IAC) can result in variable outcomes; however, preoperative evaluations have limitations in accurately predicting outcomes. In this study, we analyzed the outcomes of cochlear implantation in patients with narrow IAC and correlated the intracochlear electrically evoked auditory brainstem response (EABR) findings to postoperative performance to determine the prognostic significance of intracochlear EABR. Retrospective case series at a tertiary hospital. Thirteen profoundly deaf patients with narrow IAC who received cochlear implantation from 2002 to 2008 were included in this study. Postoperative performance was evaluated after at least 12 months of follow-up, and postoperative intracochlear EABR was measured to determine its correlation with outcome. The clinical significance of electrically evoked compound action potential (ECAP) was also analyzed. Patients with narrow IAC showed postoperative auditory performances ranging from CAP 0 to 4 after cochlear implantation. Intracochlear EABR measured postoperatively demonstrated prognostic value in the prediction of long-term outcomes, whereas ECAP measurements failed to show a significant correlation with outcome. Consistent with the advantages of intracochlear EABR over extracochlear EABR, this study demonstrates that intracochlear EABR has prognostic significance in predicting long-term outcomes in patients with narrow IAC. Intracochlear EABR measured either intraoperatively or in the early postoperative period may play an important role in deciding whether to continue with auditory rehabilitation using a cochlear implant or to switch to an auditory brainstem implant so as not to miss the optimal timing for language development.

Reconciling GRACE and GPS estimates of long-term load deformation in southern Greenland

NASA Astrophysics Data System (ADS)

Wang, Song-Yun; Chen, J. L.; Wilson, Clark R.; Li, Jin; Hu, Xiaogong

2018-02-01

We examine vertical load deformation at four continuous Global Positioning System (GPS) sites in southern Greenland relative to Gravity Recovery and Climate Experiment (GRACE) predictions of vertical deformation over the period 2002-2016. With limited spatial resolution, GRACE predictions require adjustment before they can be compared with GPS height time series. Without adjustment, both GRACE spherical harmonic (SH) and mascon solutions predict significant vertical displacement rate differences relative to GPS. We use a scaling factor method to adjust GRACE results, based on a long-term mass rate model derived from GRACE measurements, glacial geography, and ice flow data. Adjusted GRACE estimates show significantly improved agreement with GPS, both in terms of long-term rates and interannual variations. A deceleration of mass loss is observed in southern Greenland since early 2013. The success at reconciling GPS and GRACE observations with a more detailed mass rate model demonstrates the high sensitivity to load distribution in regions surrounding GPS stations. Conversely, the value of GPS observations in constraining mass changes in surrounding regions is also demonstrated. In addition, our results are consistent with recent estimates of GIA uplift (˜4.4 mm yr-1) at the KULU site.

Computational toxicology: Its essential role in reducing drug attrition.

PubMed

Naven, R T; Louise-May, S

2015-12-01

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them. © The Author(s) 2015.

Longitudinal Study-Based Dementia Prediction for Public Health

PubMed Central

Kim, HeeChel; Chun, Hong-Woo; Kim, Seonho; Coh, Byoung-Youl; Kwon, Oh-Jin; Moon, Yeong-Ho

2017-01-01

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly. PMID:28867810

Wave-Rotor-Enhanced Gas Turbine Engine Demonstrator

NASA Technical Reports Server (NTRS)

Welch, Gerard E.; Paxson, Daniel E.; Wilson, Jack; Synder, Philip H.

1999-01-01

The U.S. Army Research Laboratory, NASA Glenn Research Center, and Rolls-Royce Allison are working collaboratively to demonstrate the benefits and viability of a wave-rotor-topped gas turbine engine. The self-cooled wave rotor is predicted to increase the engine overall pressure ratio and peak temperature by 300% and 25 to 30%. respectively, providing substantial improvements in engine efficiency and specific power. Such performance improvements would significantly reduce engine emissions and the fuel logistics trails of armed forces. Progress towards a planned demonstration of a wave-rotor-topped Rolls-Royce Allison model 250 engine has included completion of the preliminary design and layout of the engine, the aerodynamic design of the wave rotor component and prediction of its aerodynamic performance characteristics in on- and off-design operation and during transients, and the aerodynamic design of transition ducts between the wave rotor and the high pressure turbine. The topping cycle increases the burner entry temperature and poses a design challenge to be met in the development of the demonstrator engine.

Predicting Gene Structures from Multiple RT-PCR Tests

NASA Astrophysics Data System (ADS)

Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.

The habenula governs the attribution of incentive salience to reward predictive cues

PubMed Central

Danna, Carey L.; Shepard, Paul D.; Elmer, Greg I.

2013-01-01

The attribution of incentive salience to reward associated cues is critical for motivation and the pursuit of rewards. Disruptions in the integrity of the neural systems controlling these processes can lead to avolition and anhedonia, symptoms that cross the diagnostic boundaries of many neuropsychiatric illnesses. Here, we consider whether the habenula (Hb), a region recently demonstrated to encode negatively valenced events, also modulates the attribution of incentive salience to a neutral cue predicting a food reward. The Pavlovian autoshaping paradigm was used in the rat as an investigative tool to dissociate Pavlovian learning processes imparting strictly predictive value from learning that attributes incentive motivational value. Electrolytic lesions of the fasciculus retroflexus (fr), the sole pathway through which descending Hb efferents are conveyed, significantly increased incentive salience as measured by conditioned approaches to a cue light predictive of reward. Conversely, generation of a fictive Hb signal via fr stimulation during CS+ presentation significantly decreased the incentive salience of the predictive cue. Neither manipulation altered the reward predictive value of the cue as measured by conditioned approach to the food. Our results provide new evidence supporting a significant role for the Hb in governing the attribution of incentive motivational salience to reward predictive cues and further imply that pathological changes in Hb activity could contribute to the aberrant pursuit of debilitating goals or avolition and depression-like symptoms. PMID:24368898

Computational fluid dynamics (CFD) using porous media modeling predicts recurrence after coiling of cerebral aneurysms.

PubMed

Umeda, Yasuyuki; Ishida, Fujimaro; Tsuji, Masanori; Furukawa, Kazuhiro; Shiba, Masato; Yasuda, Ryuta; Toma, Naoki; Sakaida, Hiroshi; Suzuki, Hidenori

2017-01-01

This study aimed to predict recurrence after coil embolization of unruptured cerebral aneurysms with computational fluid dynamics (CFD) using porous media modeling (porous media CFD). A total of 37 unruptured cerebral aneurysms treated with coiling were analyzed using follow-up angiograms, simulated CFD prior to coiling (control CFD), and porous media CFD. Coiled aneurysms were classified into stable or recurrence groups according to follow-up angiogram findings. Morphological parameters, coil packing density, and hemodynamic variables were evaluated for their correlations with aneurysmal recurrence. We also calculated residual flow volumes (RFVs), a novel hemodynamic parameter used to quantify the residual aneurysm volume after simulated coiling, which has a mean fluid domain > 1.0 cm/s. Follow-up angiograms showed 24 aneurysms in the stable group and 13 in the recurrence group. Mann-Whitney U test demonstrated that maximum size, dome volume, neck width, neck area, and coil packing density were significantly different between the two groups (P < 0.05). Among the hemodynamic parameters, aneurysms in the recurrence group had significantly larger inflow and outflow areas in the control CFD and larger RFVs in the porous media CFD. Multivariate logistic regression analyses demonstrated that RFV was the only independently significant factor (odds ratio, 1.06; 95% confidence interval, 1.01-1.11; P = 0.016). The study findings suggest that RFV collected under porous media modeling predicts the recurrence of coiled aneurysms.

Characteristics of Socially Successful Elementary School-Aged Children with Autism

PubMed Central

Locke, Jill; Williams, Justin; Shih, Wendy; Kasari, Connie

2016-01-01

Background The extant literature demonstrates that children with autism spectrum disorder (ASD) often have difficulty interacting and socially connecting with typically developing classmates. However, some children with ASD have social outcomes that are consistent with their typically developing counterparts. Little is known about this subgroup of children with ASD. This study examined the stable (unlikely to change) and malleable (changeable) characteristics of socially successful children with ASD. Methods This study used baseline data from three intervention studies performed in public schools in the Southwestern United States. A total of 148 elementary-aged children with ASD in 130 classrooms in 47 public schools participated. Measures of playground peer engagement and social network salience (inclusion in informal peer groups) were obtained. Results The results demonstrated that a number of malleable factors significantly predicted playground peer engagement (class size, autism symptom severity, peer connections) and social network salience (autism symptom severity, peer connections, received friendships). In addition, age was the only stable factor that significantly predicted social network salience. Interestingly, two malleable (i.e., peer connections and received friendships) and no stable factors (i.e., age, IQ, sex) predicted overall social success (e.g., high playground peer engagement and social network salience) in children with ASD. Conclusions School-based interventions should address malleable factors such as the number of peer connections and received friendships that predict the best social outcomes for children with ASD. PMID:27620949

Persistent hemifacial spasm after microvascular decompression: a risk assessment model.

PubMed

Shah, Aalap; Horowitz, Michael

2017-06-01

Microvascular decompression (MVD) for hemifacial spasm (HFS) provides resolution of disabling symptoms such as eyelid twitching and muscle contractions of the entire hemiface. The primary aim of this study was to evaluate the predictive value of patient demographics and spasm characteristics on long-term outcomes, with or without intraoperative lateral spread response (LSR) as an additional variable in a risk assessment model. A retrospective study was undertaken to evaluate the associations of pre-operative patient characteristics, as well as intraoperative LSR and need for a staged procedure on the presence of persistent or recurrent HFS at the time of hospital discharge and at follow-up. A risk assessment model was constructed with the inclusion of six clinically or statistically significant variables from the univariate analyses. A receiving operator characteristic curve was generated, and area under the curve was calculated to determine the strength of the predictive model. A risk assessment model was first created consisting of significant pre-operative variables (Model 1) (age >50, female gender, history of botulinum toxin use, platysma muscle involvement). This model demonstrated borderline predictive value for persistent spasm at discharge (AUC .60; p=.045) and fair predictive value at follow-up (AUC .75; p=.001). Intraoperative variables (e.g. LSR persistence) demonstrated little additive value (Model 2) (AUC .67). Patients with a higher risk score (three or greater) demonstrated greater odds of persistent HFS at the time of discharge (OR 1.5 [95%CI 1.16-1.97]; p=.035), as well as greater odds of persistent or recurrent spasm at the time of follow-up (OR 3.0 [95%CI 1.52-5.95]; p=.002) Conclusions: A risk assessment model consisting of pre-operative clinical characteristics is useful in prognosticating HFS persistence at follow-up.

Description of Selected Algorithms and Implementation Details of a Concept-Demonstration Aircraft VOrtex Spacing System (AVOSS)

NASA Technical Reports Server (NTRS)

Hinton, David A.

2001-01-01

A ground-based system has been developed to demonstrate the feasibility of automating the process of collecting relevant weather data, predicting wake vortex behavior from a data base of aircraft, prescribing safe wake vortex spacing criteria, estimating system benefit, and comparing predicted and observed wake vortex behavior. This report describes many of the system algorithms, features, limitations, and lessons learned, as well as suggested system improvements. The system has demonstrated concept feasibility and the potential for airport benefit. Significant opportunities exist however for improved system robustness and optimization. A condensed version of the development lab book is provided along with samples of key input and output file types. This report is intended to document the technical development process and system architecture, and to augment archived internal documents that provide detailed descriptions of software and file formats.

A Common Polymorphism in SCN2A Predicts General Cognitive Ability through Effects on PFC Physiology.

PubMed

Scult, Matthew A; Trampush, Joey W; Zheng, Fengyu; Conley, Emily Drabant; Lencz, Todd; Malhotra, Anil K; Dickinson, Dwight; Weinberger, Daniel R; Hariri, Ahmad R

2015-09-01

Here we provide novel convergent evidence across three independent cohorts of healthy adults (n = 531), demonstrating that a common polymorphism in the gene encoding the α2 subunit of neuronal voltage-gated type II sodium channels (SCN2A) predicts human general cognitive ability or "g." Using meta-analysis, we demonstrate that the minor T allele of a common polymorphism (rs10174400) in SCN2A is associated with significantly higher "g" independent of gender and age. We further demonstrate using resting-state fMRI data from our discovery cohort (n = 236) that this genetic advantage may be mediated by increased capacity for information processing between the dorsolateral PFC and dorsal ACC, which support higher cognitive functions. Collectively, these findings fill a gap in our understanding of the genetics of general cognitive ability and highlight a specific neural mechanism through which a common polymorphism shapes interindividual variation in "g."

Parameter Uncertainty for Aircraft Aerodynamic Modeling using Recursive Least Squares

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2016-01-01

A real-time method was demonstrated for determining accurate uncertainty levels of stability and control derivatives estimated using recursive least squares and time-domain data. The method uses a recursive formulation of the residual autocorrelation to account for colored residuals, which are routinely encountered in aircraft parameter estimation and change the predicted uncertainties. Simulation data and flight test data for a subscale jet transport aircraft were used to demonstrate the approach. Results showed that the corrected uncertainties matched the observed scatter in the parameter estimates, and did so more accurately than conventional uncertainty estimates that assume white residuals. Only small differences were observed between batch estimates and recursive estimates at the end of the maneuver. It was also demonstrated that the autocorrelation could be reduced to a small number of lags to minimize computation and memory storage requirements without significantly degrading the accuracy of predicted uncertainty levels.

The clinical evaluation of the CADence device in the acoustic detection of coronary artery disease.

PubMed

Thomas, Joseph L; Ridner, Michael; Cole, Jason H; Chambers, Jeffrey W; Bokhari, Sabahat; Yannopoulos, Demetris; Kern, Morton; Wilson, Robert F; Budoff, Matthew J

2018-06-23

The noninvasive detection of turbulent coronary flow may enable diagnosis of significant coronary artery disease (CAD) using novel sensor and analytic technology. Eligible patients (n = 1013) with chest pain and CAD risk factors undergoing nuclear stress testing were studied using the CADence (AUM Cardiovascular Inc., Northfield MN) acoustic detection (AD) system. The trial was designed to demonstrate non-inferiority of AD for diagnostic accuracy in detecting significant CAD as compared to an objective performance criteria (sensitivity 83% and specificity 80%, with 15% non-inferiority margins) for nuclear stress testing. AD analysis was blinded to clinical, core lab-adjudicated angiographic, and nuclear data. The presence of significant CAD was determined by computed tomographic (CCTA) or invasive angiography. A total of 1013 subjects without prior coronary revascularization or Q-wave myocardial infarction were enrolled. Primary analysis was performed on subjects with complete angiographic and AD data (n = 763) including 111 subjects (15%) with severe CAD based on CCTA (n = 34) and invasive angiography (n = 77). The sensitivity and specificity of AD were 78% (p = 0.012 for non-inferiority) and 35% (p < 0.001 for failure to demonstrate non-inferiority), respectively. AD results had a high 91% negative predictive value for the presence of significant CAD. AD testing failed to demonstrate non-inferior diagnostic accuracy as compared to the historical performance of a nuclear stress OPC due to low specificity. AD sensitivity was non-inferior in detecting significant CAD with a high negative predictive value supporting a potential value in excluding CAD.

One- and two-stage Arrhenius models for pharmaceutical shelf life prediction.

PubMed

Fan, Zhewen; Zhang, Lanju

2015-01-01

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.

Accuracy of the actuator disc-RANS approach for predicting the performance and wake of tidal turbines.

PubMed

Batten, W M J; Harrison, M E; Bahaj, A S

2013-02-28

The actuator disc-RANS model has widely been used in wind and tidal energy to predict the wake of a horizontal axis turbine. The model is appropriate where large-scale effects of the turbine on a flow are of interest, for example, when considering environmental impacts, or arrays of devices. The accuracy of the model for modelling the wake of tidal stream turbines has not been demonstrated, and flow predictions presented in the literature for similar modelled scenarios vary significantly. This paper compares the results of the actuator disc-RANS model, where the turbine forces have been derived using a blade-element approach, to experimental data measured in the wake of a scaled turbine. It also compares the results with those of a simpler uniform actuator disc model. The comparisons show that the model is accurate and can predict up to 94 per cent of the variation in the experimental velocity data measured on the centreline of the wake, therefore demonstrating that the actuator disc-RANS model is an accurate approach for modelling a turbine wake, and a conservative approach to predict performance and loads. It can therefore be applied to similar scenarios with confidence.

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

Lightning Scaling Laws Revisited

NASA Technical Reports Server (NTRS)

Boccippio, D. J.; Arnold, James E. (Technical Monitor)

2000-01-01

Scaling laws relating storm electrical generator power (and hence lightning flash rate) to charge transport velocity and storm geometry were originally posed by Vonnegut (1963). These laws were later simplified to yield simple parameterizations for lightning based upon cloud top height, with separate parameterizations derived over land and ocean. It is demonstrated that the most recent ocean parameterization: (1) yields predictions of storm updraft velocity which appear inconsistent with observation, and (2) is formally inconsistent with the theory from which it purports to derive. Revised formulations consistent with Vonnegut's original framework are presented. These demonstrate that Vonnegut's theory is, to first order, consistent with observation. The implications of assuming that flash rate is set by the electrical generator power, rather than the electrical generator current, are examined. The two approaches yield significantly different predictions about the dependence of charge transfer per flash on storm dimensions, which should be empirically testable. The two approaches also differ significantly in their explanation of regional variability in lightning observations.

Increased prognostic accuracy of TBI when a brain electrical activity biomarker is added to loss of consciousness (LOC).

PubMed

Hack, Dallas; Huff, J Stephen; Curley, Kenneth; Naunheim, Roseanne; Ghosh Dastidar, Samanwoy; Prichep, Leslie S

2017-07-01

Extremely high accuracy for predicting CT+ traumatic brain injury (TBI) using a quantitative EEG (QEEG) based multivariate classification algorithm was demonstrated in an independent validation trial, in Emergency Department (ED) patients, using an easy to use handheld device. This study compares the predictive power using that algorithm (which includes LOC and amnesia), to the predictive power of LOC alone or LOC plus traumatic amnesia. ED patients 18-85years presenting within 72h of closed head injury, with GSC 12-15, were study candidates. 680 patients with known absence or presence of LOC were enrolled (145 CT+ and 535 CT- patients). 5-10min of eyes closed EEG was acquired using the Ahead 300 handheld device, from frontal and frontotemporal regions. The same classification algorithm methodology was used for both the EEG based and the LOC based algorithms. Predictive power was evaluated using area under the ROC curve (AUC) and odds ratios. The QEEG based classification algorithm demonstrated significant improvement in predictive power compared with LOC alone, both in improved AUC (83% improvement) and odds ratio (increase from 4.65 to 16.22). Adding RGA and/or PTA to LOC was not improved over LOC alone. Rapid triage of TBI relies on strong initial predictors. Addition of an electrophysiological based marker was shown to outperform report of LOC alone or LOC plus amnesia, in determining risk of an intracranial bleed. In addition, ease of use at point-of-care, non-invasive, and rapid result using such technology suggests significant value added to standard clinical prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Sensitivity, Specificity, Predictive Values, and Accuracy of Three Diagnostic Tests to Predict Inferior Alveolar Nerve Blockade Failure in Symptomatic Irreversible Pulpitis

PubMed Central

Rodríguez-Wong, Laura; Noguera-González, Danny; Esparza-Villalpando, Vicente; Montero-Aguilar, Mauricio

2017-01-01

Introduction The inferior alveolar nerve block (IANB) is the most common anesthetic technique used on mandibular teeth during root canal treatment. Its success in the presence of preoperative inflammation is still controversial. The aim of this study was to evaluate the sensitivity, specificity, predictive values, and accuracy of three diagnostic tests used to predict IANB failure in symptomatic irreversible pulpitis (SIP). Methodology A cross-sectional study was carried out on the mandibular molars of 53 patients with SIP. All patients received a single cartridge of mepivacaine 2% with 1 : 100000 epinephrine using the IANB technique. Three diagnostic clinical tests were performed to detect anesthetic failure. Anesthetic failure was defined as a positive painful response to any of the three tests. Sensitivity, specificity, predictive values, accuracy, and ROC curves were calculated and compared and significant differences were analyzed. Results IANB failure was determined in 71.7% of the patients. The sensitivity scores for the three tests (lip numbness, the cold stimuli test, and responsiveness during endodontic access) were 0.03, 0.35, and 0.55, respectively, and the specificity score was determined as 1 for all of the tests. Clinically, none of the evaluated tests demonstrated a high enough accuracy (0.30, 0.53, and 0.68 for lip numbness, the cold stimuli test, and responsiveness during endodontic access, resp.). A comparison of the areas under the curve in the ROC analyses showed statistically significant differences between the three tests (p < 0.05). Conclusion None of the analyzed tests demonstrated a high enough accuracy to be considered a reliable diagnostic tool for the prediction of anesthetic failure. PMID:28694714

The predictive capacity of perceived expressed emotion as a dynamic entity of adolescents from the general community.

PubMed

Hale, William W; Raaijmakers, Quinten A W; van Hoof, Anne; Meeus, Wim H J

2011-06-01

In previous studies, it has been demonstrated that high parental expressed emotion (EE) is predictive of depressive, aggressive and delinquency symptoms of adolescents. Two issues have received much less prominence in EE research, these being studies of adolescent perceived EE and the measurement of the EE as a dynamic, developmental construct. This 4-year, three-wave, longitudinal study of perceived EE of adolescents from the general community examines if adolescent perceived EE measured with the traditional, one-measurement EE approach as well as adolescent perceived EE measured with a repeated measured, dynamic EE approach can predict adolescent depressive, aggressive and delinquency symptoms. Dutch adolescents (N = 285; 51% girls; M = 13 years) from the general community were prospectively studied annually for 4 years. At all waves, the adolescents completed the Level of Expressed Emotion (LEE) questionnaire and at the final wave also completed self-rated measures of depressive, aggressive and delinquent symptoms. Growth models were used to predict adolescent symptoms from adolescent perceived EE. Growth models significantly predicted adolescent depressive, aggressive and delinquency symptoms from adolescent perceived EE. This study of the LEE demonstrates that developmental characteristics of EE are predictive of adolescents' symptoms. These findings hold implications for current EE intervention therapies and the conceptualization of EE.

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

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

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

Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Breast Cancer Pathologic Stage

PubMed Central

Burnside, Elizabeth S.; Drukker, Karen; Li, Hui; Bonaccio, Ermelinda; Zuley, Margarita; Ganott, Marie; Net, Jose M.; Sutton, Elizabeth; Brandt, Kathleen R.; Whitman, Gary; Conzen, Suzanne; Lan, Li; Ji, Yuan; Zhu, Yitan; Jaffe, Carl; Huang, Erich; Freymann, John; Kirby, Justin; Morris, Elizabeth; Giger, Maryellen

2015-01-01

Background To demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on MRI can accurately predict pathologic stage. Methods We used a dataset of de-identified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. We analyzed 91 biopsy-proven breast cancer cases with pathologic stage (stage I = 22; stage II = 58; stage III = 11) and surgically proven nodal status (negative nodes = 46, ≥ 1 positive node = 44, no nodes examined = 1). We characterized tumors by (a) radiologist measured size, and (b) CEIP. We built models combining two CEIPs to predict tumor pathologic stage and lymph node involvement, evaluated them in leave-one-out cross-validation with area under the ROC curve (AUC) as figure of merit. Results Tumor size was the most powerful predictor of pathologic stage but CEIPs capturing biologic behavior also emerged as predictive (e.g. stage I+II vs. III demonstrated AUC = 0.83). No size measure was successful in the prediction of positive lymph nodes but adding a CEIP describing tumor “homogeneity,” significantly improved this discrimination (AUC = 0.62, p=.003) over chance. Conclusions Our results indicate that MRI phenotypes show promise for predicting breast cancer pathologic stage and lymph node status. PMID:26619259

Predicting Intentions to Breastfeed for Three Months, Six Months, and One Year Using the Theory of Planned Behavior and Body Satisfaction.

PubMed

Johnson-Young, Elizabeth A

2018-02-27

Breastfeeding is one of the top maternal priorities for many organizations, including the World Health Organization (WHO), The American Academy of Pediatrics (AAP), and the Center for Disease Control (CDC). Focusing on the goals of Healthy People 2020, as well as the recommendations of other organizations, this paper investigates the impacts on women's intentions to breastfeed newborns for 3 months, 6 months, and 1 year. This research used the theory of planned behavior (TPB) as a model to predict intentions for each duration of time. Body satisfaction was included as a moderating variable given research demonstrating a possible connection of body satisfaction to breastfeeding. A survey of 156 pregnant women was conducted. Results demonstrated the importance of the three TPB measures in predicting intentions. Further, significant interactions between body satisfaction and attitudes, as well as body satisfaction and subjective norms were present in predicting intentions to exclusively breastfeed one's baby from infant to 6 months of age. Theoretical implications are discussed, as well as practical implications for breastfeeding interventions and campaigns.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

PubMed

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

New particle formation and growth in biomass burning plumes: An important source of cloud condensation nuclei

NASA Astrophysics Data System (ADS)

Hennigan, Christopher J.; Westervelt, Daniel M.; Riipinen, Ilona; Engelhart, Gabriella J.; Lee, Taehyoung; Collett, Jeffrey L., Jr.; Pandis, Spyros N.; Adams, Peter J.; Robinson, Allen L.

2012-05-01

Experiments were performed in an environmental chamber to characterize the effects of photo-chemical aging on biomass burning emissions. Photo-oxidation of dilute exhaust from combustion of 12 different North American fuels induced significant new particle formation that increased the particle number concentration by a factor of four (median value). The production of secondary organic aerosol caused these new particles to grow rapidly, significantly enhancing cloud condensation nuclei (CCN) concentrations. Using inputs derived from these new data, global model simulations predict that nucleation in photo-chemically aging fire plumes produces dramatically higher CCN concentrations over widespread areas of the southern hemisphere during the dry, burning season (Sept.-Oct.), improving model predictions of surface CCN concentrations. The annual indirect forcing from CCN resulting from nucleation and growth in biomass burning plumes is predicted to be -0.2 W m-2, demonstrating that this effect has a significant impact on climate that has not been previously considered.

High Pressure Regenerative Turbine Engine: 21st Century Propulsion

NASA Technical Reports Server (NTRS)

Lear, W. E.; Laganelli, A. L.; Senick, Paul (Technical Monitor)

2001-01-01

A novel semi-closed cycle gas turbine engine was demonstrated and was found to meet the program goals. The proof-of-principle test of the High Pressure Regenerative Turbine Engine produced data that agreed well with models, enabling more confidence in designing future prototypes based on this concept. Emission levels were significantly reduced as predicted as a natural attribute of this power cycle. Engine testing over a portion of the operating range allowed verification of predicted power increases compared to the baseline.

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

PubMed

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

2012-05-01

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

How coping styles, cognitive distortions, and attachment predict problem gambling among adolescents and young adults.

PubMed

Calado, Filipa; Alexandre, Joana; Griffiths, Mark D

2017-12-01

Background and aims Recent research suggests that youth problem gambling is associated with several factors, but little is known how these factors might influence or interact each other in predicting this behavior. Consequently, this is the first study to examine the mediation effect of coping styles in the relationship between attachment to parental figures and problem gambling. Methods A total of 988 adolescents and emerging adults were recruited to participate. The first set of analyses tested the adequacy of a model comprising biological, cognitive, and family variables in predicting youth problem gambling. The second set of analyses explored the relationship between family and individual variables in problem gambling behavior. Results The results of the first set of analyses demonstrated that the individual factors of gender, cognitive distortions, and coping styles showed a significant predictive effect on youth problematic gambling, and the family factors of attachment and family structure did not reveal a significant influence on this behavior. The results of the second set of analyses demonstrated that the attachment dimension of angry distress exerted a more indirect influence on problematic gambling, through emotion-focused coping style. Discussion This study revealed that some family variables can have a more indirect effect on youth gambling behavior and provided some insights in how some factors interact in predicting problem gambling. Conclusion These findings suggest that youth gambling is a multifaceted phenomenon, and that the indirect effects of family variables are important in estimating the complex social forces that might influence adolescent decisions to gamble.

Examining Predictive Validity of Oral Reading Fluency Slope in Upper Elementary Grades Using Quantile Regression.

PubMed

Cho, Eunsoo; Capin, Philip; Roberts, Greg; Vaughn, Sharon

2017-07-01

Within multitiered instructional delivery models, progress monitoring is a key mechanism for determining whether a child demonstrates an adequate response to instruction. One measure commonly used to monitor the reading progress of students is oral reading fluency (ORF). This study examined the extent to which ORF slope predicts reading comprehension outcomes for fifth-grade struggling readers ( n = 102) participating in an intensive reading intervention. Quantile regression models showed that ORF slope significantly predicted performance on a sentence-level fluency and comprehension assessment, regardless of the students' reading skills, controlling for initial ORF performance. However, ORF slope was differentially predictive of a passage-level comprehension assessment based on students' reading skills when controlling for initial ORF status. Results showed that ORF explained unique variance for struggling readers whose posttest performance was at the upper quantiles at the end of the reading intervention, but slope was not a significant predictor of passage-level comprehension for students whose reading problems were the most difficult to remediate.

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

PubMed

Kiral-Kornek, Isabell; Roy, Subhrajit; Nurse, Ewan; Mashford, Benjamin; Karoly, Philippa; Carroll, Thomas; Payne, Daniel; Saha, Susmita; Baldassano, Steven; O'Brien, Terence; Grayden, David; Cook, Mark; Freestone, Dean; Harrer, Stefan

2018-01-01

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Usefulness of morning home blood pressure measurements in patients with type 2 diabetes mellitus: results of a 10-year, prospective, longitudinal study.

PubMed

Kamoi, Kyuzi

2015-01-01

Previous cross-sectional studies and 6-year longitudinal study have demonstrated that home blood pressure (HBP) measurements upon awakening have a stronger predictive power for death, micro- and macrovascular complications than clinic blood pressure (CBP) measurements in patients with type 2 diabetes (T2DM). This study investigated which of these measurements offers stronger predictive power for outcomes over 10 years. At baseline, 400 Japanese patients with T2DM were classified as having hypertension (HT) or normotension (NT) based on HBP and CBP. The mean survey duration was 95 months. Primary and secondary end-points were death and new or worsened micro- and macrovascular complications, respectively. Differences in outcomes for each end-point between HT and NT patients were analyzed using Kaplan-Meier survival curves and log-rank testing. Associated risk factors were assessed using Cox proportional hazards analysis. Based on HBP, death and micro- and macrovascular complications were significantly higher in patients with HT than with NT at baseline and end-point. Based on CBP, there were no significant differences in incidence of death, micro- or macrovascular complications between patients with HT and NT at baseline and end-point, although a significant difference in incidence of death was observed between the HT and NT groups at end-point. However, the significance was significantly lower in CBP than in HBP. One risk factor associated with micro- and macrovascular complications in patients with HBP was therapy for HT. This 10-year longitudinal study of patients with T2DM demonstrated that elevated HBP upon awakening is predictive of death, and micro- and macrovascular complications.

The predictive value of the antioxidative function of HDL for cardiovascular disease and graft failure in renal transplant recipients.

PubMed

Leberkühne, Lynn J; Ebtehaj, Sanam; Dimova, Lidiya G; Dikkers, Arne; Dullaart, Robin P F; Bakker, Stephan J L; Tietge, Uwe J F

2016-06-01

Protection of low-density lipoproteins (LDL) against oxidative modification is a key anti-atherosclerotic property of high-density lipoproteins (HDL). This study evaluated the predictive value of the HDL antioxidative function for cardiovascular mortality, all-cause mortality and chronic graft failure in renal transplant recipients (RTR). The capacity of HDL to inhibit native LDL oxidation was determined in vitro in a prospective cohort of renal transplant recipients (RTR, n = 495, median follow-up 7.0 years). The HDL antioxidative functionality was significantly higher in patients experiencing graft failure (57.4 ± 9.7%) than in those without (54.2 ± 11.3%; P = 0.039), while there were no differences for cardiovascular and all-cause mortality. Specifically glomerular filtration rate (P = 0.001) and C-reactive protein levels (P = 0.006) associated independently with antioxidative functionality in multivariate linear regression analyses. Cox regression analysis demonstrated a significant relationship between antioxidative functionality of HDL and graft failure in age-adjusted analyses, but significance was lost following adjustment for baseline kidney function and inflammatory load. No significant association was found between HDL antioxidative functionality and cardiovascular and all-cause mortality. This study demonstrates that the antioxidative function of HDL (i) does not predict cardiovascular or all-cause mortality in RTR, but (ii) conceivably contributes to the development of graft failure, however, not independent of baseline kidney function and inflammatory load. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Nuclear charge radii: density functional theory meets Bayesian neural networks

NASA Astrophysics Data System (ADS)

Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

2016-11-01

The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

Using trading strategies to detect phase transitions in financial markets.

PubMed

Forró, Z; Woodard, R; Sornette, D

2015-04-01

We show that the log-periodic power law singularity model (LPPLS), a mathematical embodiment of positive feedbacks between agents and of their hierarchical dynamical organization, has a significant predictive power in financial markets. We find that LPPLS-based strategies significantly outperform the randomized ones and that they are robust with respect to a large selection of assets and time periods. The dynamics of prices thus markedly deviate from randomness in certain pockets of predictability that can be associated with bubble market regimes. Our hybrid approach, marrying finance with the trading strategies, and critical phenomena with LPPLS, demonstrates that targeting information related to phase transitions enables the forecast of financial bubbles and crashes punctuating the dynamics of prices.

Using trading strategies to detect phase transitions in financial markets

NASA Astrophysics Data System (ADS)

Forró, Z.; Woodard, R.; Sornette, D.

2015-04-01

We show that the log-periodic power law singularity model (LPPLS), a mathematical embodiment of positive feedbacks between agents and of their hierarchical dynamical organization, has a significant predictive power in financial markets. We find that LPPLS-based strategies significantly outperform the randomized ones and that they are robust with respect to a large selection of assets and time periods. The dynamics of prices thus markedly deviate from randomness in certain pockets of predictability that can be associated with bubble market regimes. Our hybrid approach, marrying finance with the trading strategies, and critical phenomena with LPPLS, demonstrates that targeting information related to phase transitions enables the forecast of financial bubbles and crashes punctuating the dynamics of prices.

Target-motion prediction for robotic search and rescue in wilderness environments.

PubMed

Macwan, Ashish; Nejat, Goldie; Benhabib, Beno

2011-10-01

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as terrain topology, target physiology and psychology, clues found, etc. The isoprobability curves are propagated over time and space. The significant tangible benefit of the proposed target-motion prediction methodology is demonstrated through a comparison to a nonprobabilistic approach, as well as through a simulated realistic wilderness search scenario.

Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording

PubMed Central

Eliseyev, Andrey; Aksenova, Tetiana

2016-01-01

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience. PMID:27196417

Using Peer Injunctive Norms to Predict Early Adolescent Cigarette Smoking Intentions

PubMed Central

Zaleski, Adam C.; Aloise-Young, Patricia A.

2013-01-01

The present study investigated the importance of the perceived injunctive norm to predict early adolescent cigarette smoking intentions. A total of 271 6th graders completed a survey that included perceived prevalence of friend smoking (descriptive norm), perceptions of friends’ disapproval of smoking (injunctive norm), and future smoking intentions. Participants also listed their five best friends, in which the actual injunctive norm was calculated. Results showed that smoking intentions were significantly correlated with the perceived injunctive norm but not with the actual injunctive norm. Secondly, the perceived injunctive norm predicted an additional 3.4% of variance in smoking intentions above and beyond the perceived descriptive norm. These results demonstrate the importance of the perceived injunctive norm in predicting early adolescent smoking intentions. PMID:24078745

Predictive factors of alcohol and tobacco use in adolescents.

PubMed

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users.

Gas Hydrate Formation Probability Distributions: The Effect of Shear and Comparisons with Nucleation Theory.

PubMed

May, Eric F; Lim, Vincent W; Metaxas, Peter J; Du, Jianwei; Stanwix, Paul L; Rowland, Darren; Johns, Michael L; Haandrikman, Gert; Crosby, Daniel; Aman, Zachary M

2018-03-13

Gas hydrate formation is a stochastic phenomenon of considerable significance for any risk-based approach to flow assurance in the oil and gas industry. In principle, well-established results from nucleation theory offer the prospect of predictive models for hydrate formation probability in industrial production systems. In practice, however, heuristics are relied on when estimating formation risk for a given flowline subcooling or when quantifying kinetic hydrate inhibitor (KHI) performance. Here, we present statistically significant measurements of formation probability distributions for natural gas hydrate systems under shear, which are quantitatively compared with theoretical predictions. Distributions with over 100 points were generated using low-mass, Peltier-cooled pressure cells, cycled in temperature between 40 and -5 °C at up to 2 K·min -1 and analyzed with robust algorithms that automatically identify hydrate formation and initial growth rates from dynamic pressure data. The application of shear had a significant influence on the measured distributions: at 700 rpm mass-transfer limitations were minimal, as demonstrated by the kinetic growth rates observed. The formation probability distributions measured at this shear rate had mean subcoolings consistent with theoretical predictions and steel-hydrate-water contact angles of 14-26°. However, the experimental distributions were substantially wider than predicted, suggesting that phenomena acting on macroscopic length scales are responsible for much of the observed stochastic formation. Performance tests of a KHI provided new insights into how such chemicals can reduce the risk of hydrate blockage in flowlines. Our data demonstrate that the KHI not only reduces the probability of formation (by both shifting and sharpening the distribution) but also reduces hydrate growth rates by a factor of 2.

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

PubMed

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.

SNP-based heritability estimates of the personality dimensions and polygenic prediction of both neuroticism and major depression: findings from CONVERGE.

PubMed

Docherty, A R; Moscati, A; Peterson, R; Edwards, A C; Adkins, D E; Bacanu, S A; Bigdeli, T B; Webb, B T; Flint, J; Kendler, K S

2016-10-25

Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent (N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% (s.e.=0.03, P=0.0001). In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions (neuroticism, openness, conscientiousness, extraversion and agreeableness) measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case-control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10 -6 ). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.

Risk-Based, Hypothesis-Driven Framework for Hydrological Field Campaigns with Case Studies

NASA Astrophysics Data System (ADS)

Harken, B.; Rubin, Y.

2014-12-01

There are several stages in any hydrological modeling campaign, including: formulation and analysis of a priori information, data acquisition through field campaigns, inverse modeling, and prediction of some environmental performance metric (EPM). The EPM being predicted could be, for example, contaminant concentration or plume travel time. These predictions often have significant bearing on a decision that must be made. Examples include: how to allocate limited remediation resources between contaminated groundwater sites or where to place a waste repository site. Answering such questions depends on predictions of EPMs using forward models as well as levels of uncertainty related to these predictions. Uncertainty in EPM predictions stems from uncertainty in model parameters, which can be reduced by measurements taken in field campaigns. The costly nature of field measurements motivates a rational basis for determining a measurement strategy that is optimal with respect to the uncertainty in the EPM prediction. The tool of hypothesis testing allows this uncertainty to be quantified by computing the significance of the test resulting from a proposed field campaign. The significance of the test gives a rational basis for determining the optimality of a proposed field campaign. This hypothesis testing framework is demonstrated and discussed using various synthetic case studies. This study involves contaminated aquifers where a decision must be made based on prediction of when a contaminant will arrive at a specified location. The EPM, in this case contaminant travel time, is cast into the hypothesis testing framework. The null hypothesis states that the contaminant plume will arrive at the specified location before a critical amount of time passes, and the alternative hypothesis states that the plume will arrive after the critical time passes. The optimality of different field campaigns is assessed by computing the significance of the test resulting from each one. Evaluating the level of significance caused by a field campaign involves steps including likelihood-based inverse modeling and semi-analytical conditional particle tracking.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

PubMed

Dubay, Rickey; Hassan, Marwan; Li, Chunying; Charest, Meaghan

2014-09-01

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

HART-II: Prediction of Blade-Vortex Interaction Loading

NASA Technical Reports Server (NTRS)

Lim, Joon W.; Tung, Chee; Yu, Yung H.; Burley, Casey L.; Brooks, Thomas; Boyd, Doug; vanderWall, Berend; Schneider, Oliver; Richard, Hugues; Raffel, Markus

2003-01-01

During the HART-I data analysis, the need for comprehensive wake data was found including vortex creation and aging, and its re-development after blade-vortex interaction. In October 2001, US Army AFDD, NASA Langley, German DLR, French ONERA and Dutch DNW performed the HART-II test as an international joint effort. The main objective was to focus on rotor wake measurement using a PIV technique along with the comprehensive data of blade deflections, airloads, and acoustics. Three prediction teams made preliminary correlation efforts with HART-II data: a joint US team of US Army AFDD and NASA Langley, German DLR, and French ONERA. The predicted results showed significant improvements over the HART-I predicted results, computed about several years ago, which indicated that there has been better understanding of complicated wake modeling in the comprehensive rotorcraft analysis. All three teams demonstrated satisfactory prediction capabilities, in general, though there were slight deviations of prediction accuracies for various disciplines.

A new method to detect cerebral blood flow waveform in synchrony with chest compression by near-infrared spectroscopy during CPR.

PubMed

Koyama, Yasuaki; Wada, Takafumi; Lohman, Brandon D; Takamatsu, Yuka; Matsumoto, Junichi; Fujitani, Shigeki; Taira, Yasuhiko

2013-10-01

The objective of the study is to demonstrate the utility of near-infrared spectroscopy (NIRS) in evaluating chest compression (CC) quality in cardiac arrest (CA) patients as well as determine its prognosis predictive value. We present a nonconsecutive case series of adult patients with CA whose cardiopulmonary resuscitation (CPR) was monitored with NIRS and collected the total hemoglobin concentration change (ΔcHb), the tissue oxygen index (TOI), and the ΔTOI to assess CC quality in a noninvasive fashion. During CPR, ΔcHb displayed waveforms monitor, which we regarded as a surrogate for CC quality. Total hemoglobin concentration change waveforms responded accurately to variations or cessations of CCs. In addition, a TOI greater than 40% measured upon admission appears to be significant in predicting patient's outcome. Of 15 patients, 6 had a TOI greater than 40% measured upon admission, and 67% of the latter were in return of spontaneous circulation after CPR and were found to be significantly different between return of spontaneous circulation and death (P = .047; P < .05). Near-infrared spectroscopy reliably assesses the quality of CCs in patients with CA demonstrated by synchronous waveforms during CPR and possible prognostic predictive value, although further investigation is warranted. © 2013 Elsevier Inc. All rights reserved.

Assessing the risk for suicide in schizophrenia according to migration, ethnicity and geographical ancestry.

PubMed

Hettige, Nuwan C; Bani-Fatemi, Ali; Kennedy, James L; De Luca, Vincenzo

2017-02-09

Suicide is a leading cause of mortality among those afflicted by schizophrenia. Previous studies demonstrated that the stressors associated with immigration may lead to an onset of schizophrenia and suicide separately in susceptible individuals. However, no studies have shown whether immigration may lead to suicidal behaviour for individuals with schizophrenia. Our study proposes that an individual's geographical ancestry, ethnicity or migration status may be predictive of suicide risk in schizophrenia. In a sample of 276 participants with schizophrenia spectrum disorders, we conducted cross-sectional assessments to collect clinical information. Self-identified ethnicity and suicide history were collected through self-report questionnaires and interview-based scales. Ancestry was identified using 292 genetic markers from HapMap. Migrants were classified as those who immigrated to Canada during their lifetime. Using a regression analysis, we tested whether a history of migration, ethnicity or geographical ancestry were predictive of a history of suicide attempts. Our analysis failed to demonstrate a significant relationship between suicide history and migration, ethnicity or ancestry. However, ethnicity appears to be significantly associated with the number of psychiatric hospitalizations in our sample. Ethnicity and migration history are not predictive of previous suicide attempts. Ethnicity may be an important demographic factor affecting access to mental health resources and frequency of hospitalizations.

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI

PubMed Central

Churchill, Nathan W.; Spring, Robyn; Afshin-Pour, Babak; Dong, Fan; Strother, Stephen C.

2015-01-01

BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the “pipeline”) significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard “fixed” preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets. PMID:26161667

Diagnostic yield and accuracy of coronary CT angiography after abnormal nuclear myocardial perfusion imaging.

PubMed

Meinel, Felix G; Schoepf, U Joseph; Townsend, Jacob C; Flowers, Brian A; Geyer, Lucas L; Ebersberger, Ullrich; Krazinski, Aleksander W; Kunz, Wolfgang G; Thierfelder, Kolja M; Baker, Deborah W; Khan, Ashan M; Fernandes, Valerian L; O'Brien, Terrence X

2018-06-15

We aimed to determine the diagnostic yield and accuracy of coronary CT angiography (CCTA) in patients referred for invasive coronary angiography (ICA) based on clinical concern for coronary artery disease (CAD) and an abnormal nuclear stress myocardial perfusion imaging (MPI) study. We enrolled 100 patients (84 male, mean age 59.6 ± 8.9 years) with an abnormal MPI study and subsequent referral for ICA. Each patient underwent CCTA prior to ICA. We analyzed the prevalence of potentially obstructive CAD (≥50% stenosis) on CCTA and calculated the diagnostic accuracy of ≥50% stenosis on CCTA for the detection of clinically significant CAD on ICA (defined as any ≥70% stenosis or ≥50% left main stenosis). On CCTA, 54 patients had at least one ≥50% stenosis. With ICA, 45 patients demonstrated clinically significant CAD. A positive CCTA had 100% sensitivity and 84% specificity with a 100% negative predictive value and 83% positive predictive value for clinically significant CAD on a per patient basis in MPI positive symptomatic patients. In conclusion, almost half (48%) of patients with suspected CAD and an abnormal MPI study demonstrate no obstructive CAD on CCTA.

Transvaginal ultrasonographic cervical measurement in predicting failed labor induction and cesarean delivery for failure to progress in nulliparous women.

PubMed

Park, Kyo Hoon

2007-08-01

The aim of this study was to evaluate the value of transvaginal sonographic cervical measurement in predicting failed labor induction and cesarean delivery for failure to progress in nulliparous women. One hundred and sixty-one women scheduled for labor induction underwent transvaginal ultrasonography and digital cervical examinations. Logistic regression demonstrated that cervical length and gestational age at induction, but not the Bishop score, significantly and independently predicted failed labor induction. According to the receiver operating characteristic curves analysis, the best cut-off value of cervical length for predicting failed labor induction was 28 mm, with a sensitivity of 62% and a specificity of 60%. In terms of the likelihood of a cesarean delivery for failure to progress as the outcome variable, logistic regression indicated that maternal height and birth weight, but not cervical length or Bishop score, were significantly and independently associated with an increased risk of cesarean delivery for failure to progress. Transvaginal sonographic measurements of cervical length thus independently predicted failed labor induction in nulliparous women. However, the relatively poor predictive performance of this test undermines its clinical usefulness as a predictor of failed labor induction. Moreover, cervical length appears to have a poor predictive value for the likelihood of a cesarean delivery for failure to progress.

Aggression and risk of future violence in forensic psychiatric patients with and without dyslexia.

PubMed

Selenius, Heidi; Hellström, Ake; Belfrage, Henrik

2011-05-01

Dyslexia does not cause criminal behaviour, but it may worsen aggressive behaviour tendencies. In this study, aggressive behaviour and risk of future violence were compared between forensic psychiatric patients with and without dyslexia. Dyslexia was assessed using the Swedish phonological processing battery 'The Pigeon'. The patients filled in the Aggression Questionnaire, and trained assessors performed the risk assessments using HCR-20 version 2. Patients with dyslexia self-reported more aggressive behaviour compared with those without dyslexia. There was only a nearly significant tendency (p = 0.06) for the patients with dyslexia to receive higher scores in the HCR-20 compared with the patients without dyslexia, and phonological processing skills did not significantly predict aggression or risk of future violence. However, regression analyses demonstrated that poor phonological processing skills are a significant predictor of anger, which in turn significantly predicts risk of future violence. Copyright © 2011 John Wiley & Sons, Ltd.

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

Test anxiety and academic performance in chiropractic students.

PubMed

Zhang, Niu; Henderson, Charles N R

2014-01-01

Objective : We assessed the level of students' test anxiety, and the relationship between test anxiety and academic performance. Methods : We recruited 166 third-quarter students. The Test Anxiety Inventory (TAI) was administered to all participants. Total scores from written examinations and objective structured clinical examinations (OSCEs) were used as response variables. Results : Multiple regression analysis shows that there was a modest, but statistically significant negative correlation between TAI scores and written exam scores, but not OSCE scores. Worry and emotionality were the best predictive models for written exam scores. Mean total anxiety and emotionality scores for females were significantly higher than those for males, but not worry scores. Conclusion : Moderate-to-high test anxiety was observed in 85% of the chiropractic students examined. However, total test anxiety, as measured by the TAI score, was a very weak predictive model for written exam performance. Multiple regression analysis demonstrated that replacing total anxiety (TAI) with worry and emotionality (TAI subscales) produces a much more effective predictive model of written exam performance. Sex, age, highest current academic degree, and ethnicity contributed little additional predictive power in either regression model. Moreover, TAI scores were not found to be statistically significant predictors of physical exam skill performance, as measured by OSCEs.

IMPORTANCE OF THE DYNAMICS OF BACTERIOPHAGE-HOST INTERACTIONS TO BACTERIAL ABUNDANCE AND GENETIC DIVERSITY IN AQUATIC ENVIRONMENTS (RESEARCH BRIEF)

EPA Science Inventory

Using Pseudomonas aeruginosa and its bacteriophages as a model system, we have clearly demonstrated a significant potential for viral-mediated gene transfer (transduction) of both plasmid and chromosomal DNA in freshwater microbial populations. These investigations have predicted...

Graduate Student Project: Operations Management Product Plan

ERIC Educational Resources Information Center

Fish, Lynn

2007-01-01

An operations management product project is an effective instructional technique that fills a void in current operations management literature in product planning. More than 94.1% of 286 graduates favored the project as a learning tool, and results demonstrate the significant impact the project had in predicting student performance. The author…

Predicting performance of almonds shells for cleanup of almond soil fumigants from potable water.

USDA-ARS?s Scientific Manuscript database

Almond shell strength and bulk density were determined to differ significantly among almond varieties, and further research demonstrated that granular activated carbons (GAC) produced from shells of different varieties had similar abilities to adsorb DBCP from contaminated water. As an extension of...

El Niño-southern oscillation influences on the Mahaweli streamflow in Sri Lanka

NASA Astrophysics Data System (ADS)

Zubair, Lareef

2003-01-01

Despite advances over the last two decades in the capacity to predict the evolution of the El Niño-southern oscillation (ENSO) phenomenon and advances in understanding of the relationship between ENSO and climate, there has been little use of climate predictions for water resources management in the tropics. As part of an effort to develop such a prediction scheme, the ENSO influences on streamflow and rainfall in the upper catchment of the Mahaweli river in Sri Lanka were investigated with correlation analysis, composite analysis and contingency tables. El Niño conditions were often associated with decreased annual flows and La Niña with increased flows. The relationship of streamflow and rainfall with the ENSO index of NINO3 contrasted between January to September and October to December. During El Niño episodes the streamflow declines from January to September, but from October to December there is no clear relationship. On the other hand, rainfall shows a clear increase from October to December and declines during January, February, March, July and August. The simultaneous correlations of NINO3 with the aggregate January to September streamflow (r = -0.50), with January to September rainfall (r = -0.44) and with October to December rainfall (r = 0.48) are all significant at the 99% level. The correlation between one-season-in-advance NINO3 with both January to September streamflow and October to December rainfall remained significant at the 99% level.This study demonstrates the potential of using ENSO-based predictors for a seasonal hydro-climatic prediction scheme in the Mahaweli basin. It shows the significant contrasts in ENSO influence on rainfall and streamflow due to various hydrological processes. It has demonstrated that the potential for prediction is improved by investigating ENSO influences for the appropriate season for the given river catchment.

Computational fluid dynamics (CFD) using porous media modeling predicts recurrence after coiling of cerebral aneurysms

PubMed Central

Ishida, Fujimaro; Tsuji, Masanori; Furukawa, Kazuhiro; Shiba, Masato; Yasuda, Ryuta; Toma, Naoki; Sakaida, Hiroshi; Suzuki, Hidenori

2017-01-01

Objective This study aimed to predict recurrence after coil embolization of unruptured cerebral aneurysms with computational fluid dynamics (CFD) using porous media modeling (porous media CFD). Method A total of 37 unruptured cerebral aneurysms treated with coiling were analyzed using follow-up angiograms, simulated CFD prior to coiling (control CFD), and porous media CFD. Coiled aneurysms were classified into stable or recurrence groups according to follow-up angiogram findings. Morphological parameters, coil packing density, and hemodynamic variables were evaluated for their correlations with aneurysmal recurrence. We also calculated residual flow volumes (RFVs), a novel hemodynamic parameter used to quantify the residual aneurysm volume after simulated coiling, which has a mean fluid domain > 1.0 cm/s. Result Follow-up angiograms showed 24 aneurysms in the stable group and 13 in the recurrence group. Mann-Whitney U test demonstrated that maximum size, dome volume, neck width, neck area, and coil packing density were significantly different between the two groups (P < 0.05). Among the hemodynamic parameters, aneurysms in the recurrence group had significantly larger inflow and outflow areas in the control CFD and larger RFVs in the porous media CFD. Multivariate logistic regression analyses demonstrated that RFV was the only independently significant factor (odds ratio, 1.06; 95% confidence interval, 1.01–1.11; P = 0.016). Conclusion The study findings suggest that RFV collected under porous media modeling predicts the recurrence of coiled aneurysms. PMID:29284057

Associations of Sexual Victimization, Depression, and Sexual Assertiveness with Unprotected Sex: A Test of the Multifaceted Model of HIV Risk Across Gender

PubMed Central

Morokoff, Patricia J.; Redding, Colleen A.; Harlow, Lisa L.; Cho, Sookhyun; Rossi, Joseph S.; Meier, Kathryn S.; Mayer, Kenneth H.; Koblin, Beryl; Brown-Peterside, Pamela

2014-01-01

This study examined whether the Multifaceted Model of HIV Risk (MMOHR) would predict unprotected sex based on predictors including gender, childhood sexual abuse (CSA), sexual victimization (SV), depression, and sexual assertiveness for condom use. A community-based sample of 473 heterosexually active men and women, aged 18–46 years completed survey measures of model variables. Gender predicted several variables significantly. A separate model for women demonstrated excellent fit, while the model for men demonstrated reasonable fit. Multiple sample model testing supported the use of MMOHR in both men and women, while simultaneously highlighting areas of gender difference. Prevention interventions should focus on sexual assertiveness, especially for CSA and SV survivors, as well as targeting depression, especially among men. PMID:25018617

Predicting the safety and efficacy of buffer therapy to raise tumour pHe: an integrative modelling study

PubMed Central

Martin, N K; Robey, I F; Gaffney, E A; Gillies, R J; Gatenby, R A; Maini, P K

2012-01-01

Background: Clinical positron emission tomography imaging has demonstrated the vast majority of human cancers exhibit significantly increased glucose metabolism when compared with adjacent normal tissue, resulting in an acidic tumour microenvironment. Recent studies demonstrated reducing this acidity through systemic buffers significantly inhibits development and growth of metastases in mouse xenografts. Methods: We apply and extend a previously developed mathematical model of blood and tumour buffering to examine the impact of oral administration of bicarbonate buffer in mice, and the potential impact in humans. We recapitulate the experimentally observed tumour pHe effect of buffer therapy, testing a model prediction in vivo in mice. We parameterise the model to humans to determine the translational safety and efficacy, and predict patient subgroups who could have enhanced treatment response, and the most promising combination or alternative buffer therapies. Results: The model predicts a previously unseen potentially dangerous elevation in blood pHe resulting from bicarbonate therapy in mice, which is confirmed by our in vivo experiments. Simulations predict limited efficacy of bicarbonate, especially in humans with more aggressive cancers. We predict buffer therapy would be most effectual: in elderly patients or individuals with renal impairments; in combination with proton production inhibitors (such as dichloroacetate), renal glomular filtration rate inhibitors (such as non-steroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors), or with an alternative buffer reagent possessing an optimal pK of 7.1–7.2. Conclusion: Our mathematical model confirms bicarbonate acts as an effective agent to raise tumour pHe, but potentially induces metabolic alkalosis at the high doses necessary for tumour pHe normalisation. We predict use in elderly patients or in combination with proton production inhibitors or buffers with a pK of 7.1–7.2 is most promising. PMID:22382688

Patient No-Show Predictive Model Development using Multiple Data Sources for an Effective Overbooking Approach

PubMed Central

Hanauer, D.A.

2014-01-01

Summary Background Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Objective To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Methods Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. Results From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. Conclusions This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity. PMID:25298821

In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

DNA methylation-based measures of biological age: meta-analysis predicting time to death.

PubMed

Chen, Brian H; Marioni, Riccardo E; Colicino, Elena; Peters, Marjolein J; Ward-Caviness, Cavin K; Tsai, Pei-Chien; Roetker, Nicholas S; Just, Allan C; Demerath, Ellen W; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L; Murabito, Joanne M; Bandinelli, Stefania; Hernandez, Dena G; Melzer, David; Nalls, Michael; Pilling, Luke C; Price, Timothy R; Singleton, Andrew B; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M; Shah, Sonia; Wray, Naomi R; McRae, Allan F; Franco, Oscar H; Hofman, Albert; Uitterlinden, André G; Absher, Devin; Assimes, Themistocles; Levine, Morgan E; Lu, Ake T; Tsao, Philip S; Hou, Lifang; Manson, JoAnn E; Carty, Cara L; LaCroix, Andrea Z; Reiner, Alexander P; Spector, Tim D; Feinberg, Andrew P; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T; Peters, Annette; Deary, Ian J; Pankow, James S; Ferrucci, Luigi; Horvath, Steve

2016-09-28

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10 -9 ) , independent of chronological age, even after adjusting for additional risk factors (p<5.4x10 -4 ) , and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5x10 -43 ). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality.

In Silico Modelling of Transdermal and Systemic Kinetics of Topically Applied Solutes: Model Development and Initial Validation for Transdermal Nicotine.

PubMed

Chen, Tao; Lian, Guoping; Kattou, Panayiotis

2016-07-01

The purpose was to develop a mechanistic mathematical model for predicting the pharmacokinetics of topically applied solutes penetrating through the skin and into the blood circulation. The model could be used to support the design of transdermal drug delivery systems and skin care products, and risk assessment of occupational or consumer exposure. A recently reported skin penetration model [Pharm Res 32 (2015) 1779] was integrated with the kinetic equations for dermis-to-capillary transport and systemic circulation. All model parameters were determined separately from the molecular, microscopic and physiological bases, without fitting to the in vivo data to be predicted. Published clinical studies of nicotine were used for model demonstration. The predicted plasma kinetics is in good agreement with observed clinical data. The simulated two-dimensional concentration profile in the stratum corneum vividly illustrates the local sub-cellular disposition kinetics, including tortuous lipid pathway for diffusion and the "reservoir" effect of the corneocytes. A mechanistic model for predicting transdermal and systemic kinetics was developed and demonstrated with published clinical data. The integrated mechanistic approach has significantly extended the applicability of a recently reported microscopic skin penetration model by providing prediction of solute concentration in the blood.

Moderation of age, sex, and ethnicity on psychosocial predictors of increased exercise and improved eating.

PubMed

Annesi, James J

2013-01-01

Although research indicates that treatment-induced improvements in self-regulation, mood, and self-efficacy significantly predict increased exercise and improved eating, moderation by participants' personal characteristics is largely unknown. Severely obese adults (N = 414; 47% White, 53% African American) volunteered for a behavioral exercise and nutrition treatment and demonstrated significant within-group improvements in self-efficacy for exercise, self-regulation for exercise, mood, self-efficacy for controlled eating, self-regulation for controlled eating, exercise volume, and fruit and vegetable intake over 26 weeks. After testing age, sex, and race/ethnicity as possible moderators of the prediction of changes in exercise volume and fruit and vegetable consumption by changes in self-regulation, mood, and self-efficacy, only age significantly moderated change in volume of exercise. Implications for theory and treatment were discussed.

Does stress mediate the development of substance use disorders among youth transitioning to young adulthood?

PubMed

Cornelius, Jack; Kirisci, Levent; Reynolds, Maureen; Tarter, Ralph

2014-05-01

Stress is a well-documented factor in the development of addiction. However, no longitudinal studies to date have assessed the role of stress in mediating the development of substance use disorders (SUD). Our previous results have demonstrated that a measure called Transmissible Liability Index (TLI) assessed during pre-adolescent years serves as a significant predictor of risk for substance use disorder among young adults. However, it remains unclear whether life stress mediates the relationship between TLI and SUD, or whether stress predicts SUD. We conducted a longitudinal study involving 191 male subjects to assess whether life stress mediates the relationship between TLI as assessed at age 10-12 and subsequent development of SUD at age 22, after controlling for other relevant factors. Logistic regression demonstrated that the development of SUD at age 22 was associated with stress at age 19. A path analysis demonstrated that stress at age 19 significantly predicted SUD at age 22. However, stress did not mediate the relationship between the TLI assessed at age 10-12 and SUD in young adulthood. These findings confirm that stress plays a role in the development of SUD, but also shows that stress does not mediate the development of SUD. Further studies are warranted to clarify the role of stress in the etiology of SUD.

NetMHCstab – predicting stability of peptide–MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery

PubMed Central

Jørgensen, Kasper W; Rasmussen, Michael; Buus, Søren; Nielsen, Morten

2014-01-01

Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide–MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide–MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at http://www.cbs.dtu.dk/services/NetMHCstab. PMID:23927693

Assessing the Hydrologic Performance of the EPA's Nonpoint Source Water Quality Assessment Decision Support Tool Using North American Land Data Assimilation System (Products)

NASA Technical Reports Server (NTRS)

Lee, S.; Ni-Meister, W.; Toll, D.; Nigro, J.; Guiterrez-Magness, A.; Engman, T.

2010-01-01

The accuracy of streamflow predictions in the EPA's BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) decision support tool is affected by the sparse meteorological data contained in BASINS. The North American Land Data Assimilation System (NLDAS) data with high spatial and temporal resolutions provide an alternative to the NOAA National Climatic Data Center (NCDC)'s station data. This study assessed the improvement of streamflow prediction of the Hydrological Simulation Program-FORTRAN (HSPF) model contained within BASINS using the NLDAS 118 degree hourly precipitation and evapotranspiration estimates in seven watersheds of the Chesapeake Bay region. Our results demonstrated consistent improvements of daily streamflow predictions in five of the seven watersheds when NLDAS precipitation and evapotranspiration data was incorporated into BASINS. The improvement of using the NLDAS data is significant when watershed's meteorological station is either far away or not in a similar climatic region. When the station is nearby, using the NLDAS data produces similar results. The correlation coefficients of the analyses using the NLDAS data were greater than 0.8, the Nash-Sutcliffe (NS) model fit efficiency greater than 0.6, and the error in the water balance was less than 5%. Our analyses also showed that the streamflow improvements were mainly contributed by the NLDAS's precipitation data and that the improvement from using NLDAS's evapotranspiration data was not significant; partially due to the constraints of current BASINS-HSPF settings. However, NLDAS's evapotranspiration data did improve the baseflow prediction. This study demonstrates the NLDAS data has the potential to improve stream flow predictions, thus aid the water quality assessment in the EPA nonpoint water quality assessment decision tool.

Cue-Reactive Rationality, Visual Imagery and Volitional Control Predict Cue-Reactive Urge to Gamble in Poker-Machine Gamblers.

PubMed

Clark, Gavin I; Rock, Adam J; McKeith, Charles F A; Coventry, William L

2017-09-01

Poker-machine gamblers have been demonstrated to report increases in the urge to gamble following exposure to salient gambling cues. However, the processes which contribute to this urge to gamble remain to be understood. The present study aimed to investigate whether changes in the conscious experience of visual imagery, rationality and volitional control (over one's thoughts, images and attention) predicted changes in the urge to gamble following exposure to a gambling cue. Thirty-one regular poker-machine gamblers who reported at least low levels of problem gambling on the Problem Gambling Severity Index (PGSI), were recruited to complete an online cue-reactivity experiment. Participants completed the PGSI, the visual imagery, rationality and volitional control subscales of the Phenomenology of Consciousness Inventory (PCI), and a visual analogue scale (VAS) assessing urge to gamble. Participants completed the PCI subscales and VAS at baseline, following a neutral video cue and following a gambling video cue. Urge to gamble was found to significantly increase from neutral cue to gambling cue (while controlling for baseline urge) and this increase was predicted by PGSI score. After accounting for the effects of problem-gambling severity, cue-reactive visual imagery, rationality and volitional control significantly improved the prediction of cue-reactive urge to gamble. The small sample size and limited participant characteristic data restricts the generalizability of the findings. Nevertheless, this is the first study to demonstrate that changes in the subjective experience of visual imagery, volitional control and rationality predict changes in the urge to gamble from neutral to gambling cue. The results suggest that visual imagery, rationality and volitional control may play an important role in the experience of the urge to gamble in poker-machine gamblers.

A Common Polymorphism in SCN2A Predicts General Cognitive Ability Through Effects on Prefrontal Cortex Physiology

PubMed Central

Scult, Matthew A.; Trampush, Joey W.; Zheng, Fengyu; Conley, Emily Drabant; Lencz, Todd; Malhotra, Anil K.; Dickinson, Dwight; Weinberger, Daniel R.; Hariri, Ahmad R.

2015-01-01

Here we provide novel convergent evidence across three independent cohorts of healthy adults (n=531) demonstrating that a common polymorphism in the gene encoding the α2 subunit of neuronal voltage-gated type II sodium channels (SCN2A) predicts human general cognitive ability or “g.” Using meta-analysis, we demonstrate that the minor T allele of a common polymorphism (rs10174400) in SCN2A is associated with significantly higher “g” independent of gender and age. We further demonstrate using resting-state fMRI data from our discovery cohort (n=236) that this genetic advantage may be mediated by increased capacity for information processing between the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex, which support higher cognitive functions. Collectively, these findings fill a gap in our understanding of the genetics of general cognitive ability and highlight a specific neural mechanism through which a common polymorphism shapes inter-individual variation in “g.” PMID:25961639

Distinguishing between the Permeability Relationships with Absorption and Metabolism To Improve BCS and BDDCS Predictions in Early Drug Discovery

PubMed Central

2015-01-01

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug–drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption. PMID:24628254

Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery.

PubMed

Larregieu, Caroline A; Benet, Leslie Z

2014-04-07

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug-drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

PubMed Central

Marbach, Daniel; Roy, Sushmita; Ay, Ferhat; Meyer, Patrick E.; Candeias, Rogerio; Kahveci, Tamer; Bristow, Christopher A.; Kellis, Manolis

2012-01-01

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. PMID:22456606

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

Predictive factors of alcohol and tobacco use in adolescents

PubMed Central

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

OBJECTIVES: to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. METHOD: a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. RESULTS: the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. CONCLUSION: this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users. PMID:25591103

Cascade aeroacoustics including steady loading effects

NASA Astrophysics Data System (ADS)

Chiang, Hsiao-Wei D.; Fleeter, Sanford

A mathematical model is developed to analyze the effects of airfoil and cascade geometry, steady aerodynamic loading, and the characteristics of the unsteady flow field on the discrete frequency noise generation of a blade row in an incompressible flow. The unsteady lift which generates the noise is predicted with a complex first-order cascade convected gust analysis. This model was then applied to the Gostelow airfoil cascade and variations, demonstrating that steady loading, cascade solidity, and the gust direction are significant. Also, even at zero incidence, the classical flat plate cascade predictions are unacceptable.

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

PubMed Central

Huang, Li

2017-01-01

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. PMID:29253885

Particle Pollution Estimation Based on Image Analysis

PubMed Central

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. PMID:26828757

Particle Pollution Estimation Based on Image Analysis.

PubMed

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.

Differences between chronological and brain age are related to education and self-reported physical activity.

PubMed

Steffener, Jason; Habeck, Christian; O'Shea, Deirdre; Razlighi, Qolamreza; Bherer, Louis; Stern, Yaakov

2016-04-01

This study investigated the relationship between education and physical activity and the difference between a physiological prediction of age and chronological age (CA). Cortical and subcortical gray matter regional volumes were calculated from 331 healthy adults (range: 19-79 years). Multivariate analyses identified a covariance pattern of brain volumes best predicting CA (R(2) = 47%). Individual expression of this brain pattern served as a physiologic measure of brain age (BA). The difference between CA and BA was predicted by education and self-report measures of physical activity. Education and the daily number of flights of stairs climbed (FOSC) were the only 2 significant predictors of decreased BA. Effect sizes demonstrated that BA decreased by 0.95 years for each year of education and by 0.58 years for 1 additional FOSC daily. Effects of education and FOSC on regional brain volume were largely driven by temporal and subcortical volumes. These results demonstrate that higher levels of education and daily FOSC are related to larger brain volume than predicted by CA which supports the utility of regional gray matter volume as a biomarker of healthy brain aging. Copyright © 2016 Elsevier Inc. All rights reserved.

Bubble generation during transformer overload

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

Oommen, T.V.

1990-03-01

Bubble generation in transformers has been demonstrated under certain overload conditions. The release of large quantities of bubbles would pose a dielectric breakdown hazard. A bubble prediction model developed under EPRI Project 1289-4 attempts to predict the bubble evolution temperature under different overload conditions. This report details a verification study undertaken to confirm the validity of the above model using coil structures subjected to overload conditions. The test variables included moisture in paper insulation, gas content in oil, and the type of oil preservation system. Two aged coils were also tested. The results indicated that the observed bubble temperatures weremore » close to the predicted temperatures for models with low initial gas content in the oil. The predicted temperatures were significantly lower than the observed temperatures for models with high gas content. Some explanations are provided for the anomalous behavior at high gas levels in oil. It is suggested that the dissolved gas content is not a significant factor in bubble evolution. The dominant factor in bubble evolution appears to be the water vapor pressure which must reach critical levels before bubbles can be released. Further study is needed to make a meaningful revision of the bubble prediction model. 8 refs., 13 figs., 11 tabs.« less

Validity of Bioelectrical Impedance Analysis to Estimation Fat-Free Mass in the Army Cadets.

PubMed

Langer, Raquel D; Borges, Juliano H; Pascoa, Mauro A; Cirolini, Vagner X; Guerra-Júnior, Gil; Gonçalves, Ezequiel M

2016-03-11

Bioelectrical Impedance Analysis (BIA) is a fast, practical, non-invasive, and frequently used method for fat-free mass (FFM) estimation. The aims of this study were to validate predictive equations of BIA to FFM estimation in Army cadets and to develop and validate a specific BIA equation for this population. A total of 396 males, Brazilian Army cadets, aged 17-24 years were included. The study used eight published predictive BIA equations, a specific equation in FFM estimation, and dual-energy X-ray absorptiometry (DXA) as a reference method. Student's t-test (for paired sample), linear regression analysis, and Bland-Altman method were used to test the validity of the BIA equations. Predictive BIA equations showed significant differences in FFM compared to DXA (p < 0.05) and large limits of agreement by Bland-Altman. Predictive BIA equations explained 68% to 88% of FFM variance. Specific BIA equations showed no significant differences in FFM, compared to DXA values. Published BIA predictive equations showed poor accuracy in this sample. The specific BIA equations, developed in this study, demonstrated validity for this sample, although should be used with caution in samples with a large range of FFM.

Learning Behaviour and Learning Outcomes: The Roles for Social Influence and Field of Study

ERIC Educational Resources Information Center

Smyth, Lillian; Mavor, Kenneth I.; Platow, Michael J.

2017-01-01

Research has demonstrated a significant role of discipline social identification in predicting learning approaches, even controlling for individual differences. Smyth et al. ("Educ Psychol" 35(1):53-72, 2015. doi:10.1080/01443410.2013.822962) suggest that learners share discipline-based social identifications, and that this…

Listening in Early Childhood: An Interdisciplinary Review of the Literature

ERIC Educational Resources Information Center

Jalongo, Mary Renck

2010-01-01

Three general purposes of research in human development are to explain, predict, and modify behavior. Studies of listening during early childhood (birth through age eight) are of particular significance to the field because they enable researchers to describe listening processes from their very origins (explain), they demonstrate the effects of…

A novel missense Norrie disease mutation associated with a severe ocular phenotype.

PubMed

Khan, Arif O; Shamsi, Farrukh A; Al-Saif, Amr; Kambouris, Marios

2004-01-01

Clinical findings and pedigree analysis led to the diagnosis of severe Norrie disease in two brothers. DNA sequencing demonstrated a novel missense mutation (703G>T) that significantly alters predicted protein structure. Less severe retinal developmental disease may be associated with milder mutations in the Norrie disease gene.

Florida Red Tide Toxins (Brevetoxins) and Longitudinal Respiratory Effects in Asthmatics.

PubMed

Bean, Judy A; Fleming, Lora E; Kirkpatrick, Barbara; Backer, Lorraine C; Nierenberg, Kate; Reich, Andrew; Cheng, Yung Sung; Wanner, Adam; Benson, Janet; Naar, Jerome; Pierce, Richard; Abraham, William M; Kirkpatrick, Gary; Hollenbeck, Julie; Zaias, Julia; Mendes, Eliana; Baden, Daniel G

2011-09-01

Having demonstrated significant and persistent adverse changes in pulmonary function for asthmatics after 1 hour exposure to brevetoxins in Florida red tide (Karenia brevis bloom) aerosols, we assessed the possible longer term health effects in asthmatics from intermittent environmental exposure to brevetoxins over 7 years. 125 asthmatic subjects were assessed for their pulmonary function and reported symptoms before and after 1 hour of environmental exposure to Florida red tide aerosols for upto 11 studies over seven years. As a group, the asthmatics came to the studies with normal standardized percent predicted pulmonary function values. The 38 asthmatics who participated in only one exposure study were more reactive compared to the 36 asthmatics who participated in ≥4 exposure studies. The 36 asthmatics participating in ≥4 exposure studies demonstrated no significant change in their standardized percent predicted pre-exposure pulmonary function over the 7 years of the study. These results indicate that stable asthmatics living in areas with intermittent Florida red tides do not exhibit chronic respiratory effects from intermittent environmental exposure to aerosolized brevetoxins over a 7 year period.

Development of a mathematical model for the growth associated Polyhydroxybutyrate fermentation by Azohydromonas australica and its use for the design of fed-batch cultivation strategies.

PubMed

Gahlawat, Geeta; Srivastava, Ashok K

2013-06-01

In the present investigation, batch cultivation of Azohydromonas australica DSM 1124 was carried out in a bioreactor for growth associated PHB production. The observed batch PHB production kinetics data was then used for the development of a mathematical model which adequately described the substrate limitation and inhibition during the cultivation. The statistical validity test demonstrated that the proposed mathematical model predictions were significant at 99% confidence level. The model was thereafter extrapolated to fed-batch to identify various nutrients feeding regimes during the bioreactor cultivation to improve the PHB accumulation. The distinct capability of the mathematical model to predict highly dynamic fed-batch cultivation strategies was demonstrated by experimental implementation of two fed-batch cultivation strategies. A significantly high PHB concentration of 22.65 g/L & an overall PHB content of 76% was achieved during constant feed rate fed-batch cultivation which is the highest PHB content reported so far using A. australica. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

PubMed

Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

Prognostic indices of perioperative outcome following transperitoneal laparoscopic adrenalectomy.

PubMed

Kiziloz, Halil; Meraney, Anoop; Dorin, Ryan; Nip, Jonathan; Kesler, Stuart; Shichman, Steven

2014-08-01

We sought to identify preoperative patient and tumor characteristics that may be useful prognostic indicators of postsurgical outcome in patients undergoing laparoscopic adrenalectomy (LA). Data from 92 patients who underwent 93 transabdominal LA procedures between 2006-2012 were retrieved. Patients were stratified based on estimated blood loss (EBL), length of stay (LOS), and perioperative complications. Interdependencies between surgical outcome and patient demographics, tumor characteristics, comorbidities, and Charlson Comorbidity Index (CCI) were statistically analyzed. The predictive capacity of each index was assessed using receiver operating characteristic curves. Neither age, gender, tumor laterality, body mass index, American Society of Anesthesiologists (ASA) score, nor CCI predicted the occurrence of perioperative complications. EBL was significantly associated with increased age, tumor size, ASA score, and CCI, whereas prolonged LOS was associated with higher ASA score. Tumor size was related, although not significantly, to LOS and perioperative complications. Tumors ≥7.5 cm in diameter were significantly associated with worse perioperative outcomes. LA for adrenal lesions demonstrated reasonable complication rates and perioperative outcomes. Tumor size, CCI, and ASA score are predictive of increased EBL and LOS.

Local sharpening and subspace wavefront correction with predictive dynamic digital holography

NASA Astrophysics Data System (ADS)

Sulaiman, Sennan; Gibson, Steve

2017-09-01

Digital holography holds several advantages over conventional imaging and wavefront sensing, chief among these being significantly fewer and simpler optical components and the retrieval of complex field. Consequently, many imaging and sensing applications including microscopy and optical tweezing have turned to using digital holography. A significant obstacle for digital holography in real-time applications, such as wavefront sensing for high energy laser systems and high speed imaging for target racking, is the fact that digital holography is computationally intensive; it requires iterative virtual wavefront propagation and hill-climbing to optimize some sharpness criteria. It has been shown recently that minimum-variance wavefront prediction can be integrated with digital holography and image sharpening to reduce significantly large number of costly sharpening iterations required to achieve near-optimal wavefront correction. This paper demonstrates further gains in computational efficiency with localized sharpening in conjunction with predictive dynamic digital holography for real-time applications. The method optimizes sharpness of local regions in a detector plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a spectral plane.

Extending Theory-Based Quantitative Predictions to New Health Behaviors.

PubMed

Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

2016-04-01

Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Trading Network Predicts Stock Price

PubMed Central

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

2014-01-01

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices. PMID:24429767

The combination of work organizational climate and individual work commitment predicts return to work in women but not in men.

PubMed

Holmgren, Kristina; Ekbladh, Elin; Hensing, Gunnel; Dellve, Lotta

2013-02-01

To analyze if the combination of organizational climate and work commitment can predict return to work (RTW). This prospective Swedish study was based on 2285 participants, 19 to 64 years old, consecutively selected from the employed population, newly sick-listed for more than 14 days. Data were collected in 2008 through postal questionnaire and from register data. Among women, the combination of good organizational climate and fair work commitment predicted an early RTW with an adjusted relative risk of 2.05 (1.32 to 3.18). Among men, none of the adjusted variables or combinations of variables was found significantly to predict RTW. This study demonstrated the importance of integrative effects of organizational climate and individual work commitment on RTW among women. These factors did not predict RTW in men. More research is needed to understand the RTW process among men.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE PAGES

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-12-28

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Trading network predicts stock price.

PubMed

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi

2014-01-16

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

Predictive validity of the Sødring Motor Evaluation of Stroke Patients (SMES).

PubMed

Wyller, T B; Sødring, K M; Sveen, U; Ljunggren, A E; Bautz-Holter, E

1996-12-01

The Sødring Motor Evaluation of Stroke Patients (SMES) has been developed as an instrument for the evaluation by physiotherapists of motor function and activities in stroke patients. The predictive validity of the instrument was studied in a consecutive sample of 93 acute stroke patients, assessed in the acute phase and after one year. The outcome measures were: survival, residence at home or in institution, the Barthel ADL index (dichotomized at 19/20), and the Frenchay Activities Index (FAI) (dichotomized at 9/10). The SMES, scored in the acute phase, demonstrated a marginally significant predictive power regarding survival, but was a highly significant predictor regarding the other outcomes. The adjusted odds ratio for a good versus a poor outcome for patients in the upper versus the lower tertile of the SMES arm subscore was 5.4 (95% confidence interval 0.9-59) for survival, 11.5 (2.1-88) for living at home, 86.3 (11-infinity) for a high Barthel score, and 31.4 (5.2-288) for a high FAI score. We conclude that SMES has high predictive validity.

Measures of symptoms and life quality to predict emergent use of institutional health care resources in chronic obstructive airways disease.

PubMed

Traver, G A

1988-11-01

Thirty subjects with severe chronic obstructive airways disease participated in a study to identify differences in symptoms and life quality between those with high and low emergent use of institutional health care resources. Emergent use was defined as care obtained through unscheduled, nonroutine methods of access to health care providers. There were 15 subjects in each group; the groups had similar sex distribution and were not significantly different for percent predicted forced expiratory volume in 1 second (mean 29.8%), use of home oxygen (15 of 30 subjects), or prevalence of CO2 retention (nine of 30). Symptoms and life quality were measured by using three paper and pencil tests, the Bronchitis-Emphysema Symptom Checklist, the Sickness-Impact Profile, and the Katz Adjustment Scale for relatives. Findings demonstrated consistently more symptoms and impairment of life quality in the "high emergent" group. The differences reached statistical significance for irritability, anxiety, helplessness, nervousness, peripheral sensory complaints, alienation, social interaction, and emotional behavior. Discriminant analysis provided a prediction formula that yielded 80% correct prediction for the two groups.

Will they stay or will they go? The role of job embeddedness in predicting turnover in individualistic and collectivistic cultures.

PubMed

Ramesh, Anuradha; Gelfand, Michele J

2010-09-01

Although turnover is an issue of global concern, paradoxically there have been few studies of turnover across cultures. We investigated the cross-cultural generalizability of the job embeddedness model (Mitchell & Lee, 2001) by examining turnover in an individualistic country (United States) and a collectivistic country (India). Using cross-cultural data from call centers (N = 797), we demonstrated that although organization job embeddedness predicted turnover in both countries, different dimensions of job embeddedness predicted turnover in the United States and India. As hypothesized, on the basis of individualism-collectivism theory, person-job fit was a significant predictor of lower turnover in the United States, whereas person-organization fit, organization links, and community links were significant predictors of lower turnover in India. We also explored whether a newly developed construct of embeddedness-family embeddedness-predicts turnover above and beyond job embeddedness and found initial support for its utility in both the United States and India. Theoretical and practical implications are discussed. Copyright 2010 APA, all rights reserved

Metacognition Beliefs and General Health in Predicting Alexithymia in Students

PubMed Central

Babaei, Samaneh; Varandi, Shahryar Ranjbar; Hatami, Zohre; Gharechahi, Maryam

2016-01-01

Objectives: The present study was conducted to investigate the role of metacognition beliefs and general health in alexithymia in Iranian students. Methods: This descriptive and correlational study included 200 participants of high schools students, selected randomly from students of two cities (Sari and Dargaz), Iran. Metacognitive Strategies Questionnaire (MCQ-30); the General Health Questionnaire (GHQ) and Farsi Version of the Toronto Alexithymia Scale (TAS-20) were used for gathering the data. Using the Pearson’s correlation method and regression, the data were analyzed. Results: The findings indicated significant positive relationships between alexithymia and all subscales of general health. The highest correlation was between alexithymia and anxiety subscale (r=0.36, P<0.01). Also, there was a significant negative relationship between alexithymia and some metacognitive strategies. The highest significant negative relationship was seen between alexithymia and the sub-scale of risk uncontrollability (r=-0.359, P < 0.01). Based on the results of multiple regressions, three predictors explained 21% of the variance (R2=0. 21, F=7.238, P<0.01). It was found that anxiety subscale of General Health significantly predicted 13% of the variance of alexithymia (β=0.36, P<0.01) and risk uncontrollability subscale of Metacognition beliefs predicted about 8% of the variance of alexithymia (β=-0.028, P<0.01). Conclusions: The findings demonstrated that metacognition beliefs and general health had important role in predicting of alexithymia in students. PMID:26383206

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Felice, Matteo De; Catalano, Franco; Lee, June-Yi; Wang, Bin; Lee, Doo Young; Yoo, Jin-Ho; Weisheimer, Antije

2018-04-01

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

Diffusion Tensor Imaging for Outcome Prediction in Mild Traumatic Brain Injury: A TRACK-TBI Study

PubMed Central

Yuh, Esther L.; Cooper, Shelly R.; Mukherjee, Pratik; Yue, John K.; Lingsma, Hester F.; Gordon, Wayne A.; Valadka, Alex B.; Okonkwo, David O.; Schnyer, David M.; Vassar, Mary J.; Maas, Andrew I.R.; Casey, Scott S.; Cheong, Maxwell; Dams-O'Connor, Kristen; Hricik, Allison J.; Inoue, Tomoo; Menon, David K.; Morabito, Diane J.; Pacheco, Jennifer L.; Puccio, Ava M.; Sinha, Tuhin K.

2014-01-01

Abstract We evaluated 3T diffusion tensor imaging (DTI) for white matter injury in 76 adult mild traumatic brain injury (mTBI) patients at the semiacute stage (11.2±3.3 days), employing both whole-brain voxel-wise and region-of-interest (ROI) approaches. The subgroup of 32 patients with any traumatic intracranial lesion on either day-of-injury computed tomography (CT) or semiacute magnetic resonance imaging (MRI) demonstrated reduced fractional anisotropy (FA) in numerous white matter tracts, compared to 50 control subjects. In contrast, 44 CT/MRI-negative mTBI patients demonstrated no significant difference in any DTI parameter, compared to controls. To determine the clinical relevance of DTI, we evaluated correlations between 3- and 6-month outcome and imaging, demographic/socioeconomic, and clinical predictors. Statistically significant univariable predictors of 3-month Glasgow Outcome Scale-Extended (GOS-E) included MRI evidence for contusion (odds ratio [OR] 4.9 per unit decrease in GOS-E; p=0.01), ≥1 ROI with severely reduced FA (OR, 3.9; p=0.005), neuropsychiatric history (OR, 3.3; p=0.02), age (OR, 1.07/year; p=0.002), and years of education (OR, 0.79/year; p=0.01). Significant predictors of 6-month GOS-E included ≥1 ROI with severely reduced FA (OR, 2.7; p=0.048), neuropsychiatric history (OR, 3.7; p=0.01), and years of education (OR, 0.82/year; p=0.03). For the subset of 37 patients lacking neuropsychiatric and substance abuse history, MRI surpassed all other predictors for both 3- and 6-month outcome prediction. This is the first study to compare DTI in individual mTBI patients to conventional imaging, clinical, and demographic/socioeconomic characteristics for outcome prediction. DTI demonstrated utility in an inclusive group of patients with heterogeneous backgrounds, as well as in a subset of patients without neuropsychiatric or substance abuse history. PMID:24742275

Manual unloading of the lumbar spine: can it identify immediate responders to mechanical traction in a low back pain population? A study of reliability and criterion referenced predictive validity

PubMed Central

Swanson, Brian T.; Riley, Sean P.; Cote, Mark P.; Leger, Robin R.; Moss, Isaac L.; Carlos,, John

2016-01-01

Background To date, no research has examined the reliability or predictive validity of manual unloading tests of the lumbar spine to identify potential responders to lumbar mechanical traction. Purpose To determine: (1) the intra and inter-rater reliability of a manual unloading test of the lumbar spine and (2) the criterion referenced predictive validity for the manual unloading test. Methods Ten volunteers with low back pain (LBP) underwent a manual unloading test to establish reliability. In a separate procedure, 30 consecutive patients with LBP (age 50·86±11·51) were assessed for pain in their most provocative standing position (visual analog scale (VAS) 49·53±25·52 mm). Patients were assessed with a manual unloading test in their most provocative position followed by a single application of intermittent mechanical traction. Post traction, pain in the provocative position was reassessed and utilized as the outcome criterion. Results The test of unloading demonstrated substantial intra and inter-rater reliability K = 1·00, P = 0·002, K = 0·737, P = 0·001, respectively. There were statistically significant within group differences for pain response following traction for patients with a positive manual unloading test (P<0·001), while patients with a negative manual unloading test did not demonstrate a statistically significant change (P>0·05). There were significant between group differences for proportion of responders to traction based on manual unloading response (P = 0·031), and manual unloading response demonstrated a moderate to strong relationship with traction response Phi = 0·443, P = 0·015. Discussion and conclusion The manual unloading test appears to be a reliable test and has a moderate to strong correlation with pain relief that exceeds minimal clinically important difference (MCID) following traction supporting the validity of this test. PMID:27559274

An Integrated Ensemble-Based Operational Framework to Predict Urban Flooding: A Case Study of Hurricane Sandy in the Passaic and Hackensack River Basins

NASA Astrophysics Data System (ADS)

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A. F.; Wang, Y.

2016-12-01

Advances in computational resources and modeling techniques are opening the path to effectively integrate existing complex models. In the context of flood prediction, recent extreme events have demonstrated the importance of integrating components of the hydrosystem to better represent the interactions amongst different physical processes and phenomena. As such, there is a pressing need to develop holistic and cross-disciplinary modeling frameworks that effectively integrate existing models and better represent the operative dynamics. This work presents a novel Hydrologic-Hydraulic-Hydrodynamic Ensemble (H3E) flood prediction framework that operationally integrates existing predictive models representing coastal (New York Harbor Observing and Prediction System, NYHOPS), hydrologic (US Army Corps of Engineers Hydrologic Modeling System, HEC-HMS) and hydraulic (2-dimensional River Analysis System, HEC-RAS) components. The state-of-the-art framework is forced with 125 ensemble meteorological inputs from numerical weather prediction models including the Global Ensemble Forecast System, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Canadian Meteorological Centre (CMC), the Short Range Ensemble Forecast (SREF) and the North American Mesoscale Forecast System (NAM). The framework produces, within a 96-hour forecast horizon, on-the-fly Google Earth flood maps that provide critical information for decision makers and emergency preparedness managers. The utility of the framework was demonstrated by retrospectively forecasting an extreme flood event, hurricane Sandy in the Passaic and Hackensack watersheds (New Jersey, USA). Hurricane Sandy caused significant damage to a number of critical facilities in this area including the New Jersey Transit's main storage and maintenance facility. The results of this work demonstrate that ensemble based frameworks provide improved flood predictions and useful information about associated uncertainties, thus improving the assessment of risks as when compared to a deterministic forecast. The work offers perspectives for short-term flood forecasts, flood mitigation strategies and best management practices for climate change scenarios.

Predictable weathering of puparial hydrocarbons of necrophagous flies for determining the postmortem interval: a field experiment using Chrysomya rufifacies.

PubMed

Zhu, Guang-Hui; Jia, Zheng-Jun; Yu, Xiao-Jun; Wu, Ku-Sheng; Chen, Lu-Shi; Lv, Jun-Yao; Eric Benbow, M

2017-05-01

Preadult development of necrophagous flies is commonly recognized as an accurate method for estimating the minimum postmortem interval (PMImin). However, once the PMImin exceeds the duration of preadult development, the method is less accurate. Recently, fly puparial hydrocarbons were found to significantly change with weathering time in the field, indicating their potential use for PMImin estimates. However, additional studies are required to demonstrate how the weathering varies among species. In this study, the puparia of Chrysomya rufifacies were placed in the field to experience natural weathering to characterize hydrocarbon composition change over time. We found that weathering of the puparial hydrocarbons was regular and highly predictable in the field. For most of the hydrocarbons, the abundance decreased significantly and could be modeled using a modified exponent function. In addition, the weathering rate was significantly correlated with the hydrocarbon classes. The weathering rate of 2-methyl alkanes was significantly lower than that of alkenes and internal methyl alkanes, and alkenes were higher than the other two classes. For mono-methyl alkanes, the rate was significantly and positively associated with carbon chain length and branch position. These results indicate that puparial hydrocarbon weathering is highly predictable and can be used for estimating long-term PMImin.

Demonstration of GTG as an endogenous initiation codon for a human mRNA transcript revealed by molecular cloning of the serpin endopin 2B.

PubMed

Hwang, Shin-Rong; Garza, Christina Z; Wegrzyn, Jill; Hook, Vivian Y H

2004-08-16

This study demonstrates utilization of the novel GTG initiation codon for translation of a human mRNA transcript that encodes the serpin endopin 2B, a protease inhibitor. Molecular cloning revealed the nucleotide sequence of the human endopin 2B cDNA. Its deduced primary sequence shows high homology to bovine endopin 2A that possesses cross-class protease inhibition of elastase and papain. Notably, the human endopin 2B cDNA sequence revealed GTG as the predicted translation initiation codon; the predicted translation product of 46 kDa endopin 2B was produced by in vitro translation of 35S-endopin 2B with mammalian (rabbit) protein translation components. Importantly, bioinformatic studies demonstrated the presence of the entire human endopin 2B cDNA sequence with GTG as initiation codon within the human genome on chromosome 14. Further evidence for GTG as a functional initiation codon was illustrated by GTG-mediated in vitro translation of the heterologous protein EGFP, and by GTG-mediated expression of EGFP in mammalian PC12 cells. Mutagenesis of GTG to GTC resulted in the absence of EGFP expression in PC12 cells, indicating the function of GTG as an initiation codon. In addition, it was apparent that the GTG initiation codon produces lower levels of translated protein compared to ATG as initiation codon. Significantly, GTG-mediated translation of endopin 2B demonstrates a functional human gene product not previously predicted from initial analyses of the human genome. Further analyses based on GTG as an alternative initiation codon may predict new candidate genes of the human genome.

Predicting tibiotalar and subtalar joint angles from skin-marker data with dual-fluoroscopy as a reference standard.

PubMed

Nichols, Jennifer A; Roach, Koren E; Fiorentino, Niccolo M; Anderson, Andrew E

2016-09-01

Evidence suggests that the tibiotalar and subtalar joints provide near six degree-of-freedom (DOF) motion. Yet, kinematic models frequently assume one DOF at each of these joints. In this study, we quantified the accuracy of kinematic models to predict joint angles at the tibiotalar and subtalar joints from skin-marker data. Models included 1 or 3 DOF at each joint. Ten asymptomatic subjects, screened for deformities, performed 1.0m/s treadmill walking and a balanced, single-leg heel-rise. Tibiotalar and subtalar joint angles calculated by inverse kinematics for the 1 and 3 DOF models were compared to those measured directly in vivo using dual-fluoroscopy. Results demonstrated that, for each activity, the average error in tibiotalar joint angles predicted by the 1 DOF model were significantly smaller than those predicted by the 3 DOF model for inversion/eversion and internal/external rotation. In contrast, neither model consistently demonstrated smaller errors when predicting subtalar joint angles. Additionally, neither model could accurately predict discrete angles for the tibiotalar and subtalar joints on a per-subject basis. Differences between model predictions and dual-fluoroscopy measurements were highly variable across subjects, with joint angle errors in at least one rotation direction surpassing 10° for 9 out of 10 subjects. Our results suggest that both the 1 and 3 DOF models can predict trends in tibiotalar joint angles on a limited basis. However, as currently implemented, neither model can predict discrete tibiotalar or subtalar joint angles for individual subjects. Inclusion of subject-specific attributes may improve the accuracy of these models. Copyright © 2016 Elsevier B.V. All rights reserved.

A 3D-psoriatic skin model for dermatological testing: The impact of culture conditions.

PubMed

Duque-Fernandez, Alexandra; Gauthier, Lydia; Simard, Mélissa; Jean, Jessica; Gendreau, Isabelle; Morin, Alexandre; Soucy, Jacques; Auger, Michèle; Pouliot, Roxane

2016-12-01

Inadequate representation of the human tissue environment during a preclinical screen can result in inaccurate predictions of compound effects. Consequently, pharmaceutical investigators are searching for preclinical models that closely resemble original tissue for predicting clinical outcomes. The current research aims to compare the impact of using serum-free medium instead of complete culture medium during the last step of psoriatic skin substitute reconstruction. Skin substitutes were produced according to the self-assembly approach. Serum-free conditions have no negative impact on the reconstruction of healthy or psoriatic skin substitutes presented in this study regarding their macroscopic or histological appearances. ATR-FTIR results showed no significant differences in the CH 2 bands between psoriatic substitutes cultured with or without serum, thus suggesting that serum deprivation did not have a negative impact on the lipid organization of their stratum corneum . Serum deprivation could even lead to a better organization of healthy skin substitute lipids. Percutaneous analyses demonstrated that psoriatic substitutes cultured in serum-free conditions showed a higher permeability to hydrocortisone compared to controls, while no significant differences in benzoic acid and caffeine penetration profiles were observed. Results obtained with this 3D-psoriatic skin substitute demonstrate the potential and versatility of the model. It could offer good prediction of drug related toxicities at preclinical stages performed in order to avoid unexpected and costly findings in the clinic. Together, these findings offer a new approach for one of the most important challenges of the 21st century, namely, prediction of drug toxicity.

Loss of local capture of the pulmonary vein myocardium after antral isolation: prevalence and clinical significance.

PubMed

Squara, Fabien; Liuba, Ioan; Chik, William; Santangeli, Pasquale; Zado, Erica S; Callans, David J; Marchlinski, Francis E

2015-03-01

Capture of the myocardial sleeves of the pulmonary veins (PV) during PV pacing is mandatory for assessing exit block after PV isolation (PVI). However, previous studies reported that a significant proportion of PVs failed to demonstrate local capture after PVI. We designed this study to evaluate the prevalence and the clinical significance of loss of PV capture after PVI. Thirty patients (14 redo) undergoing antral PVI were included. Before and after PVI, local PV capture was assessed during circumferential pacing (10 mA/2 milliseconds) with a circular multipolar catheter (CMC), using EGM analysis from each dipole of the CMC and from the ablation catheter placed in ipsilateral PV. Pacing output was varied to optimize identification of sleeve capture. All PVs demonstrated sleeve capture before PVI, but only 81% and 40% after first time and redo PVI, respectively (P < 0.001 vs. before PVI). In multivariate analysis, absence of spontaneous PV depolarizations after PVI and previous PVI procedures were associated with less PV sleeve capture after PVI (40% sleeve capture, P < 0.001 for both). Loss of PV local capture by design was coincident with the development of PV entrance block and importantly predicted absence of acute reconnection during adenosine challenge with 96% positive predictive value (23% negative predictive value). Loss of PV local capture is common after antral PVI resulting in entrance block, and may be used as a specific alternate endpoint for PV electrical isolation. Additionally, loss of PV local capture may identify PVs at very low risk of acute reconnection during adenosine challenge. © 2014 Wiley Periodicals, Inc.

Effective grouping for energy and performance: Construction of adaptive, sustainable, and maintainable data storage

NASA Astrophysics Data System (ADS)

Essary, David S.

The performance gap between processors and storage systems has been increasingly critical over the years. Yet the performance disparity remains, and further, storage energy consumption is rapidly becoming a new critical problem. While smarter caching and predictive techniques do much to alleviate this disparity, the problem persists, and data storage remains a growing contributor to latency and energy consumption. Attempts have been made at data layout maintenance, or intelligent physical placement of data, yet in practice, basic heuristics remain predominant. Problems that early studies sought to solve via layout strategies were proven to be NP-Hard, and data layout maintenance today remains more art than science. With unknown potential and a domain inherently full of uncertainty, layout maintenance persists as an area largely untapped by modern systems. But uncertainty in workloads does not imply randomness; access patterns have exhibited repeatable, stable behavior. Predictive information can be gathered, analyzed, and exploited to improve data layouts. Our goal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts by replicating data at the storage device level. We present a comprehensive discussion of the design and construction of such a predictive engine, including workload evaluation, where we present and evaluate classical workloads as well as our own highly detailed traces collected over an extended period. We demonstrate significant gains through an initial static grouping mechanism, and compare against an optimal grouping method of our own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online) grouping, and provide motivation and solutions for addressing these challenges. These challenges include metadata storage, appropriate predictive collocation, online performance, and physical placement. We reduced the metadata needed by several orders of magnitude, reducing the required volume from more than 14% of total storage down to less than 1/2%. We also demonstrate how our collocation strategies outperform competing techniques. Finally, we present our complete model and evaluate a prototype implementation against real hardware. This model was demonstrated to be capable of reducing device-level accesses by up to 65%. Keywords: computer systems, collocation, data management, file systems, grouping, metadata, modeling and prediction, operating systems, performance, power, secondary storage.

Learning to apply models of materials while explaining their properties

NASA Astrophysics Data System (ADS)

Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

2014-09-01

Background:Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose:This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials. Sample:An experimental group is 27 Finnish upper secondary school students and control group included 18 students from the same school. Design and methods:In quasi-experimental setting, students were guided through predict, observe, explain activities in four practical work situations. It was intended that the structural models would encourage students to learn how to identify and apply appropriate models when predicting and explaining situations. The lessons, organised over a one-week period, began with a teacher's demonstration and continued with student experiments in which they described the properties and behaviours of six household products representing three different materials. Results:Most students in the experimental group learned to apply the models correctly, as demonstrated by post-test scores that were significantly higher than pre-test scores. The control group showed no significant difference between pre- and post-test scores. Conclusions:The findings indicate that the intervention where students engage in predict, observe, explain activities while several materials and models are confronted at the same time, had a positive effect on learning outcomes.

The effects of conscientiousness on the appraisals of daily stressors.

PubMed

Gartland, Nicola; O'Connor, Daryl B; Lawton, Rebecca

2012-02-01

Conscientiousness (C) is positively associated with health and longevity although the mechanisms underlying this relationship are not fully understood. Stress may play a role in explaining the C-longevity relationship. This study investigated whether C predicted the cognitive appraisals of daily stressors/hassles. Participants (N=102) completed measures of C and cognitive appraisal in relation to the most stressful hassle they had experienced in the last 7 days. Correlational analysis revealed that Total C, Order and Industriousness were positively correlated with primary appraisals, and Responsibility was positively correlated with secondary appraisals. The facets of C were then entered into hierarchical regression models, controlling for age and gender. This demonstrated that Order (β=0.27, p<0.05) and Industriousness (β=0.28, p<0.05) significantly predicted primary appraisals, accounting for 15.8% of the variance. Responsibility significantly predicted secondary appraisals (β=0.44, p<0.01), accounting for 16.3% of the variance. These findings indicate that higher Order and Industriousness are related to having a greater stake in daily stressors, whereas higher Responsibility is related to greater confidence in one's ability to deal with daily stressors. These results are the first demonstration that C is related to the appraisals of daily hassles and suggest that C may moderate the experience of stress in daily life. Copyright © 2011 John Wiley & Sons, Ltd.

The value of enduring environmental surrogates as predictors of estuarine benthic macroinvertebrate assemblages

NASA Astrophysics Data System (ADS)

Wildsmith, Michelle D.; Valesini, Fiona J.; Robinson, Samuel F.

2017-10-01

This study tested the extent to which spatial differences in the benthic macroinvertebrate assemblages of a temperate microtidal estuary were 'explained' by the enduring (biophysical) vs non-enduring (water and sediment quality) environmental attributes of a diverse range of habitats, and thus the potential of those environmental surrogates to support faunal prediction. Species composition differed significantly among habitats in each season, with the greatest differences occurring in winter and spring and the least in summer. The pattern of habitat differences, as defined by their enduring environmental characteristics, was significantly and well matched with that in the fauna in each season. In contrast, significant matches between the non-enduring environmental and faunal data were only detected in winter and/or spring, and to a lesser extent. Field validation of the faunal prediction capacity of the biophysical surrogate framework at various 'test' sites throughout the estuary showed good agreement between the actual vs predicted key species. These findings demonstrate that enduring environmental criteria, which can be readily measured from mapped data, provide a better and more cost-effective surrogate for explaining spatial differences in the invertebrate fauna of this system than non-enduring criteria, and are thus a promising basis for faunal prediction. The approaches developed in this study are also readily adapted to any estuary worldwide.

Relationship between affect and achievement in science and mathematics in Malaysia and Singapore

NASA Astrophysics Data System (ADS)

Thoe Ng, Khar; Fah Lay, Yoon; Areepattamannil, Shaljan; Treagust, David F.; Chandrasegaran, A. L.

2012-11-01

Background : The Trends in International Mathematics and Science Study (TIMSS) assesses the quality of the teaching and learning of science and mathematics among Grades 4 and 8 students across participating countries. Purpose : This study explored the relationship between positive affect towards science and mathematics and achievement in science and mathematics among Malaysian and Singaporean Grade 8 students. Sample : In total, 4466 Malaysia students and 4599 Singaporean students from Grade 8 who participated in TIMSS 2007 were involved in this study. Design and method : Students' achievement scores on eight items in the survey instrument that were reported in TIMSS 2007 were used as the dependent variable in the analysis. Students' scores on four items in the TIMSS 2007 survey instrument pertaining to students' affect towards science and mathematics together with students' gender, language spoken at home and parental education were used as the independent variables. Results : Positive affect towards science and mathematics indicated statistically significant predictive effects on achievement in the two subjects for both Malaysian and Singaporean Grade 8 students. There were statistically significant predictive effects on mathematics achievement for the students' gender, language spoken at home and parental education for both Malaysian and Singaporean students, with R 2 = 0.18 and 0.21, respectively. However, only parental education showed statistically significant predictive effects on science achievement for both countries. For Singapore, language spoken at home also demonstrated statistically significant predictive effects on science achievement, whereas gender did not. For Malaysia, neither gender nor language spoken at home had statistically significant predictive effects on science achievement. Conclusions : It is important for educators to consider implementing self-concept enhancement intervention programmes by incorporating 'affect' components of academic self-concept in order to develop students' talents and promote academic excellence in science and mathematics.

Impact of experimental design on PET radiomics in predicting somatic mutation status.

PubMed

Yip, Stephen S F; Parmar, Chintan; Kim, John; Huynh, Elizabeth; Mak, Raymond H; Aerts, Hugo J W L

2017-12-01

PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. 348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ). The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δ Overall <5%. The overall influence (δ Overall ) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56). The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Psychological impacts of challenging behaviour and motivational orientation in staff supporting individuals with autistic spectrum conditions.

PubMed

Merrick, Alistair D; Grieve, Alan; Cogan, Nicola

2017-10-01

Despite increased risk of experiencing challenging behaviour, psychological impacts on community and residential staff supporting adults with autistic spectrum conditions are under-explored. Studies examining related roles indicate protective psychological factors may help maintain staff well-being. This study investigated relationships between motivational orientation (eudaimonic or hedonic), challenging behaviour frequency and type (physical, verbal or self-injurious) and psychological impacts (anxiety, depression and life satisfaction). Participants (N = 99) were recruited from six organisations providing autism-specific adult services within Scotland. A series of binary logistic regressions demonstrated weekly challenging behaviour exposure (compared to monthly or daily) significantly increased the likelihood of anxiety caseness. Increased eudaimonic motivation significantly reduced the likelihood of anxiety caseness while also predicting higher life satisfaction. Furthermore, having high levels of eudaimonic motivation appeared to moderate the impact of weekly challenging behaviour exposure on anxiety. No motivational orientation or challenging behaviour factor significantly predicted depression. This sample also demonstrated higher anxiety, lower depression and equivalent life satisfaction levels compared with general population norms. The results highlight the need for considering staff's motivational orientations, their frequency of exposure to challenging behaviour, and both positive and negative psychological outcomes, if seeking to accurately quantify or improve well-being in this staff population.

The theory of reasoned action in prediction of breast self-examination: a comparison of two studies.

PubMed

Powell-Cope, G M; Lierman, L M; Kasprzyk, D; Young, H M; Benoliel, J Q

1991-01-01

The purpose of this article is to report the application of the theory of reasoned action (TRA) to predict breast self-examination (BSE) intention and frequency in two studies with middle-aged and older women. The sample in Study 1 consisted of 93 volunteers from church groups; the second sample consisted of 175 randomly selected subscribers to a large health maintenance organization. Questionnaires to measure attitudinal and subjective normative influences relevant to BSE were developed using guidelines specified by Ajzen and Fishbein (1980). The attitudinal components predicted BSE intention in both studies and BSE frequency in Study 1. In contrast, the subjective norm contributed significantly only to the prediction of frequency in Study 1. Findings demonstrate varying degrees of success for the TRA in predicting BSE intention and behavior. Explanations for the inconsistency in the predictive ability of the TRA can be related to differences between the two studies regarding sample and design characteristics.

Helicopter rotor trailing edge noise. [noise prediction

NASA Technical Reports Server (NTRS)

Schlinker, R. H.; Amier, R. K.

1981-01-01

A two dimensional section of a helicopter main rotor blade was tested in an acoustic wind tunnel at close to full-scale Reynolds numbers to obtain boundary layer data and acoustic data for use in developing an acoustic scaling law and testing a first principles trailing edge noise theory. Results were extended to the rotating frame coordinate system to develop a helicopter rotor trailing edge noise prediction. Comparisons of the calculated noise levels with helicopter flyover spectra demonstrate that trailing edge noise contributes significantly to the total helicopter noise spectrum at high frequencies. This noise mechanism is expected to control the minimum rotor noise. In the case of noise radiation from a local blade segment, the acoustic directivity pattern is predicted by the first principles trailing edge noise theory. Acoustic spectra are predicted by a scaling law which includes Mach number, boundary layer thickness and observer position. Spectrum shape and sound pressure level are also predicted by the first principles theory but the analysis does not predict the Strouhal value identifying the spectrum peak.

Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194

Improved accuracy of intraocular lens power calculation with the Zeiss IOLMaster.

PubMed

Olsen, Thomas

2007-02-01

This study aimed to demonstrate how the level of accuracy in intraocular lens (IOL) power calculation can be improved with optical biometry using partial optical coherence interferometry (PCI) (Zeiss IOLMaster) and current anterior chamber depth (ACD) prediction algorithms. Intraocular lens power in 461 consecutive cataract operations was calculated using both PCI and ultrasound and the accuracy of the results of each technique were compared. To illustrate the importance of ACD prediction per se, predictions were calculated using both a recently published 5-variable method and the Haigis 2-variable method and the results compared. All calculations were optimized in retrospect to account for systematic errors, including IOL constants and other off-set errors. The average absolute IOL prediction error (observed minus expected refraction) was 0.65 dioptres with ultrasound and 0.43 D with PCI using the 5-variable ACD prediction method (p < 0.00001). The number of predictions within +/- 0.5 D, +/- 1.0 D and +/- 2.0 D of the expected outcome was 62.5%, 92.4% and 99.9% with PCI, compared with 45.5%, 77.3% and 98.4% with ultrasound, respectively (p < 0.00001). The 2-variable ACD method resulted in an average error in PCI predictions of 0.46 D, which was significantly higher than the error in the 5-variable method (p < 0.001). The accuracy of IOL power calculation can be significantly improved using calibrated axial length readings obtained with PCI and modern IOL power calculation formulas incorporating the latest generation ACD prediction algorithms.

Intrinsic dimensionality predicts the saliency of natural dynamic scenes.

PubMed

Vig, Eleonora; Dorr, Michael; Martinetz, Thomas; Barth, Erhardt

2012-06-01

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

Prediction of clinical behaviour and treatment for cancers.

PubMed

Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

2003-01-01

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

Indirect and Direct Relationships between Self-Regulation and Academic Achievement during the Nursery/Elementary School Transition of French Students

ERIC Educational Resources Information Center

Hubert, Blandine; Guimard, Philippe; Florin, Agnès; Tracy, Alexis

2015-01-01

Research Findings: Several recent studies carried out in the United States and abroad (i.e., Asia and Europe) have demonstrated that the ability of young children to regulate their behavior (including inhibitory control, working memory, attentional control) significantly predicts their academic achievement. The current study examined the…

The Relationship between Deductive Reasoning Ability, Test Anxiety, and Standardized Test Scores in a Latino Sample

ERIC Educational Resources Information Center

Rich, John D., Jr.; Fullard, William; Overton, Willis

2011-01-01

One Hundred and Twelve Latino students from Philadelphia participated in this study, which examined the development of deductive reasoning across adolescence, and the relation of reasoning to test anxiety and standardized test scores. As predicted, 11th and ninth graders demonstrated significantly more advanced reasoning than seventh graders.…

Chemical kinetic model uncertainty minimization through laminar flame speed measurements

PubMed Central

Park, Okjoo; Veloo, Peter S.; Sheen, David A.; Tao, Yujie; Egolfopoulos, Fokion N.; Wang, Hai

2016-01-01

Laminar flame speed measurements were carried for mixture of air with eight C3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso-butene, n-butane, and iso-butane) at the room temperature and ambient pressure. Along with C1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011, 158, 2358–2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C3 and C4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel. PMID:27890938

Chemical kinetic model uncertainty minimization through laminar flame speed measurements.

PubMed

Park, Okjoo; Veloo, Peter S; Sheen, David A; Tao, Yujie; Egolfopoulos, Fokion N; Wang, Hai

2016-10-01

Laminar flame speed measurements were carried for mixture of air with eight C 3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso -butene, n -butane, and iso -butane) at the room temperature and ambient pressure. Along with C 1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011, 158, 2358-2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C 3 and C 4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C 3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C 4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C 4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel.

A strategy to establish Food Safety Model Repositories.

PubMed

Plaza-Rodríguez, C; Thoens, C; Falenski, A; Weiser, A A; Appel, B; Kaesbohrer, A; Filter, M

2015-07-02

Transferring the knowledge of predictive microbiology into real world food manufacturing applications is still a major challenge for the whole food safety modelling community. To facilitate this process, a strategy for creating open, community driven and web-based predictive microbial model repositories is proposed. These collaborative model resources could significantly improve the transfer of knowledge from research into commercial and governmental applications and also increase efficiency, transparency and usability of predictive models. To demonstrate the feasibility, predictive models of Salmonella in beef previously published in the scientific literature were re-implemented using an open source software tool called PMM-Lab. The models were made publicly available in a Food Safety Model Repository within the OpenML for Predictive Modelling in Food community project. Three different approaches were used to create new models in the model repositories: (1) all information relevant for model re-implementation is available in a scientific publication, (2) model parameters can be imported from tabular parameter collections and (3) models have to be generated from experimental data or primary model parameters. All three approaches were demonstrated in the paper. The sample Food Safety Model Repository is available via: http://sourceforge.net/projects/microbialmodelingexchange/files/models and the PMM-Lab software can be downloaded from http://sourceforge.net/projects/pmmlab/. This work also illustrates that a standardized information exchange format for predictive microbial models, as the key component of this strategy, could be established by adoption of resources from the Systems Biology domain. Copyright © 2015. Published by Elsevier B.V.

Demonstration of GTG as an alternative initiation codon for the serpin endopin 2B-2.

PubMed

Hwang, Shin-Rong; Garza, Christina Z; Wegrzyn, Jill L; Hook, Vivian Y H

2005-02-18

This study demonstrates GTG as a novel, alternative initiation codon for translation of bovine endopin 2B-2, a serpin protease inhibitor. Molecular cDNA cloning revealed the endopin 2B-1 and endopin 2B-2 isoforms that are predicted to inhibit papain and elastase. Notably, GTG was demonstrated as the initiation codon for endopin 2B-2, whereas endopin 2B-1 possesses ATG as its initiation codon. GTG mediated in vitro translation of 46kDa endopin 2B-2. GTG also mediated translation of EGFP by in vitro translation and by expression in mammalian cells. Notably, mutagenesis of GTG to GTC resulted in the absence of EGFP expression in cells. GTG produced a lower level of protein expression compared to ATG. The use of GTG as an initiation codon to direct translation of endopin 2B, as well as the heterologous protein EGFP, demonstrates the role of GTG in the regulation of mRNA translation in mammalian cells. Significantly, further analyses of mammalian genomes based on GTG as an alternative initiation codon may predict new candidate gene products expressed by mammalian and human genomes.

Scrutinizing MHC-I binding peptides and their limits of variation.

PubMed

Koch, Christian P; Perna, Anna M; Pillong, Max; Todoroff, Nickolay K; Wrede, Paul; Folkers, Gerd; Hiss, Jan A; Schneider, Gisbert

2013-01-01

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K(b) is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K(b) in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

PubMed

Grisafi, Andrea; Wilkins, David M; Csányi, Gábor; Ceriotti, Michele

2018-01-19

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

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

NASA Astrophysics Data System (ADS)

Önal, Orkun; Ozmenci, Cemre; Canadinc, Demircan

2014-09-01

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

Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy

NASA Astrophysics Data System (ADS)

Quyen, Michel Le Van; Martinerie, Jacques; Adam, Claude; Varela, Francisco J.

1999-03-01

The degree of interdependence between intracranial electroencephalographic (EEG) channels was investigated in epileptic patients with temporal lobe seizures during interictal (between seizures) periods. With a novel method to characterize nonlinear cross-predictability, that is, the predictability of one channel using another channel as data base, we demonstrated here a possibility to extract information on the spatio-temporal organization of interactions between multichannel recording sites. This method determines whether two channels contain common activity, and often, whether one channel contains activity induced by the activity of the other channel. In particular, the technique and the comparison with surrogate data demonstrated that transient large-scale nonlinear entrainments by the epileptogenic region can be identified, this with or without epileptic activity. Furthermore, these recurrent activities related with the epileptic foci occurred in well-defined spatio-temporal patterns. This suggests that the epileptogenic region can exhibit very subtle influences on other brain regions during an interictal period and raises the possibility that the cross-predictability analysis of interictal data may be used as a significant aid in locating epileptogenic foci.

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

NASA Astrophysics Data System (ADS)

Grisafi, Andrea; Wilkins, David M.; Csányi, Gábor; Ceriotti, Michele

2018-01-01

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

PubMed

Yin, Weiwei; Garimalla, Swetha; Moreno, Alberto; Galinski, Mary R; Styczynski, Mark P

2015-08-28

There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist. Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis. We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

DNA methylation-based measures of biological age: meta-analysis predicting time to death

PubMed Central

Chen, Brian H.; Marioni, Riccardo E.; Colicino, Elena; Peters, Marjolein J.; Ward-Caviness, Cavin K.; Tsai, Pei-Chien; Roetker, Nicholas S.; Just, Allan C.; Demerath, Ellen W.; Guan, Weihua; Bressler, Jan; Fornage, Myriam; Studenski, Stephanie; Vandiver, Amy R.; Moore, Ann Zenobia; Tanaka, Toshiko; Kiel, Douglas P.; Liang, Liming; Vokonas, Pantel; Schwartz, Joel; Lunetta, Kathryn L.; Murabito, Joanne M.; Bandinelli, Stefania; Hernandez, Dena G.; Melzer, David; Nalls, Michael; Pilling, Luke C.; Price, Timothy R.; Singleton, Andrew B.; Gieger, Christian; Holle, Rolf; Kretschmer, Anja; Kronenberg, Florian; Kunze, Sonja; Linseisen, Jakob; Meisinger, Christine; Rathmann, Wolfgang; Waldenberger, Melanie; Visscher, Peter M.; Shah, Sonia; Wray, Naomi R.; McRae, Allan F.; Franco, Oscar H.; Hofman, Albert; Uitterlinden, André G.; Absher, Devin; Assimes, Themistocles; Levine, Morgan E.; Lu, Ake T.; Tsao, Philip S.; Hou, Lifang; Manson, JoAnn E.; Carty, Cara L.; LaCroix, Andrea Z.; Reiner, Alexander P.; Spector, Tim D.; Feinberg, Andrew P.; Levy, Daniel; Baccarelli, Andrea; van Meurs, Joyce; Bell, Jordana T.; Peters, Annette; Deary, Ian J.; Pankow, James S.; Ferrucci, Luigi; Horvath, Steve

2016-01-01

Estimates of biological age based on DNA methylation patterns, often referred to as “epigenetic age”, “DNAm age”, have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2×10−9), independent of chronological age, even after adjusting for additional risk factors (p<5.4×10−4), and within the racial/ethnic groups that we examined (non-Hispanic whites, Hispanics, African Americans). Epigenetic age estimates that incorporated information on blood cell composition led to the smallest p-values for time to death (p=7.5×10−43). Overall, this study a) strengthens the evidence that epigenetic age predicts all-cause mortality above and beyond chronological age and traditional risk factors, and b) demonstrates that epigenetic age estimates that incorporate information on blood cell counts lead to highly significant associations with all-cause mortality. PMID:27690265

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

PubMed

Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

2018-01-01

Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

Thickness dependences of solar cell performance

NASA Technical Reports Server (NTRS)

Sah, C. T.

1982-01-01

The significance of including factors such as the base resistivity loss for solar cells thicker than 100 microns and emitter and BSF layer recombination for thin cells in predicting the fill factor and efficiency of solar cells is demonstrated analytically. A model for a solar cell is devised with the inclusion of the dopant impurity concentration profile, variation of the electron and hole mobility with dopant concentration, the concentration and thermal capture and emission rates of the recombination center, device temperature, the AM1 spectra and the Si absorption coefficient. Device equations were solved by means of the transmission line technique. The analytical results were compared with those of low-level theory for cell performance. Significant differences in predictions of the fill factor resulted, and inaccuracies in the low-level approximations are discussed.

Dancing with the Muses: dissociation and flow.

PubMed

Thomson, Paula; Jaque, S Victoria

2012-01-01

This study investigated dissociative psychological processes and flow (dispositional and state) in a group of professional and pre-professional dancers (n=74). In this study, high scores for global (Mdn=4.14) and autotelic (Mdn=4.50) flow suggest that dancing was inherently integrating and rewarding, although 17.6% of the dancers were identified as possibly having clinical levels of dissociation (Dissociative Experiences Scale-Taxon cutoff score≥20). The results of the multivariate analysis of variance indicated that subjects with high levels of dissociation had significantly lower levels of global flow (p<.05). Stepwise linear regression analyses demonstrated that dispositional flow negatively predicted the dissociative constructs of depersonalization and taxon (p<.05) but did not significantly predict the variance in absorption/imagination (p>.05). As hypothesized, dissociation and flow seem to operate as different mental processes.

Evaluation of Two Crew Module Boilerplate Tests Using Newly Developed Calibration Metrics

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.

2012-01-01

The paper discusses a application of multi-dimensional calibration metrics to evaluate pressure data from water drop tests of the Max Launch Abort System (MLAS) crew module boilerplate. Specifically, three metrics are discussed: 1) a metric to assess the probability of enveloping the measured data with the model, 2) a multi-dimensional orthogonality metric to assess model adequacy between test and analysis, and 3) a prediction error metric to conduct sensor placement to minimize pressure prediction errors. Data from similar (nearly repeated) capsule drop tests shows significant variability in the measured pressure responses. When compared to expected variability using model predictions, it is demonstrated that the measured variability cannot be explained by the model under the current uncertainty assumptions.

Comparison of ISS Power System Telemetry with Analytically Derived Data for Shadowed Cases

NASA Technical Reports Server (NTRS)

Fincannon, H. James

2002-01-01

Accurate International Space Station (ISS) power prediction requires the quantification of solar array shadowing. Prior papers have discussed the NASA Glenn Research Center (GRC) ISS power system tool SPACE (System Power Analysis for Capability Evaluation) and its integrated shadowing algorithms. On-orbit telemetry has become available that permits the correlation of theoretical shadowing predictions with actual data. This paper documents the comparison of a shadowing metric (total solar array current) as derived from SPACE predictions and on-orbit flight telemetry data for representative significant shadowing cases. Images from flight video recordings and the SPACE computer program graphical output are used to illustrate the comparison. The accuracy of the SPACE shadowing capability is demonstrated for the cases examined.

Evaluation of the impact of computed high b-value diffusion-weighted imaging on prostate cancer detection.

PubMed

Verma, Sadhna; Sarkar, Saradwata; Young, Jason; Venkataraman, Rajesh; Yang, Xu; Bhavsar, Anil; Patil, Nilesh; Donovan, James; Gaitonde, Krishnanath

2016-05-01

The purpose of this study was to compare high b-value (b = 2000 s/mm(2)) acquired diffusion-weighted imaging (aDWI) with computed DWI (cDWI) obtained using four diffusion models-mono-exponential (ME), intra-voxel incoherent motion (IVIM), stretched exponential (SE), and diffusional kurtosis (DK)-with respect to lesion visibility, conspicuity, contrast, and ability to predict significant prostate cancer (PCa). Ninety four patients underwent 3 T MRI including acquisition of b = 2000 s/mm(2) aDWI and low b-value DWI. High b = 2000 s/mm(2) cDWI was obtained using ME, IVIM, SE, and DK models. All images were scored on quality independently by three radiologists. Lesions were identified on all images and graded for lesion conspicuity. For a subset of lesions for which pathological truth was established, lesion-to-background contrast ratios (LBCRs) were computed and binomial generalized linear mixed model analysis was conducted to compare clinically significant PCa predictive capabilities of all DWI. For all readers and all models, cDWI demonstrated higher ratings for image quality and lesion conspicuity than aDWI except DK (p < 0.001). The LBCRs of ME, IVIM, and SE were significantly higher than LBCR of aDWI (p < 0.001). Receiver Operating Characteristic curves obtained from binomial generalized linear mixed model analysis demonstrated higher Area Under the Curves for ME, SE, IVIM, and aDWI compared to DK or PSAD alone in predicting significant PCa. High b-value cDWI using ME, IVIM, and SE diffusion models provide better image quality, lesion conspicuity, and increased LBCR than high b-value aDWI. Using cDWI can potentially provide comparable sensitivity and specificity for detecting significant PCa as high b-value aDWI without increased scan times and image degradation artifacts.

Prediction of Significant Wave Heights in the Tropics at Sub-seasonal Time Scales

NASA Astrophysics Data System (ADS)

Kinter, J. L.; Shukla, R. P.; Shin, C. S.

2017-12-01

Skillfully predicting the 14-day mean significant wave height (SWH) forecasts at 3 weeks lead-time over the Western Pacific and Indian Oceans has been demonstrated using the WAVEWATCH-3 (WW3) model coupled to a modified version of the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). In this paper, we present results on the effect of the Madden Julian Oscillation (MJO) events and El Niño and the Southern Oscillation (ENSO) on such predictions. Forecasts initialized with multiple ocean analyses in both January and May for 1979-2008 are evaluated. A significant anomaly correlation of predicted and observed SWH anomalies (SWHA) at 3 weeks lead-time is found over portions of the domain in both January and May cases. The model successfully predicts almost all the important features of the observed SWHA during El Niño events in January, including negative SWHA in the central Indian Ocean and northern western tropical Pacific, and positive SWHA over the southern Ocean and western Pacific. The model also reproduces the spatial pattern of the inverse relationship between SWHA and sea level pressure anomalies during both composite El Niño and La Niña events at 3 weeks lead-time. The model successfully predicts the sign and magnitude of SWHA in May over the Bay of Bengal and South China Sea in composites of phases 2 and 6 of MJO. The observed leading mode of SWHA in May and the third mode of SWHA in January are influenced by the combined effects of MJO and ENSO. Analysis of the mechanisms for these relationships is described.

Human Papillomavirus DNA Methylation Predicts Response to Treatment Using Cidofovir and Imiquimod in Vulval Intraepithelial Neoplasia 3.

PubMed

Jones, Sadie E F; Hibbitts, Samantha; Hurt, Christopher N; Bryant, Dean; Fiander, Alison N; Powell, Ned; Tristram, Amanda J

2017-09-15

Purpose: Response rates to treatment of vulval intraepithelial neoplasia (VIN) with imiquimod and cidofovir are approximately 57% and 61%, respectively. Treatment is associated with significant side effects and, if ineffective, risk of malignant progression. Treatment response is not predicted by clinical factors. Identification of a biomarker that could predict response is an attractive prospect. This work investigated HPV DNA methylation as a potential predictive biomarker in this setting. Experimental Design: DNA from 167 cases of VIN 3 from the RT3 VIN clinical trial was assessed. HPV-positive cases were identified using Greiner PapilloCheck and HPV 16 type-specific PCR. HPV DNA methylation status was assessed in three viral regions: E2, L1/L2, and the promoter, using pyrosequencing. Results: Methylation of the HPV E2 region was associated with response to treatment. For cidofovir ( n = 30), median E2 methylation was significantly higher in patients who responded ( P ≤ 0.0001); E2 methylation >4% predicted response with 88.2% sensitivity and 84.6% specificity. For imiquimod ( n = 33), median E2 methylation was lower in patients who responded to treatment ( P = 0.03; not significant after Bonferroni correction); E2 methylation <4% predicted response with 70.6% sensitivity and 62.5% specificity. Conclusions: These data indicate that cidofovir and imiquimod may be effective in two biologically defined groups. HPV E2 DNA methylation demonstrated potential as a predictive biomarker for the treatment of VIN with cidofovir and may warrant investigation in a biomarker-guided clinical trial. Clin Cancer Res; 23(18); 5460-8. ©2017 AACR . ©2017 American Association for Cancer Research.

Intrinsic Raman spectroscopy for quantitative biological spectroscopy Part II

PubMed Central

Bechtel, Kate L.; Shih, Wei-Chuan; Feld, Michael S.

2009-01-01

We demonstrate the effectiveness of intrinsic Raman spectroscopy (IRS) at reducing errors caused by absorption and scattering. Physical tissue models, solutions of varying absorption and scattering coefficients with known concentrations of Raman scatterers, are studied. We show significant improvement in prediction error by implementing IRS to predict concentrations of Raman scatterers using both ordinary least squares regression (OLS) and partial least squares regression (PLS). In particular, we show that IRS provides a robust calibration model that does not increase in error when applied to samples with optical properties outside the range of calibration. PMID:18711512

Low frequency vibration isolation technology for microgravity space experiments

NASA Technical Reports Server (NTRS)

Grodsinsky, Carlos M.; Brown, Gerald V.

1989-01-01

The dynamic acceleration environment observed on Space Shuttle flights to date and predicted for the Space Station has complicated the analysis of prior microgravity experiments and prompted concern for the viability of proposed space experiments requiring long-term, low-g environments. Isolation systems capable of providing significant improvements in this environment exist, but have not been demonstrated in flight configurations. This paper presents a summary of the theoretical evaluation for two one degree-of-freedom (DOF) active magnetic isolators and their predicted response to both direct and base excitations, that can be used to isolate acceleration sensitive microgravity space experiments.

Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of HERG inhibition, solubility, and lipophilicity.

PubMed

Papadatos, George; Alkarouri, Muhammad; Gillet, Valerie J; Willett, Peter; Kadirkamanathan, Visakan; Luscombe, Christopher N; Bravi, Gianpaolo; Richmond, Nicola J; Pickett, Stephen D; Hussain, Jameed; Pritchard, John M; Cooper, Anthony W J; Macdonald, Simon J F

2010-10-25

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions.

PubMed

Elhenawy, Mohammed; Jahangiri, Arash; Rakha, Hesham A; El-Shawarby, Ihab

2015-10-01

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

On the short circuit resilience of organic solar cells: prediction and validation.

PubMed

Oostra, A Jolt; Smits, Edsger C P; de Leeuw, Dago M; Blom, Paul W M; Michels, Jasper J

2015-09-07

The operational characteristics of organic solar cells manufactured with large area processing methods suffers from the occurrence of short-circuits due to defects in the photoactive thin film stack. In this work we study the effect of a shunt resistance on an organic solar cell and demonstrate that device performance is not affected negatively as long as the shunt resistance is higher than approximately 1000 Ohm. By studying charge transport across PSS-lithium fluoride/aluminum (LiF/Al) shunting junctions we show that this prerequisite is already met by applying a sufficiently thick (>1.5 nm) LiF layer. We demonstrate that this remarkable shunt-resilience stems from the formation of a significant charge transport barrier at the PSS-LiF/Al interface. We validate our predictions by fabricating devices with deliberately severed photoactive layers and find an excellent agreement between the calculated and experimental current-voltage characteristics.

Semantic effects in naming perceptual identification but not in delayed naming: implications for models and tasks.

PubMed

Wurm, Lee H; Seaman, Sean R

2008-03-01

Previous research has demonstrated that the subjective danger and usefulness of words affect lexical decision times. Usually, an interaction is found: Increasing danger predicts faster reaction times (RTs) for words low on usefulness, but increasing danger predicts slower RTs for words high on usefulness. The authors show the same interaction with immediate auditory naming. The interaction disappeared with a delayed auditory naming control experiment, suggesting that it has a perceptual basis. In an attempt to separate input (signal to ear) from output (brain to muscle) processes in word recognition, the authors ran 2 auditory perceptual identification experiments. The interaction was again significant, but performance was best for words high on both danger and usefulness. This suggests that initial demonstrations of the interaction were reflecting an output approach/withdraw response conflict induced by stimuli that are both dangerous and useful. The interaction cannot be characterized as a tradeoff of speed versus accuracy.

Polymer optical fiber compound parabolic concentrator tip for enhanced coupling efficiency for fluorescence based glucose sensors

PubMed Central

Hassan, Hafeez Ul; Nielsen, Kristian; Aasmul, Soren; Bang, Ole

2015-01-01

We demonstrate that the light excitation and capturing efficiency of fluorescence based fiber-optical sensors can be significantly increased by using a CPC (Compound Parabolic Concentrator) tip instead of the standard plane-cut tip. We use Zemax modelling to find the optimum CPC tip profile and fiber length of a polymer optical fiber diabetes sensor for continuous monitoring of glucose levels. We experimentally verify the improved performance of the CPC tipped sensor and the predicted production tolerances. Due to physical size requirements when the sensor has to be inserted into the body a non-optimal fiber length of 35 mm is chosen. For this length an average improvement in efficiency of a factor of 1.7 is experimentally demonstrated and critically compared to the predicted ideal factor of 3 in terms of parameters that should be improved through production optimization. PMID:26713213

Enhanced thermoelectric performance of graphene nanoribbon-based devices

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

Hossain, Md Sharafat, E-mail: hossain@student.unimelb.edu.au; Huynh, Duc Hau; Nguyen, Phuong Duc

There have been numerous theoretical studies on exciting thermoelectric properties of graphene nano-ribbons (GNRs); however, most of these studies are mainly based on simulations. In this work, we measure and characterize the thermoelectric properties of GNRs and compare the results with theoretical predictions. Our experimental results verify that nano-structuring and patterning graphene into nano-ribbons significantly enhance its thermoelectric power, confirming previous predictions. Although patterning results in lower conductance (G), the overall power factor (S{sup 2}G) increases for nanoribbons. We demonstrate that edge roughness plays an important role in achieving such an enhanced performance and support it through first principles simulations.more » We show that uncontrolled edge roughness, which is considered detrimental in GNR-based electronic devices, leads to enhanced thermoelectric performance of GNR-based thermoelectric devices. The result validates previously reported theoretical studies of GNRs and demonstrates the potential of GNRs for the realization of highly efficient thermoelectric devices.« less

Polymer optical fiber compound parabolic concentrator tip for enhanced coupling efficiency for fluorescence based glucose sensors.

PubMed

Hassan, Hafeez Ul; Nielsen, Kristian; Aasmul, Soren; Bang, Ole

2015-12-01

We demonstrate that the light excitation and capturing efficiency of fluorescence based fiber-optical sensors can be significantly increased by using a CPC (Compound Parabolic Concentrator) tip instead of the standard plane-cut tip. We use Zemax modelling to find the optimum CPC tip profile and fiber length of a polymer optical fiber diabetes sensor for continuous monitoring of glucose levels. We experimentally verify the improved performance of the CPC tipped sensor and the predicted production tolerances. Due to physical size requirements when the sensor has to be inserted into the body a non-optimal fiber length of 35 mm is chosen. For this length an average improvement in efficiency of a factor of 1.7 is experimentally demonstrated and critically compared to the predicted ideal factor of 3 in terms of parameters that should be improved through production optimization.

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

Dar, Roy; Shaffer, Sydney M.; Singh, Abhyudai

Recent analysis demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-tocell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: thatmore » increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. In conclusion, the data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.« less

Potential-based and non-potential-based cohesive zone formulations under mixed-mode separation and over-closure-Part II: Finite element applications

NASA Astrophysics Data System (ADS)

Máirtín, Éamonn Ó.; Parry, Guillaume; Beltz, Glenn E.; McGarry, J. Patrick

2014-02-01

This paper, the second of two parts, presents three novel finite element case studies to demonstrate the importance of normal-tangential coupling in cohesive zone models (CZMs) for the prediction of mixed-mode interface debonding. Specifically, four new CZMs proposed in Part I of this study are implemented, namely the potential-based MP model and the non-potential-based NP1, NP2 and SMC models. For comparison, simulations are also performed for the well established potential-based Xu-Needleman (XN) model and the non-potential-based model of van den Bosch, Schreurs and Geers (BSG model). Case study 1: Debonding and rebonding of a biological cell from a cyclically deforming silicone substrate is simulated when the mode II work of separation is higher than the mode I work of separation at the cell-substrate interface. An active formulation for the contractility and remodelling of the cell cytoskeleton is implemented. It is demonstrated that when the XN potential function is used at the cell-substrate interface repulsive normal tractions are computed, preventing rebonding of significant regions of the cell to the substrate. In contrast, the proposed MP potential function at the cell-substrate interface results in negligible repulsive normal tractions, allowing for the prediction of experimentally observed patterns of cell cytoskeletal remodelling. Case study 2: Buckling of a coating from the compressive surface of a stent is simulated. It is demonstrated that during expansion of the stent the coating is initially compressed into the stent surface, while simultaneously undergoing tangential (shear) tractions at the coating-stent interface. It is demonstrated that when either the proposed NP1 or NP2 model is implemented at the stent-coating interface mixed-mode over-closure is correctly penalised. Further expansion of the stent results in the prediction of significant buckling of the coating from the stent surface, as observed experimentally. In contrast, the BSG model does not correctly penalise mixed-mode over-closure at the stent-coating interface, significantly altering the stress state in the coating and preventing the prediction of buckling. Case study 3: Application of a displacement to the base of a bi-layered composite arch results in a symmetric sinusoidal distribution of normal and tangential traction at the arch interface. The traction defined mode mixity at the interface ranges from pure mode II at the base of the arch to pure mode I at the top of the arch. It is demonstrated that predicted debonding patterns are highly sensitive to normal-tangential coupling terms in a CZM. The NP2, XN, and BSG models exhibit a strong bias towards mode I separation at the top of the arch, while the NP1 model exhibits a bias towards mode II debonding at the base of the arch. Only the SMC model provides mode-independent behaviour in the early stages of debonding. This case study provides a practical example of the importance of the behaviour of CZMs under conditions of traction controlled mode mixity, following from the theoretical analysis presented in Part I of this study.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part II, External Validation and Usability Testing of a Smartphone App.

PubMed

Choo, Min Soo; Jeong, Seong Jin; Cho, Sung Yong; Yoo, Changwon; Jeong, Chang Wook; Ku, Ja Hyeon; Oh, Seung-June

2017-04-01

We aimed to externally validate the prediction model we developed for having bladder outlet obstruction (BOO) and requiring prostatic surgery using 2 independent data sets from tertiary referral centers, and also aimed to validate a mobile app for using this model through usability testing. Formulas and nomograms predicting whether a subject has BOO and needs prostatic surgery were validated with an external validation cohort from Seoul National University Bundang Hospital and Seoul Metropolitan Government-Seoul National University Boramae Medical Center between January 2004 and April 2015. A smartphone-based app was developed, and 8 young urologists were enrolled for usability testing to identify any human factor issues of the app. A total of 642 patients were included in the external validation cohort. No significant differences were found in the baseline characteristics of major parameters between the original (n=1,179) and the external validation cohort, except for the maximal flow rate. Predictions of requiring prostatic surgery in the validation cohort showed a sensitivity of 80.6%, a specificity of 73.2%, a positive predictive value of 49.7%, and a negative predictive value of 92.0%, and area under receiver operating curve of 0.84. The calibration plot indicated that the predictions have good correspondence. The decision curve showed also a high net benefit. Similar evaluation results using the external validation cohort were seen in the predictions of having BOO. Overall results of the usability test demonstrated that the app was user-friendly with no major human factor issues. External validation of these newly developed a prediction model demonstrated a moderate level of discrimination, adequate calibration, and high net benefit gains for predicting both having BOO and requiring prostatic surgery. Also a smartphone app implementing the prediction model was user-friendly with no major human factor issue.

Near Real-Time Optimal Prediction of Adverse Events in Aviation Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander; Das, Santanu

2010-01-01

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we demonstrate how to recast the anomaly prediction problem into a form whose solution is accessible as a level-crossing prediction problem. The level-crossing prediction problem has an elegant, optimal, yet untested solution under certain technical constraints, and only when the appropriate modeling assumptions are made. As such, we will thoroughly investigate the resilience of these modeling assumptions, and show how they affect final performance. Finally, the predictive capability of this method will be assessed by quantitative means, using both validation and test data containing anomalies or adverse events from real aviation data sets that have previously been identified as operationally significant by domain experts. It will be shown that the formulation proposed yields a lower false alarm rate on average than competing methods based on similarly advanced concepts, and a higher correct detection rate than a standard method based upon exceedances that is commonly used for prediction.

Market Confidence Predicts Stock Price: Beyond Supply and Demand

PubMed Central

Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

2016-01-01

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price. PMID:27391816

Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

PubMed Central

Ming, Wei; Xiong, Tao

2014-01-01

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. PMID:24723814

Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease.

PubMed

Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe

2015-10-01

Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

Tropical Mosquito Assemblages Demonstrate ‘Textbook’ Annual Cycles

PubMed Central

Franklin, Donald C.; Whelan, Peter I.

2009-01-01

Background Annual biological rhythms are often depicted as predictably cyclic, but quantitative evaluations are few and rarely both cyclic and constant among years. In the monsoon tropics, the intense seasonality of rainfall frequently drives fluctuations in the populations of short-lived aquatic organisms. However, it is unclear how predictably assemblage composition will fluctuate because the intensity, onset and cessation of the wet season varies greatly among years. Methodology/Principal Findings Adult mosquitoes were sampled using EVS suction traps baited with carbon dioxide around swamplands adjacent to the city of Darwin in northern Australia. Eleven sites were sampled weekly for five years, and one site weekly for 24 years, the sample of c. 1.4 million mosquitoes yielding 63 species. Mosquito abundance, species richness and diversity fluctuated seasonally, species richness being highly predictable. Ordination of assemblage composition demonstrated striking annual cycles that varied little from year to year. The mosquito assemblage was temporally structured by a succession of species peaks in abundance. Conclusion/Significance Ordination provided strong visual representation of annual rhythms in assemblage composition and the means to evaluate variability among years. Because most mosquitoes breed in shallow freshwater which fluctuates with rainfall, we did not anticipate such repeatability; we conclude that mosquito assemblage composition appears adapted to predictable elements of the rainfall. PMID:20011531

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.

PubMed

Rogers, Emily; Murrugarra, David; Heitsch, Christine

2017-07-25

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones. Copyright © 2017. Published by Elsevier Inc.

A two-layer composite model of the vocal fold lamina propria for fundamental frequency regulation.

PubMed

Zhang, Kai; Siegmund, Thomas; Chan, Roger W

2007-08-01

The mechanical properties of the vocal fold lamina propria, including the vocal fold cover and the vocal ligament, play an important role in regulating the fundamental frequency of human phonation. This study examines the equilibrium hyperelastic tensile deformation behavior of cover and ligament specimens isolated from excised human larynges. Ogden's hyperelastic model is used to characterize the tensile stress-stretch behaviors at equilibrium. Several statistically significant differences in the mechanical response differentiating cover and ligament, as well as gender are found. Fundamental frequencies are predicted from a string model and a beam model, both accounting for the cover and the ligament. The beam model predicts nonzero F(0) for the unstretched state of the vocal fold. It is demonstrated that bending stiffness significantly contributes to the predicted F(0), with the ligament contributing to a higher F(0), especially in females. Despite the availability of only a small data set, the model predicts an age dependence of F(0) in males in agreement with experimental findings. Accounting for two mechanisms of fundamental frequency regulation--vocal fold posturing (stretching) and extended clamping--brings predicted F(0) close to the lower bound of the human phonatory range. Advantages and limitations of the current model are discussed.

Evaluation of Variable-Depth Liner Configurations for Increased Broadband Noise Reduction

NASA Technical Reports Server (NTRS)

Jones, M. G.; Watson, W. R.; Nark, D. M.; Howerton, B. M.

2015-01-01

This paper explores the effects of variable-depth geometry on the amount of noise reduction that can be achieved with acoustic liners. Results for two variable-depth liners tested in the NASA Langley Grazing Flow Impedance Tube demonstrate significant broadband noise reduction. An impedance prediction model is combined with two propagation codes to predict corresponding sound pressure level profiles over the length of the Grazing Flow Impedance Tube. The comparison of measured and predicted sound pressure level profiles is sufficiently favorable to support use of these tools for investigation of a number of proposed variable-depth liner configurations. Predicted sound pressure level profiles for these proposed configurations reveal a number of interesting features. Liner orientation clearly affects the sound pressure level profile over the length of the liner, but the effect on the total attenuation is less pronounced. The axial extent of attenuation at an individual frequency continues well beyond the location where the liner depth is optimally tuned to the quarter-wavelength of that frequency. The sound pressure level profile is significantly affected by the way in which variable-depth segments are distributed over the length of the liner. Given the broadband noise reduction capability for these liner configurations, further development of impedance prediction models and propagation codes specifically tuned for this application is warranted.

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification.

PubMed

Ponasenko, Anastasia V; Khutornaya, Maria V; Kutikhin, Anton G; Rutkovskaya, Natalia V; Tsepokina, Anna V; Kondyukova, Natalia V; Yuzhalin, Arseniy E; Barbarash, Leonid S

2016-08-31

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification.

A Genomics-Based Model for Prediction of Severe Bioprosthetic Mitral Valve Calcification

PubMed Central

Ponasenko, Anastasia V.; Khutornaya, Maria V.; Kutikhin, Anton G.; Rutkovskaya, Natalia V.; Tsepokina, Anna V.; Kondyukova, Natalia V.; Yuzhalin, Arseniy E.; Barbarash, Leonid S.

2016-01-01

Severe bioprosthetic mitral valve calcification is a significant problem in cardiovascular surgery. Unfortunately, clinical markers did not demonstrate efficacy in prediction of severe bioprosthetic mitral valve calcification. Here, we examined whether a genomics-based approach is efficient in predicting the risk of severe bioprosthetic mitral valve calcification. A total of 124 consecutive Russian patients who underwent mitral valve replacement surgery were recruited. We investigated the associations of the inherited variation in innate immunity, lipid metabolism and calcium metabolism genes with severe bioprosthetic mitral valve calcification. Genotyping was conducted utilizing the TaqMan assay. Eight gene polymorphisms were significantly associated with severe bioprosthetic mitral valve calcification and were therefore included into stepwise logistic regression which identified male gender, the T/T genotype of the rs3775073 polymorphism within the TLR6 gene, the C/T genotype of the rs2229238 polymorphism within the IL6R gene, and the A/A genotype of the rs10455872 polymorphism within the LPA gene as independent predictors of severe bioprosthetic mitral valve calcification. The developed genomics-based model had fair predictive value with area under the receiver operating characteristic (ROC) curve of 0.73. In conclusion, our genomics-based approach is efficient for the prediction of severe bioprosthetic mitral valve calcification. PMID:27589735

Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties.

PubMed

Yue, Zhenyu; Zhang, Wenna; Lu, Yongming; Yang, Qiaoyue; Ding, Qiuying; Xia, Junfeng; Chen, Yan

2015-01-01

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

Accurate Binding Free Energy Predictions in Fragment Optimization.

PubMed

Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

2015-11-23

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Thermal Modeling in Support of the Edison Demonstration of Smallsat Networks Project

NASA Technical Reports Server (NTRS)

Coker, Robert

2013-01-01

NASA's Edison program is intending to launch a swarm of at least 8 small satellites in 2013. This swarm of 1.5U Cubesats, the Edison Demonstration of Smallsat Networks (EDSN) project, will demonstrate intra-swarm communications and multi-point in-situ space physics data acquisition. In support of the design and testing of the EDSN satellites, a geometrically accurate thermal model has been constructed. Due to the low duty cycle of most components, no significant overheating issues were found. The predicted mininum temperatures of the external antennas are low enough, however, that some mitigation may be in order. The development and application of the model will be discussed in detail.

Evaluating the Performance of a New Model for Predicting the Growth of Clostridium perfringens in Cooked, Uncured Meat and Poultry Products under Isothermal, Heating, and Dynamically Cooling Conditions.

PubMed

Huang, Lihan

2016-07-01

Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 0.6 Log CFU/g. For cooling, the end point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single-rate cooling and -0.66 Log CFU/g for dual-rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in the literature, this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single-rate cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Ensemble-based prediction of RNA secondary structures.

PubMed

Aghaeepour, Nima; Hoos, Holger H

2013-04-24

Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

PubMed Central

Mariadason, John M.; Wang, Donghai; Augenlicht, Leonard H.; Chance, Mark R.

2010-01-01

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data. PMID:20824133

Development of demi-span equations for predicting height among the Malaysian elderly.

PubMed

Ngoh, H J; Sakinah, H; Harsa Amylia, M S

2012-08-01

This study aimed to develop demi-span equations for predicting height in the Malaysian elderly and to explore the applicability of previous published demi-span equations derived from adult populations to the elderly. A cross-sectional study was conducted on Malaysian elderly aged 60 years and older. Subjects were residents of eight shelter homes in Peninsular Malaysia; 204 men and 124 women of Malay, Chinese and Indian ethnicity were included. Measurements of weight, height and demi-span were obtained using standard procedures. Statistical analyses were performed using SPSS version 18.0. The demi-span equations obtained were as follows: Men: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age) + 4.13; Women: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age). Height predicted from these new equations demonstrated good agreement with measured height and no significant differences were found between the mean values of predicted and measured heights in either gender (p>0.05). However, the heights predicted from previous published adult-derived demi-span equations failed to yield good agreement with the measured height of the elderly; significant over-estimation and underestimation of heights tended to occur (p>0.05). The new demi-span equations allow prediction of height with sufficient accuracy in the Malaysian elderly. However, further validation on other elderly samples is needed. Also, we recommend caution when using adult-derived demi-span equations to predict height in elderly people.

Women with provoked vestibulodynia experience clinically significant reductions in pain regardless of treatment: results from a 2-year follow-up study.

PubMed

Davis, Seth N P; Bergeron, Sophie; Binik, Yitzchak M; Lambert, Bernard

2013-12-01

Provoked vestibulodynia (PVD) is a prevalent genital pain syndrome that has been assumed to be chronic, with little spontaneous remission. Despite this assumption, there is a dearth of empirical evidence regarding the progression of PVD in a natural setting. Although many treatments are available, there is no single treatment that has demonstrated efficacy above others. The aims of this secondary analysis of a prospective study were to (i) assess changes over a 2-year period in pain, depressive symptoms, and sexual outcomes in women with PVD; and (ii) examine changes based on treatment(s) type. Participants completed questionnaire packages at Time 1 and a follow-up package 2 years later. Visual analog scale of genital pain, Global Measure of Sexual Satisfaction, Female Sexual Function Index, Beck Depression Inventory, Dyadic Adjustment Scale, and sexual intercourse attempts over the past month. Two hundred thirty-nine women with PVD completed both time one and two questionnaires. For the sample as a whole, there was significant improvement over 2 years on pain ratings, sexual satisfaction, sexual function, and depressive symptoms. The most commonly received treatments were physical therapy, sex/psychotherapy, and medical treatment, although 41.0% did not undergo any treatment. Women receiving no treatment also improved significantly on pain ratings. No single treatment type predicted better outcome for any variable except depressive symptoms, in which women who underwent surgery were more likely to improve. These results suggest that PVD may significantly reduce in severity over time. Participants demonstrated clinically significant pain improvement, even when they did not receive treatment. Furthermore, the only single treatment type predicting better outcomes was surgery, and only for depressive symptoms, accounting for only 2.3% of the variance. These data do not demonstrate the superiority of any one treatment and underscore the need to have control groups in PVD treatment trials, otherwise improvements may simply be the result of natural progression. © 2013 International Society for Sexual Medicine.

Validity of one-repetition maximum predictive equations in men with spinal cord injury.

PubMed

Ribeiro Neto, F; Guanais, P; Dornelas, E; Coutinho, A C B; Costa, R R G

2017-10-01

Cross-sectional study. The study aimed (a) to test the cross-validation of current one-repetition maximum (1RM) predictive equations in men with spinal cord injury (SCI); (b) to compare the current 1RM predictive equations to a newly developed equation based on the 4- to 12-repetition maximum test (4-12RM). SARAH Rehabilitation Hospital Network, Brasilia, Brazil. Forty-five men aged 28.0 years with SCI between C6 and L2 causing complete motor impairment were enrolled in the study. Volunteers were tested, in a random order, in 1RM test or 4-12RM with 2-3 interval days. Multiple regression analysis was used to generate an equation for predicting 1RM. There were no significant differences between 1RM test and the current predictive equations. ICC values were significant and were classified as excellent for all current predictive equations. The predictive equation of Lombardi presented the best Bland-Altman results (0.5 kg and 12.8 kg for mean difference and interval range around the differences, respectively). The two created equation models for 1RM demonstrated the same and a high adjusted R 2 (0.971, P<0.01), but different SEE of measured 1RM (2.88 kg or 5.4% and 2.90 kg or 5.5%). All 1RM predictive equations are accurate to assess individuals with SCI at the bench press exercise. However, the predictive equation of Lombardi presented the best associated cross-validity results. A specific 1RM prediction equation was also elaborated for individuals with SCI. The created equation should be tested in order to verify whether it presents better accuracy than the current ones.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Acid oro-pharyngeal secretions can predict gastro-oesophageal reflux in preterm infants.

PubMed

James, M E; Ewer, A K

1999-05-01

Acid gastro-oesophageal reflux (GOR) is common in preterm infants but there is a lack of a non-invasive technique to establish the diagnosis. The aim of this study was to identify whether the presence of acid in oro-pharyngeal secretions (OPS) was a valid indicator of clinically significant acid GOR in preterm infants. A total of 23 infants with suspected GOR were studied with 24 h lower-oesophageal pH monitoring and during this period the OPS were tested for acid with litmus paper at 6 hourly intervals. Median (range) gestation was 28 weeks (24-31), birth weight 1023 g (480-1750) and age at study 34 days (11-76). Significant GOR was defined as a reflux index >5%. Of the investigated infants, 18 subjects (78%) had significant GOR. Of this group, 16 infants had acid in the OPS on at least one occasion. Five infants did not demonstrate significant GOR and in four of these acid was not detected in the OPS. Our data indicate that as a predictor for significant GOR, litmus-testing OPS for acid has a sensitivity of 89%, specificity of 80%, positive predictive value of 94% and a negative predictive value of 67%. The difference in the incidence of acid OPS between the GOR and the No GOR group was significant (P<0.03). The presence of acid in the oropharyngeal secretions may help in the prediction of acid gastro-oesophageal reflux in preterm infants. The method is simple, inexpensive cheap and involves minimal disturbance. We suggest that it could aid clinical diagnosis and indicate a need for further investigation of gastro-oesophageal reflux.

The Power of the Pygmalion Effect: Teachers' Expectations Strongly Predict College Completion

ERIC Educational Resources Information Center

Boser, Ulrich; Wilhelm, Megan; Hanna, Robert

2014-01-01

People do better when more is expected of them. In education circles, this is called the Pygmalion Effect. It has been demonstrated in study after study, and the results can sometimes be quite significant. In one research project, for instance, teacher expectations of a pre-schooler's ability was a robust predictor of the child's high school GPA.…

The pharmacogenetics of body odor: as easy as ABCC?

PubMed

Brown, Sara

2013-07-01

ABCC11 genotype affects apocrine secretory cell function and determines individual body odor phenotype. Rodriguez et al. have applied genetic epidemiology using predetermined phenotype data to demonstrate an association between a single-nucleotide polymorphism (rs17822931) and the human behavior of deodorant application. Individuals with the ABCC11 genotype predicting a nonodorous phenotype report a significantly lower frequency of deodorant use.

Use of LIDAR for forest inventory and forest management application

Treesearch

Birgit Peterson; Ralph Dubayah; Peter Hyde; Michelle Hofton; J. Bryan Blair; JoAnn Fites-Kaufman

2007-01-01

A significant impediment to forest managers has been the difficulty in obtaining large-area forest structure and fuel characteristics at useful resolutions and accuracies. This paper demonstrates how LIDAR data were used to predict canopy bulk density (CBD) and canopy base height (CBH) for an area in the Sierra National Forest. The LIDAR data were used to generate maps...

Right frontal pole cortical thickness and executive functioning in children with traumatic brain injury: the impact on social problems.

PubMed

Levan, Ashley; Black, Garrett; Mietchen, Jonathan; Baxter, Leslie; Brock Kirwan, C; Gale, Shawn D

2016-12-01

Cognitive and social outcomes may be negatively affected in children with a history of traumatic brain injury (TBI). We hypothesized that executive function would mediate the association between right frontal pole cortical thickness and problematic social behaviors. Child participants with a history of TBI were recruited from inpatient admissions for long-term follow-up (n = 23; average age = 12.8, average time post-injury =3.2 years). Three measures of executive function, the Trail Making Test, verbal fluency test, and the Conners' Continuous Performance Test-Second edition (CPT-II), were administered to each participant while caregivers completed the Childhood Behavior Checklist (CBCL). All participants underwent brain magnetic resonance imaging following cognitive testing. Regression analysis demonstrated right frontal pole cortical thickness significantly predicted social problems. Measures of executive functioning also significantly predicted social problems; however, the mediation model testing whether executive function mediated the relationship between cortical thickness and social problems was not statistically significant. Right frontal pole cortical thickness and omission errors on the CPT-II predicted Social Problems on the CBCL. Results did not indicate that the association between cortical thickness and social problems was mediated by executive function.

Functional Connectivity of Child and Adolescent Attention Deficit Hyperactivity Disorder Patients: Correlation with IQ.

PubMed

Park, Bo-Yong; Hong, Jisu; Lee, Seung-Hak; Park, Hyunjin

2016-01-01

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychological disorder that affects both children and adolescents. Child and adolescent ADHD patients exhibit different behavioral symptoms such as hyperactivity and impulsivity, but not much connectivity research exists to help explain these differences. We analyzed openly accessible resting-state functional magnetic resonance imaging (rs-fMRI) data on 112 patients (28 child ADHD, 28 adolescent ADHD, 28 child normal control (NC), and 28 adolescent NC). We used group independent component analysis (ICA) and weighted degree values to identify interaction effects of age (child and adolescent) and symptom (ADHD and NC) in brain networks. The frontoparietal network showed significant interaction effects ( p = 0.0068). The frontoparietal network is known to be related to hyperactive and impulsive behaviors. Intelligence quotient (IQ) is an important factor in ADHD, and we predicted IQ scores using the results of our connectivity analysis. IQ was predicted using degree centrality values of networks with significant interaction effects of age and symptom. Actual and predicted IQ scores demonstrated significant correlation values, with an error of about 10%. Our study might provide imaging biomarkers for future ADHD and intelligence studies.

Impact of Damping Uncertainty on SEA Model Response Variance

NASA Technical Reports Server (NTRS)

Schiller, Noah; Cabell, Randolph; Grosveld, Ferdinand

2010-01-01

Statistical Energy Analysis (SEA) is commonly used to predict high-frequency vibroacoustic levels. This statistical approach provides the mean response over an ensemble of random subsystems that share the same gross system properties such as density, size, and damping. Recently, techniques have been developed to predict the ensemble variance as well as the mean response. However these techniques do not account for uncertainties in the system properties. In the present paper uncertainty in the damping loss factor is propagated through SEA to obtain more realistic prediction bounds that account for both ensemble and damping variance. The analysis is performed on a floor-equipped cylindrical test article that resembles an aircraft fuselage. Realistic bounds on the damping loss factor are determined from measurements acquired on the sidewall of the test article. The analysis demonstrates that uncertainties in damping have the potential to significantly impact the mean and variance of the predicted response.

Conceptualizing and measuring illness self-concept: a comparison with self-esteem and optimism in predicting fibromyalgia adjustment.

PubMed

Morea, Jessica M; Friend, Ronald; Bennett, Robert M

2008-12-01

Illness self-concept (ISC), or the extent to which individuals are consumed by their illness, was theoretically described and evaluated with the Illness Self-Concept Scale (ISCS), a new 23-item scale, to predict adjustment in fibromyalgia. To establish convergent and discriminant validity, illness self-concept was compared to self-esteem and optimism in predicting health status, illness intrusiveness, depression, and life satisfaction. The ISCS demonstrated good reliability (alpha = .94; test-retest r = .80) and was a strong predictor of outcomes, even after controlling for optimism or self-esteem. The ISCS predicted unique variance in health-related outcomes; optimism and self-esteem did not, providing construct validation. Illness self-concept may play a significant role in coping with fibromyalgia and may prove useful in the evaluation of other chronic illnesses. (c) 2008 Wiley Periodicals, Inc.

Persistent grief in the aftermath of mass violence: the predictive roles of posttraumatic stress symptoms, self-efficacy, and disrupted worldview.

PubMed

Smith, Andrew J; Abeyta, Andrew A; Hughes, Michael; Jones, Russell T

2015-03-01

This study tested a conceptual model merging anxiety buffer disruption and social-cognitive theories to predict persistent grief severity among students who lost a close friend, significant other, and/or professor/teacher in tragic university campus shootings. A regression-based path model tested posttraumatic stress (PTS) symptom severity 3 to 4 months postshooting (Time 1) as a predictor of grief severity 1 year postshootings (Time 2), both directly and indirectly through cognitive processes (self-efficacy and disrupted worldview). Results revealed a model that predicted 61% of the variance in Time 2 grief severity. Hypotheses were supported, demonstrating that Time 1 PTS severity indirectly, positively predicted Time 2 grief severity through undermining self-efficacy and more severely disrupting worldview. Findings and theoretical interpretation yield important insights for future research and clinical application. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Real-time flutter boundary prediction based on time series models

NASA Astrophysics Data System (ADS)

Gu, Wenjing; Zhou, Li

2018-03-01

For the purpose of predicting the flutter boundary in real time during flutter flight tests, two time series models accompanied with corresponding stability criterion are adopted in this paper. The first method simplifies a long nonstationary response signal as many contiguous intervals and each is considered to be stationary. The traditional AR model is then established to represent each interval of signal sequence. While the second employs a time-varying AR model to characterize actual measured signals in flutter test with progression variable speed (FTPVS). To predict the flutter boundary, stability parameters are formulated by the identified AR coefficients combined with Jury's stability criterion. The behavior of the parameters is examined using both simulated and wind-tunnel experiment data. The results demonstrate that both methods show significant effectiveness in predicting the flutter boundary at lower speed level. A comparison between the two methods is also given in this paper.

Diallel analysis for sex-linked and maternal effects.

PubMed

Zhu, J; Weir, B S

1996-01-01

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(θ), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

A strong diffusive ion mode in dense ionized matter predicted by Langevin dynamics

PubMed Central

Mabey, P.; Richardson, S.; White, T. G.; Fletcher, L. B.; Glenzer, S. H.; Hartley, N. J.; Vorberger, J.; Gericke, D. O.; Gregori, G.

2017-01-01

The state and evolution of planets, brown dwarfs and neutron star crusts is determined by the properties of dense and compressed matter. Due to the inherent difficulties in modelling strongly coupled plasmas, however, current predictions of transport coefficients differ by orders of magnitude. Collective modes are a prominent feature, whose spectra may serve as an important tool to validate theoretical predictions for dense matter. With recent advances in free electron laser technology, X-rays with small enough bandwidth have become available, allowing the investigation of the low-frequency ion modes in dense matter. Here, we present numerical predictions for these ion modes and demonstrate significant changes to their strength and dispersion if dissipative processes are included by Langevin dynamics. Notably, a strong diffusive mode around zero frequency arises, which is not present, or much weaker, in standard simulations. Our results have profound consequences in the interpretation of transport coefficients in dense plasmas. PMID:28134338

Memory shaped by age stereotypes over time.

PubMed

Levy, Becca R; Zonderman, Alan B; Slade, Martin D; Ferrucci, Luigi

2012-07-01

Previous studies showed that negative self-stereotypes detrimentally affect the cognitive performance of marginalized group members; however, these findings were confined to short-term experiments. In the present study, we considered whether stereotypes predicted memory over time, which had not been previously examined. We also considered whether self-relevance increased the influence of stereotypes on memory over time. Multiple waves of memory performance were analyzed using individual growth models. The sample consisted of 395 participants in the Baltimore Longitudinal Study of Aging. Those with more negative age stereotypes demonstrated significantly worse memory performance over 38 years than those with less negative age stereotypes, after adjusting for relevant covariates. The decline in memory performance for those aged 60 and above was 30.2% greater for the more negative age stereotype group than for the less negative age stereotype group. Also, the impact of age stereotypes on memory was significantly greater among those for whom the age stereotypes were self-relevant. This study shows that the adverse influence of negative self-stereotypes on cognitive performance is not limited to a short-term laboratory effect. Rather, the findings demonstrate, for the first time, that stereotypes also predict memory performance over an extended period in the community.

Psychosocial Predictors of Weight Loss among American Indian and Alaska Native Participants in a Diabetes Prevention Translational Project

PubMed Central

Dill, Edward J.; Manson, Spero M.; Jiang, Luohua; Pratte, Katherine A.; Gutilla, Margaret J.; Knepper, Stephanie L.; Beals, Janette; Roubideaux, Yvette; Special Diabetes Program for Indians Diabetes Prevention Demonstration Project

2016-01-01

The association of psychosocial factors (psychological distress, coping skills, family support, trauma exposure, and spirituality) with initial weight and weight loss among American Indians and Alaska Natives (AI/ANs) in a diabetes prevention translational project was investigated. Participants (n = 3,135) were confirmed as prediabetic and subsequently enrolled in the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project implemented at 36 Indian health care programs. Measures were obtained at baseline and after completing a 16-session educational curriculum focusing on weight loss through behavioral changes. At baseline, psychological distress and negative family support were linked to greater weight, whereas cultural spirituality was correlated with lower weight. Furthermore, psychological distress and negative family support predicted less weight loss, and positive family support predicted greater weight loss, over the course of the intervention. These bivariate relationships between psychosocial factors and weight remained statistically significant within a multivariate model, after controlling for sociodemographic characteristics. Conversely, coping skills and trauma exposure were not significantly associated with baseline weight or change in weight. These findings demonstrate the influence of psychosocial factors on weight loss in AI/AN communities and have substantial implications for incorporating adjunctive intervention components. PMID:26649314

Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset.

PubMed

Hostetter, Jason M; Morrison, James J; Morris, Michael; Jeudy, Jean; Wang, Kenneth C; Siegel, Eliot

2017-11-01

To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and ≤6 mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P < .0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6 mm were reclassified to longer-term follow-up than recommended by Fleischner. Twenty-seven percent of nodules ≤4 mm were reclassified to shorter-term follow-up. Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and follow-up recommendations. By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction.

PubMed

Lin, Yu-Hsuan; Lin, Po-Hsien; Chiang, Chih-Lin; Lee, Yang-Han; Yang, Cheryl C H; Kuo, Terry B J; Lin, Sheng-Hsuan

2017-07-01

Global smartphone expansion has brought about unprecedented addictive behaviors. The current diagnosis of smartphone addiction is based solely on information from clinical interview. This study aimed to incorporate application (app)-recorded data into psychiatric criteria for the diagnosis of smartphone addiction and to examine the predictive ability of the app-recorded data for the diagnosis of smartphone addiction. Smartphone use data of 79 college students were recorded by a newly developed app for 1 month between December 1, 2013, and May 31, 2014. For each participant, psychiatrists made a diagnosis for smartphone addiction based on 2 approaches: (1) only diagnostic interview (standard diagnosis) and (2) both diagnostic interview and app-recorded data (app-incorporated diagnosis). The app-incorporated diagnosis was further used to build app-incorporated diagnostic criteria. In addition, the app-recorded data were pooled as a score to predict smartphone addiction diagnosis. When app-incorporated diagnosis was used as a gold standard for 12 candidate criteria, 7 criteria showed significant accuracy (area under receiver operating characteristic curve [AUC] > 0.7) and were constructed as app-incorporated diagnostic criteria, which demonstrated remarkable accuracy (92.4%) for app-incorporated diagnosis. In addition, both frequency and duration of daily smartphone use significantly predicted app-incorporated diagnosis (AUC = 0.70 for frequency; AUC = 0.72 for duration). The combination of duration, frequency, and frequency trend for 1 month can accurately predict smartphone addiction diagnosis (AUC = 0.79 for app-incorporated diagnosis; AUC = 0.71 for standard diagnosis). The app-incorporated diagnosis, combining both psychiatric interview and app-recorded data, demonstrated substantial accuracy for smartphone addiction diagnosis. In addition, the app-recorded data performed as an accurate screening tool for app-incorporated diagnosis. © Copyright 2017 Physicians Postgraduate Press, Inc.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

The Predictive Value of Motor-Evoked Potentials and the Silent Period on Patient Outcome after Acute Cerebral Infarction.

PubMed

Zhang, Xueqing; Ji, Wenzhen; Li, Lancui; Yu, Changshen; Wang, Wanjun; Liu, Shoufeng; Gao, Chunlin; Qiu, Lina; Tong, Xiaoguang; Wang, Jinhuan; Wu, Jialing

2016-07-01

The predictive value of neurophysiologic assessment on patients' outcome after acute cerebral infarction is poorly understood. The aim of this study was to investigate the prognostic value of motor-evoked potentials (MEPs) and the silent period (SP) on clinical outcome. A total of 202 patients with acute cerebral infarction were prospectively recruited. MEP and SP were recorded from the abductor pollicis brevis of the affected side within 10 days after stroke onset. Patient outcome was measured as the dependency rate. Cortical MEP was induced in 78 patients whereas it was absent in 82 patients. The initial NIHSS (National Institutes of Health Stroke Scale) score was significantly lower in patients with MEP than in those without MEP (P < .001). Regression analysis demonstrated that a left-sided lesion (OR = .391, 95% CI .178-.858, P = .019), NIHSS at admission (OR = .826, 95% CI .744-.917, P < .001), and presence of MEP (OR = 3.918, 95% CI 1.770-8.672, P < .001) were independent predictors of outcome 3 months after stroke. Among patients with MEP, only the contralateral cortical SP value was significantly shorter in the good outcome subgroup (t = 2.541, P = .013). Receiver operating characteristic curve analysis demonstrated that SP was able to predict patients at higher risk of unfavorable outcome 3 months after stroke onset (area under the curve .721, 95% CI .58-.86, P = .008). These data suggested that MEP and SP were useful tools to predict patients' acute outcomes following cerebral infarction. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

History and current state of immunotherapy in glioma and brain metastasis.

PubMed

McGranahan, Tresa; Li, Gordon; Nagpal, Seema

2017-05-01

Malignant brain tumors such as glioblastoma (GBM) and brain metastasis have poor prognosis despite conventional therapies. Successful use of vaccines and checkpoint inhibitors in systemic malignancy has increased the hope that immune therapies could improve survival in patients with brain tumors. Manipulating the immune system to fight malignancy has a long history of both modest breakthroughs and pitfalls that should be considered when applying the current immunotherapy approaches to patients with brain tumors. Therapeutic vaccine trials for GBM date back to the mid 1900s and have taken many forms; from irradiated tumor lysate to cell transfer therapies and peptide vaccines. These therapies were generally well tolerated without significant autoimmune toxicity, however also did not demonstrate significant clinical benefit. In contrast, the newer checkpoint inhibitors have demonstrated durable benefit in some metastatic malignancies, accompanied by significant autoimmune toxicity. While this toxicity was not unexpected, it exceeded what was predicted from pre-clinical studies and in many ways was similar to the prior trials of immunostimulants. This review will discuss the history of these studies and demonstrate that the future use of immune therapy for brain tumors will likely need a personalized approach that balances autoimmune toxicity with the opportunity for significant survival benefit.

Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates.

PubMed

Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong

2017-04-01

Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l 2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Preschool Predictors of School-Age Academic Achievement in Autism Spectrum Disorder

PubMed Central

Miller, Lauren E.; Burke, Jeffrey D.; Troyb, Eva; Knoch, Kelley; Herlihy, Lauren E.; Fein, Deborah A.

2017-01-01

Objective Characterization of academic functioning in children with autism spectrum disorder (ASD), particularly predictors of achievement, may have important implications for intervention. The current study aimed to characterize achievement profiles, confirm associations between academic ability and concurrent intellectual and social skills, and explore preschool predictors of school-age academic achievement in a sample of children with ASD. Method Children with ASD (N = 26) were evaluated at the approximate ages of two, four, and ten years. Multiple regression was used to predict school-age academic achievement in reading and mathematics from both concurrent (i.e., school-age) and preschool variables. Results Children with ASD demonstrated a weakness in reading comprehension relative to word reading. There was a smaller difference between mathematics skills; math reasoning was lower than numerical operations, but this did not quite reach trend level significance. Concurrent IQ and social skills were associated with school-age academic achievement across domains. Preschool verbal abilities significantly predicted school-age reading comprehension, above and beyond concurrent IQ, and early motor functioning predicted later math skills. Conclusions Specific developmental features of early ASD predict specific aspects of school-age achievement. Early intervention targeting language and motor skills may improve later achievement in this population. PMID:27705180

Preschool predictors of school-age academic achievement in autism spectrum disorder.

PubMed

Miller, Lauren E; Burke, Jeffrey D; Troyb, Eva; Knoch, Kelley; Herlihy, Lauren E; Fein, Deborah A

2017-02-01

Characterization of academic functioning in children with autism spectrum disorder (ASD), particularly predictors of achievement, may have important implications for intervention. The current study aimed to characterize achievement profiles, confirm associations between academic ability and concurrent intellectual and social skills, and explore preschool predictors of school-age academic achievement in a sample of children with ASD. Children with ASD (n = 26) were evaluated at the approximate ages of two, four, and ten. Multiple regression was used to predict school-age academic achievement in reading and mathematics from both concurrent (i.e. school-age) and preschool variables. Children with ASD demonstrated a weakness in reading comprehension relative to word reading. There was a smaller difference between mathematics skills; math reasoning was lower than numerical operations, but this did not quite reach trend level significance. Concurrent IQ and social skills were associated with school-age academic achievement across domains. Preschool verbal abilities significantly predicted school-age reading comprehension, above and beyond concurrent IQ, and early motor functioning predicted later math skills. Specific developmental features of early ASD predict specific aspects of school-age achievement. Early intervention targeting language and motor skills may improve later achievement in this population.

Evaluation of a Mysis bioenergetics model

USGS Publications Warehouse

Chipps, S.R.; Bennett, D.H.

2002-01-01

Direct approaches for estimating the feeding rate of the opossum shrimp Mysis relicta can be hampered by variable gut residence time (evacuation rate models) and non-linear functional responses (clearance rate models). Bioenergetics modeling provides an alternative method, but the reliability of this approach needs to be evaluated using independent measures of growth and food consumption. In this study, we measured growth and food consumption for M. relicta and compared experimental results with those predicted from a Mysis bioenergetics model. For Mysis reared at 10??C, model predictions were not significantly different from observed values. Moreover, decomposition of mean square error indicated that 70% of the variation between model predictions and observed values was attributable to random error. On average, model predictions were within 12% of observed values. A sensitivity analysis revealed that Mysis respiration and prey energy density were the most sensitive parameters affecting model output. By accounting for uncertainty (95% CLs) in Mysis respiration, we observed a significant improvement in the accuracy of model output (within 5% of observed values), illustrating the importance of sensitive input parameters for model performance. These findings help corroborate the Mysis bioenergetics model and demonstrate the usefulness of this approach for estimating Mysis feeding rate.

Validity of Teacher-Based Vision Screening and Factors Associated with the Accuracy of Vision Screening in Vietnamese Children.

PubMed

Paudel, Prakash; Kovai, Vilas; Naduvilath, Thomas; Phuong, Ha Thanh; Ho, Suit May; Giap, Nguyen Viet

2016-01-01

To assess validity of teacher-based vision screening and elicit factors associated with accuracy of vision screening in Vietnam. After brief training, teachers independently measured visual acuity (VA) in 555 children aged 12-15 years in Ba Ria - Vung Tau Province. Teacher VA measurements were compared to those of refractionists. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for uncorrected VA (UVA) and presenting VA (PVA) 20/40 or worse in either eye. Chi-square, Fisher's exact test and multivariate logistic regression were used to assess factors associated with accuracy of vision screening. Level of significance was set at 5%. Trained teachers in Vietnam demonstrated 86.7% sensitivity, 95.7% specificity, 86.7% positive predictive value and 95.7% negative predictive value in identifying children with visual impairment using the UVA measurement. PVA measurement revealed low accuracy for teachers, which was significantly associated with child's age, sex, spectacle wear and myopic status, but UVA measurement showed no such associations. Better accuracy was achieved in measurement of VA and identification of children with visual impairment using UVA measurement compared to PVA. UVA measurement is recommended for teacher-based vision screening programs.

Do Motivational Interviewing Behaviors Predict Reductions in Partner Aggression for Men and Women?

PubMed Central

Woodin, Erica M.; Sotskova, Alina; O’Leary, K. Daniel

2011-01-01

Motivational interviewing is a directive, non-confrontational intervention to promote behavior change. The current study examined therapist behaviors during a successful brief motivational interviewing intervention for physically aggressive college dating couples (Woodin & O’Leary, 2010). Forty-five minute motivational interviews with each partner were videotaped and coded using the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, & Miller, 2003). Hierarchical modeling analyses demonstrated that therapist behaviors consistent with motivational interviewing competency predicted significantly greater reductions in physical aggression perpetration following the intervention. Specifically, greater reflection to question ratios by the therapists predicted reductions in aggression for both men and women, greater percentages of open versus closed questions predicted aggression reductions for women, and there was a trend for greater levels of global therapist empathy to predict aggression reductions for women. These findings provide evidence that motivational interviewing seems to have an effect on behavior change through therapist behaviors consistent with the theoretical underpinnings of motivational interviewing. PMID:22119133

Understanding differences between DELFT3D and empirical predictions of alongshore sediment transport gradients

USGS Publications Warehouse

List, Jeffrey; Benedet, Lindino; Hanes, Daniel M.; Ruggiero, Peter

2009-01-01

Predictions of alongshore transport gradients are critical for forecasting shoreline change. At the previous ICCE conference, it was demonstrated that alongshore transport gradients predicted by the empirical CERC equation can differ substantially from predictions made by the hydrodynamics-based model Delft3D in the case of a simulated borrow pit on the shoreface. Here we use the Delft3D momentum balance to examine the reason for this difference. Alongshore advective flow accelerations in our Delft3D simulation are mainly driven by pressure gradients resulting from alongshore variations in wave height and setup, and Delft3D transport gradients are controlled by these flow accelerations. The CERC equation does not take this process into account, and for this reason a second empirical transport term is sometimes added when alongshore gradients in wave height are thought to be significant. However, our test case indicates that this second term does not properly predict alongshore transport gradients.

Geographic and ecologic distributions of the Anopheles gambiae complex predicted using a genetic algorithm.

PubMed

Levine, Rebecca S; Peterson, A Townsend; Benedict, Mark Q

2004-02-01

The distribution of the Anopheles gambiae complex of malaria vectors in Africa is uncertain due to under-sampling of vast regions. We use ecologic niche modeling to predict the potential distribution of three members of the complex (A. gambiae, A. arabiensis, and A. quadriannulatus) and demonstrate the statistical significance of the models. Predictions correspond well to previous estimates, but provide detail regarding spatial discontinuities in the distribution of A. gambiae s.s. that are consistent with population genetic studies. Our predictions also identify large areas of Africa where the presence of A. arabiensis is predicted, but few specimens have been obtained, suggesting under-sampling of the species. Finally, we project models developed from African distribution data for the late 1900s into the past and to South America to determine retrospectively whether the deadly 1929 introduction of A. gambiae sensu lato into Brazil was more likely that of A. gambiae sensu stricto or A. arabiensis.

Competence with Fractions Predicts Gains in Mathematics Achievement

PubMed Central

Bailey, Drew H.; Hoard, Mary K.; Nugent, Lara; Geary, David C.

2012-01-01

Competence with fractions predicts later mathematics achievement, but the co-developmental pattern between fractions knowledge and mathematics achievement is not well understood. We assessed this co-development through examination of the cross-lagged relation between a measure of conceptual knowledge of fractions and mathematics achievement in sixth and seventh grade (n = 212). The cross-lagged effects indicated that performance on the sixth grade fractions concepts measure predicted one year gains in mathematics achievement (β = .14, p<.01), controlling for the central executive component of working memory and intelligence, but sixth grade mathematics achievement did not predict gains on the fractions concepts measure (β = .03, p>.50). In a follow-up assessment, we demonstrated that measures of fluency with computational fractions significantly predicted seventh grade mathematics achievement above and beyond the influence of fluency in computational whole number arithmetic, performance on number fluency and number line tasks, and central executive span and intelligence. Results provide empirical support for the hypothesis that competence with fractions underlies, in part, subsequent gains in mathematics achievement. PMID:22832199

PREOPERATIVE MRI IMPROVES PREDICTION OF EXTENSIVE OCCULT AXILLARY LYMPH NODE METASTASES IN BREAST CANCER PATIENTS WITH A POSITIVE SENTINEL LYMPH NODE BIOPSY

PubMed Central

Loiselle, Christopher; Eby, Peter R.; Kim, Janice N.; Calhoun, Kristine E.; Allison, Kimberly H.; Gadi, Vijayakrishna K.; Peacock, Sue; Storer, Barry; Mankoff, David A.; Partridge, Savannah C.; Lehman, Constance D.

2014-01-01

Rationale and Objectives To test the ability of quantitative measures from preoperative Dynamic Contrast Enhanced MRI (DCE-MRI) to predict, independently and/or with the Katz pathologic nomogram, which breast cancer patients with a positive sentinel lymph node biopsy will have ≥ 4 positive axillary lymph nodes upon completion axillary dissection. Methods and Materials A retrospective review was conducted to identify clinically node-negative invasive breast cancer patients who underwent preoperative DCE-MRI, followed by sentinel node biopsy with positive findings and complete axillary dissection (6/2005 – 1/2010). Clinical/pathologic factors, primary lesion size and quantitative DCE-MRI kinetics were collected from clinical records and prospective databases. DCE-MRI parameters with univariate significance (p < 0.05) to predict ≥ 4 positive axillary nodes were modeled with stepwise regression and compared to the Katz nomogram alone and to a combined MRI-Katz nomogram model. Results Ninety-eight patients with 99 positive sentinel biopsies met study criteria. Stepwise regression identified DCE-MRI total persistent enhancement and volume adjusted peak enhancement as significant predictors of ≥4 metastatic nodes. Receiver operating characteristic (ROC) curves demonstrated an area under the curve (AUC) of 0.78 for the Katz nomogram, 0.79 for the DCE-MRI multivariate model, and 0.87 for the combined MRI-Katz model. The combined model was significantly more predictive than the Katz nomogram alone (p = 0.003). Conclusion Integration of DCE-MRI primary lesion kinetics significantly improved the Katz pathologic nomogram accuracy to predict presence of metastases in ≥ 4 nodes. DCE-MRI may help identify sentinel node positive patients requiring further localregional therapy. PMID:24331270

Familial aggregation of MATRICS Consensus Cognitive Battery scores in a large sample of outpatients with schizophrenia and their unaffected relatives.

PubMed

Mucci, A; Galderisi, S; Green, M F; Nuechterlein, K; Rucci, P; Gibertoni, D; Rossi, A; Rocca, P; Bertolino, A; Bucci, P; Hellemann, G; Spisto, M; Palumbo, D; Aguglia, E; Amodeo, G; Amore, M; Bellomo, A; Brugnoli, R; Carpiniello, B; Dell'Osso, L; Di Fabio, F; di Giannantonio, M; Di Lorenzo, G; Marchesi, C; Monteleone, P; Montemagni, C; Oldani, L; Romano, R; Roncone, R; Stratta, P; Tenconi, E; Vita, A; Zeppegno, P; Maj, M

2018-06-01

The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relative-control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probands' scores. Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probands' MCCB scores predicted REL neurocognitive performance. SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning. In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors.

Predicting Future Reading Problems Based on Pre-reading Auditory Measures: A Longitudinal Study of Children with a Familial Risk of Dyslexia

PubMed Central

Law, Jeremy M.; Vandermosten, Maaike; Ghesquière, Pol; Wouters, Jan

2017-01-01

Purpose: This longitudinal study examines measures of temporal auditory processing in pre-reading children with a family risk of dyslexia. Specifically, it attempts to ascertain whether pre-reading auditory processing, speech perception, and phonological awareness (PA) reliably predict later literacy achievement. Additionally, this study retrospectively examines the presence of pre-reading auditory processing, speech perception, and PA impairments in children later found to be literacy impaired. Method: Forty-four pre-reading children with and without a family risk of dyslexia were assessed at three time points (kindergarten, first, and second grade). Auditory processing measures of rise time (RT) discrimination and frequency modulation (FM) along with speech perception, PA, and various literacy tasks were assessed. Results: Kindergarten RT uniquely contributed to growth in literacy in grades one and two, even after controlling for letter knowledge and PA. Highly significant concurrent and predictive correlations were observed with kindergarten RT significantly predicting first grade PA. Retrospective analysis demonstrated atypical performance in RT and PA at all three time points in children who later developed literacy impairments. Conclusions: Although significant, kindergarten auditory processing contributions to later literacy growth lack the power to be considered as a single-cause predictor; thus results support temporal processing deficits' contribution within a multiple deficit model of dyslexia. PMID:28223953

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

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

Integrated and spectral energetics of the GLAS general circulation model

NASA Technical Reports Server (NTRS)

Tenenbaum, J.

1981-01-01

Integrated and spectral error energetics of the Goddard Laboratory for Atmospheric Sciences (GLAS) general circulation model are compared with observations for periods in January 1975, 1976, and 1977. For two cases the model shows significant skill in predicting integrated energetics quantities out to two weeks, and for all three cases, the integrated monthly mean energetics show qualitative improvements over previous versions of the model in eddy kinetic energy and barotropic conversions. Fundamental difficulties remain with leakage of energy to the stratospheric level. General circulation model spectral energetics predictions are compared with the corresponding observational spectra on a day by day basis. Eddy kinetic energy can be correct while significant errors occur in the kinetic energy of wavenumber three. Single wavenumber dominance in eddy kinetic energy and the correlation of spectral kinetic and potential energy are demonstrated.

Validity of the stroke rehabilitation assessment of movement scale in acute rehabilitation: a comparison with the functional independence measure and stroke impact scale-16.

PubMed

Ward, Irene; Pivko, Susan; Brooks, Gary; Parkin, Kate

2011-11-01

To demonstrate sensitivity to change of the Stroke Rehabilitation Assessment of Movement (STREAM) as well as the concurrent and predictive validity of the STREAM in an acute rehabilitation setting. Prospective cohort study. Acute, in-patient rehabilitation department within a tertiary-care teaching hospital in the United States. Thirty adults with a newly diagnosed, first ischemic stroke. Clinical assessments were conducted on admission and then again on discharge from the rehabilitation hospital with the STREAM (total STREAM and upper extremity, lower extremity, and mobility subscales), Functional Independence Measure (FIM), and Stroke Impact Scale-16 (SIS-16). Sensitivity to change was determined with the Wilcoxon signed rank test and by the calculation of standardized response means. Spearman correlations were used to assess concurrent validity of the total STREAM and STREAM subscales with the FIM and SIS-16 on admission and discharge. We determined predictive validity for all instruments by correlating admission scores with actual and predicted length of stay and by testing associations between admission scores and discharge destination (home vs subacute facility). Not applicable. For all instruments, there was statistically significant improvement from admission to discharge. The standardized response means for the total STREAM and STREAM subscales were large. Spearman correlations between the total STREAM and STREAM subscales and the FIM and SIS-16 were moderate to excellent, both on admission and discharge. Among change scores, only the SIS-16 correlated with the total STREAM. All 3 instruments were significantly associated with discharge destination; however, the associations were strongest for the total STREAM and STREAM subscales. All instruments showed moderate-to-excellent correlations with predicted and actual length of stay. The STREAM is sensitive to change and demonstrates good concurrent and predictive validity as compared with the FIM and SIS-16 in the acute inpatient rehabilitation population. Copyright © 2011 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

GM(1,N) method for the prediction of anaerobic digestion system and sensitivity analysis of influential factors.

PubMed

Ren, Jingzheng

2018-01-01

Anaerobic digestion process has been recognized as a promising way for waste treatment and energy recovery in a sustainable way. Modelling of anaerobic digestion system is significantly important for effectively and accurately controlling, adjusting, and predicting the system for higher methane yield. The GM(1,N) approach which does not need the mechanism or a large number of samples was employed to model the anaerobic digestion system to predict methane yield. In order to illustrate the proposed model, an illustrative case about anaerobic digestion of municipal solid waste for methane yield was studied, and the results demonstrate that GM(1,N) model can effectively simulate anaerobic digestion system at the cases of poor information with less computational expense. Copyright © 2017 Elsevier Ltd. All rights reserved.

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Transfer in a GO2/GH2 Single Element Model Problem

NASA Technical Reports Server (NTRS)

West, Jeff; Westra, Doug; Lin, Jeff; Tucker, Kevin

2006-01-01

All solutions with Loci-CHEM achieved demonstrated steady state and mesh convergence. Preconditioning had no effect on solution accuracy and typically yields a 3-5times solution speed-up. The SST turbulence model has superior performance, relative to the data in the head end region, for the rise rate and peak heat flux. It was slightly worse than the others in the downstream region where all over-predicted the data by 30-100%.There was systematic mesh refinement in the unstructured volume and structured boundary layer areas produced only minor solution differences. Mesh convergence was achieved. Overall, Loci-CHEM satisfactorily predicts heat flux rise rate and peak heat flux and significantly over predicts the downstream heat flux.

Ethical Issues of Predictive Genetic Testing for Diabetes

PubMed Central

Haga, Susanne B.

2009-01-01

With the rising number of individuals affected with diabetes and the significant health care costs of treatment, the emphasis on prevention is key to controlling the health burden of this disease. Several genetic and genomic studies have identified genetic variants associated with increased risk to diabetes. As a result, commercial testing is available to predict an individual's genetic risk. Although the clinical benefits of testing have not yet been demonstrated, it is worth considering some of the ethical implications of testing for this common chronic disease. In this article, I discuss several issues that should be considered during the translation of predictive testing for diabetes, including familial implications, improvement of risk communication, implications for behavioral change and health outcomes, the Genetic Information Nondiscrimination Act, direct-to-consumer testing, and appropriate age of testing. PMID:20144329

FMRI Is a Valid Noninvasive Alternative to Wada Testing

PubMed Central

Binder, Jeffrey R.

2010-01-01

Partial removal of the anterior temporal lobe (ATL) is a highly effective surgical treatment for intractable temporal lobe epilepsy, yet roughly half of patients who undergo left ATL resection show decline in language or verbal memory function postoperatively. Two recent studies demonstrate that preoperative fMRI can predict postoperative naming and verbal memory changes in such patients. Most importantly, fMRI significantly improves the accuracy of prediction relative to other noninvasive measures used alone. Addition of language and memory lateralization data from the intracarotid amobarbital (Wada) test did not improve prediction accuracy in these studies. Thus, fMRI provides patients and practitioners with a safe, non-invasive, and well-validated tool for making better-informed decisions regarding elective surgery based on a quantitative assessment of cognitive risk. PMID:20850386

Quiescent H-mode plasmas with strong edge rotation in the cocurrent direction.

PubMed

Burrell, K H; Osborne, T H; Snyder, P B; West, W P; Fenstermacher, M E; Groebner, R J; Gohil, P; Leonard, A W; Solomon, W M

2009-04-17

For the first time in any tokamak, quiescent H-mode (QH-mode) plasmas have been created with strong edge rotation in the direction of the plasma current. This confirms the theoretical prediction that the QH mode should exist with either sign of the edge rotation provided the magnitude of the shear in the edge rotation is sufficiently large and demonstrates that counterinjection and counteredge rotation are not essential for the QH mode. Accordingly, the present work demonstrates a substantial broadening of the QH-mode operating space and represents a significant confirmation of the theory.

Probabilistic Forecasting of Coastal Morphodynamic Storm Response at Fire Island, New York

NASA Astrophysics Data System (ADS)

Wilson, K.; Adams, P. N.; Hapke, C. J.; Lentz, E. E.; Brenner, O.

2013-12-01

Site-specific probabilistic models of shoreline change are useful because they are derived from direct observations so that local factors, which greatly influence coastal response, are inherently considered by the model. Fire Island, a 50-km barrier island off Long Island, New York, is periodically subject to large storms, whose waves and storm surge dramatically alter beach morphology. Nor'Ida, which impacted the Fire Island coast in 2009, was one of the larger storms to occur in the early 2000s. In this study, we improve upon a Bayesian Network (BN) model informed with historical data to predict shoreline change from Nor'Ida. We present two BN models, referred to as 'original' model (BNo) and 'revised' model (BNr), designed to predict the most probable magnitude of net shoreline movement (NSM), as measured at 934 cross-shore transects, spanning 46 km. Both are informed with observational data (wave impact hours, shoreline and dune toe change rates, pre-storm beach width, and measured NSM) organized within five nodes, but the revised model contains a sixth node to represent the distribution of material added during an April 2009 nourishment project. We evaluate model success by examining the percentage of transects on which the model chooses the correct (observed) bin value of NSM. Comparisons of observed to model-predicted NSM show BNr has slightly higher predictive success over the total study area and significantly higher success at nourished locations. The BNo, which neglects anthropogenic modification history, correctly predicted the most probable NSM in 66.6% of transects, with ambiguous prediction at 12.7% of the locations. BNr, which incorporates anthropogenic modification history, resulted in 69.4% predictive accuracy and 13.9% ambiguity. However, across nourished transects, BNr reported 72.9% predictive success, while BNo reported 61.5% success. Further, at nourished transects, BNr reported higher ambiguity of 23.5% compared to 9.9% in BNo. These results demonstrate that BNr recognizes that nourished transects may behave differently from the expectation derived from historical data and therefore is more 'cautious' in its predictions at these locations. In contrast, BNo is more confident, but less accurate, demonstrating the risk of ignoring the influences of anthropogenic modification in a probabilistic model. Over the entire study region, both models produced greatest predictive accuracy for low retreat observations (BNo: 77.6%; BNr: 76.0%) and least success at predicting low advance observations, although BNr shows considerable improvement over BNo (39.4% vs. 28.6%, respectively). BNr also was significantly more accurate at predicting observations of no shoreline change (BNo: 56.2%; BNr: 68.93%). Both models were accurate for 60% of high advance observations, and reported high predictive success for high retreat observations (BNo: 69.1%; BNr: 67.6%), the scenario of greatest concern to coastal managers.

Analysis of renal blood flow and renal volume in normal fetuses and in fetuses with a solitary functioning kidney.

PubMed

Hindryckx, An; Raaijmakers, Anke; Levtchenko, Elena; Allegaert, Karel; De Catte, Luc

2017-12-01

To evaluate renal blood flow and renal volume for the prediction of postnatal renal function in fetuses with solitary functioning kidney (SFK). Seventy-four SFK fetuses (unilateral renal agenesis [12], multicystic dysplastic kidney [36], and severe renal dysplasia [26]) were compared with 58 healthy fetuses. Peak systolic velocity (PSV), pulsatility index (PI), and resistance index (RI) of the renal artery (RA) were measured; 2D and 3D (VOCAL) volumes were calculated. Renal length and glomerular filtration rate (GFR) were obtained in SFK children (2 years). Compared with the control group, the PSV RA was significantly lower in nonfunctioning kidneys and significantly higher in SFK. Volume measurements indicated a significantly larger volume of SFK compared with healthy kidneys. All but 4 children had GFR above 70 mL/min/1.73 m 2 , and compensatory hypertrophy was present in 69% at 2 years. PSV RA and SFK volume correlated with postnatal renal hypertrophy. No correlation between prenatal and postnatal SFK volume and GFR at 2 years was demonstrated. Low PSV RA might have a predictive value for diagnosing a nonfunctioning kidney in fetuses with a SFK. We demonstrated a higher PSV RA and larger renal volume in the SFK compared with healthy kidneys. © 2017 John Wiley & Sons, Ltd.

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

Context-sensitive network-based disease genetics prediction and its implications in drug discovery

PubMed Central

Chen, Yang; Xu, Rong

2017-01-01

Abstract Motivation: Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. Results: We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach (p

Predicting Clinical Outcome Using Brain Activation Associated with Set-Shifting and Central Coherence Skills in Anorexia Nervosa

PubMed Central

Garrett, Amy; Lock, James; Datta, Nandini; Beenhaker, Judy; Kesler, Shelli R.; Reiss, Allan L.

2014-01-01

Background Patients with Anorexia Nervosa (AN) have neuropsychological deficits in set shifting (SS) and central coherence (CC) consistent with an inflexible thinking style and overly detailed processing style, respectively. This study investigates brain activation during SS and CC tasks in patients with AN and tests whether this activation is a biomarker that predicts response to treatment. Methods : FMRI data were collected from 21 females with AN while performing a SS task (the Wisconsin Card Sort) and a CC task (embedded figures), and used to predict outcome following 16 weeks of treatment (either 16 weeks of cognitive behavioral therapy or 8 weeks cognitive remediation training followed by 8 weeks of cognitive behavioral therapy). Results Significant activation during the SS task included bilateral dorsolateral and ventrolateral prefrontal cortex and left anterior middle frontal gyrus. Higher scores on the neuropsychological test of SS (measured outside the scanner at baseline) were correlated with greater DLPFC and VLPFC activation. Improvements in SS following treatment were significantly predicted by a combination of low VLPFC and high anterior middle frontal activation (R squared = .68, p=.001). For the CC task, the visual and parietal areas were activated, but were not significantly correlated with neuropsychological measures of CC and did not predict outcome. Conclusion Cognitive flexibility requires the support of several prefrontal cortex resources. As previous studies suggest that the VLPFC is important for selecting responses, patients who demonstrate that deficit may benefit the most from cognitive therapy with or without cognitive remediation training. The ability to sustain inhibition of an unwanted response, subserved by the anterior middle frontal gyrus, is a cognitive feature that predicts favorable outcome to cognitive treatment. CC deficits may not be an effective predictor of clinical outcome. PMID:25027478

Comparison of predictive equations for resting metabolic rate in obese psychiatric patients taking olanzapine.

PubMed

Skouroliakou, Maria; Giannopoulou, Ifigenia; Kostara, Christina; Vasilopoulou, Melanie

2009-02-01

The prediction of resting metabolic rate (RMR) is important to determine the energy expenditure of obese patients with severe mental illnesses (SMIs). However, there is lack of research concerning the most accurate RMR predictive equations. The purpose of this study was to compare the validity of four RMR equations on patients with SMIs taking olanzapine. One hundred twenty-eight obese (body mass index >30 kg/m(2)) patients with SMIs (41 men and 87 women) treated with olanzapine were tested from 2005 to 2008. Measurements of anthropometric parameters (height, weight, body mass index, waist circumference) and body composition (using the BodPod) were performed at the beginning of the study. RMR was measured using indirect calorimetry. Comparisons between measured and estimated RMRs from four equations (Harris-Benedict adjusted and current body weights, Schofield, and Mifflin-St. Jeor) were performed using Pearson's correlation coefficient and Bland-Altman analysis. Significant correlations were found between the measured and predicted RMRs with all four equations (P < 0.001), with the Mifflin-St. Jeor equation demonstrating the strongest correlation in men and women (r = 0.712, P < 0.001). In men and women, the Bland-Altman analysis revealed no significant bias in the RMR prediction using the Harris-Benedict adjusted body weight and the Mifflin equations (P > 0.05). However, in men and women, the Harris-Benedict current body weight and the Schofield equations showed significant overestimation error in the RMR prediction (P < 0.001). When estimating RMR in men and women with SMIs taking olanzapine, the Mifflin-St. Jeor and Harris-Benedict adjusted body weight equations appear to be the most appropriate for clinical use.

The common occurrence of epistasis in the determination of human pigmentation and its impact on DNA-based pigmentation phenotype prediction.

PubMed

Pośpiech, Ewelina; Wojas-Pelc, Anna; Walsh, Susan; Liu, Fan; Maeda, Hitoshi; Ishikawa, Takaki; Skowron, Małgorzata; Kayser, Manfred; Branicki, Wojciech

2014-07-01

The role of epistatic effects in the determination of complex traits is often underlined but its significance in the prediction of pigmentation phenotypes has not been evaluated so far. The prediction of pigmentation from genetic data can be useful in forensic science to describe the physical appearance of an unknown offender, victim, or missing person who cannot be identified via conventional DNA profiling. Available forensic DNA prediction systems enable the reliable prediction of several eye and hair colour categories. However, there is still space for improvement. Here we verified the association of 38 candidate DNA polymorphisms from 13 genes and explored the extent to which interactions between them may be involved in human pigmentation and their impact on forensic DNA prediction in particular. The model-building set included 718 Polish samples and the model-verification set included 307 independent Polish samples and additional 72 samples from Japan. In total, 29 significant SNP-SNP interactions were found with 5 of them showing an effect on phenotype prediction. For predicting green eye colour, interactions between HERC2 rs12913832 and OCA2 rs1800407 as well as TYRP1 rs1408799 raised the prediction accuracy expressed by AUC from 0.667 to 0.697 and increased the prediction sensitivity by >3%. Interaction between MC1R 'R' variants and VDR rs731236 increased the sensitivity for light skin by >1% and by almost 3% for dark skin colour prediction. Interactions between VDR rs1544410 and TYR rs1042602 as well as between MC1R 'R' variants and HERC2 rs12913832 provided an increase in red/non-red hair prediction accuracy from an AUC of 0.902-0.930. Our results thus underline epistasis as a common phenomenon in human pigmentation genetics and demonstrate that considering SNP-SNP interactions in forensic DNA phenotyping has little impact on eye, hair and skin colour prediction. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Role of subsurface ocean in decadal climate predictability over the South Atlantic.

PubMed

Morioka, Yushi; Doi, Takeshi; Storto, Andrea; Masina, Simona; Behera, Swadhin K

2018-06-04

Decadal climate predictability in the South Atlantic is explored by performing reforecast experiments using a coupled general circulation model with two initialization schemes; one is assimilated with observed sea surface temperature (SST) only, and the other is additionally assimilated with observed subsurface ocean temperature and salinity. The South Atlantic is known to undergo decadal variability exhibiting a meridional dipole of SST anomalies through variations in the subtropical high and ocean heat transport. Decadal reforecast experiments in which only the model SST is initialized with the observation do not predict well the observed decadal SST variability in the South Atlantic, while the other experiments in which the model SST and subsurface ocean are initialized with the observation skillfully predict the observed decadal SST variability, particularly in the Southeast Atlantic. In-depth analysis of upper-ocean heat content reveals that a significant improvement of zonal heat transport in the Southeast Atlantic leads to skillful prediction of decadal SST variability there. These results demonstrate potential roles of subsurface ocean assimilation in the skillful prediction of decadal climate variability over the South Atlantic.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Analysis of free modeling predictions by RBO aleph in CASP11.

PubMed

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2016-09-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

PubMed Central

Qian, Liwei; Zheng, Haoran; Zhou, Hong; Qin, Ruibin; Li, Jinlong

2013-01-01

The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of–the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction. PMID:23516469

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

PubMed

Ołdziej, S; Czaplewski, C; Liwo, A; Chinchio, M; Nanias, M; Vila, J A; Khalili, M; Arnautova, Y A; Jagielska, A; Makowski, M; Schafroth, H D; Kaźmierkiewicz, R; Ripoll, D R; Pillardy, J; Saunders, J A; Kang, Y K; Gibson, K D; Scheraga, H A

2005-05-24

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data.

PubMed

Pathan, Sameer A; Bhutta, Zain A; Moinudheen, Jibin; Jenkins, Dominic; Silva, Ashwin D; Sharma, Yogdutt; Saleh, Warda A; Khudabakhsh, Zeenat; Irfan, Furqan B; Thomas, Stephen H

2016-01-01

Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p  < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r 2 0.51), important contributors to tMD included shift census ( p  = 0.008), shift time of day ( p  = 0.002), and physician coverage n ( p  = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p  < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.

Spatial and Temporal Dynamics of Dissolved Oxygen Concentrations and Bioactivity in the Hyporheic Zone

NASA Astrophysics Data System (ADS)

Reeder, W. Jeffery; Quick, Annika M.; Farrell, Tiffany B.; Benner, Shawn G.; Feris, Kevin P.; Tonina, Daniele

2018-03-01

Dissolved oxygen (DO) concentrations and consumption rates are primary indicators of heterotrophic respiration and redox conditions in the hyporheic zone (HZ). Due to the complexity of hyporheic flow and interactions between hyporheic hydraulics and the biogeochemical processes, a detailed, mechanistic, and predictive understanding of the biogeochemical activity in the HZ has not yet been developed. Previous studies of microbial activity in the HZ have treated the metabolic DO consumption rate constant (KDO) as a temporally fixed and spatially homogeneous property that is determined primarily by the concentration of bioavailable carbon. These studies have generally treated bioactivity as temporally steady state, failing to capture the temporal dynamics of a changeable system. We demonstrate that hyporheic hydraulics controls rate constants in a hyporheic system that is relatively abundant in bioavailable carbon, such that KDO is a linear function of the local downwelling flux. We further demonstrate that, for triangular dunes, the downwelling velocities are lognormally distributed, as are the KDO values. By comparing measured and modeled DO profiles, we demonstrate that treating KDO as a function of the downwelling flux yields a significant improvement in the accuracy of predicted DO profiles. Additionally, our results demonstrate the temporal effect of carbon consumption on microbial respiration rates.

Masculinity, moral atmosphere, and moral functioning of high school football players.

PubMed

Steinfeldt, Jesse A; Rutkowski, Leslie A; Vaughan, Ellen L; Steinfeldt, Matthew C

2011-04-01

In order to identify factors associated with on-field moral functioning among student athletes within the unique context of football, we examined masculine gender role conflict, moral atmosphere, and athletic identity. Using structural equation modeling to assess survey data from 204 high school football players, results demonstrated that moral atmosphere (i.e., the influence of coaches and teammates) was significantly associated with participants' process of on-field moral functioning across the levels of judgment, intention, and behavior. Neither masculine gender role conflict nor athletic identity significantly predicted moral functioning, but the results indicated that participants' identification with the athlete role significantly predicted conflict with socialized gender roles. Results suggest that in the aggressive and violent sport of football, coaches can have a direct influence on players' moral functioning process. Coaches can also have an indirect effect by influencing all the players so that a culture of ethical play can be cultivated among teammates and spread from the top down.

Predictive validity of the HCR-20 for inpatient aggression: the effect of intellectual disability on accuracy.

PubMed

O'Shea, L E; Picchioni, M M; McCarthy, J; Mason, F L; Dickens, G L

2015-11-01

People with intellectual disability (ID) account for a large proportion of aggressive incidents in secure and forensic psychiatric services. Although the Historical, Clinical, Risk Management 20 (HCR-20) has good predictive validity in inpatient settings, it does not perform equally in all groups and there is little evidence for its efficacy in those with ID. A pseudo-prospective cohort study of the predictive efficacy of the HCR-20 for those with ID (n = 109) was conducted in a UK secure mental health setting using routinely collected risk data. Performance of the HCR-20 in the ID group was compared with a comparison group of adult inpatients without an ID (n = 504). Analysis controlled for potential covariates including security level, length of stay, gender and diagnosis. The HCR-20 total score was a significant predictor of any aggression and of physical aggression for both groups, although the area under the curve values did not reach the threshold for a large effect size. The clinical subscale performed significantly better in those without an ID compared with those with. The ID group had a greater number of relevant historical and risk management items. The clinicians' summary judgment significantly predicted both types of aggressive outcomes in the ID group, but did not predict either in those without an ID. This study demonstrates that, after controlling for a range of potential covariates, the HCR-20 is a significant predictor of inpatient aggression in people with an ID and performs as well as for a comparison group of mentally disordered individuals without ID. The potency of HCR-20 subscales and items varied between the ID and comparison groups suggesting important target areas for improved prediction and risk management interventions in those with ID. © 2015 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

Importance of ligand reorganization free energy in protein-ligand binding-affinity prediction.

PubMed

Yang, Chao-Yie; Sun, Haiying; Chen, Jianyong; Nikolovska-Coleska, Zaneta; Wang, Shaomeng

2009-09-30

Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.

High-performance wavelet engine

NASA Astrophysics Data System (ADS)

Taylor, Fred J.; Mellot, Jonathon D.; Strom, Erik; Koren, Iztok; Lewis, Michael P.

1993-11-01

Wavelet processing has shown great promise for a variety of image and signal processing applications. Wavelets are also among the most computationally expensive techniques in signal processing. It is demonstrated that a wavelet engine constructed with residue number system arithmetic elements offers significant advantages over commercially available wavelet accelerators based upon conventional arithmetic elements. Analysis is presented predicting the dynamic range requirements of the reported residue number system based wavelet accelerator.

Predictability and Quantification of Complex Groundwater Table Dynamics Driven by Irregular Surface Water Fluctuations

NASA Astrophysics Data System (ADS)

Xin, Pei; Wang, Shen S. J.; Shen, Chengji; Zhang, Zeyu; Lu, Chunhui; Li, Ling

2018-03-01

Shallow groundwater interacts strongly with surface water across a quarter of global land area, affecting significantly the terrestrial eco-hydrology and biogeochemistry. We examined groundwater behavior subjected to unimodal impulse and irregular surface water fluctuations, combining physical experiments, numerical simulations, and functional data analysis. Both the experiments and numerical simulations demonstrated a damped and delayed response of groundwater table to surface water fluctuations. To quantify this hysteretic shallow groundwater behavior, we developed a regression model with the Gamma distribution functions adopted to account for the dependence of groundwater behavior on antecedent surface water conditions. The regression model fits and predicts well the groundwater table oscillations resulting from propagation of irregular surface water fluctuations in both laboratory and large-scale aquifers. The coefficients of the Gamma distribution function vary spatially, reflecting the hysteresis effect associated with increased amplitude damping and delay as the fluctuation propagates. The regression model, in a relatively simple functional form, has demonstrated its capacity of reproducing high-order nonlinear effects that underpin the surface water and groundwater interactions. The finding has important implications for understanding and predicting shallow groundwater behavior and associated biogeochemical processes, and will contribute broadly to studies of groundwater-dependent ecology and biogeochemistry.

LASIK versus photorefractive keratectomy for high myopic (> 3 diopter) astigmatism.

PubMed

Katz, Toam; Wagenfeld, Lars; Galambos, Peter; Darrelmann, Benedikt Große; Richard, Gisbert; Linke, Stephan Johannes

2013-12-01

To compare the efficacy, safety, predictability, and vector analysis indices of LASIK and photorefractive keratectomy (PRK) for correction of high cylinder of greater than 3 diopters (D) in myopic eyes. The efficacy, safety, and predictability of LASIK or PRK performed in 114 consecutive randomly selected myopic eyes with an astigmatism of greater than 3 D were retrospectively analyzed at the 2- to 6-month follow-up visits. Vector analysis of the cylindrical correction was compared between the treatment groups. A total of 57 eyes receiving PRK and 57 eyes receiving LASIK of 114 refractive surgery candidates were enrolled in the study. No statistically significant difference in efficacy [efficacy index = 0.76 (±0.32) for PRK vs 0.74 (±0.19) for LASIK (P = .82)], safety [safety index = 1.10 (±0.26) for PRK vs 1.01 (±0.17) for LASIK (P = .121)], or predictability [achieved astigmatism < 1 D in 39% of PRK- and 54% of LASIK-treated eyes, and < 2 D in 88% of PRK- and 89% of LASIK-treated eyes (P = .218)] was demonstrated. Using Alpins vector analysis, the surgically induced astigmatism and difference vector were not significantly different between the surgery methods, whereas the correction index showed a slight and significant advantage of LASIK over PRK (1.25 for PRK and 1.06 for LASIK, P < .001). LASIK and PRK are comparably safe, effective, and predictable procedures for excimer laser correction of high astigmatism of greater than 3 D in myopic eyes. Predictability of the correction of the cylindrical component is lower than that of the spherical equivalent. Copyright 2013, SLACK Incorporated.

A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

PubMed Central

Apgar, James; Ross, Mary; Zuo, Xiao; Dohle, Sarah; Sturtevant, Derek; Shen, Binzhang; de la Vega, Humberto; Lessard, Philip; Lazar, Gabor; Raab, R. Michael

2012-01-01

Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch. PMID:22649521

Heterogeneity in ADHD: Neurocognitive predictors of peer, family, and academic functioning.

PubMed

Kofler, Michael J; Sarver, Dustin E; Spiegel, Jamie A; Day, Taylor N; Harmon, Sherelle L; Wells, Erica L

2017-08-01

Childhood attention-deficit/hyperactivity disorder (ADHD) is associated with impairments in peer, family, and academic functioning. Although impairment is required for diagnosis, children with ADHD vary significantly in the areas in which they demonstrate clinically significant impairment. However, relatively little is known about the mechanisms and processes underlying these individual differences. The current study examined neurocognitive predictors of heterogeneity in peer, family, and academic functioning in a well-defined sample of 44 children with ADHD aged 8-13 years (M = 10.31, SD = 1.42; 31 boys, 13 girls; 81% Caucasian). Reliable change analysis indicated that 98% of the sample demonstrated objectively-defined impairment on at least one assessed outcome measure; 65% were impaired in two or all three areas of functioning. ADHD children with quantifiable deficits in academic success and family functioning performed worse on tests of working memory (d = 0.68 to 1.09), whereas children with impaired parent-reported social functioning demonstrated slower processing speed (d = 0.53). Dimensional analyses identified additional predictors of peer, family, and academic functioning. Working memory abilities were associated with individual differences in all three functional domains, processing speed predicted social functioning, and inhibitory control predicted family functioning. These results add to a growing literature implicating neurocognitive abilities not only in explaining behavioral differences between ADHD and non-ADHD groups, but also in the substantial heterogeneity in ecologically-valid functional outcomes associated with the disorder.

Controlled release of vancomycin from thin sol-gel films on implant surfaces successfully controls osteomyelitis.

PubMed

Adams, Christopher S; Antoci, Valentin; Harrison, Gerald; Patal, Payal; Freeman, Terry A; Shapiro, Irving M; Parvizi, Javad; Hickok, Noreen J; Radin, Shula; Ducheyne, Paul

2009-06-01

Peri-prosthetic infection remains a serious complication of joint replacement surgery. Herein, we demonstrate that a vancomycin-containing sol-gel film on Ti alloy rods can successfully treat bacterial infections in an animal model. The vancomycin-containing sol-gel films exhibited predictable release kinetics, while significantly inhibiting S. aureus adhesion. When evaluated in a rat osteomyelitis model, microbiological analysis indicated that the vancomycin-containing sol-gel film caused a profound decrease in S. aureus number. Radiologically, while the control side showed extensive bone degradation, including abscesses and an extensive periosteal reaction, rods coated with the vancomycin-containing sol-gel film resulted in minimal signs of infection. MicroCT analysis confirmed the radiological results, while demonstrating that the vancomycin-containing sol-gel film significantly protected dense bone from resorption and minimized remodeling. These results clearly demonstrate that this novel thin sol-gel technology can be used for the targeted delivery of antibiotics for the treatment of periprosthetic as well as other bone infections. Copyright 2008 Orthopaedic Research Society

Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao

PubMed Central

Ma, Qingguo; Zhang, Wuke

2015-01-01

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the “Diaoyu Island” event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products. PMID:25902358

Public mood and consumption choices: evidence from sales of Sony cameras on Taobao.

PubMed

Ma, Qingguo; Zhang, Wuke

2015-01-01

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the "Diaoyu Island" event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products.

The relation between receptive grammar and procedural, declarative, and working memory in specific language impairment.

PubMed

Conti-Ramsden, Gina; Ullman, Michael T; Lum, Jarrad A G

2015-01-01

What memory systems underlie grammar in children, and do these differ between typically developing (TD) children and children with specific language impairment (SLI)? Whilst there is substantial evidence linking certain memory deficits to the language problems in children with SLI, few studies have investigated multiple memory systems simultaneously, examining not only possible memory deficits but also memory abilities that may play a compensatory role. This study examined the extent to which procedural, declarative, and working memory abilities predict receptive grammar in 45 primary school aged children with SLI (30 males, 15 females) and 46 TD children (30 males, 16 females), both on average 9;10 years of age. Regression analyses probed measures of all three memory systems simultaneously as potential predictors of receptive grammar. The model was significant, explaining 51.6% of the variance. There was a significant main effect of learning in procedural memory and a significant group × procedural learning interaction. Further investigation of the interaction revealed that procedural learning predicted grammar in TD but not in children with SLI. Indeed, procedural learning was the only predictor of grammar in TD. In contrast, only learning in declarative memory significantly predicted grammar in SLI. Thus, different memory systems are associated with receptive grammar abilities in children with SLI and their TD peers. This study is, to our knowledge, the first to demonstrate a significant group by memory system interaction in predicting grammar in children with SLI and their TD peers. In line with Ullman's Declarative/Procedural model of language and procedural deficit hypothesis of SLI, variability in understanding sentences of varying grammatical complexity appears to be associated with variability in procedural memory abilities in TD children, but with declarative memory, as an apparent compensatory mechanism, in children with SLI.

Mechanistic modeling to predict the transporter- and enzyme-mediated drug-drug interactions of repaglinide.

PubMed

Varma, Manthena V S; Lai, Yurong; Kimoto, Emi; Goosen, Theunis C; El-Kattan, Ayman F; Kumar, Vikas

2013-04-01

Quantitative prediction of complex drug-drug interactions (DDIs) is challenging. Repaglinide is mainly metabolized by cytochrome-P-450 (CYP)2C8 and CYP3A4, and is also a substrate of organic anion transporting polypeptide (OATP)1B1. The purpose is to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and DDIs of repaglinide. In vitro hepatic transport of repaglinide, gemfibrozil and gemfibrozil 1-O-β-glucuronide was characterized using sandwich-culture human hepatocytes. A PBPK model, implemented in Simcyp (Sheffield, UK), was developed utilizing in vitro transport and metabolic clearance data. In vitro studies suggested significant active hepatic uptake of repaglinide. Mechanistic model adequately described repaglinide pharmacokinetics, and successfully predicted DDIs with several OATP1B1 and CYP3A4 inhibitors (<10% error). Furthermore, repaglinide-gemfibrozil interaction at therapeutic dose was closely predicted using in vitro fraction metabolism for CYP2C8 (0.71), when primarily considering reversible inhibition of OATP1B1 and mechanism-based inactivation of CYP2C8 by gemfibrozil and gemfibrozil 1-O-β-glucuronide. This study demonstrated that hepatic uptake is rate-determining in the systemic clearance of repaglinide. The model quantitatively predicted several repaglinide DDIs, including the complex interactions with gemfibrozil. Both OATP1B1 and CYP2C8 inhibition contribute significantly to repaglinide-gemfibrozil interaction, and need to be considered for quantitative rationalization of DDIs with either drug.

Intelligent Prediction of Fan Rotation Stall in Power Plants Based on Pressure Sensor Data Measured In-Situ

PubMed Central

Xu, Xiaogang; Wang, Songling; Liu, Jinlian; Liu, Xinyu

2014-01-01

Blower and exhaust fans consume over 30% of electricity in a thermal power plant, and faults of these fans due to rotation stalls are one of the most frequent reasons for power plant outage failures. To accurately predict the occurrence of fan rotation stalls, we propose a support vector regression machine (SVRM) model that predicts the fan internal pressures during operation, leaving ample time for rotation stall detection. We train the SVRM model using experimental data samples, and perform pressure data prediction using the trained SVRM model. To prove the feasibility of using the SVRM model for rotation stall prediction, we further process the predicted pressure data via wavelet-transform-based stall detection. By comparison of the detection results from the predicted and measured pressure data, we demonstrate that the SVRM model can accurately predict the fan pressure and guarantee reliable stall detection with a time advance of up to 0.0625 s. This superior pressure data prediction capability leaves significant time for effective control and prevention of fan rotation stall faults. This model has great potential for use in intelligent fan systems with stall prevention capability, which will ensure safe operation and improve the energy efficiency of power plants. PMID:24854057

Systematic bias of correlation coefficient may explain negative accuracy of genomic prediction.

PubMed

Zhou, Yao; Vales, M Isabel; Wang, Aoxue; Zhang, Zhiwu

2017-09-01

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that the minimum value for prediction accuracy should hover around zero when randomly permuted data sets are analyzed. We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by the mathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as the mean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using a modified formula. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.

PubMed

Thorwarth, Patrick; Yousef, Eltohamy A A; Schmid, Karl J

2018-02-02

Genetic resources are an important source of genetic variation for plant breeding. Genome-wide association studies (GWAS) and genomic prediction greatly facilitate the analysis and utilization of useful genetic diversity for improving complex phenotypic traits in crop plants. We explored the potential of GWAS and genomic prediction for improving curd-related traits in cauliflower ( Brassica oleracea var. botrytis ) by combining 174 randomly selected cauliflower gene bank accessions from two different gene banks. The collection was genotyped with genotyping-by-sequencing (GBS) and phenotyped for six curd-related traits at two locations and three growing seasons. A GWAS analysis based on 120,693 single-nucleotide polymorphisms identified a total of 24 significant associations for curd-related traits. The potential for genomic prediction was assessed with a genomic best linear unbiased prediction model and BayesB. Prediction abilities ranged from 0.10 to 0.66 for different traits and did not differ between prediction methods. Imputation of missing genotypes only slightly improved prediction ability. Our results demonstrate that GWAS and genomic prediction in combination with GBS and phenotyping of highly heritable traits can be used to identify useful quantitative trait loci and genotypes among genetically diverse gene bank material for subsequent utilization as genetic resources in cauliflower breeding. Copyright © 2018 Thorwarth et al.

Take charge: Personality as predictor of recovery from eating disorder.

PubMed

Levallius, Johanna; Roberts, Brent W; Clinton, David; Norring, Claes

2016-12-30

Many treatments for eating disorders (ED) have demonstrated success. However, not all patients respond the same to interventions nor achieve full recovery, and obvious candidates like ED diagnosis and symptoms have generally failed to explain this variability. The current study investigated the predictive utility of personality for outcome in ED treatment. One hundred and thirty adult patients with bulimia nervosa or eating disorder not otherwise specified enrolled in an intensive multimodal treatment for 16 weeks. Personality was assessed with the NEO Personality Inventory Revised (NEO PI-R). Outcome was defined as recovered versus still ill and also as symptom score at termination with the Eating Disorder Inventory-2 (EDI-2). Personality significantly predicted both recovery (70% of patients) and symptom improvement. Patients who recovered reported significantly higher levels of Extraversion at baseline than the still ill, and Assertiveness emerged as the personality trait best predicting variance in outcome. This study indicates that personality might hold promise as predictor of recovery after treatment for ED. Future research might investigate if adding interventions to address personality features improves outcome for ED patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Acquaintance Rape: Applying Crime Scene Analysis to the Prediction of Sexual Recidivism.

PubMed

Lehmann, Robert J B; Goodwill, Alasdair M; Hanson, R Karl; Dahle, Klaus-Peter

2016-10-01

The aim of the current study was to enhance the assessment and predictive accuracy of risk assessments for sexual offenders by utilizing detailed crime scene analysis (CSA). CSA was conducted on a sample of 247 male acquaintance rapists from Berlin (Germany) using a nonmetric, multidimensional scaling (MDS) Behavioral Thematic Analysis (BTA) approach. The age of the offenders at the time of the index offense ranged from 14 to 64 years (M = 32.3; SD = 11.4). The BTA procedure revealed three behavioral themes of hostility, criminality, and pseudo-intimacy, consistent with previous CSA research on stranger rape. The construct validity of the three themes was demonstrated through correlational analyses with known sexual offending measures and criminal histories. The themes of hostility and pseudo-intimacy were significant predictors of sexual recidivism. In addition, the pseudo-intimacy theme led to a significant increase in the incremental validity of the Static-99 actuarial risk assessment instrument for the prediction of sexual recidivism. The results indicate the potential utility and validity of crime scene behaviors in the applied risk assessment of sexual offenders. © The Author(s) 2015.

Perceived Masculinity Predicts U.S. Supreme Court Outcomes.

PubMed

Chen, Daniel; Halberstam, Yosh; Yu, Alan C L

2016-01-01

Previous studies suggest a significant role of language in the court room, yet none has identified a definitive correlation between vocal characteristics and court outcomes. This paper demonstrates that voice-based snap judgments based solely on the introductory sentence of lawyers arguing in front of the Supreme Court of the United States predict outcomes in the Court. In this study, participants rated the opening statement of male advocates arguing before the Supreme Court between 1998 and 2012 in terms of masculinity, attractiveness, confidence, intelligence, trustworthiness, and aggressiveness. We found significant correlation between vocal characteristics and court outcomes and the correlation is specific to perceived masculinity even when judgment of masculinity is based only on less than three seconds of exposure to a lawyer's speech sample. Specifically, male advocates are more likely to win when they are perceived as less masculine. No other personality dimension predicts court outcomes. While this study does not aim to establish any causal connections, our findings suggest that vocal characteristics may be relevant in even as solemn a setting as the Supreme Court of the United States.

Perceived Masculinity Predicts U.S. Supreme Court Outcomes

PubMed Central

2016-01-01

Previous studies suggest a significant role of language in the court room, yet none has identified a definitive correlation between vocal characteristics and court outcomes. This paper demonstrates that voice-based snap judgments based solely on the introductory sentence of lawyers arguing in front of the Supreme Court of the United States predict outcomes in the Court. In this study, participants rated the opening statement of male advocates arguing before the Supreme Court between 1998 and 2012 in terms of masculinity, attractiveness, confidence, intelligence, trustworthiness, and aggressiveness. We found significant correlation between vocal characteristics and court outcomes and the correlation is specific to perceived masculinity even when judgment of masculinity is based only on less than three seconds of exposure to a lawyer’s speech sample. Specifically, male advocates are more likely to win when they are perceived as less masculine. No other personality dimension predicts court outcomes. While this study does not aim to establish any causal connections, our findings suggest that vocal characteristics may be relevant in even as solemn a setting as the Supreme Court of the United States. PMID:27737008

The effect of congruence in patient and therapist alliance on patient's symptomatic levels.

PubMed

Zilcha-Mano, Sigal; Snyder, John; Silberschatz, George

2017-05-01

The ability of alliance to predict outcome has been widely demonstrated, but less is known about the effect of the level of congruence between patient and therapist alliance ratings on outcome. In the current study we examined whether the degree of congruence between patient and therapist alliance ratings can predict symptomatic levels 1 month later in treatment. The sample consisted of 127 patient-therapist dyads. Patients and therapists reported on their alliance levels, and patients reported their symptomatic levels 1 month later. Polynomial regression and response surface analysis were used to examine congruence. Findings suggest that when the congruence level of patient and therapist alliance ratings was not taken into account, only the therapist's alliance served as a significant predictor of symptomatic levels. But when the degree of congruence between patient and therapist alliance ratings was considered, the degree of congruence was a significant predictor of symptomatic levels 1 month later in treatment. Findings support the importance of the level of congruence between patient and therapist alliance ratings in predicting patient's symptomatic levels.

The mouse beam walking assay offers improved sensitivity over the mouse rotarod in determining motor coordination deficits induced by benzodiazepines.

PubMed

Stanley, Joanna L; Lincoln, Rachael J; Brown, Terry A; McDonald, Louise M; Dawson, Gerard R; Reynolds, David S

2005-05-01

The mouse rotarod test of motor coordination/sedation is commonly used to predict clinical sedation caused by novel drugs. However, past experience suggests that it lacks the desired degree of sensitivity to be predictive of effects in humans. For example, the benzodiazepine, bretazenil, showed little impairment of mouse rotarod performance, but marked sedation in humans. The aim of the present study was to assess whether the mouse beam walking assay demonstrates: (i) an increased sensitivity over the rotarod and (ii) an increased ability to predict clinically sedative doses of benzodiazepines. The study compared the effects of the full benzodiazepine agonists, diazepam and lorazepam, and the partial agonist, bretazenil, on the mouse rotarod and beam walking assays. Diazepam and lorazepam significantly impaired rotarod performance, although relatively high GABA-A receptor occupancy was required (72% and 93%, respectively), whereas beam walking performance was significantly affected at approximately 30% receptor occupancy. Bretazenil produced significant deficits at 90% and 53% receptor occupancy on the rotarod and beam walking assays, respectively. The results suggest that the mouse beam walking assay is a more sensitive tool for determining benzodiazepine-induced motor coordination deficits than the rotarod. Furthermore, the GABA-A receptor occupancy values at which significant deficits were determined in the beam walking assay are comparable with those observed in clinical positron emission tomography studies using sedative doses of benzodiazepines. These data suggest that the beam walking assay may be able to more accurately predict the clinically sedative doses of novel benzodiazepine-like drugs.

Intratumor heterogeneity predicts metastasis of triple-negative breast cancer.

PubMed

Yang, Fang; Wang, Yucai; Li, Quan; Cao, Lulu; Sun, Zijia; Jin, Juan; Fang, Hehui; Zhu, Aiyu; Li, Yan; Zhang, Wenwen; Wang, Yanru; Xie, Hui; Gustafsson, Jan-Åke; Wang, Shui; Guan, Xiaoxiang

2017-09-01

Even with the identical clinicopathological features, the ability for metastasis is vastly different among triple-negative breast cancer (TNBC) patients. Intratumor heterogeneity (ITH), which is common in breast cancer, may be a key mechanism leading to the tumor progression. In this study, we studied whether a quantitative genetic definition of ITH can predict clinical outcomes in patients with TNBC. We quantified ITH by calculating Shannon index, a measure of diversity in a population, based on Myc, epidermal growth factor receptor/centromeric probe 7 (EGFR/CEP7) and cyclin D1/centromeric probe 11 (CCND1/CEP11) copy number variations (CNVs) in 300 cells at three different locations of a tumor. Among 75 TNBC patients, those who developed metastasis had significantly higher ITH, that is Shannon indices of EGFR/CEP7 and CCND1/CEP11 CNVs. Higher Shannon indices of EGFR/CEP7 and CCND1/CEP11 CNVs were significantly associated with the development of metastasis and were predictive of significantly worse metastasis-free survival (MFS). Regional heterogeneity, defined as the difference in copy numbers of Myc, EGFR or CCND1 at different locations, was found in 52 patients. However, the presence of regional heterogeneity did not correlate with metastasis or MFS. Our findings demonstrate that higher ITH of EGFR/CEP7 and CCND1/CEP11 CNVs is predictive of metastasis and is associated with significantly worse MFS in TNBC patients, suggesting that ITH is a very promising novel prognostic factor in TNBC. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

A numerical study of fundamental shock noise mechanisms. Ph.D. Thesis - Cornell Univ.

NASA Technical Reports Server (NTRS)

Meadows, Kristine R.

1995-01-01

The results of this thesis demonstrate that direct numerical simulation can predict sound generation in unsteady aerodynamic flows containing shock waves. Shock waves can be significant sources of sound in high speed jet flows, on helicopter blades, and in supersonic combustion inlets. Direct computation of sound permits the prediction of noise levels in the preliminary design stage and can be used as a tool to focus experimental studies, thereby reducing cost and increasing the probability of a successfully quiet product in less time. This thesis reveals and investigates two mechanisms fundamental to sound generation by shocked flows: shock motion and shock deformation. Shock motion is modeled by the interaction of a sound wave with a shock. During the interaction, the shock wave begins to move and the sound pressure is amplified as the wave passes through the shock. The numerical approach presented in this thesis is validated by the comparison of results obtained in a quasi-one dimensional simulation with linear theory. Analysis of the perturbation energy demonstrated for the first time that acoustic energy is generated by the interaction. Shock deformation is investigated by the numerical simulation of a ring vortex interacting with a shock. This interaction models the passage of turbulent structures through the shock wave. The simulation demonstrates that both acoustic waves and contact surfaces are generated downstream during the interaction. Analysis demonstrates that the acoustic wave spreads cylindrically, that the sound intensity is highly directional, and that the sound pressure level increases significantly with increasing shock strength. The effect of shock strength on sound pressure level is consistent with experimental observations of shock noise, indicating that the interaction of a ring vortex with a shock wave correctly models a dominant mechanism of shock noise generation.

Can the PHS model (ISO7933) predict reasonable thermophysiological responses while wearing protective clothing in hot environments?

PubMed

Wang, Faming; Kuklane, Kalev; Gao, Chuansi; Holmér, Ingvar

2011-02-01

In this paper, the prediction accuracy of the PHS (predicted heat strain) model on human physiological responses while wearing protective clothing ensembles was examined. Six human subjects (aged 29 ± 3 years) underwent three experimental trials in three different protective garments (clothing thermal insulation I(cl) ranges from 0.63 to 2.01 clo) in two hot environments (40 °C, relative humidities: 30% and 45%). The observed and predicted mean skin temperature, core body temperature and sweat rate were presented and statistically compared. A significant difference was found in the metabolic rate between FIRE (firefighting clothing) and HV (high visibility clothing) or MIL (military clothing) (p < 0.001). Also, the development of heart rate demonstrated the significant effects of the exposure time and clothing ensembles. In addition, the predicted evaporation rate during HV, MIL and FIRE was much lower than the experimental values. Hence, the current PHS model is not applicable for protective clothing with intrinsic thermal insulations above 1.0 clo. The results showed that the PHS model generated unreliable predictions on body core temperature when human subjects wore thick protective clothing such as firefighting clothing (I(cl) > 1.0 clo). The predicted mean skin temperatures in three clothing ensembles HV, MIL and FIRE were also outside the expected limits. Thus, there is a need for further extension for the clothing insulation validation range of the PHS model. It is recommended that the PHS model should be amended and validated by individual algorithms, physical or physiological parameters, and further subject studies.

Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

PubMed

Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

2014-03-12

Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis.

PubMed

Boersen, Nathan; Carvajal, M Teresa; Morris, Kenneth R; Peck, Garnet E; Pinal, Rodolfo

2015-01-01

While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API. Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data. Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength. Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.

CURB-65 Score is Equal to NEWS for Identifying Mortality Risk of Pneumonia Patients: An Observational Study.

PubMed

Brabrand, Mikkel; Henriksen, Daniel Pilsgaard

2018-06-01

The CURB-65 score is widely implemented as a prediction tool for identifying patients with community-acquired pneumonia (cap) at increased risk of 30-day mortality. However, since most ingredients of CURB-65 are used as general prediction tools, it is likely that other prediction tools, e.g. the British National Early Warning Score (NEWS), could be as good as CURB-65 at predicting the fate of CAP patients. To determine whether NEWS is better than CURB-65 at predicting 30-day mortality of CAP patients. This was a single-centre, 6-month observational study using patients' vital signs and demographic information registered upon admission, survival status extracted from the Danish Civil Registration System after discharge and blood test results extracted from a local database. The study was conducted in the medical admission unit (MAU) at the Hospital of South West Jutland, a regional teaching hospital in Denmark. The participants consisted of 570 CAP patients, 291 female and 279 male, median age 74 (20-102) years. The CURB-65 score had a discriminatory power of 0.728 (0.667-0.789) and NEWS 0.710 (0.645-0.775), both with good calibration and no statistical significant difference. CURB-65 was not demonstrated to be significantly statistically better than NEWS at identifying CAP patients at risk of 30-day mortality.

Sequential causal inference: Application to randomized trials of adaptive treatment strategies

PubMed Central

Dawson, Ree; Lavori, Philip W.

2009-01-01

SUMMARY Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal ‘one-step’ estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies. PMID:17914714

A polynomial chaos ensemble hydrologic prediction system for efficient parameter inference and robust uncertainty assessment

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Huang, W.

2015-11-01

This paper presents a polynomial chaos ensemble hydrologic prediction system (PCEHPS) for an efficient and robust uncertainty assessment of model parameters and predictions, in which possibilistic reasoning is infused into probabilistic parameter inference with simultaneous consideration of randomness and fuzziness. The PCEHPS is developed through a two-stage factorial polynomial chaos expansion (PCE) framework, which consists of an ensemble of PCEs to approximate the behavior of the hydrologic model, significantly speeding up the exhaustive sampling of the parameter space. Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference. The PCEHPS is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability. A detailed comparison between the HYMOD hydrologic model, the ensemble of PCEs, and the ensemble of reduced PCEs is performed in terms of accuracy and efficiency. Results reveal temporal and spatial variations in parameter sensitivities due to the dynamic behavior of hydrologic systems, and the effects (magnitude and direction) of parametric interactions depending on different hydrological metrics. The case study demonstrates that the PCEHPS is capable not only of capturing both expert knowledge and probabilistic information in the calibration process, but also of implementing an acceleration of more than 10 times faster than the hydrologic model without compromising the predictive accuracy.

Integrating linear optimization with structural modeling to increase HIV neutralization breadth.

PubMed

Sevy, Alexander M; Panda, Swetasudha; Crowe, James E; Meiler, Jens; Vorobeychik, Yevgeniy

2018-02-01

Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods. We demonstrate the effectiveness of this method, which we call BROAD, by benchmarking the performance on increasing predicted breadth of anti-HIV antibodies. We use this novel method to increase predicted breadth of naturally-occurring antibody VRC23 against a panel of 180 divergent HIV viral strains and achieve 100% predicted binding against the panel. In addition, we compare the performance of this method to state-of-the-art multistate design in Rosetta and show that we can outperform the existing method significantly. We further demonstrate that sequences recovered by this method recover known binding motifs of broadly neutralizing anti-HIV antibodies. Finally, our approach is general and can be extended easily to other protein systems. Although our modeled antibodies were not tested in vitro, we predict that these variants would have greatly increased breadth compared to the wild-type antibody.

Cluster analysis as a prediction tool for pregnancy outcomes.

PubMed

Banjari, Ines; Kenjerić, Daniela; Šolić, Krešimir; Mandić, Milena L

2015-03-01

Considering specific physiology changes during gestation and thinking of pregnancy as a "critical window", classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates the use of a method based on an approach from intelligent data mining, cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices' were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branch- es or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complications during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.

Predicting response to physiotherapy treatment for musculoskeletal shoulder pain: a systematic review

PubMed Central

2013-01-01

Background People suffering from musculoskeletal shoulder pain are frequently referred to physiotherapy. Physiotherapy generally involves a multimodal approach to management that may include; exercise, manual therapy and techniques to reduce pain. At present it is not possible to predict which patients will respond positively to physiotherapy treatment. The purpose of this systematic review was to identify which prognostic factors are associated with the outcome of physiotherapy in the management of musculoskeletal shoulder pain. Methods A comprehensive search was undertaken of Ovid Medline, EMBASE, CINAHL and AMED (from inception to January 2013). Prospective studies of participants with shoulder pain receiving physiotherapy which investigated the association between baseline prognostic factors and change in pain and function over time were included. Study selection, data extraction and appraisal of study quality were undertaken by two independent assessors. Quality criteria were selected from previously published guidelines to form a checklist of 24 items. The study protocol was prospectively registered onto the International Prospective Register of Systematic Reviews. Results A total of 5023 titles were retrieved and screened for eligibility, 154 articles were assessed as full text and 16 met the inclusion criteria: 11 cohort studies, 3 randomised controlled trials and 2 controlled trials. Results were presented for the 9 studies meeting 13 or more of the 24 quality criteria. Clinical and statistical heterogeneity resulted in qualitative synthesis rather than meta-analysis. Three studies demonstrated that high functional disability at baseline was associated with poor functional outcome (p ≤ 0.05). Four studies demonstrated a significant association (p ≤ 0.05) between longer duration of shoulder pain and poorer outcome. Three studies, demonstrated a significant association (p ≤ 0.05) between increasing age and poorer function; three studies demonstrated no association (p > 0.05). Conclusion Associations between prognostic factors and outcome were often inconsistent between studies. This may be due to clinical heterogeneity or type II errors. Only two baseline prognostic factors demonstrated a consistent association with outcome in two or more studies; duration of shoulder pain and baseline function. Prior to developing a predictive model for the outcome of physiotherapy treatment for shoulder pain, a large adequately powered prospective cohort study is required in which a broad range of prognostic factors are incorporated. PMID:23834747

A novel neuroimaging model to predict early neurological deterioration after acute ischemic stroke.

PubMed

Huang, Yen-Chu; Tsai, Yuan-Hsiung; Lee, Jiann-Der; Yang, Jen-Tsung; Pan, Yi-Ting

2018-05-16

In acute ischemic stroke, early neurological deterioration (END) may occur in up to one-third of patients. However, there is still no satisfying or comprehensive predictive model for all the stroke subtypes. We propose a practical model to predict END using magnetic resonance imaging (MRI). Patients with anterior circulation infarct were recruited and they underwent an MRI within 24 hours of stroke onset. END was defined as an elevation of ≥2 points on the National Institute of Health Stroke Scale (NIHSS) within 72 hours of stroke onset. We examined the relationships of END to individual END models, including: A, infarct swelling; B, small subcortical infarct; C, mismatch; and D, recurrence. There were 163 patients recruited and 43 (26.4%) of them had END. The END models A, B and C significantly predicted END respectively after adjusting for confounding factors (p=0.022, p=0.007 and p<0.001 respectively). In END model D, we examined all imaging predictors of Recurrence Risk Estimator (RRE) individually and only the "multiple acute infarcts" pattern was significantly associated with END (p=0.032). When applying END models A, B, C and D, they successfully predicted END (p<0.001; odds ratio: 17.5[95% confidence interval: 5.1-60.8]), with 93.0% sensitivity, 60.0% specificity, 45.5% positive predictive value and 96.0% negative predictive value. The results demonstrate that the proposed model could predict END in all stroke subtypes of anterior circulation infarction. It provides a practical model for clinical physicians to select high-risk patients for more aggressive treatment to prevent END. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Serum Creatinine Versus Plasma Methotrexate Levels to Predict Toxicities in Children Receiving High-dose Methotrexate.

PubMed

Tiwari, Priya; Thomas, M K; Pathania, Subha; Dhawan, Deepa; Gupta, Y K; Vishnubhatla, Sreenivas; Bakhshi, Sameer

2015-01-01

Facilities for measuring methotrexate (MTX) levels are not available everywhere, potentially limiting administration of high-dose methotrexate (HDMTX). We hypothesized that serum creatinine alteration after HDMTX administration predicts MTX clearance. Overall, 122 cycles in 50 patients of non-Hodgkin lymphoma or acute lymphoblastic leukemia aged ≤18 years receiving HDMTX were enrolled prospectively. Plasma MTX levels were measured at 12, 24, 36, 48, 60, and 72 hours; serum creatinine was measured at baseline, 24, 48, and 72 hours. Correlation of plasma MTX levels with creatinine levels and changes in creatinine from baseline (Δ creatinine) were evaluated. Plasma MTX levels at 72 hours showed positive correlation with serum creatinine at 48 hours (P = .011) and 72 hours (P = .013) as also Δ creatinine at 48 hours (P = .042) and 72 hours (P = .045). However, cut-off value of either creatinine or Δ creatinine could not be established to reliably predict delayed MTX clearance. Greater than 50% Δ creatinine at 48 and 72 hours significantly predicted grade 3/4 leucopenia (P = .036 and P = .001, respectively) and thrombocytopenia (P = .012 and P = .009, respectively) but not mucositis (P = .827 and P = .910, respectively). Delayed MTX elimination did not predict any grade 3/4 toxicity. In spite of demonstration of significant correlation between serum creatinine and Δ creatinine with plasma MTX levels at 72 hours, cut-off value of either variable to predict MTX delay could not be established. Thus, either of these cannot be used as a surrogate for plasma MTX estimation. Interestingly, Δ creatinine effectively predicted hematological toxicities, which were not predicted by delayed MTX clearance.

Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease.

PubMed

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-06-01

See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

An investigation of the transdiagnostic model of eating disorders in the context of muscle dysmorphia.

PubMed

Murray, Stuart B; Rieger, Elizabeth; Karlov, Lisa; Touyz, Stephen W

2013-03-01

Muscle dysmorphia is a psychiatric disorder that has been conceptually linked to eating disorders, although its precise nosology remains unclear. To further investigate this notion, the present study examined the applicability of the transdiagnostic model of eating disorders to muscle dysmorphia. One hundred and nineteen male undergraduate students completed self-report measures of multidimensional perfectionism, mood intolerance, self-esteem, interpersonal problems, and muscle dysmorphia symptomatology. Self-oriented perfectionism, socially prescribed perfectionism, mood intolerance, and low self-esteem significantly predicted muscle dysmorphia symptomatology, whereas other-oriented perfectionism and interpersonal problems did not demonstrate significant predictive value when accounting for the other transdiagnostic constructs. The transdiagnostic model of eating disorders may potentially be applied to enhance our understanding of the maintenance of muscle dysmorphic features in addition to eating disorder symptomatology. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

Secondary structure of the 3'-noncoding region of flavivirus genomes: comparative analysis of base pairing probabilities.

PubMed

Rauscher, S; Flamm, C; Mandl, C W; Heinz, F X; Stadler, P F

1997-07-01

The prediction of the complete matrix of base pairing probabilities was applied to the 3' noncoding region (NCR) of flavivirus genomes. This approach identifies not only well-defined secondary structure elements, but also regions of high structural flexibility. Flaviviruses, many of which are important human pathogens, have a common genomic organization, but exhibit a significant degree of RNA sequence diversity in the functionally important 3'-NCR. We demonstrate the presence of secondary structures shared by all flaviviruses, as well as structural features that are characteristic for groups of viruses within the genus reflecting the established classification scheme. The significance of most of the predicted structures is corroborated by compensatory mutations. The availability of infectious clones for several flaviviruses will allow the assessment of these structural elements in processes of the viral life cycle, such as replication and assembly.

Romantic Partner Selection and Socialization during Early Adolescence

PubMed Central

Simon, Valerie A.; Aikins, Julie Wargo; Prinstein, Mitchell J.

2012-01-01

This prospective study examined romantic partner selection and socialization among a sample of 78 young adolescents (6th–8th graders). Independent assessments of adolescent and romantic partner adjustment were collected before and after relationships initiated via peer nomination and self-report. Prior to their relationship, adolescents and partners were significantly alike on popularity, physical attraction, and depressive symptoms. Controlling for initial similarity, partners' popularity, depressive symptoms, relational aggression and relational victimization significantly predicted changes in adolescents' functioning in these areas over time. However, the magnitude and direction of change varied according to adolescents' and partners' pre-relationship functioning. In general, adolescents who dated high-functioning partners changed more than those who dated low-functioning partners, and partner characteristics predicted greater change among low versus high-functioning adolescents. Results were consistent even when controlling for best friend characteristics. The current findings are among the first to demonstrate unique contributions of romantic partner characteristics to adolescents' psychosocial functioning. PMID:19037942

The relationship between separation anxiety and impairment

PubMed Central

Foley, Debra L; Rowe, Richard; Maes, Hermine; Silberg, Judy; Eaves, Lindon; Pickles, Andrew

2009-01-01

The goal of this study was to characterize the contemporaneous and prognostic relationship between symptoms of separation anxiety disorder (SAD) and associated functional impairment. The sample comprised n=2067 8–16 year-old twins from a community-based registry. Juvenile subjects and their parents completed a personal interview on two occasions, separated by an average follow-up period of 18 months, about the subject’s current history of SAD and associated functional impairment. Results showed that SAD symptoms typically caused very little impairment but demonstrated significant continuity over time. Older youth had significantly more persistent symptoms than younger children. Prior symptom level independently predicted future symptom level and diagnostic symptom threshold, with and without impairment. Neither diagnostic threshold nor severity of impairment independently predicted outcomes after taking account of prior symptom levels. The results indicate that impairment may index current treatment need but symptom levels provide the best information about severity and prognosis. PMID:17658718

Community integration after burn injuries.

PubMed

Esselman, P C; Ptacek, J T; Kowalske, K; Cromes, G F; deLateur, B J; Engrav, L H

2001-01-01

Evaluation of community integration is a meaningful outcome criterion after major burn injury. The Community Integration Questionnaire (CIQ) was administered to 463 individuals with major burn injuries. The CIQ results in Total, Home Integration, Social Integration, and Productivity scores. The purposes of this study were to determine change in CIQ scores over time and what burn injury and demographic factors predict CIQ scores. The CIQ scores did not change significantly from 6 to 12 to 24 months postburn injury. Home integration scores were best predicted by sex and living situation; Social Integration scores by marital status; and Productivity scores by functional outcome, burn severity, age, and preburn work factors. The data demonstrate that individuals with burn injuries have significant difficulties with community integration due to burn and nonburn related factors. CIQ scores did not improve over time but improvement may have occurred before the initial 6-month postburn injury follow-up in this study.

Chemical kinetic model uncertainty minimization through laminar flame speed measurements

DOE PAGES

Park, Okjoo; Veloo, Peter S.; Sheen, David A.; ...

2016-07-25

Laminar flame speed measurements were carried for mixture of air with eight C 3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso-butene, n-butane, and iso-butane) at the room temperature and ambient pressure. Along with C 1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011,more » 158, 2358–2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C 3 and C 4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C 3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C 4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C 4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel.« less

Chemical kinetic model uncertainty minimization through laminar flame speed measurements

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

Park, Okjoo; Veloo, Peter S.; Sheen, David A.

Laminar flame speed measurements were carried for mixture of air with eight C 3-4 hydrocarbons (propene, propane, 1,3-butadiene, 1-butene, 2-butene, iso-butene, n-butane, and iso-butane) at the room temperature and ambient pressure. Along with C 1-2 hydrocarbon data reported in a recent study, the entire dataset was used to demonstrate how laminar flame speed data can be utilized to explore and minimize the uncertainties in a reaction model for foundation fuels. The USC Mech II kinetic model was chosen as a case study. The method of uncertainty minimization using polynomial chaos expansions (MUM-PCE) (D.A. Sheen and H. Wang, Combust. Flame 2011,more » 158, 2358–2374) was employed to constrain the model uncertainty for laminar flame speed predictions. Results demonstrate that a reaction model constrained only by the laminar flame speed values of methane/air flames notably reduces the uncertainty in the predictions of the laminar flame speeds of C 3 and C 4 alkanes, because the key chemical pathways of all of these flames are similar to each other. The uncertainty in model predictions for flames of unsaturated C 3-4 hydrocarbons remain significant without considering fuel specific laminar flames speeds in the constraining target data set, because the secondary rate controlling reaction steps are different from those in the saturated alkanes. It is shown that the constraints provided by the laminar flame speeds of the foundation fuels could reduce notably the uncertainties in the predictions of laminar flame speeds of C 4 alcohol/air mixtures. Furthermore, it is demonstrated that an accurate prediction of the laminar flame speed of a particular C 4 alcohol/air mixture is better achieved through measurements for key molecular intermediates formed during the pyrolysis and oxidation of the parent fuel.« less

Histologic morphometry confirms a prophylactic effect for hyperbaric oxygen in the prevention of delayed radiation enteropathy.

PubMed

Feldmeier, J J; Davolt, D A; Court, W S; Onoda, J M; Alecu, R

1998-01-01

In a previous publication (Feldmeier et al., Radiother Oncol 1995; 35:138-144) we reported our success in preventing delayed radiation enteropathy in a murine model by the application of hyperbaric oxygen (HBO2). In this study we introduce a histologic morphometric technique for assessing fibrosis in the submucosa of these same animal specimens and relate this assay to the previous results. The histologic morphometry, like the previous gross morphometry and compliance assays, demonstrates a significant protective effect for HBO2. The present assay is related to the previous assays in a statistically significant fashion. The predictive value for the histologic morphometric assay demonstrates a sensitivity of 75% and a specificity of 62.5%. The applicability of this assay to other organ systems and its potential superiority to the compliance assay are discussed.

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).

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

Predictive validity of the UKCAT for medical school undergraduate performance: a national prospective cohort study.

PubMed

Tiffin, Paul A; Mwandigha, Lazaro M; Paton, Lewis W; Hesselgreaves, H; McLachlan, John C; Finn, Gabrielle M; Kasim, Adetayo S

2016-09-26

The UK Clinical Aptitude Test (UKCAT) has been shown to have a modest but statistically significant ability to predict aspects of academic performance throughout medical school. Previously, this ability has been shown to be incremental to conventional measures of educational performance for the first year of medical school. This study evaluates whether this predictive ability extends throughout the whole of undergraduate medical study and explores the potential impact of using the test as a selection screening tool. This was an observational prospective study, linking UKCAT scores, prior educational attainment and sociodemographic variables with subsequent academic outcomes during the 5 years of UK medical undergraduate training. The participants were 6812 entrants to UK medical schools in 2007-8 using the UKCAT. The main outcome was academic performance at each year of medical school. A receiver operating characteristic (ROC) curve analysis was also conducted, treating the UKCAT as a screening test for a negative academic outcome (failing at least 1 year at first attempt). All four of the UKCAT scale scores significantly predicted performance in theory- and skills-based exams. After adjustment for prior educational achievement, the UKCAT scale scores remained significantly predictive for most years. Findings from the ROC analysis suggested that, if used as a sole screening test, with the mean applicant UKCAT score as the cut-off, the test could be used to reject candidates at high risk of failing at least 1 year at first attempt. However, the 'number needed to reject' value would be high (at 1.18), with roughly one candidate who would have been likely to pass all years at first sitting being rejected for every higher risk candidate potentially declined entry on this basis. The UKCAT scores demonstrate a statistically significant but modest degree of incremental predictive validity throughout undergraduate training. Whilst the UKCAT could be considered a fairly crude screening tool for future academic performance, it may offer added value when used in conjunction with other selection measures. Future work should focus on the optimum role of such tests within the selection process and the prediction of post-graduate performance.

Transcriptional bursting explains the noise–versus–mean relationship in mRNA and protein levels

DOE PAGES

Dar, Roy; Shaffer, Sydney M.; Singh, Abhyudai; ...

2016-07-28

Recent analysis demonstrates that the HIV-1 Long Terminal Repeat (HIV LTR) promoter exhibits a range of possible transcriptional burst sizes and frequencies for any mean-expression level. However, these results have also been interpreted as demonstrating that cell-tocell expression variability (noise) and mean are uncorrelated, a significant deviation from previous results. Here, we re-examine the available mRNA and protein abundance data for the HIV LTR and find that noise in mRNA and protein expression scales inversely with the mean along analytically predicted transcriptional burst-size manifolds. We then experimentally perturb transcriptional activity to test a prediction of the multiple burst-size model: thatmore » increasing burst frequency will cause mRNA noise to decrease along given burst-size lines as mRNA levels increase. In conclusion, the data show that mRNA and protein noise decrease as mean expression increases, supporting the canonical inverse correlation between noise and mean.« less

A novel application of artificial neural network for wind speed estimation

NASA Astrophysics Data System (ADS)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

A systematic molecular dynamics study of nearest-neighbor effects on base pair and base pair step conformations and fluctuations in B-DNA

PubMed Central

Lavery, Richard; Zakrzewska, Krystyna; Beveridge, David; Bishop, Thomas C.; Case, David A.; Cheatham, Thomas; Dixit, Surjit; Jayaram, B.; Lankas, Filip; Laughton, Charles; Maddocks, John H.; Michon, Alexis; Osman, Roman; Orozco, Modesto; Perez, Alberto; Singh, Tanya; Spackova, Nada; Sponer, Jiri

2010-01-01

It is well recognized that base sequence exerts a significant influence on the properties of DNA and plays a significant role in protein–DNA interactions vital for cellular processes. Understanding and predicting base sequence effects requires an extensive structural and dynamic dataset which is currently unavailable from experiment. A consortium of laboratories was consequently formed to obtain this information using molecular simulations. This article describes results providing information not only on all 10 unique base pair steps, but also on all possible nearest-neighbor effects on these steps. These results are derived from simulations of 50–100 ns on 39 different DNA oligomers in explicit solvent and using a physiological salt concentration. We demonstrate that the simulations are converged in terms of helical and backbone parameters. The results show that nearest-neighbor effects on base pair steps are very significant, implying that dinucleotide models are insufficient for predicting sequence-dependent behavior. Flanking base sequences can notably lead to base pair step parameters in dynamic equilibrium between two conformational sub-states. Although this study only provides limited data on next-nearest-neighbor effects, we suggest that such effects should be analyzed before attempting to predict the sequence-dependent behavior of DNA. PMID:19850719

Repetitive and stereotyped movements in children with autism spectrum disorders late in the second year of life.

PubMed

Morgan, Lindee; Wetherby, Amy M; Barber, Angie

2008-08-01

The purpose of this study was to examine group differences and relationships with later developmental level and autism symptoms using a new clinical tool developed to measure repetitive and stereotyped movements (RSM) in young children. Videotaped behavior samples using the Communication and Symbolic Behavior Scales Developmental Profile (CSBS; Wetherby & Prizant, 2002) were coded for children with autism spectrum disorders (ASD; n = 50), developmental delays without ASD (DD; n = 25), and typical development (TD; n = 50) between 18 and 24 months of age. Children with ASD demonstrated significantly higher rate and larger inventory of RSM with objects and body during a systematic behavior sample than both the DD and TD groups. Measures of RSM were related to concurrent measures of social communication and predicted developmental outcomes and autism symptoms in the fourth year for the ASD group. None of the correlations between RSM and autism symptoms remained significant when controlling for CSBS Symbolic level. RSM with objects predicted unique variance in the severity of autism symptoms in the fourth year beyond that predicted by social communication measures alone. This study provides support for the diagnostic significance of RSM in children under 24 months of age and documents the utility of this RSM measurement tool as a companion to the CSBS.

Validation and clinical utility of the executive function performance test in persons with traumatic brain injury.

PubMed

Baum, C M; Wolf, T J; Wong, A W K; Chen, C H; Walker, K; Young, A C; Carlozzi, N E; Tulsky, D S; Heaton, R K; Heinemann, A W

2017-07-01

This study examined the relationships between the Executive Function Performance Test (EFPT), the NIH Toolbox Cognitive Function tests, and neuropsychological executive function measures in 182 persons with traumatic brain injury (TBI) and 46 controls to evaluate construct, discriminant, and predictive validity. Construct validity: There were moderate correlations between the EFPT and the NIH Toolbox Crystallized (r = -.479), Fluid Tests (r = -.420), and Total Composite Scores (r = -.496). Discriminant validity: Significant differences were found in the EFPT total and sequence scores across control, complicated mild/moderate, and severe TBI groups. We found differences in the organisation score between control and severe, and between mild and severe TBI groups. Both TBI groups had significantly lower scores in safety and judgement than controls. Compared to the controls, the severe TBI group demonstrated significantly lower performance on all instrumental activities of daily living (IADL) tasks. Compared to the mild TBI group, the controls performed better on the medication task, the severe TBI group performed worse in the cooking and telephone tasks. Predictive validity: The EFPT predicted the self-perception of independence measured by the TBI-QOL (beta = -0.49, p < .001) for the severe TBI group. Overall, these data support the validity of the EFPT for use in individuals with TBI.

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, A.; De Felice, M.; Catalano, F.; Lee, J. Y.; Wang, B.; Lee, D. Y.; Yoo, J. H.; Weisheimer, A.

2017-12-01

By initiating a novel cooperation between the European and the Asian-Pacific climate-prediction communities, this work demonstrates the potential of gathering together their Multi-Model Ensembles (MMEs) to obtain useful climate predictions at seasonal time-scale.MMEs are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles and increasing benefit is expected with the increase of the independence of the contributing Seasonal Prediction Systems (SPSs). In this work we combine the two MME SPSs independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities by establishing an unprecedented partnerships. To this aim, all the possible MME combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The Grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models.To verify the above results for a real world application, the Grand MME is used to predict energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. Above results are discussed in a Clim Dyn paper (Alessandri et al., 2017; doi:10.1007/s00382-016-3372-4).

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

PubMed Central

2014-01-01

Introduction Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown. Methods We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models. Results Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively. Conclusions Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability. Trial registration ClinicalTrials.gov NCT01237886. Registered 13 October 2010. PMID:24713049

Prediction of gene-phenotype associations in humans, mice, and plants using phenologs.

PubMed

Woods, John O; Singh-Blom, Ulf Martin; Laurent, Jon M; McGary, Kriston L; Marcotte, Edward M

2013-06-21

Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such "orthologous phenotypes," or "phenologs," are examples of deep homology, and may be used to predict additional candidate disease genes. In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data--from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans--establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene-phenotype associations, as for the Arabidopsis response to vernalization phenotype. We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.

Incremental value of the CT coronary calcium score for the prediction of coronary artery disease

PubMed Central

Genders, Tessa S. S.; Pugliese, Francesca; Mollet, Nico R.; Meijboom, W. Bob; Weustink, Annick C.; van Mieghem, Carlos A. G.; de Feyter, Pim J.

2010-01-01

Objectives: To validate published prediction models for the presence of obstructive coronary artery disease (CAD) in patients with new onset stable typical or atypical angina pectoris and to assess the incremental value of the CT coronary calcium score (CTCS). Methods: We searched the literature for clinical prediction rules for the diagnosis of obstructive CAD, defined as ≥50% stenosis in at least one vessel on conventional coronary angiography. Significant variables were re-analysed in our dataset of 254 patients with logistic regression. CTCS was subsequently included in the models. The area under the receiver operating characteristic curve (AUC) was calculated to assess diagnostic performance. Results: Re-analysing the variables used by Diamond & Forrester yielded an AUC of 0.798, which increased to 0.890 by adding CTCS. For Pryor, Morise 1994, Morise 1997 and Shaw the AUC increased from 0.838 to 0.901, 0.831 to 0.899, 0.840 to 0.898 and 0.833 to 0.899. CTCS significantly improved model performance in each model. Conclusions: Validation demonstrated good diagnostic performance across all models. CTCS improves the prediction of the presence of obstructive CAD, independent of clinical predictors, and should be considered in its diagnostic work-up. PMID:20559838

Predicting and influencing voice therapy adherence using social-cognitive factors and mobile video.

PubMed

van Leer, Eva; Connor, Nadine P

2015-05-01

Patient adherence to voice therapy is an established challenge. The purpose of this study was (a) to examine whether adherence to treatment could be predicted from three social-cognitive factors measured at treatment onset: self-efficacy, goal commitment, and the therapeutic alliance, and (b) to test whether the provision of clinician, self-, and peer model mobile treatment videos on MP4 players would influence the same triad of social cognitive factors and the adherence behavior of patients. Forty adults with adducted hyperfunction with and without benign lesions were prospectively randomized to either 4 sessions of voice therapy enhanced by MP4 support or without MP4 support. Adherence between sessions was assessed through self-report. Social cognitive factors and voice outcomes were assessed at the beginning and end of therapy. Utility of MP4 support was assessed via interviews. Self-efficacy and the therapeutic alliance predicted a significant amount of adherence variance. MP4 support significantly increased generalization, self-efficacy for generalization, and the therapeutic alliance. An interaction effect demonstrated that MP4 support was particularly effective for patients who started therapy with poor self-efficacy for generalization. Adherence may be predicted and influenced via social-cognitive means. Mobile technology can extend therapy to extraclinical settings.

Testing neoclassical and turbulent effects on poloidal rotation in the core of DIII-D

DOE PAGES

Chrystal, Colin; Burrell, Keith H.; Grierson, Brian A.; ...

2014-07-09

Experimental tests of ion poloidal rotation theories have been performed on DIII-D using a novel impurity poloidal rotation diagnostic. These tests show significant disagreements with theoretical predictions in various conditions, including L-mode plasmas with internal transport barriers (ITB), H-mode plasmas, and QH-mode plasmas. The theories tested include standard neoclassical theory, turbulence driven Reynolds stress, and fast-ion friction on the thermal ions. Poloidal rotation is observed to spin up at the formation of an ITB and makes a significant contribution to the measurement of themore » $$\\vec{E}$$ × $$\\vec{B}$$ shear that forms the ITB. In ITB cases, neoclassical theory agrees quantitatively with the experimental measurements only in the steep gradient region. Significant quantitative disagreement with neoclassical predictions is seen in the cores of ITB, QH-, and H-mode plasmas, demonstrating that neoclassical theory is an incomplete description of poloidal rotation. The addition of turbulence driven Reynolds stress does not remedy this disagreement; linear stability calculations and Doppler backscattering measurements show that disagreement increases as turbulence levels decline. Furthermore, the effect of fast-ion friction, by itself, does not lead to improved agreement; in QH-mode plasmas, neoclassical predictions are closest to experimental results in plasmas with the largest fast ion friction. Finally, predictions from a new model that combines all three effects show somewhat better agreement in the H-mode case, but discrepancies well outside the experimental error bars remain.« less

To Find a Better Dosimetric Parameter in the Predicting of Radiation-Induced Lung Toxicity Individually: Ventilation, Perfusion or CT based.

PubMed

Xiao, Lin-Lin; Yang, Guoren; Chen, Jinhu; Wang, Xiaohui; Wu, Qingwei; Huo, Zongwei; Yu, Qingxi; Yu, Jinming; Yuan, Shuanghu

2017-03-15

This study aimed to find a better dosimetric parameter in predicting of radiation-induced lung toxicity (RILT) in patients with non-small cell lung cancer (NSCLC) individually: ventilation(V), perfusion (Q) or computerized tomography (CT) based. V/Q single-photon emission computerized tomography (SPECT) was performed within 1 week prior to radiotherapy (RT). All V/Q imaging data was integrated into RT planning system, generating functional parameters based on V/Q SPECT. Fifty-seven NSCLC patients were enrolled in this prospective study. Fifteen (26.3%) patients underwent grade ≥2 RILT, the remaining forty-two (73.7%) patients didn't. Q-MLD, Q-V20, V-MLD, V-V20 of functional parameters correlated more significantly with the occurrence of RILT compared to V20, MLD of anatomical parameters (r = 0.630; r = 0.644; r = 0.617; r = 0.651 vs. r = 0.424; r = 0.520 p < 0.05, respectively). In patients with chronic obstructive pulmonary diseases (COPD), V functional parameters reflected significant advantage in predicting RILT; while in patients without COPD, Q functional parameters reflected significant advantage. Analogous results were existed in fractimal analysis of global pulmonary function test (PFT). In patients with central-type NSCLC, V parameters were better than Q parameters; while in patients with peripheral-type NSCLC, the results were inverse. Therefore, this study demonstrated that choosing a suitable dosimetric parameter individually can help us predict RILT accurately.

Toppling Trains.

ERIC Educational Resources Information Center

Parry, Malcolm

1998-01-01

Explains a novel way of approaching centripetal force: theory is used to predict an orbital period at which a toy train will topple from a circular track. The demonstration has elements of prediction (a criterion for a good model) and suspense (a criterion for a good demonstration). The demonstration proved useful in undergraduate physics and…

Retrospective evaluation of a method to predict fresh-frozen plasma dosage in anticoagulated patients.

PubMed

Frazee, Lawrence A; Bourguet, Claire C; Gutierrez, Wilson; Elder-Arrington, Jacinta; Elackattu, Alphi E P; Haller, Nairmeen Awad

2008-01-01

In the United States, fresh-frozen plasma (FFP) is commonly used for urgent reversal of warfarin; however, dosage recommendations are difficult to find. If validated, a proposed method that uses a nonlinear relationship between international normalized ratio (INR) and clotting factor activity (CFa) would be useful. This study retrospectively evaluated a proposed equation with adult medical inpatients who received FFP for warfarin reversal. For each patient the equation was used to predict the dose of FFP required to achieve the observed change in INR, which was then compared to the actual dose. The equation was considered successful if the predicted dose was within +/-20% of the actual dose. Subgroup analyses included subjects who received concomitant vitamin K; subjects with supratherapeutic INRs (>3); and subjects with significantly elevated INRs (>5). Of the 209 patients screened, 91 met criteria for inclusion in the study. Use of the equation to calculate the predicted dose of FFP was successful in 11 patients (12.1%) with use of actual body weight for prediction and in 23 patients (25.3%) with use of ideal body weight (P = 0.02). The equation performed similarly in all subgroups analyzed. The mean predicted FFP dose was significantly greater than the actual dose in all patients when actual body weight was used (925.2 mL vs. 620.6 mL; P < 0.001). Least-squares regression modeling of repeat INR (converted to CFa) produced a model that accounted for 57% of the variance in repeat INR. The value predicted from the model was closer to the actual CFa than was the value predicted from the published equation in every comparison, but it was statistically different only when actual body weight was used. This study revealed that a published equation for calculation of FFP dose to reverse oral anticoagulation resulted in doses that were significantly higher than the actual dose. Use of ideal body weight improved accuracy but was still not successful for the majority of patients. Until trials are able to prospectively demonstrate the accuracy of a dose-prediction model for FFP, dosing will remain largely empiric.

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury

PubMed Central

Wu, Xuehai; Zou, Qihong; Hu, Jin; Tang, Weijun; Mao, Ying; Gao, Liang; Zhu, Jianhong; Jin, Yi; Wu, Xin; Lu, Lu; Zhang, Yaojun; Zhang, Yao; Dai, Zhengjia; Gao, Jia-Hong; Weng, Xuchu; Northoff, Georg; Giacino, Joseph T.; He, Yong

2015-01-01

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI. SIGNIFICANCE STATEMENT Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness. PMID:26377477

Early Recurrence After Hepatectomy for Colorectal Liver Metastases: What Optimal Definition and What Predictive Factors?

PubMed Central

Imai, Katsunori; Allard, Marc-Antoine; Benitez, Carlos Castro; Vibert, Eric; Sa Cunha, Antonio; Cherqui, Daniel; Castaing, Denis; Bismuth, Henri; Baba, Hideo

2016-01-01

Background. The purpose of this study was to determine the optimal definition and elucidate the predictive factors of early recurrence after surgery for colorectal liver metastases (CRLM). Methods. Among 987 patients who underwent curative surgery for CRLM from 1990 to 2012, 846 with a minimum follow-up period of 24 months were eligible for this study. The minimum p value approach of survival after initial recurrence was used to determine the optimal cutoff for the definition of early recurrence. The predictive factors of early recurrence and prognostic factors of survival were analyzed. Results. For 667 patients (79%) who developed recurrence, the optimal cutoff point of early recurrence was determined to be 8 months after surgery. The impact of early recurrence on survival was demonstrated mainly in patients who received preoperative chemotherapy. Among the 691 patients who received preoperative chemotherapy, recurrence was observed in 562 (81%), and survival in patients with early recurrence was significantly worse than in those with late recurrence (5-year survival 18.5% vs. 53.4%, p < .0001). Multivariate logistic analysis identified age ≤57 years (p = .0022), >1 chemotherapy line (p = .03), disease progression during last-line chemotherapy (p = .024), >3 tumors (p = .0014), and carbohydrate antigen 19-9 >60 U/mL (p = .0003) as independent predictors of early recurrence. Salvage surgery for recurrence significantly improved survival, even in patients with early recurrence. Conclusion. The optimal cutoff point of early recurrence was determined to be 8 months. The preoperative prediction of early recurrence is possible and crucial for designing effective perioperative chemotherapy regimens. Implications for Practice: In this study, the optimal cutoff point of early recurrence was determined to be 8 months after surgery based on the minimum p value approach, and its prognostic impact was demonstrated mainly in patients who received preoperative chemotherapy. Five factors, including age, number of preoperative chemotherapy lines, response to last-line chemotherapy, number of tumors, and carbohydrate antigen 19-9 concentrations, were identified as predictors of early recurrence. Salvage surgery for recurrence significantly improved survival, even in patients with early recurrence. For better selection of patients who could truly benefit from surgery and should also receive strong postoperative chemotherapy, the accurate preoperative prediction of early recurrence is crucial. PMID:27125753

Phrenic Nerve Palsy Secondary to Parsonage-Turner Syndrome: A Diagnosis Commonly Overlooked.

PubMed

McEnery, Tom; Walsh, Ronan; Burke, Conor; McGowan, Aisling; Faul, John; Cormican, Liam

2017-04-01

Neuralgic Amyotrophy (NA) or Parsonage-Turner syndrome is an idiopathic neuropathy commonly affecting the brachial plexus. Associated phrenic nerve involvement, though recognised, is thought to be very rare. We present a case series of four patients (all male, mean age 53) presenting with dyspnoea preceded by severe self-limiting upper limb and shoulder pain, with an elevated hemi-diaphragm on clinical examination and chest X-ray. Neurological examination of the upper limb at the time of presentation was normal. Diaphragmatic fluoroscopy confirmed unilateral diaphragmatic paralysis. Pulmonary function testing demonstrated characteristic reduction in forced vital capacity between supine and sitting position (mean 50%, range 42-65% predicted, mean change 23%, range 22-46%), reduced maximal inspiratory pressures (mean 61%, range 43-86% predicted), reduced sniff nasal inspiratory pressure (mean 88.25, range 66-109 cm H 2 O) and preserved maximal expiratory pressure (mean 107%, range 83-130% predicted). Phrenic nerve conduction studies confirmed phrenic nerve palsy. All patients were managed conservatively. Follow-up ranged from 6 months to 3 years. Symptoms and lung function variables normalised in three patients and improved significantly in the fourth. The classic history of severe ipsilateral shoulder and upper limb neuromuscular pain should be elicited and thus NA considered in the differential for a unilateral diaphragmatic paralysis, even in the absence of neurological signs. Parsonage-Turner syndrome is likely to represent a significantly under-diagnosed aetiology of phrenic nerve palsy. Conservative management as opposed to surgical intervention is advocated as most patients demonstrate gradual resolution over time in this case series.

[Predictors of verbal memory decline following temporal lobe surgery].

PubMed

de Vanssay-Maigne, A; Boutin, M; Baudoin-Chial, S

2008-05-01

Verbal memory decline can occur after temporal lobe surgery, especially when the left dominant hemisphere is involved. This potential functional risk must be evaluated before surgery. Among all factors that have been identified by several studies, the side of surgery (left dominant) and high baseline memory performance have been found to be predictive of verbal memory decline. Other factors such as etiology, sex, age at surgery, age at seizure onset, and duration may influence memory decline, but the results are not clear. Our purpose was to identify, in our population of patients and among all risk factors, those that may be predictive of verbal memory decline. Logistic regression was used to examine the effect of each factor on the postoperative verbal memory index (WMS-R) in 101 patients who underwent a right (n=49) or left (n=52) anterior temporal lobe resection. In the group as a whole, 22 % of the patients demonstrated verbal memory decline of more than one standard deviation. The verbal memory decline was significantly related to surgery on the left side and a high level of verbal memory performance. These factors were significant predictors of decline. The other factors (etiology, sex, age at surgery, age at seizure onset, and duration) were not found to be predictive of this decline. Our analysis demonstrates that the patients who are most at risk of undergoing verbal memory deterioration are those who undergo left-sided temporal resection and have good memory scores preoperatively. The contradictions found in the literature about the other factors could be explained by the diversity of the tests and criteria used to assess memory decline.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds.

PubMed

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-12-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal-habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities.

The influence of reflection on portfolio learning in undergraduate dental education.

PubMed

Koole, S; Vanobbergen, J; De Visschere, L; Aper, L; Dornan, T; Derese, A

2013-02-01

Disparity exists between the growing consensus about the positive effects of reflection on performance and the scarcity of empirical evidence demonstrating this effect. Portfolios are considered a useful instrument to assess and supervise competence-based education and to stimulate reflection. The present study describes the introduction of a portfolio in a social dentistry and oral health promotion course and investigates student reflection as a predictor for the acquisition of the other competences in the course. Fourth year undergraduate dental students (n = 110) in the course 'Society and Health' between 2008 and 2011 collected evidence in their portfolios, demonstrating the acquisition of five competences: the ability to (1) assess the oral health profile of a target group; (2) integrate theoretical models in health promotion; (3) search for and apply scientific evidence; (4) work trans-, multi- and/or trans-disciplinarily; (5) reflect on personal development. Linear regression analysis was used to investigate the predictive value of reflection on the other course related competences. Reflection scores proved to significantly predict other course-related competences, when analysing all students between 2008 and 2011 and for each year separately, explaining between 10.7% and 25.5% of the variance in the other competences. Undergraduate dental students' competences related to social dentistry and oral health promotion were significantly predicted by the reflection scores obtained in a portfolio-based context. In line with the growing consensus about the benefits of reflection for dental students and professionals, results suggest the value to further develop the integration of reflection in dental education and practice. © 2012 John Wiley & Sons A/S.

Step selection techniques uncover the environmental predictors of space use patterns in flocks of Amazonian birds

PubMed Central

Potts, Jonathan R; Mokross, Karl; Stouffer, Philip C; Lewis, Mark A

2014-01-01

Understanding the behavioral decisions behind animal movement and space use patterns is a key challenge for behavioral ecology. Tools to quantify these patterns from movement and animal–habitat interactions are vital for transforming ecology into a predictive science. This is particularly important in environments undergoing rapid anthropogenic changes, such as the Amazon rainforest, where animals face novel landscapes. Insectivorous bird flocks are key elements of avian biodiversity in the Amazonian ecosystem. Therefore, disentangling and quantifying the drivers behind their movement and space use patterns is of great importance for Amazonian conservation. We use a step selection function (SSF) approach to uncover environmental drivers behind movement choices. This is used to construct a mechanistic model, from which we derive predicted utilization distributions (home ranges) of flocks. We show that movement decisions are significantly influenced by canopy height and topography, but depletion and renewal of resources do not appear to affect movement significantly. We quantify the magnitude of these effects and demonstrate that they are helpful for understanding various heterogeneous aspects of space use. We compare our results to recent analytic derivations of space use, demonstrating that the analytic approximation is only accurate when assuming that there is no persistence in the animals' movement. Our model can be translated into other environments or hypothetical scenarios, such as those given by proposed future anthropogenic actions, to make predictions of spatial patterns in bird flocks. Furthermore, our approach is quite general, so could potentially be used to understand the drivers of movement and spatial patterns for a wide variety of animal communities. PMID:25558353

Vitamin D, Race, and Experimental Pain Sensitivity in Older Adults with Knee Osteoarthritis

PubMed Central

Glover, T.L.; Goodin, B.R.; Horgas, A.L.; Kindler, L.L.; King, C.D.; Sibille, K.T.; Peloquin, C.A.; Riley, J.L.; Staud, R.; Bradley, L.A.; Fillingim, R.B.

2012-01-01

Objective Low levels of serum circulating 25-hydroxyvitamin D have been correlated with many health conditions, including chronic pain. Recent clinical practice guidelines define vitamin D levels < 20 ng/mL as deficient and values of 21–29 ng/mL as insufficient. Vitamin D insufficiency, including the most severe levels of deficiency, is more prevalent in black Americans. Ethnic and race group differences have been reported in both clinical and experimental pain, with black Americans reporting increased pain. The purpose of this study was to examine whether variation in vitamin D levels contribute to race differences in knee osteoarthritic pain. Methods The sample consisted of 94 participants (75% female), including 45 blacks and 49 whites with symptomatic knee osteoarthritis. Average age was 55.8 years (range 45–71 years). Participants completed a questionnaire on knee osteoarthritic symptoms and underwent quantitative sensory testing, including measures of heat and mechanical pain sensitivity. Results Blacks had significantly lower levels of vitamin D compared to whites, demonstrated greater clinical pain, and showed greater sensitivity to mechanical and heat pain. Low levels of vitamin D predicted increased experimental pain sensitivity, but did not predict self-reported clinical pain. Group differences in vitamin D significantly predicted group differences in heat pain and pressure pain thresholds on the index knee and ipsilateral forearm. Conclusion These data demonstrate race differences in experimental pain are mediated by differences in vitamin D level. Vitamin D deficiency may be a risk factor for increased knee osteoarthritic pain in black Americans. PMID:23135697

A Systems Approach to Designing Effective Clinical Trials Using Simulations

PubMed Central

Fusaro, Vincent A.; Patil, Prasad; Chi, Chih-Lin; Contant, Charles F.; Tonellato, Peter J.

2013-01-01

Background Pharmacogenetics in warfarin clinical trials have failed to show a significant benefit compared to standard clinical therapy. This study demonstrates a computational framework to systematically evaluate pre-clinical trial design of target population, pharmacogenetic algorithms, and dosing protocols to optimize primary outcomes. Methods and Results We programmatically created an end-to-end framework that systematically evaluates warfarin clinical trial designs. The framework includes options to create a patient population, multiple dosing strategies including genetic-based and non-genetic clinical-based, multiple dose adjustment protocols, pharmacokinetic/pharmacodynamics (PK/PD) modeling and international normalization ratio (INR) prediction, as well as various types of outcome measures. We validated the framework by conducting 1,000 simulations of the CoumaGen clinical trial primary endpoints. The simulation predicted a mean time in therapeutic range (TTR) of 70.6% and 72.2% (P = 0.47) in the standard and pharmacogenetic arms, respectively. Then, we evaluated another dosing protocol under the same original conditions and found a significant difference in TTR between the pharmacogenetic and standard arm (78.8% vs. 73.8%; P = 0.0065), respectively. Conclusions We demonstrate that this simulation framework is useful in the pre-clinical assessment phase to study and evaluate design options and provide evidence to optimize the clinical trial for patient efficacy and reduced risk. PMID:23261867

Memory Shaped by Age Stereotypes over Time

PubMed Central

Zonderman, Alan B.; Slade, Martin D.; Ferrucci, Luigi

2012-01-01

Objectives. Previous studies showed that negative self-stereotypes detrimentally affect the cognitive performance of marginalized group members; however, these findings were confined to short-term experiments. In the present study, we considered whether stereotypes predicted memory over time, which had not been previously examined. We also considered whether self-relevance increased the influence of stereotypes on memory over time. Method. Multiple waves of memory performance were analyzed using individual growth models. The sample consisted of 395 participants in the Baltimore Longitudinal Study of Aging. Results. Those with more negative age stereotypes demonstrated significantly worse memory performance over 38 years than those with less negative age stereotypes, after adjusting for relevant covariates. The decline in memory performance for those aged 60 and above was 30.2% greater for the more negative age stereotype group than for the less negative age stereotype group. Also, the impact of age stereotypes on memory was significantly greater among those for whom the age stereotypes were self-relevant. Discussion. This study shows that the adverse influence of negative self-stereotypes on cognitive performance is not limited to a short-term laboratory effect. Rather, the findings demonstrate, for the first time, that stereotypes also predict memory performance over an extended period in the community. PMID:22056832

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.

Rotor Broadband Noise Prediction with Comparison to Model Data

NASA Technical Reports Server (NTRS)

Brooks, Thomas F.; Burley, Casey L.

2001-01-01

This paper reports an analysis and prediction development of rotor broadband noise. The two primary components of this noise are Blade-Wake Interaction (BWI) noise, due to the blades' interaction with the turbulent wakes of the preceding blades, and "Self" noise, due to the development and shedding of turbulence within the blades' boundary layers. Emphasized in this report is the new code development for Self noise. The analysis and validation employs data from the HART program, a model BO-105 rotor wind tunnel test conducted in the German-Dutch Wind Tunnel (DNW). The BWI noise predictions are based on measured pressure response coherence functions using cross-spectral methods. The Self noise predictions are based on previously reported semiempirical modeling of Self noise obtained from isolated airfoil sections and the use of CAMRAD.Modl to define rotor performance and local blade segment flow conditions. Both BWI and Self noise from individual blade segments are Doppler shifted and summed at the observer positions. Prediction comparisons with measurements show good agreement for a range of rotor operating conditions from climb to steep descent. The broadband noise predictions, along with those of harmonic and impulsive Blade-Vortex Interaction (BVI) noise predictions, demonstrate a significant advance in predictive capability for main rotor noise.

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre- and Post-Processing in Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Wang, S.; Ancell, B. C.; Huang, G. H.; Baetz, B. W.

2018-03-01

Data assimilation using the ensemble Kalman filter (EnKF) has been increasingly recognized as a promising tool for probabilistic hydrologic predictions. However, little effort has been made to conduct the pre- and post-processing of assimilation experiments, posing a significant challenge in achieving the best performance of hydrologic predictions. This paper presents a unified data assimilation framework for improving the robustness of hydrologic ensemble predictions. Statistical pre-processing of assimilation experiments is conducted through the factorial design and analysis to identify the best EnKF settings with maximized performance. After the data assimilation operation, statistical post-processing analysis is also performed through the factorial polynomial chaos expansion to efficiently address uncertainties in hydrologic predictions, as well as to explicitly reveal potential interactions among model parameters and their contributions to the predictive accuracy. In addition, the Gaussian anamorphosis is used to establish a seamless bridge between data assimilation and uncertainty quantification of hydrologic predictions. Both synthetic and real data assimilation experiments are carried out to demonstrate feasibility and applicability of the proposed methodology in the Guadalupe River basin, Texas. Results suggest that statistical pre- and post-processing of data assimilation experiments provide meaningful insights into the dynamic behavior of hydrologic systems and enhance robustness of hydrologic ensemble predictions.

Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

PubMed

Darrington, Richard T; Jiao, Jim

2004-04-01

Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Experimental investigation of elastic mode control on a model of a transport aircraft

NASA Technical Reports Server (NTRS)

Abramovitz, M.; Heimbaugh, R. M.; Nomura, J. K.; Pearson, R. M.; Shirley, W. A.; Stringham, R. H.; Tescher, E. L.; Zoock, I. E.

1981-01-01

A 4.5 percent DC-10 derivative flexible model with active controls is fabricated, developed, and tested to investigate the ability to suppress flutter and reduce gust loads with active controlled surfaces. The model is analyzed and tested in both semispan and complete model configuration. Analytical methods are refined and control laws are developed and successfully tested on both versions of the model. A 15 to 25 percent increase in flutter speed due to the active system is demonstrated. The capability of an active control system to significantly reduce wing bending moments due to turbulence is demonstrated. Good correlation is obtained between test and analytical prediction.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Reliability and validity of Short Form 36 Version 2 to measure health perceptions in a sub-group of individuals with fatigue.

PubMed

Davenport, Todd E; Stevens, Staci R; Baroni, Katie; Van Ness, J Mark; Snell, Christopher R

2011-01-01

To determine the validity and reliability of Short Form 36 Version 2 (SF36v2) in sub-groups of individuals with fatigue. Thirty subjects participated in this study, including n = 16 subjects who met case definition criteria for chronic fatigue syndrome (CFS) and n = 14 non-disabled sedentary matched control subjects. SF36v2 and Multidimensional Fatigue Inventory (MFI-20) were administered before two maximal cardiopulmonary exercise tests (CPETs) administered 24 h apart and an open-ended recovery questionnaire was administered 7 days after CPET challenge. The main outcome measures were self-reported time to recover to pre-challenge functional and symptom status, frequency of post-exertional symptoms and SF36v2 sub-scale scores. Individuals with CFS demonstrated significantly lower SF36v2 and MFI-20 sub-scale scores prior to CPET. Between-group differences remained significant post-CPET, however, there were no significant group by test interaction effects. Subjects with CFS reported significantly more total symptoms (p < 0.001), as well as reports of fatigue (p < 0.001), neuroendocrine (p < 0.001), immune (p < 0.01), pain (p < 0.01) and sleep disturbance (p < 0.01) symptoms than control subjects as a result of CPET. Many symptom counts demonstrated significant relationships with SF36v2 sub-scale scores (p < 0.05). SF36v2 and MFI-20 sub-scale scores demonstrated significant correlations (p < 0.05). Various SF36v2 sub-scale scores demonstrated significant predictive validity to identify subjects who recovered from CPET challenge within 1 day and 7 days (p < 0.05). Potential floor effects were observed for both questionnaires for individuals with CFS. Various sub-scales of SF36v2 demonstrated adequate reliability and validity for clinical and research applications. Adequacy of sensitivity to change of SF36v2 as a result of a fatiguing stressor should be the subject of additional study.

Prediction of periodontopathic bacteria in dental plaque of periodontal healthy subjects by measurement of volatile sulfur compounds in mouth air.

PubMed

Kishi, Mitsuo; Ohara-Nemoto, Yuko; Takahashi, Masahiro; Kishi, Kayo; Kimura, Shigenobu; Aizawa, Fumie; Yonemitsu, Masami

2013-03-01

The aim of this study was to determine whether measurements of volatile sulfur compounds (VSCs) are useful to predict colonization of periodontopathic bacteria. For this purpose, we assessed the relationships among distributions of 4 species of periodontopathic bacteria in tongue coating and dental plaque, oral conditions including VSC concentration in mouth air, and smoking habit of periodontal healthy young subjects. The subjects were 108 young adults (mean age, 23.5±2.56 years) without clinical periodontal pockets. Information regarding smoking habit was obtained by interview. After VSC concentration in mouth, air was measured with a portable sulfide monitor (Halimeter(®)), non-stimulated saliva flow and dental caries status were assessed, and tongue coating and dental plaque samples were collected from the subjects. The tongue coating samples were weighed to determine the amount. The colonization of Porphyromonas gingivalis, Tannerella forsythia, Prevotella intermedia, and Treponema denticola in both tongue coating and plaque samples was investigated using species-specific polymerase chain reaction assays. Significant relationships were observed between the colonization of periodontopathic bacteria in tongue coating and plaque samples, especially that of P. gingivalis. VSC concentration showed the most significant association with colonization of P. gingivalis in both tongue coating and dental plaque. Logistic regression analysis demonstrated that the adjusted partial correlation coefficient [Exp(B)] values for VSC concentration with the colonization of P. gingivalis, P. intermedia, and T. denticola in dental plaque were 135, 35.4 and 10.4, respectively. In addition, smoking habit was also shown to be a significant variable in regression models [Exp(B)=6.19, 8.92 and 2.53, respectively]. Therefore, receiver operating characteristic analysis was performed to predict the colonization of periodontal bacteria in dental plaque in the subjects divided by smoking habit. Based on our results, we found cut-off values that indicated likelihood ratios (LR) within the efficient range for positive findings in both groups. The present results demonstrated that measurement of VSC concentration in mouth air is a useful method to predict the presence of colonization of some periodontopathic bacteria in dental plaque. Copyright © 2012 Elsevier Ltd. All rights reserved.

The predictive value of retinal vascular findings for carotid artery atherosclerosis: are further recommendations with regard to carotid atherosclerosis screening needed?

PubMed

Song, Yeo-Jeong; Cho, Kyoung-Im; Kim, Seong-Man; Jang, Hyun-Duk; Park, Jung-Min; Kim, Sang-Soo; Kim, Dong-Jun; Lee, Hyeon-Gook; Kim, Tae-Ik

2013-05-01

Vascular retinopathy is the consequence of vascular disease, and the retina is the only place where the arteries can be visualized directly. The purpose of this study was to evaluate the predictive value of retinal vascular findings for carotid artery atherosclerosis. From December 2009 to January 2011, the carotid intima-media thickness (IMT) and total plaque area (TPA) were measured in 179 consecutive patients, who received a fundoscopic examination. The patients were divided into groups as follows: normal retinal artery (normal; n = 44), diabetic retinopathy (DR; n = 25), retinal artery occlusion (RAO; n = 17), retinal vein occlusion (RVO; n = 67), and hypertensive retinopathy (HTN-R; n = 26). The subjects were classified according to the presence of an increased (≥ 1 mm) IMT and plaque. The values of the mean carotid IMT in the patients with vascular retinopathy (DR, 0.87 ± 0.14 mm; RAO, 1.18 ± 0.47 mm; RVO, 0.84 ± 0.14 mm; HTN-R, 0.90 ± 0.20 mm) were significantly increased compared with those in the normal subjects (0.77 ± 0.13 mm). A total 77 of 135 vascular retinopathy patients demonstrated an increased IMT (57 %), and 97 vascular retinopathy patients had carotid artery plaque (72 %). The relative risk of vascular retinopathy in the prediction of an increased IMT and the presence of plaque was 2.79 and 3.95, respectively. Although The TPA was significantly increased in the patients with RAO (1.87 ± 2.67 cm(2)) and RVO (0.27 ± 0.23 cm(2)) compared with the normal subjects (0.18 ± 0.23 cm(2), all Ps < 0.05), there was no significant difference in the ipsilateral carotid IMT and TPA of the affected eye compared with that of the contralateral eye. In conclusion, vascular retinopathy demonstrated a good predictive value in identifying asymptomatic carotid artery atherosclerosis, and this was not confined to the ipsilateral carotid artery of the affected eye. Further recommendations with regard to carotid atherosclerosis screening in patients with vascular retinopathy should be considered.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

PubMed Central

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

2012-01-01

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

Competence with fractions predicts gains in mathematics achievement.

PubMed

Bailey, Drew H; Hoard, Mary K; Nugent, Lara; Geary, David C

2012-11-01

Competence with fractions predicts later mathematics achievement, but the codevelopmental pattern between fractions knowledge and mathematics achievement is not well understood. We assessed this codevelopment through examination of the cross-lagged relation between a measure of conceptual knowledge of fractions and mathematics achievement in sixth and seventh grades (N=212). The cross-lagged effects indicated that performance on the sixth grade fractions concepts measure predicted 1-year gains in mathematics achievement (ß=.14, p<.01), controlling for the central executive component of working memory and intelligence, but sixth grade mathematics achievement did not predict gains on the fractions concepts measure (ß=.03, p>.50). In a follow-up assessment, we demonstrated that measures of fluency with computational fractions significantly predicted seventh grade mathematics achievement above and beyond the influence of fluency in computational whole number arithmetic, performance on number fluency and number line tasks, central executive span, and intelligence. Results provide empirical support for the hypothesis that competence with fractions underlies, in part, subsequent gains in mathematics achievement. Copyright © 2012 Elsevier Inc. All rights reserved.

A strong diffusive ion mode in dense ionized matter predicted by Langevin dynamics

DOE PAGES

Mabey, P.; Richardson, S.; White, T. G.; ...

2017-01-30

We determined the state and evolution of planets, brown dwarfs and neutron star crusts by the properties of dense and compressed matter. Furthermore, due to the inherent difficulties in modelling strongly coupled plasmas, however, current predictions of transport coefficients differ by orders of magnitude. Collective modes are a prominent feature, whose spectra may serve as an important tool to validate theoretical predictions for dense matter. With recent advances in free electron laser technology, X-rays with small enough bandwidth have become available, allowing the investigation of the low-frequency ion modes in dense matter. Here, we present numerical predictions for these ionmore » modes and demonstrate significant changes to their strength and dispersion if dissipative processes are included by Langevin dynamics. Notably, a strong diffusive mode around zero frequency arises, which is not present, or much weaker, in standard simulations. These results have profound consequences in the interpretation of transport coefficients in dense plasmas.« less

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

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

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Scaling relations of halo cores for self-interacting dark matter

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

Lin, Henry W.; Loeb, Abraham, E-mail: henrylin@college.harvard.edu, E-mail: aloeb@cfa.harvard.edu

2016-03-01

Using a simple analytic formalism, we demonstrate that significant dark matter self-interactions produce halo cores that obey scaling relations nearly independent of the underlying particle physics parameters such as the annihilation cross section and the mass of the dark matter particle. For dwarf galaxies, we predict that the core density ρ{sub c} and the core radius r{sub c} should obey ρ{sub c} r{sub c} ≈ 41 M{sub ⊙} pc{sup −2} with a weak mass dependence ∼ M{sup 0.2}. Remarkably, such a scaling relation has recently been empirically inferred. Scaling relations involving core mass, core radius, and core velocity dispersion are predicted and agree well with observationalmore » data. By calibrating against numerical simulations, we predict the scatter in these relations and find them to be in excellent agreement with existing data. Future observations can test our predictions for different halo masses and redshifts.« less

Practical quantum mechanics-based fragment methods for predicting molecular crystal properties.

PubMed

Wen, Shuhao; Nanda, Kaushik; Huang, Yuanhang; Beran, Gregory J O

2012-06-07

Significant advances in fragment-based electronic structure methods have created a real alternative to force-field and density functional techniques in condensed-phase problems such as molecular crystals. This perspective article highlights some of the important challenges in modeling molecular crystals and discusses techniques for addressing them. First, we survey recent developments in fragment-based methods for molecular crystals. Second, we use examples from our own recent research on a fragment-based QM/MM method, the hybrid many-body interaction (HMBI) model, to analyze the physical requirements for a practical and effective molecular crystal model chemistry. We demonstrate that it is possible to predict molecular crystal lattice energies to within a couple kJ mol(-1) and lattice parameters to within a few percent in small-molecule crystals. Fragment methods provide a systematically improvable approach to making predictions in the condensed phase, which is critical to making robust predictions regarding the subtle energy differences found in molecular crystals.

Understanding Eating Behaviors through Parental Communication and the Integrative Model of Behavioral Prediction.

PubMed

Scheinfeld, Emily; Shim, Minsun

2017-05-01

Emerging adulthood (EA) is an important yet overlooked period for developing long-term health behaviors. During these years, emerging adults adopt health behaviors that persist throughout life. This study applies the Integrative Model of Behavioral Prediction (IMBP) to examine the role of childhood parental communication in predicting engagement in healthful eating during EA. Participants included 239 college students, ages 18 to 25, from a large university in the southern United States. Participants were recruited and data collection occurred spring 2012. Participants responded to measures to assess perceived parental communication, eating behaviors, attitudes, subjective norms, and behavioral control over healthful eating. SEM and mediation analyses were used to address the hypotheses posited. Data demonstrated that perceived parent-child communication - specifically, its quality and target-specific content - significantly predicted emerging adults' eating behaviors, mediated through subjective norm and perceived behavioral control. This study sets the stage for further exploration and understanding of different ways parental communication influences emerging adults' healthy behavior enactment.

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

PubMed

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

2005-09-01

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

Predictors of responses to stress among families coping with poverty-related stress.

PubMed

Santiago, Catherine DeCarlo; Etter, Erica Moran; Wadsworth, Martha E; Raviv, Tali

2012-05-01

This study tested how poverty-related stress (PRS), psychological distress, and responses to stress predicted future effortful coping and involuntary stress responses one year later. In addition, we explored age, sex, ethnicity, and parental influences on responses to stress over time. Hierarchical linear modeling analyses conducted with 98 low-income families (300 family members: 136 adults, 82 school-aged children, 82 adolescents) revealed that primary control coping, secondary control coping, disengagement, involuntary engagement, and involuntary disengagement each significantly predicted future use of that response. Primary and secondary control coping also predicted less maladaptive future responses to stress, while involuntary responses to stress undermined the development of adaptive responding. Age, sex, and interactions among PRS and prior coping were also found to predict certain responses to stress. In addition, child subgroup analyses demonstrate the importance of parental modeling of coping and involuntary stress responses, and warmth/nurturance and monitoring practices. Results are discussed with regard to the implications for preventive interventions with families in poverty.

The EST Model for Predicting Progressive Damage and Failure of Open Hole Bending Specimens

NASA Technical Reports Server (NTRS)

Joseph, Ashith P. K.; Waas, Anthony M.; Pineda, Evan J.

2016-01-01

Progressive damage and failure in open hole composite laminate coupons subjected to flexural loading is modeled using Enhanced Schapery Theory (EST). Previous studies have demonstrated that EST can accurately predict the strength of open hole coupons under remote tensile and compressive loading states. This homogenized modeling approach uses single composite shell elements to represent the entire laminate in the thickness direction and significantly reduces computational cost. Therefore, when delaminations are not of concern or are active in the post-peak regime, the version of EST presented here is a good engineering tool for predicting deformation response. Standard coupon level tests provides all the input data needed for the model and they are interpreted in conjunction with finite element (FE) based simulations. Open hole bending test results of three different IM7/8552 carbon fiber composite layups agree well with EST predictions. The model is able to accurately capture the curvature change and deformation localization in the specimen at and during the post catastrophic load drop event.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exercise identity and attribution properties predict negative self-conscious emotions for exercise relapse.

PubMed

Flora, Parminder K; Strachan, Shaelyn M; Brawley, Lawrence R; Spink, Kevin S

2012-10-01

Research on exercise identity (EXID) indicates that it is related to negative affect when exercisers are inconsistent or relapse. Although identity theory suggests that causal attributions about this inconsistency elicit negative self-conscious emotions of shame and guilt, no EXID studies have examined this for exercise relapse. Weiner's attribution-based theory of interpersonal motivation (2010) offers a means of testing the attribution-emotion link. Using both frameworks, we examined whether EXID and attributional properties predicted negative emotions for exercise relapse. Participants (n = 224) read an exercise relapse vignette, and then completed EXID, attributions, and emotion measures. Hierarchical multiple regression models using EXID and the attributional property of controllability significantly predicted each of shame and guilt, R² adjusted = .09, ps ≤ .001. Results support identity theory suggestions and Weiner's specific attribution-emotion hypothesis. This first demonstration of an interlinking of EXID, controllability, and negative self-conscious emotions offers more predictive utility using complementary theories than either theory alone.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

PubMed

Correa, John B; Apolzan, John W; Shepard, Desti N; Heil, Daniel P; Rood, Jennifer C; Martin, Corby K

2016-07-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

In Vivo Validation of Predicted and Conserved T Cell Epitopes in a Swine Influenza Model

PubMed Central

Gutiérrez, Andres H.; Loving, Crystal; Moise, Leonard; Terry, Frances E.; Brockmeier, Susan L.; Hughes, Holly R.; Martin, William D.; De Groot, Anne S.

2016-01-01

Swine influenza is a highly contagious respiratory viral infection in pigs that is responsible for significant financial losses to pig farmers annually. Current measures to protect herds from infection include: inactivated whole-virus vaccines, subunit vaccines, and alpha replicon-based vaccines. As is true for influenza vaccines for humans, these strategies do not provide broad protection against the diverse strains of influenza A virus (IAV) currently circulating in U.S. swine. Improved approaches to developing swine influenza vaccines are needed. Here, we used immunoinformatics tools to identify class I and II T cell epitopes highly conserved in seven representative strains of IAV in U.S. swine and predicted to bind to Swine Leukocyte Antigen (SLA) alleles prevalent in commercial swine. Epitope-specific interferon-gamma (IFNγ) recall responses to pooled peptides and whole virus were detected in pigs immunized with multi-epitope plasmid DNA vaccines encoding strings of class I and II putative epitopes. In a retrospective analysis of the IFNγ responses to individual peptides compared to predictions specific to the SLA alleles of cohort pigs, we evaluated the predictive performance of PigMatrix and demonstrated its ability to distinguish non-immunogenic from immunogenic peptides and to identify promiscuous class II epitopes. Overall, this study confirms the capacity of PigMatrix to predict immunogenic T cell epitopes and demonstrate its potential for use in the design of epitope-driven vaccines for swine. Additional studies that match the SLA haplotype of animals with the study epitopes will be required to evaluate the degree of immune protection conferred by epitope-driven DNA vaccines in pigs. PMID:27411061

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

PubMed Central

Correa, John B.; Apolzan, John W.; Shepard, Desti N.; Heil, Daniel P.; Rood, Jennifer C.; Martin, Corby K.

2016-01-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity. PMID:27270210

Environment dominates over host genetics in shaping human gut microbiota.

PubMed

Rothschild, Daphna; Weissbrod, Omer; Barkan, Elad; Kurilshikov, Alexander; Korem, Tal; Zeevi, David; Costea, Paul I; Godneva, Anastasia; Kalka, Iris N; Bar, Noam; Shilo, Smadar; Lador, Dar; Vila, Arnau Vich; Zmora, Niv; Pevsner-Fischer, Meirav; Israeli, David; Kosower, Noa; Malka, Gal; Wolf, Bat Chen; Avnit-Sagi, Tali; Lotan-Pompan, Maya; Weinberger, Adina; Halpern, Zamir; Carmi, Shai; Fu, Jingyuan; Wijmenga, Cisca; Zhernakova, Alexandra; Elinav, Eran; Segal, Eran

2018-03-08

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

Interactions of age and cognitive functions in predicting decision making under risky conditions over the life span.

PubMed

Brand, Matthias; Schiebener, Johannes

2013-01-01

Little is known about how normal healthy aging affects decision-making competence. In this study 538 participants (age 18-80 years) performed the Game of Dice Task (GDT). Subsamples also performed the Iowa Gambling Task as well as tasks measuring logical thinking and executive functions. In a moderated regression analysis, the significant interaction between age and executive components indicates that older participants with good executive functioning perform well on the GDT, while older participants with reduced executive functions make more risky choices. The same pattern emerges for the interaction of age and logical thinking. Results demonstrate that age and cognitive functions act in concert in predicting the decision-making performance.

Suicide, hopelessness, and social desirability: a test of an interactive model.

PubMed

Holden, R R; Mendonca, J D; Serin, R C

1989-08-01

We examined the relationships among suicidal indices, hopelessness, and social desirability. Both hopelessness and a measure of social desirability that reflected a sense of general capability were significant indicators of suicidal manifestations. In particular, hierarchical multiple regression procedures demonstrated that hopelessness and social desirability interacted in the prediction of suicide variables. Results generalized across various clinical diagnostic subgroups of psychiatric patients and a sample of prisoners and across different clinically evaluated and self-reported indices of suicidal behavior. Findings are interpreted to mean that a sense of general capability buffers the link of hopelessness to suicidal behavior. Implications for understanding the cognitions associated with suicide and for improving prediction of persons at risk are discussed.

Experiments and Analyses of Distributed Exhaust Nozzles

NASA Technical Reports Server (NTRS)

Kinzie, Kevin W.; Schein, David B.; Solomon, W. David, Jr.

2002-01-01

Experimental and analytical aeroacoustic properties of several distributed exhaust nozzle (DEN) designs are presented. Significant differences between the designs are observed and correlated back to Computational Fluid Dynamics (CFD) flowfield predictions. Up to 20 dB of noise reduction on a spectral basis and 10 dB on an overall sound pressure level basis are demonstrated from the DEN designs compared to a round reference nozzle. The most successful DEN designs acoustically show a predicted thrust loss of approximately 10% compared to the reference nozzle. Characteristics of the individual mini-jet nozzles that comprise the DEN such as jet-jet shielding and coalescence are shown to play a major role in the noise signature.

Polarization sensitivity testing of off-plane reflection gratings

NASA Astrophysics Data System (ADS)

Marlowe, Hannah; McEntaffer, Randal L.; DeRoo, Casey T.; Miles, Drew M.; Tutt, James H.; Laubis, Christian; Soltwisch, Victor

2015-09-01

Off-Plane reflection gratings were previously predicted to have different efficiencies when the incident light is polarized in the transverse-magnetic (TM) versus transverse-electric (TE) orientations with respect to the grating grooves. However, more recent theoretical calculations which rigorously account for finitely conducting, rather than perfectly conducting, grating materials no longer predict significant polarization sensitivity. We present the first empirical results for radially ruled, laminar groove profile gratings in the off-plane mount which demonstrate no difference in TM versus TE efficiency across our entire 300-1500 eV bandpass. These measurements together with the recent theoretical results confirm that grazing incidence off-plane reflection gratings using real, not perfectly conducting, materials are not polarization sensitive.

Clinical judgement in the era of big data and predictive analytics.

PubMed

Chin-Yee, Benjamin; Upshur, Ross

2018-06-01

Clinical judgement is a central and longstanding issue in the philosophy of medicine which has generated significant interest over the past few decades. In this article, we explore different approaches to clinical judgement articulated in the literature, focusing in particular on data-driven, mathematical approaches which we contrast with narrative, virtue-based approaches to clinical reasoning. We discuss the tension between these different clinical epistemologies and further explore the implications of big data and machine learning for a philosophy of clinical judgement. We argue for a pluralistic, integrative approach, and demonstrate how narrative, virtue-based clinical reasoning will remain indispensable in an era of big data and predictive analytics. © 2017 John Wiley & Sons, Ltd.

Development of a model based on biochemical, real-time tissue elastography and ultrasound data for the staging of liver fibrosis and cirrhosis in patients with chronic hepatitis B

PubMed Central

Xu, Shi-Hao; Li, Qiao; Hu, Yuan-Ping; Ying, Li

2016-01-01

The liver fibrosis index (LFI), based on real-time tissue elastography (RTE), is a method currently used to assess liver fibrosis. However, this method may not consistently distinguish between the different stages of fibrosis, which limits its accuracy. The aim of the present study was to develop novel models based on biochemical, RTE and ultrasound data for predicting significant liver fibrosis and cirrhosis. A total of 85 consecutive patients with chronic hepatitis B (CHB) were prospectively enrolled and underwent a liver biopsy and RTE. The parameters for predicting significant fibrosis and cirrhosis were determined by conducting multivariate analyses. The splenoportal index (SPI; P=0.002) and LFI (P=0.023) were confirmed as independent predictors of significant fibrosis. Using multivariate analyses for identifying parameters that predict cirrhosis, significant differences in γ-glutamyl transferase (GGT; P=0.049), SPI (P=0.002) and LFI (P=0.001) were observed. Based on these observations, the novel model LFI-SPI score (LSPS) was developed to predict the occurrence of significant liver fibrosis, with an area under receiver operating characteristic curves (AUROC) of 0.87. The diagnostic accuracy of the LSPS model was superior to that of the LFI (AUROC=0.76; P=0.0109), aspartate aminotransferase-to-platelet ratio index (APRI; AUROC=0.64; P=0.0031), fibrosis-4 index (FIB-4; AUROC= 0.67; P= 0.0044) and FibroScan (AUROC=0.68; P=0.0021) models. In addition, the LFI-SPI-GGT score (LSPGS) was developed for the purposes of predicting liver cirrhosis, demonstrating an AUROC value of 0.93. The accuracy of LSPGS was similar to that of FibroScan (AUROC=0.85; P=0.134), but was superior to LFI (AUROC= 0.81; P= 0.0113), APRI (AUROC= 0.67; P<0.0001) and FIB-4 (AUROC=0.719; P=0.0005). In conclusion, the results of the present study suggest that the use of LSPS and LSPGS may complement current methods of diagnosing significant liver fibrosis and cirrhosis in patients with CHB. PMID:27573619

Validation of aspartate aminotransferase to platelet ratio for diagnosis of liver fibrosis and prediction of postoperative prognosis in infants with biliary atresia.

PubMed

Yang, Li-Yuan; Fu, Jie; Peng, Xiao-Fang; Pang, Shu-Yin; Gao, Kan-Kan; Chen, Zheng-Rong; He, Li-Juan; Wen, Zhe; Wang, Hui; Li, Le; Wang, Feng-Hua; Yu, Jia-Kang; Xu, Yi; Gong, Si-Tang; Xia, Hui-Min; Liu, Hai-Ying

2015-05-21

To validate the value of aspartate aminotransferase to platelet ratio index (APRI) in assessment of liver fibrosis and prediction of postoperative prognosis of biliary atresia (BA) infants from Mainland China. Medical records of 153 BA infants who were hospitalized from January 2010 to June 2013 were reviewed. The efficacy of APRI for diagnosis of liver fibrosis was assessed using the receiver operator characteristic (ROC) curve compared to the pathological Metavir fibrosis score of the liver wedge specimens of 91 BA infants. The prognostic value of preoperative APRI for jaundice persistence, liver injury, and occurrence of cholangitis within 6 mo after KP was studied based on the follow-up data of 48 BA infants. APRI was significantly correlated with Metavir scores (rs = 0.433; P < 0.05). The mean APRI value was 0.76 in no/mild fibrosis group (Metavir score F0-F1), 1.29 in significant fibrosis group (F2-F3), and 2.51 in cirrhosis group (F4) (P < 0.001). The area under the ROC curve (AUC) of APRI for diagnosing significant fibrosis and cirrhosis was 0.75 (P < 0.001) and 0.81 (P = 0.001), respectively. The APRI cut-off of 0.95 was 60.6% sensitive and 76.0% specific for significant fibrosis diagnosis, and a threshold of 1.66 was 70.6% sensitive and 82.7% specific for cirrhosis. The preoperative APRI in infants who maintained jaundice around 6 mo after KP was higher than that in those who did not (1.86 ± 2.13 vs 0.87 ± 0.48, P < 0.05). The AUC of APRI for prediction of postoperative jaundice occurrence was 0.67. A cut-off value of 0.60 showed a sensitivity of 66.7% and a specificity of 83.3% for the prediction of jaundice persistence. Preoperative APRI had no significant association with later liver injury or occurrence of cholangitis. Our study demonstrated that APRI could diagnose significant liver fibrosis, especially cirrhosis in BA infants, and the elevated preoperative APRI predicts jaundice persistence after KP.

Development of a model based on biochemical, real‑time tissue elastography and ultrasound data for the staging of liver fibrosis and cirrhosis in patients with chronic hepatitis B.

PubMed

Xu, Shi-Hao; Li, Qiao; Hu, Yuan-Ping; Ying, Li

2016-10-01

The liver fibrosis index (LFI), based on real‑time tissue elastography (RTE), is a method currently used to assess liver fibrosis. However, this method may not consistently distinguish between the different stages of fibrosis, which limits its accuracy. The aim of the present study was to develop novel models based on biochemical, RTE and ultrasound data for predicting significant liver fibrosis and cirrhosis. A total of 85 consecutive patients with chronic hepatitis B (CHB) were prospectively enrolled and underwent a liver biopsy and RTE. The parameters for predicting significant fibrosis and cirrhosis were determined by conducting multivariate analyses. The splenoportal index (SPI; P=0.002) and LFI (P=0.023) were confirmed as independent predictors of significant fibrosis. Using multivariate analyses for identifying parameters that predict cirrhosis, significant differences in γ‑glutamyl transferase (GGT; P=0.049), SPI (P=0.002) and LFI (P=0.001) were observed. Based on these observations, the novel model LFI‑SPI score (LSPS) was developed to predict the occurrence of significant liver fibrosis, with an area under receiver operating characteristic curves (AUROC) of 0.87. The diagnostic accuracy of the LSPS model was superior to that of the LFI (AUROC=0.76; P=0.0109), aspartate aminotransferase‑to‑platelet ratio index (APRI; AUROC=0.64; P=0.0031), fibrosis‑4 index (FIB‑4; AUROC=0.67; P=0.0044) and FibroScan (AUROC=0.68; P=0.0021) models. In addition, the LFI‑SPI‑GGT score (LSPGS) was developed for the purposes of predicting liver cirrhosis, demonstrating an AUROC value of 0.93. The accuracy of LSPGS was similar to that of FibroScan (AUROC=0.85; P=0.134), but was superior to LFI (AUROC=0.81; P=0.0113), APRI (AUROC=0.67; P<0.0001) and FIB‑4 (AUROC=0.719; P=0.0005). In conclusion, the results of the present study suggest that the use of LSPS and LSPGS may complement current methods of diagnosing significant liver fibrosis and cirrhosis in patients with CHB.

Validation of aspartate aminotransferase to platelet ratio for diagnosis of liver fibrosis and prediction of postoperative prognosis in infants with biliary atresia

PubMed Central

Yang, Li-Yuan; Fu, Jie; Peng, Xiao-Fang; Pang, Shu-Yin; Gao, Kan-Kan; Chen, Zheng-Rong; He, Li-Juan; Wen, Zhe; Wang, Hui; Li, Le; Wang, Feng-Hua; Yu, Jia-Kang; Xu, Yi; Gong, Si-Tang; Xia, Hui-Min; Liu, Hai-Ying

2015-01-01

AIM: To validate the value of aspartate aminotransferase to platelet ratio index (APRI) in assessment of liver fibrosis and prediction of postoperative prognosis of biliary atresia (BA) infants from Mainland China. METHODS: Medical records of 153 BA infants who were hospitalized from January 2010 to June 2013 were reviewed. The efficacy of APRI for diagnosis of liver fibrosis was assessed using the receiver operator characteristic (ROC) curve compared to the pathological Metavir fibrosis score of the liver wedge specimens of 91 BA infants. The prognostic value of preoperative APRI for jaundice persistence, liver injury, and occurrence of cholangitis within 6 mo after KP was studied based on the follow-up data of 48 BA infants. RESULTS: APRI was significantly correlated with Metavir scores (rs = 0.433; P < 0.05). The mean APRI value was 0.76 in no/mild fibrosis group (Metavir score F0-F1), 1.29 in significant fibrosis group (F2-F3), and 2.51 in cirrhosis group (F4) (P < 0.001). The area under the ROC curve (AUC) of APRI for diagnosing significant fibrosis and cirrhosis was 0.75 (P < 0.001) and 0.81 (P = 0.001), respectively. The APRI cut-off of 0.95 was 60.6% sensitive and 76.0% specific for significant fibrosis diagnosis, and a threshold of 1.66 was 70.6% sensitive and 82.7% specific for cirrhosis. The preoperative APRI in infants who maintained jaundice around 6 mo after KP was higher than that in those who did not (1.86 ± 2.13 vs 0.87 ± 0.48, P < 0.05). The AUC of APRI for prediction of postoperative jaundice occurrence was 0.67. A cut-off value of 0.60 showed a sensitivity of 66.7% and a specificity of 83.3% for the prediction of jaundice persistence. Preoperative APRI had no significant association with later liver injury or occurrence of cholangitis. CONCLUSION: Our study demonstrated that APRI could diagnose significant liver fibrosis, especially cirrhosis in BA infants, and the elevated preoperative APRI predicts jaundice persistence after KP. PMID:26019453

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age.

PubMed

Wang, Zichen; Li, Li; Glicksberg, Benjamin S; Israel, Ariel; Dudley, Joel T; Ma'ayan, Avi

2017-12-01

Determining the discrepancy between chronological and physiological age of patients is central to preventative and personalized care. Electronic medical records (EMR) provide rich information about the patient physiological state, but it is unclear whether such information can be predictive of chronological age. Here we present a deep learning model that uses vital signs and lab tests contained within the EMR of Mount Sinai Health System (MSHS) to predict chronological age. The model is trained on 377,686 EMR from patients of ages 18-85 years old. The discrepancy between the predicted and real chronological age is then used as a proxy to estimate physiological age. Overall, the model can predict the chronological age of patients with a standard deviation error of ∼7 years. The ages of the youngest and oldest patients were more accurately predicted, while patients of ages ranging between 40 and 60 years were the least accurately predicted. Patients with the largest discrepancy between their physiological and chronological age were further inspected. The patients predicted to be significantly older than their chronological age have higher systolic blood pressure, higher cholesterol, damaged liver, and anemia. In contrast, patients predicted to be younger than their chronological age have lower blood pressure and shorter stature among other indicators; both groups display lower weight than the population average. Using information from ∼10,000 patients from the entire cohort who have been also profiled with SNP arrays, genome-wide association study (GWAS) uncovers several novel genetic variants associated with aging. In particular, significant variants were mapped to genes known to be associated with inflammation, hypertension, lipid metabolism, height, and increased lifespan in mice. Several genes with missense mutations were identified as novel candidate aging genes. In conclusion, we demonstrate how EMR data can be used to assess overall health via a scale that is based on deviation from the patient's predicted chronological age. Copyright © 2017 Elsevier Inc. All rights reserved.

The prognostic and predictive value of vascular response parameters measured by dynamic contrast-enhanced-CT, -MRI and -US in patients with metastatic renal cell carcinoma receiving sunitinib.

PubMed

Hudson, John M; Bailey, Colleen; Atri, Mostafa; Stanisz, Greg; Milot, Laurent; Williams, Ross; Kiss, Alex; Burns, Peter N; Bjarnason, Georg A

2018-06-01

To identify dynamic contrast-enhanced (DCE) imaging parameters from MRI, CT and US that are prognostic and predictive in patients with metastatic renal cell cancer (mRCC) receiving sunitinib. Thirty-four patients were monitored by DCE imaging on day 0 and 14 of the first course of sunitinib treatment. Additional scans were performed with DCE-US only (day 7 or 28 and 2 weeks after the treatment break). Perfusion parameters that demonstrated a significant correlation (Spearman p < 0.05) with progression-free survival (PFS) and overall survival (OS) were investigated using Cox proportional hazard models/ratios (HR) and Kaplan-Meier survival analysis. A higher baseline and day 14 value for Ktrans (DCE-MRI) and a lower pre-treatment vascular heterogeneity (DCE-US) were significantly associated with a longer PFS (HR, 0.62, 0.37 and 5.5, respectively). A larger per cent decrease in blood volume on day 14 (DCE-US) predicted a longer OS (HR, 1.45). We did not find significant correlations between any of the DCE-CT parameters and PFS/OS, unless a cut-off analysis was used. DCE-MRI, -CT and ultrasound produce complementary parameters that reflect the prognosis of patients receiving sunitinib for mRCC. Blood volume measured by DCE-US was the only parameter whose change during early anti-angiogenic therapy predicted for OS and PFS. • DCE-CT, -MRI and ultrasound are complementary modalities for monitoring anti-angiogenic therapy. • The change in blood volume measured by DCE-US was predictive of OS/PFS. • Baseline vascular heterogeneity by DCE-US has the strongest prognostic value for PFS.

Improving the detection and prediction of suicidal behavior among military personnel by measuring suicidal beliefs: an evaluation of the Suicide Cognitions Scale.

PubMed

Bryan, Craig J; David Rudd, M; Wertenberger, Evelyn; Etienne, Neysa; Ray-Sannerud, Bobbie N; Morrow, Chad E; Peterson, Alan L; Young-McCaughon, Stacey

2014-04-01

Newer approaches for understanding suicidal behavior suggest the assessment of suicide-specific beliefs and cognitions may improve the detection and prediction of suicidal thoughts and behaviors. The Suicide Cognitions Scale (SCS) was developed to measure suicide-specific beliefs, but it has not been tested in a military setting. Data were analyzed from two separate studies conducted at three military mental health clinics (one U.S. Army, two U.S. Air Force). Participants included 175 active duty Army personnel with acute suicidal ideation and/or a recent suicide attempt referred for a treatment study (Sample 1) and 151 active duty Air Force personnel receiving routine outpatient mental health care (Sample 2). In both samples, participants completed self-report measures and clinician-administered interviews. Follow-up suicide attempts were assessed via clinician-administered interview for Sample 1. Statistical analyses included confirmatory factor analysis, between-group comparisons by history of suicidality, and generalized regression modeling. Two latent factors were confirmed for the SCS: Unloveability and Unbearability. Each demonstrated good internal consistency, convergent validity, and divergent validity. Both scales significantly predicted current suicidal ideation (βs >0.316, ps <0.002) and significantly differentiated suicide attempts from nonsuicidal self-injury and control groups (F(6, 286)=9.801, p<0.001). Both scales significantly predicted future suicide attempts (AORs>1.07, ps <0.050) better than other risk factors. Self-report methodology, small sample sizes, predominantly male samples. The SCS is a reliable and valid measure that predicts suicidal ideation and suicide attempts among military personnel better than other well-established risk factors. Copyright © 2014 Elsevier B.V. All rights reserved.

Increased Brahma-related Gene 1 Expression Predicts Distant Metastasis and Shorter Survival in Patients with Invasive Ductal Carcinoma of the Breast.

PubMed

Do, Sung-Im; Yoon, Gun; Kim, Hyun-Soo; Kim, Kyungeun; Lee, Hyunjoo; Do, In-Gu; Kim, Dong-Hoon; Chae, Seoung Wan; Sohn, Jin Hee

2016-09-01

Previous studies have demonstrated aberrant Brahma-related gene 1 (BRG1) expression in various tumor types. Increased BRG1 expression has recently been shown to correlate with aggressive oncogenic behavior in many different types of human cancer. However, the role of BRG1 in breast cancer development and progression is not fully understood. We evaluated BRG1 expression in 224 patients with invasive ductal carcinoma (IDC) of the breast using tissue microarray samples and immunohistochemistry. We also investigated whether BRG1 expression status is associated with clinicopathological characteristics and outcomes of patients with IDC. Among the 224 patients with IDC, 37.5% (84/224) exhibited high BRG1 expression. IDC exhibited significantly higher BRG1 expression compared to ductal carcinoma in situ (p=0.009) and normal breast tissue (p=0.005). High BRG1 expression in IDC significantly correlated with higher histological grade (p=0.035) and presence of distant metastasis (p=0.002). Furthermore, high BRG1 expression was an independent factor for predicting distant metastasis (relative risk=4.079; p=0.007). In addition, high BRG1 expression predicted shorter overall (p=0.011) and recurrence-free (p=0.003) survival in patients with IDC. In particular, BRG1 had a significant prognostic value in predicting recurrence-free survival of patients with IDC with lymph node metastasis or stage III disease. BRG1 is involved in the progression and metastasis of breast cancer and can serve as a novel biomarker predictive of distant metastasis and patient outcomes. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Aerobic power and anthropometric characteristics of elite basketball referees.

PubMed

Leicht, A S

2007-03-01

The current study aimed to document the aerobic power and body composition of elite basketball referees. Prior to the 2000/2001 Men's National Basketball League season, 25 male elite referees completed the Multistage Shuttle run test followed by body composition (body fat%) determination via bioelectrical impedance (BI) (Adult and Athlete modes) and a restricted anthropometric profile. Significant correlations between BI and anthropometric measures were examined via Pearson product correlation coefficients. Referees demonstrated a mean (SD) aerobic power of 50.8 (3.2) mL . kg-1 . min(-1) and body fat% of 23.8% (8.4%). Body fat% was similar for BI (Adult) and several anthropometric equations. Significant correlations were obtained between BI (Adult) and body fat%, and BI (Adult) and sum of skinfolds. Similar correlations were obtained for BI (Athlete) mode despite a significantly lower body fat%. Regression equations for the prediction of body fat% and sum of skinfolds from BI (Adult) were determined. Elite basketball referees demonstrated significantly greater aerobic power and similar body composition to the general community. In the euhydrated state, BI (Adult) provided a valid measurement of body fat% in elite basketball referees.

Preliminary results of fisheries investigation associated with Skylab-3. [remotely sensed distribution and abundance of gamefish in Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator); Pastula, E. J., Jr.; Woods, G.; Faller, K.

1974-01-01

The author has identified the following significant results. This investigation is to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. Data from the test area in the northeastern Gulf of Mexico has made possible the identification of fisheries significant environmental parameters for white marlin. Predictive models based on catch data and surface truth information have been developed and have demonstrated potential for reducing search significantly by identifying areas which have a high probability of being productive. Three of the parameters utilized by the model, chlorophyll-a, sea surface temperature, and turbidity have been inferred from aircraft sensor data. Cloud cover and delayed receipt have inhibited the use of Skylab data. The first step toward establishing the feasibility of utilizing remotely sensed data to assess amd monitor the distribution of ocean gamefish has been taken with the successful identification of fisheries significant oceanographic parameters and the demonstration of the capability of measuring most of these parameters remotely.

Real-Time Safety Monitoring and Prediction for the National Airspace System

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil

2016-01-01

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have both an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecasts, predicted health of assets in the airspace, and so on. To this end, we have developed a Real-Time Safety Monitoring (RTSM) that first, estimates the state of the NAS using the dynamic models. Then, given the state estimate and a probability distribution of future inputs to the NAS, the framework predicts the evolution of the NAS, i.e., the future state, and analyzes these future states to predict the occurrence of unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and or parameters. We demonstrate our overall approach by predicting the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress.

Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

PubMed

Cui, Zaixu; Su, Mengmeng; Li, Liangjie; Shu, Hua; Gong, Gaolang

2018-05-01

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

Predicting outcome in severe traumatic brain injury using a simple prognostic model.

PubMed

Sobuwa, Simpiwe; Hartzenberg, Henry Benjamin; Geduld, Heike; Uys, Corrie

2014-06-17

Several studies have made it possible to predict outcome in severe traumatic brain injury (TBI) making it beneficial as an aid for clinical decision-making in the emergency setting. However, reliable predictive models are lacking for resource-limited prehospital settings such as those in developing countries like South Africa. To develop a simple predictive model for severe TBI using clinical variables in a South African prehospital setting. All consecutive patients admitted at two level-one centres in Cape Town, South Africa, for severe TBI were included. A binary logistic regression model was used, which included three predictor variables: oxygen saturation (SpO₂), Glasgow Coma Scale (GCS) and pupil reactivity. The Glasgow Outcome Scale was used to assess outcome on hospital discharge. A total of 74.4% of the outcomes were correctly predicted by the logistic regression model. The model demonstrated SpO₂ (p=0.019), GCS (p=0.001) and pupil reactivity (p=0.002) as independently significant predictors of outcome in severe TBI. Odds ratios of a good outcome were 3.148 (SpO₂ ≥ 90%), 5.108 (GCS 6 - 8) and 4.405 (pupils bilaterally reactive). This model is potentially useful for effective predictions of outcome in severe TBI.

Thermal stability of mullite RMn₂O₅ (R  =  Bi, Y, Pr, Sm or Gd): combined density functional theory and experimental study.

PubMed

Li, Chenzhe; Thampy, Sampreetha; Zheng, Yongping; Kweun, Joshua M; Ren, Yixin; Chan, Julia Y; Kim, Hanchul; Cho, Maenghyo; Kim, Yoon Young; Hsu, Julia W P; Cho, Kyeongjae

2016-03-31

Understanding and effectively predicting the thermal stability of ternary transition metal oxides with heavy elements using first principle simulations are vital for understanding performance of advanced materials. In this work, we have investigated the thermal stability of mullite RMn2O5 (R  =  Bi, Pr, Sm, or Gd) structures by constructing temperature phase diagrams using an efficient mixed generalized gradient approximation (GGA) and the GGA  +  U method. Simulation predicted stability regions without corrections on heavy elements show a 4-200 K underestimation compared to our experimental results. We have found the number of d/f electrons in the heavy elements shows a linear relationship with the prediction deviation. Further correction on the strongly correlated electrons in heavy elements could significantly reduce the prediction deviations. Our corrected simulation results demonstrate that further correction of R-site elements in RMn2O5 could effectively reduce the underestimation of the density functional theory-predicted decomposition temperature to within 30 K. Therefore, it could produce an accurate thermal stability prediction for complex ternary transition metal oxide compounds with heavy elements.

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

PubMed

Jahed Armaghani, Danial; Hajihassani, Mohsen; Marto, Aminaton; Shirani Faradonbeh, Roohollah; Mohamad, Edy Tonnizam

2015-11-01

Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.

Physiological responses to rational-emotive self-verbalizations.

PubMed

Master, S; Gershman, L

1983-12-01

This study tested Albert Ellis' Rational Emotive Therapy (RET) theory which predicts that cognitive beliefs, not the stimulus situation, generate human emotions. According to RET, emotions created by rational beliefs are adaptive, while irrational beliefs result in an unadaptive anxiety level. Results demonstrated that at high levels of problem relevance there was (1) a significantly greater GSR in direct response to the stimulus situation, and also to irrational statements, than to rational and control statements, and (2) no significant difference between rational and neutral control statements. The authors argue that these results are more parsimoniously explained by conditioning theory than by RET theory.

Demonstration of risk based, goal driven framework for hydrological field campaigns and inverse modeling with case studies

NASA Astrophysics Data System (ADS)

Harken, B.; Geiges, A.; Rubin, Y.

2013-12-01

There are several stages in any hydrological modeling campaign, including: formulation and analysis of a priori information, data acquisition through field campaigns, inverse modeling, and forward modeling and prediction of some environmental performance metric (EPM). The EPM being predicted could be, for example, contaminant concentration, plume travel time, or aquifer recharge rate. These predictions often have significant bearing on some decision that must be made. Examples include: how to allocate limited remediation resources between multiple contaminated groundwater sites, where to place a waste repository site, and what extraction rates can be considered sustainable in an aquifer. Providing an answer to these questions depends on predictions of EPMs using forward models as well as levels of uncertainty related to these predictions. Uncertainty in model parameters, such as hydraulic conductivity, leads to uncertainty in EPM predictions. Often, field campaigns and inverse modeling efforts are planned and undertaken with reduction of parametric uncertainty as the objective. The tool of hypothesis testing allows this to be taken one step further by considering uncertainty reduction in the ultimate prediction of the EPM as the objective and gives a rational basis for weighing costs and benefits at each stage. When using the tool of statistical hypothesis testing, the EPM is cast into a binary outcome. This is formulated as null and alternative hypotheses, which can be accepted and rejected with statistical formality. When accounting for all sources of uncertainty at each stage, the level of significance of this test provides a rational basis for planning, optimization, and evaluation of the entire campaign. Case-specific information, such as consequences prediction error and site-specific costs can be used in establishing selection criteria based on what level of risk is deemed acceptable. This framework is demonstrated and discussed using various synthetic case studies. The case studies involve contaminated aquifers where a decision must be made based on prediction of when a contaminant will arrive at a given location. The EPM, in this case contaminant travel time, is cast into the hypothesis testing framework. The null hypothesis states that the contaminant plume will arrive at the specified location before a critical value of time passes, and the alternative hypothesis states that the plume will arrive after the critical time passes. Different field campaigns are analyzed based on effectiveness in reducing the probability of selecting the wrong hypothesis, which in this case corresponds to reducing uncertainty in the prediction of plume arrival time. To examine the role of inverse modeling in this framework, case studies involving both Maximum Likelihood parameter estimation and Bayesian inversion are used.

Applying a new mammographic imaging marker to predict breast cancer risk

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-02-01

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the "current" and "prior" screenings with a time interval from 365 to 600 days. All "prior" images were originally interpreted negative. In "current" screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using "prior" negative mammograms, the area under a ROC curve is 0.70+/-0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the "current" screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Analysis of factors predicting speed of hematologic recovery after transplantation with 4-hydroperoxycyclophosphamide-purged autologous bone marrow grafts.

PubMed

Rowley, S D; Piantadosi, S; Marcellus, D C; Jones, R J; Davidson, N E; Davis, J M; Kennedy, J; Wiley, J M; Wingard, J R; Yeager, A M

1991-03-01

We previously described the predictive value of graft colony-forming units granulocyte macrophage (CFU-GM) content after 4-hydroperoxycyclophosphamide (4-HC) purging for the duration of aplasia after autologous bone marrow transplantation. Despite the uniform 4-HC concentration, we observed heterogeneity in CFU-GM survival and the kinetics of engraftment. We have now analysed patient and graft characteristics for 154 patients undergoing autologous transplantation with 4-HC purged grafts to further define this heterogeneity. Patients transplanted for the treatment of malignant lymphoma reached a peripheral blood granulocyte count of greater than 0.5 x 10(9)/l (median, 20 versus 40 days; p less than 0.001) and platelet transfusion independence (median, 30 versus 70 days; p less than 0.001) significantly faster than patients transplanted for acute non-lymphoblastic leukemia. Other diagnostic groups were intermediate. These differences were independent of graft CFU-GM content. Multiple other patient and graft factors including patient age, peripheral blood counts on day of harvest, and amounts of other hematopoietic progenitors also predicted the kinetics of engraftment in univariate and multivariate analysis. Cytomegalovirus infection during the aplastic period predicted a delay in granulocyte (p = 0.024) but not platelet recovery (p = 0.174). This analysis demonstrates that multiple patient, graft, and post-transplant factors predict the engraftment capacity of autografts, and the kinetics of engraftment with 4-HC purged grafts. The multiple predictive factors explain a significant portion of the variability in engraftment kinetics observed after transplantation with 4-HC purged autografts.

Bridging the gap between computation and clinical biology: validation of cable theory in humans

PubMed Central

Finlay, Malcolm C.; Xu, Lei; Taggart, Peter; Hanson, Ben; Lambiase, Pier D.

2013-01-01

Introduction: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. Methods: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S2 extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. Results: Activation dynamics following a premature S3 were significantly different from those after an S2. Patient specific models demonstrated accurate prediction of the S3 activation wave, (Pearson's R2 = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R2 = 0.99). Conclusions: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models. PMID:24027527

Memory Binding Test Predicts Incident Amnestic Mild Cognitive Impairment.

PubMed

Mowrey, Wenzhu B; Lipton, Richard B; Katz, Mindy J; Ramratan, Wendy S; Loewenstein, David A; Zimmerman, Molly E; Buschke, Herman

2016-07-14

The Memory Binding Test (MBT), previously known as Memory Capacity Test, has demonstrated discriminative validity for distinguishing persons with amnestic mild cognitive impairment (aMCI) and dementia from cognitively normal elderly. We aimed to assess the predictive validity of the MBT for incident aMCI. In a longitudinal, community-based study of adults aged 70+, we administered the MBT to 246 cognitively normal elderly adults at baseline and followed them annually. Based on previous work, a subtle reduction in memory binding at baseline was defined by a Total Items in the Paired (TIP) condition score of ≤22 on the MBT. Cox proportional hazards models were used to assess the predictive validity of the MBT for incident aMCI accounting for the effects of covariates. The hazard ratio of incident aMCI was also assessed for different prediction time windows ranging from 4 to 7 years of follow-up, separately. Among 246 controls who were cognitively normal at baseline, 48 developed incident aMCI during follow-up. A baseline MBT reduction was associated with an increased risk for developing incident aMCI (hazard ratio (HR) = 2.44, 95% confidence interval: 1.30-4.56, p = 0.005). When varying the prediction window from 4-7 years, the MBT reduction remained significant for predicting incident aMCI (HR range: 2.33-3.12, p: 0.0007-0.04). Persons with poor performance on the MBT are at significantly greater risk for developing incident aMCI. High hazard ratios up to seven years of follow-up suggest that the MBT is sensitive to early disease.

Association of pain ratings with the prediction of early physical recovery after general and orthopaedic surgery-A quantitative study with repeated measures.

PubMed

Eriksson, Kerstin; Wikström, Lotta; Fridlund, Bengt; Årestedt, Kristofer; Broström, Anders

2017-11-01

To compare different levels of self-rated pain and determine if they predict anticipated early physical recovery in patients undergoing general and orthopaedic surgery. Previous research has indicated that average self-rated pain reflects patients' ability to recover the same day. However, there is a knowledge gap about the feasibility of using average pain ratings to predict patients' physical recovery for the next day. Descriptive, quantitative repeated measures. General and orthopaedic inpatients (n = 479) completed a questionnaire (October 2012-January 2015) about pain and recovery. Average pain intensity at rest and during activity was based on the Numeric Rating Scale and divided into three levels (0-3, 4-6, 7-10). Three out of five dimensions from the tool "Postoperative Recovery Profile" were used. Because few suffered severe pain, general and orthopaedic patients were analysed together. Binary logistic regression analysis showed that average pain intensity postoperative day 1 significantly predicted the impact on recovery day 2, except nausea, gastrointestinal function and bladder function when pain at rest and also nausea, appetite changes, and bladder function when pain during activity. High pain ratings (NRS 7-10) demonstrated to be a better predictor for recovery compared with moderate ratings (NRS 4-6), day 2, as it significantly predicted more items in recovery. Pain intensity reflected general and orthopaedic patients' physical recovery postoperative day 1 and predicted recovery for day 2. By monitoring patients' pain and impact on recovery, patients' need for support becomes visible which is valuable during hospital stays. © 2017 John Wiley & Sons Ltd.

Visuomotor training improves stroke-related ipsilesional upper extremity impairments.

PubMed

Quaney, Barbara M; He, Jianghua; Timberlake, George; Dodd, Kevin; Carr, Caitlin

2010-01-01

Unilateral middle cerebral artery infarction has been reported to impair bilateral hand grasp. Individuals (5 males and 5 females; age 33-86 years) with chronic unilateral middle cerebral artery stroke (4 right lesions and 6 left lesions) repeatedly lifted a 260-g object. Participants were then trained to lift the object using visuomotor feedback via an oscilloscope that displayed their actual grip force (GF) and a target GF, which roughly matched the physical properties of the object. The subjects failed to accurately modulate the predictive GF when relying on somatosensory information from the previous lifts. Instead, for all the lifts, they programmed excessive GF equivalent to the force used for the first lift. The predictive GF was lowered for lifts following the removal of the visual feedback. The mean difference in predictive GF between the lifts before and after visual training was significant (4.35 +/- 0.027 N; P

Stochasticity of bacterial attachment and its predictability by the extended derjaguin-landau-verwey-overbeek theory.

PubMed

Chia, Teck Wah R; Nguyen, Vu Tuan; McMeekin, Thomas; Fegan, Narelle; Dykes, Gary A

2011-06-01

Bacterial attachment onto materials has been suggested to be stochastic by some authors but nonstochastic and based on surface properties by others. We investigated this by attaching pairwise combinations of two Salmonella enterica serovar Sofia (S. Sofia) strains (with different physicochemical and attachment properties) with one strain each of S. enterica serovar Typhimurium, S. enterica serovar Infantis, or S. enterica serovar Virchow (all with similar physicochemical and attachment abilities) in ratios of 0.428, 1, and 2.333 onto glass, stainless steel, Teflon, and polysulfone. Attached bacterial cells were recovered and counted. If the ratio of attached cells of each Salmonella serovar pair recovered was the same as the initial inoculum ratio, the attachment process was deemed stochastic. Experimental outcomes from the study were compared to those predicted by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory. Significant differences (P < 0.05) between the initial and the attached ratios for serovar pairs containing S. Sofia S1296a for all different ratios were apparent for all materials. For S. Sofia S1635-containing pairs, 7 out of 12 combinations of serovar pairs and materials had attachment ratios not significantly different (P > 0.05) from the initial ratio of 0.428. Five out of 12 and 10 out of 12 samples had attachment ratios not significantly different (P > 0.05) from the initial ratios of 1 and 2.333, respectively. These results demonstrate that bacterial attachment to different materials is likely to be nonstochastic only when the key physicochemical properties of the bacteria were significantly different (P < 0.05) from each other. XDLVO theory could successfully predict the attachment of some individual isolates to particular materials but could not be used to predict the likelihood of stochasticity in pairwise attachment experiments.

Longitudinal analysis of receptive vocabulary growth in young Spanish English-speaking children from migrant families.

PubMed

Jackson, Carla Wood; Schatschneider, Christopher; Leacox, Lindsey

2014-01-01

The authors of this study described developmental trajectories and predicted kindergarten performance of Spanish and English receptive vocabulary acquisition of young Latino/a English language learners (ELLs) from socioeconomically disadvantaged migrant families. In addition, the authors examined the extent to which gender and individual initial performance in Spanish predict receptive vocabulary performance and growth rate. The authors used hierarchical linear modeling of 64 children's receptive vocabulary performance to generate growth trajectories, predict performance at school entry, and examine potential predictors of rate of growth. The timing of testing varied across children. The ELLs (prekindergarten to 2nd grade) participated in 2-5 testing sessions, each 6-12 months apart. The ELLs' average predicted standard score on an English receptive vocabulary at kindergarten was nearly 2 SDs below the mean for monolingual peers. Significant growth in the ELLs' receptive vocabulary was observed between preschool and 2nd grade, indicating that the ELLs were slowly closing the receptive vocabulary gap, although their average score remained below the standard score mean for age-matched monolingual peers. The ELLs demonstrated a significant decrease in Spanish receptive vocabulary standard scores over time. Initial Spanish receptive vocabulary was a significant predictor of growth in English receptive vocabulary. High initial Spanish receptive vocabulary was associated with greater growth in English receptive vocabulary and decelerated growth in Spanish receptive vocabulary. Gender was not a significant predictor of growth in either English or Spanish receptive vocabulary. ELLs from low socioeconomic backgrounds may be expected to perform lower in English compared with their monolingual English peers in kindergarten. Performance in Spanish at school entry may be useful in identifying children who require more intensive instructional support for English vocabulary growth. Findings substantiate the need for progress monitoring across the early school years.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Utilization of the NSQIP-Pediatric Database in Development and Validation of a New Predictive Model of Pediatric Postoperative Wound Complications.

PubMed

Maizlin, Ilan I; Redden, David T; Beierle, Elizabeth A; Chen, Mike K; Russell, Robert T

2017-04-01

Surgical wound classification, introduced in 1964, stratifies the risk of surgical site infection (SSI) based on a clinical estimate of the inoculum of bacteria encountered during the procedure. Recent literature has questioned the accuracy of predicting SSI risk based on wound classification. We hypothesized that a more specific model founded on specific patient and perioperative factors would more accurately predict the risk of SSI. Using all observations from the 2012 to 2014 pediatric National Surgical Quality Improvement Program-Pediatric (NSQIP-P) Participant Use File, patients were randomized into model creation and model validation datasets. Potential perioperative predictive factors were assessed with univariate analysis for each of 4 outcomes: wound dehiscence, superficial wound infection, deep wound infection, and organ space infection. A multiple logistic regression model with a step-wise backwards elimination was performed. A receiver operating characteristic curve with c-statistic was generated to assess the model discrimination for each outcome. A total of 183,233 patients were included. All perioperative NSQIP factors were evaluated for clinical pertinence. Of the original 43 perioperative predictive factors selected, 6 to 9 predictors for each outcome were significantly associated with postoperative SSI. The predictive accuracy level of our model compared favorably with the traditional wound classification in each outcome of interest. The proposed model from NSQIP-P demonstrated a significantly improved predictive ability for postoperative SSIs than the current wound classification system. This model will allow providers to more effectively counsel families and patients of these risks, and more accurately reflect true risks for individual surgical patients to hospitals and payers. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease.

PubMed

Tong, Tong; Gao, Qinquan; Guerrero, Ricardo; Ledig, Christian; Chen, Liang; Rueckert, Daniel; Initiative, Alzheimer's Disease Neuroimaging

2017-01-01

Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance images. We propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection, and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79-81% for the prediction of MCI-to-AD conversion within three years in tenfold cross validations. The classification AUC further increases to 84-92% when age and cognitive measures are combined with the proposed grading biomarker. The obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space, the removal of the normal aging effect, selection of discriminative voxels, the calculation of the grading biomarker using AD and normal control groups, and the integration of sparse representation technique and the combination of cognitive measures. The evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.

Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage

PubMed Central

Bozek, Katarzyna; Lengauer, Thomas; Sierra, Saleta; Kaiser, Rolf; Domingues, Francisco S.

2013-01-01

The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived ‘bulk’ sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under http://structure.bioinf.mpi-inf.mpg.de/. PMID:23555214

Meta-path based heterogeneous combat network link prediction

NASA Astrophysics Data System (ADS)

Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

2017-09-01

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

First trimester prediction of maternal glycemic status.

PubMed

Gabbay-Benziv, Rinat; Doyle, Lauren E; Blitzer, Miriam; Baschat, Ahmet A

2015-05-01

To predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics. We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state. Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC - area under the curve 0.819, CI - confidence interval 0.769-0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668-0.746). GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

DOE PAGES

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.; ...

2017-11-08

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

Simplified Models for Accelerated Structural Prediction of Conjugated Semiconducting Polymers

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

Henry, Michael M.; Jones, Matthew L.; Oosterhout, Stefan D.

We perform molecular dynamics simulations of poly(benzodithiophene-thienopyrrolodione) (BDT-TPD) oligomers in order to evaluate the accuracy with which unoptimized molecular models can predict experimentally characterized morphologies. The predicted morphologies are characterized using simulated grazing-incidence X-ray scattering (GIXS) and compared to the experimental scattering patterns. We find that approximating the aromatic rings in BDT-TPD with rigid bodies, rather than combinations of bond, angle, and dihedral constraints, results in 14% lower computational cost and provides nearly equivalent structural predictions compared to the flexible model case. The predicted glass transition temperature of BDT-TPD (410 +/- 32 K) is found to be in agreement withmore » experiments. Predicted morphologies demonstrate short-range structural order due to stacking of the chain backbones (p-p stacking around 3.9 A), and long-range spatial correlations due to the self-organization of backbone stacks into 'ribbons' (lamellar ordering around 20.9 A), representing the best-to-date computational predictions of structure of complex conjugated oligomers. We find that expensive simulated annealing schedules are not needed to predict experimental structures here, with instantaneous quenches providing nearly equivalent predictions at a fraction of the computational cost of annealing. We therefore suggest utilizing rigid bodies and fast cooling schedules for high-throughput screening studies of semiflexible polymers and oligomers to utilize their significant computational benefits where appropriate.« less

Chitinase 1 Is a Biomarker for and Therapeutic Target in Scleroderma-Associated Interstitial Lung Disease That Augments TGF-β1 Signaling

PubMed Central

Lee, Chun Geun; Herzog, Erica L.; Ahangari, Farida; Zhou, Yang; Gulati, Mridu; Lee, Chang-Min; Peng, Xueyan; Feghali-Bostwick, Carol; Jimenez, Sergio A.; Varga, John; Elias, Jack A.

2014-01-01

Interstitial lung disease (ILD) with pulmonary fibrosis is an important manifestation in systemic sclerosis (SSc, scleroderma) where it portends a poor prognosis. However, biomarkers that predict the development and or severity of SSc-ILD have not been validated, and the pathogenetic mechanisms that engender this pulmonary response are poorly understood. In this study, we demonstrate in two different patient cohorts that the levels of chitotriosidase (Chit1) bioactivity and protein are significantly increased in the circulation and lungs of SSc patients compared with demographically matched controls. We also demonstrate that, compared with patients without lung involvement, patients with ILD show high levels of circulating Chit1 activity that correlate with disease severity. Murine modeling shows that in comparison with wild-type mice, bleomycin-induced pulmonary fibrosis was significantly reduced in Chit1−/− mice and significantly enhanced in lungs from Chit1 overexpressing transgenic animals. In vitro studies also demonstrated that Chit1 interacts with TGF-β1 to augment fibroblast TGF-β receptors 1 and 2 expression and TGF-β–induced Smad and MAPK/ERK activation. These studies indicate that Chit1 is potential biomarker for ILD in SSc and a therapeutic target in SSc-associated lung fibrosis and demonstrate that Chit1 augments TGF-β1 effects by increasing receptor expression and canonical and noncanonical TGF-β1 signaling. PMID:22826322

The influence of sex difference on self-reference effects in a male-dominated culture.

PubMed

Song, Xuan; Shang, Rui; Bi, Qi; Zhang, Xin; Wu, Yanhong

2012-10-01

52 secondary school students from the Chaoshan, China, area, where males are highly valued, were examined for self-reference, mother-reference, and father-reference effects. Because the father is the primary role model in Chaoshan culture, it was predicted that male participants would demonstrate a father-reference effect while females would show a mother-reference effect. The results confirmed that females showed significant self-, mother-, and father-reference effects in terms of memory performance, while males showed only a significant father-reference effect and a marginally significant self-reference effect. This study highlights the importance of researching subcultures such as the Chaoshan subculture to gain a comprehensive understanding of self-construct.

Value of Excess Pressure Integral for Predicting 15-Year All-Cause and Cardiovascular Mortalities in End-Stage Renal Disease Patients.

PubMed

Huang, Jui-Tzu; Cheng, Hao-Min; Yu, Wen-Chung; Lin, Yao-Ping; Sung, Shih-Hsien; Wang, Jiun-Jr; Wu, Chung-Li; Chen, Chen-Huan

2017-11-29

The excess pressure integral (XSPI), derived from analysis of the arterial pressure curve, may be a significant predictor of cardiovascular events in high-risk patients. We comprehensively investigated the prognostic value of XSPI for predicting long-term mortality in end-stage renal disease patients undergoing regular hemodialysis. A total of 267 uremic patients (50.2% female; mean age 54.2±14.9 years) receiving regular hemodialysis for more than 6 months were enrolled. Cardiovascular parameters were obtained by echocardiography and applanation tonometry. Calibrated carotid arterial pressure waveforms were analyzed according to the wave-transmission and reservoir-wave theories. Multivariable Cox proportional hazard models were constructed to account for age, sex, diabetes mellitus, albumin, body mass index, and hemodialysis treatment adequacy. Incremental utility of the parameters to risk stratification was assessed by net reclassification improvement. During a median follow-up of 15.3 years, 124 deaths (46.4%) incurred. Baseline XSPI was significantly predictive of all-cause (hazard ratio per 1 SD 1.4, 95% confidence interval 1.15-1.70, P =0.0006) and cardiovascular mortalities (1.47, 1.18-1.84, P =0.0006) after accounting for the covariates. The addition of XSPI to the base prognostic model significantly improved prediction of both all-cause mortality (net reclassification improvement=0.1549, P =0.0012) and cardiovascular mortality (net reclassification improvement=0.1535, P =0.0033). XSPI was superior to carotid-pulse wave velocity, forward and backward wave amplitudes, and left ventricular ejection fraction in consideration of overall independent and incremental prognostics values. In end-stage renal disease patients undergoing regular hemodialysis, XSPI was significantly predictive of long-term mortality and demonstrated an incremental value to conventional prognostic factors. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Neuroimaging-Aided Prediction of the Effect of Methylphenidate in Children with Attention-Deficit Hyperactivity Disorder: A Randomized Controlled Trial.

PubMed

Ishii-Takahashi, Ayaka; Takizawa, Ryu; Nishimura, Yukika; Kawakubo, Yuki; Hamada, Kasumi; Okuhata, Shiho; Kawasaki, Shingo; Kuwabara, Hitoshi; Shimada, Takafumi; Todokoro, Ayako; Igarashi, Takashi; Watanabe, Kei-Ichiro; Yamasue, Hidenori; Kato, Nobumasa; Kasai, Kiyoto; Kano, Yukiko

2015-11-01

Although methylphenidate hydrochloride (MPH) is a first-line treatment for children with attention-deficit hyperactivity disorder (ADHD), the non-response rate is 30%. Our aim was to develop a supplementary neuroimaging biomarker for predicting the clinical effect of continuous MPH administration by using near-infrared spectroscopy (NIRS). After baseline assessment, we performed a double-blind, placebo-controlled, crossover trial with a single dose of MPH, followed by a prospective 4-to-8-week open trial with continuous MPH administration, and an ancillary 1-year follow-up. Twenty-two drug-naïve and eight previously treated children with ADHD (NAÏVE and NON-NAÏVE) were compared with 20 healthy controls (HCs) who underwent multiple NIRS measurements without intervention. We tested whether NIRS signals at the baseline assessment or ΔNIRS (single dose of MPH minus baseline assessment) predict the Clinical Global Impressions-Severity (CGI-S) score after 4-to-8-week or 1-year MPH administration. The secondary outcomes were the effect of MPH on NIRS signals after single-dose, 4-to-8-week, and 1-year administration. ΔNIRS significantly predicted CGI-S after 4-to-8-week MPH administration. The leave-one-out classification algorithm had 81% accuracy using the NIRS signal. ΔNIRS also significantly predicted CGI-S scores after 1 year of MPH administration. For secondary analyses, NAÏVE exhibited significantly lower prefrontal activation than HCs at the baseline assessment, whereas NON-NAÏVE and HCs showed similar activation. A single dose of MPH significantly increased activation compared with the placebo in NAÏVE. After 4-to-8-week administration, and even after MPH washout following 1-year administration, NAÏVE demonstrated normalized prefrontal activation. Supplementary NIRS measurements may serve as an objective biomarker for clinical decisions and monitoring concerning continuous MPH treatment in children with ADHD.

Phonon-tunnelling dissipation in mechanical resonators

PubMed Central

Cole, Garrett D.; Wilson-Rae, Ignacio; Werbach, Katharina; Vanner, Michael R.; Aspelmeyer, Markus

2011-01-01

Microscale and nanoscale mechanical resonators have recently emerged as ubiquitous devices for use in advanced technological applications, for example, in mobile communications and inertial sensors, and as novel tools for fundamental scientific endeavours. Their performance is in many cases limited by the deleterious effects of mechanical damping. In this study, we report a significant advancement towards understanding and controlling support-induced losses in generic mechanical resonators. We begin by introducing an efficient numerical solver, based on the 'phonon-tunnelling' approach, capable of predicting the design-limited damping of high-quality mechanical resonators. Further, through careful device engineering, we isolate support-induced losses and perform a rigorous experimental test of the strong geometric dependence of this loss mechanism. Our results are in excellent agreement with the theory, demonstrating the predictive power of our approach. In combination with recent progress on complementary dissipation mechanisms, our phonon-tunnelling solver represents a major step towards accurate prediction of the mechanical quality factor. PMID:21407197

Measurement and Modeling of the Optical Scattering Properties of Crop Canopies

NASA Technical Reports Server (NTRS)

Vanderbilt, V. C. (Principal Investigator)

1985-01-01

The specular reflection process is shown to be a key aspect of radiation transfer by plant canopies. Polarization measurements are demonstrated as the tool for determining the specular and diffuse portions of the canopy radiance. The magnitude of the specular fraction of the reflectance is significant compared to the magnitude of the diffuse fraction. Therefore, it is necessary to consider specularly reflected light in developing and evaluating light-canopy interaction models for wheat canopies. Models which assume leaves are diffuse reflectors correctly predict only the diffuse fraction of the canopy reflectance factor. The specular reflectance model, when coupled with a diffuse leaf model, would predict both the specular and diffuse portions of the reflectance factor. The specular model predicts and the data analysis confirms that the single variable, angle of incidence of specularly reflected sunlight on the leaf, explains much of variation in the polarization data as a function of view-illumination directions.

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.

Efficient first-principles prediction of solid stability: Towards chemical accuracy

NASA Astrophysics Data System (ADS)

Zhang, Yubo; Kitchaev, Daniil A.; Yang, Julia; Chen, Tina; Dacek, Stephen T.; Sarmiento-Pérez, Rafael A.; Marques, Maguel A. L.; Peng, Haowei; Ceder, Gerbrand; Perdew, John P.; Sun, Jianwei

2018-03-01

The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. Here, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.

COMSAC: Computational Methods for Stability and Control. Part 2

NASA Technical Reports Server (NTRS)

Fremaux, C. Michael (Compiler); Hall, Robert M. (Compiler)

2004-01-01

The unprecedented advances being made in computational fluid dynamic (CFD) technology have demonstrated the powerful capabilities of codes in applications to civil and military aircraft. Used in conjunction with wind-tunnel and flight investigations, many codes are now routinely used by designers in diverse applications such as aerodynamic performance predictions and propulsion integration. Typically, these codes are most reliable for attached, steady, and predominantly turbulent flows. As a result of increasing reliability and confidence in CFD, wind-tunnel testing for some new configurations has been substantially reduced in key areas, such as wing trade studies for mission performance guarantees. Interest is now growing in the application of computational methods to other critical design challenges. One of the most important disciplinary elements for civil and military aircraft is prediction of stability and control characteristics. CFD offers the potential for significantly increasing the basic understanding, prediction, and control of flow phenomena associated with requirements for satisfactory aircraft handling characteristics.

Prognostic and predictive biomarkers post curative intent therapy

PubMed Central

Feldman, Rebecca

2017-01-01

Large-scale screening trials have demonstrated that early diagnosis of lung cancer results in a significant reduction in lung cancer mortality. Despite improvements in detecting more lung cancers at early stages, the 5-year survival rates of lung cancers diagnosed before widespread disease is only 30–50%. High rates of recurrence, despite early diagnosis, suggest the need to improve treatment strategies based on the likelihood of recurrence in patient subsets, as well as explore the role of predictive markers for therapy selection in the adjuvant setting. In the era of personalized medicine, there have been a wide array of molecular alterations and signatures studied for their potential prognostic and predictive utility, however most have failed to translate into clinical tools. This review will discuss progress made in clinical management of lung cancer, and recent progress in the development of patient selection tools for the refinement of early stage lung cancers. PMID:29057234

Electrochemistry-based Battery Modeling for Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Kulkarni, Chetan Shrikant

2013-01-01

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

The interplay of personality and negative comments about appearance in predicting body image.

PubMed

Kvalem, Ingela Lundin; von Soest, Tilmann; Roald, Helge E; Skolleborg, Knut Chr

2006-09-01

This study investigates how personality traits in combination with frequency of and emotional reaction to negative comments about appearance while growing up are related to appearance evaluation and orientation among adult women. Nine hundred and seven participants from a representative sample of Norwegian women aged 22-55, answered questions measuring body image, personality (Big Five), and history of experiencing negative comments about appearance. Results indicated that only emotional reaction to negative comments about appearance significantly predicted both appearance evaluation and orientation, while frequency of negative comments did not. Being extrovert predicted more positive appearance evaluation and being more appearance oriented than being introvert. Scoring high on neuroticism was related to negative appearance evaluation and high appearance orientation. The findings demonstrate the importance of differentiating between the frequency and the emotional impact of teasing as well as including personality traits when studying body image.

Predicting catalyst-support interactions between metal nanoparticles and amorphous silica supports

NASA Astrophysics Data System (ADS)

Ewing, Christopher S.; Veser, Götz; McCarthy, Joseph J.; Lambrecht, Daniel S.; Johnson, J. Karl

2016-10-01

Metal-support interactions significantly affect the stability and activity of supported catalytic nanoparticles (NPs), yet there is no simple and reliable method for estimating NP-support interactions, especially for amorphous supports. We present an approach for rapid prediction of catalyst-support interactions between Pt NPs and amorphous silica supports for NPs of various sizes and shapes. We use density functional theory calculations of 13 atom Pt clusters on model amorphous silica supports to determine linear correlations relating catalyst properties to NP-support interactions. We show that these correlations can be combined with fast discrete element method simulations to predict adhesion energy and NP net charge for NPs of larger sizes and different shapes. Furthermore, we demonstrate that this approach can be successfully transferred to Pd, Au, Ni, and Fe NPs. This approach can be used to quickly screen stability and net charge transfer and leads to a better fundamental understanding of catalyst-support interactions.

A Spectral Analysis Approach for Acoustic Radiation from Composite Panels

NASA Technical Reports Server (NTRS)

Turner, Travis L.; Singh, Mahendra P.; Mei, Chuh

2004-01-01

A method is developed to predict the vibration response of a composite panel and the resulting far-field acoustic radiation due to acoustic excitation. The acoustic excitation is assumed to consist of obliquely incident plane waves. The panel is modeled by a finite element analysis and the radiated field is predicted using Rayleigh's integral. The approach can easily include other effects such as shape memory alloy (SMA) ber reinforcement, large detection thermal postbuckling, and non-symmetric SMA distribution or lamination. Transmission loss predictions for the case of an aluminum panel excited by a harmonic acoustic pressure are shown to compare very well with a classical analysis. Results for a composite panel with and without shape memory alloy reinforcement are also presented. The preliminary results demonstrate that the transmission loss can be significantly increased with shape memory alloy reinforcement. The mechanisms for further transmission loss improvement are identified and discussed.

Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization

PubMed Central

Yanez, Livia Z.; Han, Jinnuo; Behr, Barry B.; Pera, Renee A. Reijo; Camarillo, David B.

2016-01-01

The causes of embryonic arrest during pre-implantation development are poorly understood. Attempts to correlate patterns of oocyte gene expression with successful embryo development have been hampered by the lack of reliable and nondestructive predictors of viability at such an early stage. Here we report that zygote viscoelastic properties can predict blastocyst formation in humans and mice within hours after fertilization, with >90% precision, 95% specificity and 75% sensitivity. We demonstrate that there are significant differences between the transcriptomes of viable and non-viable zygotes, especially in expression of genes important for oocyte maturation. In addition, we show that low-quality oocytes may undergo insufficient cortical granule release and zona-hardening, causing altered mechanics after fertilization. Our results suggest that embryo potential is largely determined by the quality and maturation of the oocyte before fertilization, and can be predicted through a minimally invasive mechanical measurement at the zygote stage. PMID:26904963

MO-DE-207B-01: JACK FOWLER JUNIOR INVESTIGATOR COMPETITION WINNER: Between Somatic Mutations and PET-Based Radiomic Features in Non-Small Cell Lung Cancer

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

Yip, S; Coroller, T; Rios Velazquez, E

Purpose: Although PET-based radiomic features have been proposed to quantify tumor heterogeneity and shown promise in outcome prediction, little is known about their relationship with tumor genetics. This study assessed the association of [{sup 18}F]fluorodeoxyglucose (FDG)-PET-based radiomic features with non-small cell lung cancer (NSCLC) mutations. Methods: 348 NSCLC patients underwent FDG-PET/CT scans before treatment and were tested for genetic mutations. 13% (44/348) and 28% (96/348) patients were found to harbor EGFR (EGFR+) and KRAS (KRAS+) mutations, respectively. We evaluated nineteen PET-based radiomic features quantifying phenotypic traits, and compared them with conventional PET features (metabolic tumor volume (MTV) and maximum-SUV). Themore » association between the feature values and mutation status was evaluated using the Wilcoxcon-rank-sum-test. The ability of each measure to predict mutations was assessed by the area under the receiver operating curve (AUC). Noether’s test was used to determine if the AUCs were significantly from random (AUC=0.50). All p-values were corrected for multiple testing by controlling the false discovery rate (FDR{sub Wilcoxon} and FDR{sub Noether}) of 10%. Results: Eight radiomic features, MTV, and maximum-SUV, were significantly associated with the EGFR mutation (FDR{sub Wilcoxon}=0.01–0.10). However, KRAS+ demonstrated no significantly distinctive imaging features compared to KRAS− (FDR{sub Wilcoxon}≥0.92). EGFR+ and EGFR− were significantly discriminated by conventional PET features (AUC=0.61, FDR{sub Noether}=0.04 for MTV and AUC=0.64, FDR{sub Noether}=0.01 for maximum-SUV). Eight radiomic features were significantly predictive for EGFR+ compared to EGFR− (AUC=0.59–0.67, FDR{sub Noether}=0.0032–0.09). Normalized-inverse-difference-moment outperformed all features in predicting EGFR mutation (AUC=0.67, FDR{sub Noether}=0.0032). Moreover, only the radiomic feature normalized-inverse-difference-moment could significantly predict KRAS+ from EGFR+ (AUC=0.65, FDR{sub Noether}=0.05). All measures failed to predict KRAS+ from KRAS− (AUC=0.50–0.54, FDR{sub Noether}≥0.92). Conclusion: PET imaging features were strongly associated with EGFR mutations in NSCLC. Radiomic features have great potential in predicting EGFR mutations. Our study may help develop a non-invasive imaging biomarker for EGFR mutation. R.M. has consulting interests with Amgen.« less

Molecular Imaging of Matrix Metalloproteinase Activation to Predict Murine Aneurysm Expansion in vivo

PubMed Central

Razavian, Mahmoud; Zhang, Jiasheng; Nie, Lei; Tavakoli, Sina; Razavian, Niema; Dobrucki, Lawrence W.; Sinusas, Albert J.; Edwards, D. Scott; Azure, Michael; Sadeghi, Mehran M.

2010-01-01

Rupture and dissection are major causes of morbidity and mortality in arterial aneurysm and occur more frequently in rapidly expanding aneurysms. Current imaging modalities provide little information on aneurysm beyond size. MMP activation plays a key role in the pathogenesis of aneurysm. We investigated whether imaging matrix metalloproteinase (MMP) activation in aneurysm helps predict its propensity to expansion. Methods and Results Using a model of carotid aneurysm in apolipoprotein E−/− mice we demonstrate that several MMPs are expressed with distinct temporal patterns in aneurysm. Radiotracers with specificity for activated MMPs were used to detect and quantify MMP activation by microSPECT/CT imaging in vivo. Significant focal uptake was observed in aneurysmal carotid arteries, peaking at 4 weeks after aneurysm induction. Tracer uptake was confirmed by autoradiography and gamma-well counting, and specificity was demonstrated using excess unlabeled precursor and a specific MMP inhibitor. In a group of animals imaged serially at 2 and 4 weeks after aneurysm induction, MMP tracer uptake at 2 weeks correlated well with the vessel area assessed by histology at 4 weeks. Conclusions Molecular imaging of MMP activation is a useful experimental, and potentially clinical, tool to non-invasively predict an aneurysm’s propensity to expansion in vivo. PMID:20554725

Beyond the competition-colonization trade-off: linking multiple trait response to disturbance characteristics.

PubMed

Seifan, Merav; Seifan, Tal; Schiffers, Katja; Jeltsch, Florian; Tielbörger, Katja

2013-02-01

Disturbances' role in shaping communities is well documented but highly disputed. We suggest replacing the overused two-trait trade-off approach with a functional group scheme, constructed from combinations of four key traits that represent four classes of species' responses to disturbances. Using model results and field observations from sites affected by two highly different disturbances, we demonstrated that popular dichotomous trade-offs are not sufficient to explain community dynamics, even if some emerge under certain conditions. Without disturbances, competition was only sufficient to predict species survival but not relative success, which required some escape mechanism (e.g., long-term dormancy). With highly predictable and large-scale disturbances, successful species showed a combination of high individual tolerance to disturbance and, more surprisingly, high competitive ability. When disturbances were less predictable, high individual tolerance and long-term seed dormancy were favored, due to higher environmental uncertainty. Our study demonstrates that theories relying on a small number of predefined trade-offs among traits (e.g., competition-colonization trade-off) may lead to unrealistic results. We suggest that the understanding of disturbance-community relationships can be significantly improved by employing sets of relevant trait assemblies instead of the currently common approach in which trade-offs are assumed in advance.

Comparison of CT and MRI in patients with tibial plateau fracture: can CT findings predict ligament tear or meniscal injury?

PubMed

Mui, Leonora W; Engelsohn, Eliyahu; Umans, Hilary

2007-02-01

(1) To determine the accuracy of computed tomography (CT) in the evaluation of ligament tear and avulsion in patients with tibial plateau fracture. (2) To evaluate whether the presence or severity of fracture gap and articular depression can predict meniscal injury. A fellowship-trained musculoskeletal radiologist retrospectively reviewed knee CT and MRI examinations of 41 consecutive patients presenting to a level 1 trauma center with tibial plateau fractures. Fracture gap, articular depression, ligament tear and footprint avulsions were assessed on CT examinations. The MRI studies were examined for osseous and soft tissue injuries, including meniscal tear, meniscal displacement, ligament tear, and ligament avulsion. CT demonstrated torn ligaments with 80% sensitivity and 98% specificity. Only 2% of ligaments deemed intact on careful CT evaluation had partial or complete tears on MRI. Although the degree of fracture gap and articular depression was significantly greater in patients with meniscal injury compared with those without meniscal injury, ROC analysis demonstrated no clear threshold for gap or depression that yielded a combination of high sensitivity and specificity. In the acute setting, CT offers high sensitivity and specificity for depicting osseous avulsions, as well as high negative predictive value for excluding ligament injury. However, MRI remains necessary for the preoperative detection of meniscal injury.

Burnout and Work Demands Predict Reduced Job Satisfaction in Health Professionals Working In a Surgery Clinic

PubMed Central

Mijakoski, Dragan; Karadzinska-Bislimovska, Jovanka; Basarovska, Vera; Stoleski, Sasho; Minov, Jordan

2015-01-01

BACKGROUND: Burnout syndrome develops in health professionals (HPs) as a result of exposure to chronic emotional and interpersonal workplace stressors. Research demonstrates the links between burnout, work demands, and job satisfaction in hospital HPs. AIMS: To examine the associations between burnout, work demands and job satisfaction, and to demonstrate the mediation effect of emotional exhaustion on the relationship between work demands and job satisfaction in surgery clinic HPs. METHODS: Maslach Burnout Inventory was used for assessment of burnout. Work demands and job satisfaction were measured with Hospital Experience Scale and Job Satisfaction Survey, respectively. In order to examine the role of emotional exhaustion, depersonalization, and work demands, controlling for age, hospital tenure, and unit tenure, a hierarchical multiple regression models were tested for each job satisfaction factor. RESULTS: Job satisfaction was negatively predicted by emotional exhaustion. Certain types of work demands negatively predicted different factors of job satisfaction. Emotional exhaustion was a significant partial mediator of the relationship between work demands and job satisfaction. CONCLUSIONS: Adequate management of work demands, particularly excessive workload, time pressure, and lack of staff can lead to prevention of burnout and reduced job satisfaction in surgery clinic HPs, and contribute to better quality of patient care. PMID:27275216

Gender differences in structured risk assessment: comparing the accuracy of five instruments.

PubMed

Coid, Jeremy; Yang, Min; Ullrich, Simone; Zhang, Tianqiang; Sizmur, Steve; Roberts, Colin; Farrington, David P; Rogers, Robert D

2009-04-01

Structured risk assessment should guide clinical risk management, but it is uncertain which instrument has the highest predictive accuracy among men and women. In the present study, the authors compared the Psychopathy Checklist-Revised (PCL-R; R. D. Hare, 1991, 2003); the Historical, Clinical, Risk Management-20 (HCR-20; C. D. Webster, K. S. Douglas, D. Eaves, & S. D. Hart, 1997); the Risk Matrix 2000-Violence (RM2000[V]; D. Thornton et al., 2003); the Violence Risk Appraisal Guide (VRAG; V. L. Quinsey, G. T. Harris, M. E. Rice, & C. A. Cormier, 1998); the Offenders Group Reconviction Scale (OGRS; J. B. Copas & P. Marshall, 1998; R. Taylor, 1999); and the total previous convictions among prisoners, prospectively assessed prerelease. The authors compared predischarge measures with subsequent offending and instruments ranked using multivariate regression. Most instruments demonstrated significant but moderate predictive ability. The OGRS ranked highest for violence among men, and the PCL-R and HCR-20 H subscale ranked highest for violence among women. The OGRS and total previous acquisitive convictions demonstrated greatest accuracy in predicting acquisitive offending among men and women. Actuarial instruments requiring no training to administer performed as well as personality assessment and structured risk assessment and were superior among men for violence.

Psychometric properties of the Stroke Impairment Assessment Set (SIAS).

PubMed

Liu, Meigen; Chino, Naoichi; Tuji, Testuya; Masakado, Yoshihisa; Hase, Kimitaka; Kimura, Akio

2002-12-01

To review the psychometric properties of the Stroke Impairment Assessment Set (SAS), which was developed in 1990 as a comprehensive instrument to assess stroke impairment. Articles related to the SIAS were retrieved from the MEDLINE and the Folia Centro Japonica. Thirty-five articles were retrieved and analyzed. 1) Scale quality: Rasch analysis demonstrated the unidimensionality of the SIAS. Factor analysis produced factors corresponding to the 6 SIAS subscales. 2) Interrater reliability: The weighted kappas were high except for the unaffected side quadriceps item for which the score distribution was skewed. 3) Concurrent validity: Significant correlations were found between a) SIAS motor items and the Motricity Index or the Brunnstrom stage, b) SIAS lower extremity scores and the Functional Independence Measure (FIMSM) locomotion scores, c) trunk scores and abdominal manual muscle testing, d) visuospatial scores and line bisection and copying task scores, and e) speech scores and the FIMSM communication scores. 4) Predictive validity: Three studies attempting to predict discharge functional status demonstrated that adding the SIAS as one of the predictors enhanced the predictive power 5) Responsiveness: The SIAS was more responsive to changes than the Motricity Index, the Brunnstrom stage, or the National Institutes of Health Stroke Scale. The SIAS is a useful measure of stroke impairment with well-established psychometric properties.

Prediction of the rate of the rise of an air bubble in nanofluids in a vertical tube.

PubMed

Cho, Heon Ki; Nikolov, Alex D; Wasan, Darsh T

2018-04-19

Our recent experiments have demonstrated that when a bubble rises through a nanofluid (a liquid containing dispersed nanoparticles) in a vertical tube, a nanofluidic film with several particle layers is formed between the gas bubble and the glass tube wall, which significantly changes the bubble velocity due to the nanoparticle layering phenomenon in the film. We calculated the structural nanofilm viscosity as a function of the number of particle layers confined in it and found that the film viscosity increases rather steeply when the film contains only one or two particle layers. The nanofilm viscosity was found to be several times higher than the bulk viscosity of the fluid. Consequently, the Bretherton equation cannot accurately predict the rate of the rise of a slow-moving long bubble in a vertical tube in a nanofluid because it is valid only for very thick films and uses the bulk viscosity of the fluid. However, in this brief note, we demonstrate that the Bretherton equation can indeed be used for predicting the rate of the rise of a long single bubble through a vertical tube filled with a nanofluid by simply replacing the bulk viscosity with the proper structural nanofilm viscosity of the fluid. Copyright © 2018. Published by Elsevier Inc.

Attempted validation of the NUn score and inflammatory markers as predictors of esophageal anastomotic leak and major complications.

PubMed

Findlay, J M; Tilson, R C; Harikrishnan, A; Sgromo, B; Marshall, R E K; Maynard, N D; Gillies, R S; Middleton, M R

2015-10-01

The ability to predict complications following esophagectomy/extended total gastrectomy would be of great clinical value. A recent study demonstrated significant correlations between anastomotic leak (AL) and numerical values of C-reactive protein (CRP), white cell count (WCC) and albumin measured on postoperative day (POD) 4. A predictive model comprising all three (NUn score >10) was found to be highly sensitive and discriminant in predicting AL and complications. We attempted a retrospective validation in our center. Data were collected on all resections performed during a 5-year period (April 2008-2013) using prospectively maintained databases. Our biochemistry laboratory uses a maximum CRP value (156 mg/L), unlike that of the original study; otherwise all variables and outcome measures were comparable. Analysis was performed for all patients with complete blood results on POD4. Three hundred twenty-six patients underwent resection, of which 248 had POD4 bloods. There were 21 AL overall (6.44%); 16 among those with complete POD4 blood results (6.45%). There were 8 (2.45%) in-hospital deaths; 7 (2.82%) in those with POD4 results. No parameters were associated with AL or complication severity on univariate analysis. WCC was associated with AL in multivariate binary logistic regression with albumin and CRP (OR 1.23 [95% CI 1.03-1.47]; P = 0.021). When a binary variable of CRP ≥ 156 mg/L was used rather than an absolute value, no factors were significant. Mean NUn was 8.30 for AL, compared with 8.40 for non-AL (P = 0.710 independent t-test). NUn > 10 predicted 0 of 16 leaks (sensitivity 0.00%, specificity 94.4%, receiver operator curve [ROC] area under the curve [AUC] 0.485; P = 0.843). NUn > 7.65 was 93% sensitive and 21.6% specific. ROC for WCC alone was comparable with NUn (AUC 0.641 [0.504-0.779]; P = 0.059; WCC > 6.89 93.8% sensitive, 20.7% specific; WCC > 15 6.3% sensitive and 97% specific). There were no associations between any parameters and other complications. In a comparable cohort with the original study, we demonstrated a similar multivariate association between WCC alone on POD4 and subsequent demonstration of AL, but not albumin or CRP (measured up to 156 mg/L). The NUn score overall (calculated with this caveat) and a threshold of 10 was not found to have clinical utility in predicting AL or complications. © 2014 International Society for Diseases of the Esophagus.

Spectra of conditionalization and typicality in the multiverse

NASA Astrophysics Data System (ADS)

Azhar, Feraz

2016-02-01

An approach to testing theories describing a multiverse, that has gained interest of late, involves comparing theory-generated probability distributions over observables with their experimentally measured values. It is likely that such distributions, were we indeed able to calculate them unambiguously, will assign low probabilities to any such experimental measurements. An alternative to thereby rejecting these theories, is to conditionalize the distributions involved by restricting attention to domains of the multiverse in which we might arise. In order to elicit a crisp prediction, however, one needs to make a further assumption about how typical we are of the chosen domains. In this paper, we investigate interactions between the spectra of available assumptions regarding both conditionalization and typicality, and draw out the effects of these interactions in a concrete setting; namely, on predictions of the total number of species that contribute significantly to dark matter. In particular, for each conditionalization scheme studied, we analyze how correlations between densities of different dark matter species affect the prediction, and explicate the effects of assumptions regarding typicality. We find that the effects of correlations can depend on the conditionalization scheme, and that in each case atypicality can significantly change the prediction. In doing so, we demonstrate the existence of overlaps in the predictions of different "frameworks" consisting of conjunctions of theory, conditionalization scheme and typicality assumption. This conclusion highlights the acute challenges involved in using such tests to identify a preferred framework that aims to describe our observational situation in a multiverse.

The Gastric/Pancreatic Amylase Ratio Predicts Postoperative Pancreatic Fistula With High Sensitivity and Specificity

PubMed Central

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

Abstract This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD). POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed. In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase. Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity. This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers. PMID:25621676

The gastric/pancreatic amylase ratio predicts postoperative pancreatic fistula with high sensitivity and specificity.

PubMed

Jin, Shuo; Shi, Xiao-Ju; Sun, Xiao-Dong; Zhang, Ping; Lv, Guo-Yue; Du, Xiao-Hong; Wang, Si-Yuan; Wang, Guang-Yi

2015-01-01

This article aims to identify risk factors for postoperative pancreatic fistula (POPF) and evaluate the gastric/pancreatic amylase ratio (GPAR) on postoperative day (POD) 3 as a POPF predictor in patients who undergo pancreaticoduodenectomy (PD).POPF significantly contributes to mortality and morbidity in patients who undergo PD. Previously identified predictors for POPF often have low predictive accuracy. Therefore, accurate POPF predictors are needed.In this prospective cohort study, we measured the clinical and biochemical factors of 61 patients who underwent PD and diagnosed POPF according to the definition of the International Study Group of Pancreatic Fistula. We analyzed the association between POPF and various factors, identified POPF risk factors, and evaluated the predictive power of the GPAR on POD3 and the levels of serum and ascites amylase.Of the 61 patients, 21 developed POPF. The color of the pancreatic drain fluid, POD1 serum, POD1 median output of pancreatic drain fluid volume, and GPAR were significantly associated with POPF. The color of the pancreatic drain fluid and high GPAR were independent risk factors. Although serum and ascites amylase did not predict POPF accurately, the cutoff value was 1.24, and GPAR predicted POPF with high sensitivity and specificity.This is the first report demonstrating that high GPAR on POD3 is a risk factor for POPF and showing that GPAR is a more accurate predictor of POPF than the previously reported amylase markers.

Non-invasive prediction of forthcoming cirrhosis-related complications

PubMed Central

Kang, Wonseok; Kim, Seung Up; Ahn, Sang Hoon

2014-01-01

In patients with chronic liver diseases, identification of significant liver fibrosis and cirrhosis is essential for determining treatment strategies, assessing therapeutic response, and stratifying long-term prognosis. Although liver biopsy remains the reference standard for evaluating the extent of liver fibrosis in patients with chronic liver diseases, several non-invasive methods have been developed as alternatives to liver biopsies. Some of these non-invasive methods have demonstrated clinical accuracy for diagnosing significant fibrosis or cirrhosis in many cross-sectional studies with the histological fibrosis stage as a reference standard. However, non-invasive methods cannot be fully validated through cross-sectional studies since liver biopsy is not a perfect surrogate endpoint marker. Accordingly, recent studies have focused on assessing the performance of non-invasive methods through long-term, longitudinal, follow-up studies with solid clinical endpoints related to advanced stages of liver fibrosis and cirrhosis. As a result, current view is that these alternative methods can independently predict future cirrhosis-related complications, such as hepatic decompensation, liver failure, hepatocellular carcinoma, or liver-related death. The clinical role of non-invasive models seems to be shifting from a simple tool for predicting the extent of fibrosis to a surveillance tool for predicting future liver-related events. In this article, we will summarize recent longitudinal studies of non-invasive methods for predicting forthcoming complications related to liver cirrhosis and discuss the clinical value of currently available non-invasive methods based on evidence from the literature. PMID:24627597

Association of over-the-counter pharmaceutical sales with influenza-like-illnesses to patient volume in an urgent care setting.

PubMed

Liu, Timothy Y; Sanders, Jason L; Tsui, Fu-Chiang; Espino, Jeremy U; Dato, Virginia M; Suyama, Joe

2013-01-01

We studied the association between OTC pharmaceutical sales and volume of patients with influenza-like-illnesses (ILI) at an urgent care center over one year. OTC pharmaceutical sales explain 36% of the variance in the patient volume, and each standard deviation increase is associated with 4.7 more patient visits to the urgent care center (p<0.0001). Cross-correlation function analysis demonstrated that OTC pharmaceutical sales are significantly associated with patient volume during non-flu season (p<0.0001), but only the sales of cough and cold (p<0.0001) and thermometer (p<0.0001) categories were significant during flu season with a lag of two and one days, respectively. Our study is the first study to demonstrate and measure the relationship between OTC pharmaceutical sales and urgent care center patient volume, and presents strong evidence that OTC sales predict urgent care center patient volume year round.

Large eddy simulation applications in gas turbines.

PubMed

Menzies, Kevin

2009-07-28

The gas turbine presents significant challenges to any computational fluid dynamics techniques. The combination of a wide range of flow phenomena with complex geometry is difficult to model in the context of Reynolds-averaged Navier-Stokes (RANS) solvers. We review the potential for large eddy simulation (LES) in modelling the flow in the different components of the gas turbine during a practical engineering design cycle. We show that while LES has demonstrated considerable promise for reliable prediction of many flows in the engine that are difficult for RANS it is not a panacea and considerable application challenges remain. However, for many flows, especially those dominated by shear layer mixing such as in combustion chambers and exhausts, LES has demonstrated a clear superiority over RANS for moderately complex geometries although at significantly higher cost which will remain an issue in making the calculations relevant within the design cycle.

Differences in Expressivity Based on Attractiveness: Target or Perceiver Effects?

PubMed

Rennels, Jennifer L; Kayl, Andrea J

2015-09-01

A significant association exists between adults' expressivity and facial attractiveness, but it is unclear whether the association is linear or significant only at the extremes of attractiveness. It is also unclear whether attractive persons actually display more positive expressivity than unattractive persons (target effects) or whether high and low attractiveness influences expressivity valence judgments (perceiver effects). Experiment 1 demonstrated adult ratings of attractiveness were predictive of expressivity valence only for high and low attractive females and medium attractive males. Experiment 2 showed that low attractive females actually display more negative expressivity than medium and high attractive females, but there were no target effects for males. Also, attractiveness influenced expressivity valence judgments (perceiver effects) for both females and males. Our findings demonstrate that low attractive females are at a particular disadvantage during social interactions due to their low attractiveness, actual displays of negative expressivity, and perceptions of their negative expressivity.

Differences in Expressivity Based on Attractiveness: Target or Perceiver Effects?

PubMed Central

Rennels, Jennifer L.; Kayl, Andrea J.

2015-01-01

A significant association exists between adults’ expressivity and facial attractiveness, but it is unclear whether the association is linear or significant only at the extremes of attractiveness. It is also unclear whether attractive persons actually display more positive expressivity than unattractive persons (target effects) or whether high and low attractiveness influences expressivity valence judgments (perceiver effects). Experiment 1 demonstrated adult ratings of attractiveness were predictive of expressivity valence only for high and low attractive females and medium attractive males. Experiment 2 showed that low attractive females actually display more negative expressivity than medium and high attractive females, but there were no target effects for males. Also, attractiveness influenced expressivity valence judgments (perceiver effects) for both females and males. Our findings demonstrate that low attractive females are at a particular disadvantage during social interactions due to their low attractiveness, actual displays of negative expressivity, and perceptions of their negative expressivity. PMID:26366010

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

PubMed Central

Ngendahimana, David K.; Fagerholm, Cara L.; Sun, Jiayang; Bruckman, Laura S.

2017-01-01

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. PMID:28498875

Renal Cell Carcinoma: Comparison of RENAL Nephrometry and PADUA Scores with Maximum Tumor Diameter for Prediction of Local Recurrence after Thermal Ablation.

PubMed

Maxwell, Aaron W P; Baird, Grayson L; Iannuccilli, Jason D; Mayo-Smith, William W; Dupuy, Damian E

2017-05-01

Purpose To evaluate the performance of the radius, exophytic or endophytic, nearness to collecting system or sinus, anterior or posterior, and location relative to polar lines (RENAL) nephrometry and preoperative aspects and dimensions used for anatomic classification (PADUA) scoring systems and other tumor biometrics for prediction of local tumor recurrence in patients with renal cell carcinoma after thermal ablation. Materials and Methods This HIPAA-compliant study was performed with a waiver of informed consent after institutional review board approval was obtained. A retrospective evaluation of 207 consecutive patients (131 men, 76 women; mean age, 71.9 years ± 10.9) with 217 biopsy-proven renal cell carcinoma tumors treated with thermal ablation was conducted. Serial postablation computed tomography (CT) or magnetic resonance (MR) imaging was used to evaluate for local tumor recurrence. For each tumor, RENAL nephrometry and PADUA scores were calculated by using imaging-derived tumor morphologic data. Several additional tumor biometrics and combinations thereof were also measured, including maximum tumor diameter. The Harrell C index and hazard regression techniques were used to quantify associations with local tumor recurrence. Results The RENAL (hazard ratio, 1.43; P = .003) and PADUA (hazard ratio, 1.80; P < .0001) scores were found to be significantly associated with recurrence when regression techniques were used but demonstrated only poor to fair discrimination according to Harrell C index results (C, 0.68 and 0.75, respectively). Maximum tumor diameter showed the highest discriminatory strength of any individual variable evaluated (C, 0.81) and was also significantly predictive when regression techniques were used (hazard ratio, 2.98; P < .0001). For every 1-cm increase in diameter, the estimated rate of recurrence risk increased by 198%. Conclusion Maximum tumor diameter demonstrates superior performance relative to existing tumor scoring systems and other evaluated biometrics for prediction of local tumor recurrence after renal cell carcinoma ablation. © RSNA, 2016.

Correlation Between Acoustic Measurements and Self-Reported Voice Disorders Among Female Teachers.

PubMed

Lin, Feng-Chuan; Chen, Sheng Hwa; Chen, Su-Chiu; Wang, Chi-Te; Kuo, Yu-Ching

2016-07-01

Many studies focused on teachers' voice problems and most of them were conducted using questionnaires, whereas little research has investigated the relationship between self-reported voice disorders and objective quantification of voice. This study intends to explore the relationship of acoustic measurements according to self-reported symptoms and its predictive value of future dysphonia. This is a case-control study. Voice samples of 80 female teachers were analyzed, including 40 self-reported voice disorders (VD) and 40 self-reported normal voice (NVD) subjects. The acoustic measurements included jitter, shimmer, and noise-to-harmonics ratio (NHR). Levene's t test and logistic regression were used to analyze the differences between VD and NVD and the relationship between self-reported voice conditions and the acoustic measurements. To examine whether acoustic measurements can be used to predict further voice disorders, we applied a receiver operating characteristic (ROC) curve to determine the cutoff values and the associated sensitivity and specificity. The results showed that jitter, shimmer, and the NHR of VD were significantly higher than those of NVD. Among the parameters, the NHR and shimmer demonstrated the highest correlation with self-reported voice disorders. By using the NHR ≥0.138 and shimmer ≥0.470 dB as the cutoff values, the ROC curve displayed 72.5% of sensitivity and 75% of specificity, and the overall positive predictive value for subsequent dysphonia achieved 60%. This study demonstrated a significant correlation between acoustic measurements and self-reported dysphonic symptoms. NHR and ShdB are two acoustic parameters that are more able to reflect vocal abnormalities and, probably, to predict subsequent subjective voice disorder. Future research recruiting more subjects in other occupations and genders shall validate the preliminary results revealed in this study. Copyright © 2016 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Obstructive Airways Disease With Air Trapping Among Firefighters Exposed to World Trade Center Dust

PubMed Central

Weiden, Michael D.; Ferrier, Natalia; Nolan, Anna; Rom, William N.; Comfort, Ashley; Gustave, Jackson; Zeig-Owens, Rachel; Zheng, Shugi; Goldring, Roberta M.; Berger, Kenneth I.; Cosenza, Kaitlyn; Lee, Roy; Webber, Mayris P.; Kelly, Kerry J.; Aldrich, Thomas K.

2010-01-01

Background: The World Trade Center (WTC) collapse produced a massive exposure to respirable particulates in New York City Fire Department (FDNY) rescue workers. This group had spirometry examinations pre-September 11, 2001, and post-September 11, 2001, demonstrating declines in lung function with parallel declines in FEV1 and FVC. To date, the underlying pathophysiologic cause for this has been open to question. Methods: Of 13,234 participants in the FDNY-WTC Monitoring Program, 1,720 (13%) were referred for pulmonary subspecialty evaluation at a single institution. Evaluation included 919 full pulmonary function tests, 1,219 methacholine challenge tests, and 982 high-resolution chest CT scans. Results: At pulmonary evaluation (median 34 months post-September 11, 2001), median values were FEV1 93% predicted (interquartile range [IQR], 83%-101%), FVC 98% predicted (IQR, 89%-106%), and FEV1/FVC 0.78 (IQR, 0.72-0.82). The residual volume (RV) was 123% predicted (IQR, 106%-147%) with nearly all participants having normal total lung capacity, functional residual capacity, and diffusing capacity of carbon monoxide. Also, 1,051/1,720 (59%) had obstructive airways disease based on at least one of the following: FEV1/FVC, bronchodilator responsiveness, hyperreactivity, or elevated RV. After adjusting for age, gender, race, height and weight, and tobacco use, the decline in FEV1 post-September 11, 2001, was significantly correlated with increased RV percent predicted (P < .0001), increased bronchodilator responsiveness (P < .0001), and increased hyperreactivity (P = .0056). CT scans demonstrated bronchial wall thickening that was significantly associated with the decline in FEV1 post-September 11, 2001 (P = .024), increases in hyperreactivity (P < .0001), and increases in RV (P < .0001). Few had evidence for interstitial disease. Conclusions: Airways obstruction was the predominant physiologic finding underlying the reduction in lung function post-September 11, 2001, in FDNY WTC rescue workers presenting for pulmonary evaluation. PMID:19820077

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

Territoriality and Conflict Avoidance Explain Asociality (Solitariness) of the Endosymbiotic Pea Crab Tunicotheres moseri

PubMed Central

Ambrosio, Louis J.; Baeza, J. Antonio

2016-01-01

Host monopolization theory predicts symbiotic organisms inhabiting morphologically simple, relatively small and scarce hosts to live solitarily as a result of territorial behaviors. We tested this prediction with Tunicotheres moseri, an endosymbiotic crab dwelling in the atrial chamber of the morphologically simple, small, and relatively scarce ascidian Styela plicata. As predicted, natural populations of T. moseri inhabit ascidian hosts solitarily with greater frequency than expected by chance alone. Furthermore, laboratory experiments demonstrated that intruder crabs take significantly longer to colonize previously infected compared to uninfected hosts, indicating as expected, that resident crabs exhibit monopolization behaviors. While territoriality does occur, agonistic behaviors employed by T. moseri do not mirror the overt behaviors commonly reported for other territorial crustaceans. Documented double and triple cohabitations in the field coupled with laboratory observations demonstrating the almost invariable success of intruder crabs colonizing occupied hosts, suggest that territoriality is ineffective in completely explaining the solitary social habit of this species. Additional experiments showed that T. moseri juveniles and adults, when searching for ascidians use chemical cues to avoid hosts occupied by conspecifics. This conspecific avoidance behavior reported herein is a novel strategy most likely employed to preemptively resolve costly territorial conflicts. In general, this study supports predictions central to host monopolization theory, but also implies that alternative behavioral strategies (i.e., conflict avoidance) may be more important than originally thought in explaining the host use pattern of symbiotic organisms. PMID:26910474

Analytical performance evaluation of SAR ATR with inaccurate or estimated models

NASA Astrophysics Data System (ADS)

DeVore, Michael D.

2004-09-01

Hypothesis testing algorithms for automatic target recognition (ATR) are often formulated in terms of some assumed distribution family. The parameter values corresponding to a particular target class together with the distribution family constitute a model for the target's signature. In practice such models exhibit inaccuracy because of incorrect assumptions about the distribution family and/or because of errors in the assumed parameter values, which are often determined experimentally. Model inaccuracy can have a significant impact on performance predictions for target recognition systems. Such inaccuracy often causes model-based predictions that ignore the difference between assumed and actual distributions to be overly optimistic. This paper reports on research to quantify the effect of inaccurate models on performance prediction and to estimate the effect using only trained parameters. We demonstrate that for large observation vectors the class-conditional probabilities of error can be expressed as a simple function of the difference between two relative entropies. These relative entropies quantify the discrepancies between the actual and assumed distributions and can be used to express the difference between actual and predicted error rates. Focusing on the problem of ATR from synthetic aperture radar (SAR) imagery, we present estimators of the probabilities of error in both ideal and plug-in tests expressed in terms of the trained model parameters. These estimators are defined in terms of unbiased estimates for the first two moments of the sample statistic. We present an analytical treatment of these results and include demonstrations from simulated radar data.

Territoriality and Conflict Avoidance Explain Asociality (Solitariness) of the Endosymbiotic Pea Crab Tunicotheres moseri.

PubMed

Ambrosio, Louis J; Baeza, J Antonio

2016-01-01

Host monopolization theory predicts symbiotic organisms inhabiting morphologically simple, relatively small and scarce hosts to live solitarily as a result of territorial behaviors. We tested this prediction with Tunicotheres moseri, an endosymbiotic crab dwelling in the atrial chamber of the morphologically simple, small, and relatively scarce ascidian Styela plicata. As predicted, natural populations of T. moseri inhabit ascidian hosts solitarily with greater frequency than expected by chance alone. Furthermore, laboratory experiments demonstrated that intruder crabs take significantly longer to colonize previously infected compared to uninfected hosts, indicating as expected, that resident crabs exhibit monopolization behaviors. While territoriality does occur, agonistic behaviors employed by T. moseri do not mirror the overt behaviors commonly reported for other territorial crustaceans. Documented double and triple cohabitations in the field coupled with laboratory observations demonstrating the almost invariable success of intruder crabs colonizing occupied hosts, suggest that territoriality is ineffective in completely explaining the solitary social habit of this species. Additional experiments showed that T. moseri juveniles and adults, when searching for ascidians use chemical cues to avoid hosts occupied by conspecifics. This conspecific avoidance behavior reported herein is a novel strategy most likely employed to preemptively resolve costly territorial conflicts. In general, this study supports predictions central to host monopolization theory, but also implies that alternative behavioral strategies (i.e., conflict avoidance) may be more important than originally thought in explaining the host use pattern of symbiotic organisms.

Media exposure and dimensions of anxiety sensitivity: differential associations with PTSD symptom clusters.

PubMed

Collimore, Kelsey C; McCabe, Randi E; Carleton, R Nicholas; Asmundson, Gordon J G

2008-08-01

The present investigation examined the impact of anxiety sensitivity (AS) and media exposure on posttraumatic stress disorder (PTSD) symptoms. Reactions from 143 undergraduate students in Hamilton, Ontario were assessed in the Fall of 2003 to gather information on anxiety, media coverage, and PTSD symptoms related to exposure to a remote traumatic event (September 11th). Regression analyses revealed that the Anxiety Sensitivity Index (ASI; [Peterson, R. A., & Reiss, S. (1992). Anxiety Sensitivity Index manual, 2nd ed. Worthington, Ohio: International Diagnostic Systems]) and State-Trait Anxiety Inventory trait form (STAI-T; [Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). State-trait anxiety inventory. Palo Alto, California: Consulting Psychologists Press]) total scores were significant predictors of PTSD symptoms in general. The ASI total score was also a significant predictor of hyperarousal and avoidance symptoms. Subsequent analyses further demonstrated differential relationships based on subscales and symptom clusters. Specifically, media exposure and trait anxiety predicted hyperarousal and re-experiencing symptoms, whereas the ASI fear of somatic sensations subscale significantly predicted avoidance and overall PTSD symptoms. Implications and directions for future research are discussed.

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1

NASA Astrophysics Data System (ADS)

Day, Jonathan J.; Tietsche, Steffen; Collins, Mat; Goessling, Helge F.; Guemas, Virginie; Guillory, Anabelle; Hurlin, William J.; Ishii, Masayoshi; Keeley, Sarah P. E.; Matei, Daniela; Msadek, Rym; Sigmond, Michael; Tatebe, Hiroaki; Hawkins, Ed

2016-06-01

Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño-Southern Oscillation.

Human Cortical θ during Free Exploration Encodes Space and Predicts Subsequent Memory

PubMed Central

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric

2013-01-01

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration. PMID:24048836

Body mass index predicts risk for complications from transtemporal cerebellopontine angle surgery.

PubMed

Mantravadi, Avinash V; Leonetti, John P; Burgette, Ryan; Pontikis, George; Marzo, Sam J; Anderson, Douglas

2013-03-01

To determine the relationship between body mass index (BMI) and risk for specific complications from transtemporal cerebellopontine angle (CPA) surgery for nonmalignant disease. Case series with chart review. Tertiary-care academic hospital. Retrospective review of 134 consecutive patients undergoing transtemporal cerebellopontine angle surgery for nonmalignant disease from 2009 to 2011. Data were collected regarding demographics, body mass index, intraoperative details, hospital stay, and complications including cerebrospinal fluid leak, wound complications, and brachial plexopathy. One hundred thirty-four patients were analyzed with a mean preoperative body mass index of 28.58. Statistical analysis demonstrated a significant difference in body mass index between patients with a postoperative cerebrospinal fluid leak and those without (P = .04), as well as a similar significant difference between those experiencing postoperative brachial plexopathy and those with no such complication (P = .03). Logistical regression analysis confirmed that body mass index is significant in predicting both postoperative cerebrospinal fluid leak (P = .004; odds ratio, 1.10) and brachial plexopathy (P = .04; odds ratio, 1.07). Elevated body mass index was not significant in predicting wound complications or increased hospital stay beyond postoperative day 3. Risk of cerebrospinal fluid leak and brachial plexopathy is increased in patients with elevated body mass index undergoing surgery of the cerebellopontine angle. Consideration should be given to preoperative optimization via dietary and lifestyle modifications as well as intraoperative somatosensory evoked potential monitoring of the brachial plexus to decrease these risks.

Human cortical θ during free exploration encodes space and predicts subsequent memory.

PubMed

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric; Poizner, Howard

2013-09-18

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration.

Near-Infrared Spectroscopy Enhances Intravascular Ultrasound Assessment of Vulnerable Coronary Plaque: A Combined Pathological and In Vivo Study.

PubMed

Puri, Rishi; Madder, Ryan D; Madden, Sean P; Sum, Stephen T; Wolski, Kathy; Muller, James E; Andrews, Jordan; King, Karilane L; Kataoka, Yu; Uno, Kiyoko; Kapadia, Samir R; Tuzcu, E Murat; Nissen, Steven E; Virmani, Renu; Maehara, Akiko; Mintz, Gary S; Nicholls, Stephen J

2015-11-01

Pathological studies demonstrate the dual significance of plaque burden (PB) and lipid composition for mediating coronary plaque vulnerability. We evaluated relationships between intravascular ultrasound (IVUS)-derived PB and arterial remodeling with near-infrared spectroscopy (NIRS)-derived lipid content in ex vivo and in vivo human coronary arteries. Ex vivo coronary NIRS and IVUS imaging was performed through blood in 116 coronary arteries of 51 autopsied hearts, followed by 2-mm block sectioning (n=2070) and histological grading according to modified American Heart Association criteria. Lesions were defined as the most heavily diseased 2-mm block per imaged artery on IVUS. IVUS-derived PB and NIRS-derived lipid core burden index (LCBI) of each block and lesion were analyzed. Block-level analysis demonstrated significant trends of increasing PB and LCBI across more complex atheroma (Ptrend <0.001 for both LCBI and PB). Lesion-based analyses demonstrated the highest LCBI and remodeling index within coronary fibroatheroma (Ptrend <0.001 and 0.02 versus all plaque groups, respectively). Prediction models demonstrated similar abilities of PB, LCBI, and remodeling index for discriminating fibroatheroma (c indices: 0.675, 0.712, and 0.672, respectively). A combined PB+LCBI analysis significantly improved fibroatheroma detection accuracy (c index 0.77, P=0.028 versus PB; net-reclassification index 43%, P=0.003), whereas further adding remodeling index did not (c index 0.80, P=0.27 versus PB+LCBI). In vivo comparisons of 43 age- and sex-matched patients (to the autopsy cohort) undergoing combined NIRS-IVUS coronary imaging yielded similar associations to those demonstrated ex vivo. Adding NIRS to conventional IVUS-derived PB imaging significantly improves the ability to detect more active, potentially vulnerable coronary atheroma. © 2015 American Heart Association, Inc.

Simultaneous construction of PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps.

PubMed

Liao, C; Peng, Z Y; Li, J B; Cui, X W; Zhang, Z H; Malakar, P K; Zhang, W J; Pan, Y J; Zhao, Y

2015-03-01

The aim of this study was to simultaneously construct PCR-DGGE-based predictive models of Listeria monocytogenes and Vibrio parahaemolyticus on cooked shrimps at 4 and 10°C. Calibration curves were established to correlate peak density of DGGE bands with microbial counts. Microbial counts derived from PCR-DGGE and plate methods were fitted by Baranyi model to obtain molecular and traditional predictive models. For L. monocytogenes, growing at 4 and 10°C, molecular predictive models were constructed. It showed good evaluations of correlation coefficients (R(2) > 0.92), bias factors (Bf ) and accuracy factors (Af ) (1.0 ≤ Bf ≤ Af ≤ 1.1). Moreover, no significant difference was found between molecular and traditional predictive models when analysed on lag phase (λ), maximum growth rate (μmax ) and growth data (P > 0.05). But for V. parahaemolyticus, inactivated at 4 and 10°C, molecular models show significant difference when compared with traditional models. Taken together, these results suggest that PCR-DGGE based on DNA can be used to construct growth models, but it is inappropriate for inactivation models yet. This is the first report of developing PCR-DGGE to simultaneously construct multiple molecular models. It has been known for a long time that microbial predictive models based on traditional plate methods are time-consuming and labour-intensive. Denaturing gradient gel electrophoresis (DGGE) has been widely used as a semiquantitative method to describe complex microbial community. In our study, we developed DGGE to quantify bacterial counts and simultaneously established two molecular predictive models to describe the growth and survival of two bacteria (Listeria monocytogenes and Vibrio parahaemolyticus) at 4 and 10°C. We demonstrated that PCR-DGGE could be used to construct growth models. This work provides a new approach to construct molecular predictive models and thereby facilitates predictive microbiology and QMRA (Quantitative Microbial Risk Assessment). © 2014 The Society for Applied Microbiology.

The interactive effects of estrogen and progesterone on changes in emotional eating across the menstrual cycle.

PubMed

Klump, Kelly L; Keel, Pamela K; Racine, Sarah E; Burt, S Alexandra; Burt, Alexandra S; Neale, Michael; Sisk, Cheryl L; Boker, Steven; Hu, Jean Yueqin

2013-02-01

Studies suggest that within-person changes in estrogen and progesterone predict changes in binge eating across the menstrual cycle. However, samples have been extremely small (maximum N = 9), and analyses have not examined the interactive effects of hormones that are critical for changes in food intake in animals. The aims of the current study were to examine ovarian hormone interactions in the prediction of within-subject changes in emotional eating in the largest sample of women to date (N = 196). Participants provided daily ratings of emotional eating and saliva samples for hormone measurement for 45 consecutive days. Results confirmed that changes in ovarian hormones predict changes in emotional eating across the menstrual cycle, with a significant estradiol × progesterone interaction. Emotional eating scores were highest during the midluteal phase, when progesterone peaks and estradiol demonstrates a secondary peak. Findings extend previous work by highlighting significant interactions between estrogen and progesterone that explain midluteal increases in emotional eating. Future work should explore mechanisms (e.g., gene-hormone interactions) that contribute to both within- and between-subjects differences in emotional eating. 2013 APA, all rights reserved

Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada

NASA Astrophysics Data System (ADS)

Zhang, Kongwen; Hu, Baoxin; Robinson, Justin

2014-01-01

The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.

A proteomic analysis identifies candidate early biomarkers to predict ovarian hyperstimulation syndrome in polycystic ovarian syndrome patients.

PubMed

Wu, Lan; Sun, Yazhou; Wan, Jun; Luan, Ting; Cheng, Qing; Tan, Yong

2017-07-01

Ovarian hyperstimulation syndrome (OHSS) is a potentially life‑threatening, iatrogenic complication that occurs during assisted reproduction. Polycystic ovarian syndrome (PCOS) significantly increases the risk of OHSS during controlled ovarian stimulation. Therefore, a more effective early prediction technique is required in PCOS patients. Quantitative proteomic analysis of serum proteins indicates the potential diagnostic value for disease. In the present study, the authors revealed the differentially expressed proteins in OHSS patients with PCOS as new diagnostic biomarkers. The promising proteins obtained from liquid chromatography‑mass spectrometry were subjected to ELISA and western blotting assay for further confirmation. A total of 57 proteins were identified with significant difference, of which 29 proteins were upregulated and 28 proteins were downregulated in OHSS patients. Haptoglobin, fibrinogen and lipoprotein lipase were selected as candidate biomarkers. Receiver operating characteristic curve analysis demonstrated all three proteins may have potential as biomarkers to discriminate OHSS in PCOS patients. Haptoglobin, fibrinogen and lipoprotein lipase have never been reported as a predictive marker of OHSS in PCOS patients, and their potential roles in OHSS occurrence deserve further studies. The proteomic results reported in the present study may gain deeper insights into the pathophysiology of OHSS.

Can the big five factors of personality predict lymphocyte counts?

PubMed

Ožura, Ana; Ihan, Alojz; Musek, Janek

2012-03-01

Psychological stress is known to affect the immune system. The Limbic Hypothalamic Pituitary Adrenal (LHPA) axis has been identified as the principal path of the bidirectional communication between the immune system and the central nervous system with significant psychological activators. Personality traits acted as moderators of the relationship between life conflicts and psychological distress. This study focuses on the relationship between the Big Five factors of personality and immune regulation as indicated by Lymphocyte counts. Our study included 32 professional soldiers from the Slovenian Army that completed the Big Five questionnaire (Goldberg IPIP-300). We also assessed their white blood cell counts with a detailed lymphocyte analysis using flow cytometry. The correlations between personality variables and immune system parameters were calculated. Furthermore, regression analyses were performed using personality variables as predictors and immune parameters as criteria. The results demonstrated that the model using the Big Five factors as predictors of Lymphocyte counts is significant in predicting the variance in NK and B cell counts. Agreeableness showed the strongest predictive function. The results offer support for the theoretical models that stressed the essential links between personality and immune regulation. Further studies with larger samples examining the Big five factors and immune system parameters are needed.