Predicting employment status and subjective quality of life in patients with schizophrenia

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Haruo Fujino

2016-03-01

Full Text Available Although impaired social functioning, particularly poor employment status, is a cardinal feature of patients with schizophrenia and leads to decreased quality of life (QOL, few studies have addressed the relationship between these two clinical issues. The aim of this study was to determine whether employment status predicts subjective QOL and to evaluate a model in which functional capacity mediates the relationship between general cognitive performance and employment status. Ninety-three patients with schizophrenia were administered a comprehensive battery of cognitive tests, the UCSD Performance-based Skills Assessment-Brief version (UPSA-B, the Social Functioning Scale (SFS, and the Subjective Quality of Life Scale (SQLS. First, we evaluated a model for predicting the employment/occupation subscale score of the SFS using path analysis, and the model fitted well (χ2 (4 = 3.6, p = 0.46; CFI = 1.0; RMSEA < 0.001, with 90% CIs: 0–0.152. Employment status was predicted by negative symptoms and functional capacity, which was in turn predicted by general cognitive performance. Second, we added subjective QOL to this model. In a final path model, QOL was predicted by negative symptoms and employment status. This model also satisfied good fit criteria (χ2 (7 = 10.3, p = 0.17; CFI = 0.987; RMSEA = 0.072, with 90% CIs: 0–0.159. The UPSA-B and SFS scores were moderately correlated with most measures of cognitive performance. These results support the notion that better employment status enhances subjective QOL in patients with schizophrenia.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

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Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

2017-10-01

Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

Communal and Agentic Interpersonal and Intergroup Motives Predict Preferences for Status Versus Power.

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Locke, Kenneth D; Heller, Sonja

2017-01-01

Seven studies involving 1,343 participants showed how circumplex models of social motives can help explain individual differences in preferences for status (having others' admiration) versus power (controlling valuable resources). Studies 1 to 3 and 7 concerned interpersonal motives in workplace contexts, and found that stronger communal motives (to have mutual trust, support, and cooperation) predicted being more attracted to status (but not power) and achieving more workplace status, while stronger agentic motives (to be firm, decisive, and influential) predicted being more attracted to and achieving more workplace power, and experiencing a stronger connection between workplace power and job satisfaction. Studies 4 to 6 found similar effects for intergroup motives: Stronger communal motives predicted wanting one's ingroup (e.g., country) to have status-but not power-relative to other groups. Finally, most people preferred status over power, and this was especially true for women, which was partially explained by women having stronger communal motives.

Clinical implication of negative conversion of predicted circumferential resection margin status after preoperative chemoradiotherapy for locally advanced rectal cancer

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Lee, Nam Kwon [Department of Radiation Oncology, Korea University Medical Center, Korea University College of Medicine, Seoul (Korea, Republic of); Kim, Chul Yong, E-mail: kcyro@korea.ac.kr [Department of Radiation Oncology, Korea University Medical Center, Korea University College of Medicine, Seoul (Korea, Republic of); Park, Young Je; Yang, Dae Sik; Yoon, Won Sup [Department of Radiation Oncology, Korea University Medical Center, Korea University College of Medicine, Seoul (Korea, Republic of); Kim, Seon Hahn; Kim, Jin [Division of Colorectal Surgery, Department of Surgery, Korea University Medical Center, Korea University College of Medicine, Seoul (Korea, Republic of)

2014-02-15

Objective: To evaluate the prognostic implication of the negative conversion of predicted circumferential resection margin status before surgery in patients with locally advanced rectal cancer with predicted circumferential resection margin involvement. Methods: Thirty-eight patients (28 men, 10 women; median age, 61 years; age range, 39–80 years) with locally advanced rectal cancer with predicted circumferential resection margin involvement who underwent preoperative chemoradiotherapy followed by radical surgery were analyzed. Involvement of the circumferential resection margin was predicted on the basis of pre- and post-chemoradiotherapy magnetic resonance imaging. The primary endpoints were 3-year local recurrence-free survival and overall survival. Results: The median follow-up time was 41.1 months (range, 13.9–85.2 months). The negative conversion rate of predicted circumferential resection margin status after preoperative chemoradiotherapy was 65.8%. Patients who experienced negative conversion of predicted circumferential resection margin status had a significantly higher 3-year local recurrence-free survival rate (100.0% vs. 76.9%; P = 0.013), disease-free survival rate (91.7% vs. 59.3%; P = 0.023), and overall survival rate (96.0% vs. 73.8%; P = 0.016) than those who had persistent circumferential resection margin involvement. Conclusions: The negative conversion of the predicted circumferential resection margin status as predicted by magnetic resonance imaging will assist in individual risk stratification as a predictive factor for treatment response and survival before surgery. These findings may help physicians determine whether to administer more intense adjuvant chemotherapy or change the surgical plan for patients displaying resistance to preoperative chemoradiotherapy.

A neural network - based algorithm for predicting stone - free status after ESWL therapy.

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Seckiner, Ilker; Seckiner, Serap; Sen, Haluk; Bayrak, Omer; Dogan, Kazim; Erturhan, Sakip

2017-01-01

The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones. Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data. Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group. Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones. Copyright® by the International Brazilian Journal of Urology.

Predicting dating behavior from aggression and self-perceived social status in adolescence.

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Lee, Kirsty S; Brittain, Heather; Vaillancourt, Tracy

2018-03-14

We investigated the longitudinal associations between self-reported aggression, self-perceived social status, and dating in adolescence using an intrasexual competition theoretical framework. Participants consisted of 536 students in Grade 9 (age 15), recruited from a community sample, who were assessed on a yearly basis until they were in Grade 11 (age 17). Adolescents self-reported their use of direct and indirect aggression, social status, and number of dating partners. A cross-lagged panel model that controlled for within-time covariance and across-time stability while examining cross-lagged pathways was used to analyze the data. The findings revealed that direct aggression did not predict dating behavior and was negatively associated with self-perceived social status in Grade 10. Self-perceived social status in Grade 9 was positively associated with greater use of indirect aggression in Grade 10. Regarding dating, in Grade 9, self-perceived social status positively predicted more dating partners the following year, while in Grade 10, it was higher levels of indirect aggression that predicted greater dating activity the following year. Overall, there were no significant sex differences in the model. The study supports the utility of evolutionary psychological theory in explaining peer aggression, and suggests that although social status can increase dating opportunities, as adolescents mature, indirect aggression becomes the most successful and strategic means of competing intrasexually and gaining mating advantages. © 2018 Wiley Periodicals, Inc.

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

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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.

Current Status and Prediction on Development of PE Market

Institute of Scientific and Technical Information of China (English)

Yu Jiao

2003-01-01

This article comprehensively analyzes the status of market demand/supply and import/export volumes of PE in the world and in China, and predicts the future development trends in the fields of PE production and consumption.

Clinical utility of EMSE and STESS in predicting hospital mortality for status epilepticus.

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Zhang, Yu; Chen, Deng; Xu, Da; Tan, Ge; Liu, Ling

2018-05-25

To explore the applicability of the epidemiology-based mortality score in status epilepticus (EMSE) and the status epilepticus severity score (STESS) in predicting hospital mortality in patients with status epilepticus (SE) in western China. Furthermore, we sought to compare the abilities of the two scales to predict mortality from convulsive status epilepticus (CSE) and non-convulsive status epilepticus (NCSE). Patients with epilepsy (n = 253) were recruited from the West China Hospital of Sichuan University from January 2012 to January 2016. The EMSE and STESS for all patients were calculated immediately after admission. The main outcome was in-hospital death. The predicted values were analysed using SPSS 22.0 receiver operating characteristic (ROC) curves. Of the 253 patients with SE who were included in the study, 39 (15.4%) died in the hospital. Using STESS ≥4 points to predict SE mortality, the area under the ROC curve (AUC) was 0.724 (P  0.05), while EMSE ≥90 points gave an AUC of 0.666 (P > 0.05). The hospital mortality rate from SE in this study was 15.4%. Those with STESS ≥4 points or EMSE ≥79 points had higher rates of SE mortality. Both STESS and EMSE are less useful predicting in-hospital mortality in NCSE compared to CSE. Furthermore, the EMSE has some advantages over the STESS. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Patient-rated health status predicts prognosis following percutaneous coronary intervention with drug-eluting stenting

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Pedersen, Susanne S.; Versteeg, Henneke; Denollet, Johan

2011-01-01

In patients treated with percutaneous coronary intervention (PCI) with the paclitaxel-eluting stent, we examined whether patient-rated health status predicts adverse clinical events.......In patients treated with percutaneous coronary intervention (PCI) with the paclitaxel-eluting stent, we examined whether patient-rated health status predicts adverse clinical events....

Earthquake prediction by Kina Method

International Nuclear Information System (INIS)

Kianoosh, H.; Keypour, H.; Naderzadeh, A.; Motlagh, H.F.

2005-01-01

Earthquake prediction has been one of the earliest desires of the man. Scientists have worked hard to predict earthquakes for a long time. The results of these efforts can generally be divided into two methods of prediction: 1) Statistical Method, and 2) Empirical Method. In the first method, earthquakes are predicted using statistics and probabilities, while the second method utilizes variety of precursors for earthquake prediction. The latter method is time consuming and more costly. However, the result of neither method has fully satisfied the man up to now. In this paper a new method entitled 'Kiana Method' is introduced for earthquake prediction. This method offers more accurate results yet lower cost comparing to other conventional methods. In Kiana method the electrical and magnetic precursors are measured in an area. Then, the time and the magnitude of an earthquake in the future is calculated using electrical, and in particular, electrical capacitors formulas. In this method, by daily measurement of electrical resistance in an area we make clear that the area is capable of earthquake occurrence in the future or not. If the result shows a positive sign, then the occurrence time and the magnitude can be estimated by the measured quantities. This paper explains the procedure and details of this prediction method. (authors)

SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

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Zhengfeng, Huang; Pengjun, Zheng; Wenjun, Xu; Gang, Ren

Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

Predicting Collateral Status With Magnetic Resonance Perfusion Parameters: Probabilistic Approach With a Tmax-Derived Prediction Model.

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Lee, Mi Ji; Son, Jeong Pyo; Kim, Suk Jae; Ryoo, Sookyung; Woo, Sook-Young; Cha, Jihoon; Kim, Gyeong-Moon; Chung, Chin-Sang; Lee, Kwang Ho; Bang, Oh Young

2015-10-01

Good collateral flow is an important predictor for favorable responses to recanalization therapy and successful outcomes after acute ischemic stroke. Magnetic resonance perfusion-weighted imaging (MRP) is widely used in patients with stroke. However, it is unclear whether the perfusion parameters and thresholds would predict collateral status. The present study evaluated the relationship between hypoperfusion severity and collateral status to develop a predictive model for good collaterals using MRP parameters. Patients who were eligible for recanalization therapy that underwent both serial diffusion-weighted imaging and serial MRP were enrolled into the study. A collateral flow map derived from MRP source data was generated through automatic postprocessing. Hypoperfusion severity, presented as proportions of every 2-s Tmax strata to the entire hypoperfusion volume (Tmax≥2 s), was compared between patients with good and poor collaterals. Prediction models for good collaterals were developed with each Tmax strata proportion and cerebral blood volumes. Among 66 patients, 53 showed good collaterals based on MRP-based collateral grading. Although no difference was noted in delays within 16 s, more severe Tmax delays (Tmax16-18 s, Tmax18-22 s, Tmax22-24 s, and Tmax>24 s) were associated with poor collaterals. The probability equation model using Tmax strata proportion demonstrated high predictive power in a receiver operating characteristic analysis (area under the curve=0.9303; 95% confidence interval, 0.8682-0.9924). The probability score was negatively correlated with the volume of infarct growth (P=0.030). Collateral status is associated with more severe Tmax delays than previously defined. The present Tmax severity-weighted model can determine good collaterals and subsequent infarct growth. © 2015 American Heart Association, Inc.

Leptomeningeal collateral status predicts outcome after middle cerebral artery occlusion

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Madelung, Christopher Fugl; Ovesen, C; Trampedach, C

2017-01-01

NCCT and according to European Cooperative Acute Stroke Study (ECASS) criteria. Modified Rankin Scale score was assessed at 90 days, and mortality at 1 year. RESULTS: At 90 days, median (IQR) modified Rankin Scale score in patients with poor collateral status was 4 (3-6) compared to 2 (1-4) in patients...... population (P = .001). CONCLUSIONS: Leptomeningeal collateral status predicts functional outcome, mortality, and hemorrhagic transformation following middle cerebral artery occlusion.......OBJECTIVES: Perfusion through leptomeningeal collateral vessels is a likely pivotal factor in the outcome of stroke patients. We aimed to investigate the effect of collateral status on outcome in a cohort of unselected, consecutive stroke patients with middle cerebral artery occlusion undergoing...

Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction

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2017-12-01

19 NIH Exploiting drivers of androgen receptor signaling negative prostate cancer for precision medicine Goal(s): Identify novel potential drivers...AWARD NUMBER: W81XWH-14-1-0466 TITLE: Clonal evaluation of prostate cancer by ERG/SPINK1 status to improve prognosis prediction PRINCIPAL...Sept 2017 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Clonal Evaluation of Prostate Cancer by ERG/SPINK1 Status to Improve Prognosis Prediction 5b

Socioeconomic status predicts second cardiovascular event in 29,226 survivors of a first myocardial infarction.

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Ohm, Joel; Skoglund, Per H; Discacciati, Andrea; Sundström, Johan; Hambraeus, Kristina; Jernberg, Tomas; Svensson, Per

2018-01-01

Background Risk assessment post-myocardial infarction needs improvement, and risk factors derived from general populations apply differently in secondary prevention. The prediction of subsequent cardiovascular events post-myocardial infarction by socioeconomic status has previously been poorly studied. Design Swedish nationwide cohort study. Methods A total of 29,226 men and women (27%), 40-76 years of age, registered at the standardised one year revisit after a first myocardial infarction in the secondary prevention quality registry of SWEDEHEART 2006-2014. Personal-level data on socioeconomic status measured by disposable income and educational level, marital status, and the primary endpoint, first recurrent event of atherosclerotic cardiovascular disease, defined as non-fatal myocardial infarction or coronary heart disease death or fatal or non-fatal stroke were obtained from linked national registries. Results During the mean 4.1-year follow-up, 2284 (7.8%) first recurrent manifestations of atherosclerotic cardiovascular disease occurred. Both socioeconomic status indicators and marital status were associated with the primary endpoint in multivariable Cox regression models. In a comprehensively adjusted model, including secondary preventive treatment, the hazard ratio for the highest versus lowest quintile of disposable income was 0.73 (95% confidence interval 0.62-0.83). The association between disposable income and first recurrent manifestation of atherosclerotic cardiovascular disease was stronger in men as was the risk associated with being unmarried (tests for interaction P < 0.05). Conclusions Among one year survivors of a first myocardial infarction, first recurrent manifestation of atherosclerotic cardiovascular disease was predicted by disposable income, level of education and marital status. The association between disposable income and first recurrent manifestation of atherosclerotic cardiovascular disease was independent of secondary preventive

Mortality, morbidity and refractoriness prediction in status epilepticus: Comparison of STESS and EMSE scores.

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Giovannini, Giada; Monti, Giulia; Tondelli, Manuela; Marudi, Andrea; Valzania, Franco; Leitinger, Markus; Trinka, Eugen; Meletti, Stefano

2017-03-01

Status epilepticus (SE) is a neurological emergency, characterized by high short-term morbidity and mortality. We evaluated and compared two scores that have been developed to evaluate status epilepticus prognosis: STESS (Status Epilepticus Severity Score) and EMSE (Epidemiology based Mortality score in Status Epilepticus). A prospective observational study was performed on consecutive patients with SE admitted between September 2013 and August 2015. Demographics, clinical variables, STESS-3 and -4, and EMSE-64 scores were calculated for each patient at baseline. SE drug response, 30-day mortality and morbidity were the outcomes measure. 162 episodes of SE were observed: 69% had a STESS ≥3; 34% had a STESS ≥4; 51% patients had an EMSE ≥64. The 30-days mortality was 31.5%: EMSE-64 showed greater negative predictive value (NPV) (97.5%), positive predictive value (PPV) (59.8%) and accuracy in the prediction of death than STESS-3 and STESS-4 (pstatus epilepticus proved refractory to non-anaesthetic treatment. All three scales showed a high NPV (EMSE-64: 87.3%; STESS-4: 89.4%; STESS-3: 87.5%) but a low PPV (EMSE-64: 40.9%; STESS-4: 52.9%; STESS-3: 32%) for the prediction of refractoriness to first and second line drugs. This means that accuracy for the prediction of refractoriness was equally poor for all scales. EMSE-64 appears superior to STESS-3 and STESS-4 in the prediction of 30-days mortality and morbidity. All scales showed poor accuracy in the prediction of response to first and second line antiepileptic drugs. At present, there are no reliable scores capable of predicting treatment responsiveness. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

g-2 and α(MZ2): Status of the Standard Model predictions

International Nuclear Information System (INIS)

Teubner, T.; Hagiwara, K.; Liao, R.; Martin, A.D.; Nomura, D.

2012-01-01

We review the status of the Standard Model prediction of the anomalous magnetic moment of the muon and the electromagnetic coupling at the scale M Z . Recent progress in the evaluation of the hadronic contributions have consolidated the prediction of both quantities. For g-2, the discrepancy between the measurement from BNL and the Standard Model prediction stands at a level of more than three standard deviations.

Changes in Pilot Behavior with Predictive System Status Information

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Trujillo, Anna C.

1998-01-01

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

Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

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Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

2018-01-01

Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

Low Social Status Markers: Do They Predict Depressive Symptoms in Adolescence?

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Jackson, Benita; Goodman, Elizabeth

2011-07-01

Some markers of social disadvantage are associated robustly with depressive symptoms among adolescents: female gender and lower socioeconomic status (SES), respectively. Others are associated equivocally, notably Black v. White race/ethnicity. Few studies examine whether markers of social disadvantage by gender, SES, and race/ethnicity jointly predict self-reported depressive symptoms during adolescence; this was our goal. Secondary analyses were conducted on data from a socioeconomically diverse community-based cohort study of non-Hispanic Black and White adolescents (N = 1,263, 50.4% female). Multivariable general linear models tested if female gender, Black race/ethnicity, and lower SES (assessed by parent education and household income), and their interactions predicted greater depressive symptoms reported on the Center for Epidemiological Studies-Depression scale. Models adjusted for age and pubertal status. Univariate analyses revealed more depressive symptoms in females, Blacks, and participants with lower SES. Multivariable models showed females across both racial/ethnic groups reported greater depressive symptoms; Blacks demonstrated more depressive symptoms than did Whites but when SES was included this association disappeared. Exploratory analyses suggested Blacks gained less mental health benefit from increased SES. However there were no statistically significant interactions among gender, race/ethnicity, or SES. Taken together, we conclude that complex patterning among low social status domains within gender, race/ethnicity, and SES predicts depressive symptoms among adolescents.

Review of the status of reactor physics predictive methods for burnable poisons in CAGRs

International Nuclear Information System (INIS)

Edens, D.J.; McEllin, M.

1983-01-01

An essential component of the design of Commercial Advanced Gas Cooled Reactor fuel necessary to achieve higher discharge irradiations is the incorporation of burnable poisons. The poisons enable the more highly enriched fuel required to reach higher irradiation to be loaded without increasing the peak channel power. The optimum choice of fuel enrichment and poison loading will be made using reactor physics predictive methods developed by Berkeley Nuclear Laboratories. These methods and the evidence available to support them from theoretical comparisons, zero energy experiments, WAGR irradiations, and measurements on operating CAGRs are described. (author)

Review of the status of reactor physics predictive methods for burnable poisons in CAGRs

International Nuclear Information System (INIS)

Edens, D.J.; McEllin, M.

1983-01-01

An essential component of the design of Commercial Advanced Gas Cooled Reactor fuel necessary to achieve higher discharge irradiations is the incorporation of burnable poisons. The poisons enable the more highly enriched fuel required to reach higher irradiation to be loaded without increasing the peak channel power. The optimum choice of fuel enrichment and poison loading will be made using reactor physics predictive methods developed by Berkeley Nuclear Laboratories. The paper describes these methods and the evidence available to support them from theoretical comparisons, zero energy experiments, WAGR irradiations, and measurements on operating CAGR's. (author)

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

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Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Next-generation sequencing for antenatal prediction of KEL1 blood group status

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Rieneck, Klaus; Clausen, Frederik Banch; Dziegiel, Morten Hanefeld

2015-01-01

The KEL1 antigen can give rise to immunization of KEL2 mothers. Maternal antibodies can be transferred to the fetus and destroy fetal red blood cells and their stem cell precursors and give rise to serious fetal disease. It is important to be able to predict the fetal KEL status in order...... to intervene in those pregnancies where the fetus is at risk, and to ascertain when the fetus is not at risk. Technically it can be demanding to predict KEL1 status from a maternal blood sample. The KEL1 allele is based on a single SNP present in about 1–10 % of cell-free maternal DNA after gestation week 10...

Vitamin D status predicts 30 day mortality in hospitalised cats.

Directory of Open Access Journals (Sweden)

Helen Titmarsh

Full Text Available Vitamin D insufficiency, defined as low serum concentrations of the major circulating form of vitamin D, 25 hydroxyvitamin D (25(OHD, has been associated with the development of numerous infectious, inflammatory, and neoplastic disorders in humans. In addition, vitamin D insufficiency has been found to be predictive of mortality for many disorders. However, interpretation of human studies is difficult since vitamin D status is influenced by many factors, including diet, season, latitude, and exposure to UV radiation. In contrast, domesticated cats do not produce vitamin D cutaneously, and most cats are fed a commercial diet containing a relatively standard amount of vitamin D. Consequently, domesticated cats are an attractive model system in which to examine the relationship between serum 25(OHD and health outcomes. The hypothesis of this study was that vitamin D status would predict short term, all-cause mortality in domesticated cats. Serum concentrations of 25(OHD, together with a wide range of other clinical, hematological, and biochemical parameters, were measured in 99 consecutively hospitalised cats. Cats which died within 30 days of initial assessment had significantly lower serum 25(OHD concentrations than cats which survived. In a linear regression model including 12 clinical variables, serum 25(OHD concentration in the lower tertile was significantly predictive of mortality. The odds ratio of mortality within 30 days was 8.27 (95% confidence interval 2.54-31.52 for cats with a serum 25(OHD concentration in the lower tertile. In conclusion, this study demonstrates that low serum 25(OHD concentration status is an independent predictor of short term mortality in cats.

Health and Maintenance Status Determination and Predictive Fault Diagnosis System, Phase I

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National Aeronautics and Space Administration — The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket...

Current status of methods for shielding analysis

International Nuclear Information System (INIS)

Engle, W.W.

1980-01-01

Current methods used in shielding analysis and recent improvements in those methods are discussed. The status of methods development is discussed based on needs cited at the 1977 International Conference on Reactor Shielding. Additional areas where methods development is needed are discussed

Status epilepticus severity score (STESS): A useful tool to predict outcome of status epilepticus.

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Goyal, Manoj Kumar; Chakravarthi, Sudheer; Modi, Manish; Bhalla, Ashish; Lal, Vivek

2015-12-01

The treatment protocols for status epilepticus (SE) range from small doses of intravenous benzodiazepines to induction of coma. The pros and cons of more aggressive treatment regimen remain debatable. The importance of an index need not be overemphasized which can predict outcome of SE and guide the intensity of treatment. We tried to evaluate utility of one such index Status epilepticus severity score (STESS). 44 consecutive patients of SE were enrolled in the study. STESS results were compared with various outcome measures: (a) mortality, (b) final neurological outcome at discharge as defined by functional independence measure (FIM) (good outcome: FIM score 5-7; bad outcome: FIM score 1-4), (c) control of SE within 1h of start of treatment and (d) need for coma induction. A higher STESS score correlated significantly with poor neurological outcome at discharge (p=0.0001), need for coma induction (p=0.0001) and lack of response to treatment within 1h (p=0.001). A STESS of status epilepticus. Further studies on STESS based treatment approach may help in designing better therapeutic regimens for SE. Copyright © 2015 Elsevier B.V. All rights reserved.

G-cimp status prediction of glioblastoma samples using mRNA expression data.

Science.gov (United States)

Baysan, Mehmet; Bozdag, Serdar; Cam, Margaret C; Kotliarova, Svetlana; Ahn, Susie; Walling, Jennifer; Killian, Jonathan K; Stevenson, Holly; Meltzer, Paul; Fine, Howard A

2012-01-01

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

Restraint status improves the predictive value of motor vehicle crash criteria for pediatric trauma team activation.

Science.gov (United States)

Bozeman, Andrew P; Dassinger, Melvin S; Recicar, John F; Smith, Samuel D; Rettiganti, Mallikarjuna R; Nick, Todd G; Maxson, Robert T

2012-12-01

Most trauma centers incorporate mechanistic criteria (MC) into their algorithm for trauma team activation (TTA). We hypothesized that characteristics of the crash are less reliable than restraint status in predicting significant injury and the need for TTA. We identified 271 patients (age, <15 y) admitted with a diagnosis of motor vehicle crash. Mechanistic criteria and restraint status of each patient were recorded. Both MC and MC plus restraint status were evaluated as separate measures for appropriately predicting TTA based on treatment outcomes and injury scores. Improper restraint alone predicted a need for TTA with an odds ratios of 2.69 (P = .002). MC plus improper restraint predicted the need for TTA with an odds ratio of 2.52 (P = .002). In contrast, the odds ratio when using MC alone was 1.65 (P = .16). When the 5 MC were evaluated individually as predictive of TTA, ejection, death of occupant, and intrusion more than 18 inches were statistically significant. Improper restraint is an independent predictor of necessitating TTA in this single-institution study. Copyright © 2012 Elsevier Inc. All rights reserved.

Support vector machine learning model for the prediction of sentinel node status in patients with cutaneous melanoma.

Science.gov (United States)

Mocellin, Simone; Ambrosi, Alessandro; Montesco, Maria Cristina; Foletto, Mirto; Zavagno, Giorgio; Nitti, Donato; Lise, Mario; Rossi, Carlo Riccardo

2006-08-01

Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested. The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient's age and sex and the tumor's histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression. The SLN positivity rate was 22% (52 of 234). When the accuracy was > or = 80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively. The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.

Can personality traits and intelligence compensate for background disadvantage? Predicting status attainment in adulthood.

Science.gov (United States)

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W

2015-09-01

This study investigated the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large U.S. representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige 11 years later. Specifically, we tested whether individual differences followed 1 of 3 patterns in relation to parental socioeconomic status (SES) when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., "the rich get richer"; individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, the interaction models being more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a "full catch-up" effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. (c) 2015 APA, all rights reserved).

Role of specimen US for predicting resection margin status in breast conserving therapy.

Science.gov (United States)

Moschetta, M; Telegrafo, M; Introna, T; Coi, L; Rella, L; Ranieri, V; Cirili, A; Stabile Ianora, A A; Angelelli, G

2015-01-01

To assess the diagnostic accuracy of specimen ultrasound (US) for predicting resection margin status in women undergoing breast conserving therapy for US-detected cancer, having the histological findings as the reference standard. A total of 132 consecutive patients (age range, 34-87 years; mean, 51 years) underwent breast-conserving surgery for US-detected invasive breast cancer. All surgical specimens underwent US examination. The presence of lesion within the specimen and its distance from the specimen margins were assessed considering a threshold distance between the lesion and specimen margins of 10 mm. US findings were then compared with the pathological ones and specimen US. Sensitivity, specificity, diagnostic accuracy, positive (PPV) and negative predictive values (NPV) for predicting histological margin status were evaluated, having the histological findings as the reference standard. The histological examination detected invasive ductal carcinoma in 96/132 (73%) cases, invasive lobular carcinoma in 32/132 (24%), mucinous carcinoma in 4/132 (3%). The pathological margin analysis revealed 96/132 (73%) negative margins and 36 (27%) close/positive margins. US examination detected all 132 breast lesions within the surgical specimens. 110 (83%) negative margins and 22 (17%) positive margins were found on US. Sensitivity, specificity, diagnostic accuracy, PPV and NPV of 44%, 94%, 80%, 73% and 82%, respectively, were found for specimen US. Specimen US represents a time and cost saving imaging tool for evaluating the presence of US detected-breast lesion within surgical specimen and for predicting the histological margin status.

G-cimp status prediction of glioblastoma samples using mRNA expression data.

Directory of Open Access Journals (Sweden)

Mehmet Baysan

Full Text Available Glioblastoma Multiforme (GBM is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

Subjective social status predicts in vivo responsiveness of β-adrenergic receptors.

Science.gov (United States)

Euteneuer, Frank; Mills, Paul J; Rief, Winfried; Ziegler, Michael G; Dimsdale, Joel E

2012-07-01

Several poor health outcomes, including cardiovascular risk, have been associated with both subjective social status (SSS) and sympathetic overactivity. Because prolonged sympathetic overactivation down regulates beta adrenergic receptor (β-AR) function, reduced β-AR responsiveness is considered an indicator of sympathetic overactivity and a cardiovascular risk factor. Though prior research has focused on objective social status and β-AR function, no studies have examined the association between SSS and β-AR function. We aimed to learn whether SSS predicts the in vivo responsiveness of β-ARs. We assessed the chronotropic 25 dose (CD25), an in vivo marker of β-AR responsiveness, in 94 healthy participants. The MacArthur scales of subjective social status were used to assess SSS in the U.S.A. (SSS-USA) and in the local community (SSS-C). Objective social status was analyzed by calculating the Hollingshead two-factor index. β-AR responsiveness was reduced (as indicated by higher CD25 values) in participants with lower SSS-USA (p = .007) and lower SSS-C (p social status. Our results indicate that β-AR function may be an important component of the link between SSS and health.

Present status of processing method

Energy Technology Data Exchange (ETDEWEB)

Kosako, Kazuaki [Sumitomo Atomic Energy Industries Ltd., Tokyo (Japan)

1998-11-01

Present status of processing method for a high-energy nuclear data file was examined. The NJOY94 code is the only one available to the processing. In Japan, present processing used NJOY94 is orienting toward the production of traditional cross section library, because a high-energy transport code using a high-energy cross section library is indistinct. (author)

Leptomeningeal collateral status predicts outcome after middle cerebral artery occlusion.

Science.gov (United States)

Madelung, C F; Ovesen, C; Trampedach, C; Christensen, A; Havsteen, I; Hansen, C K; Christensen, H

2018-01-01

Perfusion through leptomeningeal collateral vessels is a likely pivotal factor in the outcome of stroke patients. We aimed to investigate the effect of collateral status on outcome in a cohort of unselected, consecutive stroke patients with middle cerebral artery occlusion undergoing reperfusion therapy. This retrospectively planned analysis was passed on prospectively collected data from 187 consecutive patients with middle cerebral artery occlusion admitted within 4.5 hours to one center and treated with intravenous thrombolysis alone (N = 126), mechanical thrombectomy alone (N = 5), or both (N = 56) from May 2009 to April 2014. Non-contrast CT (NCCT) and computed tomography angiography (CTA) were provided on admission and NCCT repeated at 24 hours. Collateral status was assessed based on the initial CTA. Hemorrhagic transformation was evaluated on the 24-hour NCCT and according to European Cooperative Acute Stroke Study (ECASS) criteria. Modified Rankin Scale score was assessed at 90 days, and mortality at 1 year. At 90 days, median (IQR) modified Rankin Scale score in patients with poor collateral status was 4 (3-6) compared to 2 (1-4) in patients with good collateral status (P collateral status were less likely to achieve a good 90-day outcome (modified Rankin Scale score 0-2) (Adjusted odds ratio 0.27, 95% CI: 0.09-0.86). During the first year, 40.9% of patients with poor collateral status died vs 18.2% of the remaining population (P = .001). Leptomeningeal collateral status predicts functional outcome, mortality, and hemorrhagic transformation following middle cerebral artery occlusion. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Preventive child health care findings on early childhood predict peer-group social status in early adolescence.

Science.gov (United States)

Jaspers, Merlijne; de Winter, Andrea F; Veenstra, René; Ormel, Johan; Verhulst, Frank C; Reijneveld, Sijmen A

2012-12-01

A disputed social status among peers puts children and adolescents at risk for developing a wide range of problems, such as being bullied. However, there is a lack of knowledge about which early predictors could be used to identify (young) adolescents at risk for a disputed social status. The aim of this study was to assess whether preventive child health care (PCH) findings on early childhood predict neglected and rejected status in early adolescence in a large longitudinal community-based sample. Data came from 898 participants who participated in TRAILS, a longitudinal study. Information on early childhood factors was extracted from the charts of routine PCH visits registered between infancy and age of 4 years. To assess social status, peer nominations were used at age of 10-12 years. Multinomial logistic regression showed that children who had a low birth weight, motor problems, and sleep problems; children of parents with a low educational level (odds ratios [ORs] between 1.71 and 2.90); and those with fewer attention hyperactivity problems (ORs = .43) were more likely to have a neglected status in early adolescence. Boys, children of parents with a low educational level, and children with early externalizing problems were more likely to have a rejected status in early adolescence (ORs between 1.69 and 2.56). PCH findings on early childhood-on motor and social development-are predictive of a neglected and a rejected status in early adolescence. PCH is a good setting to monitor risk factors that predict the social status of young adolescents. Copyright © 2012 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Prediction of sentinel lymph node status using single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging of breast cancer.

Science.gov (United States)

Tomiguchi, Mai; Yamamoto-Ibusuki, Mutsuko; Yamamoto, Yutaka; Fujisue, Mamiko; Shiraishi, Shinya; Inao, Touko; Murakami, Kei-ichi; Honda, Yumi; Yamashita, Yasuyuki; Iyama, Ken-ichi; Iwase, Hirotaka

2016-02-01

Single-photon emission computed tomography (SPECT)/computed tomography (CT) improves the anatomical identification of sentinel lymph nodes (SNs). We aimed to evaluate the possibility of predicting the SN status using SPECT/CT. SN mapping using a SPECT/CT system was performed in 381 cases of clinically node-negative, operable invasive breast cancer. We evaluated and compared the values of SN mapping on SPECT/CT, the findings of other modalities and clinicopathological factors in predicting the SN status. Patients with SNs located in the Level I area were evaluated. Of the 355 lesions (94.8 %) assessed, six cases (1.6 %) were not detected using any imaging method. According to the final histological diagnosis, 298 lesions (78.2 %) were node negative and 83 lesions (21.7 %) were node positive. The univariate analysis showed that SN status was significantly correlated with the number of SNs detected on SPECT/CT in the Level I area (P = 0.0048), total number of SNs detected on SPECT/CT (P = 0.011), findings of planar lymphoscintigraphy (P = 0.011) and findings of a handheld gamma probe during surgery (P = 0.012). According to the multivariate analysis, the detection of multiple SNs on SPECT/CT imaging helped to predict SN metastasis. The number of SNs located in the Level I area detected using the SPECT/CT system may be a predictive factor for SN metastasis.

Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

Science.gov (United States)

Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

2009-06-01

Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

Machine learning methods for metabolic pathway prediction

Directory of Open Access Journals (Sweden)

Karp Peter D

2010-01-01

Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

Machine learning methods for metabolic pathway prediction

Science.gov (United States)

2010-01-01

Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods

Directory of Open Access Journals (Sweden)

Daniel Doktor

2014-12-01

Full Text Available The machine learning method, random forest (RF, is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare. Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL simulated data. Furthermore, it is almost unaffected by introduced noise and bias in the data. When applied to laboratory data, the prediction accuracy is still good (C\\(_{ab}\\: \\(R^2\\ = 0.94/ LAI: \\(R^2\\ = 0.80/BBCH (Growth stages of mono-and dicotyledonous plants : \\(R^2\\ = 0.91, but not as high as for simulated spectra. Transferability to field measurements is given with prediction levels as high as for laboratory data (C\\(_{ab}\\: \\(R^2\\ = 0.89/LAI: \\(R^2\\ = 0.89/BBCH: \\(R^2\\ = \\(\\sim\\0.8. Wavelengths for deriving plant physiological status based on simulated and measured hyperspectral signatures are mostly selected from appropriate spectral regions (both field and laboratory: 700–800 nm regressing on C\\(_{ab}\\ and 800–1300

Early changes in socioeconomic status do not predict changes in body mass in the first decade of life.

Science.gov (United States)

Starkey, Leighann; Revenson, Tracey A

2015-04-01

Many studies link childhood socioeconomic status (SES) to body mass index (BMI), but few account for the impact of socioeconomic mobility throughout the lifespan. This study aims to investigate the impact of socioeconomic mobility on changes in BMI in childhood. Analyses tested whether [1] socioeconomic status influences BMI, [2] changes in socioeconomic status impact changes in BMI, and [3] timing of socioeconomic status mobility impacts BMI. Secondary data spanning birth to age 9 were analyzed. SES and BMI were investigated with gender, birth weight, maternal race/ethnicity, and maternal nativity as covariates. Autoregressive structural equation modeling and latent growth modeling were used. Socioeconomic status in the first year of life predicted body mass index. Child covariates were consistently associated with body mass index. Rate of change in socioeconomic status did not predict change in body mass index. The findings suggest that early socioeconomic status may most influence body mass in later childhood.

Predictive value of the Status Epilepticus Severity Score (STESS) and its components for long-term survival

DEFF Research Database (Denmark)

Aukland, Preben; Lando, Martin; Vilholm, Ole

2016-01-01

BACKGROUND: The "Status Epilepticus Severity Score" (STESS) is the most important clinical score to predict in-hospital mortality of patients with status epilepticus (SE), but its prognostic relevance for long-term survival is unknown. This study therefore examined if STESS and its components...

Prediction methods and databases within chemoinformatics

DEFF Research Database (Denmark)

Jónsdóttir, Svava Osk; Jørgensen, Flemming Steen; Brunak, Søren

2005-01-01

MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability...

Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis

Directory of Open Access Journals (Sweden)

Charles Frank

2018-03-01

Full Text Available Smoking has been proven to negatively affect health in a multitude of ways. As of 2009, smoking has been considered the leading cause of preventable morbidity and mortality in the United States, continuing to plague the country’s overall health. This study aims to investigate the viability and effectiveness of some machine learning algorithms for predicting the smoking status of patients based on their blood tests and vital readings results. The analysis of this study is divided into two parts: In part 1, we use One-way ANOVA analysis with SAS tool to show the statistically significant difference in blood test readings between smokers and non-smokers. The results show that the difference in INR, which measures the effectiveness of anticoagulants, was significant in favor of non-smokers which further confirms the health risks associated with smoking. In part 2, we use five machine learning algorithms: Naïve Bayes, MLP, Logistic regression classifier, J48 and Decision Table to predict the smoking status of patients. To compare the effectiveness of these algorithms we use: Precision, Recall, F-measure and Accuracy measures. The results show that the Logistic algorithm outperformed the four other algorithms with Precision, Recall, F-Measure, and Accuracy of 83%, 83.4%, 83.2%, 83.44%, respectively.

NEURAL METHODS FOR THE FINANCIAL PREDICTION

OpenAIRE

Jerzy Balicki; Piotr Dryja; Waldemar Korłub; Piotr Przybyłek; Maciej Tyszka; Marcin Zadroga; Marcin Zakidalski

2016-01-01

Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

NEURAL METHODS FOR THE FINANCIAL PREDICTION

Directory of Open Access Journals (Sweden)

Jerzy Balicki

2016-06-01

Full Text Available Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

What Predicts Method Effects in Child Behavior Ratings

Science.gov (United States)

Low, Justin A.; Keith, Timothy Z.; Jensen, Megan

2015-01-01

The purpose of this research was to determine whether child, parent, and teacher characteristics such as sex, socioeconomic status (SES), parental depressive symptoms, the number of years of teaching experience, number of children in the classroom, and teachers' disciplinary self-efficacy predict deviations from maternal ratings in a…

Can Personality Traits and Intelligence Compensate for Background Disadvantage? Predicting Status Attainment in Adulthood

Science.gov (United States)

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W.

2014-01-01

This paper investigates the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large US representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige eleven years later. Specifically, we tested whether individual differences followed one of three patterns in relation to parental SES when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer,” individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, where interaction models were more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a “full catch up” effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. PMID:25402679

Rainfall prediction with backpropagation method

Science.gov (United States)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients

Directory of Open Access Journals (Sweden)

Johnson Denise L

2008-03-01

Full Text Available Abstract Background Current practice is to perform a completion axillary lymph node dissection (ALND for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs, although fewer than half will have non-sentinel node (NSLN metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model. Methods We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC, boosted Classification and Regression Trees (CART, and multivariate logistic regression (MLR informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram in our dataset and an independent dataset from Northwestern University. Results 285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93% patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%. 101 (35% of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC was 0.83/0.85 for MLR (n = 213/n = 171 and 0.77 for Nomogram (n = 171. When applied to an independent dataset (n = 77, AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of

Horticultural activity predicts later localized limb status in a contemporary pre-industrial population.

Science.gov (United States)

Stieglitz, Jonathan; Trumble, Benjamin C; Kaplan, Hillard; Gurven, Michael

2017-07-01

Modern humans may have gracile skeletons due to low physical activity levels and mechanical loading. Tests using pre-historic skeletons are limited by the inability to assess behavior directly, while modern industrialized societies possess few socio-ecological features typical of human evolutionary history. Among Tsimane forager-horticulturalists, we test whether greater activity levels and, thus, increased loading earlier in life are associated with greater later-life bone status and diminished age-related bone loss. We used quantitative ultrasonography to assess radial and tibial status among adults aged 20+ years (mean ± SD age = 49 ± 15; 52% female). We conducted systematic behavioral observations to assess earlier-life activity patterns (mean time lag between behavioural observation and ultrasound = 12 years). For a subset of participants, physical activity was again measured later in life, via accelerometry, to determine whether earlier-life time use is associated with later-life activity levels. Anthropometric and demographic data were collected during medical exams. Structural decline with age is reduced for the tibia (female: -0.25 SDs/decade; male: 0.05 SDs/decade) versus radius (female: -0.56 SDs/decade; male: -0.20 SDs/decade), which is expected if greater loading mitigates bone loss. Time allocation to horticulture, but not hunting, positively predicts later-life radial status (β Horticulture  = 0.48, p = 0.01), whereas tibial status is not significantly predicted by subsistence or sedentary leisure participation. Patterns of activity- and age-related change in bone status indicate localized osteogenic responses to loading, and are generally consistent with the logic of bone functional adaptation. Nonmechanical factors related to subsistence lifestyle moderate the association between activity patterns and bone structure. © 2017 Wiley Periodicals, Inc.

Status self-validation of a multifunctional sensor using a multivariate relevance vector machine and predictive filters

International Nuclear Information System (INIS)

Shen, Zhengguang; Wang, Qi

2013-01-01

A novel strategy by using a multivariable relevance vector machine coupled with predictive filters for status self-validation of a multifunctional sensor is proposed. The working principle and online updating algorithm of predictive filters are emphasized for multiple fault detection, isolation and recovery (FDIR), and the incorrect sensor measurements are validated online. The multivariable relevance vector machine is then employed for the signal reconstruction of the multifunctional sensor to generate the final validated measurement values (VMV) of multiple measured components, in which its advantages of sparse models and multivariable simultaneous outputs are fully used. With all likely uncertainty sources of the multifunctional self-validating sensor taken into account, the uncertainty propagation model is deduced in detail to evaluate the online validated uncertainty (VU) under a fault-free situation while a qualitative uncertainty component is appended to indicate the accuracy changes of VMV under different types of fault. A real experimental system of a multifunctional self-validating sensor is designed to verify the performance of the proposed strategy. From the real-time capacity and fault recovery accuracy of FDIR, and runtime of signal reconstruction under small samples, a performance comparison among different methods is made. Results demonstrate that the proposed scheme provides a better solution to the status self-validation of a multifunctional self-validating sensor under both normal and abnormal situations. (paper)

Development of a regional ensemble prediction method for probabilistic weather prediction

International Nuclear Information System (INIS)

Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

2015-01-01

A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources

Directory of Open Access Journals (Sweden)

Mengyi Ren

2016-03-01

Full Text Available Seafloor polymetallic sulfide resources exhibit significant development potential. In 2011, China received the exploration rights for 10,000 km2 of a polymetallic sulfides area in the Southwest Indian Ocean; China will be permitted to retain only 25% of the area in 2021. However, an exploration of seafloor hydrothermal sulfide deposits in China remains in the initial stage. According to the quantitative prediction theory and the exploration status of seafloor sulfides, this paper systematically proposes a quantitative prediction evaluation process of oceanic polymetallic sulfide resources and divides it into three stages: prediction in a large area, prediction in the prospecting region, and the verification and evaluation of targets. The first two stages of the prediction process have been employed in seafloor sulfides prospecting of the Chinese contract area. The results of stage one suggest that the Chinese contract area is located in the high posterior probability area, which indicates good prospecting potential area in the Indian Ocean. In stage two, the Chinese contract area of 48°–52°E has the highest posterior probability value, which can be selected as the reserved region for additional exploration. In stage three, the method of numerical simulation is employed to reproduce the ore-forming process of sulfides to verify the accuracy of the reserved targets obtained from the three-stage prediction. By narrowing the exploration area and gradually improving the exploration accuracy, the prediction will provide a basis for the exploration and exploitation of seafloor polymetallic sulfide resources.

Kajian metode Subjective Global Assessment (SGA dan Nutrition Services Screening Assesment (NSSA sebagai status gizi awal pasien dewasa sebagai prediktor lama rawat inap dan status pulang

Directory of Open Access Journals (Sweden)

Agustinus I Wayan Harimawan

2011-03-01

Full Text Available Background: Assessment of nutrition status of newly hospitalized patients is an initial stage of nutrition intervention which will bring effects to the duration of stay and the history of patients' diseases during hospitalization. Appropriate nutrition intervention as part of  patients' care can be used as an indicator of the quality of hospital service. Objective: The study aimed to identify preliminary nutrition status of newly hospitalized adult patients using SGA method, its effects to length of stay and status of discharge and compare the capacity of SGA and NSSA indicators in predicting length of stay and status of discharge of adult patients. Method: This observational study used prospective cohort study design. It was carried out at Anuntaloko Hospital of Parigi, District of Parigi Moutong, Sulawesi Tengah from July to September 2008. Subject consisted of 162 people comprising 82 undernourished people and 80 people with good nutrition status based on assessment using SGA method. Data analysis used bivariable and multivariable, receiver operating characteristics (ROC curve and diagnostic methods using computer program. Result: The majority of newly hospitalized patients were undernourished (50.6%; preliminary status of patients assessed using SGA method could affect length of stay, relative risk (RR=3.67 but not status of discharge (RR=0.97. The capacity of SGA indicator, area under the curve (AUC=0.81 and maximum sum of sensitivity and specifcity (MSS =1.57 was better than NSSA indicator (AUC=0.76 and MSS 1.43 in predicting length of stay. The capacity of SGA indicator (AUC=0.50 and MSS=1.01 was better than NSSA indicator (AUC=0.49 and MSS=0.98 in predicting discharge status of the patient. Conclusion: SGA and NSSA indicators could be implemented in assessing preliminary nutrition status of newly hospitalized adult patients; SGA indicator had better capacity than NSSA indicator.

A review on real time physical measurement techniques and their attempt to predict wear-out status of IGBT

DEFF Research Database (Denmark)

Ghimire, Pramod; Beczkowski, Szymon; Munk-Nielsen, Stig

2013-01-01

Insulated Gate Bipolar Transistors (IGBTs) are key component in power converters. Reliability of power converters depend on wear-out process of power modules. A physical parameter such as the on-state collector-emitter voltage (Vce) shows the status of degradation of the IGBT after a certain cycles...... of difficulties in the measurement, the offline and online Vce measurement topologies are implemented to study the reliability of the power converters. This paper presents a review in wear-out prediction methods of IGBT power modules and freewheeling diodes based on the real time Vce measurement. The measurement...

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method

Energy Technology Data Exchange (ETDEWEB)

Rundle-Thiele, Dayle [Centre for Clinical Research, University of Queensland, Brisbane, Queensland (Australia); Day, Bryan; Stringer, Brett [Brain Cancer Research Unit, Queensland Institute of Medical Research, Brisbane, Queensland (Australia); Fay, Michael [Department of Radiation Oncology, Royal Brisbane and Women' s Hospital, Brisbane, Queensland (Australia); Martin, Jennifer [Discipline of Clinical Pharmacology, School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales (Australia); Jeffree, Rosalind L [Department of Neurosurgery, Royal Brisbane and Women' s Hospital, Brisbane, Queensland (Australia); Thomas, Paul [Queensland PET Service, Royal Brisbane and Women' s Hospital, Brisbane, Queensland (Australia); Bell, Christopher [Centre for Clinical Research, University of Queensland, Brisbane, Queensland (Australia); Salvado, Olivier [CSIRO Digital Productivity Flagship, CSIRO, Herston, Queensland (Australia); Gal, Yaniv [Centre for Medical Diagnostic Technologies in Queensland, University of Queensland, Brisbane, Queensland (Australia); Coulthard, Alan [Discipline of Medical Imaging, University of Queensland, St Lucia, Queensland (Australia); Department of Medical Imaging, Royal Brisbane and Women' s Hospital, Brisbane, Queensland (Australia); Crozier, Stuart [Centre for Medical Diagnostic Technologies in Queensland, University of Queensland, Brisbane, Queensland (Australia); Rose, Stephen, E-mail: stephen.rose@csiro.au [CSIRO Digital Productivity Flagship, CSIRO, Herston, Queensland (Australia); Centre for Clinical Research, University of Queensland, Brisbane, Queensland (Australia)

2015-06-15

Accurate knowledge of O{sup 6}-methylguanine methyltransferase (MGMT) gene promoter subtype in patients with glioblastoma (GBM) is important for treatment. However, this test is not always available. Pre-operative diffusion MRI (dMRI) can be used to probe tumour biology using the apparent diffusion coefficient (ADC); however, its ability to act as a surrogate to predict MGMT status has shown mixed results. We investigated whether this was due to variations in the method used to analyse ADC. We undertook a retrospective study of 32 patients with GBM who had MGMT status measured. Matching pre-operative MRI data were used to calculate the ADC within contrast enhancing regions of tumour. The relationship between ADC and MGMT was examined using two published ADC methods. A strong trend between a measure of ‘minimum ADC’ and methylation status was seen. An elevated minimum ADC was more likely in the methylated compared to the unmethylated MGMT group (U = 56, P = 0.0561). In contrast, utilising a two-mixture model histogram approach, a significant reduction in mean measure of the ‘low ADC’ component within the histogram was associated with an MGMT promoter methylation subtype (P < 0.0246). This study shows that within the same patient cohort, the method selected to analyse ADC measures has a significant bearing on the use of that metric as a surrogate marker of MGMT status. Thus for dMRI data to be clinically useful, consistent methods of data analysis need to be established prior to establishing any relationship with genetic or epigenetic profiling.

Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method

International Nuclear Information System (INIS)

Rundle-Thiele, Dayle; Day, Bryan; Stringer, Brett; Fay, Michael; Martin, Jennifer; Jeffree, Rosalind L; Thomas, Paul; Bell, Christopher; Salvado, Olivier; Gal, Yaniv; Coulthard, Alan; Crozier, Stuart; Rose, Stephen

2015-01-01

Accurate knowledge of O 6 -methylguanine methyltransferase (MGMT) gene promoter subtype in patients with glioblastoma (GBM) is important for treatment. However, this test is not always available. Pre-operative diffusion MRI (dMRI) can be used to probe tumour biology using the apparent diffusion coefficient (ADC); however, its ability to act as a surrogate to predict MGMT status has shown mixed results. We investigated whether this was due to variations in the method used to analyse ADC. We undertook a retrospective study of 32 patients with GBM who had MGMT status measured. Matching pre-operative MRI data were used to calculate the ADC within contrast enhancing regions of tumour. The relationship between ADC and MGMT was examined using two published ADC methods. A strong trend between a measure of ‘minimum ADC’ and methylation status was seen. An elevated minimum ADC was more likely in the methylated compared to the unmethylated MGMT group (U = 56, P = 0.0561). In contrast, utilising a two-mixture model histogram approach, a significant reduction in mean measure of the ‘low ADC’ component within the histogram was associated with an MGMT promoter methylation subtype (P < 0.0246). This study shows that within the same patient cohort, the method selected to analyse ADC measures has a significant bearing on the use of that metric as a surrogate marker of MGMT status. Thus for dMRI data to be clinically useful, consistent methods of data analysis need to be established prior to establishing any relationship with genetic or epigenetic profiling

Lean mass predicts conditioned pain modulation in adolescents across weight status.

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Stolzman, S; Hoeger Bement, M

2016-07-01

There is a wide continuum of conditioned pain modulation (CPM) in adults with older adults experiencing an attenuated CPM response compared with younger adults. Less is known for adolescents and the role of anthropometrics. Fifty-six adolescents (15.1 ± 1.8 years; 32 normal weight and 24 overweight/obese; 27 boys) completed in a CPM session that included anthropometric testing. Pressure pain thresholds were measured at the nailbed and deltoid muscle (test stimuli) with the foot submerged in a cool or ice water bath (conditioning stimulus). Weight status, body composition (Dual-energy X-ray absorptiometry scan), physical activity levels and clinical pain were also evaluated. The CPM response in adolescents was similar across sites (nailbed vs. deltoid), weight status (normal vs. overweight/obese) and sex. CPM measured at the deltoid muscle was positively associated with left arm lean mass but not fat mass; lean mass of the arm uniquely predicted 10% of the CPM magnitude. CPM measured at the nailbed was positively correlated with physical activity levels. These results suggest that lean mass and physical activity levels may contribute to endogenous pain inhibition in adolescents across weight status. © 2016 European Pain Federation - EFIC®

Functional status predicts acute care readmission in the traumatic spinal cord injury population.

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Huang, Donna; Slocum, Chloe; Silver, Julie K; Morgan, James W; Goldstein, Richard; Zafonte, Ross; Schneider, Jeffrey C

2018-03-29

Context/objective Acute care readmission has been identified as an important marker of healthcare quality. Most previous models assessing risk prediction of readmission incorporate variables for medical comorbidity. We hypothesized that functional status is a more robust predictor of readmission in the spinal cord injury population than medical comorbidities. Design Retrospective cross-sectional analysis. Setting Inpatient rehabilitation facilities, Uniform Data System for Medical Rehabilitation data from 2002 to 2012 Participants traumatic spinal cord injury patients. Outcome measures A logistic regression model for predicting acute care readmission based on demographic variables and functional status (Functional Model) was compared with models incorporating demographics, functional status, and medical comorbidities (Functional-Plus) or models including demographics and medical comorbidities (Demographic-Comorbidity). The primary outcomes were 3- and 30-day readmission, and the primary measure of model performance was the c-statistic. Results There were a total of 68,395 patients with 1,469 (2.15%) readmitted at 3 days and 7,081 (10.35%) readmitted at 30 days. The c-statistics for the Functional Model were 0.703 and 0.654 for 3 and 30 days. The Functional Model outperformed Demographic-Comorbidity models at 3 days (c-statistic difference: 0.066-0.096) and outperformed two of the three Demographic-Comorbidity models at 30 days (c-statistic difference: 0.029-0.056). The Functional-Plus models exhibited negligible improvements (0.002-0.010) in model performance compared to the Functional models. Conclusion Readmissions are used as a marker of hospital performance. Function-based readmission models in the spinal cord injury population outperform models incorporating medical comorbidities. Readmission risk models for this population would benefit from the inclusion of functional status.

Predicted vitamin D status during pregnancy in relation to offspring forearm fractures in childhood

DEFF Research Database (Denmark)

Petersen, Sesilje B.; Strøm, Marin; Maslova, Ekaterina

2015-01-01

fractures among offspring between birth and end of follow-up. Diagnoses were extracted from the Danish National Patient Register. Multivariable Cox regression models using age as the underlying time scale indicated no overall association between predicted vitamin D status (based on smoking, season, dietary...

Predicted vitamin D status and colon cancer recurrence and mortality in CALGB 89803 (Alliance).

Science.gov (United States)

Fuchs, M A; Yuan, C; Sato, K; Niedzwiecki, D; Ye, X; Saltz, L B; Mayer, R J; Mowat, R B; Whittom, R; Hantel, A; Benson, A; Atienza, D; Messino, M; Kindler, H; Venook, A; Innocenti, F; Warren, R S; Bertagnolli, M M; Ogino, S; Giovannucci, E L; Horvath, E; Meyerhardt, J A; Ng, K

2017-06-01

Observational studies suggest that higher levels of 25-hydroxyvitamin D3 (25(OH)D) are associated with a reduced risk of colorectal cancer and improved survival of colorectal cancer patients. However, the influence of vitamin D status on cancer recurrence and survival of patients with stage III colon cancer is unknown. We prospectively examined the influence of post-diagnosis predicted plasma 25(OH)D on outcome among 1016 patients with stage III colon cancer who were enrolled in a National Cancer Institute-sponsored adjuvant therapy trial (CALGB 89803). Predicted 25(OH)D scores were computed using validated regression models. We examined the influence of predicted 25(OH)D scores on cancer recurrence and mortality (disease-free survival; DFS) using Cox proportional hazards. Patients in the highest quintile of predicted 25(OH)D score had an adjusted hazard ratio (HR) for colon cancer recurrence or mortality (DFS) of 0.62 (95% confidence interval [CI], 0.44-0.86), compared with those in the lowest quintile (Ptrend = 0.005). Higher predicted 25(OH)D score was also associated with a significant improvement in recurrence-free survival and overall survival (Ptrend = 0.01 and 0.0004, respectively). The benefit associated with higher predicted 25(OH)D score appeared consistent across predictors of cancer outcome and strata of molecular tumor characteristics, including microsatellite instability and KRAS, BRAF, PIK3CA, and TP53 mutation status. Higher predicted 25(OH)D levels after a diagnosis of stage III colon cancer may be associated with decreased recurrence and improved survival. Clinical trials assessing the benefit of vitamin D supplementation in the adjuvant setting are warranted. NCT00003835. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Hierarchical Status Predicts Behavioral Vulnerability and Nucleus Accumbens Metabolic Profile Following Chronic Social Defeat Stress.

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Larrieu, Thomas; Cherix, Antoine; Duque, Aranzazu; Rodrigues, João; Lei, Hongxia; Gruetter, Rolf; Sandi, Carmen

2017-07-24

Extensive data highlight the existence of major differences in individuals' susceptibility to stress [1-4]. While genetic factors [5, 6] and exposure to early life stress [7, 8] are key components for such neurobehavioral diversity, intriguing observations revealed individual differences in response to stress in inbred mice [9-12]. This raised the possibility that other factors might be critical in stress vulnerability. A key challenge in the field is to identify non-invasively risk factors for vulnerability to stress. Here, we investigated whether behavioral factors, emerging from preexisting dominance hierarchies, could predict vulnerability to chronic stress [9, 13-16]. We applied a chronic social defeat stress (CSDS) model of depression in C57BL/6J mice to investigate the predictive power of hierarchical status to pinpoint which individuals will exhibit susceptibility to CSDS. Given that the high social status of dominant mice would be the one particularly challenged by CSDS, we predicted and found that dominant individuals were the ones showing a strong susceptibility profile as indicated by strong social avoidance following CSDS, while subordinate mice were not affected. Data from 1 H-NMR spectroscopy revealed that the metabolic profile in the nucleus accumbens (NAc) relates to social status and vulnerability to stress. Under basal conditions, subordinates show lower levels of energy-related metabolites compared to dominants. In subordinates, but not dominants, levels of these metabolites were increased after exposure to CSDS. To the best of our knowledge, this is the first study that identifies non-invasively the origin of behavioral risk factors predictive of stress-induced depression-like behaviors associated with metabolic changes. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Prediction of Labor Force Status: Implications from International Adult Skill Assessments. Research Report. ETS RR-16-11

Science.gov (United States)

Li, Tongyun; von Davier, Matthias; Hancock, Gregory R.

2016-01-01

This report investigates the prediction of labor force status using observed variables, such as gender, age, and immigrant status, and more importantly, measured skill variables, including literacy proficiency and a categorical rating of educational attainment based on the 1994 International Adult Literacy Survey (IALS), the 2003 Adult Literacy…

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

KAUST Repository

Fan, Ming

2017-12-08

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Retrospective study.Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.T1 -weighted 3.0T DCE-MR images.Each tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.Univariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.In the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable

Role of nutritional status in predicting quality of life outcomes in cancer--a systematic review of the epidemiological literature.

Science.gov (United States)

Lis, Christopher G; Gupta, Digant; Lammersfeld, Carolyn A; Markman, Maurie; Vashi, Pankaj G

2012-04-24

Malnutrition is a significant factor in predicting cancer patients' quality of life (QoL). We systematically reviewed the literature on the role of nutritional status in predicting QoL in cancer. We searched MEDLINE database using the terms "nutritional status" in combination with "quality of life" together with "cancer". Human studies published in English, having nutritional status as one of the predictor variables, and QoL as one of the outcome measures were included. Of the 26 included studies, 6 investigated head and neck cancer, 8 gastrointestinal, 1 lung, 1 gynecologic and 10 heterogeneous cancers. 24 studies concluded that better nutritional status was associated with better QoL, 1 study showed that better nutritional status was associated with better QoL only in high-risk patients, while 1 study concluded that there was no association between nutritional status and QoL. Nutritional status is a strong predictor of QoL in cancer patients. We recommend that more providers implement the American Society of Parenteral and Enteral Nutrition (ASPEN) guidelines for oncology patients, which includes nutritional screening, nutritional assessment and intervention as appropriate. Correcting malnutrition may improve QoL in cancer patients, an important outcome of interest to cancer patients, their caregivers, and families.

Novel hyperspectral prediction method and apparatus

Science.gov (United States)

Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

2009-05-01

Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

Functional status and mortality prediction in community-acquired pneumonia.

Science.gov (United States)

Jeon, Kyeongman; Yoo, Hongseok; Jeong, Byeong-Ho; Park, Hye Yun; Koh, Won-Jung; Suh, Gee Young; Guallar, Eliseo

2017-10-01

Poor functional status (FS) has been suggested as a poor prognostic factor in both pneumonia and severe pneumonia in elderly patients. However, it is still unclear whether FS is associated with outcomes and improves survival prediction in community-acquired pneumonia (CAP) in the general population. Data on hospitalized patients with CAP and FS, assessed by the Eastern Cooperative Oncology Group (ECOG) scale were prospectively collected between January 2008 and December 2012. The independent association of FS with 30-day mortality in CAP patients was evaluated using multivariable logistic regression. Improvement in mortality prediction when FS was added to the CRB-65 (confusion, respiratory rate, blood pressure and age 65) score was evaluated for discrimination, reclassification and calibration. The 30-day mortality of study participants (n = 1526) was 10%. Mortality significantly increased with higher ECOG score (P for trend <0.001). In multivariable analysis, ECOG ≥3 was strongly associated with 30-day mortality (adjusted OR: 5.70; 95% CI: 3.82-8.50). Adding ECOG ≥3 significantly improved the discriminatory power of CRB-65. Reclassification indices also confirmed the improvement in discrimination ability when FS was combined with the CRB-65, with a categorized net reclassification index (NRI) of 0.561 (0.437-0.686), a continuous NRI of 0.858 (0.696-1.019) and a relative integrated discrimination improvement in the discrimination slope of 139.8 % (110.8-154.6). FS predicted 30-day mortality and improved discrimination and reclassification in consecutive CAP patients. Assessment of premorbid FS should be considered in mortality prediction in patients with CAP. © 2017 Asian Pacific Society of Respirology.

Modified-Fibonacci-Dual-Lucas method for earthquake prediction

Science.gov (United States)

Boucouvalas, A. C.; Gkasios, M.; Tselikas, N. T.; Drakatos, G.

2015-06-01

The FDL method makes use of Fibonacci, Dual and Lucas numbers and has shown considerable success in predicting earthquake events locally as well as globally. Predicting the location of the epicenter of an earthquake is one difficult challenge the other being the timing and magnitude. One technique for predicting the onset of earthquakes is the use of cycles, and the discovery of periodicity. Part of this category is the reported FDL method. The basis of the reported FDL method is the creation of FDL future dates based on the onset date of significant earthquakes. The assumption being that each occurred earthquake discontinuity can be thought of as a generating source of FDL time series The connection between past earthquakes and future earthquakes based on FDL numbers has also been reported with sample earthquakes since 1900. Using clustering methods it has been shown that significant earthquakes (conjunct Sun, Moon opposite Sun, Moon conjunct or opposite North or South Modes. In order to test improvement of the method we used all +8R earthquakes recorded since 1900, (86 earthquakes from USGS data). We have developed the FDL numbers for each of those seeds, and examined the earthquake hit rates (for a window of 3, i.e. +-1 day of target date) and for <6.5R. The successes are counted for each one of the 86 earthquake seeds and we compare the MFDL method with the FDL method. In every case we find improvement when the starting seed date is on the planetary trigger date prior to the earthquake. We observe no improvement only when a planetary trigger coincided with the earthquake date and in this case the FDL method coincides with the MFDL. Based on the MDFL method we present the prediction method capable of predicting global events or localized earthquakes and we will discuss the accuracy of the method in as far as the prediction and location parts of the method. We show example calendar style predictions for global events as well as for the Greek region using

Predicting Infant Maltreatment in Low-Income Families: The Interactive Effects of Maternal Attributions and Child Status at Birth

Science.gov (United States)

Bugental, Daphne Blunt; Happaney, Keith

2004-01-01

Maternal attributions and child neonatal status at birth were assessed as predictors of infant maltreatment (harsh parenting and safety neglect). The population included low-income, low-education families who were primarily Hispanic. Child maltreatment during the 1st year of life (N = 73) was predicted by neonatal status (low Apgar scores, preterm…

Prediction of Helicobacter pylori status by conventional endoscopy, narrow-band imaging magnifying endoscopy in stomach after endoscopic resection of gastric cancer.

Science.gov (United States)

Yagi, Kazuyoshi; Saka, Akiko; Nozawa, Yujiro; Nakamura, Atsuo

2014-04-01

To reduce the incidence of metachronous gastric carcinoma after endoscopic resection of early gastric cancer, Helicobacter pylori eradication therapy has been endorsed. It is not unusual for such patients to be H. pylori negative after eradication or for other reasons. If it were possible to predict H. pylori status using endoscopy alone, it would be very useful in clinical practice. To clarify the accuracy of endoscopic judgment of H. pylori status, we evaluated it in the stomach after endoscopic submucosal dissection (ESD) of gastric cancer. Fifty-six patients treated by ESD were enrolled. The diagnostic criteria for H. pylori status by conventional endoscopy and narrow-band imaging (NBI)-magnifying endoscopy were decided, and H. pylori status was judged by two endoscopists. Based on the H. pylori stool antigen test as a diagnostic gold standard, conventional endoscopy and NBI-magnifying endoscopy were compared for their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Interobserver agreement was assessed in terms of κ value. Interobserver agreement was moderate (0.56) for conventional endoscopy and substantial (0.77) for NBI-magnifying endoscopy. The sensitivity, specificity, PPV, and NPV were 0.79, 0.52, 0.70, and 0.63 for conventional endoscopy and 0.91, 0.83, 0.88, and 0.86 for NBI-magnifying endoscopy, respectively. Prediction of H. pylori status using NBI-magnifying endoscopy is practical, and interobserver agreement is substantial. © 2013 John Wiley & Sons Ltd.

The Subjective Well-Being Method of Valuation: An Application to General Health Status.

Science.gov (United States)

Brown, Timothy T

2015-12-01

To introduce the subjective well-being (SWB) method of valuation and provide an example by valuing health status. The SWB method allows monetary valuations to be performed in the absence of market relationships. Data are from the 1975-2010 General Social Survey. The value of health status is determined via the estimation of an implicit derivative based on a happiness equation. Two-stage least-squares was used to estimate happiness as a function of poor-to-fair health status, annual household income adjusted for household size, age, sex, race, marital status, education, year, and season. Poor-to-fair health status and annual household income are instrumented using a proxy for intelligence, a temporal version of the classic distance instrument, and the average health status of individuals who are demographically similar but geographically separated. Instrument validity is evaluated. Moving from good/excellent health to poor/fair health (1 year of lower health status) is equivalent to the loss of $41,654 of equivalized household income (2010 constant dollars) per annum, which is larger than median equivalized household income. The SWB method may be useful in making monetary valuations where fundamental market relationships are not present. © Health Research and Educational Trust.

DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail

2016-03-16

Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind

Computational prediction of chemical reactions: current status and outlook.

Science.gov (United States)

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

Science.gov (United States)

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis

The Roles of Negative Career Thoughts and Sense of Coherence in Predicting Career Decision Status

Science.gov (United States)

Austin, R. Kirk; Dahl, A. Dennis; Wagner, Bruce D.

2010-01-01

The relationship between sense of coherence and negative career thoughts was investigated in a non-college-based population to determine the relationship and predictive value of these factors toward career decision status. Participants completed the Orientation to Life Questionnaire, Career Thoughts Inventory, and Career Decision Profile's…

Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms.

Science.gov (United States)

Stoica, C; Camejo, J; Banciu, A; Nita-Lazar, M; Paun, I; Cristofor, S; Pacheco, O R; Guevara, M

2016-01-01

Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

Science.gov (United States)

Girela, Jose L; Gil, David; Johnsson, Magnus; Gomez-Torres, María José; De Juan, Joaquín

2013-04-01

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.

Prediction methods environmental-effect reporting

International Nuclear Information System (INIS)

Jonker, R.J.; Koester, H.W.

1987-12-01

This report provides a survey of prediction methods which can be applied to the calculation of emissions in cuclear-reactor accidents, in the framework of environment-effect reports (dutch m.e.r.) or risk analyses. Also emissions during normal operation are important for m.e.r.. These can be derived from measured emissions of power plants being in operation. Data concerning the latter are reported. The report consists of an introduction into reactor technology, among which a description of some reactor types, the corresponding fuel cycle and dismantling scenarios - a discussion of risk-analyses for nuclear power plants and the physical processes which can play a role during accidents - a discussion of prediction methods to be employed and the expected developments in this area - some background information. (aughor). 145 refs.; 21 figs.; 20 tabs

Prognostic Value of Histology and Lymph Node Status in Bilharziasis-Bladder Cancer: Outcome Prediction Using Neural Networks

National Research Council Canada - National Science Library

Ji, W

2001-01-01

.... Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer...

The effects of lymph node status on predicting outcome in ER+ /HER2- tamoxifen treated breast cancer patients using gene signatures

International Nuclear Information System (INIS)

Cockburn, Jessica G.; Hallett, Robin M.; Gillgrass, Amy E.; Dias, Kay N.; Whelan, T.; Levine, M. N.; Hassell, John A.; Bane, Anita

2016-01-01

Lymph node (LN) status is the most important prognostic variable used to guide ER positive (+) breast cancer treatment. While a positive nodal status is traditionally associated with a poor prognosis, a subset of these patients respond well to treatment and achieve long-term survival. Several gene signatures have been established as a means of predicting outcome of breast cancer patients, but the development and indication for use of these assays varies. Here we compare the capacity of two approved gene signatures and a third novel signature to predict outcome in distinct LN negative (-) and LN+ populations. We also examine biological differences between tumours associated with LN- and LN+ disease. Gene expression data from publically available data sets was used to compare the ability of Oncotype DX and Prosigna to predict Distant Metastasis Free Survival (DMFS) using an in silico platform. A novel gene signature (Ellen) was developed by including patients with both LN- and LN+ disease and using Prediction Analysis of Microarrays (PAM) software. Gene Set Enrichment Analysis (GSEA) was used to determine biological pathways associated with patient outcome in both LN- and LN+ tumors. The Oncotype DX gene signature, which only used LN- patients during development, significantly predicted outcome in LN- patients, but not LN+ patients. The Prosigna gene signature, which included both LN- and LN+ patients during development, predicted outcome in both LN- and LN+ patient groups. Ellen was also able to predict outcome in both LN- and LN+ patient groups. GSEA suggested that epigenetic modification may be related to poor outcome in LN- disease, whereas immune response may be related to good outcome in LN+ disease. We demonstrate the importance of incorporating lymph node status during the development of prognostic gene signatures. Ellen may be a useful tool to predict outcome of patients regardless of lymph node status, or for those with unknown lymph node status. Finally we

Methane prediction in collieries

CSIR Research Space (South Africa)

Creedy, DP

1999-06-01

Full Text Available The primary aim of the project was to assess the current status of research on methane emission prediction for collieries in South Africa in comparison with methods used and advances achieved elsewhere in the world....

Status of the observed and predicted b anti-b production at the Tevatron

Energy Technology Data Exchange (ETDEWEB)

Happacher, F.; Giromini, P.; /Frascati; Ptohos, F.; /Cyprus U.

2005-09-01

The authors review the experimental status of the b-quark production at the Fermilab Tevatron. They compare all available measurements to perturbative QCD predictions (NLO and FONLL) and also to the parton-level cross section evaluated with parton-shower Monte Carlo generators. They examine both the single b cross section and the so called b{bar b} correlations. The review shows that the experimental situation is quite complicated because the measurements appear to be inconsistent among themselves. In this situation, there is no solid basis to either claim that perturbative QCD is challenged by these measurements or, in contrast, that long-standing discrepancies between data and theory have been resolved by incrementally improving the measurements and the theoretical prediction.

The trajectory prediction of spacecraft by grey method

International Nuclear Information System (INIS)

Wang, Qiyue; Wang, Zhongyu; Zhang, Zili; Wang, Yanqing; Zhou, Weihu

2016-01-01

The real-time and high-precision trajectory prediction of a moving object is a core technology in the field of aerospace engineering. The real-time monitoring and tracking technology are also significant guarantees of aerospace equipment. A dynamic trajectory prediction method called grey dynamic filter (GDF) which combines the dynamic measurement theory and grey system theory is proposed. GDF can use coordinates of the current period to extrapolate coordinates of the following period. At meantime, GDF can also keep the instantaneity of measured coordinates by the metabolism model. In this paper the optimal model length of GDF is firstly selected to improve the prediction accuracy. Then the simulation for uniformly accelerated motion and variably accelerated motion is conducted. The simulation results indicate that the mean composite position error of GDF prediction is one-fifth to that of Kalman filter (KF). By using a spacecraft landing experiment, the prediction accuracy of GDF is compared with the KF method and the primitive grey method (GM). The results show that the motion trajectory of spacecraft predicted by GDF is much closer to actual trajectory than the other two methods. The mean composite position error calculated by GDF is one-eighth to KF and one-fifth to GM respectively. (paper)

The interplay of parental monitoring and socioeconomic status in predicting minor delinquency between and within adolescents

NARCIS (Netherlands)

Rekker, Roderik; Keijsers, L.G.M.T.; Branje, Susan; Koot, Hans; Meeus, W.H.J.

This six-wave multi-informant longitudinal study on Dutch adolescents (N = 824; age 12 18) examined the interplay of socioeconomic status with parental monitoring in predicting minor delinquency. Fixed-effects negative binomial regression analyses revealed that this interplay is different within

Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.

Directory of Open Access Journals (Sweden)

Huiling Chen

Full Text Available The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC curve criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01. According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.

Reliability of non-lethal assessment methods of body composition and energetic status exemplified by applications to eel (Anguilla anguilla) and carp (Cyprinus carpio)

DEFF Research Database (Denmark)

Klefoth, Thomas; Skov, Christian; Aarestrup, Kim

2013-01-01

tNon-lethal assessments of proximate body composition of fish can help unravelling the physiologicaland condition-dependent mechanisms of individual responses to ecological challenges. Common non-lethal methods designed to index nutrient composition in fish include the relative condition factor (Kn......),bioelectric impedance-based assessments of body composition (BIA), and microwave-based “fat” meters(FM). Previous studies have revealed mixed findings as to the reliability of each of these. We compared theperformance of Kn, BIA and FM at different temperatures to predict energetic status of the whole bodiesof live eel...... approach isthe most suitable method to non-lethally estimate energetic status in both, carp and eel, whereas BIA is oflimited use for energetic measurements in the same species, in contrast to other reports in the literature...

Pre-fracture nutritional status is predictive of functional status at discharge during the acute phase with hip fracture patients: A multicenter prospective cohort study.

Science.gov (United States)

Inoue, Tatsuro; Misu, Syogo; Tanaka, Toshiaki; Sakamoto, Hiroki; Iwata, Kentaro; Chuman, Yuki; Ono, Rei

2017-10-01

Malnutrition is common in patients with hip fractures, and elderly patients with hip fractures lose functional independence and often fail to recover previous functional status. The aim of this study was to determine whether pre-fracture nutritional status predicts functional status of patients with hip fracture at discharge from acute hospitals. In the present multicenter prospective cohort study, pre-fracture nutritional status was assessed using the Mini Nutritional Assessment Short-Form (MNA-SF). At discharge from acute hospitals, functional status was evaluated using a functional independent measurement instrument (FIM). Subsequently, multiple regression analyses were performed using FIM as the dependent variable and MNA-SF as the independent variable. Among the 204 patients analyzed in the present study, the mean length of hospital stay was 26.2 ± 12.6 days, and according to MNA-SF assessments, 51 (25.0%) patients were malnourished, 98 (48.0%) were at risk of malnutrition, and 55 (27.0%) were well-nourished before fracture. At discharge, FIM scores were higher in patients who were well-nourished than in those who were malnourished or were at risk of malnutrition (p fracture nutritional status was a significant independent predictor for functional status at discharge during the acute phase, warranting early assessment of nutritional status and early intervention for successful postoperative rehabilitation. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Current status of the EPR method to detect irradiated food

International Nuclear Information System (INIS)

Desrosiers, M.F.

1996-01-01

This review gives a brief outline of the principles of the EPR detection method for irradiated foods by food type. For each food type, the scope, limitations and status of the method are given. The extensive reference list aims to include all which define the method, as well as some rarely cited works of historical importance. (author)

Preference for High Status Predicts Implicit Outgroup Bias among Children from Low-Status Groups

Science.gov (United States)

Newheiser, Anna-Kaisa; Dunham, Yarrow; Merrill, Anna; Hoosain, Leah; Olson, Kristina R.

2014-01-01

Whereas members of high-status racial groups show ingroup preference when attitudes are measured implicitly, members of low-status racial groups--both adults and children--typically show no bias, potentially reflecting awareness of the ingroup's low status. We hypothesized that when status differences are especially pronounced, children from…

Proceeding of 27th domestic symposium on trends in aging management and current status of aging degradation studies in nuclear power plants

International Nuclear Information System (INIS)

2000-11-01

As the 27th domestic symposium of Atomic Energy Research Committee, the Japan Welding Engineering Society, the symposium was held titled as 'Trends of aging managements and current status of aging effect studies in nuclear power plants'. Six speakers gave lectures titled as 'Present status of research on mechanism and prediction method of neutron irradiation embrittlement of pressure vessel steels', 'Present status of research on mechanism and prediction method of environmentally assisted cracking in the LWR environments', 'Domestic and overseas trends of aging management of the LWR plants', 'Trends of prediction/evaluation, inspection/monitoring and repair/replacement technologies for aging of the LWR plants', 'Present status of research on mechanism and prediction method of high cycle thermal fatigue due to the thermal fluid-structure interaction in the LWR environments' and Present status of research on very high cycle fatigue of structural materials'. (T. Tanaka)

Methods and techniques for prediction of environmental impact

International Nuclear Information System (INIS)

1992-04-01

Environmental impact assessment (EIA) is the procedure that helps decision makers understand the environmental implications of their decisions. The prediction of environmental effects or impact is an extremely important part of the EIA procedure and improvements in existing capabilities are needed. Considerable attention is paid within environmental impact assessment and in handbooks on EIA to methods for identifying and evaluating environmental impacts. However, little attention is given to the issue distribution of information on impact prediction methods. The quantitative or qualitative methods for the prediction of environmental impacts appear to be the two basic approaches for incorporating environmental concerns into the decision-making process. Depending on the nature of the proposed activity and the environment likely to be affected, a combination of both quantitative and qualitative methods is used. Within environmental impact assessment, the accuracy of methods for the prediction of environmental impacts is of major importance while it provides for sound and well-balanced decision making. Pertinent and effective action to deal with the problems of environmental protection and the rational use of natural resources and sustainable development is only possible given objective methods and techniques for the prediction of environmental impact. Therefore, the Senior Advisers to ECE Governments on Environmental and Water Problems, decided to set up a task force, with the USSR as lead country, on methods and techniques for the prediction of environmental impacts in order to undertake a study to review and analyse existing methodological approaches and to elaborate recommendations to ECE Governments. The work of the task force was completed in 1990 and the resulting report, with all relevant background material, was approved by the Senior Advisers to ECE Governments on Environmental and Water Problems in 1991. The present report reflects the situation, state of

The interplay of parental monitoring and socioeconomic status in predicting minor delinquency between and within adolescents

NARCIS (Netherlands)

Rekker, Roderik; Keijsers, Loes; Branje, Susan; Koot, Hans M.; Meeus, Wim

2017-01-01

This six-wave multi-informant longitudinal study on Dutch adolescents (N = 824; age 12–18) examined the interplay of socioeconomic status with parental monitoring in predicting minor delinquency. Fixed-effects negative binomial regression analyses revealed that this interplay is different within

Connecting clinical and actuarial prediction with rule-based methods

NARCIS (Netherlands)

Fokkema, M.; Smits, N.; Kelderman, H.; Penninx, B.W.J.H.

2015-01-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction

Prediction Methods for Blood Glucose Concentration

DEFF Research Database (Denmark)

“Recent Results on Glucose–Insulin Predictions by Means of a State Observer for Time-Delay Systems” by Pasquale Palumbo et al. introduces a prediction model which in real time predicts the insulin concentration in blood which in turn is used in a control system. The method is tested in simulation...... EEG signals to predict upcoming hypoglycemic situations in real-time by employing artificial neural networks. The results of a 30-day long clinical study with the implanted device and the developed algorithm are presented. The chapter “Meta-Learning Based Blood Glucose Predictor for Diabetic......, but the insulin amount is chosen using factors that account for this expectation. The increasing availability of more accurate continuous blood glucose measurement (CGM) systems is attracting much interest to the possibilities of explicit prediction of future BG values. Against this background, in 2014 a two...

Role of nutritional status in predicting quality of life outcomes in cancer – a systematic review of the epidemiological literature

Science.gov (United States)

2012-01-01

Malnutrition is a significant factor in predicting cancer patients’ quality of life (QoL). We systematically reviewed the literature on the role of nutritional status in predicting QoL in cancer. We searched MEDLINE database using the terms “nutritional status” in combination with “quality of life” together with “cancer”. Human studies published in English, having nutritional status as one of the predictor variables, and QoL as one of the outcome measures were included. Of the 26 included studies, 6 investigated head and neck cancer, 8 gastrointestinal, 1 lung, 1 gynecologic and 10 heterogeneous cancers. 24 studies concluded that better nutritional status was associated with better QoL, 1 study showed that better nutritional status was associated with better QoL only in high-risk patients, while 1 study concluded that there was no association between nutritional status and QoL. Nutritional status is a strong predictor of QoL in cancer patients. We recommend that more providers implement the American Society of Parenteral and Enteral Nutrition (ASPEN) guidelines for oncology patients, which includes nutritional screening, nutritional assessment and intervention as appropriate. Correcting malnutrition may improve QoL in cancer patients, an important outcome of interest to cancer patients, their caregivers, and families. PMID:22531478

Artificial neural network intelligent method for prediction

Science.gov (United States)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

New prediction methods for collaborative filtering

Directory of Open Access Journals (Sweden)

Hasan BULUT

2016-05-01

Full Text Available Companies, in particular e-commerce companies, aims to increase customer satisfaction, hence in turn increase their profits, using recommender systems. Recommender Systems are widely used nowadays and they provide strategic advantages to the companies that use them. These systems consist of different stages. In the first stage, the similarities between the active user and other users are computed using the user-product ratings matrix. Then, the neighbors of the active user are found from these similarities. In prediction calculation stage, the similarities computed at the first stage are used to generate the weight vector of the closer neighbors. Neighbors affect the prediction value by the corresponding value of the weight vector. In this study, we developed two new methods for the prediction calculation stage which is the last stage of collaborative filtering. The performance of these methods are measured with evaluation metrics used in the literature and compared with other studies in this field.

A method for predicting monthly rainfall patterns

International Nuclear Information System (INIS)

Njau, E.C.

1987-11-01

A brief survey is made of previous methods that have been used to predict rainfall trends or drought spells in different parts of the earth. The basic methodologies or theoretical strategies used in these methods are compared with contents of a recent theory of Sun-Weather/Climate links (Njau, 1985a; 1985b; 1986; 1987a; 1987b; 1987c) which point towards the possibility of practical climatic predictions. It is shown that not only is the theoretical basis of each of these methodologies or strategies fully incorporated into the above-named theory, but also this theory may be used to develop a technique by which future monthly rainfall patterns can be predicted in further and finer details. We describe the latter technique and then illustrate its workability by means of predictions made on monthly rainfall patterns in some East African meteorological stations. (author). 43 refs, 11 figs, 2 tabs

An Approximate Method for Pitch-Damping Prediction

National Research Council Canada - National Science Library

Danberg, James

2003-01-01

...) method for predicting the pitch-damping coefficients has been employed. The CFD method provides important details necessary to derive the correlation functions that are unavailable from the current experimental database...

Epitope prediction methods

DEFF Research Database (Denmark)

Karosiene, Edita

Analysis. The chapter provides detailed explanations on how to use different methods for T cell epitope discovery research, explaining how input should be given as well as how to interpret the output. In the last chapter, I present the results of a bioinformatics analysis of epitopes from the yellow fever...... peptide-MHC interactions. Furthermore, using yellow fever virus epitopes, we demonstrated the power of the %Rank score when compared with the binding affinity score of MHC prediction methods, suggesting that this score should be considered to be used for selecting potential T cell epitopes. In summary...... immune responses. Therefore, it is of great importance to be able to identify peptides that bind to MHC molecules, in order to understand the nature of immune responses and discover T cell epitopes useful for designing new vaccines and immunotherapies. MHC molecules in humans, referred to as human...

Connecting clinical and actuarial prediction with rule-based methods.

Science.gov (United States)

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

A new automated colorimetric method for measuring total oxidant status.

Science.gov (United States)

Erel, Ozcan

2005-12-01

To develop a new, colorimetric and automated method for measuring total oxidation status (TOS). The assay is based on the oxidation of ferrous ion to ferric ion in the presence of various oxidant species in acidic medium and the measurement of the ferric ion by xylenol orange. The oxidation reaction of the assay was enhanced and precipitation of proteins was prevented. In addition, autoxidation of ferrous ion present in the reagent was prevented during storage. The method was applied to an automated analyzer, which was calibrated with hydrogen peroxide and the analytical performance characteristics of the assay were determined. There were important correlations with hydrogen peroxide, tert-butyl hydroperoxide and cumene hydroperoxide solutions (r=0.99, Ptotal antioxidant capacity (TAC) (r=-0.66 Ptotal oxidant status.

FREEZING AND THAWING TIME PREDICTION METHODS OF FOODS II: NUMARICAL METHODS

Directory of Open Access Journals (Sweden)

Yahya TÜLEK

1999-03-01

Full Text Available Freezing is one of the excellent methods for the preservation of foods. If freezing and thawing processes and frozen storage method are carried out correctly, the original characteristics of the foods can remain almost unchanged over an extended periods of time. It is very important to determine the freezing and thawing time period of the foods, as they strongly influence the both quality of food material and process productivity and the economy. For developing a simple and effectively usable mathematical model, less amount of process parameters and physical properties should be enrolled in calculations. But it is a difficult to have all of these in one prediction method. For this reason, various freezing and thawing time prediction methods were proposed in literature and research studies have been going on.

Investigation into Methods for Predicting Connection Temperatures

Directory of Open Access Journals (Sweden)

K. Anderson

2009-01-01

Full Text Available The mechanical response of connections in fire is largely based on material strength degradation and the interactions between the various components of the connection. In order to predict connection performance in fire, temperature profiles must initially be established in order to evaluate the material strength degradation over time. This paper examines two current methods for predicting connection temperatures: The percentage method, where connection temperatures are calculated as a percentage of the adjacent beam lower-flange, mid-span temperatures; and the lumped capacitance method, based on the lumped mass of the connection. Results from the percentage method do not correlate well with experimental results, whereas the lumped capacitance method shows much better agreement with average connection temperatures. A 3D finite element heat transfer model was also created in Abaqus, and showed good correlation with experimental results. 

Depressive vulnerabilities predict depression status and trajectories of depression over 1 year in persons with acute coronary syndrome.

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Doyle, Frank; McGee, Hannah; Delaney, Mary; Motterlini, Nicola; Conroy, Ronán

2011-01-01

Depression is prevalent in patients hospitalized with acute coronary syndrome (ACS). We determined whether theoretical vulnerabilities for depression (interpersonal life events, reinforcing events, cognitive distortions, Type D personality) predicted depression, or depression trajectories, post-hospitalization. We followed 375 ACS patients who completed depression scales during hospital admission and at least once during three follow-up intervals over 1 year (949 observations). Questionnaires assessing vulnerabilities were completed at baseline. Logistic regression for panel/longitudinal data predicted depression status during follow-up. Latent class analysis determined depression trajectories. Multinomial logistic regression modeled the relationship between vulnerabilities and trajectories. Vulnerabilities predicted depression status over time in univariate and multivariate analysis, even when controlling for baseline depression. Proportions in each depression trajectory category were as follows: persistent (15%), subthreshold (37%), never depressed (48%). Vulnerabilities independently predicted each of these trajectories, with effect sizes significantly highest for the persistent depression group. Self-reported vulnerabilities - stressful life events, reduced reinforcing events, cognitive distortions, personality - measured during hospitalization can identify those at risk for depression post-ACS and especially those with persistent depressive episodes. Interventions should focus on these vulnerabilities. Copyright © 2011 Elsevier Inc. All rights reserved.

Different protein-protein interface patterns predicted by different machine learning methods.

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Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

Sentinel node status prediction by four statistical models: results from a large bi-institutional series (n = 1132).

Science.gov (United States)

Mocellin, Simone; Thompson, John F; Pasquali, Sandro; Montesco, Maria C; Pilati, Pierluigi; Nitti, Donato; Saw, Robyn P; Scolyer, Richard A; Stretch, Jonathan R; Rossi, Carlo R

2009-12-01

To improve selection for sentinel node (SN) biopsy (SNB) in patients with cutaneous melanoma using statistical models predicting SN status. About 80% of patients currently undergoing SNB are node negative. In the absence of conclusive evidence of a SNBassociated survival benefit, these patients may be over-treated. Here, we tested the efficiency of 4 different models in predicting SN status. The clinicopathologic data (age, gender, tumor thickness, Clark level, regression, ulceration, histologic subtype, and mitotic index) of 1132 melanoma patients who had undergone SNB at institutions in Italy and Australia were analyzed. Logistic regression, classification tree, random forest, and support vector machine models were fitted to the data. The predictive models were built with the aim of maximizing the negative predictive value (NPV) and reducing the rate of SNB procedures though minimizing the error rate. After cross-validation logistic regression, classification tree, random forest, and support vector machine predictive models obtained clinically relevant NPV (93.6%, 94.0%, 97.1%, and 93.0%, respectively), SNB reduction (27.5%, 29.8%, 18.2%, and 30.1%, respectively), and error rates (1.8%, 1.8%, 0.5%, and 2.1%, respectively). Using commonly available clinicopathologic variables, predictive models can preoperatively identify a proportion of patients ( approximately 25%) who might be spared SNB, with an acceptable (1%-2%) error. If validated in large prospective series, these models might be implemented in the clinical setting for improved patient selection, which ultimately would lead to better quality of life for patients and optimization of resource allocation for the health care system.

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

Energy Technology Data Exchange (ETDEWEB)

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit

2016-05-02

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

International Nuclear Information System (INIS)

Liu, Guangming; Ouyang, Minggao; Lu, Languang; Li, Jianqiu; Hua, Jianfeng

2015-01-01

Highlights: • An energy prediction (EP) method is introduced for battery E RDE determination. • EP determines E RDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved E RDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the

Acoustical method of whole-body hydration status monitoring

Science.gov (United States)

Sarvazyan, A. P.; Tsyuryupa, S. N.; Calhoun, M.; Utter, A.

2016-07-01

An acoustical handheld hydration monitor (HM) for assessing the water balance of the human body was developed. Dehydration is a critical public health problem. Many elderly over age of 65 are particularly vulnerable as are infants and young children. Given that dehydration is both preventable and reversible, the need for an easy-to-perform method for the detection of water imbalance is of the utmost clinical importance. The HM is based on an experimental fact that ultrasound velocity in muscle is a linear function of water content and can be referenced to the hydration status of the body. Studies on the validity of HM for the assessment of whole-body hydration status were conducted in the Appalachian State University, USA, on healthy young adults and on elderly subjects residing at an assisted living facility. The HM was able to track changes in total body water during periods of acute dehydration and rehydration in athletes and day-to-day and diurnal variability of hydration in elderly. Results of human studies indicate that HM has a potential to become an efficient tool for detecting abnormal changes in the body hydration status.

Mini Nutritional Assessment predicts gait status and mortality 6 months after hip fracture.

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Gumieiro, David N; Rafacho, Bruna P M; Gonçalves, Andrea F; Tanni, Suzana E; Azevedo, Paula S; Sakane, Daniel T; Carneiro, Carlos A S; Gaspardo, David; Zornoff, Leonardo A M; Pereira, Gilberto J C; Paiva, Sergio A R; Minicucci, Marcos F

2013-05-01

The aim of the present study was to evaluate the Mini Nutritional Assessment (MNA), the Nutritional Risk Screening (NRS) 2002 and the American Society of Anesthesiologists Physical Status Score (ASA) as predictors of gait status and mortality 6 months after hip fracture. A total of eighty-eight consecutive patients over the age of 65 years with hip fracture admitted to an orthopaedic unit were prospectively evaluated. Within the first 72 h of admission, each patient's characteristics were recorded, and the MNA, the NRS 2002 and the ASA were performed. Gait status and mortality were evaluated 6 months after hip fracture. Of the total patients, two were excluded because of pathological fractures. The remaining eighty-six patients (aged 80·2 (sd 7·3) years) were studied. Among these patients 76·7 % were female, 69·8 % walked with or without support and 12·8 % died 6 months after the fracture. In a multivariate analysis, only the MNA was associated with gait status 6 months after hip fracture (OR 0·773, 95 % CI 0·663, 0·901; P= 0·001). In the Cox regression model, only the MNA was associated with mortality 6 months after hip fracture (hazard ratio 0·869, 95 % CI 0·757, 0·998; P= 0·04). In conclusion, the MNA best predicts gait status and mortality 6 months after hip fracture. These results suggest that the MNA should be included in the clinical stratification of patients with hip fracture to identify and treat malnutrition in order to improve the outcomes.

Does socioeconomic status predict course and outcome in patients with psychosis?

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Samele, C; van Os, J; McKenzie, K; Wright, A; Gilvarry, C; Manley, C; Tattan, T; Murray, R

2001-12-01

We examined the relationship between socioeconomic status (SES) and course and outcome of patients with psychosis. Two hypotheses were examined: a) patients with higher best-ever SES will have better course and outcome than those with lower best-ever SES, and b) patients with greater downward drift in SES will have poorer course and outcome than those with less downward drift. Data were drawn from the baseline and 2-year follow-up assessments of the UK700 Case Management Trial of 708 patients with severe psychosis. The indicators of SES used were occupational status and educational achievement. Drift in SES was defined as change from best-ever occupation to occupation at baseline. For the baseline data highly significant differences were found between best-ever groups and negative symptoms (non-manual vs. unemployed--coef -10.5, p=0.000, 95% CIs 5.1-15.8), functioning (non-manual vs. unemployed--coef -0.6, p=0.000, 95% CIs 0.3 to -0.8) and unmet needs (manual vs. unemployed - coef 0.5, p=0.004, 95% CIs 0.2-0.9). No significant differences between best-ever groups were found for days in hospital, symptoms, perceived quality of life and dissatisfaction with services. Significant differences for clinical and social variables were found between drift and non-drift SES groups. There were no significant findings between educational groups and clinical and social variables. Best-ever occupation, but not educational qualifications, appeared to predict prognosis in patients with severe psychosis. Downward drift in occupational status did not result in poorer illness course and outcome.

Winter birth, urbanicity and immigrant status predict psychometric schizotypy dimensions in adolescents.

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Mimarakis, D; Roumeliotaki, T; Roussos, P; Giakoumaki, S G; Bitsios, P

2018-01-01

Urbanicity, immigration and winter-birth are stable epidemiological risk factors for schizophrenia, but their relationship to schizotypy is unknown. This is a first examination of the association of these epidemiological risk factors with positive schizotypy, in nonclinical adolescents, controlling for a range of potential and known confounders. We collected socio-demographics, life-style, family and school circumstances, positive schizotypy dimensions and other personality traits from 445 high school pupils (192 males, 158 immigrants) from 9 municipalities in Athens and Heraklion, Greece, which covered a range of host population and migrant densities. Using multivariate hierarchical linear regressions models, we estimated the association of schizotypy dimensions with: (1) demographics of a priori interest (winter-birth, immigrant status, urban characteristics), including family financial and mental health status; (2) factors resulting from principal component analysis (PCA) of the demographic and personal data; (3) factors resulting from PCA of the personality questionnaires. Adolescent women scored higher on schizotypy than men. High anxiety/neuroticism was the most consistent and significant predictor of all schizotypy dimensions in both sexes. In the fully adjusted models, urbanicity predicted magical thinking and unusual experiences in women, while winter-birth and immigration predicted paranoid ideation and unusual experiences respectively in men. These results support the continuum hypothesis and offer potential insights in the nature of risk conferred by winter-birth, urbanicity and immigration and the nature of important sex differences. Controlling for a wide range of potential confounding factors increases the robustness of these results and confidence that these were not spurious associations. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Quality status display for a vibration welding process

Science.gov (United States)

Spicer, John Patrick; Abell, Jeffrey A.; Wincek, Michael Anthony; Chakraborty, Debejyo; Bracey, Jennifer; Wang, Hui; Tavora, Peter W.; Davis, Jeffrey S.; Hutchinson, Daniel C.; Reardon, Ronald L.; Utz, Shawn

2017-11-28

A method includes receiving, during a vibration welding process, a set of sensory signals from a collection of sensors positioned with respect to a work piece during formation of a weld on or within the work piece. The method also includes receiving control signals from a welding controller during the process, with the control signals causing the welding horn to vibrate at a calibrated frequency, and processing the received sensory and control signals using a host machine. Additionally, the method includes displaying a predicted weld quality status on a surface of the work piece using a status projector. The method may include identifying and display a quality status of a suspect weld. The laser projector may project a laser beam directly onto or immediately adjacent to the suspect welds, e.g., as a red, green, blue laser or a gas laser having a switched color filter.

Investigation on water status and distribution in broccoli and the effects of drying on water status using NMR and MRI methods

NARCIS (Netherlands)

Xu, Fangfang; Jin, Xin; Zhang, Lu; Chen, Xiao Dong

2017-01-01

Many quality attributes of food products are influenced by the water status and the microstructure. Low-field nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) methods are applied to non-destructively monitor the water status and structure of food. The aim of this study is to

Predicting IDH mutation status of intrahepatic cholangiocarcinomas based on contrast-enhanced CT features

Energy Technology Data Exchange (ETDEWEB)

Zhu, Yong [Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Department of Radiology, Nanjing, Jiangsu Province (China); Chen, Jun [Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Department of Pathology, Nanjing, Jiangsu Province (China); Kong, Weiwei [Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Department of Oncology, Nanjing, Jiangsu Province (China); Mao, Liang; Qiu, Yudong [Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Department of Hepatopancreatobiliary Surgery, Nanjing, Jiangsu Province (China); Kong, Wentao [Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Department of Ultrasonography, Nanjing, Jiangsu Province (China); Zhou, Qun [Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Department of Radiology, Nanjing, Jiangsu Province (China); Zhou, Zhengyang; Zhu, Bin; He, Jian [Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Department of Radiology, Nanjing, Jiangsu Province (China); Wang, Zhongqiu [Jiangsu Province Hospital of Traditional Chinese Medicine, the Affiliated Hospital of Nanjing University of Chinese Medicine, Department of Radiology, Nanjing, Jiangsu Province (China)

2018-01-15

To explore the difference in contrast-enhanced computed tomography (CT) features of intrahepatic cholangiocarcinomas (ICCs) with different isocitrate dehydrogenase (IDH) mutation status. Clinicopathological and contrast-enhanced CT features of 78 patients with 78 ICCs were retrospectively analysed and compared based on IDH mutation status. There were 11 ICCs with IDH mutation (11/78, 14.1%) and 67 ICCs without IDH mutation (67/78, 85.9%). IDH-mutated ICCs showed intratumoral artery more often than IDH-wild ICCs (p = 0.023). Most ICCs with IDH mutation showed rim and internal enhancement (10/11, 90.9%), while ICCs without IDH mutation often appeared diffuse (26/67, 38.8%) or with no enhancement (4/67, 6.0%) in the arterial phase (p = 0.009). IDH-mutated ICCs showed significantly higher CT values, enhancement degrees and enhancement ratios in arterial and portal venous phases than IDH-wild ICCs (all p < 0.05). The CT value of tumours in the portal venous phase performed best in distinguishing ICCs with and without IDH mutation, with an area under the curve of 0.798 (p = 0.002). ICCs with and without IDH mutation differed significantly in arterial enhancement mode, and the tumour enhancement degree on multiphase contrast-enhanced CT was helpful in predicting IDH mutation status. (orig.)

Improvement of gas entrainment prediction method. Introduction of surface tension effect

International Nuclear Information System (INIS)

Ito, Kei; Sakai, Takaaki; Ohshima, Hiroyuki; Uchibori, Akihiro; Eguchi, Yuzuru; Monji, Hideaki; Xu, Yongze

2010-01-01

A gas entrainment (GE) prediction method has been developed to establish design criteria for the large-scale sodium-cooled fast reactor (JSFR) systems. The prototype of the GE prediction method was already confirmed to give reasonable gas core lengths by simple calculation procedures. However, for simplification, the surface tension effects were neglected. In this paper, the evaluation accuracy of gas core lengths is improved by introducing the surface tension effects into the prototype GE prediction method. First, the mechanical balance between gravitational, centrifugal, and surface tension forces is considered. Then, the shape of a gas core tip is approximated by a quadratic function. Finally, using the approximated gas core shape, the authors determine the gas core length satisfying the mechanical balance. This improved GE prediction method is validated by analyzing the gas core lengths observed in simple experiments. Results show that the analytical gas core lengths calculated by the improved GE prediction method become shorter in comparison to the prototype GE prediction method, and are in good agreement with the experimental data. In addition, the experimental data under different temperature and surfactant concentration conditions are reproduced by the improved GE prediction method. (author)

Method for Predicting Thermal Buckling in Rails

Science.gov (United States)

2018-01-01

A method is proposed herein for predicting the onset of thermal buckling in rails in such a way as to provide a means of avoiding this type of potentially devastating failure. The method consists of the development of a thermomechanical model of rail...

Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging

Energy Technology Data Exchange (ETDEWEB)

Cui, Chunyan; Liu, Lizhi; Li, Li [Sun Yat-sen University, State Key Laboratory of Oncology in Southern China, Imaging Diagnosis and Interventional Center, Cancer Center, Guangzhou, Guangdong (China); Cai, Hongmin; Tian, Haiying [Sun Yat-Sen University, Department of Automation, School of Science Information and Technology, Guangzhou (China); Li, Liren [Sun Yat-sen University, State Key Laboratory of Oncology in Southern China, Department of Abdominal (colon and rectal) Surgery, Cancer Center, Guangzhou (China)

2011-11-15

To quantitatively evaluate regional lymph nodes in rectal cancer patients by using an automated, computer-aided approach, and to assess the accuracy of this approach in differentiating benign and malignant lymph nodes. Patients (228) with newly diagnosed rectal cancer, confirmed by biopsy, underwent enhanced computed tomography (CT). Patients were assigned to the benign node or malignant node group according to histopathological analysis of node samples. All CT-detected lymph nodes were segmented using the edge detection method, and seven quantitative parameters of each node were measured. To increase the prediction accuracy, a hierarchical model combining the merits of the support and relevance vector machines was proposed to achieve higher performance. Of the 220 lymph nodes evaluated, 125 were positive and 95 were negative for metastases. Fractal dimension obtained by the Minkowski box-counting approach was higher in malignant nodes than in benign nodes, and there was a significant difference in heterogeneity between metastatic and non-metastatic lymph nodes. The overall performance of the proposed model is shown to have accuracy as high as 88% using morphological characterisation of lymph nodes. Computer-aided quantitative analysis can improve the prediction of node status in rectal cancer. (orig.)

The Application of Determining Students’ Graduation Status of STMIK Palangkaraya Using K-Nearest Neighbors Method

Science.gov (United States)

Rusdiana, Lili; Marfuah

2017-12-01

K-Nearest Neighbors method is one of methods used for classification which calculate a value to find out the closest in distance. It is used to group a set of data such as students’ graduation status that are got from the amount of course credits taken by them, the grade point average (AVG), and the mini-thesis grade. The study is conducted to know the results of using K-Nearest Neighbors method on the application of determining students’ graduation status, so it can be analyzed from the method used, the data, and the application constructed. The aim of this study is to find out the application results by using K-Nearest Neighbors concept to determine students’ graduation status using the data of STMIK Palangkaraya students. The development of the software used Extreme Programming, since it was appropriate and precise for this study which was to quickly finish the project. The application was created using Microsoft Office Excel 2007 for the training data and Matlab 7 to implement the application. The result of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5%. It could determine the predicate graduation of 94 data used from the initial data before the processing as many as 136 data which the maximal training data was 50data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study. The results of K-Nearest Neighbors method on the application of determining students’ graduation status was 92.5% could determine the predicate graduation which is the maximal training data. The K-Nearest Neighbors method is one of methods used to group a set of data based on the closest value, so that using K-Nearest Neighbors method agreed with this study.

Overview of mycotoxin methods, present status and future needs.

Science.gov (United States)

Gilbert, J

1999-01-01

This article reviews current requirements for the analysis for mycotoxins in foods and identifies legislative as well as other factors that are driving development and validation of new methods. New regulatory limits for mycotoxins and analytical quality assurance requirements for laboratories to only use validated methods are seen as major factors driving developments. Three major classes of methods are identified which serve different purposes and can be categorized as screening, official and research. In each case the present status and future needs are assessed. In addition to an overview of trends in analytical methods, some other areas of analytical quality assurance such as participation in proficiency testing and reference materials are identified.

Motor degradation prediction methods

Energy Technology Data Exchange (ETDEWEB)

Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

1996-12-01

Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.

Motor degradation prediction methods

International Nuclear Information System (INIS)

Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

1996-01-01

Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor's duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures

Evaluation and comparison of mammalian subcellular localization prediction methods

Directory of Open Access Journals (Sweden)

Fink J Lynn

2006-12-01

Full Text Available Abstract Background Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER, peroxisome, and lysosome. The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE

Seasonal climate prediction for North Eurasia

International Nuclear Information System (INIS)

Kryjov, Vladimir N

2012-01-01

An overview of the current status of the operational seasonal climate prediction for North Eurasia is presented. It is shown that the performance of existing climate models is rather poor in seasonal prediction for North Eurasia. Multi-model ensemble forecasts are more reliable than single-model ones; however, for North Eurasia they tend to be close to climatological ones. Application of downscaling methods may improve predictions for some locations (or regions). However, general improvement of the reliability of seasonal forecasts for North Eurasia requires improvement of the climate prediction models. (letter)

Performance prediction method for a multi-stage Knudsen pump

Science.gov (United States)

Kugimoto, K.; Hirota, Y.; Kizaki, Y.; Yamaguchi, H.; Niimi, T.

2017-12-01

In this study, the novel method to predict the performance of a multi-stage Knudsen pump is proposed. The performance prediction method is carried out in two steps numerically with the assistance of a simple experimental result. In the first step, the performance of a single-stage Knudsen pump was measured experimentally under various pressure conditions, and the relationship of the mass flow rate was obtained with respect to the average pressure between the inlet and outlet of the pump and the pressure difference between them. In the second step, the performance of a multi-stage pump was analyzed by a one-dimensional model derived from the mass conservation law. The performances predicted by the 1D-model of 1-stage, 2-stage, 3-stage, and 4-stage pumps were validated by the experimental results for the corresponding number of stages. It was concluded that the proposed prediction method works properly.

Role of [18F]FDG PET in prediction of KRAS and EGFR mutation status in patients with advanced non-small-cell lung cancer

International Nuclear Information System (INIS)

Caicedo, Carlos; Garcia-Velloso, Maria Jose; Vigil Diaz, Carmen; Richter Echevarria, Jose Angel; Lozano, Maria Dolores; Labiano, Tania; Lopez-Picazo, Jose Maria; Gurpide, Alfonso; Perez Gracia, Jose Luis; Zulueta, Javier

2014-01-01

The tumour molecular profile predicts the activity of epidermal growth factor receptor (EGFR) inhibitors in non-small-cell lung cancer (NSCLC). However, tissue availability and tumour heterogeneity limit its assessment. We evaluated whether [ 18 F]FDG PET might help predict KRAS and EFGR mutation status in NSCLC. Between January 2005 and October 2011, 340 NSCLC patients were tested for KRAS and EGFR mutation status. We identified patients with stage III and IV disease who had undergone [ 18 F]FDG PET/CT scanning for initial staging. SUVpeak, SUVmax and SUVmean of the single hottest tumour lesions were calculated, and their association with KRAS and EGFR mutation status was assessed. A receiver operator characteristic (ROC) curve analysis and a multivariate analysis (including SUVmean, gender, age and AJCC stage) were performed to identify the potential value of [ 18 F]FDG PET/CT for predicting KRAS mutation. From 102 patients staged using [ 18 F]FDG PET/CT, 28 (27 %) had KRAS mutation (KRAS+), 22 (22 %) had EGFR mutation (EGFR+) and 52 (51 %) had wild-type KRAS and EGFR profiles (WT). KRAS+ patients showed significantly higher [ 18 F]FDG uptake than EGFR+ and WT patients (SUVmean 9.5, 5.7 and 6.6, respectively; p 18 F]FDG uptake between EGFR+ patients and WT patients. ROC curve analysis for KRAS mutation status discrimination yielded an area under the curve of 0.740 for SUVmean (p 18 F]FDG uptake than WT patients, as assessed in terms of SUVpeak, SUVmax and SUVmean. A multivariate model based on age, gender, AJCC stage and SUVmean might be used as a predictive marker of KRAS mutation status in patients with stage III or IV NSCLC. (orig.)

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

Science.gov (United States)

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Interrelationship between family history of alcoholism and generational status in the prediction of alcohol dependence in US Hispanics.

Science.gov (United States)

Chartier, K G; Thomas, N S; Kendler, K S

2017-01-01

Both a family history of alcoholism and migration-related factors like US v. foreign nativity increase the risk for developing alcohol use disorders in Hispanic Americans. For this study, we integrated these two lines of research to test whether the relationship between familial alcoholism and alcohol dependence changes with successive generations in the United States. Data were from the waves 1 and 2 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Subjects self-identified Hispanic ethnicity (N = 4122; n = 1784 first, n = 1169 second, and n = 1169 third or later generation) and reported ever consuming ⩾12 drinks in a 1-year period. A family history of alcoholism was assessed in first- and second-degree relatives. Analyses predicting the number of alcohol dependence symptoms were path models. Alcohol dependence symptoms were associated with a stronger family history of alcoholism and later generational status. There was a significant interaction effect between familial alcoholism and generational status; the relationship of familial alcoholism with alcohol dependence symptoms increased significantly with successive generations in the United States, more strongly in women than men. Acculturation partially mediated the interaction effect between familial alcoholism and generational status on alcohol dependence, although not in the expected direction. Familial alcoholism interacted with generational status in predicting alcohol dependence symptoms in US Hispanic drinkers. This relationship suggests that heritability for alcoholism is influenced by a higher-order environmental factor, likely characterized by a relaxing of social restrictions on drinking.

Ensemble method for dengue prediction.

Science.gov (United States)

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Ensemble method for dengue prediction.

Directory of Open Access Journals (Sweden)

Anna L Buczak

Full Text Available In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico during four dengue seasons: 1 peak height (i.e., maximum weekly number of cases during a transmission season; 2 peak week (i.e., week in which the maximum weekly number of cases occurred; and 3 total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date.Our approach used ensemble models created by combining three disparate types of component models: 1 two-dimensional Method of Analogues models incorporating both dengue and climate data; 2 additive seasonal Holt-Winters models with and without wavelet smoothing; and 3 simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations.Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week.The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

Development of motion image prediction method using principal component analysis

International Nuclear Information System (INIS)

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

2012-01-01

Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail; Soufan, Othman; Essack, Magbubah; Kalnis, Panos; Bajic, Vladimir B.

2016-01-01

DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://​www.​cbrc.​kaust.​edu.​sa/​daspfind.

[Research advances in indices and methods for nutritional status evaluation in patients with liver cirrhosis].

Science.gov (United States)

Li, H; Zhang, L

2017-03-20

In recent years, malnutrition in patients with liver cirrhosis has been taken more and more seriously in clinical physicians, and patients' nutritional status is closely associated with prognosis. At present, there are many methods for the evaluation of nutritional status in patients with liver cirrhosis, but there are still no unified standards. This article reviews the common evaluation indices and methods used in clinical practice in China and foreign countries, in order to provide a basis for accurately evaluating nutritional status and guiding nutritional therapy in patients with liver cirrhosis.

Predicting Voice Disorder Status From Smoothed Measures of Cepstral Peak Prominence Using Praat and Analysis of Dysphonia in Speech and Voice (ADSV).

Science.gov (United States)

Sauder, Cara; Bretl, Michelle; Eadie, Tanya

2017-09-01

The purposes of this study were to (1) determine and compare the diagnostic accuracy of a single acoustic measure, smoothed cepstral peak prominence (CPPS), to predict voice disorder status from connected speech samples using two software systems: Analysis of Dysphonia in Speech and Voice (ADSV) and Praat; and (2) to determine the relationship between measures of CPPS generated from these programs. This is a retrospective cross-sectional study. Measures of CPPS were obtained from connected speech recordings of 100 subjects with voice disorders and 70 nondysphonic subjects without vocal complaints using commercially available ADSV and freely downloadable Praat software programs. Logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate and compare the diagnostic accuracy of CPPS measures. Relationships between CPPS measures from the programs were determined. Results showed acceptable overall accuracy rates (75% accuracy, ADSV; 82% accuracy, Praat) and area under the ROC curves (area under the curve [AUC] = 0.81, ADSV; AUC = 0.91, Praat) for predicting voice disorder status, with slight differences in sensitivity and specificity. CPPS measures derived from Praat were uniquely predictive of disorder status above and beyond CPPS measures from ADSV (χ 2 (1) = 40.71, P disorder status using either program. Clinicians may consider using CPPS to complement clinical voice evaluation and screening protocols. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Non-enzymatic antioxidant status of women using four different methods of contraception

International Nuclear Information System (INIS)

Akinloye, O.; Oyabiyi, S.A.; Oguntibeju, O.O.; Arowojolu, A.O.

2010-01-01

Objective: To investigate antioxidant status of women on four different methods of contraception. Methodology: Sixty non-pregnant women aged 16-45 years on oral contraceptive pills, injectables, Norplant and intra-uterine contraceptive devices (IUD) attending the Family Planning Clinics of the University College Hospital (UCH) and Adeoyo Maternity Hospital, Ibadan were recruited for the study. Fifty-eight apparently healthy women aged 16-45 years who were not on any contraceptive served as a control group. The body mass index (BMI) of all participants (subjects and controls) was determined following standard protocol. Serum levels of ascorbic acid, tocopherol, malondialdehyde, bilirubin, creatinine, uric acid, total protein and albumin were determined using standard spectrophotometric methods. Progesterone was estimated by the chemilumiscence method while selenium was determined by atomic absorption spectrophotometry (AAS). Results: The BMI was significant in women on oral contraceptive pills (OCP) when compared to the control group (P 0.05) in intra-uterine device (IUD), injectables and Norplant users. The mean serum ascorbic acid (P 0.05) in users of other contraceptive methods. Serum levels of malondialdehyde was significantly elevated in women on OCP (P 0.05) in users of other contraceptive methods. There was no significant association between progesterone and antioxidants in women on OCP, IUD, injectables and Norplant. Conclusion: Oral contraceptive pills showed a significant decreasing effect on the antioxidant status of its users while IUD, injectables and Norplant did not indicate any significant effect. Routine monitoring of the antioxidant status of women on different methods of contraceptive particularly those on OCP is recommended. (author)

An assessment on epitope prediction methods for protozoa genomes

Directory of Open Access Journals (Sweden)

Resende Daniela M

2012-11-01

Full Text Available Abstract Background Epitope prediction using computational methods represents one of the most promising approaches to vaccine development. Reduction of time, cost, and the availability of completely sequenced genomes are key points and highly motivating regarding the use of reverse vaccinology. Parasites of genus Leishmania are widely spread and they are the etiologic agents of leishmaniasis. Currently, there is no efficient vaccine against this pathogen and the drug treatment is highly toxic. The lack of sufficiently large datasets of experimentally validated parasites epitopes represents a serious limitation, especially for trypanomatids genomes. In this work we highlight the predictive performances of several algorithms that were evaluated through the development of a MySQL database built with the purpose of: a evaluating individual algorithms prediction performances and their combination for CD8+ T cell epitopes, B-cell epitopes and subcellular localization by means of AUC (Area Under Curve performance and a threshold dependent method that employs a confusion matrix; b integrating data from experimentally validated and in silico predicted epitopes; and c integrating the subcellular localization predictions and experimental data. NetCTL, NetMHC, BepiPred, BCPred12, and AAP12 algorithms were used for in silico epitope prediction and WoLF PSORT, Sigcleave and TargetP for in silico subcellular localization prediction against trypanosomatid genomes. Results A database-driven epitope prediction method was developed with built-in functions that were capable of: a removing experimental data redundancy; b parsing algorithms predictions and storage experimental validated and predict data; and c evaluating algorithm performances. Results show that a better performance is achieved when the combined prediction is considered. This is particularly true for B cell epitope predictors, where the combined prediction of AAP12 and BCPred12 reached an AUC value

Fast Prediction Method for Steady-State Heat Convection

KAUST Repository

Wáng, Yì

2012-03-14

A reduced model by proper orthogonal decomposition (POD) and Galerkin projection methods for steady-state heat convection is established on a nonuniform grid. It was verified by thousands of examples that the results are in good agreement with the results obtained from the finite volume method. This model can also predict the cases where model parameters far exceed the sample scope. Moreover, the calculation time needed by the model is much shorter than that needed for the finite volume method. Thus, the nonuniform POD-Galerkin projection method exhibits high accuracy, good suitability, and fast computation. It has universal significance for accurate and fast prediction. Also, the methodology can be applied to more complex modeling in chemical engineering and technology, such as reaction and turbulence. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Science.gov (United States)

Oakden-Rayner, Luke; Carneiro, Gustavo; Bessen, Taryn; Nascimento, Jacinto C; Bradley, Andrew P; Palmer, Lyle J

2017-05-10

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Directory of Open Access Journals (Sweden)

Fei Gao

2015-01-01

Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

Predictive ability of machine learning methods for massive crop yield prediction

Directory of Open Access Journals (Sweden)

Alberto Gonzalez-Sanchez

2014-04-01

Full Text Available An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS, root relative square error (RRSE, normalized mean absolute error (MAE, and correlation factor (R. Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91, the lowest RRSE errors (79.46% and 79.78%, the lowest average MAE errors (18.12% and 19.42%, and the highest average correlation factors (0.41 and 0.42. Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

Predicting chaos in memristive oscillator via harmonic balance method.

Science.gov (United States)

Wang, Xin; Li, Chuandong; Huang, Tingwen; Duan, Shukai

2012-12-01

This paper studies the possible chaotic behaviors in a memristive oscillator with cubic nonlinearities via harmonic balance method which is also called the method of describing function. This method was proposed to detect chaos in classical Chua's circuit. We first transform the considered memristive oscillator system into Lur'e model and present the prediction of the existence of chaotic behaviors. To ensure the prediction result is correct, the distortion index is also measured. Numerical simulations are presented to show the effectiveness of theoretical results.

DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers

KAUST Repository

Fan, Ming; Cheng, Hu; Zhang, Peng; Gao, Xin; Zhang, Juan; Shao, Guoliang; Li, Lihua

2017-01-01

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.To predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor

A Method for Driving Route Predictions Based on Hidden Markov Model

Directory of Open Access Journals (Sweden)

Ning Ye

2015-01-01

Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

Soft Computing Methods for Disulfide Connectivity Prediction.

Science.gov (United States)

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Method of Fusion Diagnosis for Dam Service Status Based on Joint Distribution Function of Multiple Points

Directory of Open Access Journals (Sweden)

Zhenxiang Jiang

2016-01-01

Full Text Available The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.

Comparison of four statistical and machine learning methods for crash severity prediction.

Science.gov (United States)

Iranitalab, Amirfarrokh; Khattak, Aemal

2017-11-01

Crash severity prediction models enable different agencies to predict the severity of a reported crash with unknown severity or the severity of crashes that may be expected to occur sometime in the future. This paper had three main objectives: comparison of the performance of four statistical and machine learning methods including Multinomial Logit (MNL), Nearest Neighbor Classification (NNC), Support Vector Machines (SVM) and Random Forests (RF), in predicting traffic crash severity; developing a crash costs-based approach for comparison of crash severity prediction methods; and investigating the effects of data clustering methods comprising K-means Clustering (KC) and Latent Class Clustering (LCC), on the performance of crash severity prediction models. The 2012-2015 reported crash data from Nebraska, United States was obtained and two-vehicle crashes were extracted as the analysis data. The dataset was split into training/estimation (2012-2014) and validation (2015) subsets. The four prediction methods were trained/estimated using the training/estimation dataset and the correct prediction rates for each crash severity level, overall correct prediction rate and a proposed crash costs-based accuracy measure were obtained for the validation dataset. The correct prediction rates and the proposed approach showed NNC had the best prediction performance in overall and in more severe crashes. RF and SVM had the next two sufficient performances and MNL was the weakest method. Data clustering did not affect the prediction results of SVM, but KC improved the prediction performance of MNL, NNC and RF, while LCC caused improvement in MNL and RF but weakened the performance of NNC. Overall correct prediction rate had almost the exact opposite results compared to the proposed approach, showing that neglecting the crash costs can lead to misjudgment in choosing the right prediction method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Self-Fitting Hearing Aids: Status Quo and Future Predictions.

Science.gov (United States)

Keidser, Gitte; Convery, Elizabeth

2016-04-12

A self-contained, self-fitting hearing aid (SFHA) is a device that enables the user to perform both threshold measurements leading to a prescribed hearing aid setting and fine-tuning, without the need for audiological support or access to other equipment. The SFHA has been proposed as a potential solution to address unmet hearing health care in developing countries and remote locations in the developed world and is considered a means to lower cost and increase uptake of hearing aids in developed countries. This article reviews the status of the SFHA and the evidence for its feasibility and challenges and predicts where it is heading. Devices that can be considered partly or fully self-fitting without audiological support were identified in the direct-to-consumer market. None of these devices are considered self-contained as they require access to other hardware such as a proprietary interface, computer, smartphone, or tablet for manipulation. While there is evidence that self-administered fitting processes can provide valid and reliable results, their success relies on user-friendly device designs and interfaces and easy-to-interpret instructions. Until these issues have been sufficiently addressed, optional assistance with the self-fitting process and on-going use of SFHAs is recommended. Affordability and a sustainable delivery system remain additional challenges for the SFHA in developing countries. Future predictions include a growth in self-fitting products, with most future SFHAs consisting of earpieces that connect wirelessly with a smartphone and providers offering assistance through a telehealth infrastructure, and the integration of SFHAs into the traditional hearing health-care model. © The Author(s) 2016.

Generic methods for aero-engine exhaust emission prediction

NARCIS (Netherlands)

Shakariyants, S.A.

2008-01-01

In the thesis, generic methods have been developed for aero-engine combustor performance, combustion chemistry, as well as airplane aerodynamics, airplane and engine performance. These methods specifically aim to support diverse emission prediction studies coupled with airplane and engine

Supplementary Material for: DASPfind: new efficient method to predict drug–target interactions

KAUST Repository

Ba Alawi, Wail

2016-01-01

Abstract Background Identification of novel drug–target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. Results Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. Conclusions DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery

Assessment of a method for the prediction of mandibular rotation.

Science.gov (United States)

Lee, R S; Daniel, F J; Swartz, M; Baumrind, S; Korn, E L

1987-05-01

A new method to predict mandibular rotation developed by Skieller and co-workers on a sample of 21 implant subjects with extreme growth patterns has been tested against an alternative sample of 25 implant patients with generally similar mean values, but with less extreme facial patterns. The method, which had been highly successful in retrospectively predicting changes in the sample of extreme subjects, was much less successful in predicting individual patterns of mandibular rotation in the new, less extreme sample. The observation of a large difference in the strength of the predictions for these two samples, even though their mean values were quite similar, should serve to increase our awareness of the complexity of the problem of predicting growth patterns in individual cases.

Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows.

Science.gov (United States)

McParland, S; Banos, G; McCarthy, B; Lewis, E; Coffey, M P; O'Neill, B; O'Donovan, M; Wall, E; Berry, D P

2012-12-01

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Drug-Target Interactions: Prediction Methods and Applications.

Science.gov (United States)

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The Accuracy Of Fuzzy Sugeno Method With Antropometry On Determination Natural Patient Status

Science.gov (United States)

Syahputra, Dinur; Tulus; Sawaluddin

2017-12-01

Anthropometry is one of the processes that can be used to assess nutritional status. In general anthropometry is defined as body size in terms of nutrition, then anthropometry is reviewed from various age levels and nutritional levels. Nutritional status is a description of the balance between nutritional intake with the needs of the body individually. Fuzzy logic is a logic that has a vagueness between right and wrong or between 0 and 1. Sugeno method is used because in the process of calculating nutritional status so far is still done by anthropometry. Currently information technology is growing in any aspect, one of them in the aspect of calculation with data taken from anthropometry. In this case the calculation can use the Fuzzy Sugeno Method, in order to know the great accuracy obtained. Then the results obtained using fuzzy sugeno integrated with anthropometry has an accuracy of 81.48%.

Is self-regard a sociometer or a hierometer? Self-esteem tracks status and inclusion, narcissism tracks status.

Science.gov (United States)

Mahadevan, Nikhila; Gregg, Aiden P; Sedikides, Constantine

2018-04-02

What adaptive function does self-regard serve? Sociometer theory predicts that it positively tracks social inclusion. A new theory, hierometer theory, predicts that it positively tracks social status. We tested both predictions with respect to two types of self-regard: self-esteem and narcissism. Study 1 (N = 940), featuring a cross-sectional design, found that both status and inclusion covaried positively with self-esteem, but that status alone covaried positively with narcissism. These links held independently of gender, age, and the Big Five personality traits. Study 2 (N = 627), a preregistered cross-sectional study, obtained similar results with alternative measures of self-esteem and narcissism. Studies 3-4 featured experimental designs in which status and inclusion were orthogonally manipulated. Study 3 (N = 104) found that both higher status and higher inclusion promoted higher self-esteem, whereas only higher status promoted higher narcissism. Study 4 (N = 259) obtained similar results with alternative measures of self-esteem and narcissism. The findings suggest that self-esteem operates as both sociometer and hierometer, positively tracking both status and inclusion, whereas narcissism operates primarily as a hierometer, positively tracking status. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Real-time prediction of respiratory motion based on local regression methods

International Nuclear Information System (INIS)

Ruan, D; Fessler, J A; Balter, J M

2007-01-01

Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

Methods for early prediction of lactation flow in Holstein heifers

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Vesna Gantner

2010-12-01

Full Text Available The aim of this research was to define methods for early prediction (based on I. milk control record of lactation flow in Holstein heifers as well as to choose optimal one in terms of prediction fit and application simplicity. Total of 304,569 daily yield records automatically recorded on a 1,136 first lactation Holstein cows, from March 2003 till August 2008., were included in analysis. According to the test date, calving date, the age at first calving, lactation stage when I. milk control occurred and to the average milk yield in first 25th, T1 (and 25th-45th, T2 lactation days, measuring monthcalving month-age-production-time-period subgroups were formed. The parameters of analysed nonlinear and linear methods were estimated for each defined subgroup. As models evaluation measures,adjusted coefficient of determination, and average and standard deviation of error were used. Considering obtained results, in terms of total variance explanation (R2 adj, the nonlinear Wood’s method showed superiority above the linear ones (Wilmink’s, Ali-Schaeffer’s and Guo-Swalve’s method in both time-period subgroups (T1 - 97.5 % of explained variability; T2 - 98.1 % of explained variability. Regarding the evaluation measures based on prediction error amount (eavg±eSD, the lowest average error of daily milk yield prediction (less than 0.005 kg/day, as well as of lactation milk yield prediction (less than 50 kg/lactation (T1 time-period subgroup and less than 30 kg/lactation (T2 time-period subgroup; were determined when Wood’s nonlinear prediction method were applied. Obtained results indicate that estimated Wood’s regression parameters could be used in routine work for early prediction of Holstein heifer’s lactation flow.

Advantages and disadvantages of hysterosonosalpingography in the assessment of the reproductive status of uterine cavity and fallopian tubes

International Nuclear Information System (INIS)

Radic, Vanja; Canic, Tomislav; Valetic, Josip; Duic, Zeljko

2005-01-01

Background: Hysterosonosalpingography as a contrast ultrasound method is safer, cheaper and easier to perform than hysterosalpingography in the assessment of the uterine cavity and fallopian tubes. Is it feasible for all patients? Which is the main problem in the evaluation of target structures by ultrasound? Methods: In a prospective study, 68 patients in the initial stage of the infertility treatment were examined by hysterosonosalpingography using saline NaCl infundibile[reg] and Echovist[reg] as contrast media. Subsequently, further status of the tubes and uterine cavity was assessed by the 'gold standards', laparoscopy and hysteroscopy. Results: Sensitivity and specificity of hysterosonosalpingography using NaCl infundibile[reg] for evaluation of the uterine cavity was 100 and 88.8%, respectively. Negative predictive value was 100% and positive predictive value 97%. Sensitivity and specificity of the method for the assessment of the tubal status was 100 and 66%, respectively, negative predictive value was 100% and positive predictive value was 61%. For the assessment of tubal patency using positive contrast Echovist[reg] the method has shown 100% sensibility and negative predictive value again but it reached a specificity of 77% and a positive predictive value of 70%. There were no evident complications during or after the procedure. Conclusion: Hysterosonosalpingography is useful in making decisions regarding further procedures for the diagnosis and treatment of infertility. Uterine cavity evaluation using saline is the method of choice. Tubal patency can be assessed only under ideal sonographic conditions. The method is feasible for early assessment of the reproductive status of uterine cavity and fallopian tubes as a simple, safe and cheap outpatient method prior to any following invasive procedure or even histerosalpingography

Advantages and disadvantages of hysterosonosalpingography in the assessment of the reproductive status of uterine cavity and fallopian tubes

Energy Technology Data Exchange (ETDEWEB)

Radic, Vanja; Canic, Tomislav; Valetic, Josip; Duic, Zeljko

2005-02-01

Background: Hysterosonosalpingography as a contrast ultrasound method is safer, cheaper and easier to perform than hysterosalpingography in the assessment of the uterine cavity and fallopian tubes. Is it feasible for all patients? Which is the main problem in the evaluation of target structures by ultrasound? Methods: In a prospective study, 68 patients in the initial stage of the infertility treatment were examined by hysterosonosalpingography using saline NaCl infundibile[reg] and Echovist[reg] as contrast media. Subsequently, further status of the tubes and uterine cavity was assessed by the 'gold standards', laparoscopy and hysteroscopy. Results: Sensitivity and specificity of hysterosonosalpingography using NaCl infundibile[reg] for evaluation of the uterine cavity was 100 and 88.8%, respectively. Negative predictive value was 100% and positive predictive value 97%. Sensitivity and specificity of the method for the assessment of the tubal status was 100 and 66%, respectively, negative predictive value was 100% and positive predictive value was 61%. For the assessment of tubal patency using positive contrast Echovist[reg] the method has shown 100% sensibility and negative predictive value again but it reached a specificity of 77% and a positive predictive value of 70%. There were no evident complications during or after the procedure. Conclusion: Hysterosonosalpingography is useful in making decisions regarding further procedures for the diagnosis and treatment of infertility. Uterine cavity evaluation using saline is the method of choice. Tubal patency can be assessed only under ideal sonographic conditions. The method is feasible for early assessment of the reproductive status of uterine cavity and fallopian tubes as a simple, safe and cheap outpatient method prior to any following invasive procedure or even histerosalpingography.

An ensemble method for predicting subnuclear localizations from primary protein structures.

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Guo Sheng Han

Full Text Available BACKGROUND: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL FINDINGS: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. CONCLUSIONS: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method

Alternative methods for skin irritation testing: the current status : ECVAM skin irritation task force report 1

NARCIS (Netherlands)

Botham, P.A.; Earl, L.K.; Fentem, J.H.; Roguet, R.; Sandt, J.J.M.

1998-01-01

The ECVAM Skin Irritation Task Force was established in November 1996, primarily to prepare a report on the current status of the development and validation of alternative tests for skin irritation and corrosion and, in particular, to identify any appropriate non-animal tests for predicting human

Available Prediction Methods for Corrosion under Insulation (CUI: A Review

Directory of Open Access Journals (Sweden)

Burhani Nurul Rawaida Ain

2014-07-01

Full Text Available Corrosion under insulation (CUI is an increasingly important issue for the piping in industries especially petrochemical and chemical plants due to its unexpected catastrophic disaster. Therefore, attention towards the maintenance and prediction of CUI occurrence, particularly in the corrosion rates, has grown in recent years. In this study, a literature review in determining the corrosion rates by using various prediction models and method of the corrosion occurrence between the external surface piping and its insulation was carried out. The results, prediction models and methods available were presented for future research references. However, most of the prediction methods available are based on each local industrial data only which might be different based on the plant location, environment, temperature and many other factors which may contribute to the difference and reliability of the model developed. Thus, it is more reliable if those models or method supported by laboratory testing or simulation which includes the factors promoting CUI such as environment temperature, insulation types, operating temperatures, and other factors.

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

Science.gov (United States)

Notaro, Marco; Schubach, Max; Robinson, Peter N; Valentini, Giorgio

2017-10-12

The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

Short-term prediction method of wind speed series based on fractal interpolation

International Nuclear Information System (INIS)

Xiu, Chunbo; Wang, Tiantian; Tian, Meng; Li, Yanqing; Cheng, Yi

2014-01-01

Highlights: • An improved fractal interpolation prediction method is proposed. • The chaos optimization algorithm is used to obtain the iterated function system. • The fractal extrapolate interpolation prediction of wind speed series is performed. - Abstract: In order to improve the prediction performance of the wind speed series, the rescaled range analysis is used to analyze the fractal characteristics of the wind speed series. An improved fractal interpolation prediction method is proposed to predict the wind speed series whose Hurst exponents are close to 1. An optimization function which is composed of the interpolation error and the constraint items of the vertical scaling factors in the fractal interpolation iterated function system is designed. The chaos optimization algorithm is used to optimize the function to resolve the optimal vertical scaling factors. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Simulation results show that the fractal interpolation prediction method can get better prediction result than others for the wind speed series with the fractal characteristic, and the prediction performance of the proposed method can be improved further because the fractal characteristic of its iterated function system is similar to that of the predicted wind speed series

Quality status display for a vibration welding process

Science.gov (United States)

Spicer, John Patrick; Abell, Jeffrey A.; Wincek, Michael Anthony; Chakraborty, Debejyo; Bracey, Jennifer; Wang, Hui; Tavora, Peter W.; Davis, Jeffrey S.; Hutchinson, Daniel C.; Reardon, Ronald L.; Utz, Shawn

2017-03-28

A system includes a host machine and a status projector. The host machine is in electrical communication with a collection of sensors and with a welding controller that generates control signals for controlling the welding horn. The host machine is configured to execute a method to thereby process the sensory and control signals, as well as predict a quality status of a weld that is formed using the welding horn, including identifying any suspect welds. The host machine then activates the status projector to illuminate the suspect welds. This may occur directly on the welds using a laser projector, or on a surface of the work piece in proximity to the welds. The system and method may be used in the ultrasonic welding of battery tabs of a multi-cell battery pack in a particular embodiment. The welding horn and welding controller may also be part of the system.

In silico toxicology: computational methods for the prediction of chemical toxicity

KAUST Repository

Raies, Arwa B.; Bajic, Vladimir B.

2016-01-01

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

In silico toxicology: computational methods for the prediction of chemical toxicity

KAUST Repository

Raies, Arwa B.

2016-01-06

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

Predicting weight status stability and change from fifth grade to eighth grade: the significant role of adolescents' social-emotional well-being.

Science.gov (United States)

Chang, Yiting; Gable, Sara

2013-04-01

The primary objective of this study was to predict weight status stability and change across the transition to adolescence using parent reports of child and household routines and teacher and child self-reports of social-emotional development. Data were from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K), a nationally representative sample of children who entered kindergarten during 1998-1999 and were followed through eighth grade. At fifth grade, parents reported on child and household routines and the study child and his/her primary classroom teacher reported on the child's social-emotional functioning. At fifth and eighth grade, children were directly weighed and measured at school. Nine mutually-exclusive weight trajectory groups were created to capture stability or change in weight status from fifth to eighth grade: (1) stable obese (ObeSta); (2) obese to overweight (ObePos1); (3) obese to healthy (ObePos2); (4) stable overweight (OverSta); (5) overweight to healthy (OverPos); (6) overweight to obese (OverNeg); (7) stable healthy (HelSta); (8) healthy to overweight (HelNeg1); and (9) healthy to obese (HelNeg2). Except for breakfast consumption at home, school-provided lunches, nighttime sleep duration, household and child routines did not predict stability or change in weight status. Instead, weight status trajectory across the transition to adolescence was significantly predicted by measures of social-emotional functioning at fifth grade. Assessing children's social-emotional well-being in addition to their lifestyle routines during the transition to adolescence is a noteworthy direction for adolescent obesity prevention and intervention. Copyright © 2013 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Creep/fatigue damage prediction of fast reactor components using shakedown methods

International Nuclear Information System (INIS)

Buckthorpe, D.E.

1997-01-01

The present status of the shakedown method is reviewed, the application of the shakedown based principles to complex hardening and creep behaviour is described and justified and the prediction of damage against design criteria outlined. Comparisons are made with full inelastic analysis solutions where these are available and against damage assessments using elastic and inelastic design code methods. Current and future developments of the method are described including a summary of the advances made in the development of the post process ADAPT, which has enabled the method to be applied to complex geometry features and loading cases. The paper includes a review of applications of the method to typical Fast Reactor structural example cases within the primary and secondary circuits. For the primary circuit this includes structures such as the large diameter internal shells which are surrounded by hot sodium and subject to slow and rapid thermal transient loadings. One specific case is the damage assessment associated with thermal stratifications within sodium and the effects of moving sodium surfaces arising from reactor trip conditions. Other structures covered are geometric features within components such as the Above Core structure and Intermediate Heat Exchanger. For the secondary circuit the method has been applied to alternative and more complex forms of geometry namely thick section tubeplates of the Steam Generator and a typical secondary circuit piping run. Both of these applications are in an early stage of development but are expected to show significant advantages with respect to creep and fatigue damage estimation compared with existing code methods. The principle application of the method to design has so far been focused on Austenitic Stainless steel components however current work shows some significant benefits may be possible from the application of the method to structures made from Ferritic steels such as Modified 9Cr 1Mo. This aspect is briefly

Mechatronics technology in predictive maintenance method

Science.gov (United States)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

Associations among arbuscular mycorrhizal fungi and seedlings are predicted to change with tree successional status.

Science.gov (United States)

Bachelot, Benedicte; Uriarte, María; Muscarella, Robert; Forero-Montaña, Jimena; Thompson, Jill; McGuire, Krista; Zimmerman, Jess; Swenson, Nathan G; Clark, James S

2018-03-01

Arbuscular mycorrhizal (AM) fungi in the soil may influence tropical tree dynamics and forest succession. The mechanisms are poorly understood, because the functional characteristics and abundances of tree species and AM fungi are likely to be codependent. We used generalized joint attribute modeling to evaluate if AM fungi are associated with three forest community metrics for a sub-tropical montane forest in Puerto Rico. The metrics chosen to reflect changes during forest succession are the abundance of seedlings of different successional status, the amount of foliar damage on seedlings of different successional status, and community-weighted mean functional trait values (adult specific leaf area [SLA], adult wood density, and seed mass). We used high-throughput DNA sequencing to identify fungal operational taxonomic units (OTUs) in the soil. Model predictions showed that seedlings of mid- and late-successional species had less leaf damage when the 12 most common AM fungi were abundant compared to when these fungi were absent. We also found that seedlings of mid-successional species were predicted to be more abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. In contrast, early-successional tree seedlings were predicted to be less abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. Finally, we showed that, among the 12 most common AM fungi, different AM fungi were correlated with functional trait characteristics of early- or late-successional species. Together, these results suggest that early-successional species might not rely as much as mid- and late-successional species on AM fungi, and AM fungi might accelerate forest succession. © 2017 by the Ecological Society of America.

Combining gene prediction methods to improve metagenomic gene annotation

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Rosen Gail L

2011-01-01

Full Text Available Abstract Background Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation. Results We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resulting in more false-positives and false-negatives than desired. In fact, the specificities of the algorithms are notably worse than the sensitivities. By combining the programs' predictions, we show significant improvement in specificity at minimal cost to sensitivity, resulting in 4% improvement in accuracy for 100 bp reads with ~1% improvement in accuracy for 200 bp reads and above. To correctly annotate the start and stop of the genes, we find that a consensus of all the predictors performs best for shorter read lengths while a unanimous agreement is better for longer read lengths, boosting annotation accuracy by 1-8%. We also demonstrate use of the classifier combinations on a real dataset. Conclusions To optimize the performance for both prediction and annotation accuracies, we conclude that the consensus of all methods (or a majority vote is the best for reads 400 bp and shorter, while using the intersection of GeneMark and Orphelia predictions is the best for reads 500 bp and longer. We demonstrate that most methods predict over 80% coding (including partially coding reads on a real human gut sample sequenced by Illumina technology.

Seminal quality prediction using data mining methods.

Science.gov (United States)

Sahoo, Anoop J; Kumar, Yugal

2014-01-01

Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of

Prediction of Protein–Protein Interactions by Evidence Combining Methods

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Ji-Wei Chang

2016-11-01

Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Force prediction in cold rolling mills by polynomial methods

Directory of Open Access Journals (Sweden)

Nicu ROMAN

2007-12-01

Full Text Available A method for steel and aluminium strip thickness control is provided including a new technique for predictive rolling force estimation method by statistic model based on polynomial techniques.

Three-dimensional protein structure prediction: Methods and computational strategies.

Science.gov (United States)

Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

2014-10-12

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

Science.gov (United States)

Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

River Flow Prediction Using the Nearest Neighbor Probabilistic Ensemble Method

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H. Sanikhani

2016-02-01

Full Text Available Introduction: In the recent years, researchers interested on probabilistic forecasting of hydrologic variables such river flow.A probabilistic approach aims at quantifying the prediction reliability through a probability distribution function or a prediction interval for the unknown future value. The evaluation of the uncertainty associated to the forecast is seen as a fundamental information, not only to correctly assess the prediction, but also to compare forecasts from different methods and to evaluate actions and decisions conditionally on the expected values. Several probabilistic approaches have been proposed in the literature, including (1 methods that use resampling techniques to assess parameter and model uncertainty, such as the Metropolis algorithm or the Generalized Likelihood Uncertainty Estimation (GLUE methodology for an application to runoff prediction, (2 methods based on processing the forecast errors of past data to produce the probability distributions of future values and (3 methods that evaluate how the uncertainty propagates from the rainfall forecast to the river discharge prediction, as the Bayesian forecasting system. Materials and Methods: In this study, two different probabilistic methods are used for river flow prediction.Then the uncertainty related to the forecast is quantified. One approach is based on linear predictors and in the other, nearest neighbor was used. The nonlinear probabilistic ensemble can be used for nonlinear time series analysis using locally linear predictors, while NNPE utilize a method adapted for one step ahead nearest neighbor methods. In this regard, daily river discharge (twelve years of Dizaj and Mashin Stations on Baranduz-Chay basin in west Azerbijan and Zard-River basin in Khouzestan provinces were used, respectively. The first six years of data was applied for fitting the model. The next three years was used to calibration and the remained three yeas utilized for testing the models

A Prediction Method of Airport Noise Based on Hybrid Ensemble Learning

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Tao XU

2014-05-01

Full Text Available Using monitoring history data to build and to train a prediction model for airport noise is a normal method in recent years. However, the single model built in different ways has various performances in the storage, efficiency and accuracy. In order to predict the noise accurately in some complex environment around airport, this paper presents a prediction method based on hybrid ensemble learning. The proposed method ensembles three algorithms: artificial neural network as an active learner, nearest neighbor as a passive leaner and nonlinear regression as a synthesized learner. The experimental results show that the three learners can meet forecast demands respectively in on- line, near-line and off-line. And the accuracy of prediction is improved by integrating these three learners’ results.

Ensemble approach combining multiple methods improves human transcription start site prediction

LENUS (Irish Health Repository)

Dineen, David G

2010-11-30

Abstract Background The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets. Results We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (\\'Profisi Ensemble\\') using predictions from 7 programs, along with 2 other data sources. Support vector machines using \\'full\\' and \\'reduced\\' data sets are combined in an either\\/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool. Conclusions Supervised learning methods are a useful way to combine predictions from diverse sources.

Prediction of BRAF mutation status of craniopharyngioma using magnetic resonance imaging features.

Science.gov (United States)

Yue, Qi; Yu, Yang; Shi, Zhifeng; Wang, Yongfei; Zhu, Wei; Du, Zunguo; Yao, Zhenwei; Chen, Liang; Mao, Ying

2017-10-06

OBJECTIVE Treatment with a BRAF mutation inhibitor might shrink otherwise refractory craniopharyngiomas and is a promising preoperative treatment to facilitate tumor resection. The aim of this study was to investigate the noninvasive diagnosis of BRAF-mutated craniopharyngiomas based on MRI characteristics. METHODS Fifty-two patients with pathologically diagnosed craniopharyngioma were included in this study. Polymerase chain reaction was performed on tumor tissue specimens to detect BRAF and CTNNB1 mutations. MRI manifestations-including tumor location, size, shape, and composition; signal intensity of cysts; enhancement pattern; pituitary stalk morphology; and encasement of the internal carotid artery-were analyzed by 2 neuroradiologists blinded to patient identity and clinical characteristics, including BRAF mutation status. Results were compared between the BRAF-mutated and wild-type (WT) groups. Characteristics that were significantly more prevalent (p < 0.05) in the BRAF-mutated craniopharyngiomas were defined as diagnostic features. The minimum number of diagnostic features needed to make a diagnosis was determined by analyzing the receiver operating characteristic (ROC) curve. RESULTS Eight of the 52 patients had BRAF-mutated craniopharyngiomas, and the remaining 44 had BRAF WT tumors. The clinical characteristics did not differ significantly between the 2 groups. Interobserver agreement for MRI data analysis was relatively reliable, with values of Cohen κ ranging from 0.65 to 0.97 (p < 0.001). A comparison of findings in the 2 patient groups showed that BRAF-mutated craniopharyngiomas tended to be suprasellar (p < 0.001), spherical (p = 0.005), predominantly solid (p = 0.003), and homogeneously enhancing (p < 0.001), and that patients with these tumors tended to have a thickened pituitary stalk (p = 0.014). When at least 3 of these 5 features were present, a tumor might be identified as BRAF mutated with a sensitivity of 1.00 and a specificity of 0

The energetic cost of walking: a comparison of predictive methods.

Directory of Open Access Journals (Sweden)

Patricia Ann Kramer

Full Text Available BACKGROUND: The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is "best", but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1 to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2 to investigate to what degree the prediction methods explain the variation in energy expenditure. METHODOLOGY/PRINCIPAL FINDINGS: We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. CONCLUSION: Our results indicate that the choice of predictive method is dependent on the question(s of interest and the data available for use as inputs. Although we

The energetic cost of walking: a comparison of predictive methods.

Science.gov (United States)

Kramer, Patricia Ann; Sylvester, Adam D

2011-01-01

The energy that animals devote to locomotion has been of intense interest to biologists for decades and two basic methodologies have emerged to predict locomotor energy expenditure: those based on metabolic and those based on mechanical energy. Metabolic energy approaches share the perspective that prediction of locomotor energy expenditure should be based on statistically significant proxies of metabolic function, while mechanical energy approaches, which derive from many different perspectives, focus on quantifying the energy of movement. Some controversy exists as to which mechanical perspective is "best", but from first principles all mechanical methods should be equivalent if the inputs to the simulation are of similar quality. Our goals in this paper are 1) to establish the degree to which the various methods of calculating mechanical energy are correlated, and 2) to investigate to what degree the prediction methods explain the variation in energy expenditure. We use modern humans as the model organism in this experiment because their data are readily attainable, but the methodology is appropriate for use in other species. Volumetric oxygen consumption and kinematic and kinetic data were collected on 8 adults while walking at their self-selected slow, normal and fast velocities. Using hierarchical statistical modeling via ordinary least squares and maximum likelihood techniques, the predictive ability of several metabolic and mechanical approaches were assessed. We found that all approaches are correlated and that the mechanical approaches explain similar amounts of the variation in metabolic energy expenditure. Most methods predict the variation within an individual well, but are poor at accounting for variation between individuals. Our results indicate that the choice of predictive method is dependent on the question(s) of interest and the data available for use as inputs. Although we used modern humans as our model organism, these results can be extended

Emotion perception and executive functioning predict work status in euthymic bipolar disorder.

Science.gov (United States)

Ryan, Kelly A; Vederman, Aaron C; Kamali, Masoud; Marshall, David; Weldon, Anne L; McInnis, Melvin G; Langenecker, Scott A

2013-12-15

Functional recovery, including return to work, in Bipolar Disorder (BD) lags behind clinical recovery and may be incomplete when acute mood symptoms have subsided. We examined impact of cognition on work status and underemployment in a sample of 156 Euthymic-BD and 143 controls (HC) who were divided into working/not working groups. Clinical, health, social support, and personality data were collected, and eight cognitive factors were derived from a battery of neuropsychological tests. The HC groups outperformed the BD groups on seven of eight cognitive factors. The working-BD group outperformed the not working-BD group on 4 cognitive factors composed of tasks of emotion processing and executive functioning including processing speed and set shifting. Emotion processing and executive tasks were predictive of BD unemployment, after accounting for number of mood episodes. Four cognitive factors accounted for a significant amount of the variance in work status among the BD participants. Results indicate that patients with BD who are unemployed/unable to work exhibit greater difficulties processing emotional information and on executive tasks that comprise a set shifting or interference resolution component as compared to those who are employed, independent of other factors. These cognitive and affective factors are suggested as targets for treatment and/or accommodations. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Performance of Comorbidity, Risk Adjustment, and Functional Status Measures in Expenditure Prediction for Patients With Diabetes

OpenAIRE

Maciejewski, Matthew L.; Liu, Chuan-Fen; Fihn, Stephan D.

2009-01-01

OBJECTIVE?To compare the ability of generic comorbidity and risk adjustment measures, a diabetes-specific measure, and a self-reported functional status measure to explain variation in health care expenditures for individuals with diabetes. RESEARCH DESIGN AND METHODS?This study included a retrospective cohort of 3,092 diabetic veterans participating in a multisite trial. Two comorbidity measures, four risk adjusters, a functional status measure, a diabetes complication count, and baseline ex...

Present status of transport code development based on Monte Carlo method

International Nuclear Information System (INIS)

Nakagawa, Masayuki

1985-01-01

The present status of development in Monte Carlo code is briefly reviewed. The main items are the followings; Application fields, Methods used in Monte Carlo code (geometry spectification, nuclear data, estimator and variance reduction technique) and unfinished works, Typical Monte Carlo codes and Merits of continuous energy Monte Carlo code. (author)

What Predicts Use of Learning-Centered, Interactive Engagement Methods?

Science.gov (United States)

Madson, Laura; Trafimow, David; Gray, Tara; Gutowitz, Michael

2014-01-01

What makes some faculty members more likely to use interactive engagement methods than others? We use the theory of reasoned action to predict faculty members' use of interactive engagement methods. Results indicate that faculty members' beliefs about the personal positive consequences of using these methods (e.g., "Using interactive…

Method of estimating thermal power distribution of core of BWR type reactor

International Nuclear Information System (INIS)

Sekimizu, Koichi

1982-01-01

Purpose: To accurately and rapidly predict the thermal power of the core of a BWR they reactor at load follow-up operating time. Method: A parameter value corrected from a correction coefficient deciding unit and a xenon density distribution value predicted and calculated from a xenon density distributor are inputted to a thermal power distribution predicting devise, the status amount such as coolant flow rate or the like predetermined at this and next high power operating times is substituted for physical model to predict and calculate the thermal power distribution. The status amount of a nuclear reactor at the time of operating in previous high power corresponding to the next high power operation to be predicted is read from the status amount of the reactor stored in time series manner is a reactor core status memory, and the physical model used in the prediction and calculation of the thermal power distribution at the time of next high power operation is corrected. (Sikiya, K.)

Gender and offender status predicting treatment success in refugees and asylum seekers with PTSD

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Håkon Stenmark

2014-01-01

Full Text Available Background: Current knowledge is limited regarding patient characteristics related to treatment outcome of posttraumatic stress disorders (PTSD in refugees and asylum seekers. Objective: Gender, torture status, offender status, level of anger, and level of depression were investigated for possible effects on the treatment outcome. Method: Patient characteristics were explored in 54 refugees and asylum seekers who had completed a treatment program for PTSD. Non-responders (10, those who had the same or higher levels of symptom severity after treatment, were compared with responders, those who had lower symptom severity after treatment (44. Symptom severity was measured by Clinician-Administered PTSD Scale. The non-responders and responders constituted the dichotomous, dependent variable. The independent variables were gender, torture status, offender status, level of anger, and level of depression. T-tests and Exact Unconditional Homogeneity/Independence Tests for 2X2 Tables were used to study the relationship to treatment outcome. Results: Being male and reporting to have been a violent offender were significantly more frequent characteristics among the non-responders compared to the responders. The levels of pretreatment anger, depression and torture status did not affect the treatment outcome. Conclusions: The study adds support to findings that females benefit more from treatment of PTSD than males and that violent offenders are difficult to treat within the standard treatment programs.

A dynamic particle filter-support vector regression method for reliability prediction

International Nuclear Information System (INIS)

Wei, Zhao; Tao, Tao; ZhuoShu, Ding; Zio, Enrico

2013-01-01

Support vector regression (SVR) has been applied to time series prediction and some works have demonstrated the feasibility of its use to forecast system reliability. For accuracy of reliability forecasting, the selection of SVR's parameters is important. The existing research works on SVR's parameters selection divide the example dataset into training and test subsets, and tune the parameters on the training data. However, these fixed parameters can lead to poor prediction capabilities if the data of the test subset differ significantly from those of training. Differently, the novel method proposed in this paper uses particle filtering to estimate the SVR model parameters according to the whole measurement sequence up to the last observation instance. By treating the SVR training model as the observation equation of a particle filter, our method allows updating the SVR model parameters dynamically when a new observation comes. Because of the adaptability of the parameters to dynamic data pattern, the new PF–SVR method has superior prediction performance over that of standard SVR. Four application results show that PF–SVR is more robust than SVR to the decrease of the number of training data and the change of initial SVR parameter values. Also, even if there are trends in the test data different from those in the training data, the method can capture the changes, correct the SVR parameters and obtain good predictions. -- Highlights: •A dynamic PF–SVR method is proposed to predict the system reliability. •The method can adjust the SVR parameters according to the change of data. •The method is robust to the size of training data and initial parameter values. •Some cases based on both artificial and real data are studied. •PF–SVR shows superior prediction performance over standard SVR

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010.

Science.gov (United States)

Adler, Sarah; Basketter, David; Creton, Stuart; Pelkonen, Olavi; van Benthem, Jan; Zuang, Valérie; Andersen, Klaus Ejner; Angers-Loustau, Alexandre; Aptula, Aynur; Bal-Price, Anna; Benfenati, Emilio; Bernauer, Ulrike; Bessems, Jos; Bois, Frederic Y; Boobis, Alan; Brandon, Esther; Bremer, Susanne; Broschard, Thomas; Casati, Silvia; Coecke, Sandra; Corvi, Raffaella; Cronin, Mark; Daston, George; Dekant, Wolfgang; Felter, Susan; Grignard, Elise; Gundert-Remy, Ursula; Heinonen, Tuula; Kimber, Ian; Kleinjans, Jos; Komulainen, Hannu; Kreiling, Reinhard; Kreysa, Joachim; Leite, Sofia Batista; Loizou, George; Maxwell, Gavin; Mazzatorta, Paolo; Munn, Sharon; Pfuhler, Stefan; Phrakonkham, Pascal; Piersma, Aldert; Poth, Albrecht; Prieto, Pilar; Repetto, Guillermo; Rogiers, Vera; Schoeters, Greet; Schwarz, Michael; Serafimova, Rositsa; Tähti, Hanna; Testai, Emanuela; van Delft, Joost; van Loveren, Henk; Vinken, Mathieu; Worth, Andrew; Zaldivar, José-Manuel

2011-05-01

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Hybrid methods for airframe noise numerical prediction

Energy Technology Data Exchange (ETDEWEB)

Terracol, M.; Manoha, E.; Herrero, C.; Labourasse, E.; Redonnet, S. [ONERA, Department of CFD and Aeroacoustics, BP 72, Chatillon (France); Sagaut, P. [Laboratoire de Modelisation en Mecanique - UPMC/CNRS, Paris (France)

2005-07-01

This paper describes some significant steps made towards the numerical simulation of the noise radiated by the high-lift devices of a plane. Since the full numerical simulation of such configuration is still out of reach for present supercomputers, some hybrid strategies have been developed to reduce the overall cost of such simulations. The proposed strategy relies on the coupling of an unsteady nearfield CFD with an acoustic propagation solver based on the resolution of the Euler equations for midfield propagation in an inhomogeneous field, and the use of an integral solver for farfield acoustic predictions. In the first part of this paper, this CFD/CAA coupling strategy is presented. In particular, the numerical method used in the propagation solver is detailed, and two applications of this coupling method to the numerical prediction of the aerodynamic noise of an airfoil are presented. Then, a hybrid RANS/LES method is proposed in order to perform some unsteady simulations of complex noise sources. This method allows for significant reduction of the cost of such a simulation by considerably reducing the extent of the LES zone. This method is described and some results of the numerical simulation of the three-dimensional unsteady flow in the slat cove of a high-lift profile are presented. While these results remain very difficult to validate with experiments on similar configurations, they represent up to now the first 3D computations of this kind of flow. (orig.)

Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

DEFF Research Database (Denmark)

Jonsdottir, Svava Osk; Jorgensen, FS; Brunak, Søren

2005-01-01

about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability......MOTIVATION: To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS: We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information...... of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described....

An Adjusted Probability Method for the Identification of Sociometric Status in Classrooms

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Francisco J. García Bacete

2017-10-01

Full Text Available Objective: The aim of this study was to test the performance of an adjusted probability method for sociometric classification proposed by García Bacete (GB in comparison with two previous methods. Specific goals were to examine the overall agreement between methods, the behavioral correlates of each sociometric group, the sources for discrepant classifications between methods, the behavioral profiles of discrepant and consistent cases between methods, and age differences.Method: We compared the GB adjusted probability method with the standard score model proposed by Coie and Dodge (CD and the probability score model proposed by Newcomb and Bukowski (NB. The GB method is an adaptation of the NB method, cutoff scores are derived from the distribution of raw liked most and liked least scores in each classroom instead of using fixed and absolute scores as does NB method. The criteria for neglected status are also modified by the GB method. Participants were 569 children (45% girls from 23 elementary school classrooms (13 Grades 1–2, 10 Grades 5–6.Results: We found agreement as well as differences between the three methods. The CD method yielded discrepancies in the classifications because of its dependence on z-scores and composite dimensions. The NB method was less optimal in the validation of the behavioral characteristics of the sociometric groups, because of its fixed cutoffs for identifying preferred, rejected, and controversial children, and not differentiating between positive and negative nominations for neglected children. The GB method addressed some of the limitations of the other two methods. It improved the classified of neglected students, as well as discrepant cases of the preferred, rejected, and controversial groups. Agreement between methods was higher with the oldest children.Conclusion: GB is a valid sociometric method as evidences by the behavior profiles of the sociometric status groups identified with this method.

A generic method for assignment of reliability scores applied to solvent accessibility predictions

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Nielsen Morten

2009-07-01

Full Text Available Abstract Background Estimation of the reliability of specific real value predictions is nontrivial and the efficacy of this is often questionable. It is important to know if you can trust a given prediction and therefore the best methods associate a prediction with a reliability score or index. For discrete qualitative predictions, the reliability is conventionally estimated as the difference between output scores of selected classes. Such an approach is not feasible for methods that predict a biological feature as a single real value rather than a classification. As a solution to this challenge, we have implemented a method that predicts the relative surface accessibility of an amino acid and simultaneously predicts the reliability for each prediction, in the form of a Z-score. Results An ensemble of artificial neural networks has been trained on a set of experimentally solved protein structures to predict the relative exposure of the amino acids. The method assigns a reliability score to each surface accessibility prediction as an inherent part of the training process. This is in contrast to the most commonly used procedures where reliabilities are obtained by post-processing the output. Conclusion The performance of the neural networks was evaluated on a commonly used set of sequences known as the CB513 set. An overall Pearson's correlation coefficient of 0.72 was obtained, which is comparable to the performance of the currently best public available method, Real-SPINE. Both methods associate a reliability score with the individual predictions. However, our implementation of reliability scores in the form of a Z-score is shown to be the more informative measure for discriminating good predictions from bad ones in the entire range from completely buried to fully exposed amino acids. This is evident when comparing the Pearson's correlation coefficient for the upper 20% of predictions sorted according to reliability. For this subset, values of 0

Use of quantitative diffusion-weighted magnetic resonance imaging to predict human papilloma virus status in patients with oropharyngeal squamous cell carcinoma.

Science.gov (United States)

Nakahira, Mitsuhiko; Saito, Naoko; Yamaguchi, Hiroshi; Kuba, Kiyomi; Sugasawa, Masashi

2014-05-01

Although identification of human papilloma virus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC) is essential in predicting treatment response, no imaging modality can currently determine whether a tumor is HPV-related. In this retrospective study, 26 patients with OPSCC confined to the lateral wall or the base of tongue underwent neck magnetic resonance imaging, using T1-, T2- and diffusion-weighted imaging (DWI). Apparent diffusion coefficients (ADCs) in a region of interest covering the largest available primary tumor area of OPSCC on a single slice of the ADC map were calculated using two b values (0 and 1,000 s/mm(2)). Mean and minimum ADCs were compared with HPV status, using p16 immunohistochemistry as a surrogate marker for HPV infection. Mean and minimum ADCs for HPV(+) OPSCC were significantly lower than those for HPV(-) OPSCC. A cut-off value of mean ADC for HPV(+) OPSCC of 1.027 × 10(-3) mm(2)/s yielded sensitivity and specificity of 83.33 and 78.57%, respectively. In conclusion, the present study indicates that ADC could be used to predict HPV status in patients with OPSCC.

Comparison of selected methods of prediction of wine exports and imports

Directory of Open Access Journals (Sweden)

Radka Šperková

2008-01-01

Full Text Available For prediction of future events, there exist a number of methods usable in managerial practice. Decision on which of them should be used in a particular situation depends not only on the amount and quality of input information, but also on a subjective managerial judgement. Paper performs a practical application and consequent comparison of results of two selected methods, which are statistical method and deductive method. Both methods were used for predicting wine exports and imports in (from the Czech Republic. Prediction was done in 2003 and it related to the economic years 2003/2004, 2004/2005, 2005/2006, and 2006/2007, within which it was compared with the real values of the given indicators.Within the deductive methods there were characterized the most important factors of external environment including the most important influence according to authors’ opinion, which was the integration of the Czech Republic into the EU from 1st May, 2004. On the contrary, the statistical method of time-series analysis did not regard the integration, which is comes out of its principle. Statistics only calculates based on data from the past, and cannot incorporate the influence of irregular future conditions, just as the EU integration. Because of this the prediction based on deductive method was more optimistic and more precise in terms of its difference from real development in the given field.

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Directory of Open Access Journals (Sweden)

Kedong Yin

2017-12-01

Full Text Available The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick–Prescott filter (HP filter to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1 to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA.

Usefulness of the Mini Nutritional Assessment (MNA) in predicting the nutritional status of people with mental disorders in Taiwan.

Science.gov (United States)

Tsai, Alan C; Chou, Yuan-Ti; Chang, Tsui-Lan

2011-02-01

The study was to evaluate the ability of the Mini Nutritional Assessment in predicting malnutrition in people with three subtypes of mental disorder (schizophrenia, major depression and bipolar disorder) in Taiwan. The study involved a convenience sample of 120 residents of psychiatric wards managed by a hospital in central Taiwan (52 with schizophrenia, 36 with major depression and 32 with bipolar disorder) classified according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria. A structured questionnaire elicited subjects' personal data, disease history and answers to questions in the Mini Nutritional Assessment. Serum and anthropometrical parameters were measured. Nutritional status was evaluated with a content-equivalent version of the Mini Nutritional Assessment (Taiwan version-1, T1). The Mini Nutritional Assessment-Taiwan version-1 was effective in assessing the nutritional status of people of all three subtypes of disorder. Nutritional statuses predicted with the Mini Nutritional Assessment-Taiwan version-1 agreed well with other nutritional indicators such as BMI, waist circumference and appetite status. According to the Mini Nutritional Assessment-Taiwan version-1, people with major depression were more likely to be at risk of undernutrition, whereas people with schizophrenia or bipolar disorder were more likely to be at risk of overnutrition. The Mini Nutritional Assessment-Taiwan version-1 can effectively grade both undernutrition and overnutrition of people with schizophrenia, major depression or bipolar disorder. The Mini Nutritional Assessment enables nurses to monitor emerging nutritional problems in people with psychiatric disorder without relying on subjective judgement. With proper intervention, it can help reduce nutrition-related chronic conditions in these individuals and save on healthcare cost. © 2011 Blackwell Publishing Ltd.

Methods for predicting isochronous stress-strain curves

International Nuclear Information System (INIS)

Kiyoshige, Masanori; Shimizu, Shigeki; Satoh, Keisuke.

1976-01-01

Isochronous stress-strain curves show the relation between stress and total strain at a certain temperature with time as a parameter, and they are drawn up from the creep test results at various stress levels at a definite temperature. The concept regarding the isochronous stress-strain curves was proposed by McVetty in 1930s, and has been used for the design of aero-engines. Recently the high temperature characteristics of materials are shown as the isochronous stress-strain curves in the design guide for the nuclear energy equipments and structures used in high temperature creep region. It is prescribed that these curves are used as the criteria for determining design stress intensity or the data for analyzing the superposed effects of creep and fatigue. In case of the isochronous stress-strain curves used for the design of nuclear energy equipments with very long service life, it is impractical to determine the curves directly from the results of long time creep test, accordingly the method of predicting long time stress-strain curves from short time creep test results must be established. The method proposed by the authors, for which the creep constitution equations taking the first and second creep stages into account are used, and the method using Larson-Miller parameter were studied, and it was found that both methods were reliable for the prediction. (Kako, I.)

Analysis of the uranium price predicted to 24 months, implementing neural networks and the Monte Carlo method like predictive tools

International Nuclear Information System (INIS)

Esquivel E, J.; Ramirez S, J. R.; Palacios H, J. C.

2011-11-01

The present work shows predicted prices of the uranium, using a neural network. The importance of predicting financial indexes of an energy resource, in this case, allows establishing budgetary measures, as well as the costs of the resource to medium period. The uranium is part of the main energy generating fuels and as such, its price rebounds in the financial analyses, due to this is appealed to predictive methods to obtain an outline referent to the financial behaviour that will have in a certain time. In this study, two methodologies are used for the prediction of the uranium price: the Monte Carlo method and the neural networks. These methods allow predicting the indexes of monthly costs, for a two years period, starting from the second bimonthly of 2011. For the prediction the uranium costs are used, registered from the year 2005. (Author)

Role of [{sup 18}F]FDG PET in prediction of KRAS and EGFR mutation status in patients with advanced non-small-cell lung cancer

Energy Technology Data Exchange (ETDEWEB)

Caicedo, Carlos; Garcia-Velloso, Maria Jose; Vigil Diaz, Carmen; Richter Echevarria, Jose Angel [University of Navarra, Nuclear Medicine Department, University Clinic of Navarra, Pamplona (Spain); Lozano, Maria Dolores; Labiano, Tania [University of Navarra, Pathology Department, University Clinic of Navarra, Pamplona (Spain); Lopez-Picazo, Jose Maria; Gurpide, Alfonso; Perez Gracia, Jose Luis [University of Navarra, Oncology Department, University Clinic of Navarra, Pamplona (Spain); Zulueta, Javier [University of Navarra, Pulmonology Department, University Clinic of Navarra, Pamplona (Spain)

2014-11-15

The tumour molecular profile predicts the activity of epidermal growth factor receptor (EGFR) inhibitors in non-small-cell lung cancer (NSCLC). However, tissue availability and tumour heterogeneity limit its assessment. We evaluated whether [{sup 18}F]FDG PET might help predict KRAS and EFGR mutation status in NSCLC. Between January 2005 and October 2011, 340 NSCLC patients were tested for KRAS and EGFR mutation status. We identified patients with stage III and IV disease who had undergone [{sup 18}F]FDG PET/CT scanning for initial staging. SUVpeak, SUVmax and SUVmean of the single hottest tumour lesions were calculated, and their association with KRAS and EGFR mutation status was assessed. A receiver operator characteristic (ROC) curve analysis and a multivariate analysis (including SUVmean, gender, age and AJCC stage) were performed to identify the potential value of [{sup 18}F]FDG PET/CT for predicting KRAS mutation. From 102 patients staged using [{sup 18}F]FDG PET/CT, 28 (27 %) had KRAS mutation (KRAS+), 22 (22 %) had EGFR mutation (EGFR+) and 52 (51 %) had wild-type KRAS and EGFR profiles (WT). KRAS+ patients showed significantly higher [{sup 18}F]FDG uptake than EGFR+ and WT patients (SUVmean 9.5, 5.7 and 6.6, respectively; p < 0.001). No significant differences were observed in [{sup 18}F]FDG uptake between EGFR+ patients and WT patients. ROC curve analysis for KRAS mutation status discrimination yielded an area under the curve of 0.740 for SUVmean (p < 0.001). The multivariate analysis showed a sensitivity and specificity of 78.6 % and 62.2 %, respectively, and the AUC was 0.773. NSCLC patients with tumours harbouring KRAS mutations showed significantly higher [{sup 18}F]FDG uptake than WT patients, as assessed in terms of SUVpeak, SUVmax and SUVmean. A multivariate model based on age, gender, AJCC stage and SUVmean might be used as a predictive marker of KRAS mutation status in patients with stage III or IV NSCLC. (orig.)

CREME96 and Related Error Rate Prediction Methods

Science.gov (United States)

Adams, James H., Jr.

2012-01-01

Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

An Influence Function Method for Predicting Store Aerodynamic Characteristics during Weapon Separation,

Science.gov (United States)

1981-05-14

8217 AO-Ail 777 GRUMMAN AEROSPACE CORP BETHPAGE NY F/G 20/4 AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC C--ETCCU) MAY 8 1 R MEYER, A...CENKO, S YARDS UNCLASSIFIED N ’.**~~N**n I EHEEKI j~j .25 Q~4 111110 111_L 5. AN INFLUENCE FUNCTION METHOD FOR PREDICTING STORE AERODYNAMIC...extended to their logical conclusion one is led quite naturally to consideration of an " Influence Function Method" for I predicting store aerodynamic

Novel Methods for Drug-Target Interaction Prediction using Graph Mining

KAUST Repository

Ba Alawi, Wail

2016-08-31

The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

The Impact of Conscious Sedation versus General Anesthesia for Stroke Thrombectomy on the Predictive Value of Collateral Status: A Post Hoc Analysis of the SIESTA Trial.

Science.gov (United States)

Schönenberger, S; Pfaff, J; Uhlmann, L; Klose, C; Nagel, S; Ringleb, P A; Hacke, W; Kieser, M; Bendszus, M; Möhlenbruch, M A; Bösel, J

2017-08-01

Radiologic selection criteria to identify patients likely to benefit from endovascular stroke treatment are still controversial. In this post hoc analysis of the recent randomized Sedation versus Intubation for Endovascular Stroke TreAtment (SIESTA) trial, we aimed to investigate the impact of sedation mode (conscious sedation versus general anesthesia) on the predictive value of collateral status. Using imaging data from SIESTA, we assessed collateral status with the collateral score of Tan et al and graded it from absent to good collaterals (0-3). We examined the association of collateral status with 24-hour improvement of the NIHSS score, infarct volume, and mRS at 3 months according to the sedation regimen. In a cohort of 104 patients, the NIHSS score improved significantly in patients with moderate or good collaterals (2-3) compared with patients with no or poor collaterals (0-1) ( P = .011; mean, -5.8 ± 7.6 versus -1.1 ± 10.7). Tan 2-3 was also associated with significantly higher ASPECTS before endovascular stroke treatment (median, 9 versus 7; P collateral status (0.1 versus 2.3), the sedation modes conscious sedation and general anesthesia were not associated with significant differences in the predictive value of collateral status regarding infarction size or functional outcome. The sedation mode, conscious sedation or general anesthesia, did not influence the predictive value of collaterals in patients with large-vessel occlusion anterior circulation stroke undergoing thrombectomy in the SIESTA trial. © 2017 by American Journal of Neuroradiology.

A method for uncertainty quantification in the life prediction of gas turbine components

Energy Technology Data Exchange (ETDEWEB)

Lodeby, K.; Isaksson, O.; Jaervstraat, N. [Volvo Aero Corporation, Trolhaettan (Sweden)

1998-12-31

A failure in an aircraft jet engine can have severe consequences which cannot be accepted and high requirements are therefore raised on engine reliability. Consequently, assessment of the reliability of life predictions used in design and maintenance are important. To assess the validity of the predicted life a method to quantify the contribution to the total uncertainty in the life prediction from different uncertainty sources is developed. The method is a structured approach for uncertainty quantification that uses a generic description of the life prediction process. It is based on an approximate error propagation theory combined with a unified treatment of random and systematic errors. The result is an approximate statistical distribution for the predicted life. The method is applied on life predictions for three different jet engine components. The total uncertainty became of reasonable order of magnitude and a good qualitative picture of the distribution of the uncertainty contribution from the different sources was obtained. The relative importance of the uncertainty sources differs between the three components. It is also highly dependent on the methods and assumptions used in the life prediction. Advantages and disadvantages of this method is discussed. (orig.) 11 refs.

Ultrasound-guided diffuse optical tomography (DOT) of invasive breast carcinoma: Does tumour total haemoglobin concentration contribute to the prediction of axillary lymph node status?

Energy Technology Data Exchange (ETDEWEB)

Zhu, Qingli, E-mail: qinglizhu@gmail.com [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); Xiao, Mengsu, E-mail: xiaomengsu_2000@sina.com [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); You, Shanshan, E-mail: shanshan_0531@sina.com [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); Zhang, Jing, E-mail: zhang.jing1029@163.com [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); Jiang, Yuxin, E-mail: yuxinjiangxh@yahoo.com.cn [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); Lai, Xingjian, E-mail: lxjpumch@yahoo.com.cn [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China); Dai, Qing, E-mail: qingdai_2000@yahoo.com [Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Shuaifuyuan 1, Dongcheng District, Beijing 100730 (China)

2012-11-15

Objectives: To prospectively study the ultrasound-guided near-infrared diffuse optical tomography (DOT) findings of the total haemoglobin concentration (THC) detected in invasive breast carcinomas and its contribution to the prediction of axillary lymph node (LN) status. Methods: A total of 195 invasive breast carcinomas were prospectively studied with DOT before surgery. Lumpectomy or mastectomy with full axillary nodal dissection was performed. Tumour size and THC level were correlated with LN status by a logistic regression analysis. Results: One hundred twenty-four patients (63.59%) was LN(-) and 71 (36.41%) was LN(+). The average THC was significantly higher in the LN(+) group than in the LN(-) group (252.94 {+-} 69.19 {mu}mol/L versus 203.86 {+-} 83.13 {mu}mol/L, P = 0.01). A multivariate analysis showed an independent relationship between the probability of axillary metastasis, elevated THC level (P = 0.01), and tumour size (P = 0.001). The odds ratio with THC {>=} 140 {mu}mol/L was 13.651 (1.781-104.560), whereas that of tumour size with a 1 cm increment was only 1.777 (1.283-2.246). Conclusions: The THC level and the tumour size are independent and preoperative predictors of axillary nodal status; these variables may improve the diagnosis of patients with lymph node metastasis.

Improving protein function prediction methods with integrated literature data

Directory of Open Access Journals (Sweden)

Gabow Aaron P

2008-04-01

Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

Science.gov (United States)

Saxe, Glenn N; Ma, Sisi; Ren, Jiwen; Aliferis, Constantin

2017-07-10

The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to

Ensemble approach combining multiple methods improves human transcription start site prediction.

LENUS (Irish Health Repository)

Dineen, David G

2010-01-01

The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.

Prediction of protein post-translational modifications: main trends and methods

Science.gov (United States)

Sobolev, B. N.; Veselovsky, A. V.; Poroikov, V. V.

2014-02-01

The review summarizes main trends in the development of methods for the prediction of protein post-translational modifications (PTMs) by considering the three most common types of PTMs — phosphorylation, acetylation and glycosylation. Considerable attention is given to general characteristics of regulatory interactions associated with PTMs. Different approaches to the prediction of PTMs are analyzed. Most of the methods are based only on the analysis of the neighbouring environment of modification sites. The related software is characterized by relatively low accuracy of PTM predictions, which may be due both to the incompleteness of training data and the features of PTM regulation. Advantages and limitations of the phylogenetic approach are considered. The prediction of PTMs using data on regulatory interactions, including the modular organization of interacting proteins, is a promising field, provided that a more carefully selected training data will be used. The bibliography includes 145 references.

The All Particle Method: 1991 status report

International Nuclear Information System (INIS)

Cullen, D.E.; Ballinger, C.T.; Perkins, S.T.

1991-07-01

At the present time a Monte Carlo transport computer code is being designed and implemented at Lawrence Livermore National Laboratory to include the transport of: neutrons, photons, electrons and light charged particles as well as the coupling between all species of particles, e.g. photon induced electron emission. Since this code is being designed to handle all particles, this approach is called the ''All Particle Method.'' This paper describes the current design philosophy and status of the Monte Carlo transport code and its supporting data bases. The treatment of neutrons and photons used by the All Particle Method code is conventional and as such this topic will not be discussed in this paper. Here emphasis is on discussion of our recent work to extend our ability to perform electron transport, as well as photon transport, as it is effected by coupling to electron transport, and atomic relaxation. First we discuss our new extended photon and electron interaction and atomic relaxation data bases. Next we illustrate the extended capabilities that these new data bases provide by presenting the results of several Monte Carlo transport calculations

Deterministic Role of CEA and MSI Status in Predicting Outcome of CRC Patients: a Perspective Study Amongst Hospital Attending Eastern Indian Populations.

Science.gov (United States)

Koyel, Banerjee; Priyabrata, Das; Rittwika, Bhattacharya; Swati, Dasgupta; Soma, Mukhopadhyay; Jayasri, Basak; Ashis, Mukhopadhyay

2017-12-01

Carcinoembryonic antigen (CEA) is an important deterministic factor in predicting colorectal carcinoma (CRC) progression. It is also evident that microsatellite instability (MSI) which results in a hypermutable phenotype of genomic DNA is common in CRC. Owing to the scarcity of reports from India, our aim of this study was to understand the clinicopathological correlations of CEA status with surgery and chemotherapy, correlate the same with socio-demographic status of the patients, determine the MSI status amongst them and understand the prognostic implications of CEA and MSI as CRC progression marker amongst patients. The serum CEA level was estimated by chemiluminescence assay (CLIA). Serum liver enzyme assay was carried out following the manufacturer's instructions using auto-analysers (E. Merck and Sera mol. Health Care, India). MSI analysis was carried out by PCR-SSCP. From our study, most frequently detected colorectal cancer was in 40-49 years age group (25.26%) with 61.05% male and 38.95% females. CEA showed a significant association with higher TNM staging, tumour size, smoking habit and MSI status ( p   0.05). After surgery and chemotherapy, CEA and WBCs were decreased significantly ( p   0.05). Overall, microsatellite instability was observed in approximately 40% of the populations. From our study, it was also evident that for both, MSI and abnormal CEA level predicted poor prognosis for the patient (by using Kaplan-Meier survival analysis; p  = 0.04). Thus, CEA and initial MSI status can be used as prognostic markers of CRC.

Fast Prediction Method for Steady-State Heat Convection

KAUST Repository

Wá ng, Yì ; Yu, Bo; Sun, Shuyu

2012-01-01

, the nonuniform POD-Galerkin projection method exhibits high accuracy, good suitability, and fast computation. It has universal significance for accurate and fast prediction. Also, the methodology can be applied to more complex modeling in chemical engineering

An ensemble method to predict target genes and pathways in uveal melanoma

Directory of Open Access Journals (Sweden)

Wei Chao

2018-04-01

Full Text Available This work proposes to predict target genes and pathways for uveal melanoma (UM based on an ensemble method and pathway analyses. Methods: The ensemble method integrated a correlation method (Pearson correlation coefficient, PCC, a causal inference method (IDA and a regression method (Lasso utilizing the Borda count election method. Subsequently, to validate the performance of PIL method, comparisons between confirmed database and predicted miRNA targets were performed. Ultimately, pathway enrichment analysis was conducted on target genes in top 1000 miRNA-mRNA interactions to identify target pathways for UM patients. Results: Thirty eight of the predicted interactions were matched with the confirmed interactions, indicating that the ensemble method was a suitable and feasible approach to predict miRNA targets. We obtained 50 seed miRNA-mRNA interactions of UM patients and extracted target genes from these interactions, such as ASPG, BSDC1 and C4BP. The 601 target genes in top 1,000 miRNA-mRNA interactions were enriched in 12 target pathways, of which Phototransduction was the most significant one. Conclusion: The target genes and pathways might provide a new way to reveal the molecular mechanism of UM and give hand for target treatments and preventions of this malignant tumor.

Predicting recovery at home after Ambulatory Surgery

Directory of Open Access Journals (Sweden)

Ayala Guillermo

2011-10-01

Full Text Available Abstract The correct implementation of Ambulatory Surgery must be accompanied by an accurate monitoring of the patient post-discharge state. We fit different statistical models to predict the first hours postoperative status of a discharged patient. We will also be able to predict, for any discharged patient, the probability of needing a closer follow-up, or of having a normal progress at home. Background The status of a discharged patient is predicted during the first 48 hours after discharge by using variables routinely used in Ambulatory Surgery. The models fitted will provide the physician with an insight into the post-discharge progress. These models will provide valuable information to assist in educating the patient and their carers about what to expect after discharge as well as to improve their overall level of satisfaction. Methods A total of 922 patients from the Ambulatory Surgery Unit of the Dr. Peset University Hospital (Valencia, Spain were selected for this study. Their post-discharge status was evaluated through a phone questionnaire. We pretend to predict four variables which were self-reported via phone interviews with the discharged patient: sleep, pain, oral tolerance of fluid/food and bleeding status. A fifth variable called phone score will be built as the sum of these four ordinal variables. The number of phone interviews varies between patients, depending on the evolution. The proportional odds model was used. The predictors were age, sex, ASA status, surgical time, discharge time, type of anaesthesia, surgical specialty and ambulatory surgical incapacity (ASI. This last variable reflects, before the operation, the state of incapacity and severity of symptoms in the discharged patient. Results Age, ambulatory surgical incapacity and the surgical specialty are significant to explain the level of pain at the first call. For the first two phone calls, ambulatory surgical incapacity is significant as a predictor for all

Mining Chemical Activity Status from High-Throughput Screening Assays

KAUST Repository

Soufan, Othman; Ba Alawi, Wail; Afeef, Moataz A.; Essack, Magbubah; Rodionov, Valentin; Kalnis, Panos; Bajic, Vladimir B.

2015-01-01

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.

Mining Chemical Activity Status from High-Throughput Screening Assays

KAUST Repository

Soufan, Othman

2015-12-14

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones. We developed a method, DRAMOTE, to predict activity status of chemical compounds in HTP activity assays. For a class of HTP assays, our method achieves considerably better results than the current state-of-the-art-solutions. We achieved this by modification of a minority oversampling technique. To demonstrate that DRAMOTE is performing better than the other methods, we performed a comprehensive comparison analysis with several other methods and evaluated them on data from 11 PubChem assays through 1,350 experiments that involved approximately 500,000 interactions between chemicals and their target proteins. As an example of potential use, we applied DRAMOTE to develop robust models for predicting FDA approved drugs that have high probability to interact with the thyroid stimulating hormone receptor (TSHR) in humans. Our findings are further partially and indirectly supported by 3D docking results and literature information. The results based on approximately 500,000 interactions suggest that DRAMOTE has performed the best and that it can be used for developing robust virtual screening models. The datasets and implementation of all solutions are available as a MATLAB toolbox online at www.cbrc.kaust.edu.sa/dramote and can be found on Figshare.

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Directory of Open Access Journals (Sweden)

Mitsunori Kayano

2017-01-01

Full Text Available This paper proposes Bayesian methods for the shape estimation of Chinese yam (Dioscorea opposita using a few key diameters of yam. Shape prediction of yam is applicable to determining optimal cutoff positions of a yam for producing seed yams. Our Bayesian method, which is a combination of Bayesian estimation model and predictive model, enables automatic, rapid, and low-cost processing of yam. After the construction of the proposed models using a sample data set in Japan, the models provide whole shape prediction of yam based on only a few key diameters. The Bayesian method performed well on the shape prediction in terms of minimizing the mean squared error between measured shape and the prediction. In particular, a multiple regression method with key diameters at two fixed positions attained the highest performance for shape prediction. We have developed automatic, rapid, and low-cost yam-processing machines based on the Bayesian estimation model and predictive model. Development of such shape prediction approaches, including our Bayesian method, can be a valuable aid in reducing the cost and time in food processing.

Pre-typhoon socioeconomic status factors predict post-typhoon psychiatric symptoms in a Vietnamese sample.

Science.gov (United States)

Brown, Ruth C; Trapp, Stephen K; Berenz, Erin C; Bigdeli, Tim Bernard; Acierno, Ron; Tran, Trinh Luong; Trung, Lam Tu; Tam, Nguyen Thanh; Tuan, Tran; Buoi, La Thi; Ha, Tran Thu; Thach, Tran Duc; Amstadter, Ananda B

2013-11-01

Exposure to natural disasters has been associated with increased risk for various forms of psychopathology. Evidence indicates that socioeconomic status (SES) may be important for understanding post-disaster psychiatric distress; however, studies of SES-relevant factors in non-Western, disaster-exposed samples are lacking. The primary aim of the current study was to examine the role of pre-typhoon SES-relevant factors in relation to post-typhoon psychiatric symptoms among Vietnamese individuals exposed to Typhoon Xangsane. In 2006, Typhoon Xangsane disrupted a mental health needs assessment in Vietnam in which the Self Reporting Questionnaire-20 (SRQ-20), and the Demographic and Health Surveys Wealth Index, a measure of SES created for use in low-income countries, were administered pre-typhoon. The SRQ-20 was re-administered post-typhoon. Results of a linear mixed model indicated that the covariates of older age, female sex, and higher levels of pre-typhoon psychiatric symptoms were associated with higher levels of post-typhoon psychiatric symptoms. Analysis of SES indicators revealed that owning fewer consumer goods, having lower quality of household services, and having attained less education were associated with higher levels of post-typhoon symptoms, above and beyond the covariates, whereas quality of the household build, employment status, and insurance status were not related to post-typhoon psychiatric symptoms. Even after controlling for demographic characteristics and pre-typhoon psychiatric symptoms, certain SES factors uniquely predicted post-typhoon psychiatric distress. These SES characteristics may be useful for identifying individuals in developing countries who are in need of early intervention following disaster exposure.

Post-treatment PET/CT and p16 status for predicting treatment outcomes in locally advanced head and neck cancer after definitive radiation

Energy Technology Data Exchange (ETDEWEB)

Awan, Musaddiq J.; Machtay, Mitchell; Yao, Min [Case Western Reserve University and University Hospitals, Department of Radiation Oncology, Cleveland, OH (United States); Lavertu, Pierre; Zender, Chad; Rezaee, Rod; Fowler, Nicole [University Hospitals, Department of Otolaryngology and Head and Neck Surgery, Cleveland, OH (United States); Karapetyan, Lilit; Gibson, Michael [University Hospitals, Department of Medical Oncology, Cleveland, OH (United States); Wasman, Jay [University Hospitals, Department of Pathology, Cleveland, OH (United States); Faulhaber, Peter [University Hospitals, Department of Nuclear Medicine and Radiology, Cleveland, OH (United States)

2017-06-15

To retrospectively review post-treatment (post-tx) FDG-PET/CT scans in patients with advanced head and neck squamous cell carcinoma (HNSCC) and known p16 status, treated with definitive (chemo)radiation (RT). A total of 108 eligible patients had N2A or greater HNSCC treated with chemoRT from August 1, 2008, to February 28, 2015, with post-tx PET/CT within 6 months after RT. Kaplan-Meier curves, log-rank statistics, and Cox proportional hazards regression were used for statistical analysis. Median follow-up was 2.38 years. Sixty-eight (63.0%) patients had p16+ and 40 (37.0%) had p16- status. Two-year overall survival and recurrence-free survival were 93.4% and 77.8%, respectively. The negative predictive value (NPV) of PET/CT for local recurrence (LR) was 100%. The NPV for regional recurrence (RR) was 96.5% for all patients, 100% for p16+ patients, and 88.5% for p16- patients. The positive predictive value (PPV) of PET/CT for recurrence was 77.3% for all patients, 50.0% for p16+, and 78.6% for p16-. The PPV for LR was 72.7% for all patients, 50.0% for p16+ patients, and 72.7% for p16- patients. The PPV for RR was 50.0% for all patients, 33% for p16+, and 66.6% for p16-. Post-tx PET/CT and p16 status were independent predictors of recurrence-free survival (p < 0.01). Post-tx PET/CT predicts treatment outcomes in both p16 + and p16- patients, and does so independently of p16 status. P16- patients with negative PET have a 10% risk of nodal recurrence, and closer follow-up in these patients is warranted. (orig.)

Prediction Methods for Blood Glucose Concentration

DEFF Research Database (Denmark)

-day workshop on the design, use and evaluation of prediction methods for blood glucose concentration was held at the Johannes Kepler University Linz, Austria. One intention of the workshop was to bring together experts working in various fields on the same topic, in order to shed light from different angles...... discussions which allowed to receive direct feedback from the point of view of different disciplines. This book is based on the contributions of that workshop and is intended to convey an overview of the different aspects involved in the prediction. The individual chapters are based on the presentations given...... in the process of writing this book: All authors for their individual contributions, all reviewers of the book chapters, Daniela Hummer for the entire organization of the workshop, Boris Tasevski for helping with the typesetting, Florian Reiterer for his help editing the book, as well as Oliver Jackson and Karin...

Benchmarking pKa prediction methods for Lys115 in acetoacetate decarboxylase.

Science.gov (United States)

Liu, Yuli; Patel, Anand H G; Burger, Steven K; Ayers, Paul W

2017-05-01

Three different pK a prediction methods were used to calculate the pK a of Lys115 in acetoacetate decarboxylase (AADase): the empirical method PROPKA, the multiconformation continuum electrostatics (MCCE) method, and the molecular dynamics/thermodynamic integration (MD/TI) method with implicit solvent. As expected, accurate pK a prediction of Lys115 depends on the protonation patterns of other ionizable groups, especially the nearby Glu76. However, since the prediction methods do not explicitly sample the protonation patterns of nearby residues, this must be done manually. When Glu76 is deprotonated, all three methods give an incorrect pK a value for Lys115. If protonated, Glu76 is used in an MD/TI calculation, the pK a of Lys115 is predicted to be 5.3, which agrees well with the experimental value of 5.9. This result agrees with previous site-directed mutagenesis studies, where the mutation of Glu76 (negative charge when deprotonated) to Gln (neutral) causes no change in K m , suggesting that Glu76 has no effect on the pK a shift of Lys115. Thus, we postulate that the pK a of Glu76 is also shifted so that Glu76 is protonated (neutral) in AADase. Graphical abstract Simulated abundances of protonated species as pH is varied.

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Value of geriatric frailty and nutritional status assessment in predicting postoperative mortality in gastric cancer surgery.

Science.gov (United States)

Tegels, Juul J W; de Maat, M F G; Hulsewé, K W E; Hoofwijk, A G M; Stoot, J H M B

2014-03-01

This study seeks to evaluate assessment of geriatric frailty and nutritional status in predicting postoperative mortality in gastric cancer surgery. Preoperatively, patients operated for gastric adenocarcinoma underwent assessment of Groningen Frailty Indicator (GFI) and Short Nutritional Assessment Questionnaire (SNAQ). We studied retrospectively whether these scores were associated with in-hospital mortality. From 2005 to September 2012 180 patients underwent surgery with an overall mortality of 8.3%. Patients with a GFI ≥ 3 (n = 30, 24%) had a mortality rate of 23.3% versus 5.2% in the lower GFI group (OR 4.0, 95%CI 1.1-14.1, P = 0.03). For patients who underwent surgery with curative intent (n = 125), this was 27.3% for patients with GFI ≥ 3 (n = 22, 18%) versus 5.7% with GFI gastric cancer surgical mortality and geriatric frailty as well as nutritional status using a simple questionnaire. This may have implications in preoperative decision making in selecting patients who optimally benefit from surgery.

Prediction-based dynamic load-sharing heuristics

Science.gov (United States)

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

1993-01-01

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

Towards a unified fatigue life prediction method for marine structures

CERN Document Server

Cui, Weicheng; Wang, Fang

2014-01-01

In order to apply the damage tolerance design philosophy to design marine structures, accurate prediction of fatigue crack growth under service conditions is required. Now, more and more people have realized that only a fatigue life prediction method based on fatigue crack propagation (FCP) theory has the potential to explain various fatigue phenomena observed. In this book, the issues leading towards the development of a unified fatigue life prediction (UFLP) method based on FCP theory are addressed. Based on the philosophy of the UFLP method, the current inconsistency between fatigue design and inspection of marine structures could be resolved. This book presents the state-of-the-art and recent advances, including those by the authors, in fatigue studies. It is designed to lead the future directions and to provide a useful tool in many practical applications. It is intended to address to engineers, naval architects, research staff, professionals and graduates engaged in fatigue prevention design and survey ...

Bicycle Frame Prediction Techniques with Fuzzy Logic Method

Directory of Open Access Journals (Sweden)

Rafiuddin Syam

2015-03-01

Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.

Bicycle Frame Prediction Techniques with Fuzzy Logic Method

Directory of Open Access Journals (Sweden)

Rafiuddin Syam

2017-03-01

Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.

Predictive probability methods for interim monitoring in clinical trials with longitudinal outcomes.

Science.gov (United States)

Zhou, Ming; Tang, Qi; Lang, Lixin; Xing, Jun; Tatsuoka, Kay

2018-04-17

In clinical research and development, interim monitoring is critical for better decision-making and minimizing the risk of exposing patients to possible ineffective therapies. For interim futility or efficacy monitoring, predictive probability methods are widely adopted in practice. Those methods have been well studied for univariate variables. However, for longitudinal studies, predictive probability methods using univariate information from only completers may not be most efficient, and data from on-going subjects can be utilized to improve efficiency. On the other hand, leveraging information from on-going subjects could allow an interim analysis to be potentially conducted once a sufficient number of subjects reach an earlier time point. For longitudinal outcomes, we derive closed-form formulas for predictive probabilities, including Bayesian predictive probability, predictive power, and conditional power and also give closed-form solutions for predictive probability of success in a future trial and the predictive probability of success of the best dose. When predictive probabilities are used for interim monitoring, we study their distributions and discuss their analytical cutoff values or stopping boundaries that have desired operating characteristics. We show that predictive probabilities utilizing all longitudinal information are more efficient for interim monitoring than that using information from completers only. To illustrate their practical application for longitudinal data, we analyze 2 real data examples from clinical trials. Copyright © 2018 John Wiley & Sons, Ltd.

Computational predictive methods for fracture and fatigue

Science.gov (United States)

Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

1994-09-01

The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

Investigation of a redox-sensitive predictive model of mouse embryonic stem cells differentiation using quantitative nuclease protection assays and glutathione redox status

Science.gov (United States)

Investigation of a redox-sensitive predictive model of mouse embryonic stem cell differentiation via quantitative nuclease protection assays and glutathione redox status Chandler KJ,Hansen JM, Knudsen T,and Hunter ES 1. U.S. Environmental Protection Agency, Research Triangl...

Using deuterated PAH amendments to validate chemical extraction methods to predict PAH bioavailability in soils

International Nuclear Information System (INIS)

Gomez-Eyles, Jose L.; Collins, Chris D.; Hodson, Mark E.

2011-01-01

Validating chemical methods to predict bioavailable fractions of polycyclic aromatic hydrocarbons (PAHs) by comparison with accumulation bioassays is problematic. Concentrations accumulated in soil organisms not only depend on the bioavailable fraction but also on contaminant properties. A historically contaminated soil was freshly spiked with deuterated PAHs (dPAHs). dPAHs have a similar fate to their respective undeuterated analogues, so chemical methods that give good indications of bioavailability should extract the fresh more readily available dPAHs and historic more recalcitrant PAHs in similar proportions to those in which they are accumulated in the tissues of test organisms. Cyclodextrin and butanol extractions predicted the bioavailable fraction for earthworms (Eisenia fetida) and plants (Lolium multiflorum) better than the exhaustive extraction. The PAHs accumulated by earthworms had a larger dPAH:PAH ratio than that predicted by chemical methods. The isotope ratio method described here provides an effective way of evaluating other chemical methods to predict bioavailability. - Research highlights: → Isotope ratios can be used to evaluate chemical methods to predict bioavailability. → Chemical methods predicted bioavailability better than exhaustive extractions. → Bioavailability to earthworms was still far from that predicted by chemical methods. - A novel method using isotope ratios to assess the ability of chemical methods to predict PAH bioavailability to soil biota.

Using deuterated PAH amendments to validate chemical extraction methods to predict PAH bioavailability in soils

Energy Technology Data Exchange (ETDEWEB)

Gomez-Eyles, Jose L., E-mail: j.l.gomezeyles@reading.ac.uk [University of Reading, School of Human and Environmental Sciences, Soil Research Centre, Reading, RG6 6DW Berkshire (United Kingdom); Collins, Chris D.; Hodson, Mark E. [University of Reading, School of Human and Environmental Sciences, Soil Research Centre, Reading, RG6 6DW Berkshire (United Kingdom)

2011-04-15

Validating chemical methods to predict bioavailable fractions of polycyclic aromatic hydrocarbons (PAHs) by comparison with accumulation bioassays is problematic. Concentrations accumulated in soil organisms not only depend on the bioavailable fraction but also on contaminant properties. A historically contaminated soil was freshly spiked with deuterated PAHs (dPAHs). dPAHs have a similar fate to their respective undeuterated analogues, so chemical methods that give good indications of bioavailability should extract the fresh more readily available dPAHs and historic more recalcitrant PAHs in similar proportions to those in which they are accumulated in the tissues of test organisms. Cyclodextrin and butanol extractions predicted the bioavailable fraction for earthworms (Eisenia fetida) and plants (Lolium multiflorum) better than the exhaustive extraction. The PAHs accumulated by earthworms had a larger dPAH:PAH ratio than that predicted by chemical methods. The isotope ratio method described here provides an effective way of evaluating other chemical methods to predict bioavailability. - Research highlights: > Isotope ratios can be used to evaluate chemical methods to predict bioavailability. > Chemical methods predicted bioavailability better than exhaustive extractions. > Bioavailability to earthworms was still far from that predicted by chemical methods. - A novel method using isotope ratios to assess the ability of chemical methods to predict PAH bioavailability to soil biota.

Microsatellite Status of Primary Colorectal Cancer Predicts the Incidence of Postoperative Colorectal Neoplasms.

Science.gov (United States)

Takiyama, Aki; Tanaka, Toshiaki; Yamamoto, Yoko; Hata, Keisuke; Ishihara, Soichiro; Nozawa, Hiroaki; Kawai, Kazushige; Kiyomatsu, Tomomichi; Nishikawa, Takeshi; Otani, Kensuke; Sasaki, Kazuhito; Watanabe, Toshiaki

2017-10-01

Few studies have evaluated the risk of postoperative colorectal neoplasms stratified by the nature of primary colorectal cancer (CRC). In this study, we revealed it on the basis of the microsatellite (MS) status of primary CRC. We retrospectively reviewed 338 patients with CRC and calculated the risk of neoplasms during postoperative surveillance colonoscopy in association with the MS status of primary CRC. A propensity score method was applied. We identified a higher incidence of metachronous rectal neoplasms after the resection of MS stable CRC than MS instable CRC (adjusted HR 5.74, p=0.04). We also observed a higher incidence of colorectal tubular adenoma in patients with MSS CRC (adjusted hazard ratio 7.09, pcolorectal cancer influenced the risk of postoperative colorectal neoplasms. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method.

Science.gov (United States)

Li, Jing; Wang, Jing; Li, Jing

2017-12-01

As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREAL W , which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREAL W has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREAL W could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .

Evaluation of mathematical methods for predicting optimum dose of gamma radiation in sugarcane (Saccharum sp.)

International Nuclear Information System (INIS)

Wu, K.K.; Siddiqui, S.H.; Heinz, D.J.; Ladd, S.L.

1978-01-01

Two mathematical methods - the reversed logarithmic method and the regression method - were used to compare the predicted and the observed optimum gamma radiation dose (OD 50 ) in vegetative propagules of sugarcane. The reversed logarithmic method, usually used in sexually propagated crops, showed the largest difference between the predicted and observed optimum dose. The regression method resulted in a better prediction of the observed values and is suggested as a better method for the prediction of optimum dose for vegetatively propagated crops. (author)

Candidate Prediction Models and Methods

DEFF Research Database (Denmark)

Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

2005-01-01

This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

Science.gov (United States)

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multilingual Validation of the Questionnaire for Verifying Stroke-Free Status in West Africa.

Science.gov (United States)

Sarfo, Fred; Gebregziabher, Mulugeta; Ovbiagele, Bruce; Akinyemi, Rufus; Owolabi, Lukman; Obiako, Reginald; Akpa, Onoja; Armstrong, Kevin; Akpalu, Albert; Adamu, Sheila; Obese, Vida; Boa-Antwi, Nana; Appiah, Lambert; Arulogun, Oyedunni; Mensah, Yaw; Adeoye, Abiodun; Tosin, Aridegbe; Adeleye, Osimhiarherhuo; Tabi-Ajayi, Eric; Phillip, Ibinaiye; Sani, Abubakar; Isah, Suleiman; Tabari, Nasir; Mande, Aliyu; Agunloye, Atinuke; Ogbole, Godwin; Akinyemi, Joshua; Laryea, Ruth; Melikam, Sylvia; Uvere, Ezinne; Adekunle, Gregory; Kehinde, Salaam; Azuh, Paschal; Dambatta, Abdul; Ishaq, Naser; Saulson, Raelle; Arnett, Donna; Tiwari, Hemnant; Jenkins, Carolyn; Lackland, Dan; Owolabi, Mayowa

2016-01-01

The Questionnaire for Verifying Stroke-Free Status (QVSFS), a method for verifying stroke-free status in participants of clinical, epidemiological, and genetic studies, has not been validated in low-income settings where populations have limited knowledge of stroke symptoms. We aimed to validate QVSFS in 3 languages, Yoruba, Hausa and Akan, for ascertainment of stroke-free status of control subjects enrolled in an on-going stroke epidemiological study in West Africa. Data were collected using a cross-sectional study design where 384 participants were consecutively recruited from neurology and general medicine clinics of 5 tertiary referral hospitals in Nigeria and Ghana. Ascertainment of stroke status was by neurologists using structured neurological examination, review of case records, and neuroimaging (gold standard). Relative performance of QVSFS without and with pictures of stroke symptoms (pictograms) was assessed using sensitivity, specificity, positive predictive value, and negative predictive value. The overall median age of the study participants was 54 years and 48.4% were males. Of 165 stroke cases identified by gold standard, 98% were determined to have had stroke, whereas of 219 without stroke 87% were determined to be stroke-free by QVSFS. Negative predictive value of the QVSFS across the 3 languages was 0.97 (range, 0.93-1.00), sensitivity, specificity, and positive predictive value were 0.98, 0.82, and 0.80, respectively. Agreement between the questionnaire with and without the pictogram was excellent/strong with Cohen k=0.92. QVSFS is a valid tool for verifying stroke-free status across culturally diverse populations in West Africa. © 2015 American Heart Association, Inc.

Creep-fatigue life prediction method using Diercks equation for Cr-Mo steel

International Nuclear Information System (INIS)

Sonoya, Keiji; Nonaka, Isamu; Kitagawa, Masaki

1990-01-01

For dealing with the situation that creep-fatigue life properties of materials do not exist, a development of the simple method to predict creep-fatigue life properties is necessary. A method to predict the creep-fatigue life properties of Cr-Mo steels is proposed on the basis of D. Diercks equation which correlates the creep-fatigue lifes of SUS 304 steels under various temperatures, strain ranges, strain rates and hold times. The accuracy of the proposed method was compared with that of the existing methods. The following results were obtained. (1) Fatigue strength and creep rupture strength of Cr-Mo steel are different from those of SUS 304 steel. Therefore in order to apply Diercks equation to creep-fatigue prediction for Cr-Mo steel, the difference of fatigue strength was found to be corrected by fatigue life ratio of both steels and the difference of creep rupture strength was found to be corrected by the equivalent temperature corresponding to equal strength of both steels. (2) Creep-fatigue life can be predicted by the modified Diercks equation within a factor of 2 which is nearly as precise as the accuracy of strain range partitioning method. Required test and analysis procedure of this method are not so complicated as strain range partitioning method. (author)

Predicting metabolic syndrome using decision tree and support vector machine methods

Directory of Open Access Journals (Sweden)

Farzaneh Karimi-Alavijeh

2016-06-01

Full Text Available BACKGROUND: Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. METHODS: This study aims to employ decision tree and support vector machine (SVM to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP, diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs, total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. RESULTS: SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758, 0.74 (0.72 and 0.757 (0.739 in SVM (decision tree method. CONCLUSION: The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most

Is Subjective Status Influenced by Psychosocial Factors?

OpenAIRE

Lundberg, Johanna; Kristenson, Margareta

2008-01-01

Objective Associations between subjective status and health are still relatively unexplored. This study aimed at testing whether subjective status is uniquely confounded by psychosocial factors compared to objective status, and what factors that may predict subjective status. Design A cross-sectional analysis of a population-based, random sample of 795 middle-aged men and women from the southeast of Sweden. Questionnaires included subjective status, objective measures of socioeconomic status,...

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Prediction of polymer flooding performance using an analytical method

International Nuclear Information System (INIS)

Tan Czek Hoong; Mariyamni Awang; Foo Kok Wai

2001-01-01

The study investigated the applicability of an analytical method developed by El-Khatib in polymer flooding. Results from a simulator UTCHEM and experiments were compared with the El-Khatib prediction method. In general, by assuming a constant viscosity polymer injection, the method gave much higher recovery values than the simulation runs and the experiments. A modification of the method gave better correlation, albeit only oil production. Investigation is continuing on modifying the method so that a better overall fit can be obtained for polymer flooding. (Author)

Development of an integrated method for long-term water quality prediction using seasonal climate forecast

Directory of Open Access Journals (Sweden)

J. Cho

2016-10-01

Full Text Available The APEC Climate Center (APCC produces climate prediction information utilizing a multi-climate model ensemble (MME technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1 the Simple Bias Correction (SBC method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2 the Moving Window Regression (MWR method, which indirectly utilizes dynamic prediction data; (3 the Climate Index Regression (CIR method, which predominantly uses observation-based climate indices; and (4 the Integrated Time Regression (ITR method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Directory of Open Access Journals (Sweden)

César Hernández-Hernández

2017-06-01

Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

The role of quantitative estrogen receptor status in predicting tumor response at surgery in breast cancer patients treated with neoadjuvant chemotherapy.

Science.gov (United States)

Raphael, Jacques; Gandhi, Sonal; Li, Nim; Lu, Fang-I; Trudeau, Maureen

2017-07-01

Estrogen receptor (ER) negative (-) breast cancer (BC) patients have better tumor response rates than ER-positive (+) patients after neoadjuvant chemotherapy (NCT). We conducted a retrospective review using the institutional database "Biomatrix" to assess the value of quantitative ER status in predicting tumor response at surgery and to identify potential predictors of survival outcomes. Univariate followed by multivariable regression analyses were conducted to assess the association between quantitative ER and tumor response assessed as tumor size reduction and pathologic complete response (pCR). Predictors of recurrence-free survival (RFS) were identified using a cox proportional hazards model (CPH). A log-rank test was used to compare RFS between groups if a significant predictor was identified. 304 patients were included with a median follow-up of 43.3 months (Q1-Q3 28.7-61.1) and a mean age of 49.7 years (SD 10.9). Quantitative ER was inversely associated with tumor size reduction and pCR (OR 0.99, 95% CI 0.99-1.00, p = 0.027 and 0.98 95% CI 0.97-0.99, p Quantitative ER status is inversely associated with tumor response in BC patients treated with NCT. A cut-off of 60 and 80% predicts best the association with tumor size reduction and pCR, respectively. Therefore, patients with an ER status higher than the cut-off might benefit from a neoadjuvant endocrine therapy approach. Patients with pCR had better survival outcomes independently of their tumor phenotype. Further prospective studies are needed to validate the clinical utility of quantitative ER as a predictive marker of tumor response.

PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

Science.gov (United States)

Shin, Woong-Hee; Bures, Mark Gregory; Kihara, Daisuke

2016-01-15

Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

Science.gov (United States)

DeGrandchamp, Joseph B.; Whisenant, Jennifer G.; Arlinghaus, Lori R.; Abramson, V. G.; Yankeelov, Thomas E.; Cárdenas-Rodríguez, Julio

2016-03-01

The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.

Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

OpenAIRE

Shan, Min

2017-01-01

With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

Classication of Status of the Region on Java Island using C4.5, CHAID, and CART Methods

Science.gov (United States)

Syaraswati, R. A.; Slamet, I.; Winarno, B.

2017-06-01

The indicator of region economic success can be measured by economic growth, presented by value of Gross Regional Domestic Product (GRDP). Java island has the biggest GDP contribution toward the Indonesian government, but not all of the region gives equality contribution. The C4.5, CHAID, and CART methods can be used for classifying the status of the region with nonparametric approach. The C4.5 and CHAID methods are non-binary decision tree, meanwhile the CART methods is binary decision tree. The purposes of this paper are to know how the classification and to determine the factors that influence on classification of the region. The dependent variable is status of the region which is divided into four categories based on Klassen typology. The result shows factors that have the biggest contribution on classification of status of the region on Java island based on C4.5 method are economic growth rate, electricity, gas, and water sector, and area. The factors that have the biggest contribution based on CHAID method are growth rate, manufacturing sector, and electricity, gas, and water sector, while based on CART method are growth rate, manufacturing sector, and electricity, gas, and water sector.

Analysis of Margin Index as a Method for Predicting Residual Disease After Breast-Conserving Surgery in a European Cancer Center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2011-06-03

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) × 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

Analysis of margin index as a method for predicting residual disease after breast-conserving surgery in a European cancer center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2012-02-01

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) x 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

[Predictive methods versus clinical titration for the initiation of lithium therapy. A systematic review].

Science.gov (United States)

Geeraerts, I; Sienaert, P

2013-01-01

When lithium is administered, the clinician needs to know when the lithium in the patient’s blood has reached a therapeutic level. At the initiation of treatment the level is usually achieved gradually through the application of the titration method. In order to increase the efficacy of this procedure several methods for dosing lithium and for predicting lithium levels have been developed. To conduct a systematic review of the publications relating to the various methods for dosing lithium or predicting lithium levels at the initiation of therapy. We searched Medline systematically for articles published in English, French or Dutch between 1966 and April 2012 which described or studied a method for dosing lithium or for predicting the lithium level reached following a specific dosage. We screened the reference lists of relevant articles in order to locate additional papers. We found 38 lithium prediction methods, in addition to the clinical titration method. These methods can be divided into two categories: the ‘a priori’ methods and the ‘test-dose’ methods, the latter requiring the administration of a test dose of lithium. The lithium prediction methods generally achieve a therapeutic blood level faster than the clinical titration method, but none of the methods achieves convincing results. On the basis of our review, we propose that the titration method should be used as the standard method in clinical practice.

Behavior, Expectations and Status

Science.gov (United States)

Webster, Jr, Murray; Rashotte, Lisa Slattery

2010-01-01

We predict effects of behavior patterns and status on performance expectations and group inequality using an integrated theory developed by Fisek, Berger and Norman (1991). We next test those predictions using new experimental techniques we developed to control behavior patterns as independent variables. In a 10-condition experiment, predictions…

Predicting metabolic syndrome using decision tree and support vector machine methods.

Science.gov (United States)

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

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Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Water hammer prediction and control: the Green's function method

Science.gov (United States)

Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi

2012-04-01

By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.

Prediction of 90 Day and Overall Survival after Chemoradiotherapy for Lung Cancer: Role of Performance Status and Body Composition.

Science.gov (United States)

Bowden, J C S; Williams, L J; Simms, A; Price, A; Campbell, S; Fallon, M T; Fearon, K C H

2017-09-01

If appropriate patients are to be selected for lung cancer treatment, an understanding of who is most at risk of adverse outcomes after treatment is needed. The aim of the present study was to identify predictive factors for 30 and 90 day mortality after chemoradiotherapy (CRT), and factors that were prognostic for overall survival. A retrospective cohort study of 194 patients with lung cancer who had undergone CRT in South East Scotland from 2008 to 2010 was undertaken. Gender, age, cancer characteristics, weight loss, body mass index (BMI), performance status (Eastern Cooperative Oncology Group; ECOG) and computed tomography-derived body composition variables were examined for prognostic significance using Cox's proportional hazards model and logistic regression. The median overall survival was 19 months (95% confidence interval 16.3, 21.7). Four of 194 patients died within 30 days of treatment completion, for which there were no independent predictive variables; 22/194 (11%) died within 90 days of treatment completion. BMI < 20 and ECOG performance status ≥2 were independent predictors of death within 90 days of treatment completion (P = 0.001 and P = 0.004, respectively). Patients with either BMI < 20 or ECOG performance status ≥ 2 had an odds ratio of death within 90 days of 5.97 (95% confidence interval 2.20, 16.19), rising to an odds ratio of 13.27 (1.70, 103.47) for patients with both BMI < 20 and ECOG performance status ≥ 2. Patients with low muscle attenuation had significantly reduced overall survival (P = 0.004); individuals with low muscle attenuation had a median survival of 15.2 months (95% confidence interval 12.7, 17.7) compared with 23.0 months (95% confidence interval 18.3, 27.8) for those with high muscle attenuation, equating to a hazard ratio of death of 1.62 (95% confidence interval 1.17, 2.23, P = 0.003). Poor performance status, low BMI and low muscle attenuation identify patients at increased risk of premature death after

Socioeconomic Status and Race Outperform Concussion History and Sport Participation in Predicting Collegiate Athlete Baseline Neurocognitive Scores.

Science.gov (United States)

Houck, Zac; Asken, Breton; Clugston, James; Perlstein, William; Bauer, Russell

2018-01-01

The purpose of this study was to assess the contribution of socioeconomic status (SES) and other multivariate predictors to baseline neurocognitive functioning in collegiate athletes. Data were obtained from the Concussion Assessment, Research and Education (CARE) Consortium. Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) baseline assessments for 403 University of Florida student-athletes (202 males; age range: 18-23) from the 2014-2015 and 2015-2016 seasons were analyzed. ImPACT composite scores were consolidated into one memory and one speed composite score. Hierarchical linear regressions were used for analyses. In the overall sample, history of learning disability (β=-0.164; p=.001) and attention deficit-hyperactivity disorder (β=-0.102; p=.038) significantly predicted worse memory and speed performance, respectively. Older age predicted better speed performance (β=.176; pAmerican race predicted worse memory (β=-0.113; p=.026) and speed performance (β=-.242; pfootball players, higher maternal SES predicted better memory performance (β=0.308; p=.007); older age predicted better speed performance (β=0.346; p=.001); while Black/African American race predicted worse speed performance (β=-0.397; phistory of neurodevelopmental disorder, age, and race. In football players, specifically, maternal SES independently predicted baseline memory scores, but concussion history and years exposed to sport were not predictive. SES, race, and medical history beyond exposure to brain injury or subclinical brain trauma are important factors when interpreting variability in cognitive scores among collegiate athletes. Additionally, sport-specific differences in the proportional representation of various demographic variables (e.g., SES and race) may also be an important consideration within the broader biopsychosocial attributional model. (JINS, 2018, 24, 1-10).

Chemical shift effect predicting lymph node status in rectal cancer using high-resolution MR imaging with node-for-node matched histopathological validation

Energy Technology Data Exchange (ETDEWEB)

Zhang, Hongmei; Zhang, Chongda; Ye, Feng; Liu, Yuan; Zhou, Chunwu [Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, ChaoYang District, Beijing (China); Zheng, Zhaoxu [Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Colorectal Oncology, National Cancer Center/Cancer Hospital, ChaoYang District, Beijing (China); Zou, Shuangmei [Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Pathology, National Cancer Center/Cancer Hospital, ChaoYang District, Beijing (China)

2017-09-15

To evaluate the value of the chemical shift effect (CSE) as well as other criteria for the prediction of lymph node status. Twenty-nine patients who underwent radical surgery of rectal cancers were studied with pre- and postoperative specimen MRI. Lymph nodes were harvested from transverse whole-mount specimens and compared with in vivo and ex vivo images to obtain a precise slice-for-section match. Preoperative MR characteristics including CSE, as well as other predictors, were evaluated by two readers independently between benign and metastatic nodes. A total of 255 benign and 35 metastatic nodes were obtained; 71.4% and 69.4% of benign nodes were detected with regular CSE for two readers, whereas 80.0% and 74.3% of metastatic nodes with absence of CSE. The CSE rendered areas under the ROC curve (AUC) of 0.879 and 0.845 for predicting nodal status for two readers. The criteria of nodal location, border, signal intensity and minimum distance to the rectal wall were also useful but with AUCs (0.629-0.743) lower than those of CSE. CSE is a reliable predictor for differentiating benign from metastatic nodes. Additional criteria should be taken into account when it is difficult to determine the nodal status by using only a single predictor. (orig.)

A data-driven prediction method for fast-slow systems

Science.gov (United States)

Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

2016-04-01

In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

Development of laboratory acceleration test method for service life prediction of concrete structures

International Nuclear Information System (INIS)

Cho, M. S.; Song, Y. C.; Bang, K. S.; Lee, J. S.; Kim, D. K.

1999-01-01

Service life prediction of nuclear power plants depends on the application of history of structures, field inspection and test, the development of laboratory acceleration tests, their analysis method and predictive model. In this study, laboratory acceleration test method for service life prediction of concrete structures and application of experimental test results are introduced. This study is concerned with environmental condition of concrete structures and is to develop the acceleration test method for durability factors of concrete structures e.g. carbonation, sulfate attack, freeze-thaw cycles and shrinkage-expansion etc

Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Directory of Open Access Journals (Sweden)

Rohit Babbar

2018-03-01

Full Text Available IntroductionImpaired glucose tolerance (IGT is diagnosed by a standardized oral glucose tolerance test (OGTT. However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.MethodsGiven a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA, proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year.ResultsThe most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78. For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.ConclusionMachine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a

Link prediction based on nonequilibrium cooperation effect

Science.gov (United States)

Li, Lanxi; Zhu, Xuzhen; Tian, Hui

2018-04-01

Link prediction in complex networks has become a common focus of many researchers. But most existing methods concentrate on neighbors, and rarely consider degree heterogeneity of two endpoints. Node degree represents the importance or status of endpoints. We describe the large-degree heterogeneity as the nonequilibrium between nodes. This nonequilibrium facilitates a stable cooperation between endpoints, so that two endpoints with large-degree heterogeneity tend to connect stably. We name such a phenomenon as the nonequilibrium cooperation effect. Therefore, this paper proposes a link prediction method based on the nonequilibrium cooperation effect to improve accuracy. Theoretical analysis will be processed in advance, and at the end, experiments will be performed in 12 real-world networks to compare the mainstream methods with our indices in the network through numerical analysis.

Analysis of deep learning methods for blind protein contact prediction in CASP12.

Science.gov (United States)

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2018-03-01

Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.; Motwalli, Olaa Amin; Oliva, Romina; Jankovic, Boris R.; Medvedeva, Yulia; Ashoor, Haitham; Essack, Magbubah; Gao, Xin; Bajic, Vladimir B.

2018-01-01

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

A novel method for improved accuracy of transcription factor binding site prediction

KAUST Repository

Khamis, Abdullah M.

2018-03-20

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

Science.gov (United States)

Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

2016-01-01

Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

Maternal education and intelligence predict offspring diet and nutritional status.

Science.gov (United States)

Wachs, Theodore D; Creed-Kanashiro, Hilary; Cueto, Santiago; Jacoby, Enrique

2005-09-01

The traditional assumption that children's nutritional deficiencies are essentially due either to overall food scarcity or to a lack of family resources to purchase available food has been increasingly questioned. Parental characteristics represent 1 type of noneconomic factor that may be related to variability in children's diets and nutritional status. We report evidence on the relation of 2 parental characteristics, maternal education level and maternal intelligence, to infant and toddler diet and nutritional status. Our sample consisted of 241 low-income Peruvian mothers and their infants assessed from 3 to 12 mo, with a further follow-up of 104 of these infants at 18 mo of age. Using a nonexperimental design, we related measures of level of maternal education, maternal intelligence, and family socioeconomic status to infant anthropometry, duration of exclusive breast-feeding, adequacy of dietary intake, and iron status. Results indicated unique positive relations between maternal education level and the extent of exclusive breast-feeding. Significant relations between maternal education and offspring length were partially mediated by maternal height. There also were unique positive relations between maternal intelligence and quality of offspring diet and hemoglobin level. All findings remained significant even after controlling for family socioeconomic characteristics. This pattern of results illustrates the importance of parental characteristics in structuring the adequacy of offspring diet. Maternal education and intelligence appear to have unique influences upon different aspects of the diet and nutritional status of offspring.

Poor nutritional status of older subacute patients predicts clinical outcomes and mortality at 18 months of follow-up.

Science.gov (United States)

Charlton, K; Nichols, C; Bowden, S; Milosavljevic, M; Lambert, K; Barone, L; Mason, M; Batterham, M

2012-11-01

Older malnourished patients experience increased surgical complications and greater morbidity compared with their well-nourished counterparts. This study aimed to assess whether nutritional status at hospital admission predicted clinical outcomes at 18 months follow-up. A retrospective analysis of N=2076 patient admissions (65+ years) from two subacute hospitals, New South Wales, Australia. Analysis of outcomes at 18 months, according to nutritional status at index admission, was performed in a subsample of n = 476. Nutritional status was determined within 72 h of admission using the Mini Nutritional Assessment (MNA). Outcomes, obtained from electronic patient records, included hospital readmission rate, total Length of Stay (LOS), change in level of care at discharge and mortality. Survival analysis, using a Cox proportional hazards model, included age, sex, Major Disease Classification, mobility and LOS at index admission as covariates. At baseline, 30% of patients were malnourished and 53% were at risk of malnutrition. LOS was higher in malnourished and at risk, compared with well-nourished patients (median (interquartile range): 34 (21, 58); 26 (15, 41); 20 (14, 26) days, respectively; Pclinical outcomes and identifies a need to target this population for nutritional intervention following hospital discharge.

Psoriasis prediction from genome-wide SNP profiles

Directory of Open Access Journals (Sweden)

Fang Xiangzhong

2011-01-01

Full Text Available Abstract Background With the availability of large-scale genome-wide association study (GWAS data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs to predict psoriasis from searching GWAS data. Methods Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB method was compared with classical linear discriminant analysis(LDA for classification performance. Results The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698, while only 0.520(95% CI: 0.472-0.524 was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study. Conclusions The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.

Predictors of burnout and health status in Samaritans' listening volunteers.

Science.gov (United States)

Roche, Adeline; Ogden, Jane

2017-12-01

Samaritan listening volunteers provide emotional support to people in distress or suicidal. Samaritans' has high volunteer turnover, which may be due to burnout. This study evaluated the role of demographic and psychosocial factors in predicting Samaritans listening volunteers' burnout and health status. Samaritans' listening volunteers (n = 216) from seven branches across UK completed an online survey to assess their levels of burnout (emotional exhaustion, depersonalisation, personal accomplishment), subjective health status, coping, empathy and social support. Overall, listeners showed low levels of burnout and good health. Regression analysis revealed that higher emotional exhaustion was predicted by younger age and avoidant coping style; higher depersonalisation was predicted by lower empathy fantasy and higher avoidant coping style; lower personal accomplishment scores were predicted by higher empathy personal distress and worse health status was predicted by more hours per week spent on listening duties, lower social support and higher avoidant coping style. Overall, different factors influenced different facets of burnout. However, higher use of avoidant coping style consistently predicted higher burnout and worse health status, suggesting avoidant coping is an important target for intervention.

Some new results on correlation-preserving factor scores prediction methods

NARCIS (Netherlands)

Ten Berge, J.M.F.; Krijnen, W.P.; Wansbeek, T.J.; Shapiro, A.

1999-01-01

Anderson and Rubin and McDonald have proposed a correlation-preserving method of factor scores prediction which minimizes the trace of a residual covariance matrix for variables. Green has proposed a correlation-preserving method which minimizes the trace of a residual covariance matrix for factors.

New imaging characteristics for predicting postoperative neurologic status in patients with metastatic epidural spinal cord compression. A retrospective analysis of 81 cases.

Science.gov (United States)

Lei, Mingxing; Liu, Shubin; Yang, Shaoxing; Liu, Yaosheng; Wang, Cheng; Gao, Hongjun

2017-06-01

Several clinical features have been proposed for the prediction of postoperative functional outcome in patients with metastatic epidural spinal cord compression (MESCC). However, few articles address the relationship between preoperative imaging characteristics and the postoperative neurologic status. This study aims to analyze the postoperative functional outcome and to identify new imaging parameters for predicting postoperative neurologic status in patients with MESCC. This study is a retrospective consecutive case series of patients with MESCC who were treated surgically. We assessed 81 consecutive patients who were treated with decompressive surgery for MESCC between 2013 and 2015. Eight imaging characteristics were analyzed for postoperative motor status by logistic regression models. Neurologic function was assessed using the Frankel grade preoperatively and postoperatively. The following imaging characteristics were assessed for postoperative motor status: location of lesions in the spine, lamina involvement, retropulsion of the posterior wall, number of vertebrae involved, pedicle involvement, fracture of any involved vertebrae, T2 signal of the spinal cord at the compression site, and circumferential angle of spinal cord compression (CASCC). The postoperative neurologic outcome was better than the preoperative neurologic status (p<.01). In the entire group, 40.7% of the patients were non-ambulatory before the surgical procedure, whereas 77.8% of the patients could walk after surgery (p=.01). In the multivariate analysis, the location of the lesions (odds ratio [OR]: 3.89, 95% confidence interval [CI]: 1.19-12.77, p=.02) and CASCC (OR: 2.31, 95% CI: 1.44-3.71, p<.01) were significantly associated with postoperative neurologic outcome. A CASCC of more than 180° was associated with an increased OR that approached significance, and the larger the CASCC, the higher the risk of poor postoperative neurologic status. The postoperative neurologic status was

Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

Science.gov (United States)

Pôças, Isabel; Gonçalves, João; Costa, Patrícia Malva; Gonçalves, Igor; Pereira, Luís S.; Cunha, Mario

2017-06-01

In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support

Improved time series prediction with a new method for selection of model parameters

International Nuclear Information System (INIS)

Jade, A M; Jayaraman, V K; Kulkarni, B D

2006-01-01

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

Assessment of NASA and RAE viscous-inviscid interaction methods for predicting transonic flow over nozzle afterbodies

Science.gov (United States)

Putnam, L. E.; Hodges, J.

1983-01-01

The Langley Research Center of the National Aeronautics and Space Administration and the Royal Aircraft Establishment have undertaken a cooperative program to conduct an assessment of their patched viscous-inviscid interaction methods for predicting the transonic flow over nozzle afterbodies. The assessment was made by comparing the predictions of the two methods with experimental pressure distributions and boattail pressure drag for several convergent circular-arc nozzle configurations. Comparisons of the predictions of the two methods with the experimental data showed that both methods provided good predictions of the flow characteristics of nozzles with attached boundary layer flow. The RAE method also provided reasonable predictions of the pressure distributions and drag for the nozzles investigated that had separated boundary layers. The NASA method provided good predictions of the pressure distribution on separated flow nozzles that had relatively thin boundary layers. However, the NASA method was in poor agreement with experiment for separated nozzles with thick boundary layers due primarily to deficiencies in the method used to predict the separation location.

PREDICTIVE ANALYSIS SOFTWARE FOR MODELING THE ALTMAN Z-SCORE FINANCIAL DISTRESS STATUS OF COMPANIES

Directory of Open Access Journals (Sweden)

ILIE RĂSCOLEAN

2012-10-01

Full Text Available Literature shows some bankruptcy methods for determining the financial distress status of companies and based on this information we chosen Altman statistical model because it has been used a lot in the past and like that it has become a benchmark for other methods. Based on this financial analysis flowchart, programming software was developed that allows the calculation and determination of the bankruptcy probability for a certain rate of failure Z-score, corresponding to a given interval that is equal to the ratio of the number of bankrupt companies and the total number of companies (bankrupt and healthy interval.

A FISH-based method for assessment of HER-2 amplification status in breast cancer circulating tumor cells following CellSearch isolation

Directory of Open Access Journals (Sweden)

Frithiof H

2016-11-01

Full Text Available Henrik Frithiof,1 Kristina Aaltonen,1 Lisa Rydén2,3 1Division of Oncology and Pathology, 2Division of Surgery, Department of Clinical Sciences Lund, Lund University, Lund, 3Department of Surgery, Skåne University Hospital, Malmö, Sweden Introduction: Amplification of the HER-2/neu (HER-2 proto-oncogene occurs in 10%–15% of primary breast cancer, leading to an activated HER-2 receptor, augmenting growth of cancer cells. Tumor classification is determined in primary tumor tissue and metastatic biopsies. However, malignant cells tend to alter their phenotype during disease progression. Circulating tumor cell (CTC analysis may serve as an alternative to repeated biopsies. The Food and Drug Administration-approved CellSearch system allows determination of the HER-2 protein, but not of the HER-2 gene. The aim of this study was to optimize a fluorescence in situ hybridization (FISH-based method to quantitatively determine HER-2 amplification in breast cancer CTCs following CellSearch-based isolation and verify the method in patient samples. Methods: Using healthy donor blood spiked with human epidermal growth factor receptor 2 (HER-2-positive breast cancer cell lines, SKBr-3 and BT-474, and a corresponding negative control (the HER-2-negative MCF-7 cell line, an in vitro CTC model system was designed. Following isolation in the CellSearch system, CTC samples were further enriched and fixed on microscope slides. Immunocytochemical staining with cytokeratin and 4',6-diamidino-2'-phenylindole dihydrochloride identified CTCs under a fluorescence microscope. A FISH-based procedure was optimized by applying the HER2 IQFISH pharmDx assay for assessment of HER-2 amplification status in breast cancer CTCs. Results: A method for defining the presence of HER-2 amplification in single breast cancer CTCs after CellSearch isolation was established using cell lines as positive and negative controls. The method was validated in blood from breast cancer patients

Development of incident progress prediction technologies for nuclear emergency preparedness. Current status and future subjects

International Nuclear Information System (INIS)

Yoshida, Yoshitaka; Yamamoto, Yasunori; Kusunoki, Takayoshi; Kawasaki, Ikuo; Yanagi, Chihiro; Kinoshita, Ikuo; Iwasaki, Yoshito

2014-01-01

Nuclear licensees are required to maintain a prediction system during normal condition for using a nuclear emergency by the Basic Plan for Disaster Prevention of government. With prediction of the incident progress, if the present condition of nuclear power plant is understood appropriately and it grows more serious with keeping the present situation, it is in predicting what kind of situation will be occurred in the near future, choosing the effective countermeasures against the coming threat, and understanding the time available of intervention time. Following the accident on September 30 1999 in the nuclear fuel fabrication facility in Tokai Village of Ibaraki Prefecture, the Institute of Nuclear Safety System started development of incident progress prediction technologies for nuclear emergency preparedness. We have performed technical applications and made improvements in nuclear emergency exercises and verified the developed systems using the observed values of the Fukushima Daiichi Nuclear Power Plant accident. As a result, our developed Incident Progress Prediction System was applied to nuclear emergency exercises and we accumulated knowledge and experience by which we improved the system to make predictions more rapidly and more precisely, including for example, the development of a prediction method for leak size of reactor coolant. On the other hand, if a rapidly progressing incident occurs, since end users need simple and quick predictions about the public's protection and evacuation areas, we developed the Radioactive Materials Release, Radiation Dose and Radiological Protection Area Prediction System which changed solving an inverse problem into a forward problem solution. In view of the water-level-decline incident of the spent fuel storage facility at the Fukushima Daiichi Nuclear Power Plant, the spent fuel storage facility water level and the water temperature evaluation tool were improved. Such incident progress prediction technologies were

Hybrid robust predictive optimization method of power system dispatch

Science.gov (United States)

Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

2011-08-02

A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

Orthology prediction methods: a quality assessment using curated protein families.

Science.gov (United States)

Trachana, Kalliopi; Larsson, Tomas A; Powell, Sean; Chen, Wei-Hua; Doerks, Tobias; Muller, Jean; Bork, Peer

2011-10-01

The increasing number of sequenced genomes has prompted the development of several automated orthology prediction methods. Tests to evaluate the accuracy of predictions and to explore biases caused by biological and technical factors are therefore required. We used 70 manually curated families to analyze the performance of five public methods in Metazoa. We analyzed the strengths and weaknesses of the methods and quantified the impact of biological and technical challenges. From the latter part of the analysis, genome annotation emerged as the largest single influencer, affecting up to 30% of the performance. Generally, most methods did well in assigning orthologous group but they failed to assign the exact number of genes for half of the groups. The publicly available benchmark set (http://eggnog.embl.de/orthobench/) should facilitate the improvement of current orthology assignment protocols, which is of utmost importance for many fields of biology and should be tackled by a broad scientific community. Copyright © 2011 WILEY Periodicals, Inc.

Prediction of intestinal absorption and blood-brain barrier penetration by computational methods.

Science.gov (United States)

Clark, D E

2001-09-01

This review surveys the computational methods that have been developed with the aim of identifying drug candidates likely to fail later on the road to market. The specifications for such computational methods are outlined, including factors such as speed, interpretability, robustness and accuracy. Then, computational filters aimed at predicting "drug-likeness" in a general sense are discussed before methods for the prediction of more specific properties--intestinal absorption and blood-brain barrier penetration--are reviewed. Directions for future research are discussed and, in concluding, the impact of these methods on the drug discovery process, both now and in the future, is briefly considered.

Methods, apparatus and system for notification of predictable memory failure

Energy Technology Data Exchange (ETDEWEB)

Cher, Chen-Yong; Andrade Costa, Carlos H.; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

2017-01-03

A method for providing notification of a predictable memory failure includes the steps of: obtaining information regarding at least one condition associated with a memory; calculating a memory failure probability as a function of the obtained information; calculating a failure probability threshold; and generating a signal when the memory failure probability exceeds the failure probability threshold, the signal being indicative of a predicted future memory failure.

Self-esteem and peer-perceived social status in early adolescence and prediction of eating pathology in young adulthood.

Science.gov (United States)

Smink, Frédérique R E; van Hoeken, Daphne; Dijkstra, Jan Kornelis; Deen, Mathijs; Oldehinkel, Albertine J; Hoek, Hans W

2018-04-27

Self-esteem is implied as a factor in the development of eating disorders. In adolescence peers have an increasing influence. Support for the role of self-esteem in eating disorders is ambiguous and little is known about the influence of social status as judged by others. The present study investigates whether self-esteem and peer status in early adolescence are associated with eating pathology in young adulthood. This study is part of TRAILS, a longitudinal cohort study on mental health and social development from preadolescence into adulthood. At age 11, participants completed the Self-Perception Profile for Children, assessing global self-esteem and self-perceptions regarding social acceptance, physical appearance, and academic competence. At age 13, peer status among classmates was assessed regarding likeability, physical attractiveness, academic performance, and popularity in a subsample of 1,007 participants. The Eating Disorder Diagnostic Scale was administered at age 22. The present study included peer-nominated participants with completed measures of self-perception at age 11 and eating pathology at age 22 (N = 732; 57.8% female). In a combined model, self-perceived physical attractiveness at age 11 and peer popularity at age 13 were inversely correlated with eating pathology at 22 years, while likeability by peers at age 13 was positively related to eating pathology. Both self-perceptions and peer status in early adolescence are significant predictors of eating pathology in young adults. Specific measures of self-esteem and peer-perceived status may be more relevant to the prediction of eating pathology than a global measure of self-esteem. © 2018 The Authors International Journal of Eating Disorders Published by Wiley Periodicals, Inc.

A prediction method based on grey system theory in equipment condition based maintenance

International Nuclear Information System (INIS)

Yan, Shengyuan; Yan, Shengyuan; Zhang, Hongguo; Zhang, Zhijian; Peng, Minjun; Yang, Ming

2007-01-01

Grey prediction is a modeling method based on historical or present, known or indefinite information, which can be used for forecasting the development of the eigenvalues of the targeted equipment system and setting up the model by using less information. In this paper, the postulate of grey system theory, which includes the grey generating, the sorts of grey generating and the grey forecasting model, is introduced first. The concrete application process, which includes the grey prediction modeling, grey prediction, error calculation, equal dimension and new information approach, is introduced secondly. Application of a so-called 'Equal Dimension and New Information' (EDNI) technology in grey system theory is adopted in an application case, aiming at improving the accuracy of prediction without increasing the amount of calculation by replacing old data with new ones. The proposed method can provide a new way for solving the problem of eigenvalue data exploding in equal distance effectively, short time interval and real time prediction. The proposed method, which was based on historical or present, known or indefinite information, was verified by the vibration prediction of induced draft fan of a boiler of the Yantai Power Station in China, and the results show that the proposed method based on grey system theory is simple and provides a high accuracy in prediction. So, it is very useful and significant to the controlling and controllable management in safety production. (authors)

Reliable B cell epitope predictions: impacts of method development and improved benchmarking

DEFF Research Database (Denmark)

Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole

2012-01-01

biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping...... evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version...

Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

Science.gov (United States)

Moura, Antonio Divino; Hastenrath, Stefan

2004-07-01

Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

Prediction of critical heat flux in fuel assemblies using a CHF table method

Energy Technology Data Exchange (ETDEWEB)

Chun, Tae Hyun; Hwang, Dae Hyun; Bang, Je Geon [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of); Baek, Won Pil; Chang, Soon Heung [Korea Advance Institute of Science and Technology, Taejon (Korea, Republic of)

1998-12-31

A CHF table method has been assessed in this study for rod bundle CHF predictions. At the conceptual design stage for a new reactor, a general critical heat flux (CHF) prediction method with a wide applicable range and reasonable accuracy is essential to the thermal-hydraulic design and safety analysis. In many aspects, a CHF table method (i.e., the use of a round tube CHF table with appropriate bundle correction factors) can be a promising way to fulfill this need. So the assessment of the CHF table method has been performed with the bundle CHF data relevant to pressurized water reactors (PWRs). For comparison purposes, W-3R and EPRI-1 were also applied to the same data base. Data analysis has been conducted with the subchannel code COBRA-IV-I. The CHF table method shows the best predictions based on the direct substitution method. Improvements of the bundle correction factors, especially for the spacer grid and cold wall effects, are desirable for better predictions. Though the present assessment is somewhat limited in both fuel geometries and operating conditions, the CHF table method clearly shows potential to be a general CHF predictor. 8 refs., 3 figs., 3 tabs. (Author)

Prediction of critical heat flux in fuel assemblies using a CHF table method

Energy Technology Data Exchange (ETDEWEB)

Chun, Tae Hyun; Hwang, Dae Hyun; Bang, Je Geon [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of); Baek, Won Pil; Chang, Soon Heung [Korea Advance Institute of Science and Technology, Taejon (Korea, Republic of)

1997-12-31

A CHF table method has been assessed in this study for rod bundle CHF predictions. At the conceptual design stage for a new reactor, a general critical heat flux (CHF) prediction method with a wide applicable range and reasonable accuracy is essential to the thermal-hydraulic design and safety analysis. In many aspects, a CHF table method (i.e., the use of a round tube CHF table with appropriate bundle correction factors) can be a promising way to fulfill this need. So the assessment of the CHF table method has been performed with the bundle CHF data relevant to pressurized water reactors (PWRs). For comparison purposes, W-3R and EPRI-1 were also applied to the same data base. Data analysis has been conducted with the subchannel code COBRA-IV-I. The CHF table method shows the best predictions based on the direct substitution method. Improvements of the bundle correction factors, especially for the spacer grid and cold wall effects, are desirable for better predictions. Though the present assessment is somewhat limited in both fuel geometries and operating conditions, the CHF table method clearly shows potential to be a general CHF predictor. 8 refs., 3 figs., 3 tabs. (Author)

Time dependent patient no-show predictive modelling development.

Science.gov (United States)

Huang, Yu-Li; Hanauer, David A

2016-05-09

Purpose - The purpose of this paper is to develop evident-based predictive no-show models considering patients' each past appointment status, a time-dependent component, as an independent predictor to improve predictability. Design/methodology/approach - A ten-year retrospective data set was extracted from a pediatric clinic. It consisted of 7,291 distinct patients who had at least two visits along with their appointment characteristics, patient demographics, and insurance information. Logistic regression was adopted to develop no-show models using two-thirds of the data for training and the remaining data for validation. The no-show threshold was then determined based on minimizing the misclassification of show/no-show assignments. There were a total of 26 predictive model developed based on the number of available past appointments. Simulation was employed to test the effective of each model on costs of patient wait time, physician idle time, and overtime. Findings - The results demonstrated the misclassification rate and the area under the curve of the receiver operating characteristic gradually improved as more appointment history was included until around the 20th predictive model. The overbooking method with no-show predictive models suggested incorporating up to the 16th model and outperformed other overbooking methods by as much as 9.4 per cent in the cost per patient while allowing two additional patients in a clinic day. Research limitations/implications - The challenge now is to actually implement the no-show predictive model systematically to further demonstrate its robustness and simplicity in various scheduling systems. Originality/value - This paper provides examples of how to build the no-show predictive models with time-dependent components to improve the overbooking policy. Accurately identifying scheduled patients' show/no-show status allows clinics to proactively schedule patients to reduce the negative impact of patient no-shows.

Discriminative and predictive properties of disease-specific and generic health status indexes in elderly COPD patients

Directory of Open Access Journals (Sweden)

Forastiere Francesco

2008-08-01

Full Text Available Abstract Background The association between bronchial obstruction severity and mortality in Chronic Obstructive Pulmonary Disease (COPD is well established, but it is unknown whether disease-specific health status measures and multidimensional assessment (MDA have comparable prognostic value. Methods We analyzed data coming from the Salute Respiratoria nell'Anziano (Respiratory Health in the Elderly – SaRA study, enrolling elderly people attending outpatient clinics for respiratory and non-respiratory problems. From this population we selected 449 patients with bronchial obstruction (77.3% men, mean age 73.1. We classified patients' health status using tertiles of the Saint George Respiratory Questionnaire (SGRQ and a MDA including functional (the 6' walking test, WT, cognitive (Mini-Mental State Examination, MMSE and affective status (Geriatric Depression Scale, GDS. The agreement of the classification methods was calculated using the kappa statistic, and survival associated with group membership was evaluated using survival analysis. Results Pulmonary function, expressed by the FEV1, worsened with increasing SGRQ or MDA scores. Cognitive function was not associated with the SGRQ, while physical performance and mood status were impaired only in the highest tertile of SGRQ. A poor agreement was found between the two classification systems tested (k = 0.194. Compared to people in the first tertile of SGRQ score, those in the second tertile had a sex-adjusted HR of 1.22 (0.75 – 1.98 and those in the third tertile of 2.90 (1.92 – 4.40. The corresponding figures of the MDA were 1.49 (95% CI 1.02 – 2.18 and 2.01 (95% CI: 1.31 – 3.08. After adjustment for severity of obstruction, only a SGRQ in the upper tertile was associated with mortality (HR: 1.86; 95% CI: 1.14 – 3.02. Conclusion In elderly outpatients with mild-moderate COPD, a disease-specific health status index seems to be a better predictor of death compared to a MDA.

Genomic prediction based on data from three layer lines: a comparison between linear methods

NARCIS (Netherlands)

Calus, M.P.L.; Huang, H.; Vereijken, J.; Visscher, J.; Napel, ten J.; Windig, J.J.

2014-01-01

Background The prediction accuracy of several linear genomic prediction models, which have previously been used for within-line genomic prediction, was evaluated for multi-line genomic prediction. Methods Compared to a conventional BLUP (best linear unbiased prediction) model using pedigree data, we

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Science.gov (United States)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Method for Predicting Solubilities of Solids in Mixed Solvents

DEFF Research Database (Denmark)

Ellegaard, Martin Dela; Abildskov, Jens; O'Connell, J. P.

2009-01-01

A method is presented for predicting solubilities of solid solutes in mixed solvents, based on excess Henry's law constants. The basis is statistical mechanical fluctuation solution theory for composition derivatives of solute/solvent infinite dilution activity coefficients. Suitable approximatio...

Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

Science.gov (United States)

Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

2017-01-01

Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

Predicting Handwriting Difficulties through Spelling Processes

Science.gov (United States)

Rodríguez, Cristina; Villarroel, Rebeca

2017-01-01

This study examined whether spelling tasks contribute to the prediction of the handwriting status of children with poor and good handwriting skills in a cross-sectional study with 276 Spanish children from Grades 1 and 3. The main hypothesis was that the spelling tasks would predict the handwriting status of the children, although this influence…

Available Prediction Methods for Corrosion under Insulation (CUI): A Review

OpenAIRE

Burhani Nurul Rawaida Ain; Muhammad Masdi; Ismail Mokhtar Che

2014-01-01

Corrosion under insulation (CUI) is an increasingly important issue for the piping in industries especially petrochemical and chemical plants due to its unexpected catastrophic disaster. Therefore, attention towards the maintenance and prediction of CUI occurrence, particularly in the corrosion rates, has grown in recent years. In this study, a literature review in determining the corrosion rates by using various prediction models and method of the corrosion occurrence between the external su...

A Practical Radiosity Method for Predicting Transmission Loss in Urban Environments

Directory of Open Access Journals (Sweden)

Liang Ming

2004-01-01

Full Text Available The ability to predict transmission loss or field strength distribution is crucial for determining coverage in planning personal communication systems. This paper presents a practical method to accurately predict entire average transmission loss distribution in complicated urban environments. The method uses a 3D propagation model based on radiosity and a simplified city information database including surfaces of roads and building groups. Narrowband validation measurements with line-of-sight (LOS and non-line-of-sight (NLOS cases at 1800 MHz give excellent agreement in urban environments.

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

DEFF Research Database (Denmark)

Larsen, Mette Voldby; Lundegaard, Claus; Lamberth, K.

2007-01-01

BACKGROUND: Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein....... of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at http://www.cbs.dtu.dk/services/NetCTL.All used datasets are available at http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php....

Application of artificial intelligence methods for prediction of steel mechanical properties

Directory of Open Access Journals (Sweden)

Z. Jančíková

2008-10-01

Full Text Available The target of the contribution is to outline possibilities of applying artificial neural networks for the prediction of mechanical steel properties after heat treatment and to judge their perspective use in this field. The achieved models enable the prediction of final mechanical material properties on the basis of decisive parameters influencing these properties. By applying artificial intelligence methods in combination with mathematic-physical analysis methods it will be possible to create facilities for designing a system of the continuous rationalization of existing and also newly developing industrial technologies.

What effects performance status of patients with hepatocellular carcinoma: stage of tumor versus underlying liver status

International Nuclear Information System (INIS)

Sarwar, S.; Tarique, S.

2015-01-01

Objective: To identify variables associated with poor performance status of hepatocellular carcinoma (HCC) patients and to compare impact of stage of liver disease and that of hepatoma on functional status of patient. Patients and Methods: We included 254 confirmed cases of liver cancer in a crosssectional analytical study carried out at Doctors Hospital Lahore. Patient's clinical, biochemical and radiological variables were correlated with Karnofsky's performance status (KPS) using pearson correlation. Model for End stage Liver Disease (MELD) and Cancer of Liver Italian Program (CLIP) were evaluated for predicting performance status using Receiver Operating Characteristic (ROC) curve. Results: Mean age of patients was 56.69 (±10.34) and male to female ratio was 2.47: 1 (181/73). On KPS evaluation 84 (33.1%) patients scored between 80-100, 147 (57.9%) had score of 50-70 while in 23 (9.1%) KPS score was between 0-40. Variables associated with poor performance status were bilirubin> 3mg/dl (p value 0.00), albumin< 2.5 g/dl (p value 0.00), creatinine > 1.2mg/dl (p 0.00), prothrombin time> 16seconds (p value 0.00), size of tumor >7cm (p value 0.02), tumor involving > 50% of liver mass (p value 0.00) and vascular invasion (p value 0.00). Both stage of liver disease as determined by MELD and stage of liver cancer as per CLIP scores had strong correlation (p value 0.00) with poor performance status of patient. Area under ROC curve was 0.764 for MELD score and 0.785 for CLIP score. Conclusion: Performance status of liver cancer patients is affected by both stage of liver disease and that of liver tumor. Patients with MELD score above 16 and CLIP score above 4 have poor performance status. (author)

A comparison of methods for cascade prediction

OpenAIRE

Guo, Ruocheng; Shakarian, Paulo

2016-01-01

Information cascades exist in a wide variety of platforms on Internet. A very important real-world problem is to identify which information cascades can go viral. A system addressing this problem can be used in a variety of applications including public health, marketing and counter-terrorism. As a cascade can be considered as compound of the social network and the time series. However, in related literature where methods for solving the cascade prediction problem were proposed, the experimen...

Preface to the Focus Issue: Chaos Detection Methods and Predictability

International Nuclear Information System (INIS)

Gottwald, Georg A.; Skokos, Charalampos

2014-01-01

This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17–21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue

Preface to the Focus Issue: chaos detection methods and predictability.

Science.gov (United States)

Gottwald, Georg A; Skokos, Charalampos

2014-06-01

This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17-21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

Risk prediction, safety analysis and quantitative probability methods - a caveat

International Nuclear Information System (INIS)

Critchley, O.H.

1976-01-01

Views are expressed on the use of quantitative techniques for the determination of value judgements in nuclear safety assessments, hazard evaluation, and risk prediction. Caution is urged when attempts are made to quantify value judgements in the field of nuclear safety. Criteria are given the meaningful application of reliability methods but doubts are expressed about their application to safety analysis, risk prediction and design guidances for experimental or prototype plant. Doubts are also expressed about some concomitant methods of population dose evaluation. The complexities of new designs of nuclear power plants make the problem of safety assessment more difficult but some possible approaches are suggested as alternatives to the quantitative techniques criticized. (U.K.)

Prediction of pKa values using the PM6 semiempirical method

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Jimmy C. Kromann

2016-08-01

Full Text Available The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6, slightly worse (0.5–1.0, and worse (1.0–2.5, provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Method for estimating capacity and predicting remaining useful life of lithium-ion battery

International Nuclear Information System (INIS)

Hu, Chao; Jain, Gaurav; Tamirisa, Prabhakar; Gorka, Tom

2014-01-01

Highlights: • We develop an integrated method for the capacity estimation and RUL prediction. • A state projection scheme is derived for capacity estimation. • The Gauss–Hermite particle filter technique is used for the RUL prediction. • Results with 10 years’ continuous cycling data verify the effectiveness of the method. - Abstract: Reliability of lithium-ion (Li-ion) rechargeable batteries used in implantable medical devices has been recognized as of high importance from a broad range of stakeholders, including medical device manufacturers, regulatory agencies, physicians, and patients. To ensure Li-ion batteries in these devices operate reliably, it is important to be able to assess the capacity of Li-ion battery and predict the remaining useful life (RUL) throughout the whole life-time. This paper presents an integrated method for the capacity estimation and RUL prediction of Li-ion battery used in implantable medical devices. A state projection scheme from the author’s previous study is used for the capacity estimation. Then, based on the capacity estimates, the Gauss–Hermite particle filter technique is used to project the capacity fade to the end-of-service (EOS) value (or the failure limit) for the RUL prediction. Results of 10 years’ continuous cycling test on Li-ion prismatic cells in the lab suggest that the proposed method achieves good accuracy in the capacity estimation and captures the uncertainty in the RUL prediction. Post-explant weekly cycling data obtained from field cells with 4–7 implant years further verify the effectiveness of the proposed method in the capacity estimation

The Predictive Value of Cognitive Impairments Measured at the Start of Clinical Rehabilitation for Health Status 1 Year and 3 Years Poststroke

Science.gov (United States)

Verhoeven, Clara L.; Schepers, Vera P.; Post, Marcel W.; van Heugten, Caroline M.

2011-01-01

The objective of this study was to investigate the value of screening for cognitive functions at the start of an inpatient rehabilitation programme to predict the health status 1 and 3 years poststroke. In this longitudinal cohort study of stroke patients in inpatient rehabilitation data of 134 participants were analysed. Cognitive and clinical…

Accuracy assessment of the ERP prediction method based on analysis of 100-year ERP series

Science.gov (United States)

Malkin, Z.; Tissen, V. M.

2012-12-01

A new method has been developed at the Siberian Research Institute of Metrology (SNIIM) for highly accurate prediction of UT1 and Pole motion (PM). In this study, a detailed comparison was made of real-time UT1 predictions made in 2006-2011 and PMpredictions made in 2009-2011making use of the SNIIM method with simultaneous predictions computed at the International Earth Rotation and Reference Systems Service (IERS), USNO. Obtained results have shown that proposed method provides better accuracy at different prediction lengths.

Application of the backstepping method to the prediction of increase or decrease of infected population.

Science.gov (United States)

Kuniya, Toshikazu; Sano, Hideki

2016-05-10

In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors. Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps. Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error. Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.

Impacts of education level and employment status on healthrelated quality of life in multiple sclerosis patients

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Selma Šabanagić-Hajrić

2015-02-01

Full Text Available Aim To evaluate the impacts of education level and employment status on health-related quality of life (HRQoL in multiple sclerosis patients. Methods This study included 100 multiple sclerosis patients treated at the Department of Neurology, Clinical Center of the University of Sarajevo. Inclusion criteria were the Expanded Disability Status Scale (EDSS score between 1.0 and 6.5, age between 18 and 65 years, stable disease on enrollment. Quality of life (QoL was evaluated by the Multiple Sclerosis Quality of Life-54 questionnaire (MSQoL-54. Mann-Whitney and Kruskal-Wallis test were used for comparisons. Linear regression analyses were performed to evaluate prediction value of educational level and employment status in predicting MSQOL-54 physical and mental composite scores. Results Full employment status had positive impact on physical health (54.85 vs. 37.90; p<0.001 and mental health (59.55 vs. 45.90; p<0.001 composite scores. Employment status retained its independent predictability for both physical (r2=0.105 and mental (r2=0.076 composite scores in linear regression analysis. Patients with college degree had slightly higher median value of physical (49.36 vs. 45.30 and mental health composite score (66.74 vs. 55.62 comparing to others, without statistically significant difference. Conclusion Employment proved to be an important factor in predicting quality of life in multiple sclerosis patients. Higher education level may determine better QOL but without significant predictive value. Sustained employment and development of vocational rehabilitation programs for MS patients living in the country with high unemployment level is an important factor in improving both physical and mental health outcomes in MS patients.

Polyadenylation site prediction using PolyA-iEP method.

Science.gov (United States)

Kavakiotis, Ioannis; Tzanis, George; Vlahavas, Ioannis

2014-01-01

This chapter presents a method called PolyA-iEP that has been developed for the prediction of polyadenylation sites. More precisely, PolyA-iEP is a method that recognizes mRNA 3'ends which contain polyadenylation sites. It is a modular system which consists of two main components. The first exploits the advantages of emerging patterns and the second is a distance-based scoring method. The outputs of the two components are finally combined by a classifier. The final results reach very high scores of sensitivity and specificity.

Customer churn prediction using a hybrid method and censored data

Directory of Open Access Journals (Sweden)

Reza Tavakkoli-Moghaddam

2013-05-01

Full Text Available Customers are believed to be the main part of any organization’s assets and customer retention as well as customer churn management are important responsibilities of organizations. In today’s competitive environment, organization must do their best to retain their existing customers since attracting new customers cost significantly more than taking care of existing ones. In this paper, we present a hybrid method based on neural network and Cox regression analysis where neural network is used for outlier data and Cox regression method is implemented for prediction of future events. The proposed model of this paper has been implemented on some data and the results are compared based on five criteria including prediction accuracy, errors’ type I and II, root mean square error and mean absolute deviation. The preliminary results indicate that the proposed model of this paper performs better than alternative methods.

The local-ladder effect: social status and subjective well-being.

Science.gov (United States)

Anderson, Cameron; Kraus, Michael W; Galinsky, Adam D; Keltner, Dacher

2012-07-01

Dozens of studies in different nations have revealed that socioeconomic status only weakly predicts an individual's subjective well-being (SWB). These results imply that although the pursuit of social status is a fundamental human motivation, achieving high status has little impact on one's SWB. However, we propose that sociometric status-the respect and admiration one has in face-to-face groups (e.g., among friends or coworkers)-has a stronger effect on SWB than does socioeconomic status. Using correlational, experimental, and longitudinal methodologies, four studies found consistent evidence for a local-ladder effect: Sociometric status significantly predicted satisfaction with life and the experience of positive and negative emotions. Longitudinally, as sociometric status rose or fell, SWB rose or fell accordingly. Furthermore, these effects were driven by feelings of power and social acceptance. Overall, individuals' sociometric status matters more to their SWB than does their socioeconomic status.

Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

Science.gov (United States)

Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

2017-12-01

Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

Validity of a Manual Soft Tissue Profile Prediction Method Following Mandibular Setback Osteotomy

OpenAIRE

Kolokitha, Olga-Elpis

2007-01-01

Objectives The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. Methods To test the validity of the manu...

A GPS Satellite Clock Offset Prediction Method Based on Fitting Clock Offset Rates Data

Directory of Open Access Journals (Sweden)

WANG Fuhong

2016-12-01

Full Text Available It is proposed that a satellite atomic clock offset prediction method based on fitting and modeling clock offset rates data. This method builds quadratic model or linear model combined with periodic terms to fit the time series of clock offset rates, and computes the model coefficients of trend with the best estimation. The clock offset precisely estimated at the initial prediction epoch is directly adopted to calculate the model coefficient of constant. The clock offsets in the rapid ephemeris (IGR provided by IGS are used as modeling data sets to perform certain experiments for different types of GPS satellite clocks. The results show that the clock prediction accuracies of the proposed method for 3, 6, 12 and 24 h achieve 0.43, 0.58, 0.90 and 1.47 ns respectively, which outperform the traditional prediction method based on fitting original clock offsets by 69.3%, 61.8%, 50.5% and 37.2%. Compared with the IGU real-time clock products provided by IGS, the prediction accuracies of the new method have improved about 15.7%, 23.7%, 27.4% and 34.4% respectively.

The influence of posttraumatic stress disorder numbing and hyperarousal symptom clusters in the prediction of physical health status in veterans with chronic tobacco dependence and posttraumatic stress disorder.

Science.gov (United States)

Harder, Laura H; Chen, Shuo; Baker, Dewleen G; Chow, Bruce; McFall, Miles; Saxon, Andrew; Smith, Mark W

2011-12-01

Smoking and PTSD are predictors of poor physical health status. This study examined the unique contribution of PTSD symptoms in the prediction of the SF-36 physical health status subscales accounting for cigarette smoking, chronic medical conditions, alcohol and drug use disorders, and depression. This study examined baseline interview and self-report data from a national tobacco cessation randomized, controlled trial (Veterans Affairs Cooperative Study 519) that enrolled tobacco-dependent veterans with chronic PTSD (N = 943). A series of blockwise multiple regression analyses indicated that PTSD numbing and hyperarousal symptom clusters explained a significant proportion of the variance across all physical health domains except for the Physical Functioning subscale, which measures impairments in specific physical activities. Our findings further explain the impact of PTSD on health status by exploring the way PTSD symptom clusters predict self-perceptions of health, role limitations, pain, and vitality.

Empirical Flutter Prediction Method.

Science.gov (United States)

1988-03-05

been used in this way to discover species or subspecies of animals, and to discover different types of voter or comsumer requiring different persuasions...respect to behavior or performance or response variables. Once this were done, corresponding clusters might be sought among descriptive or predictive or...jump in a response. The first sort of usage does not apply to the flutter prediction problem. Here the types of behavior are the different kinds of

Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

Science.gov (United States)

Nath, Abhigyan; Kumari, Priyanka; Chaube, Radha

2018-01-01

Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug targets and their interactions can be very useful for drug repurposing. Supervised machine learning methods have been very useful in drug target prediction and in prediction of drug target interactions. Here, we describe the details for developing prediction models using supervised learning techniques for human drug target prediction and their interactions.

Unbiased and non-supervised learning methods for disruption prediction at JET

International Nuclear Information System (INIS)

Murari, A.; Vega, J.; Ratta, G.A.; Vagliasindi, G.; Johnson, M.F.; Hong, S.H.

2009-01-01

The importance of predicting the occurrence of disruptions is going to increase significantly in the next generation of tokamak devices. The expected energy content of ITER plasmas, for example, is such that disruptions could have a significant detrimental impact on various parts of the device, ranging from erosion of plasma facing components to structural damage. Early detection of disruptions is therefore needed with evermore increasing urgency. In this paper, the results of a series of methods to predict disruptions at JET are reported. The main objective of the investigation consists of trying to determine how early before a disruption it is possible to perform acceptable predictions on the basis of the raw data, keeping to a minimum the number of 'ad hoc' hypotheses. Therefore, the chosen learning techniques have the common characteristic of requiring a minimum number of assumptions. Classification and Regression Trees (CART) is a supervised but, on the other hand, a completely unbiased and nonlinear method, since it simply constructs the best classification tree by working directly on the input data. A series of unsupervised techniques, mainly K-means and hierarchical, have also been tested, to investigate to what extent they can autonomously distinguish between disruptive and non-disruptive groups of discharges. All these independent methods indicate that, in general, prediction with a success rate above 80% can be achieved not earlier than 180 ms before the disruption. The agreement between various completely independent methods increases the confidence in the results, which are also confirmed by a visual inspection of the data performed with pseudo Grand Tour algorithms.

Ability of High-Resolution Manometry to Determine Feeding Method and to Predict Aspiration Pneumonia in Patients With Dysphagia.

Science.gov (United States)

Park, Chul-Hyun; Lee, Yong-Taek; Yi, Youbin; Lee, Jung-Sang; Park, Jung Ho; Yoon, Kyung Jae

2017-07-01

The introduction of high-resolution manometry (HRM) offered an improved method to objectively analyze the status of pharynx and esophagus. At present, HRM for patients with oropharyngeal dysphagia has been poorly studied. We aimed to determine feeding method and predict the development of aspiration pneumonia in patients with oropharyngeal dysphagia using HRM. We recruited 120 patients with dysphagia who underwent both HRM and videofluoroscopic swallow study. HRM was used to estimate pressure events from velopharynx (VP) to upper esophageal sphincter (UES). Feeding methods were determined to non-oral or oral feeding according to dysphagia severity. We prospectively followed patients to assess the development of aspiration pneumonia. VP maximal pressure and UES relaxation duration were independently associated with non-oral feeding. Non-oral feeding was determined based on optimal cutoff value of 105.0 mm Hg for VP maximal pressure (95.0% sensitivity and 70.0% specificity) and 0.45 s for UES relaxation duration (76.3% sensitivity and 57.5% specificity), respectively. During a mean follow-up of 18.8 months, 15.8% of patients developed aspiration pneumonia. On multivariate Cox regression analysis, VP maximal pressure (Pdysphagia.

Predicting maternal parenting stress in middle childhood: the roles of child intellectual status, behaviour problems and social skills.

Science.gov (United States)

Neece, C; Baker, B

2008-12-01

Parents of children with intellectual disabilities (ID) typically report elevated levels of parenting stress, and child behaviour problems are a strong predictor of heightened parenting stress. Interestingly, few studies have examined child characteristics beyond behaviour problems that may also contribute to parenting stress. The present longitudinal study examined the contribution of child social skills to maternal parenting stress across middle childhood, as well as the direction of the relationship between child social skills and parenting stress. Families of children with ID (n = 74) or typical development (TD) (n = 115) participated over a 2-year period. Maternal parenting stress, child behaviour problems and child social skills were assessed at child ages six and eight. Child social skills accounted for unique variance in maternal parenting stress above and beyond child intellectual status and child behaviour problems. As the children matured, there was a significant interaction between child social skills and behaviour problems in predicting parenting stress. With respect to the direction of these effects, a cross-lagged panel analysis indicated that early parenting stress contributed to later social skills difficulties for children, but the path from children's early social skills to later parenting stress was not supported, once child behaviour problems and intellectual status were accounted for. When examining parenting stress, child social skills are an important variable to consider, especially in the context of child behaviour problems. Early parenting stress predicted child social skills difficulties over time, highlighting parenting stress as a key target for intervention.

Different methods for assessment of nutritional status in newborn infants based on physical and anthropometric indexes: a short review article

Directory of Open Access Journals (Sweden)

Ali Asghar Rashidi

2017-01-01

Full Text Available Several complications during childhood is associated with nutritional status of infants at birth. Therefore, nutritional status of newborns must be evaluated properly after birth. Assessment of the nutritional status of neonates based on anthropometric and physical indices is simple and inexpensive without the need for advanced medical equipment. However, no previous studies have focused on the assessment methods of the nutritional status of infants via anthropometric and physical indices. This study aimed to review some of the key methods used to determine the nutritional status of neonates using anthropometric and physical indices. To date, most studies have focused on the diagnosis of fetal malnutrition (FM and growth monitoring. In order to diagnose FM, researchers have used growth charts and Ponderal index (PI based on anthropometric indices, as well as Clinical Assessment of Nutritional (CAN Score based on physical features. Moreover, in order to assess the growth status of infants, growth charts were used. According to the findings of this study, standard intrauterine growth curves and the PI are common measurement tools in the diagnosis of FM. Furthermore, CAN score is widely used in the evaluation of the nutritional status of neonates. Given the differences in the physical features of term and preterm infants, this index should be adjusted for preterm neonates. Longitudinal growth charts are one of the most prominent methods used for monitoring of the growth patterns of infants.

The Use of Data Mining Methods to Predict the Result of Infertility Treatment Using the IVF ET Method

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Malinowski Paweł

2014-12-01

Full Text Available The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.

A computational method to predict fluid-structure interaction of pressure relief valves

Energy Technology Data Exchange (ETDEWEB)

Kang, S. K.; Lee, D. H.; Park, S. K.; Hong, S. R. [Korea Electric Power Research Institute, Taejon (Korea, Republic of)

2004-07-01

An effective CFD (Computational fluid dynamics) method to predict important performance parameters, such as blowdown and chattering, for pressure relief valves in NPPs is provided in the present study. To calculate the valve motion, 6DOF (six degree of freedom) model is used. A chimera overset grid method is utilized to this study for the elimination of grid remeshing problem, when the disk moves. Further, CFD-Fastran which is developed by CFD-RC for compressible flow analysis is applied to an 1' safety valve. The prediction results ensure the applicability of the presented method in this study.

A method of predicting the reliability of CDM coil insulation

International Nuclear Information System (INIS)

Kytasty, A.; Ogle, C.; Arrendale, H.

1992-01-01

This paper presents a method of predicting the reliability of the Collider Dipole Magnet (CDM) coil insulation design. The method proposes a probabilistic treatment of electrical test data, stress analysis, material properties variability and loading uncertainties to give the reliability estimate. The approach taken to predict reliability of design related failure modes of the CDM is to form analytical models of the various possible failure modes and their related mechanisms or causes, and then statistically assess the contributions of the various contributing variables. The probability of the failure mode occurring is interpreted as the number of times one would expect certain extreme situations to combine and randomly occur. One of the more complex failure modes of the CDM will be used to illustrate this methodology

Anisotropic Elastoplastic Damage Mechanics Method to Predict Fatigue Life of the Structure

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Hualiang Wan

2016-01-01

Full Text Available New damage mechanics method is proposed to predict the low-cycle fatigue life of metallic structures under multiaxial loading. The microstructure mechanical model is proposed to simulate anisotropic elastoplastic damage evolution. As the micromodel depends on few material parameters, the present method is very concise and suitable for engineering application. The material parameters in damage evolution equation are determined by fatigue experimental data of standard specimens. By employing further development on the ANSYS platform, the anisotropic elastoplastic damage mechanics-finite element method is developed. The fatigue crack propagation life of satellite structure is predicted using the present method and the computational results comply with the experimental data very well.

HEMODYNAMIC STATUS IN AIRWAY MANAGEMENT DURING GENERAL ANESTHESIA: COMPARISON OF THREE METHODS

OpenAIRE

K MONTAZERI; KH NAGHIBI; A.A AKHOUNDI

2000-01-01

Introduction. The laryngeal mask airway (LMA) was recently introduced in general anesthesia as an alternative to the face mask or tracheal intubation for airway maintenance. Methods. The effects of LMA insertion, face mask or tracheal intubation on homodynamic status were studied in 195 normotensive patients who underwent elective transurethral lithotripsy (TUL). The patients were monitored with blood pressure measurement and pulse oximetry. Anesthesia was induced with sodium thiopental,...

A novel method for predicting the power outputs of wave energy converters

Science.gov (United States)

Wang, Yingguang

2018-03-01

This paper focuses on realistically predicting the power outputs of wave energy converters operating in shallow water nonlinear waves. A heaving two-body point absorber is utilized as a specific calculation example, and the generated power of the point absorber has been predicted by using a novel method (a nonlinear simulation method) that incorporates a second order random wave model into a nonlinear dynamic filter. It is demonstrated that the second order random wave model in this article can be utilized to generate irregular waves with realistic crest-trough asymmetries, and consequently, more accurate generated power can be predicted by subsequently solving the nonlinear dynamic filter equation with the nonlinearly simulated second order waves as inputs. The research findings demonstrate that the novel nonlinear simulation method in this article can be utilized as a robust tool for ocean engineers in their design, analysis and optimization of wave energy converters.

RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method

KAUST Repository

Ganesan, Pugalenthi; Kandaswamy, Krishna Kumar Umar; Chou -, Kuochen; Vivekanandan, Saravanan; Kolatkar, Prasanna R.

2012-01-01

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/. - See more at: http://www.eurekaselect.com/89216/article#sthash.pwVGFUjq.dpuf

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Science.gov (United States)

Sharafoddini, Anis; Dubin, Joel A

2017-01-01

Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health

L2 Vocabulary Acquisition in Children: Effects of Learning Method and Cognate Status

Science.gov (United States)

Tonzar, Claudio; Lotto, Lorella; Job, Remo

2009-01-01

In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…

A NEW METHOD FOR PREDICTING SURVIVAL AND ESTIMATING UNCERTAINTY IN TRAUMA PATIENTS

Directory of Open Access Journals (Sweden)

V. G. Schetinin

2017-01-01

Full Text Available The Trauma and Injury Severity Score (TRISS is the current “gold” standard of screening patient’s condition for purposes of predicting survival probability. More than 40 years of TRISS practice revealed a number of problems, particularly, 1 unexplained fluctuation of predicted values caused by aggregation of screening tests, and 2 low accuracy of uncertainty intervals estimations. We developed a new method made it available for practitioners as a web calculator to reduce negative effect of factors given above. The method involves Bayesian methodology of statistical inference which, being computationally expensive, in theory provides most accurate predictions. We implemented and tested this approach on a data set including 571,148 patients registered in the US National Trauma Data Bank (NTDB with 1–20 injuries. These patients were distributed over the following categories: (1 174,647 with 1 injury, (2 381,137 with 2–10 injuries, and (3 15,364 with 11–20 injuries. Survival rates in each category were 0.977, 0.953, and 0.831, respectively. The proposed method has improved prediction accuracy by 0.04%, 0.36%, and 3.64% (p-value <0.05 in the categories 1, 2, and 3, respectively. Hosmer-Lemeshow statistics showed a significant improvement of the new model calibration. The uncertainty 2σ intervals were reduced from 0.628 to 0.569 for patients of the second category and from 1.227 to 0.930 for patients of the third category, both with p-value <0.005. The new method shows the statistically significant improvement (p-value <0.05 in accuracy of predicting survival and estimating the uncertainty intervals. The largest improvement has been achieved for patients with 11–20 injuries. The method is available for practitioners as a web calculator http://www.traumacalc.org.

Modification of an Existing In vitro Method to Predict Relative Bioavailable Arsenic in Soils

Science.gov (United States)

The soil matrix can sequester arsenic (As) and reduces its exposure by soil ingestion. In vivo dosing studies and in vitro gastrointestinal (IVG) methods have been used to predict relative bioavailable (RBA) As. Originally, the Ohio State University (OSU-IVG) method predicted R...

Interior Noise Prediction of the Automobile Based on Hybrid FE-SEA Method

Directory of Open Access Journals (Sweden)

S. M. Chen

2011-01-01

created using hybrid FE-SEA method. The modal density was calculated using analytical method and finite element method; the damping loss factors of the structural and acoustic cavity subsystems were also calculated with analytical method; the coupling loss factors between structure and structure, structure and acoustic cavity were both calculated. Four different kinds of excitations including road excitations, engine mount excitations, sound radiation excitations of the engine, and wind excitations are exerted on the body of automobile when the automobile is running on the road. All the excitations were calculated using virtual prototype technology, computational fluid dynamics (CFD, and experiments realized in the design and development stage. The interior noise of the automobile was predicted and verified at speed of 120 km/h. The predicted and tested overall SPLs of the interior noise were 73.79 and 74.44 dB(A respectively. The comparison results also show that the prediction precision is satisfied, and the effectiveness and reliability of the hybrid FE-SEA model of the automobile is verified.

Predictive Distribution of the Dirichlet Mixture Model by the Local Variational Inference Method

DEFF Research Database (Denmark)

Ma, Zhanyu; Leijon, Arne; Tan, Zheng-Hua

2014-01-01

the predictive likelihood of the new upcoming data, especially when the amount of training data is small. The Bayesian estimation of a Dirichlet mixture model (DMM) is, in general, not analytically tractable. In our previous work, we have proposed a global variational inference-based method for approximately...... calculating the posterior distributions of the parameters in the DMM analytically. In this paper, we extend our previous study for the DMM and propose an algorithm to calculate the predictive distribution of the DMM with the local variational inference (LVI) method. The true predictive distribution of the DMM...... is analytically intractable. By considering the concave property of the multivariate inverse beta function, we introduce an upper-bound to the true predictive distribution. As the global minimum of this upper-bound exists, the problem is reduced to seek an approximation to the true predictive distribution...

Predicting proteasomal cleavage sites: a comparison of available methods

DEFF Research Database (Denmark)

Saxova, P.; Buus, S.; Brunak, Søren

2003-01-01

-terminal, in particular, of CTL epitopes is cleaved precisely by the proteasome, whereas the N-terminal is produced with an extension, and later trimmed by peptidases in the cytoplasm and in the endoplasmic reticulum. Recently, three publicly available methods have been developed for prediction of the specificity...

Child Mortality as Predicted by Nutritional Status and Recent Weight Velocity in Children under Two in Rural Africa.

LENUS (Irish Health Repository)

2012-01-31

WHO has released prescriptive child growth standards for, among others, BMI-for-age (BMI-FA), mid-upper arm circumference-for-age, and weight velocity. The ability of these indices to predict child mortality remains understudied, although growth velocity prognostic value underlies current growth monitoring programs. The study aims were first to assess, in children under 2, the independent and combined ability of these indices and of stunting to predict all-cause mortality within 3 mo, and second, the comparative abilities of weight-for-length (WFL) and BMI-FA to predict short-term (<3 mo) mortality. We used anthropometry and survival data from 2402 children aged between 0 and 24 mo in a rural area of the Democratic Republic of Congo with high malnutrition and mortality rates and limited nutritional rehabilitation. Analyses used Cox proportional hazard models and receiver operating characteristic curves. Univariate analysis and age-adjusted analysis showed predictive ability of all indices. Multivariate analysis without age adjustment showed that only very low weight velocity [HR = 3.82 (95%CI = 1.91, 7.63); P < 0.001] was independently predictive. With age adjustment, very low weight velocity [HR = 3.61 (95%CI = 1.80, 7.25); P < 0.001] was again solely retained as an independent predictor. There was no evidence for a difference in predictive ability between WFL and BMI-FA. This paper shows the value of attained BMI-FA, a marker of wasting status, and recent weight velocity, a marker of the wasting process, in predicting child death using the WHO child growth standards. WFL and BMI-FA appear equivalent as predictors.

The Dissolved Oxygen Prediction Method Based on Neural Network

Directory of Open Access Journals (Sweden)

Zhong Xiao

2017-01-01

Full Text Available The dissolved oxygen (DO is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture’s dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF, autoregression (AR, grey model (GM, and support vector machines (SVM, the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

International Nuclear Information System (INIS)

Ernst, Floris; Schweikard, Achim

2008-01-01

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN)

Energy Technology Data Exchange (ETDEWEB)

Ernst, Floris; Schweikard, Achim [University of Luebeck, Institute for Robotics and Cognitive Systems, Luebeck (Germany)

2008-06-15

Forecasting of respiration motion in image-guided radiotherapy requires algorithms that can accurately and efficiently predict target location. Improved methods for respiratory motion forecasting were developed and tested. MULIN, a new family of prediction algorithms based on linear expansions of the prediction error, was developed and tested. Computer-generated data with a prediction horizon of 150 ms was used for testing in simulation experiments. MULIN was compared to Least Mean Squares-based predictors (LMS; normalized LMS, nLMS; wavelet-based multiscale autoregression, wLMS) and a multi-frequency Extended Kalman Filter (EKF) approach. The in vivo performance of the algorithms was tested on data sets of patients who underwent radiotherapy. The new MULIN methods are highly competitive, outperforming the LMS and the EKF prediction algorithms in real-world settings and performing similarly to optimized nLMS and wLMS prediction algorithms. On simulated, periodic data the MULIN algorithms are outperformed only by the EKF approach due to its inherent advantage in predicting periodic signals. In the presence of noise, the MULIN methods significantly outperform all other algorithms. The MULIN family of algorithms is a feasible tool for the prediction of respiratory motion, performing as well as or better than conventional algorithms while requiring significantly lower computational complexity. The MULIN algorithms are of special importance wherever high-speed prediction is required. (orig.)

Predicting Job Crafting From the Socially Embedded Perspective: The Interactive Effect of Job Autonomy, Social Skill, and Employee Status

OpenAIRE

Sekiguchi, Tomoki; Li, Jie; Hosomi, Masaki

2017-01-01

Job crafting represents the bottom-up process of change employees make in their work boundaries and plays an important role in the management of organizational change. Following the socially embedded perspective, we examine the roles of job autonomy, social skill, and employee status in predicting job crafting. Study 1 with a sample of 509 part-time employees found that job autonomy and social skill not only directly but also interactively influenced job crafting. Study 2 with a sample of 564...

Indirect dark matter searches: current status and perspectives

CERN Multimedia

CERN. Geneva

2016-01-01

Many theoretical ideas for the particle nature of dark matter exist. The  most popular models often predict that dark matter particles self-annihilate or decay, giving rise to potentially detectable signatures in astronomical observations.  I will summarize the current status of searches for such signatures and critically reassess recent claims for dark matter signals.  I will further provide an outlook on anticipated developments in the next 10 years, and discuss new methods to facilitate strategy development.

Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches

KAUST Repository

Olayan, Rawan S.

2017-12-01

Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Computational determination of DTIs is a convenient strategy for systematic screening of a large number of drugs in the attempt to identify new DTIs at low cost and with reasonable accuracy. This necessitates development of accurate computational methods that can help focus on the follow-up experimental validation on a smaller number of highly likely targets for a drug. Although many methods have been proposed for computational DTI prediction, they suffer the high false positive prediction rate or they do not predict the effect that drugs exert on targets in DTIs. In this report, first, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithm-centric perspectives. The aim is to provide a comprehensive review of computational methods for identifying DTIs, which could help in constructing more reliable methods. Then, we present DDR, an efficient method to predict the existence of DTIs. DDR achieves significantly more accurate results compared to the other state-of-theart methods. As supported by independent evidences, we verified as correct 22 out of the top 25 DDR DTIs predictions. This validation proves the practical utility of DDR, suggesting that DDR can be used as an efficient method to identify 5 correct DTIs. Finally, we present DDR-FE method that predicts the effect types of a drug on its target. On different representative datasets, under various test setups, and using different performance measures, we show that DDR-FE achieves extremely good performance. Using blind test data, we verified as correct 2,300 out of 3,076 DTIs effects predicted by DDR-FE. This suggests that DDR-FE can be used as an efficient method to identify correct effects of a drug on its target.

In/Out Status Monitoring in Mobile Asset Tracking with Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Kwangsoo Kim

2010-03-01

Full Text Available A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the mobile asset is inside the region, the system might determine that it is outside, or vice versa. In this paper, we propose a method to detect and correct the incorrect in/out status of the mobile asset. To solve this problem, our approach uses data about the connection state transition and the battery lifetime of the mobile node attached to the mobile asset. The connection state transition is used to classify the mobile node as normal or abnormal. The battery lifetime is used to predict a valid working period for the mobile node. We evaluate our method using real data generated by a medical asset tracking system. The experimental results show that our method, by using the estimated battery life time or by using the invalid connection state, can detect and correct most cases of incorrect in/out statuses generated by the conventional approach.

In/out status monitoring in mobile asset tracking with wireless sensor networks.

Science.gov (United States)

Kim, Kwangsoo; Chung, Chin-Wan

2010-01-01

A mobile asset with a sensor node in a mobile asset tracking system moves around a monitoring area, leaves it, and then returns to the region repeatedly. The system monitors the in/out status of the mobile asset. Due to the continuous movement of the mobile asset, the system may generate an error for the in/out status of the mobile asset. When the mobile asset is inside the region, the system might determine that it is outside, or vice versa. In this paper, we propose a method to detect and correct the incorrect in/out status of the mobile asset. To solve this problem, our approach uses data about the connection state transition and the battery lifetime of the mobile node attached to the mobile asset. The connection state transition is used to classify the mobile node as normal or abnormal. The battery lifetime is used to predict a valid working period for the mobile node. We evaluate our method using real data generated by a medical asset tracking system. The experimental results show that our method, by using the estimated battery life time or by using the invalid connection state, can detect and correct most cases of incorrect in/out statuses generated by the conventional approach.

A prediction method of natural gas hydrate formation in deepwater gas well and its application

Directory of Open Access Journals (Sweden)

Yanli Guo

2016-09-01

Full Text Available To prevent the deposition of natural gas hydrate in deepwater gas well, the hydrate formation area in wellbore must be predicted. Herein, by comparing four prediction methods of temperature in pipe with field data and comparing five prediction methods of hydrate formation with experiment data, a method based on OLGA & PVTsim for predicting the hydrate formation area in wellbore was proposed. Meanwhile, The hydrate formation under the conditions of steady production, throttling and shut-in was predicted by using this method based on a well data in the South China Sea. The results indicate that the hydrate formation area decreases with the increase of gas production, inhibitor concentrations and the thickness of insulation materials and increases with the increase of thermal conductivity of insulation materials and shutdown time. Throttling effect causes a plunge in temperature and pressure in wellbore, thus leading to an increase of hydrate formation area.

Assessing smoking status in disadvantaged populations: is computer administered self report an accurate and acceptable measure?

Directory of Open Access Journals (Sweden)

Bryant Jamie

2011-11-01

Full Text Available Abstract Background Self report of smoking status is potentially unreliable in certain situations and in high-risk populations. This study aimed to determine the accuracy and acceptability of computer administered self-report of smoking status among a low socioeconomic (SES population. Methods Clients attending a community service organisation for welfare support were invited to complete a cross-sectional touch screen computer health survey. Following survey completion, participants were invited to provide a breath sample to measure exposure to tobacco smoke in expired air. Sensitivity, specificity, positive predictive value and negative predictive value were calculated. Results Three hundred and eighty three participants completed the health survey, and 330 (86% provided a breath sample. Of participants included in the validation analysis, 59% reported being a daily or occasional smoker. Sensitivity was 94.4% and specificity 92.8%. The positive and negative predictive values were 94.9% and 92.0% respectively. The majority of participants reported that the touch screen survey was both enjoyable (79% and easy (88% to complete. Conclusions Computer administered self report is both acceptable and accurate as a method of assessing smoking status among low SES smokers in a community setting. Routine collection of health information using touch-screen computer has the potential to identify smokers and increase provision of support and referral in the community setting.

ANTIOXIDANT STATUS IN DIABETIC NEUROPATHY

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Giriraja Vrushabaiah Kanakapura

2017-09-01

Full Text Available BACKGROUND Diabetic neuropathy, retinopathy and nephropathy are the chronic complications of diabetes mellitus. Neuropathy, retinopathy and nephropathy are microvascular complication of diabetes mellitus. Antioxidant status is reduced in DM-induced retinopathy and nephropathy. Present study is undertaken to evaluate the degree of oxidative stress in diabetic neuropathy patients. The aim of the study is to study on oxidative stress as measured by lipid peroxidation marker, malondialdehyde and antienzyme status in type II DM patients with neuropathy and compared them with a controlled nondiabetic group. MATERIALS AND METHODS The study included 100 subjects from Sapthagiri Medical College, Bangalore, from January 1, 2015, to December 31, 2015, of age group 50 to 70 yrs. out of which 50 patients were non-insulin-dependent DM with neuropathy and rest 50 age and sex matched apparently healthy individuals (control group. Antioxidant status was assessed by measuring superoxide dismutase (SOD, glutathione peroxidase (GPx, glutathione reductase (GR, Catalase and Reduced Glutathione (GSH. RESULTS It showed a significant increase p<0.001 in FBS, PPBS, TC, TG, LDL, VLDL, CAT, MDA, while HDL, GSH, GPX, GR and SOD were found to be decreased significantly (p 0.001. CONCLUSION MDA was significantly elevated in diabetic group, whereas antioxidant enzymes superoxide dismutase, glutathione peroxidase, glutathione reductase and reduced glutathione were significantly decreased, which might be helpful in risk assessment of various complications of DM. The data suggests that alteration in antioxidant status and MDA may help to predict the risk of diabetic neuropathy.

Statistical Analysis of a Method to Predict Drug-Polymer Miscibility

DEFF Research Database (Denmark)

Knopp, Matthias Manne; Olesen, Niels Erik; Huang, Yanbin

2016-01-01

In this study, a method proposed to predict drug-polymer miscibility from differential scanning calorimetry measurements was subjected to statistical analysis. The method is relatively fast and inexpensive and has gained popularity as a result of the increasing interest in the formulation of drug...... as provided in this study. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci....

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

Science.gov (United States)

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-07-04

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. The SMM-align method was shown to outperform other

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

Directory of Open Access Journals (Sweden)

Lund Ole

2007-07-01

Full Text Available Abstract Background Antigen presenting cells (APCs sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR, we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion

Variable importance and prediction methods for longitudinal problems with missing variables.

Directory of Open Access Journals (Sweden)

Iván Díaz

Full Text Available We present prediction and variable importance (VIM methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed. Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner, which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO. In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC for a receiver-operator curve (ROC. Thus, given that 1 our VIM

A study on the fatigue life prediction of tire belt-layers using probabilistic method

International Nuclear Information System (INIS)

Lee, Dong Woo; Park, Jong Sang; Lee, Tae Won; Kim, Seong Rae; Sung, Ki Deug; Huh, Sun Chul

2013-01-01

Tire belt separation failure is occurred by internal cracks generated in *1 and *2 belt layers and by its growth. And belt failure seriously affects tire endurance. Therefore, to improve the tire endurance, it is necessary to analyze tire crack growth behavior and predict fatigue life. Generally, the prediction of tire endurance is performed by the experimental method using tire test machine. But it takes much cost and time to perform experiment. In this paper, to predict tire fatigue life, we applied deterministic fracture mechanics approach, based on finite element analysis. Also, probabilistic analysis method based on statistics using Monte Carlo simulation is presented. Above mentioned two methods include a global-local finite element analysis to provide the detail necessary to model explicitly an internal crack and calculate the J-integral for tire life prediction.

Electronic structure prediction via data-mining the empirical pseudopotential method

Energy Technology Data Exchange (ETDEWEB)

Zenasni, H; Aourag, H [LEPM, URMER, Departement of Physics, University Abou Bakr Belkaid, Tlemcen 13000 (Algeria); Broderick, S R; Rajan, K [Department of Materials Science and Engineering, Iowa State University, Ames, Iowa 50011-2230 (United States)

2010-01-15

We introduce a new approach for accelerating the calculation of the electronic structure of new materials by utilizing the empirical pseudopotential method combined with data mining tools. Combining data mining with the empirical pseudopotential method allows us to convert an empirical approach to a predictive approach. Here we consider tetrahedrally bounded III-V Bi semiconductors, and through the prediction of form factors based on basic elemental properties we can model the band structure and charge density for these semi-conductors, for which limited results exist. This work represents a unique approach to modeling the electronic structure of a material which may be used to identify new promising semi-conductors and is one of the few efforts utilizing data mining at an electronic level. (Abstract Copyright [2010], Wiley Periodicals, Inc.)

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

DEFF Research Database (Denmark)