WorldWideScience

Sample records for clinical parameters predicting

  1. Pre and per operative prediction of difficult laparoscopic cholecystectomy using clinical and ultrasonographic parameters

    Directory of Open Access Journals (Sweden)

    Gaurav Gupta

    2015-11-01

    Methods: In 50 consecutive patients who underwent LC during 2013 to 2014 patient's characteristics, clinical history, laboratory data, ultrasonography results and intraoperative details were prospectively analyzed to determine predictors of difficult LC. Results: Of 50 patients 3 (06% required conversion to open cholecystectomy. Significant predictors of conversion were obscured anatomy of Calot's due to adhesions, sessile gall bladder, male gender and gall bladder wall thickness >3 mm. Conclusions: With preoperative clinical and ultrasonographic parameters, proper patient selection can be made to help predict difficult LC and a likelihood of conversion to open cholecystectomy. [Int J Res Med Sci 2015; 3(11.000: 3342-3346

  2. Multidetector-CT angiography in pulmonary embolism - can image parameters predict clinical outcome?

    Energy Technology Data Exchange (ETDEWEB)

    Heyer, Christoph M.; Lemburg, Stefan P.; Nicolas, Volkmar; Roggenland, Daniela [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Ruhr-University of Bochum, Institute of Diagnostic Radiology, Interventional Radiology and Nuclear Medicine, Bochum (Germany); Knoop, Heiko [Berufsgenossenschaftliches Universitaetsklinikum Bergmannsheil GmbH, Medical Clinic III - Pneumology, Allergology, and Sleep Medicine, Bochum (Germany); Holland-Letz, Tim [Ruhr-University of Bochum, Department of Medical Informatics, Biometry and Epidemiology, Bochum (Germany)

    2011-09-15

    To assess if pulmonary CT angiography (CTA) can predict outcome in patients with pulmonary embolism (PE). Retrospective analysis of CTA studies of patients with PE and documentation of pulmonary artery (PA)/aorta ratio, right ventricular (RV)/left ventricular (LV) ratio, superior vena cava (SVC) diameter, pulmonary obstruction index (POI), ventricular septal bowing (VSB), venous contrast reflux (VCR), pulmonary infarction and pleural effusion. Furthermore, duration of total hospital stay, necessity for/duration of ICU therapy, necessity for mechanical ventilation and mortality were recorded. Comparison was performed by logistic/linear regression analysis with significance at 5%. 152 patients were investigated. Mean duration of hospital stay was 21 {+-} 24 days. 66 patients were admitted to the ICU; 20 received mechanical ventilation. Mean duration of ICU therapy was 3 {+-} 8 days. Mortality rate was 8%. Significant positive associations of POI, VCR and pulmonary infarction with necessity for ICU therapy were shown. VCR was significantly associated with necessity for mechanical ventilation and duration of ICU treatment. Pleural effusions were significantly associated with duration of total hospital stay whereas the RV/LV ratio correlated with mortality. Selected CTA findings showed significant associations with the clinical course of PE and may thus be used as predictive parameters. (orig.)

  3. [Using CTS and PK-PD models to predict the effect of uncertainty about population parameters on clinical trial power].

    Science.gov (United States)

    Zhu, Ling; Shi, Xinling; Liu, Yajie

    2009-02-01

    The traditional clinical trail designs always depend on expert opinions and lack statistical evaluations. In this article, we present a method and illustrate how population parameter uncertainty may be incorporated in the overall simulation model. Using the techniques of clinical trail simulation (CTS) and setting up predictions on the basis of pharmacokinetics-pharmacodynamics (PK-PD) models, we advance the modeling methods for simulation, for treatment effects, and for the clinical trail power under the given PK-PD conditions. Then we discuss the model of uncertainty, suggest an ANOVA-based method, add eta2 statistics for sensitivity analysis, and canvass the effect of uncertainty about population parameters on clinical trail power. The results from simulations and the indices derived from this type of sensitivity analysis may be used for grading the influence on the prediction quality of uncertainty about different population parameters. The experiment results are satisfactory and the approach presented has practical value in clinical trails.

  4. How to develop, validate, and compare clinical prediction models involving radiological parameters: Study design and statistical methods

    Energy Technology Data Exchange (ETDEWEB)

    Han, Kyung Hwa; Choi, Byoung Wook [Dept. of Radiology, and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul (Korea, Republic of); Song, Ki Jun [Dept. of Biostatistics and Medical Informatics, Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-06-15

    Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.

  5. Thyroid Hormones, Autoantibodies, Ultrasonography, and Clinical Parameters for Predicting Thyroid Cancer

    Science.gov (United States)

    He, Lin-zheng; Zeng, Tian-shu; Pu, Lin; Pan, Shi-xiu; Xia, Wen-fang; Chen, Lu-lu

    2016-01-01

    Our objective was to evaluate thyroid nodule malignancy prediction using thyroid function tests, autoantibodies, ultrasonographic imaging, and clinical data. We conducted a retrospective cohort study in 1400 patients with nodular thyroid disease (NTD). The thyroid stimulating hormone (TSH) concentration was significantly higher in patients with differentiated thyroid cancer (DTC) versus benign thyroid nodular disease (BTND) (p = 0.004). The receiver operating characteristic curve of TSH showed an AUC of 0.58 (95% CI 0.53–0.62, p = 0.001), sensitivity of 74%, and specificity of 57% at a cut-off of 1.59 mIU/L. There was an incremental increase in TSH concentration along with the increasing tumor size (p < 0.001). Thyroglobulin antibody (TgAb) concentration was associated with an increased risk of malignancy (p = 0.029), but this association was lost when the effect of TSH was taken into account (p = 0.11). Thyroid ultrasonographic characteristics, including fewer than three nodules, hypoechoic appearance, solid component, poorly defined margin, intranodular or peripheral-intranodular flow, and punctate calcification, can be used to predict the risk of thyroid cancer. In conclusion, our study suggests that preoperative serum TSH concentration, age, and ultrasonographic features can be used to predict the risk of malignancy in patients with NTD. PMID:27313612

  6. STUDY OF CLINICAL AND BIOCHEMICAL PARAMETERS IN PREDICTING THE NEED FOR VENTILATOR SUPPORT IN ORGANOPHOSPHORUS COMPOUND POISONING

    Directory of Open Access Journals (Sweden)

    Rajeev

    2013-12-01

    Full Text Available Organophosphorus compound is used for committing suicidal are on upswing in developing countries A grading system of severity of OP poisoning suggests that most cases can be managed in the ICU which cannot be applied to developing countries, where facilities for ICU management are rather limited. Hence, the pr esent study is undertaken to identify the factors both clinical and biochemical, which help in predicting the need for ventilator support and thus helping to reduce the mortality by timely institution of ventilator support. AIMS OF THE STUDY: To study the clinical and biochemical parameters in organophosphate poisoning, which help to predict the need for ventilator support. MATERIAL AND METHODS: This is a Descriptive Study done at Kempegowda Institute of Medical Sciences, Bangalore, with a sample size of 50 cases. Patients who fulfilled the inclusion criteria are assessed as per proforma specifically designed for the study. RESULTS: In this study population, 12 patients who reached the hospital for treatment > 4 hour of consumption, 11(91.7% required ventil ator support . In this study, out of patients with pinpoint pupils at admission required ventilator support. In this study, all patients ( required ventilator support with a fasciculation score of more than as compared to none with a bsent fasciculation. In this study, lower the Glasgow coma scale at admission, more vulnerable are the patients for ventilator support . In This study, patients with reduced levels of Pseudo cholinesterase i.e. out of patients ( required ventilato r support. CONCLUSION: Clinical and biochemical parameters such as Greater the time lag from consumption of OP poison till getting specific treatment, Lower GCS scoring, Generalized Fasiculations, Low Pseudo cholinesterase levels, Larger initial dose of At ropine required for Atropinization were strong predictors for the need for Assisted Ventilation in OP poisoning. Grading of the degree of the poisoning taking

  7. Non-invasive clinical parameters for the prediction of urodynamic bladder outlet obstruction: analysis using causal Bayesian networks.

    Directory of Open Access Journals (Sweden)

    Myong Kim

    Full Text Available To identify non-invasive clinical parameters to predict urodynamic bladder outlet obstruction (BOO in patients with benign prostatic hyperplasia (BPH using causal Bayesian networks (CBN.From October 2004 to August 2013, 1,381 eligible BPH patients with complete data were selected for analysis. The following clinical variables were considered: age, total prostate volume (TPV, transition zone volume (TZV, prostate specific antigen (PSA, maximum flow rate (Qmax, and post-void residual volume (PVR on uroflowmetry, and International Prostate Symptom Score (IPSS. Among these variables, the independent predictors of BOO were selected using the CBN model. The predictive performance of the CBN model using the selected variables was verified through a logistic regression (LR model with the same dataset.Mean age, TPV, and IPSS were 6.2 (±7.3, SD years, 48.5 (±25.9 ml, and 17.9 (±7.9, respectively. The mean BOO index was 35.1 (±25.2 and 477 patients (34.5% had urodynamic BOO (BOO index ≥40. By using the CBN model, we identified TPV, Qmax, and PVR as independent predictors of BOO. With these three variables, the BOO prediction accuracy was 73.5%. The LR model showed a similar accuracy (77.0%. However, the area under the receiver operating characteristic curve of the CBN model was statistically smaller than that of the LR model (0.772 vs. 0.798, p = 0.020.Our study demonstrated that TPV, Qmax, and PVR are independent predictors of urodynamic BOO.

  8. Which clinical parameters predict a positive CSF diagnosis of meningitis in a population with high HIV prevalence?

    Directory of Open Access Journals (Sweden)

    Will Loughborough

    2014-05-01

    Full Text Available Background. The HIV epidemic has changed the aetiology of meningitis in sub-Saharan Africa, and frontline clinicians are faced with a variety of meningitic presentations. Doctors working in resource-limited settings have the challenge of appropriately selecting patients for lumbar puncture (LP, a potentially risky procedure that requires laboratory analysis. Methods. In a rural South African hospital, the practice of performing LPs was audited against local guidelines. Data were collected retrospectively between February and June 2013. Symptoms and signs of meningitis, HIV status, investigations performed prior to LP and cerebrospinal fluid (CSF results were recorded. With the aim of determining statistically significant clinical predictors of meningitis, parameters were explored using univariate and multivariate logistic regression analyses.Results. A total of 107 patients were included, of whom 43% had an abnormal CSF result. The majority (76% of patients were HIV-positive (CD4+ cell count <200 cells/µl in 46%. Cryptococcal meningitis (CCM was the most prevalent microbiological diagnosis, confirmed in 10 out of 12 patients. Of the non-microbiological diagnoses, lymphocytic predominance was the most common abnormality, present in 17 out of 33 patients. Confusion (p=0.011 was the most statistically significant predictor of an abnormal CSF result. Headache (p=0.355, fever (p=0.660 and photophobia (p=0.634 were not statistically predictive.Conclusion. The high incidence of CCM correlates with previous data from sub-Saharan Africa. In populations with high HIV prevalence, the classic meningitic symptoms of headache, fever and photophobia, while common presenting symptoms, are significantly less predictive of a meningitis diagnosis than confusion.

  9. Benefit of combining quantitative cardiac CT parameters with troponin I for predicting right ventricular dysfunction and adverse clinical events in patients with acute pulmonary embolism

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, Mathias, E-mail: mr.meyer.mathias@gmail.com [Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Fink, Christian, E-mail: Christian.Fink@umm.de [Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Roeger, Susanne, E-mail: susanne.roeger@umm.de [1st Department of Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Apfaltrer, Paul, E-mail: Paul.Apfaltrer@umm.de [Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Haghi, Dariush, E-mail: dariush.haghi@umm.de [1st Department of Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Kaminski, Wolfgang E., E-mail: wolfgang.kaminski@umm.de [Department of Clinical Chemistry, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Neumaier, Michael, E-mail: michael.neumaier@medma.uni-heidelberg.de [Department of Clinical Chemistry, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); Schoenberg, Stefan O., E-mail: Stefan.Schoenberg@umm.de [Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, D-68167 Mannheim (Germany); and others

    2012-11-15

    Objective: To prospectively evaluate the diagnostic accuracy of quantitative cardiac CT parameters alone and in combination with troponin I for the assessment of right ventricular dysfunction (RVD) and adverse clinical events in patients with acute pulmonary embolism (PE). Materials and results: This prospective study had institutional review board approval and was HIPAA compliant. In total 83 patients with confirmed PE underwent echocardiography and troponin I serum level measurements within 24 h. Three established cardiac CT measurements for the assessment of RVD were obtained (RV/LV{sub axial}, RV/LV{sub 4-CH}, and RV/LV{sub volume}). CT measurements and troponin I serum levels were correlated with RVD found on echocardiography and adverse clinical events according to Management Strategies and Prognosis in Pulmonary Embolism Trial-3 (MAPPET-3 criteria. 31 of 83 patients with PE had RVD on echocardiography and 39 of 83 patients had adverse clinical events. A RV/LV{sub volume} ratio > 1.43 showed the highest area under the curve (AUC) (0.65) for the prediction of adverse clinical events when compared to RV/LV{sub axial}, RV/LV{sub 4Ch} and troponin I. The AUC for the detection of RVD of RV/LV{sub axial}, RV/LV{sub 4Ch}, RV/LV{sub volume}, and troponin I were 0.86, 0.86, 0.92, and 0.69, respectively. Combination of RV/LV{sub axial}, RV/LV{sub 4Ch}, RV/LV{sub volume} with troponin I increased the AUC to 0.87, 0.87 and 0.93, respectively. Conclusion: A combination of cardiac CT parameters and troponin I measurements improves the diagnostic accuracy for detecting RVD and predicting adverse clinical events if compared to either test alone.

  10. Factors predictive of abnormal semen parameters in male partners of couples attending the infertility clinic of a tertiary hospital in south-western Nigeria

    Directory of Open Access Journals (Sweden)

    Peter Olusola Aduloju

    2016-11-01

    Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility. Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital. Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti. Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters. Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism. Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

  11. Predicting Engine Parameters using the Optical Spectrum

    Data.gov (United States)

    National Aeronautics and Space Administration — The Optical Plume Anomaly Detection (OPAD) system is under development to predict engine anomalies and engine parameters of the Space Shuttle's Main Engine (SSME)....

  12. Optimal design criteria - prediction vs. parameter estimation

    Science.gov (United States)

    Waldl, Helmut

    2014-05-01

    G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

  13. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    Directory of Open Access Journals (Sweden)

    Hao Ke

    2011-11-01

    Full Text Available Abstract Background The prognosis of hepatocellular carcinoma (HCC varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Methods Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. Results HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. Conclusion When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.

  14. Prediction of ventricular fibrillation based on nonlinear multi-parameter

    Institute of Scientific and Technical Information of China (English)

    SI Junfeng; NING Xinbao; ZHOU Lingling; ZHANG Song

    2003-01-01

    Ventricular fibrillation (VF) caused by myocardial ischemia is one of the leading factors of death attributed to cardiovascular diseases. It is particularly significant to predict VF and gain valuable time for clinic therapy. Fivedogs are taken as the research objects and a VF model is introduced. The nonlinear characteristics of the ECGs before and after VF are investigated with nonlinear multi-parame- ter analysis methods, Gaussian kernel (GK) correlation estimation algorithm and Lyapunov exponent estimation algorithm. Correlation entropy h2is also presented. The results indicate that there are three parameters which will change at the same time with the conditions of myocardial ischemia, and any changes of a single parameter may be caused by other factors and mislead the judgment. Multi-parameter analysis is more reliable to reveal the heart conditions,and to predict VF without misjudgments.

  15. Vertebral Geometry Parameters Can Predict Fractures

    Directory of Open Access Journals (Sweden)

    P Tofighi

    2007-08-01

    Full Text Available Background: The aim of this study was to investigate vertebral geometry changes and determine cutoff value of vertebral height to predict fractures. Methods: In a cross-sectional study, 280 postmenopausal women recruited .In all subjects bone mineral density and radiog¬raphy of the lumbar spine performed. Lateral radiographs were evaluated for identification of vertebral fractures, using a validated semiquantitative method. T-score of vertebral height was calculated based on data extracted from Iranian Multi¬center Osteoporosis Study. ROC curve used to determine cut off value of vertebral height T-score to predict fractures. Results: The mean of age and BMI were 55.34±8.7 years and 27.73±5 kg/m2, respectively. Among osteoporotic women, 59.8% had one or more vertebral fractures and 23.8% had at least 2 fractures. In fracture group the T-score of spine and femur BMD was lower than the others. The mean of vertebral height in women without fractures was 12.94±0.6 cm, and in the patient with 4 or more fractures was12.3, thus every fracture accompany with 1.2% decreases in the height of vertebrae. The prevalence of vertebral fracture in osteoporotic patients was 71.4% and in healthy cases 39.5%. Better estimation of vertebral height T score in ROC curve was less than -0.7.The sensitivity and specificity of the cut off value were 81.3% and 52.9%, respectively. Conclusion: Vertebral fractures are common fractures in postmenopausal women. There was a correlation between verte¬bral height and fractures. Vertebral geometric parameters especially height T score can be used for fracture screening.

  16. The clot burden score, the Boston Acute Stroke Imaging Scale, the cerebral blood volume ASPECTS, and two novel imaging parameters in the prediction of clinical outcome of ischemic stroke patients receiving intravenous thrombolytic therapy

    Energy Technology Data Exchange (ETDEWEB)

    Sillanpaa, Niko; Hakomaki, Jari; Lahteela, Arto; Dastidar, Prasun; Soimakallio, Seppo [Tampere University Hospital, Medical Imaging Center, Tampere (Finland); Saarinen, Jukka T.; Numminen, Heikki; Elovaara, Irina [Tampere University Hospital, Department of Neurology, Tampere (Finland); Rusanen, Harri [Oulu University Hospital, Department of Neurology, Oulu (Finland)

    2012-07-15

    Recently two classification methods based on the location and the extent of thrombosis detected with CT angiography have been introduced: the Boston Acute Stroke Imaging Scale (BASIS) and the clot burden score (CBS). We studied the performance of BASIS and CBS in predicting good clinical outcome (mRS {<=}2 at 90 days) in an acute (<3 h) stroke cohort treated with intravenous thrombolytic therapy. Eighty-three consecutive patients who underwent multimodal CT were analyzed. Binary logistic regression model was used to assess how BASIS, CBS, and cerebral blood volume (CBV) ASPECTS predict favorable clinical outcome. Diagnostic sensitivities and specificities were calculated and compared. Patients with low CBS and CBV ASPECTS scores and major strokes according to BASIS had significantly higher admission NIHSS scores, larger perfusion defects, and more often poor clinical outcome. In logistic regression analysis, CBV ASPECTS, CBS and BASIS were significantly associated with the clinical outcome. The performance of BASIS improved when patients with thrombosis of the M2 segment of the middle cerebral artery were classified as having minor stroke (M1-BASIS). In the anterior circulation, the sum of CBS and CBV ASPECTS (CBSV) proved to be the most robust predictor of favorable outcome. CBV ASPECTS and CBS had high sensitivity but moderate to poor specificity while BASIS was only moderately sensitive and specific. CBS, BASIS, and CBV ASPECTS are statistically robust and sensitive but unspecific predictors of good clinical outcome. Two new derived imaging parameters, CBSV and M1-BASIS, share these properties and may have increased prognostic value. (orig.)

  17. Predictive value of PSA velocity over early clinical and pathological parameters in patients with localized prostate cancer who undergo radical retropubic prostatectomy

    Directory of Open Access Journals (Sweden)

    Martinez Carlos A.L.

    2004-01-01

    Full Text Available OBJECTIVES: To analyze the behavior of the prostate specific antigen velocity (PSAV in localized prostate adenocarcinoma. MATERIALS AND METHODS: We conducted a retrospective study of 500 men who had localized prostate adenocarcinoma, who underwent radical retropubic prostatectomy between January 1986 and December 1999. The PSAV was calculated for each patient and subsequently, the values were correlated with 5 groups: age, initial PSA value, clinical stage, tumor volume and Gleason score. RESULTS: The behavior of PSAV presented statistic significance with an increment between 1.3 ng/mL and 9.6 ng/mL, ranging from 38.6% and 59.8% when compared with the initial PSA value (p < 0.0001, clinical stage (p = 0.0002, tumor volume (p < 0.0001 and Gleason score (p = 0.0009. CONCLUSION: PSAV up to 2.5 ng/mL/year is associated with factors of good prognosis, such as initial PSA below 10 mg/mL, clinical stage T1, tumor volume below 20% and Gleason score lower than 7.

  18. Vertebral Geometry Parameters Can Predict Fractures

    Directory of Open Access Journals (Sweden)

    P Tofighi

    2007-01-01

    Conclusion: Vertebral fractures are common fractures in postmenopausal women. There was a correlation between verte¬bral height and fractures. Vertebral geometric parameters especially height T score can be used for fracture screening.

  19. How to Establish Clinical Prediction Models

    Directory of Open Access Journals (Sweden)

    Yong-ho Lee

    2016-03-01

    Full Text Available A clinical prediction model can be applied to several challenging clinical scenarios: screening high-risk individuals for asymptomatic disease, predicting future events such as disease or death, and assisting medical decision-making and health education. Despite the impact of clinical prediction models on practice, prediction modeling is a complex process requiring careful statistical analyses and sound clinical judgement. Although there is no definite consensus on the best methodology for model development and validation, a few recommendations and checklists have been proposed. In this review, we summarize five steps for developing and validating a clinical prediction model: preparation for establishing clinical prediction models; dataset selection; handling variables; model generation; and model evaluation and validation. We also review several studies that detail methods for developing clinical prediction models with comparable examples from real practice. After model development and vigorous validation in relevant settings, possibly with evaluation of utility/usability and fine-tuning, good models can be ready for the use in practice. We anticipate that this framework will revitalize the use of predictive or prognostic research in endocrinology, leading to active applications in real clinical practice.

  20. Length of stay in surgical patients: nutritional predictive parameters revisited.

    Science.gov (United States)

    Almeida, Ana Isabel; Correia, Marta; Camilo, Maria; Ravasco, Paula

    2013-01-28

    Nutritional evaluation may predict clinical outcomes, such as hospital length of stay (LOS). We aimed to assess the value of nutritional risk and status methods, and to test standard anthropometry percentiles v. the 50th percentile threshold in predicting LOS, and to determine nutritional status changes during hospitalisation and their relation with LOS. In this longitudinal prospective study, 298 surgical patients were evaluated at admission and discharge. At admission, nutritional risk was assessed by Nutritional Risk Screening-2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST) and nutritional status by Subjective Global Assessment (SGA), involuntary % weight loss in the previous 6 months and anthropometric parameters; % weight loss and anthropometry were reassessed at discharge. At admission, risk/undernutrition results by NRS-2002 (PMAC) or a mid-arm muscle circumference (MAMA) under the 15th and the 50th percentile, which was considered indicative of undernutrition, did predict longer LOS (PMAC+MAMA (n 158, 53 %) had longer LOS than patients with a TSF+MAC+MAMA positive variation (11 (8-15) v. 8 (7-12) d, PMAC and MAMA measurements and their classification according to the 50th percentile threshold seem reliable undernutrition indicators.

  1. Representative parameter of immunostimulatory ginseng polysaccharide to predict radioprotection

    Energy Technology Data Exchange (ETDEWEB)

    Son, Hyeog Jin; Shim, Ji Young; Ahn, Ji Yeon; Yun, Yeon Sook; Song, Jie Young [Korea Institute of Radiological and Medical Sciences, Seoul (Korea, Republic of)

    2008-09-15

    According to the increase in the use of radiotherapy to cancer patients, many approaches have been tried to develop new agents for the protection of surrounding normal tissues. However, it is still few applied in the clinic as a radioprotector. We aim to find a representative parameter for radioprotection to easily predict the activity of in vivo experiment from the results of in vitro screening. The polysaccharide extracted from Panax ginseng was used in this study because the immunostimulator has been regarded as one of the radioprotective agent category and was already reported having a promising radioprotective activity through the increase of hematopoietic cells and the production of several cytokines. Mitogenic activity, AK cells activity and nitric oxide production were monitored for the in vitro immunological assay, and endogenous Colony-Forming Unit (e-CFU) was measured as in vivo radioprotective parameter. The immunological activity was increased by the galactose contents of ginseng polysaccharide dependently. The result of this study suggests that mitogenic activity of splenocytes demonstrated a good correlation with in vivo radioprotective effect, and may be used as a representative parameter to screen the candidates for radioprotector.

  2. Prediction of Betavoltaic Battery Output Parameters Based on SEM Measurements

    Directory of Open Access Journals (Sweden)

    E.B. Yakimov

    2016-12-01

    Full Text Available The approach for the prediction of betavoltaic battery output parameters based on EBIC investigations of semiconductor converters of beta-radiation energy into electric power is presented. Using this approach the parameters of battery based on porous Si are calculated. These parameters are compared with those of battery based on a planar Si p-n junction.

  3. The Clinical Prediction of Dangerousness.

    Science.gov (United States)

    1985-05-01

    executive potential, psychopathy , suicidality and so forth. Unfor- tunately, this is not the case. There tend to be substantial dif- ferences among...Prediction from case material to personality data. New York Archives of Psychology, 29 (No. 207). Hare, R. D. (1970). Psychopathy : Theory and research. New...1967). Psychopathy , mental deficiency, aggressiveness, and the XYY syndrome. Nature, 214, (5087), 500-501. Wexler, D. (1979). Patients, therapists

  4. Parameter prediction in laser bending of aluminum alloy sheet

    Institute of Scientific and Technical Information of China (English)

    Xuyue WANG; Weixing XU; Hua CHEN; Jinsong WANG

    2008-01-01

    Based on the basic platform of BP neural net-works, a BP network model is established to predict the bending angle in the laser bending process of an aluminum alloy sheet (1-2 mm in thickness) and to optimize laser bending parameters for bending control. The sample experimental data is used to train the BP network. The nonlinear regularities of sample data are fitted through the trained BP network; the predicted results include laser bending angles and parameters. Experimental results indi-cate that the prediction allowance is controlled less than 5%-8% and can provide a theoretical and experimental basis for industry purpose.

  5. Predicting Clinical Outcomes Using Molecular Biomarkers.

    Science.gov (United States)

    Burke, Harry B

    2016-01-01

    Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician's screen during the physician-patient interaction, they will be an integral part of physician-patient-shared decision-making, and they will improve clinical care and patient outcomes.

  6. Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

    KAUST Repository

    Sawlan, Zaid A

    2012-12-01

    Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.

  7. Solar flare prediction using highly stressed longitudinal magnetic field parameters

    Institute of Scientific and Technical Information of China (English)

    Xin Huang; Hua-Ning Wang

    2013-01-01

    Three new longitudinal magnetic field parameters are extracted from SOHO/MDI magnetograms to characterize properties of the stressed magnetic field in active regions,and their flare productivities are calculated for 1055 active regions.We find that the proposed parameters can be used to distinguish flaring samples from non-flaring samples.Using the long-term accumulated MDI data,we build the solar flare prediction model by using a data mining method.Furthermore,the decision boundary,which is used to divide flaring from non-flaring samples,is determined by the decision tree algorithm.Finally,the performance of the prediction model is evaluated by 10-fold cross validation technology.We conclude that an efficient solar flare prediction model can be built by the proposed longitudinal magnetic field parameters with the data mining method.

  8. A Universal Parameter to Predict Subaerial Landslide Tsunamis?

    Directory of Open Access Journals (Sweden)

    Valentin Heller

    2014-04-01

    Full Text Available The significance of the impulse product parameter P is reviewed, which is believed to be the most universal parameter for subaerial landslide tsunami (impulse wave prediction. This semi-empirical parameter is based on the streamwise slide momentum flux component and it was refined with a multiple regression laboratory data analysis. Empirical equations based on P allow for a simple prediction of wave features under diverse conditions (landslides and ice masses, granular and block slides, etc.. Analytical evidence reveals that a mass sliding down a hill slope of angle 51.6° results in the highest waves. The wave height “observed” in the 1958 Lituya Bay case was well predicted using P. Other real-world case studies illustrate how efficient empirical equations based on P deliver wave estimates which support hazard assessment. Future applications are hoped to further confirm the applicability of P to cases with more complex water body geometries and bathymetries.

  9. Autocovariance prediction of short period Earth rotation parameters.

    Science.gov (United States)

    Kosek, W.

    The autocovariance prediction of equidistant model and Earth Rotation Parameters (ERP) time series are presented. It enables computation of a forecast without any a priori information. It has been applied to short period polar motion and Length of Day (LOD) time series. The differences between the predicted short-period polar motion and LOD data computed for the 7th, 14th and 21st day in the future for different starting prediction epochs point out on irregular variations in Earth rotation. Similar computations were made using the autoregressive prediction method. The irregular (unpredictable) variations computed by the autoregressive prediction are very similar to those computed by the autocovariance prediction. The frequency and time-frequency analysis of these irregular variations shows that they affect oscillations with different periods from about 20 to ≡130 days and in different epochs.

  10. Using string invariants for prediction searching for optimal parameters

    Science.gov (United States)

    Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

    2016-02-01

    We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

  11. Parameter variations in prediction skill optimization at ECMWF

    Science.gov (United States)

    Ollinaho, P.; Bechtold, P.; Leutbecher, M.; Laine, M.; Solonen, A.; Haario, H.; Järvinen, H.

    2013-11-01

    Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our target criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the target criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the target criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general target criterion should be developed

  12. Towards predictive food process models: A protocol for parameter estimation.

    Science.gov (United States)

    Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E

    2016-05-31

    Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.

  13. Convergence in parameters and predictions using computational experimental design.

    Science.gov (United States)

    Hagen, David R; White, Jacob K; Tidor, Bruce

    2013-08-06

    Typically, biological models fitted to experimental data suffer from significant parameter uncertainty, which can lead to inaccurate or uncertain predictions. One school of thought holds that accurate estimation of the true parameters of a biological system is inherently problematic. Recent work, however, suggests that optimal experimental design techniques can select sets of experiments whose members probe complementary aspects of a biochemical network that together can account for its full behaviour. Here, we implemented an experimental design approach for selecting sets of experiments that constrain parameter uncertainty. We demonstrated with a model of the epidermal growth factor-nerve growth factor pathway that, after synthetically performing a handful of optimal experiments, the uncertainty in all 48 parameters converged below 10 per cent. Furthermore, the fitted parameters converged to their true values with a small error consistent with the residual uncertainty. When untested experimental conditions were simulated with the fitted models, the predicted species concentrations converged to their true values with errors that were consistent with the residual uncertainty. This paper suggests that accurate parameter estimation is achievable with complementary experiments specifically designed for the task, and that the resulting parametrized models are capable of accurate predictions.

  14. Radiogenomics: predicting clinical normal tissue radiosensitivity

    DEFF Research Database (Denmark)

    Alsner, Jan

    2006-01-01

    of subcutaneous fibrosis in breast cancer patients will be presented and discussed in relation to possible future studies in radiogenomics. One important and necessary basis for future studies is the collection of carefully designed clinical studies with the accrual of very large numbers of patients (the ESTRO......Studies on the genetic basis of normal tissue radiosensitivity, or  'radiogenomics', aims at predicting clinical radiosensitivity and optimize treatment from individual genetic profiles. Several studies have now reported links between variations in certain genes related to the biological response...... to radiation injury and risk of normal tissue morbidity in cancer patients treated with radiotherapy. However, after these initial association studies including few genes, we are still far from being able to predict clinical radiosensitivity on an individual level. Recent data from our own studies on risk...

  15. Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hoda Hosny Abuzied

    2012-01-01

    Full Text Available Electrochemical machining (ECM is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR, and surface roughness (Ra using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.

  16. Prediction of acoustic feature parameters using myoelectric signals.

    Science.gov (United States)

    Lee, Ki-Seung

    2010-07-01

    It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test.

  17. Prediction and simulation errors in parameter estimation for nonlinear systems

    Science.gov (United States)

    Aguirre, Luis A.; Barbosa, Bruno H. G.; Braga, Antônio P.

    2010-11-01

    This article compares the pros and cons of using prediction error and simulation error to define cost functions for parameter estimation in the context of nonlinear system identification. To avoid being influenced by estimators of the least squares family (e.g. prediction error methods), and in order to be able to solve non-convex optimisation problems (e.g. minimisation of some norm of the free-run simulation error), evolutionary algorithms were used. Simulated examples which include polynomial, rational and neural network models are discussed. Our results—obtained using different model classes—show that, in general the use of simulation error is preferable to prediction error. An interesting exception to this rule seems to be the equation error case when the model structure includes the true model. In the case of error-in-variables, although parameter estimation is biased in both cases, the algorithm based on simulation error is more robust.

  18. Hansen solubility parameter as a tool to predict cocrystal formation.

    Science.gov (United States)

    Mohammad, Mohammad Amin; Alhalaweh, Amjad; Velaga, Sitaram P

    2011-04-04

    The objective of this study was to investigate whether the miscibility of a drug and coformer, as predicted by Hansen solubility parameters (HSPs), can indicate cocrystal formation and guide cocrystal screening. It was also our aim to evaluate various HSPs-based approaches in miscibility prediction. HSPs for indomethacin (the model drug) and over thirty coformers were calculated according to the group contribution method. Differences in the HSPs between indomethacin and each coformer were then calculated using three established approaches, and the miscibility was predicted. Subsequently, differential scanning calorimetry was used to investigate the experimental miscibility and cocrystal formation. The formation of cocrystals was also verified using liquid-assisted grinding. All except one of the drug-coformers that were predicted to be miscible were confirmed experimentally as miscible. All tested theoretical approaches were in agreement in predicting miscibility. All systems that formed cocrystals were miscible. Remarkably, two new cocrystals of indomethacin were discovered in this study. Though it may be necessary to test this approach in a wide range of different coformer and drug compound types for accurate generalizations, the trends with tested systems were clear and suggest that the drug and coformer should be miscible for cocrystal formation. Thus, predicting the miscibility of cocrystal components using solubility parameters can guide the selection of potential coformers prior to exhaustive cocrystal screening work.

  19. Temperature-based bioclimatic parameters can predict nematode metabolic footprints.

    Science.gov (United States)

    Bhusal, Daya Ram; Tsiafouli, Maria A; Sgardelis, Stefanos P

    2015-09-01

    Nematode metabolic footprints (MFs) refer to the lifetime amount of metabolized carbon per individual, indicating a connection to soil food web functions and eventually to processes supporting ecosystem services. Estimating and managing these at a convenient scale requires information upscaling from the soil sample to the landscape level. We explore the feasibility of predicting nematode MFs from temperature-based bioclimatic parameters across a landscape. We assume that temperature effects are reflected in MFs, since temperature variations determine life processes ranging from enzyme activities to community structure. We use microclimate data recorded for 1 year from sites differing by orientation, altitude and vegetation cover. At the same sites we estimate MFs for each nematode trophic group. Our models show that bioclimatic parameters, specifically those accounting for temporal variations in temperature and extremities, predict most of the variation in nematode MFs. Higher fungivorous and lower bacterivorous nematode MFs are predicted for sites with high seasonality and low isothermality (sites of low vegetation, mostly at low altitudes), indicating differences in the relative contribution of the corresponding food web channels to the metabolism of carbon across the landscape. Higher plant-parasitic MFs were predicted for sites with high seasonality. The fitted models provide realistic predictions of unknown cases within the range of the predictor's values, allowing for the interpolation of MFs within the sampled region. We conclude that upscaling of the bioindication potential of nematode communities is feasible and can provide new perspectives not only in the field of soil ecology but other research areas as well.

  20. Neural networks for predicting mass transfer parameters in supercritical extraction

    Directory of Open Access Journals (Sweden)

    A.P. Fonseca

    2000-12-01

    Full Text Available Neural networks have been investigated for predicting mass transfer coefficients from supercritical Carbon Dioxide/Ethanol/Water system. To avoid the difficulties associated with reduce experimental data set available for supercritical extraction in question, it was chosen to use a technique to generate new semi-empirical data. It combines experimental mass transfer coefficient with those obtained from correlation available in literature, producing an extended data set enough for efficient neural network identification. With respect to available experimental data, the results obtained to benefit neural networks in comparing with empirical correlations for predicting mass transfer parameters.

  1. Prediction of mortality rates using a model with stochastic parameters

    Science.gov (United States)

    Tan, Chon Sern; Pooi, Ah Hin

    2016-10-01

    Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.

  2. Prediction of interest rate using CKLS model with stochastic parameters

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)

    2014-06-19

    The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

  3. Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness.

    Science.gov (United States)

    Hodges-Simeon, Carolyn R; Gaulin, Steven J C; Puts, David A

    2010-12-01

    Low mean fundamental frequency (F(0)) in men's voices has been found to positively influence perceptions of dominance by men and attractiveness by women using standardized speech. Using natural speech obtained during an ecologically valid social interaction, we examined relationships between multiple vocal parameters and dominance and attractiveness judgments. Male voices from an unscripted dating game were judged by men for physical and social dominance and by women in fertile and non-fertile menstrual cycle phases for desirability in short-term and long-term relationships. Five vocal parameters were analyzed: mean F(0) (an acoustic correlate of vocal fold size), F(0) variation, intensity (loudness), utterance duration, and formant dispersion (D(f), an acoustic correlate of vocal tract length). Parallel but separate ratings of speech transcripts served as controls for content. Multiple regression analyses were used to examine the independent contributions of each of the predictors. Physical dominance was predicted by low F(0) variation and physically dominant word content. Social dominance was predicted only by socially dominant word content. Ratings of attractiveness by women were predicted by low mean F(0), low D(f), high intensity, and attractive word content across cycle phase and mating context. Low D(f) was perceived as attractive by fertile-phase women only. We hypothesize that competitors and potential mates may attend more strongly to different components of men's voices because of the different types of information these vocal parameters provide.

  4. Researches on High Accuracy Prediction Methods of Earth Orientation Parameters

    Science.gov (United States)

    Xu, X. Q.

    2015-09-01

    The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients

  5. Prediction of Earth rotation parameters by fuzzy inference systems

    Science.gov (United States)

    Akyilmaz, O.; Kutterer, H.

    2004-09-01

    The short-term prediction of Earth rotation parameters (ERP) (length-of-day and polar motion) is studied up to 10 days by means of ANFIS (adaptive network based fuzzy inference system). The prediction is then extended to 40 days into the future by using the formerly predicted values as input data. The ERP C04 time series with daily values from the International Earth Rotation Service (IERS) serve as the data base. Well-known effects in the ERP series, such as the impact of the tides of the solid Earth and the oceans or seasonal variations of the atmosphere, were removed a priori from the C04 series. The residual series were used for both training and validation of the network. Different network architectures are discussed and compared in order to optimize the network solution. The results of the prediction are analyzed and compared with those of other methods. Short-term ERP values predicted by ANFIS show root-mean-square errors which are equal to or even lower than those from the other considered methods. The presented method is easy to use.

  6. Using neural networks for prediction of nuclear parameters

    Energy Technology Data Exchange (ETDEWEB)

    Pereira Filho, Leonidas; Souto, Kelling Cabral, E-mail: leonidasmilenium@hotmail.com, E-mail: kcsouto@bol.com.br [Instituto Federal de Educacao, Ciencia e Tecnologia do Rio de Janeiro (IFRJ), Rio de Janeiro, RJ (Brazil); Machado, Marcelo Dornellas, E-mail: dornemd@eletronuclear.gov.br [Eletrobras Termonuclear S.A. (GCN.T/ELETRONUCLEAR), Rio de Janeiro, RJ (Brazil). Gerencia de Combustivel Nuclear

    2013-07-01

    Dating from 1943, the earliest work on artificial neural networks (ANN), when Warren Mc Cullock and Walter Pitts developed a study on the behavior of the biological neuron, with the goal of creating a mathematical model. Some other work was done until after the 80 witnessed an explosion of interest in ANNs, mainly due to advances in technology, especially microelectronics. Because ANNs are able to solve many problems such as approximation, classification, categorization, prediction and others, they have numerous applications in various areas, including nuclear. Nodal method is adopted as a tool for analyzing core parameters such as boron concentration and pin power peaks for pressurized water reactors. However, this method is extremely slow when it is necessary to perform various core evaluations, for example core reloading optimization. To overcome this difficulty, in this paper a model of Multi-layer Perceptron (MLP) artificial neural network type backpropagation will be trained to predict these values. The main objective of this work is the development of Multi-layer Perceptron (MLP) artificial neural network capable to predict, in very short time, with good accuracy, two important parameters used in the core reloading problem - Boron Concentration and Power Peaking Factor. For the training of the neural networks are provided loading patterns and nuclear data used in cycle 19 of Angra 1 nuclear power plant. Three models of networks are constructed using the same input data and providing the following outputs: 1- Boron Concentration and Power Peaking Factor, 2 - Boron Concentration and 3 - Power Peaking Factor. (author)

  7. New Predictive Hematologic Parameters in Chronic Rhinosinusitis: A Multicenter Study

    Directory of Open Access Journals (Sweden)

    Beyhan Yilmaz

    2016-12-01

    Full Text Available INTRODUCTION: Our aim was to investigate whether Neutrophil-Lymphocyte Ratio (NLR, Platelet-Lymphocyte Ratio (PLR and Mean Platelet Volume parameters (MPV may be utilized as inflammatory markers of chronic rhinosinusitis with nasal polyps (CRSwNP and without nasal polyps (CRSsNP. METHODS: This retrospective multicenter study was performed on 647 patients who were underwent endoscopic sinus surgery. Clinical and preoperative laboratory data of patients were screened retrospectively. The study and control groups were compared for the parameters NLR, PLR, MPV, neutrophils, lymphocytes, and platelets. RESULTS: Of the 647 patients, 313 were in the CRSwNP group, 334 were in the CRSsNP group. There were 93 individuals in the control group. NLR and PLR levels were significantly higher in study groups compared to control group (p < 0.001. But no statistically significant differences were identified between CRSwNP group and CRSsNP group in terms of NLR, PLR, MPV levels. DISCUSSION AND CONCLUSION: We speculate that high NLR and PLR values may be useful inflammatory indicator for CRSwNP and CRSsNP groups. We believe these parameters will have increasing clinical use in the future on treatment options and prognosis.

  8. Method for Predicting and Optimizing System Parameters for Electrospinning System

    Science.gov (United States)

    Wincheski, Russell A. (Inventor)

    2011-01-01

    An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

  9. Clinical predictive factors of pathologic tumor response

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Chi Hwan; Kim, Won Dong; Lee, Sang Jeon; Park, Woo Yoon [Chungbuk National University College of Medicine, Cheongju (Korea, Republic of)

    2012-09-15

    The aim of this study was to identify clinical predictive factors for tumor response after preoperative chemoradiotherapy (CRT) in rectal cancer. The study involved 51 patients who underwent preoperative CRT followed by surgery between January 2005 and February 2012. Radiotherapy was delivered to the whole pelvis at a dose of 45 Gy in 25 fractions, followed by a boost of 5.4 Gy in 3 fractions to the primary tumor with 5 fractions per week. Three different chemotherapy regimens were used. Tumor responses to preoperative CRT were assessed in terms of tumor downstaging and pathologic complete response (ypCR). Statistical analyses were performed to identify clinical factors associated with pathologic tumor response. Tumor downstaging was observed in 28 patients (54.9%), whereas ypCR was observed in 6 patients (11.8%). Multivariate analysis found that predictors of downstaging was pretreatment relative lymphocyte count (p = 0.023) and that none of clinical factors was significantly associated with ypCR. Pretreatment relative lymphocyte count (%) has a significant impact on the pathologic tumor response (tumor downstaging) after preoperative CRT for locally advanced rectal cancer. Enhancement of lymphocyte-mediated immune reactions may improve the effect of preoperative CRT for rectal cancer.

  10. Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Ahmed M. Al Rajeh

    2016-11-01

    Full Text Available Introduction: The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review. Objectives: To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation. Methods: An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline, Excerpta Medica dataBASE (Embase, Allied and Complementary Medicine Database (AMED, Cumulative Index of Nursing and Allied Health Literature (CINAHL and the Cochrane clinical trials database. Results: Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO2% prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients. Conclusion: There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.

  11. Correlation of salivary immunoglobulin A against lipopolysaccharide of Porphyromonas gingivalis with clinical periodontal parameters

    Directory of Open Access Journals (Sweden)

    Pushpa S Pudakalkatti

    2015-01-01

    Full Text Available Background: A major challenge in clinical periodontics is to find a reliable molecular marker of periodontal tissue destruction. Aim: The aim of the present study was to assess, whether any correlation exists between salivary immunoglobulin A (IgA level against lipopolysaccharide of Porphyromonas gingivalis and clinical periodontal parameters (probing depth and clinical attachment loss. Materials and Methods: Totally, 30 patients with chronic periodontitis were included for the study based on clinical examination. Unstimulated saliva was collected from each study subject. Probing depth and clinical attachment loss were recorded in all selected subjects using University of North Carolina-15 periodontal probe. Extraction and purification of lipopolysaccharide were done from the standard strain of P. gingivalis (ATCC 33277. Enzyme linked immunosorbent assay (ELISA was used to detect the level of IgA antibodies against lipopolysaccharide of P. gingivalis in the saliva of each subject by coating wells of ELISA kit with extracted lipopolysaccharide antigen. Statistical Analysis: The correlation between salivary IgA and clinical periodontal parameters was checked using Karl Pearson′s correlation coefficient method and regression analysis. Results: The significant correlation was observed between salivary IgA level and clinical periodontal parameters in chronic periodontitis patients. Conclusion: A significant strong correlation was observed between salivary IgA against lipopolysaccharide of P. gingivalis and clinical periodontal parameters which suggest that salivary IgA level against lipopolysaccharide of P. gingivalis can be used to predict the severity of periodontal destruction in chronic periodontitis patients.

  12. Clinical parameters associated with periodontitis in untreated persons

    NARCIS (Netherlands)

    Lembariti, BS; Van't Hof, MA; Pilot, T; Van Palenstein-Helderman, WH

    1997-01-01

    The purpose of this study was to investigate the relationship between clinical parameters and periodontitis in a population receiving no regular prophylactic dental care. From a sample of 164 adult rural and urban Tanzanian subjects aged between 30 and 44 years, 16% were identified with periodontiti

  13. Prediction of solubility parameters using partial least square regression.

    Science.gov (United States)

    Tantishaiyakul, Vimon; Worakul, Nimit; Wongpoowarak, Wibul

    2006-11-15

    The total solubility parameter (delta) values were effectively predicted by using computed molecular descriptors and multivariate partial least squares (PLS) statistics. The molecular descriptors in the derived models included heat of formation, dipole moment, molar refractivity, solvent-accessible surface area (SA), surface-bounded molecular volume (SV), unsaturated index (Ui), and hydrophilic index (Hy). The values of these descriptors were computed by the use of HyperChem 7.5, QSPR Properties module in HyperChem 7.5, and Dragon Web version. The other two descriptors, hydrogen bonding donor (HD), and hydrogen bond-forming ability (HB) were also included in the models. The final reduced model of the whole data set had R(2) of 0.853, Q(2) of 0.813, root mean squared error from the cross-validation of the training set (RMSEcv(tr)) of 2.096 and RMSE of calibration (RMSE(tr)) of 1.857. No outlier was observed from this data set of 51 diverse compounds. Additionally, the predictive power of the developed model was comparable to the well recognized systems of Hansen, van Krevelen and Hoftyzer, and Hoy.

  14. Predicting Endometrium Receptivity with Parameters of Spiral Artery Blood Flow

    Institute of Scientific and Technical Information of China (English)

    GONG Xuehao; LI Quanshui; ZHANG Qingping; ZHU Guijin

    2005-01-01

    Summary: In order To evaluate whether the parameters of spiral artery blood flow, as measured by transvaginal color Doppler, may be used to assess endometrium receptivity prior to embryo transfer (ET), a retrospective study of 94 infertile women who had undergone ART treatments with different outcomes (pregnant or nonpregnant) was done. Subendometrial blood flow was evaluated. The resistance index (RI), systolic/diastolic ratio (S/D) and pulsatility index (PI) were significantly lower in those who achieved pregnancy as compared with those who did not: 0.62±0.04 vs 0.68±0.04 (P<0.001), 2.66±0.33 vs 3.19±0.39 (P<0.01) and 1.15±0.17 vs 1.34±0.22 (P<0.05), respectively. Furthermore, when RI>0.72, PI>1.6, and S/D>3.6, no pregnancy occurred. These data suggest that the parameters of spiral artery blood flow could be used as a new assay in predicting endometrial receptivity before ET.

  15. Clinical and blood gasometric parameters during Vaquejada competition

    Directory of Open Access Journals (Sweden)

    Silvana S.B. Arruda

    2015-11-01

    Full Text Available ABSTRACT: Clinical and complementary analysis are good alternatives to evaluate physiological demand in performance horses. The aim of this study was to assess whether the physical effort variation of the three-day Vaquejada competition (a Brazilian form of bullfighting reflected in clinical and blood gasometric changes. During the competition eight sprints have been performed on the first day (D1, eight on the second (D2 and three on the last one (D3. Ten horses were evaluated by checking heart and respiratory rates and collecting blood samples for use in portable chemistry analyzer. Through that, it was assessed potential of hydrogen ion (pH, carbon dioxide pressure (pCO2, bicarbonate (HCO3- and titratable base concentration (cBase. Evaluations were carried with resting of at least twenty hours, before physical activity (D0, as control parameter, and up to thirty minutes after each sprint. Clinical parameters have increased on D1, D2 and D3, when compared to D0, which demonstrated the increased demand for substrate and oxygen to the cells.. Blood gasometric trial showed reductions of all variables, most marked between D1 and D2. It was verified less alteration of all clinical and blood gasometric parameters in D3 against D0. We concluded that the change effort between days of competition influenced the clinical and blood gas parameters, demonstrating appropriate physiological response. The data were presented as mean and standard error of the mean (mean ± SEM obtained in different days. Normality was confirmed by the Kolmogorov-Sminov test and data were compared by one-way ANOVA, followed by post-test Holm-Sidak (GraphPad Prism 2.6 for Windows, GraphPad Software, San Diego, CA, USA. P≤0.05 was considered as statistically significant.

  16. Use of Feedback in Clinical Prediction

    Science.gov (United States)

    Schroeder, Harold E.

    1972-01-01

    Results indicated that predictive accuracy is greater when feedback is applied to the basis for the prediction than when applied to gut" impressions. Judges forming hypotheses were also able to learn from experience. (Author)

  17. Evaluation of Neonatal Hemolytic Jaundice: Clinical and Laboratory Parameters

    OpenAIRE

    Anet Papazovska Cherepnalkovski; Vjekoslav Krzelj; Beti Zafirovska-Ivanovska; Todor Gruev; Josko Markic; Natasa Aluloska; Nikolina Zdraveska; Katica Piperkovska

    2015-01-01

    BACKGROUND: Neonatal jaundice that occurs in ABO or Rhesus issoimunisation has been recognized as one of the major risk factors for development of severe hyperbilirubinemia and bilirubin neurotoxicity. AIM: Aim of our study was to investigate clinical and laboratory parameters associated with hemolytic jaundice due to Rh and ABO incompatibility and compare results with the group of unspecific jaundice. MATERIAL AND METHODS: One hundred sixty seven (167) neonatal hyperbilirubinemia cas...

  18. Clinical Prediction Rule of Drug Resistant Epilepsy in Children

    OpenAIRE

    2015-01-01

    Background and Purpose: Clinical prediction rules (CPR) are clinical decision-making tools containing variables such as history, physical examination, diagnostic tests by developing scoring model from potential risk factors. This study is to establish clinical prediction scoring of drug-resistant epilepsy (DRE) in children using clinical manifestationa and only basic electroencephalography (EEG). Methods: Retrospective cohort study was conducted. A total of 308 children with diagnosed epileps...

  19. Functional clinical typology of the foot and kinematic gait parameters

    Directory of Open Access Journals (Sweden)

    Jitka Marenčáková

    2016-06-01

    Full Text Available Background: The foot plays a key role in a standing posture, walking and running performance. Changes in its structure or function may alter upper segments of kinematic chain which can lead to formation of musculoskeletal disorders. Although functional clinical typology provides a complex view of foot kinesiology there is a lack of knowledge and evidence about influences of different foot types on human gait. Objective: The aim of the study was to analyse differences of kinematic gait parameters of lower extremity joints and pelvis between functional clinical foot types in healthy young men. Methods: Three-dimensional kinematic analysis by the Vicon Motion Capture MX System device in synchronization with 2 Kistler force platforms was used to obtain kinematic data from 18 healthy men (mean age 23.2 ± 1.9 years. The functional clinical foot type was clinically examined and sorted into 3 basic foot type groups - forefoot varus (FFvar, rearfoot varus (RFvar and forefoot valgus (FFvalg. Peak angular values and range of an angular displacement in all of three movement planes were analysed for pelvis, hip, knee and ankle joint. For statistical analysis of kinematic gait parameters differences between foot types Mann Whitney U test at a statistical significance level p < .05 and Cohen's coefficient d for effect size were used. Results: This study showed that functional clinical foot type can affect kinematic parameters of gait in the joints of the lower limb and pelvis. Significant differences were presented in the FFvar in comparison with other two foot type groups with middle and high size of effect. The most alterations were observed in pelvis area and in a sagittal plane of movement. Nevertheless, significant differences between FFvalg and RFvar foot types were not noticed. Conclusions: Functional clinical foot typology provides one of the possible methods to describe foot structure and function. Our results showed that foot type could

  20. Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Nonlinear time series prediction is studied by using an improved least squares support vector machine (LSSVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization.We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

  1. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Science.gov (United States)

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

    2016-07-01

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

  2. Predicting hospital-acquired infections by scoring system with simple parameters.

    Directory of Open Access Journals (Sweden)

    Ying-Jui Chang

    Full Text Available BACKGROUND: Hospital-acquired infections (HAI are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR and validated by Artificial Neural Networks (ANN simultaneously. METHODOLOGY/PRINCIPAL FINDINGS: A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507 to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447. The scoring system also performed extremely well in the internal (AUC: 0.965 and external (AUC: 0.871 validations. CONCLUSIONS: We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction

  3. A clinical prediction rule for histological chorioamnionitis in preterm newborns.

    Directory of Open Access Journals (Sweden)

    Jasper V Been

    Full Text Available BACKGROUND: Histological chorioamnionitis (HC is an intrauterine inflammatory process highly associated with preterm birth and adverse neonatal outcome. HC is often clinically silent and diagnosed postnatally by placental histology. Earlier identification could facilitate treatment individualisation to improve outcome in preterm newborns. AIM: Develop a clinical prediction rule at birth for HC and HC with fetal involvement (HCF in preterm newborns. METHODS: Clinical data and placental pathology were obtained from singleton preterm newborns (gestational age ≤ 32.0 weeks born at Erasmus UMC Rotterdam from 2001 to 2003 (derivation cohort; n = 216 or Máxima MC Veldhoven from 2009 to 2010 (validation cohort; n = 206. HC and HCF prediction rules were developed with preference for high sensitivity using clinical variables available at birth. RESULTS: HC and HCF were present in 39% and 24% in the derivation cohort and in 44% and 22% in the validation cohort, respectively. HC was predicted with 87% accuracy, yielding an area under ROC curve of 0.95 (95%CI = 0.92-0.98, a positive predictive value of 80% (95%CI = 74-84%, and a negative predictive value of 93% (95%CI = 88-96%. Corresponding figures for HCF were: accuracy 83%, area under ROC curve 0.92 (95%CI = 0.88-0.96, positive predictive value 59% (95%CI = 52-62%, and negative predictive value 97% (95%CI = 93-99%. External validation expectedly resulted in some loss of test performance, preferentially affecting positive predictive rather than negative predictive values. CONCLUSION: Using a clinical prediction rule composed of clinical variables available at birth, HC and HCF could be predicted with good test characteristics in preterm newborns. Further studies should evaluate the clinical value of these rules to guide early treatment individualisation.

  4. Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications

    Directory of Open Access Journals (Sweden)

    Morufu Olusola Ibitoye

    2014-12-01

    Full Text Available The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

  5. Mechanomyographic parameter extraction methods: an appraisal for clinical applications.

    Science.gov (United States)

    Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Zuniga, Jorge M; Hasnan, Nazirah; Wahab, Ahmad Khairi Abdul

    2014-12-03

    The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.

  6. Electrocardiographic parameters in the clinically healthy Zamorano-leones donkey.

    Science.gov (United States)

    Escudero, Andrés; González, José R; Benedito, José L; Prieto, Felipe R; Ayala, Ignacio

    2009-12-01

    Limited information exists regarding electrocardiographic parameters in clinically healthy donkeys. The study was carried out in 75 healthy adult animals (40 females and 35 males) using the Einthoven standard II and base-apex leads. The P wave showed usually a bifid shape deflection. The QRS complex of the donkeys appeared in several forms: QR and R were the most frequent in limb lead II, and QS and QR in the base-apex lead. Most T waves presented a simple negative configuration in lead II and biphasic shape in the base-apex one. Mean heart rate value was 52 beats per minute. The direction of the QRS vector in lead II had a mean value of 91.4 degrees. We observed a lack of detected arrhythmias. Statistically significant differences were observed between sexes for several parameters. The electrocardiogram of Zamorano-leones donkey differs in several duration, amplitude and morphologic parameters from that of several breeds of horses and donkeys. This fact justifies obtaining values for a specific breed against which to compare values for the same breed.

  7. Trend modelling of wave parameters and application in onboard prediction of ship responses

    DEFF Research Database (Denmark)

    Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, J. Juncher

    2015-01-01

    This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in-service ......This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in...

  8. Clinical refinement of the automatic lung parameter estimator (ALPE).

    Science.gov (United States)

    Thomsen, Lars P; Karbing, Dan S; Smith, Bram W; Murley, David; Weinreich, Ulla M; Kjærgaard, Søren; Toft, Egon; Thorgaard, Per; Andreassen, Steen; Rees, Stephen E

    2013-06-01

    The automatic lung parameter estimator (ALPE) method was developed in 2002 for bedside estimation of pulmonary gas exchange using step changes in inspired oxygen fraction (FIO₂). Since then a number of studies have been conducted indicating the potential for clinical application and necessitating systems evolution to match clinical application. This paper describes and evaluates the evolution of the ALPE method from a research implementation (ALPE1) to two commercial implementations (ALPE2 and ALPE3). A need for dedicated implementations of the ALPE method was identified: one for spontaneously breathing (non-mechanically ventilated) patients (ALPE2) and one for mechanically ventilated patients (ALPE3). For these two implementations, design issues relating to usability and automation are described including the mixing of gasses to achieve FIO₂ levels, and the automatic selection of FIO₂. For ALPE2, these improvements are evaluated against patients studied using the system. The major result is the evolution of the ALPE method into two dedicated implementations, namely ALPE2 and ALPE3. For ALPE2, the usability and automation of FIO₂ selection has been evaluated in spontaneously breathing patients showing that variability of gas delivery is 0.3 % (standard deviation) in 1,332 breaths from 20 patients. Also for ALPE2, the automated FIO2 selection method was successfully applied in 287 patient cases, taking 7.2 ± 2.4 min and was shown to be safe with only one patient having SpO₂ < 86 % when the clinician disabled the alarms. The ALPE method has evolved into two practical, usable systems targeted at clinical application, namely ALPE2 for spontaneously breathing patients and ALPE3 for mechanically ventilated patients. These systems may promote the exploration of the use of more detailed descriptions of pulmonary gas exchange in clinical practice.

  9. Variable input parameter influence on river corridor prediction

    NARCIS (Netherlands)

    Zerfu, T.; Beevers, L.; Crosato, A.; Wright, N.

    2015-01-01

    This paper considers the erodible river corridor, which is the area in which the main river channel is free to migrate over a period of time. Due to growing anthropogenic pressure, predicting the corridor width has become increasingly important for the planning of development along rivers. Several a

  10. Cervical Vertebral Body’s Volume as a New Parameter for Predicting the Skeletal Maturation Stages

    Directory of Open Access Journals (Sweden)

    Youn-Kyung Choi

    2016-01-01

    Full Text Available This study aimed to determine the correlation between the volumetric parameters derived from the images of the second, third, and fourth cervical vertebrae by using cone beam computed tomography with skeletal maturation stages and to propose a new formula for predicting skeletal maturation by using regression analysis. We obtained the estimation of skeletal maturation levels from hand-wrist radiographs and volume parameters derived from the second, third, and fourth cervical vertebrae bodies from 102 Japanese patients (54 women and 48 men, 5–18 years of age. We performed Pearson’s correlation coefficient analysis and simple regression analysis. All volume parameters derived from the second, third, and fourth cervical vertebrae exhibited statistically significant correlations (P<0.05. The simple regression model with the greatest R-square indicated the fourth-cervical-vertebra volume as an independent variable with a variance inflation factor less than ten. The explanation power was 81.76%. Volumetric parameters of cervical vertebrae using cone beam computed tomography are useful in regression models. The derived regression model has the potential for clinical application as it enables a simple and quantitative analysis to evaluate skeletal maturation level.

  11. Wear prediction on total ankle replacement effect of design parameters

    CERN Document Server

    Saad, Amir Putra Bin Md; Harun, Muhamad Noor; Kadir, Mohammed Rafiq Abdul

    2016-01-01

    This book develops and analyses computational wear simulations of the total ankle replacement for the stance phase of gait cycle. The emphasis is put on the relevant design parameters. The book presents a model consisting of three components; tibial, bearing and talar representing their physiological functions.

  12. Translating biological parameters into clinically useful diagnostic tests.

    Science.gov (United States)

    Arfken, Cynthia L; Carney, Stuart; Boutros, Nash N

    2009-08-01

    Psychiatry has lagged behind other specialties in developing diagnostic laboratory tests for the purpose of confirming or ruling out a diagnosis. Biological research into the pathophysiology of psychiatric disorders has, however, yielded some highly replicable abnormalities that have the potential for development into clinically useful diagnostic tests. To achieve this goal, a process for systematic translation must be developed and implemented. Building on our previous work, we review a proposed process using four clearly defined steps. We conclude that biological parameters currently face challenges in their pathways to becoming diagnostic tests because of both the premature release and premature abandonment of tests. Attention to a systematic translation process aided by these principles may help to avoid these problems.

  13. Nutritional screening: control of clinical undernutrition with analytical parameters

    Directory of Open Access Journals (Sweden)

    José Ignacio de Ulíbarri Pérez

    2014-04-01

    Full Text Available Objective: To update the system for nutritional screening. The high prevalence of nutritional unstability that causes the Clinical Undernutrition (CU, especially within the hospitals and assisted residencies, makes it necessary to use screening tools for the constant control of undernutrition to combat it during its development. CU is not so much due to a nutritional deficiency but to the illness and its treatments. However, the screening systems currently used are aimed at detecting an already established undernutrition rather than at detecting any nutritional risk that may be present. The metabolic changes of the nutritional status that have a trophopathic effect, can be easily and automatically detected in plasma, which allows to make the necessary changes in treatments that might be too aggressive, as well as to apply nutritional support according to each case. The manual screening systems can detect those somatic changes typical of undernutrition only after many days or weeks, which might be too late. Plasma albumin is a very reliable parameter for nutritional control. A lowered amount of it, due to whatever reason, is a clear sign of a possible deficit as well as of a nutritional risk suffered by the cell way before the somatic signs of undernutrition will become apparent. A fast detection of nutritional risk, anticipating undernutrition, offers prognostic abilities, which makes screening tools based on analytic parameters the most useful, ergonomic, reliable and efficient system for nutritional screening and prognosis in the clinical practice. Conclusion: It is necessary to update some concepts, to leave behind old myths and to choose modern screening systems that have proven to be efficient. This is the only way achieving the dream of controlling CU among ill and vulnerable patients.

  14. Prediction of Milk Quality Parameters Using Vibrational Spectroscopy and Chemometrics

    DEFF Research Database (Denmark)

    Eskildsen, Carl Emil Aae

    Vibrational spectroscopic techniques are widely used throughout all stages of food production. The analysis of raw materials, real-time process control, and end-product quality evaluation are all crucial steps in food production. In order to increase production throughput there is a need for speed...... fatty acids, protein fractions and coagulation properties from Fourier transform infrared measurements. This thesis shows how such predictions are trapped in a cage of covariance with major milk constituents like total fat and protein content. The prediction models for detailed milk composition...... are not based on causal relationships and this may seriously compromise calibration robustness. It is not recommended to implement indirect models for detailed milk composition in milk recording or breeding programs as such model are providing information on, for example, total protein rather than the specific...

  15. Improving hot region prediction by parameter optimization of density clustering in PPI.

    Science.gov (United States)

    Hu, Jing; Zhang, Xiaolong

    2016-11-01

    This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius.

  16. Combining clinical variables to optimize prediction of antidepressant treatment outcomes

    OpenAIRE

    Iniesta, R.; Malki, K.; Maier, W; Rietschel, M.; Mors, O; Hauser, J; Henigsberg, N.; Dernovsek, M. Z.; Souery, D.; Stahl, D.; Dobson, R.; Aitchison, K. J.; Farmer, A; Lewis, C.M.; McGuffin, P.

    2016-01-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remissio...

  17. Insomnia Complaint Versus Sleep Diary Parameters: Predictions of Suicidal Ideation.

    Science.gov (United States)

    Woosley, Julie A; Lichstein, Kenneth L; Taylor, Daniel J; Riedel, Brant W; Bush, Andrew J

    2016-02-01

    The purpose of this study was to determine which aspects of insomnia best predict suicidal ideation (SI). Participants were grouped according to whether they complained of insomnia and whether their sleep would be characterized as poor or good by applying quantitative criteria for insomnia to their sleep diary data. Analyses revealed that insomnia complaint was more strongly associated with SI than was poor sleep. These findings suggest that patients who complain of insomnia, regardless of the presence or absence of poor sleep, may be at greater risk for suicide than those who are content with their sleep.

  18. Endometrial histology and predictable clinical factors for endometrial disease in women with polycystic ovary syndrome

    OpenAIRE

    Park, Joon Cheol; Lim, Su Yeon; Jang, Tae Kyu; Bae, Jin Gon; Kim, Jong In; Rhee, Jeong Ho

    2011-01-01

    Objective This study was aimed to investigate endometrial histology and to find predictable clinical factors for endometrial disease (hyperplasia or cancer) in women with polycystic ovary syndrome (PCOS). Methods We investigated the endometrial histology and analyzed the relationship between endometrial histology and clinical parameters, such as LH, FSH, estradiol, testosterone, fasting and 2 hours postprandial glucose and insulin, insulin resistance, body mass index, endometrial thickness, m...

  19. Partial solubility parameters and solvatochromic parameters for predicting the solubility of single and multiple drugs in individual solvents.

    Science.gov (United States)

    Bustamante, P; Martin, A; Gonzalez-Guisandez, M A

    1993-06-01

    A modification of the extended Hansen method is used for estimating the solubility of sulfadiazine and other organic drug molecules in a number of individual solvents ranging from nonpolar to highly polar. The equations obtained for each drug involve the partial solubility parameters of the solvents and allow the prediction of solubility of these drugs in a new solvent. Furthermore, a number of drugs (e.g., sulfadiazine, sulfamethoxypyridazine, naphthalene, and some benzoic acid derivatives) are combined in a single expression including the ideal solubility of the drugs and the partial solubility parameters of the solvents. The equation fits the solubilities of these drugs in a wide variety of solvents and may be used to predict the solubility of other sulfonamides and benzoic acid derivatives in semipolar and highly polar solvents. The solvatochromic parameter approach is also used in models for predicting the solubility of single drugs in individual solvents. It was tested with multiple solutes as was the partial solubility parameter approach. However, the latter approach is superior; the parameters of the solubility parameter method are all statistically significant for drugs tested individually or together in a single equation, a condition that is not obtained with the solvatochromic model.

  20. Predictive parameters of survival in hemodialysis patients with restless leg syndrome

    Directory of Open Access Journals (Sweden)

    Radojica V Stolic

    2014-01-01

    Full Text Available Restless leg syndrome (RLS affects the quality of life and survival in patients on hemodialysis (HD. The aim of this study was to determine the characteristics and survival parameters in patients on HD with RLS. This study was a non-randomized clinical study involving 204 patients on HD, of whom 71 were female and 133 were male. Symptoms of RLS were defined as positive responses to four questions comprising the criteria of RLS. We recorded the outcome of treatment, biochemical analyses, demographic, sexual, anthropometric and clinical characteristics in all study patients. Patients with RLS who completed the study had a significantly higher body mass index and lower intima-media thickness and flow through the arteriovenous fistula. Among patients with RLS who died, there were more smokers as well as higher incidences of cardiovascular disease and diabetes mellitus. Among patients with RLS who survived, there were a greater number of patients with preserved diuresis and receiving erythropoietin therapy. Patients who completed the study had significantly higher levels of hemoglobin, creatinine, serum iron and transferrin satura-tion. Diabetes mellitus (B = 1.802; P = 0.002 and low Kt/V (B = -5.218; P = 0.001 were major predictive parameters for survival.

  1. Prediction of compressibility parameters of the soils using artificial neural network.

    Science.gov (United States)

    Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan

    2016-01-01

    The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.

  2. Molecular physicochemical parameters predicting antioxidant activity of Brazilian natural products

    Directory of Open Access Journals (Sweden)

    Luciana Scotti

    2009-12-01

    Full Text Available Reactive oxygen species (ROS are capable of oxidizing cellular proteins, nucleic acids and lipids, contributing to cellular aging, mutagenesis, carcinogenesis, coronary heart and neurodegenerative diseases. Free radicals-scavenging by phenolic compounds occurs by the transfer of one electron followed by the H-abstraction. In order to evaluate the antioxidant activity of a series of seventeen phenolic compounds extracted from Brazilian flora (Chimarrhis turbinata and Arrabidea samydoides, some physicochemical parameters (heat formation of the neutral, radical, and cationic compounds; orbitals' energies; ClogP; ΔH OX; and ΔHf were calculated. Considering the results from the calculated descriptors, the molecules 10a-f can be classified as having a higher antioxidant activity.

  3. Estimation of boundary parameters and prediction of transmission loss based upon ray acoustics

    Institute of Scientific and Technical Information of China (English)

    GUO Yuhong; FAN Minyi; HUI Junying

    2000-01-01

    Estimation of boundary parameters and prediction of transmission loss using a coherent channel model based upon ray acoustics and sound propagation data collected in field experiments are presented. Comparison between the prediction results and the experiment data indicates that the adopted sound propagation model is valuable, both selection and estimation methods on boundary parameters are reasonable, and the prediction performance of transmission loss is favorable.

  4. Predictive parameters of infectiologic complications in patients after TIPSS; Praediktive Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage

    Energy Technology Data Exchange (ETDEWEB)

    Cohnen, M.; Saleh, A.; Moedder, U. [Institut fuer Diagnostische Radiologie, Universitaetsklinikum Duesseldorf (Germany); Luethen, R.; Bode, J.; Haeussinger, D. [Klinik fuer Gastroenterologie, Hepatologie und Infektiologie, Universitaetsklinikum Duesseldorf (Germany); Daeubener, W. [Institut fuer Mikrobiologie und Virologie, Universitaetsklinikum Duesseldorf (Germany)

    2003-02-01

    Aim To define predictive parameters of a complicated clinical course after the TIPSS procedure. Blood cultures were drawn prospectively in 41 patients from a central line and from the portal venous blood before stent placement as well as from the central line 20 min after intervention. C-reactive proteine (CRP) (mg/dl) and white blood cell count (WBC,/{mu}l) on the day of TIPSS-procedure (d0), the first (d1) and seven (d7) days after TIPSS were compared in patients with a complicated clinical course (spontaneous bacterial peritonitis, pneumonia, sepsis; group I) to patients without clinical complications (group II) Group I showed a significant increase in CRP (d0: 1.8{+-}1.0; d1: 3.2{+-}1.5; d7: 4.3{+-}3.2), and white blood cell count (d0: 7700{+-}2600; d1: 10800{+-}2800; d7: 7500{+-}1800) on the first day after TIPSS-procedure in comparison to group II (CRP: d0: 1.6{+-}0.6; d1: 1.8{+-}1.0; d7: 1.9{+-}0.6. WBC: d0: 6900{+-}1500; d1: 8000{+-}1600; d7: 7600{+-}1400).Microbiological analysis showed in 12% skin or oral flora in the last sample. The course of CRP and WBC-count during the first week after TIPSS procedure may indicate patients with a potential risk of a complicated clinical course. (orig.) [German] Fragestellung Definition praediktiver Parameter infektiologischer Komplikationen bei Patienten nach TIPSS-Anlage.Methodik Bei 41 Patienten wurden Blutproben prospektiv vor intrahepatischer Stentanlage zentralvenoes und portalvenoes sowie 20 min postinterventionell erneut zentralvenoes entnommen und mikrobiologisch analysiert. C-reaktives Protein (CRP) (mg/dl) und Leukozytenzahl (/{mu}l) wurden am Interventionstag (d0), am 1. (d1) sowie 7 Tage (d7) postinterventionell bestimmt. Patienten mit kompliziertem Verlauf (spontane bakterielle Peritonitis,Pneumonie, Sepsis; Gruppe 1) wurden von Patienten ohne klinische Komplikationen (Gruppe 2) unterschieden.Ergebnisse Gruppe 1 wies einen signifikanten Anstieg des CRP (d0: 1,8{+-}1,0; d1: 3,2{+-}1,5; d7: 4,3{+-}3,2) und

  5. Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

  6. Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot

    Directory of Open Access Journals (Sweden)

    D. Hazry

    2006-01-01

    Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture

  7. Cognitive Models of Risky Choice: Parameter Stability and Predictive Accuracy of Prospect Theory

    Science.gov (United States)

    Glockner, Andreas; Pachur, Thorsten

    2012-01-01

    In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are…

  8. Predicting parameters of degradation succession processes of Tibetan Kobresia grasslands

    Science.gov (United States)

    Lin, L.; Li, Y. K.; Xu, X. L.; Zhang, F. W.; Du, Y. G.; Liu, S. L.; Guo, X. W.; Cao, G. M.

    2015-11-01

    In the past two decades, increasing human activity (i.e., overgrazing) in the Tibetan Plateau has strongly influenced plant succession processes, resulting in the degradation of alpine grasslands. Therefore, it is necessary to diagnose the degree of degradation to enable implementation of appropriate management for sustainable exploitation and protection of alpine grasslands. Here, we investigated environmental factors and plant functional group (PFG) quantity factors during the alpine grassland succession processes. Principal component analysis (PCA) was used to identify the parameters indicative of degradation. We divided the entire degradation process into six stages. PFG types shifted from rhizome bunchgrasses to rhizome plexus and dense-plexus grasses during the degradation process. Leguminosae and Gramineae plants were replaced by sedges during the advanced stages of degradation. The PFGs were classified into two reaction groups: the grazing-sensitive group, containing Kobresia humilis Mey, and Gramineae and Leguminosae plants, and the grazing-insensitive group, containing Kobresia pygmaea Clarke. The first group was correlated with live root biomass in the surface soil (0-10 cm), whereas the second group was strongly correlated with mattic epipedon thickness and K. pygmaea characteristics. The degree of degradation of alpine meadows may be delineated by development of mattic epipedon and PFG composition. Thus, meadows could be easily graded and their use adjusted based on our scaling system, which would help prevent irreversible degradation of important grasslands. Because relatively few environmental factors are investigated, this approach can save time and labor to formulate a conservation management plan for degraded alpine meadows.

  9. Evaluation of clinical, biochemical and hematological parameters in macrocytic anemia

    Directory of Open Access Journals (Sweden)

    Aarthi Kannan

    2016-07-01

    Results: Primary bone marrow disorders were the most common cause of macrocytosis (46%. The other causes in decreasing order of frequency were megaloblastic anaemia (38%, hemolytic anemia (6%, drug induced (5%, alcoholism and liver disease (4% and idiopathic thrombocytopenic purpura (1%. There was a significant difference in the mean values of MCV and serum LDH between megaloblastic and non and ndash; megaloblastic macrocytosis. When serum LDH >1345.2 IU/L or MCV>121fl (criterion values of ROC curve with reticulocyte count <2% was taken as criteria, the sensitivity was 92.1% and specificity was 93.5% for diagnosing megaloblastic anemia. Conclusions: Systematic evaluation of macrocytosis will help us to distinguish megaloblastic and non and ndash; megaloblastic macrocytosis. The blood and biochemical parameters especially CBC, RC, and serum LDH along with supporting clinical features help us in diagnosing megaloblastic anemia in a setup where vitamin and metabolite levels are difficult to obtain. [Int J Res Med Sci 2016; 4(7.000: 2670-2678

  10. Clinical and Biochemical Parameters of Children and Adolescents Applying Pesticides

    Directory of Open Access Journals (Sweden)

    O Hendy

    2010-06-01

    Full Text Available Background: The primary agricultural product in Egypt is the cotton crop. Children and adolescents work seasonally in the cotton fields applying pesticides.Objectives: To examine the effect of pesticide exposure on clinical and biochemical parameters in children and adolescents applying pesticides.Methods: Male children currently applying pesticides and aged between 9 and 19 years (n = 50 were recruited for this study. They were asked to complete work, health, and exposure questionnaires; examined for any medical and neurological problems with particular attention to sensory and motor functions including cranial nerves, sensory and motor system, and reflexes. From each participant, a blood sample was taken to measure acetylcholinesterase activity, and liver and kidney functions. Children who have never worked in agriculture (n = 50, matched on age, education, and socioeconomic status were also studied and served as controls.Results: More neuromuscular disorders were identified in pesticide applicators than controls. A significant lower level of acetylcholinesterase was found in the applicator group compared to the controls. There was also a significant difference in hematological, renal and hepatic indices in the exposed children compared to the control children. Working more days in the current season and also working more years as a pesticide applicator were both associated with an increase in the prevalence of neuromuscular abnormalities and significant changes in the laboratory tests.Conclusion: Children and adolescent pesticide applicators working in farms of Egypt are at risk of developing serious health problems similar to those of adults.

  11. DYNAMICALLY EVOLVING CLINICAL PRACTICES AND IMPLICATIONS FOR PREDICTING MEDICAL DECISIONS

    Science.gov (United States)

    CHEN, JONATHAN H; GOLDSTEIN, MARY K; ASCH, STEVEN M; ALTMAN, RUSS B

    2015-01-01

    Automatically data-mining clinical practice patterns from electronic health records (EHR) can enable prediction of future practices as a form of clinical decision support (CDS). Our objective is to determine the stability of learned clinical practice patterns over time and what implication this has when using varying longitudinal historical data sources towards predicting future decisions. We trained an association rule engine for clinical orders (e.g., labs, imaging, medications) using structured inpatient data from a tertiary academic hospital. Comparing top order associations per admission diagnosis from training data in 2009 vs. 2012, we find practice variability from unstable diagnoses with rank biased overlap (RBO)0.6. Predicting admission orders for future (2013) patients with associations trained on recent (2012) vs. older (2009) data improved accuracy evaluated by area under the receiver operating characteristic curve (ROC-AUC) 0.89 to 0.92, precision at ten (positive predictive value of the top ten predictions against actual orders) 30% to 37%, and weighted recall (sensitivity) at ten 2.4% to 13%, (P<10−10). Training with more longitudinal data (2009-2012) was no better than only using recent (2012) data. Secular trends in practice patterns likely explain why smaller but more recent training data is more accurate at predicting future practices. PMID:26776186

  12. Parameter optimization of parenchymal texture analysis for prediction of false-positive recalls from screening mammography

    Science.gov (United States)

    Ray, Shonket; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

    2016-03-01

    This work details a methodology to obtain optimal parameter values for a locally-adaptive texture analysis algorithm that extracts mammographic texture features representative of breast parenchymal complexity for predicting falsepositive (FP) recalls from breast cancer screening with digital mammography. The algorithm has two components: (1) adaptive selection of localized regions of interest (ROIs) and (2) Haralick texture feature extraction via Gray- Level Co-Occurrence Matrices (GLCM). The following parameters were systematically varied: mammographic views used, upper limit of the ROI window size used for adaptive ROI selection, GLCM distance offsets, and gray levels (binning) used for feature extraction. Each iteration per parameter set had logistic regression with stepwise feature selection performed on a clinical screening cohort of 474 non-recalled women and 68 FP recalled women; FP recall prediction was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC) and associations between the extracted features and FP recall were assessed via odds ratios (OR). A default instance of mediolateral (MLO) view, upper ROI size limit of 143.36 mm (2048 pixels2), GLCM distance offset combination range of 0.07 to 0.84 mm (1 to 12 pixels) and 16 GLCM gray levels was set. The highest ROC performance value of AUC=0.77 [95% confidence intervals: 0.71-0.83] was obtained at three specific instances: the default instance, upper ROI window equal to 17.92 mm (256 pixels2), and gray levels set to 128. The texture feature of sum average was chosen as a statistically significant (p<0.05) predictor and associated with higher odds of FP recall for 12 out of 14 total instances.

  13. Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials

    Science.gov (United States)

    Tétreault, Pascal; Mansour, Ali; Vachon-Presseau, Etienne; Schnitzer, Thomas J.; Apkarian, A. Vania

    2016-01-01

    Placebo response in the clinical trial setting is poorly understood and alleged to be driven by statistical confounds, and its biological underpinnings are questioned. Here we identified and validated that clinical placebo response is predictable from resting-state functional magnetic-resonance-imaging (fMRI) brain connectivity. This also led to discovering a brain region predicting active drug response and demonstrating the adverse effect of active drug interfering with placebo analgesia. Chronic knee osteoarthritis (OA) pain patients (n = 56) underwent pretreatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-wk single-blinded placebo pill trial. Study 2 (n = 39) was a 3-mo double-blinded randomized trial comparing placebo pill to duloxetine. Study 3, which was conducted in additional knee OA pain patients (n = 42), was observational. fMRI-derived brain connectivity maps in study 1 were contrasted between placebo responders and nonresponders and compared to healthy controls (n = 20). Study 2 validated the primary biomarker and identified a brain region predicting drug response. In both studies, approximately half of the participants exhibited analgesia with placebo treatment. In study 1, right midfrontal gyrus connectivity best identified placebo responders. In study 2, the same measure identified placebo responders (95% correct) and predicted the magnitude of placebo’s effectiveness. By subtracting away linearly modeled placebo analgesia from duloxetine response, we uncovered in 6/19 participants a tendency of duloxetine enhancing predicted placebo response, while in another 6/19, we uncovered a tendency for duloxetine to diminish it. Moreover, the approach led to discovering that right parahippocampus gyrus connectivity predicts drug analgesia after correcting for modeled placebo-related analgesia. Our evidence is consistent with clinical placebo response having biological underpinnings and shows that the method can also reveal that active

  14. Influence and prediction of hot deformation parameters on microstructure of Ti-15-3 alloy

    Institute of Scientific and Technical Information of China (English)

    李萍; 薛克敏; 吕炎; 谭建荣

    2002-01-01

    The effect of hot processing parameters on the microstructure of Ti-15-3 alloy after solution treatment was studied by isothermal compression test and metallurgical analysis. Predicting models for the relations between equivalent grain size and recrystallized grain volume percent with strain,strain rate and temperature have been developed with an artificial neural network method. The coincidence of predicted results with measured ones shows that the neural network can predict the influence of hot deformation parameters on the microstructure of Ti-15-3 alloy after solution treatment successfully. These studies are significant for determining hot-forging processing parameters of Ti-15-3 alloy.

  15. Probabilistic Fatigue Life Prediction of Turbine Disc Considering Model Parameter Uncertainty

    Science.gov (United States)

    He, Liping; Yu, Le; Zhu, Shun-Peng; Ding, Liangliang; Huang, Hong-Zhong

    2016-06-01

    Aiming to improve the predictive ability of Walker model for fatigue life prediction and taking the turbine disc alloy GH4133 as the application example, this paper investigates a new approach for probabilistic fatigue life prediction when considering parameter uncertainty inherent in the life prediction model. Firstly, experimental data are used to update the model parameters using Bayes' theorem, so as to obtain the posterior probability distribution functions of two parameters of the Walker model, as well to achieve the probabilistic life prediction model for turbine disc. During the updating process, Markov Chain Monte Carlo (MCMC) technique is used to generate samples of the given distribution and estimating the parameters distinctly. After that, the turbine disc life is predicted using the probabilistic Walker model based on Monte Carlo simulation technique. The experimental results indicate that: (1) after using the small sample test data obtained from turbine disc, parameter uncertainty of the Walker model can be quantified and the corresponding probabilistic model for fatigue life prediction can be established using Bayes' theorem; (2) there exists obvious dispersion of life data for turbine disc when predicting fatigue life in practical engineering application.

  16. Predictive dosimetric parameters for gastrointestinal toxicity with hypofractioned radiotherapy in pancreatic adenocarcinoma

    Directory of Open Access Journals (Sweden)

    Liu X

    2016-04-01

    Full Text Available Xian Liu,* Gang Ren,* Liqin Li, Tingyi Xia Department of Radiation Oncology, Air Force General Hospital, Beijing, People’s Republic of China *These authors contributed equally to this work Abstract: To better guide the development and optimization of radiotherapy planning, to reduce the incidence of radiation reactions, and to improve the quality of life of the patients with pancreatic cancer using radiotherapy, we conducted this study to explore the dosimetric parameters that predict the risk of gastrointestinal (GI toxicity with hypofractioned radiotherapy for pancreatic cancer. Between January 2014 and January 2015, the medical records of 68 patients with pancreatic cancer who underwent helical tomotherapy at the Air Force General Hospital were analyzed. The doses delivered to the planning target volume, clinical target volume, and gross tumor volume–internal gross tumor volume of the primary pancreatic lesions were 50, 60, and 70–80 Gy in 15–20 fractions, respectively. GI toxicity was scored according to version 4.0 of the National Cancer Institute Common Terminology Criteria for Adverse Events. The stomach and duodenum were contoured separately to determine the dose–volume histogram parameters. Univariate and multivariate analyses were adopted to identify clinical and physical risk factors associated with GI toxicity. The median follow-up was 9 months (range: 4–16 months. Eighteen patients had grade II acute GI toxicity, one patient had grade III acute GI toxicity, 17 patients had grade II late GI toxicity, and one patient had grade III late GI toxicity. On univariate analysis, the volume, the average dose Dmean, the maximum dose to 1, 3, 5, and 10 cm3 of the stomach and duodenum (D1, D3, D5, and D10, and the relative volumes receiving 5–40 Gy (V5–V40, and the absolute volumes receiving 5–45 Gy (aV5–aV45 of the duodenum were significantly associated with grade II or higher GI toxicity (P<0.05. On multivariate

  17. Hemorheological and Glycemic Parameters and HDL Cholesterol for the Prediction of Cardiovascular Events

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sung Woo [Division of Cardiology - Department of Internal Medicine - Sanggye Paik Hospital, Inje University College of Medicine, Seoul (Korea, Republic of); Division of Cardiology - Department of Medicine - Samsung Medical Center, Seoul (Korea, Republic of); Kim, Byung Gyu; Kim, Byung Ok; Byun, Young Sup; Goh, Choong Won; Rhee, Kun Joo [Division of Cardiology - Department of Internal Medicine - Sanggye Paik Hospital, Inje University College of Medicine, Seoul (Korea, Republic of); Kwon, Hyuck Moon; Lee, Byoung Kwon, E-mail: cardiobk@yuhs.ac [Division of Cardiology - Department of Internal Medicine - Gangnam Severance Hospital - Yonsei University College of Medicine, Seoul (Korea, Republic of)

    2016-01-15

    Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients.

  18. Prediction of Hansen Solubility Parameters with a New Group-Contribution Method

    Science.gov (United States)

    Stefanis, Emmanuel; Panayiotou, Costas

    2008-04-01

    A group-contribution method for the estimation of Hansen solubility parameters of pure organic compounds is presented. It uses two kinds of characteristic groups: first-order groups that describe the basic molecular structure of compounds and second-order groups, which are based on the conjugation theory and improve the accuracy of predictions. A large variety of characteristic groups ensure the prediction of Hansen solubility parameters for a broad series of organic compounds, including those having complex multi-ring, heterocyclic, and aromatic structures. The predictions are exclusively based on the molecular structure of compounds, and no experimental data are needed. The predicted values permit a fairly reliable selection of solvents based on the radius of a Hansen solubility parameter sphere or on a Teas parameter ternary plot. Especially designed algorithms permit the preparation of a list of new molecular structures which, if synthesized, could be the ideally suited solvents for a series of corresponding applications.

  19. SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hua; WANG Yun-jia; LI Yong-feng

    2009-01-01

    A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed. Based on least squares support vector machine (LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction. Many of the geological and mining factors involved are related in a nonlinear way. The new model is based on statistical theory (SLT) and empirical risk minimization (ERM) principles. Typical data collected from observation stations were used for the learning and training samples. The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network (BPNN) model. The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model. The LS-SVM model was faster in computation and had better generalized performance. It provides a highly effective method for calculating the predicting parameters of the probability-integral method.

  20. The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

    Science.gov (United States)

    Dahl, Milo D.; Khavaran, Abbas

    2010-01-01

    Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

  1. Data mining of solubility parameters for computational prediction of drug-excipient miscibility.

    Science.gov (United States)

    Alhalaweh, Amjad; Alzghoul, Ahmad; Kaialy, Waseem

    2014-07-01

    Abstract Computational data mining is of interest in the pharmaceutical arena for the analysis of massive amounts of data and to assist in the management and utilization of the data. In this study, a data mining approach was used to predict the miscibility of a drug and several excipients, using Hansen solubility parameters (HSPs) as the data set. The K-means clustering algorithm was applied to predict the miscibility of indomethacin with a set of more than 30 compounds based on their partial solubility parameters [dispersion forces (δd), polar forces (δp) and hydrogen bonding (δh)]. The miscibility of the compounds was determined experimentally, using differential scanning calorimetry (DSC), in a separate study. The results of the K-means algorithm and DSC were compared to evaluate the K-means clustering prediction performance using the HSPs three-dimensional parameters, the two-dimensional parameters such as volume-dependent solubility (δv) and hydrogen bonding (δh) and selected single (one-dimensional) parameters. Using HSPs, the prediction of miscibility by the K-means algorithm correlated well with the DSC results, with an overall accuracy of 94%. The prediction accuracy was the same (94%) when the two-dimensional parameters or the hydrogen-bonding (one-dimensional) parameter were used. The hydrogen-bonding parameter was thus a determining factor in predicting miscibility in such set of compounds, whereas the dispersive and polar parameters had only a weak correlation. The results show that data mining approach is a valuable tool for predicting drug-excipient miscibility because it is easy to use, is time and cost-effective, and is material sparing.

  2. The Prediction of Item Parameters Based on Classical Test Theory and Latent Trait Theory

    Science.gov (United States)

    Anil, Duygu

    2008-01-01

    In this study, the prediction power of the item characteristics based on the experts' predictions on conditions try-out practices cannot be applied was examined for item characteristics computed depending on classical test theory and two-parameters logistic model of latent trait theory. The study was carried out on 9914 randomly selected students…

  3. Prediction of chemical, physical and sensory data from process parameters for frozen cod using multivariate analysis

    DEFF Research Database (Denmark)

    Bechmann, Iben Ellegaard; Jensen, H.S.; Bøknæs, Niels

    1998-01-01

    Physical, chemical and sensory quality parameters were determined for 115 cod (Gadus morhua) samples stored under varying frozen storage conditions. Five different process parameters (period of frozen storage, frozen storage. temperature, place of catch, season for catching and state of rigor) were...... varied systematically at two levels. The data obtained were evaluated using the multivariate methods, principal component analysis (PCA) and partial least squares (PLS) regression. The PCA models were used to identify which process parameters were actually most important for the quality of the frozen cod....... PLS models that were able to predict the physical, chemical and sensory quality parameters from the process parameters of the frozen raw material were generated. The prediction abilities of the PLS models were good enough to give reasonable results even when the process parameters were characterised...

  4. Prediction of Liquid-Liquid Equilibrium Using the Group Solubility Parameter Model

    Institute of Scientific and Technical Information of China (English)

    ZHAO Mo; CHEN Fuming

    2005-01-01

    The group solubility parameter (GSP) model was used to analyze the liquid-liquid equilibrium (LLE) of ternary and quaternary systems. The GSP parameters are divided into four dimensions representing the four major intermolecular forces. The values of the parameters were determined by regression using the nonlinear SIMPLEX optimization method to fit the LLE data of 548 ternary and 26 quaternary systems selected from the literature. LLE predictions of 8 ternary systems were then made using the fit parameters. Comparison of the results with predictions using the modified UNIFAC model shows that the GSP model has less adjustable parameters to achieve a similar accuracy and that the parameter values are easily acquired by analysis of available data.

  5. Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

    OpenAIRE

    Ling-Yuan Hsu; Tsung-Lin Chen

    2012-01-01

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficie...

  6. Using Kalman filtering to predict time-varying parameters in a model predicting baroreflex regulation during head-up tilt

    DEFF Research Database (Denmark)

    Matzuka, Brett; Mehlsen, Jesper; Tran, Hien;

    2015-01-01

    , while the second uses the ensemble transform Kalman filter (ETKF) [1], [12], [13], [35]. In addition, we show that the delayed rejection adaptive Metropolis (DRAM) algorithm can be used for predicting parameter uncertainties within the spline methodology, which is compared with the variability obtained...

  7. Usefulness of Clinical Prediction Rules, D-dimer, and Arterial Blood Gas Analysis to Predict Pulmonary Embolism in Cancer Patients

    Directory of Open Access Journals (Sweden)

    Shazia Awan

    2017-03-01

    Full Text Available Objectives: Pulmonary embolism (PE is seven times more common in cancer patients than non-cancer patients. Since the existing clinical prediction rules (CPRs were validated predominantly in a non-cancer population, we decided to look at the utility of arterial blood gas (ABG analysis and D-dimer in predicting PE in cancer patients. Methods: Electronic medical records were reviewed between December 2005 and November 2010. A total of 177 computed tomography pulmonary angiograms (CTPAs were performed. We selected 104 individuals based on completeness of laboratory and clinical data. Patients were divided into two groups, CTPA positive (patients with PE and CTPA negative (PE excluded. Wells score, Geneva score, and modified Geneva score were calculated for each patient. Primary outcomes of interest were the sensitivities, specificities, positive, and negative predictive values for all three CPRs. Results: Of the total of 104 individuals who had CTPAs, 33 (31.7% were positive for PE and 71 (68.3% were negative. There was no difference in basic demographics between the two groups. Laboratory parameters were compared and partial pressure of oxygen was significantly lower in patients with PE (68.1 mmHg vs. 71 mmHg, p = 0.030. Clinical prediction rules showed good sensitivities (88−100% and negative predictive values (93−100%. An alveolar-arterial (A-a gradient > 20 had 100% sensitivity and negative predictive values. Conclusions: CPRs and a low A-a gradient were useful in excluding PE in cancer patients. There is a need for prospective trials to validate these results.

  8. Toward an Efficient Prediction of Solar Flares: Which Parameters, and How?

    Directory of Open Access Journals (Sweden)

    Manolis K. Georgoulis

    2013-11-01

    Full Text Available Solar flare prediction has become a forefront topic in contemporary solar physics, with numerous published methods relying on numerous predictive parameters, that can even be divided into parameter classes. Attempting further insight, we focus on two popular classes of flare-predictive parameters, namely multiscale (i.e., fractal and multifractal and proxy (i.e., morphological parameters, and we complement our analysis with a study of the predictive capability of fundamental physical parameters (i.e., magnetic free energy and relative magnetic helicity. Rather than applying the studied parameters to a comprehensive statistical sample of flaring and non-flaring active regions, that was the subject of our previous studies, the novelty of this work is their application to an exceptionally long and high-cadence time series of the intensely eruptive National Oceanic and Atmospheric Administration (NOAA active region (AR 11158, observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Aiming for a detailed study of the temporal evolution of each parameter, we seek distinctive patterns that could be associated with the four largest flares in the AR in the course of its five-day observing interval. We find that proxy parameters only tend to show preflare impulses that are practical enough to warrant subsequent investigation with sufficient statistics. Combining these findings with previous results, we conclude that: (i carefully constructed, physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting; and (ii the time series of promising parameters may be as important as their instantaneous values. Value-based prediction is the only approach followed so far. Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities.

  9. Interval finite difference method for steady-state temperature field prediction with interval parameters

    Science.gov (United States)

    Wang, Chong; Qiu, Zhi-Ping

    2014-04-01

    A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters. [Figure not available: see fulltext.

  10. How much are radiological parameters related to clinical symptoms and function in osteoarthritis of the shoulder?

    Science.gov (United States)

    Kircher, Jörn; Morhard, Markus; Magosch, Petra; Ebinger, Nina; Lichtenberg, Sven; Habermeyer, Peter

    2010-06-01

    Loss of joint space, formation of osteophytes and deformation are common features of osteoarthritis. Little information exists about the radiological features of arthrosis in relation to clinical findings and the radiological appearance in degenerative shoulder joint disease especially with regard to decision making about the timing of joint replacement. We retrospectively examined 120 standardised X-rays of patients with advanced osteoarthritis of the shoulder. Exclusion criteria included rotator cuff tear, severe glenoid erosion or protrusion. Measurements of joint space width at three levels in each plane (anteroposterior and axillary view), humeral head diameter and size of humeral osteophytes were made by two independent examiners. Osteoarthritis was graded according to Samilson and Prieto. Seventy-five of these patients had a complete record from the clinical investigation (pain record on VAS scale, active and passive range of motion) and the constant score (CS). Mean joint space width in the central anteroposterior level was 1.46 mm +/- 1.08 and in the central axillary 0.98 mm +/- 1.02. Increasing age was positively correlated with joint space narrowing at all measured levels. The joint space width was not correlated with the Samilson grade or the size of osteophytes. The joint space width was neither correlated with pain nor active or passive ROM. Pain was correlated with active and passive flexion and abduction but not for internal or external rotation. The size of the osteophytes was negatively correlated (active and passive) with flexion, abduction and external and internal rotation. The study illustrates that joint space narrowing and development of osteophytes are reliable but independent parameters of primary shoulder arthrosis and should be recorded separately. The size of the caudal humeral osteophyte is a predictive factor for function and should be taken into account for clinical decision making. The primary clinical feature, pain, as the main

  11. Vehicle dynamic prediction systems with on-line identification of vehicle parameters and road conditions.

    Science.gov (United States)

    Hsu, Ling-Yuan; Chen, Tsung-Lin

    2012-01-01

    This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

  12. Evaluation of Neonatal Hemolytic Jaundice: Clinical and Laboratory Parameters

    Directory of Open Access Journals (Sweden)

    Anet Papazovska Cherepnalkovski

    2015-12-01

    CONCLUSIONS: The laboratory profile in ABO/Rh isoimmunisation cases depicts hemolytic mechanism of jaundice. These cases carry a significant risk for early and severe hyperbilirubinemia and are eligible for neurodevelopmental follow-up. Hematological parameters and blood grouping are simple diagnostic methods that assist the etiological diagnosis of neonatal hyperbilirubinemia.

  13. Trabecular bone structure analysis of the spine using clinical MDCT: can it predict vertebral bone strength?

    Science.gov (United States)

    Baum, Thomas; Gräbeldinger, Martin; Räth, Christoph; Garcia, Eduardo Grande; Burgkart, Rainer; Patsch, Janina M; Rummeny, Ernst J; Link, Thomas M; Bauer, Jan S

    2014-01-01

    Recent technical improvements have made it possible to determine trabecular bone structure parameters of the spine using clinical multi-detector computed tomography (MDCT). Therefore, the purpose of this study was to analyze trabecular bone structure parameters obtained from clinical MDCT in relation to high resolution peripheral quantitative computed tomography (HR-pQCT) as a standard of reference and to investigate whether clinical MDCT can predict vertebral bone strength. Fourteen functional spinal segment units between T7 and L3 were harvested from 14 formalin-fixed human cadavers (11 women and 3 men; age 84 ± 10 years). All functional spinal segment units were examined using HR-pQCT (isotropic voxel size of 41 μm(3)) and a clinical whole-body MDCT (interpolated voxel size of 146 × 146 × 300 μm(3)). Trabecular bone structure analyses (histomorphometric and texture measures) were performed in the HR-pQCT as well as MDCT images. Vertebral failure load (FL) of the functional spinal segment units was determined in an uniaxial biomechanical test. The HR-pQCT and MDCT derived trabecular bone structure parameters showed correlations ranging from r = 0.60 to r = 0.90 (p parameters and FL amounted up to r = 0.86 (p parameters obtained with HR-pQCT and MDCT were not significantly different (p > 0.05). In this cadaver model, the spatial resolution of clinically available whole-body MDCT scanners was suitable for trabecular bone structure analysis of the spine and to predict vertebral bone strength.

  14. Clinical prediction and the idea of a population.

    Science.gov (United States)

    Armstrong, David

    2017-01-01

    Using an analysis of the British Medical Journal over the past 170 years, this article describes how changes in the idea of a population have informed new technologies of medical prediction. These approaches have largely replaced older ideas of clinical prognosis based on understanding the natural histories of the underlying pathologies. The 19(th)-century idea of a population, which provided a denominator for medical events such as births and deaths, was constrained in its predictive power by its method of enumerating individual bodies. During the 20(th) century, populations were increasingly constructed through inferential techniques based on patient groups and samples seen to possess variable characteristics. The emergence of these new virtual populations created the conditions for the emergence of predictive algorithms that are used to foretell our medical futures.

  15. Soil Parameter Identification for Wheel-terrain Interaction Dynamics and Traversability Prediction

    Institute of Scientific and Technical Information of China (English)

    Suksun Hutangkabodee; Yahya Hashem Zweiri; Lakmal Dasarath Seneviratne; Kaspar Althoefer

    2006-01-01

    This paper presents a novel technique for identifying soil parameters for a wheeled vehicle traversing unknown terrain. The identified soil parameters are required for predicting vehicle drawbar pull and wheel drive torque, which in turn can be used for traversability prediction, traction control, and performance optimization of a wheeled vehicle on unknown terrain. The proposed technique is based on the Newton Raphson method. An approximated form of a wheel-soil interaction model based on Composite Simpson's Rule is employed for this purpose. The key soil parameters to be identified are internal friction angle, shear deformation modulus, and lumped pressure-sinkage coefficient. The fourth parameter, cohesion, is not too relevant to vehicle drawbar pull, and is assigned an average value during the identification process. Identified parameters are compared with known values, and shown to be in agreement. The identification method is relatively fast and robust.The identified soil parameters can effectively be used to predict drawbar pull and wheel drive torque with good accuracy.The use of identified soil parameters to design a traversability criterion for wheeled vehicles traversing unknown terrain is presented.

  16. Efficiency of Calatonia on clinical parameters in the immediate post-surgery period: a clinical study

    Directory of Open Access Journals (Sweden)

    Elaine Ferreira Lasaponari

    2013-09-01

    Full Text Available OBJECTIVE: to assess the efficiency of the Calatonia technique about clinical parameters and pain in the immediate post-surgical phase. METHOD: a randomised study was carried out with 116 patients subjected to a cholecystectomy, by laparoscopy, divided into an experimental group (58 patients and a placebo group (58 patients. The experimental group received the Calatonia technique, while the placebo was only subjected to non-intentional touches. RESULTS: The placebo group and the experimental group were considered homogeneous in terms of the variables: sex, age, physical status classification, duration of surgical procedures and also the time spent recovering in the Post-Anaesthetic Recovery Room. The only variable to show a statistically significant difference was the axillary temperature of the body. In relation to pain, the experimental group showed significant results, and hence it is possible to deduce that the relaxation caused by the Calatonia technique brought some relief of the general situation of pain. CONCLUSION: The application of Calatonia can take up the function of a resource complementary to assistance in the period immediately after surgery. Brazilian Register of Clinical Trials, UTN U1111-1129-9629.

  17. Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2015-07-01

    Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.

  18. The relationships between preoperative urodynamic parameters and clinical outcomes in urinary stress incontinence

    Directory of Open Access Journals (Sweden)

    Yaşar Bozkurt

    2008-12-01

    Full Text Available The aim of present study was to evaluate the influence of urodynamic parameters on preoperative and postoperative clinical pictures in stress incontinence.Charts of patients, who were operated for stress incontinence using autologous rectus fascia sling between March 1999 and January 2005 in Tepecik Training and Research Hospital Urology Clinic, were evaluated retrospectively.A total of 41 patients were divided into two subgroups as, pure (10 patients and mixed stress incontinence (31 patients groups. Mean age of patients was 50.4 (33-70 years. Fifteen patients had intrinsic sphincter insufficiency (ISI. Mixed incontinence group had lower volume for first sensation and more detrusor overactivity than pure group. ISI did not alter the success of operation. Urodynamically no relationship was found between detrusor pressure and postoperative postvoiding residual urine (P>0.05.In conclusion, urodynamic evaluation before surgery was not related to preoperative and postoperative clinical picture of patients, but first sensation of bladder is only predictive for the success in fascial sling surgery.

  19. Nutritional screening: control of clinical undernutrition with analytical parameters

    OpenAIRE

    José Ignacio de Ulíbarri Pérez; Guillermo Fernández; Francisco Rodríguez Salvanés; Ana María Díaz López

    2014-01-01

    Objective: To update the system for nutritional screening. The high prevalence of nutritional unstability that causes the Clinical Undernutrition (CU), especially within the hospitals and assisted residencies, makes it necessary to use screening tools for the constant control of undernutrition to combat it during its development. CU is not so much due to a nutritional deficiency but to the illness and its treatments. However, the screening systems currently used are aimed at detecting an alre...

  20. Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters.

    Directory of Open Access Journals (Sweden)

    Alanna Weisman

    Full Text Available OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP is hindered by the need for complex nerve conduction study (NCS protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity. Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC. RESULTS: At baseline there were 246 (60% Prevalent Cases. After 3.9 years mean follow-up, 25 (23% of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively. Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively. DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols.

  1. Predictions of the marviken subcooled critical mass flux using the critical flow scaling parameters

    Energy Technology Data Exchange (ETDEWEB)

    Park, Choon Kyung; Chun, Se Young; Cho, Seok; Yang, Sun Ku; Chung, Moon Ki [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    A total of 386 critical flow data points from 19 runs of 27 runs in the Marviken Test were selected and compared with the predictions by the correlations based on the critical flow scaling parameters. The results show that the critical mass flux in the very large diameter pipe can be also characterized by two scaling parameters such as discharge coefficient and dimensionless subcooling (C{sub d,ref} and {Delta}{Tau}{sup *} {sub sub}). The agreement between the measured data and the predictions are excellent. 8 refs., 8 figs. 1 tab. (Author)

  2. Prediction of Permeation Resistance of Protective Gloves, etc. from Solubility Parameters

    DEFF Research Database (Denmark)

    Henriksen, H. Risvig; Madsen, Jørgen Øgaard

    1997-01-01

    be valuable. This paper is concerned with a method of prediction. The three dimensional solubility parameter was developed to make the formulation af paints and varnishes more easy (Hansen 1967). Later it was transferred (Henriksen 1982) to the field of diffusion and, particularly, permeation in continuous...... layers of polymers as used in chemical protective clothing. Application of a relatively simple mathematical equation made permeability data correlate with solubility parameters of permeants and polymer materials. Permeation could actually be predicted and a concept on selection emerged (MAXIPARDIF...

  3. Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

    Directory of Open Access Journals (Sweden)

    Gergely Takács

    2014-01-01

    Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

  4. Modeling Nonlinear Adsorption with a Single Chemical Parameter: Predicting Chemical Median Langmuir Binding Constants.

    Science.gov (United States)

    Davis, Craig Warren; Di Toro, Dominic M

    2015-07-07

    Procedures for accurately predicting linear partition coefficients onto various sorbents (e.g., organic carbon, soils, clay) are reliable and well established. However, similar procedures for the prediction of sorption parameters of nonlinear isotherm models are not. The purpose of this paper is to present a procedure for predicting nonlinear isotherm parameters, specifically the median Langmuir binding constants, K̃L, obtained utilizing the single-chemical parameter log-normal Langmuir isotherm developed in the accompanying work. A reduced poly parameter linear free energy relationship (pp-LFER) is able to predict median Langmuir binding constants for graphite, charcoal, and Darco granular activated carbon (GAC) adsorption data. For the larger F400 GAC data set, a single pp-LFER model was insufficient, as a plateau is observed for the median Langmuir binding constants of larger molecular volume sorbates. This volumetric cutoff occurs in proximity to the median pore diameter for F400 GAC. A log-linear relationship exists between the aqueous solubility of these large compounds and their median Langmuir binding constants. Using this relationship for the chemicals above the volumetric cutoff and the pp-LFER below the cutoff, the median Langmuir binding constants can be predicted with a root-mean square error for graphite (n = 13), charcoal (n = 11), Darco GAC (n = 14), and F400 GAC (n = 44) of 0.129, 0.307, 0.407, and 0.424, respectively.

  5. Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters.

    Science.gov (United States)

    Zare Abyaneh, Hamid

    2014-01-01

    This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD.

  6. Computer simulation for prediction of performance and thermodynamic parameters of high energy materials.

    Science.gov (United States)

    Muthurajan, H; Sivabalan, R; Talawar, M B; Asthana, S N

    2004-08-09

    A new code viz., Linear Output Thermodynamic User-friendly Software for Energetic Systems (LOTUSES) developed during this work predicts the theoretical performance parameters such as density, detonation factor, velocity of detonation, detonation pressure and thermodynamic properties such as heat of detonation, heat of explosion, volume of explosion gaseous products. The same code also assists in the prediction of possible explosive decomposition products after explosion and power index. The developed code has been validated by calculating the parameters of standard explosives such as TNT, PETN, RDX, and HMX. Theoretically predicated parameters are accurate to the order of +/-5% deviation. To the best of our knowledge, no such code is reported in literature which can predict a wide range of characteristics of known/unknown explosives with minimum input parameters. The code can be used to obtain thermochemical and performance parameters of high energy materials (HEMs) with reasonable accuracy. The code has been developed in Visual Basic having enhanced windows environment, and thereby advantages over the conventional codes, written in Fortran. The theoretically predicted HEMs performance can be directly printed as well as stored in text (.txt) or HTML (.htm) or Microsoft Word (.doc) or Adobe Acrobat (.pdf) format in the hard disk. The output can also be copied into the Random Access Memory as clipboard text which can be imported/pasted in other software as in the case of other codes.

  7. Computer simulation for prediction of performance and thermodynamic parameters of high energy materials

    Energy Technology Data Exchange (ETDEWEB)

    Muthurajan, H.; Sivabalan, R.; Talawar, M.B.; Asthana, S.N

    2004-08-09

    A new code viz., Linear Output Thermodynamic User-friendly Software for Energetic Systems (LOTUSES) developed during this work predicts the theoretical performance parameters such as density, detonation factor, velocity of detonation, detonation pressure and thermodynamic properties such as heat of detonation, heat of explosion, volume of explosion gaseous products. The same code also assists in the prediction of possible explosive decomposition products after explosion and power index. The developed code has been validated by calculating the parameters of standard explosives such as TNT, PETN, RDX, and HMX. Theoretically predicated parameters are accurate to the order of {+-}5% deviation. To the best of our knowledge, no such code is reported in literature which can predict a wide range of characteristics of known/unknown explosives with minimum input parameters. The code can be used to obtain thermochemical and performance parameters of high energy materials (HEMs) with reasonable accuracy. The code has been developed in Visual Basic having enhanced windows environment, and thereby advantages over the conventional codes, written in Fortran. The theoretically predicted HEMs performance can be directly printed as well as stored in text (.txt) or HTML (.htm) or Microsoft Word (.doc) or Adobe Acrobat (.pdf) format in the hard disk. The output can also be copied into the Random Access Memory as clipboard text which can be imported/pasted in other software as in the case of other codes.

  8. Optimization of fluoroscopy parameters using pattern matching prediction in the real-time tumor-tracking radiotherapy system.

    Science.gov (United States)

    Miyamoto, Naoki; Ishikawa, Masayori; Bengua, Gerard; Sutherland, Kenneth; Suzuki, Ryusuke; Kimura, Suguru; Shimizu, Shinichi; Onimaru, Rikiya; Shirato, Hiroki

    2011-08-07

    In the real-time tumor-tracking radiotherapy system, fluoroscopy is used to determine the real-time position of internal fiducial markers. The pattern recognition score (PRS) ranging from 0 to 100 is computed by a template pattern matching technique in order to determine the marker position on the fluoroscopic image. The PRS depends on the quality of the fluoroscopic image. However, the fluoroscopy parameters such as tube voltage, current and exposure duration are selected manually and empirically in the clinical situation. This may result in an unnecessary imaging dose from the fluoroscopy or loss of the marker because of too much or insufficient x-ray exposure. In this study, a novel optimization method is proposed in order to minimize the fluoroscopic dose while keeping the image quality usable for marker tracking. The PRS can be predicted in a region where the marker appears to move in the fluoroscopic image by the proposed method. The predicted PRS can be utilized to judge whether the marker can be tracked with accuracy. In this paper, experiments were performed to show the feasibility of the PRS prediction method under various conditions. The predicted PRS showed good agreement with the measured PRS. The root mean square error between the predicted PRS and the measured PRS was within 1.44. An experiment using a motion controller and an anthropomorphic chest phantom was also performed in order to imitate a clinical fluoroscopy situation. The result shows that the proposed prediction method is expected to be applicable in a real clinical situation.

  9. Prediction of 28-day Compressive Strength of Concrete from Early Strength and Accelerated Curing Parameters

    OpenAIRE

    T.R. Neelakantan; S. Ramasundaram; Shanmugavel, R.; R. Vinoth

    2013-01-01

    Predicting 28-day compressive strength of concrete is an important research task for many years. In this study, concrete specimens were cured in two phases, initially at room temperature for a maximum of 30 h and later at a higher temperature for accelerated curing for a maximum of 3 h. Using the early strength obtained after the two-phase curing and the curing parameters, regression equations were developed to predict the 28-day compressive strength. For the accelerated curing (higher temper...

  10. Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

    Directory of Open Access Journals (Sweden)

    Bo Zeng

    2014-01-01

    Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

  11. Electrocardiographic parameters in captive, clinically healthy, Amazona ochrocephala

    Directory of Open Access Journals (Sweden)

    Claudia Guerrero S

    2015-11-01

    Full Text Available Objective. To stablish the electrocardiographic parameters of individuals of the species Amazona ochrocephala, from the Unidad de Rescate y Rehabilitacion de Animales Silvestres at the Universidad Nacional de Colombia. Materials and methods. The electrocardiographic examination was performed under inhaled anesthesia with isoflurane. Leads I, II, III, aVL, aVR and aVF were measured. Results. Electrocardiographic parameters obtained in Lead II. P wave Duration: 0.015-0.044 s, P wave amplitude: 0.031 to 0.6 mv, R wave duration: 0.015-0.022 s, amplitude R: 0.034-0.038 mv, S wave Duration: 0.019- 0.042 s, amplitude S: 0.194-0.815 mv, T wave Duration: 0.025-0.064 s, T-wave amplitude: 0.010 to 0.5 mv, PQ Duration: 0.021-0.076 s, QRS Duration: 0.036-0.068 s, QT Duration: 0.070-0.015 s, RR Duration: 0.104-0.324 s, EEM: -111° to -80°, FC: 240-600 ppm. Conclusions. The results showed different values for amplitude and duration of the P, R and T waves in comparison to those obtained in other studies. However, they were similar for heart rate, MEA and duration of the PQ/R, QT and QRS segments.

  12. A clinical prediction score for upper extremity deep venous thrombosis.

    Science.gov (United States)

    Constans, Joel; Salmi, Louis-Rachid; Sevestre-Pietri, Marie-Antoinette; Perusat, Sophie; Nguon, Monika; Degeilh, Maryse; Labarere, Jose; Gattolliat, Olivier; Boulon, Carine; Laroche, Jean-Pierre; Le Roux, Philippe; Pichot, Olivier; Quéré, Isabelle; Conri, Claude; Bosson, Jean-Luc

    2008-01-01

    It was the objective of this study to design a clinical prediction score for the diagnosis of upper extremity deep venous thrombosis (UEDVT). A score was built by multivariate logistic regression in a sample of patients hospitalized for suspicion of UEDVT (derivation sample). It was validated in a second sample in the same university hospital, then in a sample from the multicenter OPTIMEV study that included both outpatients and inpatients. In these three samples, UEDVT diagnosis was objectively confirmed by ultrasound. The derivation sample included 140 patients among whom 50 had confirmed UEDVT, the validation sample included 103 patients among whom 46 had UEDVT, and the OPTIMEV sample included 214 patients among whom 65 had UEDVT. The clinical score identified a combination of four items (venous material, localized pain, unilateral pitting edema and other diagnosis as plausible). One point was attributed to each item (positive for the first 3 and negative for the other diagnosis). A score of -1 or 0 characterized low probability patients, a score of 1 identified intermediate probability patients, and a score of 2 or 3 identified patients with high probability. Low probability score identified a prevalence of UEDVT of 12, 9 and 13%, respectively, in the derivation, validation and OPTIMEV samples. High probability score identified a prevalence of UEDVT of 70, 64 and 69% respectively. In conclusion we propose a simple score to calculate clinical probability of UEDVT. This score might be a useful test in clinical trials as well as in clinical practice.

  13. American tegumentary leishmaniasis: correlations among immunological, histopathological and clinical parameters*

    Science.gov (United States)

    Martins, Ana Luiza Grizzo Peres; Barreto, Jaison Antonio; Lauris, José Roberto Pereira; Martins, Ana Claudia Grizzo Peres

    2014-01-01

    BACKGROUND American tegumentary leishmaniasis has an annual incidence of 1 to 1.5 million cases. In some cases, the patient's immune response can eliminate the parasite, and the lesion spontaneously resolves. However, when this does not occur, patients develop the disseminated form of the disease. OBJECTIVE To investigate the association between clinical, laboratory and pathological findings in cases of American tegumentary leishmaniasis. METHODS A retrospective study of the medical records of 47 patients with American cutaneous leishmaniasis. Clinical, laboratory and epidemiological data were collected, and semi-quantitative histopathological analyses were performed using the Spearman correlation coefficient (p Montenegro reaction, degree of granulomatous transformation and epithelioid cell count; duration of disease, Montenegro reaction and number of lymphocytes; epithelial hyperplasia and edema, hemorrhaging, and epithelial aggression; number of plasmocytes and number of parasites. The main negative correlations found were as follows: age and serology; time and parasite load; epithelial hyperplasia and degree of granulomatous transformation. CONCLUSION The long duration of the disease could be explained by the fact that lesions were relatively asymptomatic, and therefore ignored by patients with low literacy levels. Individuals may have simply waited for spontaneous healing, which proved to be dependent on the activation of hypersensitivity mechanisms. PMID:24626648

  14. Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

    Science.gov (United States)

    Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

    2016-07-01

    Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological

  15. Hydrological model parameter dimensionality is a weak measure of prediction uncertainty

    Directory of Open Access Journals (Sweden)

    S. Pande

    2015-04-01

    Full Text Available This paper shows that instability of hydrological system representation in response to different pieces of information and associated prediction uncertainty is a function of model complexity. After demonstrating the connection between unstable model representation and model complexity, complexity is analyzed in a step by step manner. This is done measuring differences between simulations of a model under different realizations of input forcings. Algorithms are then suggested to estimate model complexity. Model complexities of the two model structures, SAC-SMA (Sacramento Soil Moisture Accounting and its simplified version SIXPAR (Six Parameter Model, are computed on resampled input data sets from basins that span across the continental US. The model complexities for SIXPAR are estimated for various parameter ranges. It is shown that complexity of SIXPAR increases with lower storage capacity and/or higher recession coefficients. Thus it is argued that a conceptually simple model structure, such as SIXPAR, can be more complex than an intuitively more complex model structure, such as SAC-SMA for certain parameter ranges. We therefore contend that magnitudes of feasible model parameters influence the complexity of the model selection problem just as parameter dimensionality (number of parameters does and that parameter dimensionality is an incomplete indicator of stability of hydrological model selection and prediction problems.

  16. The Importance of the Range Parameter for Estimation and Prediction in Geostatistics

    CERN Document Server

    Kaufman, Cari

    2011-01-01

    Two canonical problems in geostatistics are estimating the parameters in a specified family of stochastic process models and predicting the process at new locations. A number of asymptotic results for these problems over a fixed spatial domain indicate that, for a Gaussian process with Mat\\'ern covariance function, one can fix the range parameter controlling the rate of decay of the process and obtain results that are asymptotically equivalent to the case that the range parameter is known. We discuss why these results do not always provide the appropriate intuition for finite samples. Moreover, we prove that a number of these asymptotic results may be extended to the case that the variance and range parameters are jointly estimated via maximum likelihood or maximum tapered likelihood. Our simulation results show that performance on a variety of metrics is improved and asymptotic approximations are applicable for smaller sample sizes when the range parameter is estimated. These effects are particularly apparen...

  17. Nomogram including pretherapeutic parameters for prediction of survival after SIRT of hepatic metastases from colorectal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Fendler, Wolfgang Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Klinik und Poliklinik fuer Nuklearmedizin, Munich (Germany); Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Paprottka, Philipp M. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Jakobs, Tobias F. [Hospital Barmherzige Brueder, Department of Diagnostic and Interventional Radiology, Munich (Germany); Heinemann, Volker [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter; Haug, Alexander R. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Khalaf, Feras [University Hospital Bonn, Department of Nuclear Medicine, Bonn (Germany); Ezziddin, Samer [Saarland University Medical Center, Department of Nuclear Medicine, Homburg (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

    2015-09-15

    Pre-therapeutic prediction of outcome is important for clinicians and patients in determining whether selective internal radiation therapy (SIRT) is indicated for hepatic metastases of colorectal cancer (CRC). Pre-therapeutic characteristics of 100 patients with colorectal liver metastases (CRLM) treated by radioembolization were analyzed to develop a nomogram for predicting survival. Prognostic factors were selected by univariate Cox regression analysis and subsequent tested by multivariate analysis for predicting patient survival. The nomogram was validated with reference to an external patient cohort (n = 25) from the Bonn University Department of Nuclear Medicine. Of the 13 parameters tested, four were independently associated with reduced patient survival in multivariate analysis. These parameters included no liver surgery before SIRT (HR:1.81, p = 0.014), CEA serum level ≥ 150 ng/ml (HR:2.08, p = 0.001), transaminase toxicity level ≥2.5 x upper limit of normal (HR:2.82, p = 0.001), and summed computed tomography (CT) size of the largest two liver lesions ≥10 cm (HR:2.31, p < 0.001). The area under the receiver-operating characteristic curve for our prediction model was 0.83 for the external patient cohort, indicating superior performance of our multivariate model compared to a model ignoring covariates. The nomogram developed in our study entailing four pre-therapeutic parameters gives good prediction of patient survival post SIRT. (orig.)

  18. Predictable Outcomes with Porcelain Laminate Veneers: A Clinical Report.

    Science.gov (United States)

    Pimentel, Welson; Teixeira, Marcelo Lucchesi; Costa, Priscila Paganini; Jorge, Mônica Zacharias; Tiossi, Rodrigo

    2016-06-01

    This clinical report describes how to achieve predictable outcomes for anterior teeth esthetic restorations with porcelain laminate veneers by associating the digital planning and design of the restoration with interim restorations. The previous digital smile design of the restoration eliminates the communication barrier with the patient and assists the clinician throughout patient treatment. Interim restorations (diagnostic mock-ups) further enhance communication with the patient and prevent unnecessary tooth reduction for conservative tooth preparation. Adequate communication between patient and clinician contributes to successful definitive restorations and patient satisfaction with the final esthetic outcome.

  19. Predictability of malaria parameters in Sahel under the S4CAST Model.

    Science.gov (United States)

    Diouf, Ibrahima; Rodríguez-Fonseca, Belen; Deme, Abdoulaye; Cisse, Moustapha; Ndione, Jaques-Andre; Gaye, Amadou; Suárez-Moreno, Roberto

    2016-04-01

    An extensive literature exists documenting the ENSO impacts on infectious diseases, including malaria. Other studies, however, have already focused on cholera, dengue and Rift Valley Fever. This study explores the seasonal predictability of malaria outbreaks over Sahel from previous SSTs of Pacific and Atlantic basins. The SST may be considered as a source of predictability due to its direct influence on rainfall and temperature, thus also other related variables like malaria parameters. In this work, the model has been applied to the study of predictability of the Sahelian malaria parameters from the leading MCA covariability mode in the framework of climate and health issue. The results of this work will be useful for decision makers to better access to climate forecasts and application on malaria transmission risk.

  20. Prediction of data stream parameters in atmospheric turbulent wireless communication links

    Science.gov (United States)

    Tiker, A.; Yarkoni, N.; Blaunstein, N.; Zilberman, A.; Kopeika, N.

    2007-01-01

    A unified approach for calculation of information data stream parameters in the atmospheric optical communication channel is presented based on irradiance fluctuations of optical wave propagation through turbulence and on a generalized Ricean K-parameter distribution. The effects of turbulence are described via the well-known Kolmogorov scheme of turbulent structure relaxation in terms of stochastic scintillation theory described by the gamma-gamma distribution along with measurements of the values of the refractive index structure parameter, Cn 2. The relation between the Ricean parameter K and the signal scintillation parameter σI 2 is considered to develop a unified description of the corresponding probability density function (pdf) of signal fading within an atmospheric wireless communication link. Through the corresponding pdf and parameter K, signal data stream parameters such as the signal-to-noise ratio (SNR), bit error rate (BER), and capacity of the optical atmospheric channel (C) are estimated. Such an approach permits the reliable prediction of the effects of fading caused by different levels of turbulence and agrees with experimental data observed at different atmospheric levels, at the heights of both 100-200 m and above 1-2 km. It is shown that at heights of 100-200 m, effects of fading, caused by turbulence, occur much more frequently than those at the heights of 1-2 km. Data stream parameters such as channel capacity, SNR, and spectral efficiency become stronger at higher altitudes, while at the same time the BER becomes relatively negligible.

  1. Formalized prediction of clinically significant prostate cancer: is it possible?

    Institute of Scientific and Technical Information of China (English)

    Carvell T Nguyen; Michael W Kattan

    2012-01-01

    Greater understanding of the biology and epidemiology of prostate cancer in the last several decades have led to significant advances in its management.Prostate cancer is now detected in greater numbers at lower stages of disease and is amenable to multiple forms of efficacious treatment.However,there is a lack of conclusive data demonstrating a definitive mortality benefit from this earlier diagnosis and treatment of prostate cancer.It is likely due to the treatment of a large proportion of indolent cancers that would have had little adverse impact on health or lifespan if left alone.Due to this overtreatment phenomenon,active surveillance with delayed intervention is gaining traction as a viable management approach in contemporary practice.The ability to distinguish clinically insignificant cancers from those with a high risk of progression and/or lethality is critical to the appropriate selection of patients for surveillance protocols versus immediate intervention.This chapter will review the ability of various prediction models,including risk groupings and nomograms,to predict indolent disease and determine their role in the contemporary management of clinically localized prostate cancer.

  2. Predictive value of semen parameters and age of the couple in pregnancy outcome after Intrauterine insemination

    Directory of Open Access Journals (Sweden)

    Marjan Sabbaghian

    2013-11-01

    Full Text Available Background: Intrauterine insemination (IUI is one the most common methods in infertility treatment, but its efficiency in infertile couples with male factor is controversial. This study is a retrospective study about correlation between semen parameters and male and female age with successful rate of IUI in patients attending to Royan Institute.Methods: A total of 998 consecutive couples in a period of 6 months undergoing IUI were included. They were classified into two groups: couples with successful and unsuccessful pregnancy. Main outcome was clinical pregnancy. Data about male and female ages and semen analysis including concentration, total sperm motility, class A motility, class B motility, class A+B motility and normal morphology was extracted from patients’ records. Semen samples were collected by masturbation or coitus after 2 to 7 days of abstinence. Their female partners were reported to have no chronic medi-cal conditions and have normal menstrual cycles.Results: One hundred and fifty seven of total 998 cycles (15.7% achieved pregnancy. The average of female age in successful and unsuccessful group was 28.95±4.19 and 30.00±4.56 years, respectively. Mean of male age was 33.97±4.85 years in successful group and 34.44±4.62 years in unsuccessful group. In successful and unsuccessful groups, average of sperm concentration was 53.62±38.45 and 46.26±26.59 (million sperm/ml, normal morphology of sperm was 8.98±4.31 (% and 8.68±4.81 (%, sperm total motility was 47.24±18.92 (% and 43.70±20.22 (% and total motile sperm count was 80.10±63.61 million and 78.57±68.22 million, respectively.Conclusion: There was no significant difference in mean of females’ age and males’ age between successful and unsuccessful groups (P<0.05. In addition, there was no significant difference in semen parameters including concentration, total sperm motility, class A motility, class B motility, class A+B motility and normal morphology between two

  3. Performance Prediction of Two-Phase Geothermal Reservoir using Lumped Parameter Model

    Science.gov (United States)

    Nurlaela, F.; Sutopo

    2016-09-01

    Many studies have been conducted to simulate performance of low-temperature geothermal reservoirs using lumped parameter method. Limited work had been done on applying non-isothermal lumped parameter models to higher temperature geothermal reservoirs. In this study, the lumped parameter method was applied to high-temperature two phase geothermal reservoirs. The model couples both energy and mass balance equations thus can predict temperature, pressure and fluid saturation changes in the reservoir as a result of production, reinjection of water, and/or natural recharge. This method was validated using reservoir simulation results of TOUGH2. As the results, the two phase lumped parameter model simulation without recharge shows good matching, however reservoir model with recharge condition show quite good conformity.

  4. Comparing Parameter Estimation Techniques for an Electrical Power Transformer Oil Temperature Prediction Model

    Science.gov (United States)

    Morris, A. Terry

    1999-01-01

    This paper examines various sources of error in MIT's improved top oil temperature rise over ambient temperature model and estimation process. The sources of error are the current parameter estimation technique, quantization noise, and post-processing of the transformer data. Results from this paper will show that an output error parameter estimation technique should be selected to replace the current least squares estimation technique. The output error technique obtained accurate predictions of transformer behavior, revealed the best error covariance, obtained consistent parameter estimates, and provided for valid and sensible parameters. This paper will also show that the output error technique should be used to minimize errors attributed to post-processing (decimation) of the transformer data. Models used in this paper are validated using data from a large transformer in service.

  5. Adaptability and Prediction of Anticipatory Muscular Activity Parameters to Different Movements in the Sitting Position.

    Science.gov (United States)

    Chikh, Soufien; Watelain, Eric; Faupin, Arnaud; Pinti, Antonio; Jarraya, Mohamed; Garnier, Cyril

    2016-08-01

    Voluntary movement often causes postural perturbation that requires an anticipatory postural adjustment to minimize perturbation and increase the efficiency and coordination during execution. This systematic review focuses specifically on the relationship between the parameters of anticipatory muscular activities and movement finality in sitting position among adults, to study the adaptability and predictability of anticipatory muscular activities parameters to different movements and conditions in sitting position in adults. A systematic literature search was performed using PubMed, Science Direct, Web of Science, Springer-Link, Engineering Village, and EbscoHost. Inclusion and exclusion criteria were applied to retain the most rigorous and specific studies, yielding 76 articles, Seventeen articles were excluded at first reading, and after the application of inclusion and exclusion criteria, 23 were retained. In a sitting position, central nervous system activity precedes movement by diverse anticipatory muscular activities and shows the ability to adapt anticipatory muscular activity parameters to the movement direction, postural stability, or charge weight. In addition, these parameters could be adapted to the speed of execution, as found for the standing position. Parameters of anticipatory muscular activities (duration, order, and amplitude of muscle contractions constituting the anticipatory muscular activity) could be used as a predictive indicator of forthcoming movement. In addition, this systematic review may improve methodology in empirical studies and assistive technology for people with disabilities.

  6. Integration of preclinical and clinical knowledge to predict intravenous PK in human: bilastine case study.

    Science.gov (United States)

    Vozmediano, Valvanera; Ortega, Ignacio; Lukas, John C; Gonzalo, Ana; Rodriguez, Monica; Lucero, Maria Luisa

    2014-03-01

    Modern pharmacometrics can integrate and leverage all prior proprietary and public knowledge. Such methods can be used to scale across species or comparators, perform clinical trial simulation across alternative designs, confirm hypothesis and potentially reduce development burden, time and costs. Crucial yet typically lacking in integration is the pre-clinical stage. Prediction of PK in man, using in vitro and in vivo studies in different animal species, is increasingly well theorized but could still find wider application in drug development. The aim of the present work was to explore methods for bridging pharmacokinetic knowledge from animal species (i.v. and p.o.) and man (p.o.) into i.v. in man using the antihistamine drug bilastine as example. A model, predictive of i.v. PK in man, was developed on data from two pre-clinical species (rat and dog) and p.o. in man bilastine trials performed earlier. In the knowledge application stage, two different approaches were used to predict human plasma concentration after i.v. of bilastine: allometry (several scaling methods) and a semi-physiological method. Both approaches led to successful predictions of key i.v. PK parameters of bilastine in man. The predictive i.v. PK model was validated using later data from a clinical study of i.v. bilastine. Introduction of such knowledge in development permits proper leveraging of all emergent knowledge as well as quantification-based exploration of PK scenario, e.g. in special populations (pediatrics, renal insufficiency, comedication). In addition, the methods permit reduction or elimination and certainly optimization of learning trials, particularly those concerning alternative off-label administration routes.

  7. Recursive Parameter Method for Computing the Predicting Function of the Multivariable ARMAX Model

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    New method for computing the predicting function of the ARMAX model is proposed. The proposed method constructs a set of schemes for recursively computing the parameters in predicting function of the ARMAX model. In contrast to the existing method, that only gives results for the special case of the ARX model, the method presented is suitable not only for an SISO system, but also for an MIMO system. For the SISO system, the method presented here is even more convenient than the exisiting ones.

  8. Fast Predicting Statistical Subsurface Damage Parameters of the K9 Sample

    Science.gov (United States)

    Wang, Hairong; Chen, Hongfeng; Xiao, Lihui; Zhang, Bike; Jiang, Zhuangde

    2015-07-01

    Based on the subsurface damage model and the material removal rate of K9 glass in HF acid solution, a fast method is proposed to calculate the parameters of characterizing the subsurface damage of a polished sample. When micro cracks of the etched sample's subsurface can be clearly observed, lengths, widths, angles, densities of the micro cracks can be calculated by using the image processing algorithm, and depths of the micro cracks may be predicted by the load-crack model. Eventually a set of the parameters are proposed as a complete description about subsurface damage of the sample.

  9. Conditioning rainfall-runoff model parameters to reduce prediction uncertainty in ungauged basins

    Science.gov (United States)

    Visessri, S.; McIntyre, N.; Maksimovic, C.

    2012-12-01

    Conditioning rainfall-runoff model parameters in ungauged catchments in Thailand presents problems common to ungauged basins involving data availability, data quality, and rainfall-runoff model suitability, which all contribute to prediction uncertainty. This paper attempts to improve the estimation of streamflow in ungauged basins and reduce associated uncertainties using the approaches of conditioning the prior parameter space. 35 catchments from the upper Ping River basin, Thailand are selected as a case study. The catchments have a range of attributes e.g. catchment sizes 20-6350 km2, elevations 632-1529 m above sea level. and annual rainfall 846-1447 mm/year. For each catchment, three indices - rainfall-runoff elasticity, base flow index and runoff coefficient - are calculated using the observed rainfall-runoff data and regression equations relating these indices to the catchment attributes are identified. Uncertainty in expected indices is defined by the regression error distribution, approximated by a Gaussian model. The IHACRES model is applied for simulating streamflow. The IHACRES parameters are randomly sampled from their presumed prior parameter space. For each sampled parameter set, the streamflow and hence the three indices are modelled. The parameter sets are conditioned on the probability distributions of the regionalised indices, allowing ensemble predictions to be made. The objective function, NSE, calculated for daily and weekly time steps from the water years 1995-2000, is used to assess model performance. Ability to capture observed streamflow and the precision of the estimate is evaluated using reliability and sharpness measures. Similarity in modelled and expected indices contributes to good objective function values. Using only the regionalised runoff coefficient to condition the model yields better NSE values compared to using either only the rainfall-runoff elasticity or only the base flow index. Conditioning on the runoff coefficient

  10. Prediction of HAZ grain size in welding of ultra fine grained steel with different parameters

    Institute of Scientific and Technical Information of China (English)

    Zhao Hongyun; Zhang Hongtao; Li Dongqing; Wang Guodong

    2010-01-01

    The temperature field and thermal cycling curve in the heat-affected zone during welding 400 MPa ultra fine grained steel by plasma arc were simulated using finite element method.The principle of grain growth kinetics was used to predict the grain size in the heat-affected zone under different welding parameters.The simulation results show that the growing tendency of HAZ grain could be controlled by adjusting the welding parameters,but the growth of HAZ grain could not be eliminated at all.The HAZ grain size became small with increasing of the cooling rate and added with increasing of welding current,arc voltage and welding speed.

  11. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance☆

    Institute of Scientific and Technical Information of China (English)

    Baocang Ding; Hongguang Pan

    2016-01-01

    The output feedback model predictive control (MPC), for a linear parameter varying (LPV) process system including unmeasurable model parameters and disturbance (all lying in known polytopes), is considered. Some previously developed tools, including the norm-bounding technique for relaxing the disturbance-related constraint handling, the dynamic output feedback law, the notion of quadratic boundedness for specifying the closed-loop stability, and the el ipsoidal state estimation error bound for guaranteeing the recursive feasibility, are merged in the control design. Some previous approaches are shown to be the special cases. An example of continuous stirred tank reactor (CSTR) is given to show the effectiveness of the proposed approaches.

  12. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.

    Science.gov (United States)

    Clifton, Lei; Clifton, David A; Pimentel, Marco A F; Watkinson, Peter J; Tarassenko, Lionel

    2014-05-01

    The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

  13. Parameters Online Detection and Model Predictive Control during the Grain Drying Process

    Directory of Open Access Journals (Sweden)

    Lihui Zhang

    2013-01-01

    Full Text Available In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

  14. Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

    NARCIS (Netherlands)

    Mourik, van S.; Braak, ter C.J.F.; Stigter, J.D.; Molenaar, J.

    2014-01-01

    Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate

  15. Multi-Variable Model-Based Parameter Estimation Model for Antenna Radiation Pattern Prediction

    Science.gov (United States)

    Deshpande, Manohar D.; Cravey, Robin L.

    2002-01-01

    A new procedure is presented to develop multi-variable model-based parameter estimation (MBPE) model to predict far field intensity of antenna. By performing MBPE model development procedure on a single variable at a time, the present method requires solution of smaller size matrices. The utility of the present method is demonstrated by determining far field intensity due to a dipole antenna over a frequency range of 100-1000 MHz and elevation angle range of 0-90 degrees.

  16. Alternative Schemes of Predicting Lepton Mixing Parameters from Discrete Flavor and CP Symmetry

    CERN Document Server

    Lu, Jun-Nan

    2016-01-01

    We suggest two alternative schemes to predict lepton mixing angles as well as $CP$ violating phases from a discrete flavor symmetry group combined with $CP$ symmetry. In the first scenario, the flavor and $CP$ symmetry is broken to the residual groups of the structure $Z_2\\times CP$ in the neutrino and charged lepton sectors. The resulting lepton mixing matrix depends on two free parameters $\\theta_{\

  17. Correlation analysis of clinical parameters with epigenetic modifications in the DUX4 promoter in FSHD.

    NARCIS (Netherlands)

    Balog, J.; Thijssen, P.E.; Greef, J.C. de; Shah, B.; Engelen, B.G.M. van; Yokomori, K.; Tapscott, S.J.; Tawil, R.; Maarel, S.M. van der

    2012-01-01

    The aim of our study was to identify relationships between epigenetic parameters correlating with a relaxed chromatin state of the DUX4 promoter region and clinical severity as measured by a clinical severity score or muscle pathologic changes in D4Z4 contraction-dependent (FSHD1) and -independent (

  18. The predicted influence of climate change on lesser prairie-chicken reproductive parameters

    Science.gov (United States)

    Grisham, Blake A.; Boal, Clint W.; Haukos, David A.; Davis, Dawn M.; Boydston, Kathy K.; Dixon, Charles; Heck, Willard R.

    2013-01-01

    The Southern High Plains is anticipated to experience significant changes in temperature and precipitation due to climate change. These changes may influence the lesser prairie-chicken (Tympanuchus pallidicinctus) in positive or negative ways. We assessed the potential changes in clutch size, incubation start date, and nest survival for lesser prairie-chickens for the years 2050 and 2080 based on modeled predictions of climate change and reproductive data for lesser prairie-chickens from 2001–2011 on the Southern High Plains of Texas and New Mexico. We developed 9 a priori models to assess the relationship between reproductive parameters and biologically relevant weather conditions. We selected weather variable(s) with the most model support and then obtained future predicted values from climatewizard.org. We conducted 1,000 simulations using each reproductive parameter’s linear equation obtained from regression calculations, and the future predicted value for each weather variable to predict future reproductive parameter values for lesser prairie-chickens. There was a high degree of model uncertainty for each reproductive value. Winter temperature had the greatest effect size for all three parameters, suggesting a negative relationship between above-average winter temperature and reproductive output. The above-average winter temperatures are correlated to La Niña events, which negatively affect lesser prairie-chickens through resulting drought conditions. By 2050 and 2080, nest survival was predicted to be below levels considered viable for population persistence; however, our assessment did not consider annual survival of adults, chick survival, or the positive benefit of habitat management and conservation, which may ultimately offset the potentially negative effect of drought on nest survival.

  19. Graphics and statistics for cardiology: clinical prediction rules.

    Science.gov (United States)

    Woodward, Mark; Tunstall-Pedoe, Hugh; Peters, Sanne Ae

    2017-04-01

    Graphs and tables are indispensable aids to quantitative research. When developing a clinical prediction rule that is based on a cardiovascular risk score, there are many visual displays that can assist in developing the underlying statistical model, testing the assumptions made in this model, evaluating and presenting the resultant score. All too often, researchers in this field follow formulaic recipes without exploring the issues of model selection and data presentation in a meaningful and thoughtful way. Some ideas on how to use visual displays to make wise decisions and present results that will both inform and attract the reader are given. Ideas are developed, and results tested, using subsets of the data that were used to develop the ASSIGN cardiovascular risk score, as used in Scotland.

  20. PREDICTION OF CLINICAL EFFICIENCY OF SIMVASTATIN TREATMENT IN PATIENTS WITH RHEUMATOID ARTHRITIS

    Directory of Open Access Journals (Sweden)

    I. V. Shirinsky

    2009-01-01

    Full Text Available Abstract. Treatment with statins results in reduction of disease activity in one-third of patients with rheumatoid arthritis (RA. The aim of this study was to assess some factors that may predict clinical response to simvastatin therapy before starting the treatment. We evaluated an association of treatment efficacy with baseline clinical and laboratory parameters including disease activity measures, cytokine profiles in sera and culture supernatants of peripheral blood mononuclear cells. Thirty-three patients with active RA were enrolled in the study. The patients were treated with simvastatin at 40 mg/day for three months. Eleven patients (33% developed a moderate response according to EULAR criteria. It was shown that serum IL-10 concentrations was higher in responders, and positively correlated with clinical response to simvastatin. We carried out a receiver operating characteristic curve (ROC analysis in order to assess the accuracy of serum IL-10 for the predicting of EULAR response development. The cut-off threshold corresponding to the highest sensitivity (89% and specificity (62% was a value of 6.5 pg/ml. In conclusion, the performance characteristics of serum IL-10 measurement proved to be good enough to predict EULAR response to simvastatin therapy in RA patients.

  1. Predictive value of CASA parameters in IUI with frozen donor sperm.

    Science.gov (United States)

    Freour, Thomas; Jean, Miguel; Mirallie, Sophie; Langlois, Marie-Laure; Dubourdieu, Sophie; Barriere, Paul

    2009-10-01

    The objective of this study was to determine if characteristics of sperm motion determined by computer-aided semen analysis (CASA) after thawing and preparation on discontinuous gradient could predict pregnancy outcome after intrauterine insemination (IUI) from frozen donor sperm. A retrospective analysis of 100 non-selected women undergoing 171 consecutive donor insemination cycles was conducted between January 2006 and April 2007. Semen samples from all donors were analysed after thawing and density gradient preparation. Women who became pregnant and those who did not were comparable in terms of age, ovarian stimulation regimen and indication of IUI with donor semen. Pregnancy rate per cycle was 21.8%, and pregnancy occurred after 2.5 IUI cycles on average. Motility parameters of sperm measured by CASA (VAP, VCL, VSL, LIN, STR, and ALH) and total spermatozoa concentration after preparation on discontinuous gradient showed no difference in both groups. Progressive and total motile spermatozoa concentration, as well as progressive and total motile percentages was significantly higher in pregnancy group. The receiver operating characteristic (ROC) curve analysis showed that total motile percentage >17% and motile concentration >0.9 x 10(6)/mL best predicted pregnancy. In a multivariate analysis, only total motility percentage was able to predict pregnancy. Sperm motility parameters of frozen-thawed prepared donor sperm obtained by CASA do not seem to predict pregnancy in IUI cycles. Total motile and progressive percentages and concentrations remain the best prognostic elements for pregnancy in IUI with donor semen.

  2. Clinical parameters for assessment of udder health in Danish dairy herds.

    Science.gov (United States)

    Houe, H; Vaarst, M; Enevoldsen, C

    2002-01-01

    This study examined the possibilities of using clinical parameters related to the bovine udder for characterisation of udder health. Five clinicians performed systematic clinical recordings of udder health at 3 visits to 4 dairy herds. Several of the clinical parameters were scored on an ordinal scale. The agreement between clinicians was compared using kappa statistics. Factor analysis was used to identify udder types. The clinical evaluations showed substantial variation among clinicians. Parameters that were not directly related to pathological conditions showed the highest variation e.g. length of the claws, teat shape and hardness of the udder parenchyma. On the other hand, evaluation of pathological parameters such as nodes in the udder, skin lesions and oedema showed good agreement between clinicians. Udder types identified by means of factor analysis were found to be suitable for characterisation of udder health. Especially one factor related to dry quarters and udder asymmetry showed a more consistent relationship to milk yield than traditionally applied udder health parameters such as treatment rate and cell count. It is concluded that there is a considerable need for increased efforts among clinicians in order to standardise clinical recordings. It is further concluded that certain combinations of extended clinical recordings have significant perspectives for future characterisation of udder health.

  3. Dynamic Contrast-Enhanced MRI of Cervical Cancers: Temporal Percentile Screening of Contrast Enhancement Identifies Parameters for Prediction of Chemoradioresistance

    Energy Technology Data Exchange (ETDEWEB)

    Andersen, Erlend K.F. [Department of Medical Physics, Oslo University Hospital, Oslo (Norway); Hole, Knut Hakon; Lund, Kjersti V. [Department of Radiology, Oslo University Hospital, Oslo (Norway); Sundfor, Kolbein [Department of Gynaecological Oncology, Oslo University Hospital, Oslo (Norway); Kristensen, Gunnar B. [Department of Gynaecological Oncology, Oslo University Hospital, Oslo (Norway); Institute for Medical Informatics, Oslo University Hospital, Oslo (Norway); Lyng, Heidi [Department of Radiation Biology, Oslo University Hospital, Oslo (Norway); Malinen, Eirik, E-mail: eirik.malinen@fys.uio.no [Department of Medical Physics, Oslo University Hospital, Oslo (Norway); Department of Physics, University of Oslo, Oslo (Norway)

    2012-03-01

    Purpose: To systematically screen the tumor contrast enhancement of locally advanced cervical cancers to assess the prognostic value of two descriptive parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods and Materials: This study included a prospectively collected cohort of 81 patients who underwent DCE-MRI with gadopentetate dimeglumine before chemoradiotherapy. The following descriptive DCE-MRI parameters were extracted voxel by voxel and presented as histograms for each time point in the dynamic series: normalized relative signal increase (nRSI) and normalized area under the curve (nAUC). The first to 100th percentiles of the histograms were included in a log-rank survival test, resulting in p value and relative risk maps of all percentile-time intervals for each DCE-MRI parameter. The maps were used to evaluate the robustness of the individual percentile-time pairs and to construct prognostic parameters. Clinical endpoints were locoregional control and progression-free survival. The study was approved by the institutional ethics committee. Results: The p value maps of nRSI and nAUC showed a large continuous region of percentile-time pairs that were significantly associated with locoregional control (p < 0.05). These parameters had prognostic impact independent of tumor stage, volume, and lymph node status on multivariate analysis. Only a small percentile-time interval of nRSI was associated with progression-free survival. Conclusions: The percentile-time screening identified DCE-MRI parameters that predict long-term locoregional control after chemoradiotherapy of cervical cancer.

  4. Multi-output ANN Model for Prediction of Seven Meteorological Parameters in a Weather Station

    Science.gov (United States)

    Raza, Khalid; Jothiprakash, V.

    2014-12-01

    The meteorological parameters plays a vital role for determining various water demand in the water resource systems, planning, management and operation. Thus, accurate prediction of meteorological variables at different spatial and temporal intervals is the key requirement. Artificial Neural Network (ANN) is one of the most widely used data driven modelling techniques with lots of good features like, easy applications, high accuracy in prediction and to predict the multi-output complex non-linear relationships. In this paper, a Multi-input Multi-output (MIMO) ANN model has been developed and applied to predict seven important meteorological parameters, such as maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours, dew point temperature and evaporation concurrently. Several types of ANN, such as multilayer perceptron, generalized feedforward neural network, radial basis function and recurrent neural network with multi hidden layer and varying number of neurons at the hidden layer, has been developed, trained, validated and tested. From the results, it is found that the recurrent MIMO-ANN having 28 neurons in a single hidden layer, trained using hyperbolic tangent transfer function with a learning rate of 0.3 and momentum factor of 0.7 performed well over the other types of MIMO-ANN models. The MIMO ANN model performed well for all parameters with higher correlation and other performance indicators except for sunshine hours. Due to erratic nature, the importance of each of the input over the output through sensitivity analysis indicated that relative humidity has highest influence while others have equal influence over the output.

  5. Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Duan, X-Y.; Wang, W.; Li, M.; Li, Y.; Guo, Y-M. [PET-CT Center, The First Affiliated Hospital of Xi' an, Jiaotong University, Xi' an, Shaanxi (China)

    2015-02-03

    {sup 18}F-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) is widely used to diagnose and stage non-small cell lung cancer (NSCLC). The aim of this retrospective study was to evaluate the predictive ability of different FDG standardized uptake values (SUVs) in 74 patients with newly diagnosed NSCLC. {sup 18}F-FDG PET/CT scans were performed and different SUV parameters (SUV{sub max}, SUV{sub avg}, SUV{sub T/L}, and SUV{sub T/A}) obtained, and their relationship with clinical characteristics were investigated. Meanwhile, correlation and multiple stepwise regression analyses were performed to determine the primary predictor of SUVs for NSCLC. Age, gender, and tumor size significantly affected SUV parameters. The mean SUVs of squamous cell carcinoma were higher than those of adenocarcinoma. Poorly differentiated tumors exhibited higher SUVs than well-differentiated ones. Further analyses based on the pathologic type revealed that the SUV{sub max}, SUV{sub avg}, and SUV{sub T/L} of poorly differentiated adenocarcinoma tumors were higher than those of moderately or well-differentiated tumors. Among these four SUV parameters, SUV{sub T/L} was the primary predictor for tumor differentiation. However, in adenocarcinoma, SUV{sub max} was the determining factor for tumor differentiation. Our results showed that these four SUV parameters had predictive significance related to NSCLC tumor differentiation; SUV{sub T/L} appeared to be most useful overall, but SUV{sub max} was the best index for adenocarcinoma tumor differentiation.

  6. Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

    Directory of Open Access Journals (Sweden)

    Kennedy Curtis E

    2011-10-01

    Full Text Available Abstract Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1 selecting candidate variables; 2 specifying measurement parameters; 3 defining data format; 4 defining time window duration and resolution; 5 calculating latent variables for candidate variables not directly measured; 6 calculating time series features as latent variables; 7 creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8

  7. 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.

  8. Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time

    Directory of Open Access Journals (Sweden)

    X. He

    2013-08-01

    Full Text Available Uncertainty of groundwater model predictions has in the past mostly been related to uncertainty in the hydraulic parameters, whereas uncertainty in the geological structure has not been considered to the same extent. Recent developments in theoretical methods for quantifying geological uncertainty have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS integrated in the Stanford Geostatistical Modeling Software (SGeMS for exploring the impact of geological uncertainty on groundwater flow patterns for a site in Denmark. Realizations from the geostatistical model were used as input to a groundwater model developed from Modular three-dimensional finite-difference ground-water model (MODFLOW within the Groundwater Modeling System (GMS modeling environment. The uncertainty analysis was carried out in three scenarios involving simulation of groundwater head distribution and travel time. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model optimization, where the hydraulic parameters for each of them were estimated by calibration against observations of hydraulic head and stream discharge. In the third scenario each geological model was run with 216 randomized sets of parameters. The analysis documented that the uncertainty on the conceptual geological model was as significant as the uncertainty related to the embedded hydraulic parameters.

  9. Prediction of water quality parameters from SAR images by using multivariate and texture analysis models

    Science.gov (United States)

    Shareef, Muntadher A.; Toumi, Abdelmalek; Khenchaf, Ali

    2014-10-01

    Remote sensing is one of the most important tools for monitoring and assisting to estimate and predict Water Quality parameters (WQPs). The traditional methods used for monitoring pollutants are generally relied on optical images. In this paper, we present a new approach based on the Synthetic Aperture Radar (SAR) images which we used to map the region of interest and to estimate the WQPs. To achieve this estimation quality, the texture analysis is exploited to improve the regression models. These models are established and developed to estimate six common concerned water quality parameters from texture parameters extracted from Terra SAR-X data. In this purpose, the Gray Level Cooccurrence Matrix (GLCM) is used to estimate several regression models using six texture parameters such as contrast, correlation, energy, homogeneity, entropy and variance. For each predicted model, an accuracy value is computed from the probability value given by the regression analysis model of each parameter. In order to validate our approach, we have used tow dataset of water region for training and test process. To evaluate and validate the proposed model, we applied it on the training set. In the last stage, we used the fuzzy K-means clustering to generalize the water quality estimation on the whole of water region extracted from segmented Terra SAR-X image. Also, the obtained results showed that there are a good statistical correlation between the in situ water quality and Terra SAR-X data, and also demonstrated that the characteristics obtained by texture analysis are able to monitor and predicate the distribution of WQPs in large rivers with high accuracy.

  10. 四参数疲劳定寿方法%Four-parameter life prediction method

    Institute of Scientific and Technical Information of China (English)

    宁向荣; 陈伟; 蔡显新; 廖学军; 米栋

    2012-01-01

    对直升机传动系统采用的一种疲劳定寿方法——四参数法进行了研究:首先介绍了四参数应力一循环(S—N)曲线,然后详细介绍了通过材料平均S—N曲线获取构件安全S—N曲线的步骤以及直升机传动系统四参数疲劳定寿的程序和方法,研究了定寿流程中的各个技术细节.同时介绍了传动系统机匣疲劳试验中的多路协调加载技术以及某传动系统尾减机匣(TGB)的全尺寸疲劳试验情况.最后采用四参数疲劳定寿方法,根据某传动系统尾减机匣全尺寸疲劳试验结果和飞行实测载荷谱,对尾减机匣进行了安全寿命评估.该实例分析表明:四参数疲劳定寿方法为一种有效、可靠的寿命评估方法,具有推广价值.%Four-parameter life prediction method for helicopter transmission system was studied. Four-parameter stress-number of cycles (S-N) curve was presented first, and then, the derivation of helicopter transmission component safe S-N curve from material mean S-N curve and the scheme for the component life prediction were introduced in detail. Multi-load coordinating loading technology in fatigue test and a tail gear box (TGB) casing full-scale fatigue test were introduced at the same time. Finally, according to the full-scale fatigue test result and flight load survey data, applying the four-parameter life prediction method, the TGB casing life prediction was performed, which indicates that four-parameter life prediction method is a reliable and effective method and is worth to be generalized.

  11. Relationship between clinical features of facial dry skin and biophysical parameters in Asians.

    Science.gov (United States)

    Baek, J H; Lee, M Y; Koh, J S

    2011-06-01

    There have been few reports classifying the biophysical characteristics of Korean women with healthy skin. Consequently, the aim of this study was to find the most useful parameters for categorizing skin types based on a clinical assessment. One hundred and three female volunteers, aged 20-59, participated in this study. We conducted a self-evaluation questionnaire, a clinical assessment of the facial skin, and non-invasive measurements on the cheek under controlled environmental conditions. The questionnaire survey indicated that 72% of respondents had dry skin. However, results of the clinical assessment focusing on skin roughness and scaling of the cheek showed that 6 subjects had very dry skin (6%), 29 had dry skin (28%) and 68 had normal skin with sufficient moisture (66%). We analysed the correlation between the clinical assessment and biophysical parameters. As a result, we obtained six biophysical parameters that had relatively higher correlations with clinical assessment than other parameters. Our study provided general information about the physiological characteristics of normal skin in Korean women and suggested useful parameters for characterizing dry skin.

  12. Neural network predictions of acoustical parameters in multi-purpose performance halls.

    Science.gov (United States)

    Cheung, L Y; Tang, S K

    2013-09-01

    A detailed binaural sound measurement was carried out in two multi-purpose performance halls of different seating capacities and designs in Hong Kong in the present study. The effectiveness of using neural network in the predictions of the acoustical properties using a limited number of measurement points was examined. The root-mean-square deviation from measurements, statistical parameter distribution matching, and the results of a t-test for vanishing mean difference between simulations and measurements were adopted as the evaluation criteria for the neural network performance. The audience locations relative to the sound source were used as the inputs to the neural network. Results show that the neural network training scheme using nine uniformly located measurement points in each specific hall area is the best choice regardless of the hall setting and design. It is also found that the neural network prediction of hall spaciousness does not require a large amount of training data, but the accuracy of the reverberance related parameter predictions increases with increasing volume of training data.

  13. Lumped Parameter Modeling as a Predictive Tool for a Battery Status Monitor

    Energy Technology Data Exchange (ETDEWEB)

    Jon P. Christophersen; Chester G. Motloch; Chinh D. Ho; John L. Morrison; Ronald C. Fenton; Vincent S. Battaglia; Tien Q. Duong

    2003-10-01

    The Advanced Technology Development Program is currently evaluating the performance of the second generation of lithium-ion cells (i.e., Gen 2 cells). Both the Gen 2 Baseline and Variant C cells are tested in accordance with the cell-specific test plan, and are removed at roughly equal power fade increments and sent for destructive diagnostic analysis. The diagnostic laboratories did not need all test cells for analysis, and returned five spare cells to the Idaho National Engineering and Environmental Laboratory (INEEL). INEEL used these cells for special pulse testing at various duty cycles, amplitudes, and durations to investigate the usefulness of the lumped parameter model (LPM) as a predictive tool in a battery status monitor (BSM). The LPM is a simplified linear model that accurately predicts the voltage response during certain pulse conditions. A database of parameter trends should enable dynamic predictions of state-of-charge and state-of-health conditions during in-vehicle pulsing. This information could be used by the BSM to provide accurate information to the vehicle control system.

  14. Predictive value of routine hematological and biochemical parameters on 30-day fatality in acute stroke

    Directory of Open Access Journals (Sweden)

    Bhatia R

    2004-04-01

    Full Text Available OBJECTIVE: This prospective study was planned to study the prognostic value of routine clinical, hematological and biochemical parameters, including platelet aggregation in patients of acute stroke, on fatality occurring during the first 30 days. MATERIAL AND METHODS: In this study 116 consecutive patients (77 males and 39 females of stroke (within 72 hours of onset were included. After clinical evaluation and neuroimaging, blood investigations, hemoglobin, total leukocyte count, platelet count, platelet aggregation, erythrocyte sedimentation rate (ESR, blood sugar, urea, creatinine, sodium, potassium, serum cholesterol, serum bilirubin, aspartate aminotransferase (SGOT, alanine aminotransferase (SGPT, albumin, and globulin estimations were performed. The patients were followed up for a maximum period of 30 days from the onset of stroke, and patients who expired were grouped as ′expired′ and the rest as ′survivors′. Logistic regression analysis was carried out among the significant parameters to identify independent predictors of 30-day fatality. RESULTS: Univariate analysis demonstrated that among hematological parameters, high total leukocyte count and ESR, at admission, correlated significantly with an undesirable outcome during the initial 30 days. Among biochemical parameters, elevated urea, creatinine, serum transaminases (SGOT and SGPT and globulin levels correlated significantly with death. Logistic regression analysis demonstrated that a low Glasgow Coma Scale (GCS score along with biochemical parameters such as high serum creatinine, SGPT, ESR and total leukocyte count correlated with death. CONCLUSION: Impaired consciousness, high total leukocyte count, raised ESR, elevated creatinine and SGPT levels, estimated within 24 hours of hospitalization, are the most important indicators of 30-day mortality in patients with first-time ischemic stroke.

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

    Science.gov (United States)

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

    2016-04-01

    The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood

  16. Predicting trace organic compound attenuation with spectroscopic parameters in powdered activated carbon processes.

    Science.gov (United States)

    Ziska, Austin D; Park, Minkyu; Anumol, Tarun; Snyder, Shane A

    2016-08-01

    The removal of trace organic compounds (TOrCs) is of growing interest in water research and society. Powdered activated carbon (PAC) has been proven to be an effective method of removal for TOrCs in water, with the degree of effectiveness depending on dosage, contact time, and activated carbon type. In this study, the attenuation of TOrCs in three different secondary wastewater effluents using four PAC materials was studied in order to elucidate the effectiveness and efficacy of PAC for TOrC removal. With the notable exception of hydrochlorothiazide, all 14 TOrC indicators tested in this study exhibited a positive correlation of removal rate with their log Dow values, demonstrating that the main adsorption mechanism was hydrophobic interaction. As a predictive model, the modified Chick-Watson model, often used for the prediction of microorganism inactivation by disinfectants, was applied. The applied model exhibited good predictive power for TOrC attenuation by PAC in wastewater. In addition, surrogate models based upon spectroscopic measurements including UV absorbance at 254 nm and total fluorescence were applied to predict TOrC removal by PAC. The surrogate model was found to provide an excellent prediction of TOrC attenuation for all combinations of water quality and PAC type included in this study. The success of spectrometric parameters as surrogates in predicting TOrC attenuation by PAC are particularly useful because of their potential application in real-time on-line sensor monitoring and process control at full-scale water treatment plants, which could lead to significantly reduced operator response times and PAC operational optimization.

  17. A Priori Intrinsic PTM Size Parameters for Predicting the Ion Mobilities of Modified Peptides

    Science.gov (United States)

    Kaszycki, Julia L.; Shvartsburg, Alexandre A.

    2017-02-01

    The rising profile of ion mobility spectrometry (IMS) in proteomics has driven the efforts to predict peptide cross-sections. In the simplest approach, these are derived by adding the contributions of all amino acid residues and post-translational modifications (PTMs) defined by their intrinsic size parameters (ISPs). We show that the ISPs for PTMs can be calculated from properties of constituent atoms, and introduce the "impact scores" that govern the shift of cross-sections from the central mass-dependent trend for unmodified peptides. The ISPs and scores tabulated for 100 more common PTMs enable predicting the domains for modified peptides in the IMS/MS space that would guide subproteome investigations.

  18. A new model to predict weak-lensing peak counts II. Parameter constraint strategies

    CERN Document Server

    Lin, Chieh-An

    2015-01-01

    Peak counts have been shown to be an excellent tool to extract the non-Gaussian part of the weak lensing signal. Recently, we developped a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analyses. In this work, we explore and compare various strategies for constraining parameter using our model, focusing on the matter density $\\Omega_\\mathrm{m}$ and the density fluctuation amplitude $\\sigma_8$. First, we examine the impact from the cosmological dependency of covariances (CDC). Second, we perform the analysis with the copula likelihood, a technique which makes a weaker assumption compared to the Gaussian likelihood. Third, direct, non-analytic parameter estimations are applied using the full information of the distribution. Fourth, we obtain constraints with approximate Bayesian computation (ABC), an efficient, robust, and likelihood-free algorithm based on accept-reject sampling. We find that neglecting the CDC ...

  19. A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model

    CERN Document Server

    Higdon, Dave; Schunck, Nicolas; Sarich, Jason; Wild, Stefan M

    2014-01-01

    Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model $\\eta(\\theta)$ where $\\theta$ denotes the uncertain, best input setting. Hence the statistical model is of the form $y = \\eta(\\theta) + \\epsilon$, where $\\epsilon$ accounts for measurement, and possibly other error sources. When non-linearity is present in $\\eta(\\cdot)$, the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and non-standard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. While quite generally applicable, MCMC requires thousands, or even millions of evaluations of the physics model $\\eta(\\cdot)$. This is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we pr...

  20. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

    Directory of Open Access Journals (Sweden)

    Chunxiao Zhang

    2012-01-01

    Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

  1. Use of remote sensing derived parameters in a crop model for biomass prediction of hay crop

    Science.gov (United States)

    El Hajj, Mohammad; Baghdadi, Nicolas; Cheviron, Bruno; Belaud, Gilles; Zribi, Mehrez

    2016-04-01

    Pre-harvest yield forecasting is a critical challenge for producers, especially for large agricultural areas. During previous decades, numerous crop models were developed to predict crop growth and yield at daily time, most often for wheat or maize, and also for grasslands. Crop models require several input parameters that describe soil properties (e.g. field capacity), plant characteristics (e.g. maximal rooting depth) and management options (e.g. sowing dates, irrigation and harvest dates), which are referred to as the soil, plant and management families of parameters. Remote sensing technology has been extensively applied to identify spatially distributed values of some of the accessible parameters in the soil, plant and management families. The aim of this study was to address the feasibility, merits and limitations of forcing remote-sensing-derived parameters (LAI values, harvest and irrigation dates) in the PILOTE crop model, targeting the Total Dry Matter (TDM) of hay crops. Results show that optical images are suitable to feed PILOTE with LAI values without inducing significant errors on the predicted Total Dry Matter (TDM) values (Root Mean Square Error "RMSE" = 0.41 t/ha and Mean Absolute Percentage Error "MAPE" = 22%). Moreover, optical images with revisit times lower than 16 days are adequate to feed PILOTE with remotely sensed harvest dates (RMSE < 0.44 t/ha, MAPE < 10.8%). Finally, feeding PILOTE with noisy irrigation dates that were estimated from SAR images also enabled reliable model predictions, at least when attaching a random uncertainty of "only" 3 days to the real known irrigation dates. The case of one or several undetected irrigations has also been explored, with the expected conclusion that undetected irrigations significantly affect model predictions only in dry periods. For the tested soil properties and climatic conditions, a maximum underestimation of TDM of approximately 1.55 t/ha (reference TDM of 3.43 t/ha) was observed in the second

  2. Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

    Science.gov (United States)

    Lin, Zhiming; Liao, Zetao; Huang, Jianlin; Ai, Maixing; Pan, Yunfeng; Wu, Henglian; Lu, Jun; Cao, Shuangyan; Li, Li; Wei, Qiujing; Tang, Deshen; Wei, Yanlin; Li, Tianwang; Wu, Yuqiong; Xu, Manlong; Li, Qiuxia; Jin, Ou; Yu, Buyun; Gu, Jieruo

    2015-01-01

    Objectives. To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients. Methods. Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively. Results. Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791; P = 0.005), CRP (AUC = 0.75; P = 0.017), and ASDAS (AUC = 0.778, P = 0.007) significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696, P = 0.040) significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (wald χ2 = 6.87, P = 0.009, and wald χ2 = 5.171, P = 0.023). Conclusions. Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients. PMID:26273654

  3. The influence of estimated body segment parameters on predicted joint kinetics during diplegic cerebral palsy gait.

    Science.gov (United States)

    Kiernan, D; Walsh, M; O'Sullivan, R; O'Brien, T; Simms, C K

    2014-01-01

    Inverse Dynamic calculations are routinely used in joint moment and power estimates during gait with anthropometric data often taken from published sources. Many biomechanical analyses have highlighted the need to obtain subject-specific anthropometric data (e.g. Mass, Centre of Mass, Moments of Inertia) yet the types of imaging techniques required to achieve this are not always available in the clinical setting. Differences in anthropometric sets have been shown to affect the reactive force and moment calculations in normal subjects but the effect on a paediatric diplegic cerebral palsy group has not been investigated. The aim of this study was to investigate the effect of using different anthropometric sets on predicted sagittal plane moments during normal and diplegic cerebral palsy gait. Three published anthropometric sets were applied to the reactive force and moment calculations of 14 Cerebral Palsy and 14 Control subjects. Statistically significant differences were found when comparing the different anthropometric sets but variability in the resulting sagittal plane moment calculations between sets was low (0.01-0.07 Nm/kg). In addition, the GDI-Kinetic, used as an outcome variable to assess whether differences were clinically meaningful, indicated no clinically meaningful difference between sets. The results suggest that the effects of using different anthropometric sets on the kinetic profiles of normal and diplegic cerebral palsy subjects are clinically insignificant.

  4. Relative importance of parameters affecting wind speed prediction using artificial neural networks

    Science.gov (United States)

    Ghorbani, M. A.; Khatibi, R.; Hosseini, B.; Bilgili, M.

    2013-10-01

    In traditional artificial neural networks (ANN) models, the relative importance of the individual meteorological input variables is often overlooked. A case study is presented in this paper to model monthly wind speed values using meteorological data (air pressure, air temperature, relative humidity, and precipitation), where the study also includes an estimate of the relative importance of these variables. Recorded monthly mean data are available at a gauging site in Tabriz, Azerbaijan, Iran, for the period from 2000 to 2005, gauged in the city at the outskirt of alluvial funneling mountains with an established microclimatic conditions and a diurnal wind regime. This provides a sufficiently severe test for the ANN model with a good predictive capability of 1 year of lead time but without any direct approach to refer the predicted results to local microclimatic conditions. A method is used in this paper to calculate the relative importance of each meteorological input parameters affecting wind speed, showing that air pressure and precipitation are the most and least influential parameters with approximate values of 40 and 10 %, respectively. This gained knowledge corresponds to the local knowledge of the microclimatic and geomorphologic conditions surrounding Tabriz.

  5. Nonlinear model predictive control using parameter varying BP-ARX combination model

    Science.gov (United States)

    Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.

    2012-03-01

    A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.

  6. Mapping of ionospheric parameters for space weather predictions: A concise review

    Institute of Scientific and Technical Information of China (English)

    Y. KAMIDE; A. IEDA

    2008-01-01

    Reviewing brieflythe recent progress in a joint program of specifying the polar ionosphere primarily on the basis of ground magnetometer data, this paper em-phasizes the importance of processing data from around the world in real time for space weather predictions. The output parameters from the program include ionospheric electric fields and currents and field-aligned currents. These real-time records are essential for running computer simulations under realistic boundary conditions and thus for making numerical predictions of space weather efficient as reliable as possible. Data from individual ground magnetometers as well as from the solar wind are collected and are used as input for the KRM and AMIE mag-netogram-inversion algorithms, through which the two-dimensional distribution of the ionospheric parameters is calculated. One of the goals of the program is to specify the solar-terrestrial environment in terms of ionospheric processes and to provide the scientific community with more than what geomagnetic activity Indices and statistical models indicate.

  7. Mapping of ionospheric parameters for space weather predictions: A concise review

    Institute of Scientific and Technical Information of China (English)

    Y.; KAMIDE; A.; IEDA

    2008-01-01

    Reviewing briefly the recent progress in a joint program of specifying the polar ionosphere primarily on the basis of ground magnetometer data, this paper em-phasizes the importance of processing data from around the world in real time for space weather predictions. The output parameters from the program include ionospheric electric fields and currents and field-aligned currents. These real-time records are essential for running computer simulations under realistic boundary conditions and thus for making numerical predictions of space weather efficient as reliable as possible. Data from individual ground magnetometers as well as from the solar wind are collected and are used as input for the KRM and AMIE mag-netogram-inversion algorithms, through which the two-dimensional distribution of the ionospheric parameters is calculated. One of the goals of the program is to specify the solar-terrestrial environment in terms of ionospheric processes and to provide the scientific community with more than what geomagnetic activity indices and statistical models indicate.

  8. A Modelling Study for Predicting Life of Downhole Tubes Considering Service Environmental Parameters and Stress

    Directory of Open Access Journals (Sweden)

    Tianliang Zhao

    2016-09-01

    Full Text Available A modelling effort was made to try to predict the life of downhole tubes or casings, synthetically considering the effect of service influencing factors on corrosion rate. Based on the discussed corrosion mechanism and corrosion processes of downhole tubes, a mathematic model was established. For downhole tubes, the influencing factors are environmental parameters and stress, which vary with service duration. Stress and the environmental parameters including water content, partial pressure of H2S and CO2, pH value, total pressure and temperature, were considered to be time-dependent. Based on the model, life-span of an L80 downhole tube in oilfield Halfaya, an oilfield in Iraq, was predicted. The results show that life-span of the L80 downhole tube in Halfaya is 247 months (approximately 20 years under initial stress of 0.1 yield strength and 641 months (approximately 53 years under no initial stress, which indicates that an initial stress of 0.1 yield strength will reduce the life-span by more than half.

  9. MODEL JARINGAN SYARAF TIRUAN UNTUK MEMPREDIKSI PARAMETER KUALITAS TOMAT BERDASARKAN PARAMETER WARNA RGB (An artificial neural network model for predicting tomato quality parameters based on color

    Directory of Open Access Journals (Sweden)

    Rudiati Evi Masithoh

    2013-03-01

    Full Text Available Artificial neural networks (ANN was used to predict the quality parameters of tomato, i.e. Brix, citric acid, total carotene, and vitamin C. ANN was developed from Red Green Blue (RGB image data of tomatoes measured using a developed computer vision system (CVS. Qualitative analysis of tomato compositions were obtained from laboratory experiments. ANN model was based on a feedforward backpropagation network with different training functions, namely gradient descent (traingd, gradient descent with the resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab and Shanno (BFGS quasi-Newton (trainbfg, as well as Levenberg Marquardt (trainlm.  The network structure using logsig and linear (purelin activation function at the hidden and output layer, respectively, and using  the trainlm as a training function resulted in the best performance. Correlation coefficient (r of training and validation process were 0.97 - 0.99 and 0.92 - 0.99, whereas the MAE values ​​ranged from 0.01 to 0.23 and 0.03 to 0.59, respectively. Keywords: Artificial neural network, trainlm, tomato, RGB   Jaringan syaraf tiruan (JST digunakan untuk memprediksi parameter kualitas tomat, yaitu Brix, asam sitrat, karoten total, dan vitamin C. JST dikembangkan dari data Red Green Blue (RGB  citra tomat yang diukur menggunakan computer vision system. Data kualitas tomat diperoleh dari analisis di laboratorium. Struktur model JST didasarkan pada jaringan feedforward backpropagation dengan berbagai fungsi pelatihan, yaitu gradient descent (traingd, gradient descent dengan resilient backpropagation (trainrp, Broyden, Fletcher, Goldfrab dan Shanno (BFGS quasi-Newton (trainbfg, serta Levenberg Marquardt (trainlm. Fungsi pelatihan yang terbaik adalah menggunakan trainlm, serta pada struktur jaringan digunakan fungsi aktivasi logsig pada lapisan tersembunyi dan linier (purelin pada lapisan keluaran. dengan 1000 epoch. Nilai koefisien korelasi (r pada tahap pelatihan dan validasi

  10. Chest X-rays and associated clinical parameters in pulmonary Tubercolosis cases from the National Tubercolosis Program, Mumbai, India

    Directory of Open Access Journals (Sweden)

    Yatin N. Dholakia

    2012-01-01

    Full Text Available The study was carried out in pulmonary tuberculosis (PTB patients from the local Tuberculosis control programme, Mumbai, India. It examined features of chest X-rays and their correlation with clinical parameters for possible application in suspected multidrug resistant TB (MDRTB and to predict outcome in new and treatment failure PTB cases. X-ray features (infiltrate, cavitation, miliary shadows, pleural effusion, mediastinal lymphadenopathy and extent of lesions were analyzed to identify associations with biological/clinical parameters through univariate and multivariate logistic regression. Failures demonstrated associations between extensive lesions and high glycosylated hemoglobin (GHb levels (P=0.028 and male gender (P=0.03. An association was also detected between cavitation and MDR (P=0.048. In new cases, bilateral cavities were associated with MDR (P=0.018 and male gender (P=0.01, low body mass index with infiltrates (P=0.008, and smoking with cavitation (P=0.0238. Strains belonging to the Manu1 spoligotype were associated with mild lesions (P=0.002. Poor outcome showed borderline significance with extensive lesions at onset (P=0.053. Furthermore, amongst new cases, smoking, the Central Asian Strain (CAS spoligotype and high GHb were associated with cavitation, whereas only CAS spoligotypes and high GHb were associated with extensive lesions. The study highlighted associations between certain clinical parameters and X-ray evidence which support the potential of X-rays to predict TB, MDRTB and poor outcome. The use of Xrays as an additional tool to shorten diagnostic delay and shortlist MDR suspects amongst nonresponders to TB treatment should be explored in a setting with limited resources coping with a high MDR case load such as Mumbai.

  11. Prediction of positron-annihilation parameters for vacancy-type defects in ternary alloy semiconductors by data-scientific approach

    Science.gov (United States)

    Ishibashi, Shoji; Kino, Hiori; Uedono, Akira; Miyake, Takashi; Terakura, Kiyoyuki

    2017-01-01

    We calculated positron annihilation parameters for mono- and di-vacancies in ternary semiconductors Al0.5Ga0.5N and In0.5Ga0.5N. It has been found that the obtained annihilation parameters are well correlated with structural parameters. By constructing multiple linear regression models using selected (about 1/4 of the total) datasets as training sets in order to reduce computational cost, we could predict annihilation parameters for the rest.

  12. Rorschach Prediction of Success in Clinical Training: A Second Look

    Science.gov (United States)

    Carlson, Rae

    1969-01-01

    A Rorschach Index based on ego-psychological conceptualization of an optimal personality picture predicted for 155 trainees was compared with predictions from the Miller Analogies Test (MAT) and the Strong Vocational Interest Blank (SVIB). The Index predicted success and failure more effectively. (Author)

  13. A combined clinical and biomarker approach to predict diuretic response in acute heart failure

    NARCIS (Netherlands)

    Ter Maaten, Jozine M; Valente, Mattia A E; Metra, Marco; Bruno, Noemi; O'Connor, Christopher M; Ponikowski, Piotr; Teerlink, John R; Cotter, Gad; Davison, Beth; Cleland, John G; Givertz, Michael M; Bloomfield, Daniel M; Dittrich, Howard C; van Veldhuisen, Dirk J; Hillege, Hans L; Damman, Kevin; Voors, Adriaan A

    2015-01-01

    BACKGROUND: Poor diuretic response in acute heart failure is related to poor clinical outcome. The underlying mechanisms and pathophysiology behind diuretic resistance are incompletely understood. We evaluated a combined approach using clinical characteristics and biomarkers to predict diuretic resp

  14. Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care

    Science.gov (United States)

    Cancer Pharmacogenomics: Integrating Discoveries in Basic, Clinical and Population Sciences to Advance Predictive Cancer Care, a 2010 workshop sponsored by the Epidemiology and Genomics Research Program.

  15. Impact of Hybrid Intelligent Computing in Identifying Constructive Weather Parameters for Modeling Effective Rainfall Prediction

    Directory of Open Access Journals (Sweden)

    M. Sudha

    2015-12-01

    Full Text Available Uncertain atmosphere is a prevalent factor affecting the existing prediction approaches. Rough set and fuzzy set theories as proposed by Pawlak and Zadeh have become an effective tool for handling vagueness and fuzziness in the real world scenarios. This research work describes the impact of Hybrid Intelligent System (HIS for strategic decision support in meteorology. In this research a novel exhaustive search based Rough set reduct Selection using Genetic Algorithm (RSGA is introduced to identify the significant input feature subset. The proposed model could identify the most effective weather parameters efficiently than other existing input techniques. In the model evaluation phase two adaptive techniques were constructed and investigated. The proposed Artificial Neural Network based on Back Propagation learning (ANN-BP and Adaptive Neuro Fuzzy Inference System (ANFIS was compared with existing Fuzzy Unordered Rule Induction Algorithm (FURIA, Structural Learning Algorithm on Vague Environment (SLAVE and Particle Swarm OPtimization (PSO. The proposed rainfall prediction models outperformed when trained with the input generated using RSGA. A meticulous comparison of the performance indicates ANN-BP model as a suitable HIS for effective rainfall prediction. The ANN-BP achieved 97.46% accuracy with a nominal misclassification rate of 0.0254 %.

  16. Changes in Predicted Muscle Coordination with Subject-Specific Muscle Parameters for Individuals after Stroke

    Directory of Open Access Journals (Sweden)

    Brian A. Knarr

    2014-01-01

    Full Text Available Muscle weakness is commonly seen in individuals after stroke, characterized by lower forces during a maximal volitional contraction. Accurate quantification of muscle weakness is paramount when evaluating individual performance and response to after stroke rehabilitation. The objective of this study was to examine the effect of subject-specific muscle force and activation deficits on predicted muscle coordination when using musculoskeletal models for individuals after stroke. Maximum force generating ability and central activation ratio of the paretic plantar flexors, dorsiflexors, and quadriceps muscle groups were obtained using burst superimposition for four individuals after stroke with a range of walking speeds. Two models were created per subject: one with generic and one with subject-specific activation and maximum isometric force parameters. The inclusion of subject-specific muscle data resulted in changes in the model-predicted muscle forces and activations which agree with previously reported compensation patterns and match more closely the timing of electromyography for the plantar flexor and hamstring muscles. This was the first study to create musculoskeletal simulations of individuals after stroke with subject-specific muscle force and activation data. The results of this study suggest that subject-specific muscle force and activation data enhance the ability of musculoskeletal simulations to accurately predict muscle coordination in individuals after stroke.

  17. Beta Dips in the Gaia Era: Simulation Predictions of the Galactic Velocity Anisotropy Parameter (β)

    Science.gov (United States)

    Loebman, Sarah; Valluri, Monica; Hattori, Kohei; Debattista, Victor P.; Bell, Eric F.; Stinson, Greg; Christensen, Charlotte; Brooks, Alyson; Quinn, Thomas R.; Governato, Fabio

    2017-01-01

    Milky Way (MW) science has entered a new era with the advent of Gaia. Combined with spectroscopic survey data, we have newfound access to full 6D phase space information for halo stars. Such data provides an invaluable opportunity to assess kinematic trends as a function of radius and confront simulations with these observations to draw insight about our merger history. I will discuss predictions for the velocity anisotropy parameter, β, drawn from three suites of state-of-the-art cosmological N-body and N-body+SPH MW-like simulations. On average, all three suites predict a monotonically increasing value of β that is radially biased, and beyond 10 kpc, β > 0.5. I will also discuss β as a function of time for individual simulated galaxies. I will highlight when "dips" in β form, the severity (the rarity of β < 0), origin (in situ versus accreted halo), and persistence of these dips. Thereby, I present a cohesive set of predictions of β from simulations for comparison to forthcoming observations.

  18. A multi-domain Chebyshev collocation method for predicting ultrasonic field parameters in complex material geometries

    DEFF Research Database (Denmark)

    Nielsen, S.A.; Hesthaven, J.S.

    2002-01-01

    The use of ultrasound to measure elastic field parameters as well as to detect cracks in solid materials has received much attention, and new important applications have been developed recently, e.g., the use of laser generated ultrasound in non-destructive evaluation (NDE). To model such applica......The use of ultrasound to measure elastic field parameters as well as to detect cracks in solid materials has received much attention, and new important applications have been developed recently, e.g., the use of laser generated ultrasound in non-destructive evaluation (NDE). To model...... such applications requires a realistic calculation of field parameters in complex geometries with discontinuous, layered materials. In this paper we present an approach for solving the elastic wave equation in complex geometries with discontinuous layered materials. The approach is based on a pseudospectral...... solutions by means of characteristic variables. Finally, the global solution is advanced in time using a fourth order Runge-Kutta scheme. Examples of field prediction in discontinuous solids with complex geometries are given and related to ultrasonic NDE. (C) 2002 Elsevier Science B.V. All rights reserved....

  19. Predictive Models for Different Roughness Parameters During Machining Process of Peek Composites Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    Mata-Cabrera Francisco

    2013-10-01

    Full Text Available Polyetheretherketone (PEEK composite belongs to a group of high performance thermoplastic polymers and is widely used in structural components. To improve the mechanical and tribological properties, short fibers are added as reinforcement to the material. Due to its functional properties and potential applications, it’s impor- tant to investigate the machinability of non-reinforced PEEK (PEEK, PEEK rein- forced with 30% of carbon fibers (PEEK CF30, and reinforced PEEK with 30% glass fibers (PEEK GF30 to determine the optimal conditions for the manufacture of the parts. The present study establishes the relationship between the cutting con- ditions (cutting speed and feed rate and the roughness (Ra , Rt , Rq , Rp , by develop- ing second order mathematical models. The experiments were planned as per full factorial design of experiments and an analysis of variance has been performed to check the adequacy of the models. These state the adequacy of the derived models to obtain predictions for roughness parameters within ranges of parameters that have been investigated during the experiments. The experimental results show that the most influence of the cutting parameters is the feed rate, furthermore, proved that glass fiber reinforcements produce a worse machinability.

  20. ECG dispersion mapping predicts clinical deterioration, measured by increase in the Simple Clinical Score.

    LENUS (Irish Health Repository)

    Kellett, J

    2012-01-01

    Objective: ECG dispersion mapping (ECG-DM) is a novel technique that reports abnormal ECG microalternations. We report the ability of ECG-DM to predict clinical deterioration of acutely ill medical patients, as measured by an increase in the Simple Clinical Score (SCS) the day after admission to hospital. Methods: 453 acutely ill medical patients (mean age 69.7 +\\/- 14.0 years) had the SCS recorded and ECGDM performed immediately after admission to hospital. Results: 46 patients had an SCS increase 20.8 +\\/- 7.6 hours after admission. Abnormal micro-alternations during left ventricular re-polarization had the highest association with SCS increase (p=0.0005). Logistic regression showed that only nursing home residence and abnormal micro-alternations during re-polarization of the left ventricle were independent predictors of SCS increase with an odds ratio of 2.84 and 3.01, respectively. Conclusion: ECG-DM changes during left ventricular re-polarization are independent predictors of clinical deterioration the day after hospital admission.

  1. Identification of Clinical and Genetic Parameters Associated with Hidradenitis Suppurativa in Inflammatory Bowel Disease

    NARCIS (Netherlands)

    Janse, Ineke C; Koldijk, Marjolein J; Spekhorst, Lieke M; Vila, Arnau Vich; Weersma, Rinse K; Dijkstra, Gerard; Horváth, Barbara

    2016-01-01

    BACKGROUND: Hidradenitis suppurativa (HS) has recently been associated with inflammatory bowel disease (IBD). The objective of this study is to investigate the prevalence of HS in IBD and to identify clinical and genetic parameters associated with HS in IBD. METHODS: A questionnaire, validated for H

  2. New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs

    Institute of Scientific and Technical Information of China (English)

    Robert A.Beckman; Cong Chen

    2013-01-01

    Predictive biomarkers are important to the future of oncology; they can be used to identify patient populations who will benefit from therapy,increase the value of cancer medicines,and decrease the size and cost of clinical trials while increasing their chance of success.But predictive biomarkers do not always work.When unsuccessful,they add cost,complexity,and time to drug development.This perspective describes phases 2 and 3 development methods that efficiently and adaptively check the ability of a biomarker to predict clinical outcomes.In the end,the biomarker is emphasized to the extent that it can actually predict.

  3. Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

    Science.gov (United States)

    Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

    2010-01-01

    Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in

  4. Clinical prediction of 5-year survival in systemic sclerosis

    DEFF Research Database (Denmark)

    Fransen, Julie Munk; Popa-Diaconu, D; Hesselstrand, R;

    2011-01-01

    Systemic sclerosis (SSc) is associated with a significant reduction in life expectancy. A simple prognostic model to predict 5-year survival in SSc was developed in 1999 in 280 patients, but it has not been validated in other patients. The predictions of a prognostic model are usually less accurate...... in other patients, especially from other centres or countries. A study was undertaken to validate the prognostic model to predict 5-year survival in SSc in other centres throughout Europe....

  5. On-Line Flutter Prediction Tool for Wind Tunnel Flutter Testing using Parameter Varying Estimation Methodology Project

    Data.gov (United States)

    National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool for wind tunnel model using the parameter varying estimation (PVE) technique to...

  6. On-Line Flutter Prediction Tool for Wind Tunnel Flutter Testing using Parameter Varying Estimation Methodology Project

    Data.gov (United States)

    National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes to develop an on-line flutter prediction tool using the parameter varying estimation (PVE) methodology, called the PVE Toolbox,...

  7. Prediction efficiency of the hydrographical parameters as related to distribution patterns of the Pleuromamma species in the Indian Ocean

    Digital Repository Service at National Institute of Oceanography (India)

    Jayalakshmy, K.V.; Saraswathy, M.

    the equatorial region and southern coast off South Africa could explain only 30% of the variations in the predicted total abundance and it was attained only when average values of the parameters were considered in both cases. Hence average values...

  8. Predicting future conflict between team-members with parameter-free models of social networks.

    Science.gov (United States)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-01-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  9. Predicting future conflict between team-members with parameter-free models of social networks

    CERN Document Server

    Rovira-Asenjo, Nuria; Sales-Pardo, Marta; Guimera, Roger

    2014-01-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  10. Proposed parameters of specific rain attenuation prediction for Free Space Optics link operating in tropical region

    Science.gov (United States)

    Suriza, A. Z.; Md Rafiqul, Islam; Wajdi, A. K.; Naji, A. W.

    2013-03-01

    As the demand for higher and unlimited bandwidth for communication channel is increased, Free Space Optics (FSO) is a good alternative solution. As it is protocol transparent, easy to install, cost effective and have capabilities like fiber optics, its demand rises very fast. Weather condition, however is the limiting factor for FSO link. In the temperate region the major blockage for FSO link feasibility is fog. In the tropical region high rainfall rate is expected to be the major drawback of FSO link availability. Rain attenuation is the most significant to influence FSO link availability in tropical region. As for now the available k and α values are developed using data from temperate regions. Therefore, the objective of this paper is to propose new parameters for specific rain attenuation prediction model that represents tropical weather condition. The proposed values are derived from data measured in Malaysia and using methods recommended by ITU-R.

  11. Predicting future conflict between team-members with parameter-free models of social networks

    Science.gov (United States)

    Rovira-Asenjo, Núria; Gumí, Tània; Sales-Pardo, Marta; Guimerà, Roger

    2013-06-01

    Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

  12. Predicting Future Clinical Adjustment from Treatment Outcome and Process Variables.

    Science.gov (United States)

    Patterson, G. R.; Forgatch, Marion S.

    1995-01-01

    Issues related to the use of outcome and process data from the treatment of antisocial children to predict future childhood adjustment were examined through a study of 69 children. Data supported the hypothesis that measures of processes thought to produce changes in child behavior would serve to predict future adjustment. (SLD)

  13. Prediction of pyrolysis kinetic parameters from biomass constituents based on simplex-lattice mixture design☆

    Institute of Scientific and Technical Information of China (English)

    Panusit Sungsuk; Sasiporn Chayaporn; Sasithorn Sunphorka; Prapan Kuchonthara; Pornpote Piumsomboon; Benjapon Chalermsinsuwan

    2016-01-01

    The aim of this study is to determine the effect of the main chemical components of biomass:cel ulose, hemicel-lulose and lignin, on chemical kinetics of biomass pyrolysis. The experiments were designed based on a simplex-lattice mixture design. The pyrolysis was observed by using a thermogravimetric analyzer. The curves obtained from the employed analytical method fit the experimental data (R2 N 0.9). This indicated that this method has the potential to determine the kinetic parameters such as the activation energy (Ea), frequency factor (A) and re-action order (n) for each point of the experimental design. The results obtained from the simplex-lattice mixture design indicated that cellulose had a significant effect on Ea and A, and the interaction between cellulose and lignin had an important effect on the reaction order, n. The proposed models were then proved to be useful for predicting pyrolysis behavior in real biomass and so could be used as a simple approximation for predicting the overall trend of chemical reaction kinetics.

  14. Performance improvement of artificial neural networks designed for safety key parameters prediction in nuclear research reactors

    Energy Technology Data Exchange (ETDEWEB)

    Mazrou, Hakim [Division de Physique Radiologique, Centre de Recherche Nucleaire d' Alger (CRNA), 02 Boulevard Frantz, Fanon, B.P. 399, 16000 Alger (Algeria)], E-mail: mazrou_h@crna.dz

    2009-10-15

    The present work explores, through a comprehensive sensitivity study, a new methodology to find a suitable artificial neural network architecture which improves its performances capabilities in predicting two significant parameters in safety assessment i.e. the multiplication factor k{sub eff} and the fuel powers peaks P{sub max} of the benchmark 10 MW IAEA LEU core research reactor. The performances under consideration were the improvement of network predictions during the validation process and the speed up of computational time during the training phase. To reach this objective, we took benefit from Neural Network MATLAB Toolbox to carry out a widespread sensitivity study. Consequently, the speed up of several popular algorithms has been assessed during the training process. The comprehensive neural system was subsequently trained on different transfer functions, number of hidden neurons, levels of error and size of generalization corpus. Thus, using a personal computer with data created from preceding work, the final results obtained for the treated benchmark were improved in both network generalization phase and much more in computational time during the training process in comparison to the results obtained previously.

  15. Antioxidant defense parameters as predictive biomarkers for fermentative capacity of active dried wine yeast.

    Science.gov (United States)

    Gamero-Sandemetrio, Esther; Gómez-Pastor, Rocío; Matallana, Emilia

    2014-08-01

    The production of active dried yeast (ADY) is a common practice in industry for the maintenance of yeast starters and as a means of long term storage. The process, however, causes multiple cell injuries, with oxidative damage being one of the most important stresses. Consequentially, dehydration tolerance is a highly appreciated property in yeast for ADY production. In this study we analyzed the cellular redox environment in three Saccharomyces cerevisiae wine strains, which show markedly different fermentative capacities after dehydration. To measure/quantify the effect of dehydration on the S. cerevisiae strains, we used: (i) fluorescent probes; (ii) antioxidant enzyme activities; (ii) intracellular damage; (iii) antioxidant metabolites; and (iv) gene expression, to select a minimal set of biochemical parameters capable of predicting desiccation tolerance in wine yeasts. Our results show that naturally enhanced antioxidant defenses prevent oxidative damage after wine yeast biomass dehydration and improve fermentative capacity. Based on these results we chose four easily assayable parameters/biomarkers for the selection of industrial yeast strains of interest for ADY production: trehalose and glutathione levels, and glutathione reductase and catalase enzymatic activities. Yeast strains selected in accordance with this process display high levels of trehalose, low levels of oxidized glutathione, a high induction of glutathione reductase activity, as well as a high basal level and sufficient induction of catalase activity, which are properties inherent in superior ADY strains.

  16. A multi-domain Chebyshev collocation method for predicting ultrasonic field parameters in complex material geometries.

    Science.gov (United States)

    Nielsen, S A; Hesthaven, J S

    2002-05-01

    The use of ultrasound to measure elastic field parameters as well as to detect cracks in solid materials has received much attention, and new important applications have been developed recently, e.g., the use of laser generated ultrasound in non-destructive evaluation (NDE). To model such applications requires a realistic calculation of field parameters in complex geometries with discontinuous, layered materials. In this paper we present an approach for solving the elastic wave equation in complex geometries with discontinuous layered materials. The approach is based on a pseudospectral elastodynamic formulation, giving a direct solution of the time-domain elastodynamic equations. A typical calculation is performed by decomposing the global computational domain into a number of subdomains. Every subdomain is then mapped on a unit square using transfinite blending functions and spatial derivatives are calculated efficiently by a Chebyshev collocation scheme. This enables that the elastodynamic equations can be solved within spectral accuracy, and furthermore, complex interfaces can be approximated smoothly, hence avoiding staircasing. A global solution is constructed from the local solutions by means of characteristic variables. Finally, the global solution is advanced in time using a fourth order Runge-Kutta scheme. Examples of field prediction in discontinuous solids with complex geometries are given and related to ultrasonic NDE.

  17. Serum parameters predict the severity of ultrasonographicifndingsinnon-alcoholic fatty liver disease

    Institute of Scientific and Technical Information of China (English)

    Mohsen Razavizade; Raika Jamali; Abbas Arj; Hamidreza Talari

    2012-01-01

    BACKGROUND: Controversy exists about the correlation between liver ultrasonography and serum parameters for evaluating the severity of liver involvement in non-alcoholic fatty liver disease (NAFLD). This study was designed to determine the association between liver ultrasonography staging in NAFLD and serum parameters correlated with disease severity in previous studies; and set optimal cut-off points for those serum parameters correlated with NAFLD staging at ultrasonography, in order to differentiate ultrasonographic groups (USGs). METHODS: This cross-sectional study evaluated outpatients with evidence of NAFLD in ultrasonography referred to a general hospital. Those with positive viral markers, abnormal serum ceruloplasmin or gamma-globulin concentrations were excluded. A radiologist performed the ultrasonography staging and stratiifed the patients into mild, moderate, and severe groups. Fasting serum alanine aminotransferase (ALT), aspartate aminotransferase, alkaline phosphatase, triglyceride (TG), high and low density lipoprotein (HDL, LDL), and cholesterol were checked. RESULTS:Two hundred and forty-ifve patients with a mean age (±standard deviation) of 41.63(±11.46) years were included. There were no signiifcant differences when mean laboratory concentrations were compared between moderate and severe USGs. Therefore, these groups were combined to create revised USGs ("mild"versus"moderate or severe"). There were associations between the revised USGs, and ALT, TG, HDL levels, and diabetes mellitus [odds ratios=2.81 (95%conifdence interval (CI):1.37-5.76), 2.48 (95%CI:1.29-4.78), 0.36 (95%CI:0.18-0.74), and 5.65 (95%CI:2.86-11.16) respectively;all P values CONCLUSIONS: Serum ALT, TG, and HDL concentrations seem to be associated with the staging by liver ultrasonography in NAFLD. They might be used to predict the staging of liver ultrasonography in these patients.

  18. Predicting the wheel rolling resistance regarding important motion parameters using the artificial neural network

    Directory of Open Access Journals (Sweden)

    F Gheshlaghi

    2016-04-01

    the analytical and statistical methods. It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated. Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a single-wheel tester and the carrier. Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network. Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling

  19. Predictive value of clinical history compared with urodynamic study in 1,179 women

    Directory of Open Access Journals (Sweden)

    Jorge Milhem Haddad

    2016-02-01

    Full Text Available SUMMARY Objective: to determine the positive predictive value of clinical history in comparison with urodynamic study for the diagnosis of urinary incontinence. Methods: retrospective analysis comparing clinical history and urodynamic evaluation of 1,179 women with urinary incontinence. The urodynamic study was considered the gold standard, whereas the clinical history was the new test to be assessed. This was established after analyzing each method as the gold standard through the difference between their positive predictive values. Results: the positive predictive values of clinical history compared with urodynamic study for diagnosis of stress urinary incontinence, overactive bladder and mixed urinary incontinence were, respectively, 37% (95% CI 31-44, 40% (95% CI 33-47 and 16% (95% CI 14-19. Conclusion: we concluded that the positive predictive value of clinical history was low compared with urodynamic study for urinary incontinence diagnosis. The positive predictive value was low even among women with pure stress urinary incontinence.

  20. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  1. Towards clinical application: repetitive sensor position re-calibration for improved reliability of gait parameters.

    Science.gov (United States)

    Hamacher, Daniel; Hamacher, Dennis; Taylor, William R; Singh, Navrag B; Schega, Lutz

    2014-04-01

    While camera-based motion tracking systems are considered to be the gold standard for kinematic analysis, these systems are not practical in clinical practice. However, the collection of gait parameters using inertial sensors is feasible in clinical settings and less expensive, but suffers from drift error that excludes accurate analyses. The goal of this study was to apply a combination of repetitive sensor position re-calibration techniques in order to improve the intra-day and inter-day reliability of gait parameters using inertial sensors. Kinematic data of nineteen healthy elderly individuals were captured twice within the first day and once on a second day after one week using inertial sensors fixed on the subject's forefoot during gait. Parameters of walking speed, minimum foot clearance (MFC), minimum toe clearance (MTC), stride length, stance time and swing time, as well as their corresponding measures of variability were calculated. Intra-day and inter-day differences were rated using intra-class correlation coefficients (ICC(3,1)), as well as the bias and limits of agreement. The results indicate excellent reliability for all intra-day and inter-day mean parameters (ICC: MFC 0.83-stride length 0.99). While good to excellent reliability was observed during intra-day parameters of variability (ICC: walking speed 0.71-MTC 0.98), corresponding inter-day reliability ranged from poor to excellent (ICC: walking speed 0.32-MTC 0.95). In conclusion, the system is suitable for reliable measurement of mean temporo-spatial parameters and the variability of MFC and MTC. However, the system's accuracy needs to be improved before remaining parameters of variability can reliably be collected.

  2. A biometric approach to predictable treatment of clinical crown discrepancies.

    Science.gov (United States)

    Chu, Stephen J

    2007-08-01

    Dental professionals have long been guided by mathematical principles when interpreting aesthetic and tooth proportions for their patients. While many acknowledge that such principles are merely launch points for a smile design or reconstructive procedure, their existence appears to indicate practitioners' desire for predictable, objective, and reproducible means of achieving success in aesthetic dentistry. This article introduces innovative aesthetic measurement gauges as a means of objectively quantifying tooth size discrepancies and enabling the clinician to perform aesthetic restorative dentistry with success and predictability.

  3. Comparison of clinical and paraclinical parameters as tools for early diagnosis of classical swine fever

    DEFF Research Database (Denmark)

    Lohse, Louise; Uttenthal, Åse; Nielsen, Jens

    Comparison of clinical and paraclinical parameters as tools for early diagnosis of classical swine fever. Louise Lohse, Åse Uttenthal, Jens Nielsen. National Veterinary Institute, Division of Virology, Lindholm, Technical University of Denmark. Introduction: In order to limit the far-reaching socio......-economic as well as the animal welfare consequences of an outbreak of classical swine fever (CSF), early diagnosis is essential. However, host-virus interactions strongly influence the course of CSF disease, and the clinical feature is not clear, thus complicating the diagnostic perspective. At the National...... were investigated for their potential as indicators for early diagnosis of CSF. Together, they constitute a promising panel for detection of CSF, however, one single parameter does not by itself hold the potential as a safe indicator of CSF in the early phase of infection. In addition, the results...

  4. Self-care and clinical parameters in patients with type 2 diabetes mellitus

    Directory of Open Access Journals (Sweden)

    José Thiago de Sousa

    2015-09-01

    Full Text Available Objective: to verify characteristics related to self-care and clinical parameters in patients with type 2 diabetes mellitus. Methods: descriptive and exploratory, cross-sectional study, conducted with 173 patients assisted in 12 Family Health Units in the urban area of a city in the Northeast region of Brazil. Results: most participants (61.3% were female, aged less than 60 years old. There were significant differences in the lower glycemic control (p = 0.014, capillary glycemia (p = 0.018 and alcohol consumption (p = 0.015 for men as well as higher central obesity indexes for women (p = 0.000. It was observed high frequency of overweight, abdominal obesity, high blood pressure, elevated blood glucose levels and insufficient levels of physical activity. Conclusion: there is the need for nursing actions aimed at improving self-care and control of the clinical parameters in these patients.

  5. Comparison of statistical and clinical predictions of functional outcome after ischemic stroke.

    Directory of Open Access Journals (Sweden)

    Douglas D Thompson

    Full Text Available To determine whether the predictions of functional outcome after ischemic stroke made at the bedside using a doctor's clinical experience were more or less accurate than the predictions made by clinical prediction models (CPMs.A prospective cohort study of nine hundred and thirty one ischemic stroke patients recruited consecutively at the outpatient, inpatient and emergency departments of the Western General Hospital, Edinburgh between 2002 and 2005. Doctors made informal predictions of six month functional outcome on the Oxford Handicap Scale (OHS. Patients were followed up at six months with a validated postal questionnaire. For each patient we calculated the absolute predicted risk of death or dependence (OHS≥3 using five previously described CPMs. The specificity of a doctor's informal predictions of OHS≥3 at six months was good 0.96 (95% CI: 0.94 to 0.97 and similar to CPMs (range 0.94 to 0.96; however the sensitivity of both informal clinical predictions 0.44 (95% CI: 0.39 to 0.49 and clinical prediction models (range 0.38 to 0.45 was poor. The prediction of the level of disability after stroke was similar for informal clinical predictions (ordinal c-statistic 0.74 with 95% CI 0.72 to 0.76 and CPMs (range 0.69 to 0.75. No patient or clinician characteristic affected the accuracy of informal predictions, though predictions were more accurate in outpatients.CPMs are at least as good as informal clinical predictions in discriminating between good and bad functional outcome after ischemic stroke. The place of these models in clinical practice has yet to be determined.

  6. Association between magnesium-deficient status and anthropometric and clinical-nutritional parameters in posmenopausal women

    OpenAIRE

    L??pez-Gonz??lez, Beatriz; Molina-L??pez, Jorge; Florea, Daniela Ioana; Quintero Osso, Bartolom??; P??rez de la Cruz, Antonio; Planells del Pozo, Elena Mar??a

    2014-01-01

    Background: During menopause occurs weight gain and bone loss occurs due to the hormone decline during this period and other factors such as nutrition. Magnesium deficiency suggests a risk factor for obesity and osteo porosis. Objective: To evaluate the clinical and nutritional magnesium status in a population of postmenopausal women, assessing intake and serum levels of magnesium in the study population and correlation with anthropometric parameters such as body mass index ...

  7. Reporting and methods in clinical prediction research: a systematic review.

    Directory of Open Access Journals (Sweden)

    Walter Bouwmeester

    Full Text Available BACKGROUND: We investigated the reporting and methods of prediction studies, focusing on aims, designs, participant selection, outcomes, predictors, statistical power, statistical methods, and predictive performance measures. METHODS AND FINDINGS: We used a full hand search to identify all prediction studies published in 2008 in six high impact general medical journals. We developed a comprehensive item list to systematically score conduct and reporting of the studies, based on recent recommendations for prediction research. Two reviewers independently scored the studies. We retrieved 71 papers for full text review: 51 were predictor finding studies, 14 were prediction model development studies, three addressed an external validation of a previously developed model, and three reported on a model's impact on participant outcome. Study design was unclear in 15% of studies, and a prospective cohort was used in most studies (60%. Descriptions of the participants and definitions of predictor and outcome were generally good. Despite many recommendations against doing so, continuous predictors were often dichotomized (32% of studies. The number of events per predictor as a measure of statistical power could not be determined in 67% of the studies; of the remainder, 53% had fewer than the commonly recommended value of ten events per predictor. Methods for a priori selection of candidate predictors were described in most studies (68%. A substantial number of studies relied on a p-value cut-off of p<0.05 to select predictors in the multivariable analyses (29%. Predictive model performance measures, i.e., calibration and discrimination, were reported in 12% and 27% of studies, respectively. CONCLUSIONS: The majority of prediction studies in high impact journals do not follow current methodological recommendations, limiting their reliability and applicability.

  8. Effect of clinical parameters on the control of myoelectric robotic prosthetic hands.

    Science.gov (United States)

    Atzori, Manfredo; Gijsberts, Arjan; Castellini, Claudio; Caputo, Barbara; Hager, Anne-Gabrielle Mittaz; Elsig, Simone; Giatsidis, Giorgio; Bassetto, Franco; Müller, Henning

    2016-01-01

    Improving the functionality of prosthetic hands with noninvasive techniques is still a challenge. Surface electromyography (sEMG) currently gives limited control capabilities; however, the application of machine learning to the analysis of sEMG signals is promising and has recently been applied in practice, but many questions still remain. In this study, we recorded the sEMG activity of the forearm of 11 male subjects with transradial amputation who were mentally performing 40 hand and wrist movements. The classification performance and the number of independent movements (defined as the subset of movements that could be distinguished with >90% accuracy) were studied in relationship to clinical parameters related to the amputation. The analysis showed that classification accuracy and the number of independent movements increased significantly with phantom limb sensation intensity, remaining forearm percentage, and temporal distance to the amputation. The classification results suggest the possibility of naturally controlling up to 11 movements of a robotic prosthetic hand with almost no training. Knowledge of the relationship between classification accuracy and clinical parameters adds new information regarding the nature of phantom limb pain as well as other clinical parameters, and it can lay the foundations for future "functional amputation" procedures in surgery.

  9. Pressure Ulcers in Adults: Prediction and Prevention. Clinical Practice Guideline Number 3.

    Science.gov (United States)

    Agency for Health Care Policy and Research (DHHS/PHS), Rockville, MD.

    This package includes a clinical practice guideline, quick reference guide for clinicians, and patient's guide to predicting and preventing pressure ulcers in adults. The clinical practice guideline includes the following: overview of the incidence and prevalence of pressure ulcers; clinical practice guideline (introduction, risk assessment tools…

  10. Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

    Science.gov (United States)

    Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

    2013-01-01

    Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

  11. Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code.

    Science.gov (United States)

    Hartman, Jessica H; Cothren, Steven D; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A; Miller, Grover P

    2013-07-01

    Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

  12. Relationship between Gram negative enteric rods, Aggregatibacter actinomycetemcomitans, and clinical parameters in periodontal disease

    Directory of Open Access Journals (Sweden)

    Carlos M Ardila

    2012-01-01

    Full Text Available Background: The association between Gram negative enteric rods and Aggregatibacter actinomycetemcomitans in periodontal diseases has received little attention in the literature. The objective of this study was to explore the relationship between these organisms and clinical parameters of periodontal disease. Materials and Methods: Clinical parameters and occurrence of Gram-negative enteric rods and A. actinomycetemcomitans were examined in 76 patients with chronic periodontitis. Chi-square and Mann-Whitney tests were used to determine differences in clinical variables versus the presence or absence of both microorganisms. Correlation among both organisms and clinical data were determined using Spearman rank correlation coefficient. Results: Gram-negative enteric rods and A. actinomycetemcomitans were detected in 20 (26.3% and 18 (23.7% individuals, respectively. A total of 14 (18.4% patients harbored both microorganisms studied. There were significantly positive correlations between enteric rods and presence of A. actinomycetemcomitans (r=0.652, P<0.0001. Both microorganisms were significant and positively correlated with probing depth (PD, clinical attachment level, and bleeding on probing (P<0.0001. The mean PD (mm of the sampled sites was significantly deeper in patients with presence of A. actinomycetemcomitans and Gram-negative enteric rods. Conclusion: The results of the present study suggest a strong positive correlation between Gram-negative enteric rods and A. actinomycetemcomitans in the population studied. This finding must be taken into account when considering the best therapeutic approach, including the utilization of antimicrobials. The adverse clinical outcomes observed in presence of these microorganisms could have implications in the pathogenesis of periodontal disease and a possible impact on outcomes after treatment.

  13. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain

    Science.gov (United States)

    Grech-Sollars, Matthew; Hales, Patrick W; Miyazaki, Keiko; Raschke, Felix; Rodriguez, Daniel; Wilson, Martin; Gill, Simrandip K; Banks, Tina; Saunders, Dawn E; Clayden, Jonathan D; Gwilliam, Matt N; Barrick, Thomas R; Morgan, Paul S; Davies, Nigel P; Rossiter, James; Auer, Dorothee P; Grundy, Richard; Leach, Martin O; Howe, Franklyn A; Peet, Andrew C; Clark, Chris A

    2015-01-01

    The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice–water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials. © 2015 The Authors NMR in

  14. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain.

    Science.gov (United States)

    Grech-Sollars, Matthew; Hales, Patrick W; Miyazaki, Keiko; Raschke, Felix; Rodriguez, Daniel; Wilson, Martin; Gill, Simrandip K; Banks, Tina; Saunders, Dawn E; Clayden, Jonathan D; Gwilliam, Matt N; Barrick, Thomas R; Morgan, Paul S; Davies, Nigel P; Rossiter, James; Auer, Dorothee P; Grundy, Richard; Leach, Martin O; Howe, Franklyn A; Peet, Andrew C; Clark, Chris A

    2015-04-01

    The purpose of this work was to assess the reproducibility of diffusion imaging, and in particular the apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM) parameters and diffusion tensor imaging (DTI) parameters, across multiple centres using clinically available protocols with limited harmonization between sequences. An ice-water phantom and nine healthy volunteers were scanned across fives centres on eight scanners (four Siemens 1.5T, four Philips 3T). The mean ADC, IVIM parameters (diffusion coefficient D and perfusion fraction f) and DTI parameters (mean diffusivity MD and fractional anisotropy FA), were measured in grey matter, white matter and specific brain sub-regions. A mixed effect model was used to measure the intra- and inter-scanner coefficient of variation (CV) for each of the five parameters. ADC, D, MD and FA had a good intra- and inter-scanner reproducibility in both grey and white matter, with a CV ranging between 1% and 7.4%; mean 2.6%. Other brain regions also showed high levels of reproducibility except for small structures such as the choroid plexus. The IVIM parameter f had a higher intra-scanner CV of 8.4% and inter-scanner CV of 24.8%. No major difference in the inter-scanner CV for ADC, D, MD and FA was observed when analysing the 1.5T and 3T scanners separately. ADC, D, MD and FA all showed good intra-scanner reproducibility, with the inter-scanner reproducibility being comparable or faring slightly worse, suggesting that using data from multiple scanners does not have an adverse effect compared with using data from the same scanner. The IVIM parameter f had a poorer inter-scanner CV when scanners of different field strengths were combined, and the parameter was also affected by the scan acquisition resolution. This study shows that the majority of diffusion MRI derived parameters are robust across 1.5T and 3T scanners and suitable for use in multi-centre clinical studies and trials.

  15. Predicting biological parameters of estuarine benthic communities using models based on environmental data

    Directory of Open Access Journals (Sweden)

    José Souto Rosa-Filho

    2004-08-01

    Full Text Available This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth. Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA, and the second based on Multiple Linear Regression (MLR. Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.Este trabalho objetivou predizer parâmetros da estrutura de associações macrobentônicas (composição específica, abundância, riqueza, diversidade e equitatividade em estuários do Sul do Brasil, utilizando modelos baseados em dados ambientais (características dos sedimentos, salinidade, temperaturas do ar e da água, e profundidade. As amostragens foram realizadas sazonalmente em cinco estuários entre o inverno de 1996 e o verão de 1998. Em cada estuário as amostras foram coletadas em áreas não poluídas, com características semelhantes quanto a presença ou ausência de vegetação, profundidade e distância da desenbocadura. Para a obtenção dos modelos de predição, foram utilizados dois métodos: o primeiro baseado em Análise Discriminante Múltipla (ADM e o segundo em Regressão Linear Múltipla (RLM. Os modelos baseados em ADM apresentaram resultados melhores do que os baseados em regressão linear. Os melhores

  16. Utilizing observations of vegetation patterns to infer ecosystem parameters and test model predictions

    Science.gov (United States)

    Penny, G.; Daniels, K. E.; Thompson, S. E.

    2012-12-01

    Periodic vegetation patterns arise globally in arid and semi-arid environments, and are believed to indicate competing positive and negative feedbacks between resource availability and plant uptake at different length scales. The patterns have become the object of two separate research themes, one focusing on observation of ecosystem properties and vegetation morphology, and another focusing on the development of theoretical models and descriptions of pattern behavior. Given the growing body of work in both directions, there is a compelling need to unify both strands of research by bringing together observations of large-scale pattern morphology with predictions made by various models. Previous attempts have employed spectral analysis on pattern images and inverse modeling on one-dimensional transects of patterns images, yet have not made a concerted effort to rigorously confront predictions with observational data in two dimensions. This study makes the first steps towards unification, utilizing high resolution landscape-scale images of vegetation patterns over multiple years at five different locations, including Niger, Central Mexico, Baja California, Texas, and Australia. Initial analyses of the observed patterns reveal considerable departures from the idealized morphologies predicted by models. Pattern wavelengths, while clustered around a local average, vary through space and are frequently altered by pattern defects such as missing or broken bands. While often locally homogeneous, pattern orientation also varies through space, allowing the correlations between landscape features and changes in local pattern morphology to be explored. Stationarity of the pattern can then be examined by comparing temporal changes in morphology with local climatic fluctuations. Ultimately, by identifying homogeneous regions of coherent pattern, inversion approaches can be applied to infer model parameters and build links between observable pattern and landscape features and the

  17. Assessment of clinical and laboratory parameters that reflect inflammatory response and organ function in sepsis

    Directory of Open Access Journals (Sweden)

    Herdiman T. Pohan

    2005-03-01

    Full Text Available Sepsis is a spectrum of clinical conditions caused by the host immune response to infection or other inflammatory stimuli characterized by systemic inflammation. Clinical response to sepsis could be varies according to compensate or decompensate state, inflammatory process and host condition. Aims of this study is to assess the role of some parameters (clinical and biochemical, hematology, arterial blood gas analysis and coagulation in supporting the diagnosis of sepsis. A cross-sectional study was performed in the Internal Medicine Inpatient Unit of Dr. Cipto Mangunkusumo National General Hospital, Jakarta, from February to July 2002. Forty-two patients who fulfilled the criteria of sepsis, severe sepsis, and septic shock were enrolled in this study. Clinical details and blood specimens for hematological, biochemical, arterial blood gas analysis and coagulation were collected. There were 42 subjects who participated in the study, aged from 19 to 78 years old. Eleven subjects fulfilled the criteria for early sepsis, 20 severe sepsis and 11 septic shock. Clinical examination showed that the Glasgow coma scale (GCS was significantly reduced in severe sepsis and septic shock. Heart rate, respiration rate and body temperature were increased in all groups. Hemoglobin levels mostly below 10 g/dl and hematocrite levels below 30 %. The leucocyte counts were increased in more than 80%, mostly above 15.000/mm3. The platelet count were low (below 50.000/mm3 especially in septic shock.  The serum creatinine were significantly increased (>2 mg/dl in severe sepsis and septic shock. Albumin was decreased,  lactate dehydrogenase/LDH and procalcitonin  were increased. The arterial blood gas analysis showed that: pH and HCO3 were decreased especially in  septic shock; the PO2 was lower in severe sepsis and septic shock; and PCO2 was below 32 mmHg in all groups. Coagulation examinations showed that fibrinogen was significantly decreased in septic shock; PT and

  18. Analysis of direct contact membrane distillation based on a lumped-parameter dynamic predictive model

    KAUST Repository

    Karam, Ayman M.

    2016-10-03

    Membrane distillation (MD) is an emerging technology that has a great potential for sustainable water desalination. In order to pave the way for successful commercialization of MD-based water desalination techniques, adequate and accurate dynamical models of the process are essential. This paper presents the predictive capabilities of a lumped-parameter dynamic model for direct contact membrane distillation (DCMD) and discusses the results under wide range of steady-state and dynamic conditions. Unlike previous studies, the proposed model captures the time response of the spacial temperature distribution along the flow direction. It also directly solves for the local temperatures at the membrane interfaces, which allows to accurately model and calculate local flux values along with other intrinsic variables of great influence on the process, like the temperature polarization coefficient (TPC). The proposed model is based on energy and mass conservation principles and analogy between thermal and electrical systems. Experimental data was collected to validated the steady-state and dynamic responses of the model. The obtained results shows great agreement with the experimental data. The paper discusses the results of several simulations under various conditions to optimize the DCMD process efficiency and analyze its response. This demonstrates some potential applications of the proposed model to carry out scale up and design studies. © 2016

  19. Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus)

    Science.gov (United States)

    Chapman, Robert W.; Somerville, Stephen E.; Guillette, Matthew P.; Botha, Hannes; Hoffman, Andre; Luus-Powell, Wilmien J.; Smit, Willem J.; Lebepe, Jeffrey; Myburgh, Jan; Govender, Danny; Tucker, Jonathan; Boggs, Ashley S. P.

    2016-01-01

    One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various “outbreaks” of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River. PMID:27115488

  20. Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus).

    Science.gov (United States)

    Bowden, John A; Cantu, Theresa M; Chapman, Robert W; Somerville, Stephen E; Guillette, Matthew P; Botha, Hannes; Hoffman, Andre; Luus-Powell, Wilmien J; Smit, Willem J; Lebepe, Jeffrey; Myburgh, Jan; Govender, Danny; Tucker, Jonathan; Boggs, Ashley S P; Guillette, Louis J

    2016-01-01

    One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various "outbreaks" of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus) collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin), in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight) were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.

  1. Predictive Blood Chemistry Parameters for Pansteatitis-Affected Mozambique Tilapia (Oreochromis mossambicus.

    Directory of Open Access Journals (Sweden)

    John A Bowden

    Full Text Available One of the largest river systems in South Africa, the Olifants River, has experienced significant changes in water quality due to anthropogenic activities. Since 2005, there have been various "outbreaks" of the inflammatory disease pansteatitis in several vertebrate species. Large-scale pansteatitis-related mortality events have decimated the crocodile population at Lake Loskop and decreased the population at Kruger National Park. Most pansteatitis-related diagnoses within the region are conducted post-mortem by either gross pathology or histology. The application of a non-lethal approach to assess the prevalence and pervasiveness of pansteatitis in the Olifants River region would be of great importance for the development of a management plan for this disease. In this study, several plasma-based biomarkers accurately classified pansteatitis in Mozambique tilapia (Oreochromis mossambicus collected from Lake Loskop using a commercially available benchtop blood chemistry analyzer combined with data interpretation via artificial neural network analysis. According to the model, four blood chemistry parameters (calcium, sodium, total protein and albumin, in combination with total length, diagnose pansteatitis to a predictive accuracy of 92 percent. In addition, several morphometric traits (total length, age, weight were also associated with pansteatitis. On-going research will focus on further evaluating the use of blood chemistry to classify pansteatitis across different species, trophic levels, and within different sites along the Olifants River.

  2. Prevalence of herpesviruses in gingivitis and chronic periodontitis: relationship to clinical parameters and effect of treatment

    Directory of Open Access Journals (Sweden)

    Rucha Shah

    2016-01-01

    Full Text Available Background: Assess the prevalence of herpesviruses in healthy subjects, gingivitis, and chronic periodontitis patients, to assess the relationship between the prevalence of herpesviruses and periodontal clinical parameters, and to evaluate the effect of phase-I therapy on the level of viral detection. Materials and Methods: Hundred patients consisting of 20 healthy subjects, 40 gingivitis, and 40 chronic periodontitis were included in the study. Clinical parameters recorded included plaque index, gingival index, sulcus bleeding index, probing depth, and clinical attachment level. The gingivitis and chronic periodontitis patients received phase-I periodontal therapy including oral hygiene instructions, full mouth scaling for gingivitis patients and scaling and root planing for chronic periodontitis patients. Gingival crevicular fluid (GCF was collected, and the presence of herpes simplex virus-1 (HSV-1, HSV-2, cytomegalovirus, and Epstein–Barr virus (EBV was analyzed using polymerase chain reaction (PCR. Recording of periodontal parameters as well as GCF collection was performed at baseline and 6 weeks postphase-I therapy. Results: At baseline, the levels of HSV-1 and EBV detection were lower in healthy controls as compared to gingivitis (P < 0.05 and chronic periodontitis cases (P < 0.001. Phase-I therapy led to reduction in the amount of HSV-1 and EBV in gingivitis patients (P < 0.05 and for HSV-1, human cytomegalovirus and EBV in chronic periodontitis patients (P < 0.05 in comparison to baseline. The prevalence of EBV in chronic periodontitis patients was positively associated with increased gingival index, probing depth and loss of clinical attachment (P < 0.05. Conclusions: Higher prevalence of HSV-1 and EBV viruses in GCF of gingivitis and chronic periodontitis suggests a strong association between these viruses and periodontal diseases and periodontal therapy can lead to a reduction in herpesviruses at infected sites.

  3. Somatic cell count distributions during lactation predict clinical mastitis

    NARCIS (Netherlands)

    Green, M.J.; Green, L.E.; Schukken, Y.H.; Bradley, A.J.; Peeler, E.J.; Barkema, H.W.; Haas, de Y.; Collis, V.J.; Medley, G.F.

    2004-01-01

    This research investigated somatic cell count (SCC) records during lactation, with the purpose of identifying distribution characteristics (mean and measures of variation) that were most closely associated with clinical mastitis. Three separate data sets were used, one containing quarter SCC (n = 14

  4. Gene expression profiling predicts clinical outcome of breast cancer

    NARCIS (Netherlands)

    Veer, L.J. van 't; Dai, H.; Vijver, H. van de; He, Y.D.; Hart, A.A.M.; Mao, M.; Peterse, H.L.; Kooy, K. van der; Marton, M.J.; Witteveen, A.T.; Schreiber, G.J.; Kerkhoven, R.M.; Roberts, C.; Linsley, P.S.; Bernards, R.A.; Friend, S.H.

    2002-01-01

    Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour.

  5. An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

    Science.gov (United States)

    Chen, Xi; Wang, Lily; Ishwaran, Hemant

    2010-09-01

    Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

  6. Are clinical parameters sufficient to model gait patterns in patients with cerebral palsy using a multilinear approach?

    Science.gov (United States)

    Bonnefoy-Mazure, Alice; Sagawa, Yoshisama; Pomero, Vincent; Lascombes, Pierre; De Coulon, Geraldo; Armand, Stéphane

    2016-01-01

    The aim of this study was to evaluate whether clinical parameters are sufficient using, a multilinear regression model, to reproduce the sagittal plane joint angles (hip, knee, and ankle) in cerebral palsy gait. A total of 154 patients were included. The two legs were considered (308 observations). Thirty-six clinical parameters were used as regressors (range of motion, muscle strength, and spasticity of the lower). From the clinical gait analysis, the joint angles of the sagittal plane were selected. Results showed that clinical parameter does not provide sufficient information to recover joint angles and/or that the multilinear regression model is not an appropriate solution.

  7. Evaluation of the usefulness of 2 prediction models of clinical prediction models in physical therapy: a qualitative process evaluation.

    NARCIS (Netherlands)

    Oort, L. van; Verhagen, A.F.; Koes, B.; Vet, R. de; Anema, H.; Heymans, M.

    2014-01-01

    OBJECTIVE: The purposes of this study were to (1) evaluate the usefulness of 2 prediction models by assessing the actual use and advantages/disadvantages of application in daily clinical practice and (2) propose recommendations to enhance their implementation. METHODS: Physical therapists working in

  8. Predicting the activity coefficients of free-solvent for concentrated globular protein solutions using independently determined physical parameters.

    Directory of Open Access Journals (Sweden)

    Devin W McBride

    Full Text Available The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

  9. Predicting the activity coefficients of free-solvent for concentrated globular protein solutions using independently determined physical parameters.

    Science.gov (United States)

    McBride, Devin W; Rodgers, Victor G J

    2013-01-01

    The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

  10. Parameter definition using vibration prediction software leads to significant drilling performance improvements

    Energy Technology Data Exchange (ETDEWEB)

    Amorim, Dalmo; Hanley, Chris Hanley; Fonseca, Isaac; Santos, Juliana [National Oilwell Varco, Houston TX (United States); Leite, Daltro J.; Borella, Augusto; Gozzi, Danilo [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil)

    2012-07-01

    field monitoring. Vibration prediction diminishes the importance of trial-and-error procedures such as drill-off tests, which are valid only for short sections. It also solves an existing lapse in Mechanical Specific Energy (MSE) real-time drilling control programs applying the theory of Teale, which states that a drilling system is perfectly efficient when it spends the exact energy to overcome the in situ rock strength. Using the proprietary software tool this paper will examine the resonant vibration modes that may be initiated while drilling with different BHA's and drill string designs, showing that the combination of a proper BHA design along with the correct selection of input parameters results in an overall improvement to drilling efficiency. Also, being the BHA predictively analyzed, it will be reduced the potential for vibration or stress fatigue in the drill string components, leading to a safer operation. In the recent years there has been an increased focus on vibration detection, analysis, and mitigation techniques, where new technologies, like the Drilling Dynamics Data Recorders (DDDR), may provide the capability to capture high frequency dynamics data at multiple points along the drilling system. These tools allow the achievement of drilling performance improvements not possible before, opening a whole new array of opportunities for optimization and for verification of predictions calculated by the drill string dynamics modeling software tool. The results of this study will identify how the dynamics from the drilling system, interacting with formation, directly relate to inefficiencies and to the possible solutions to mitigate drilling vibrations in order to improve drilling performance. Software vibration prediction and downhole measurements can be used for non-drilling operations like drilling out casing or reaming, where extremely high vibration levels - devastating to the cutting structure of the bit before it has even touched bottom - have

  11. DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

    Directory of Open Access Journals (Sweden)

    Kirill Litovkin

    Full Text Available Prostate cancer (PCa is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF in high-risk patients.A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation.Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07 and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72 as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27 in multivariate analysis.Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

  12. Predictive microbiology models vs. modeling microbial growth within Listeria monocytogenes risk assessment: what parameters matter and why.

    Science.gov (United States)

    Pouillot, Régis; Lubran, Meryl B

    2011-06-01

    Predictive microbiology models are essential tools to model bacterial growth in quantitative microbial risk assessments. Various predictive microbiology models and sets of parameters are available: it is of interest to understand the consequences of the choice of the growth model on the risk assessment outputs. Thus, an exercise was conducted to explore the impact of the use of several published models to predict Listeria monocytogenes growth during food storage in a product that permits growth. Results underline a gap between the most studied factors in predictive microbiology modeling (lag, growth rate) and the most influential parameters on the estimated risk of listeriosis in this scenario (maximum population density, bacterial competition). The mathematical properties of an exponential dose-response model for Listeria accounts for the fact that the mean number of bacteria per serving and, as a consequence, the highest achievable concentrations in the product under study, has a strong influence on the estimated expected number of listeriosis cases in this context.

  13. Prenatal prediction of pulmonary hypoplasia: clinical, biometric, and Doppler velocity correlates

    NARCIS (Netherlands)

    J.A.M. Laudij (Jacqueline); D. Tibboel (Dick); S.G.F. Robben (Simon); R.R. de Krijger (Ronald); M.A.J. de Ridder (Maria); J.W. Wladimiroff (Juriy)

    2002-01-01

    textabstractOBJECTIVES: To determine the value of pulmonary artery Doppler velocimetry relative to fetal biometric indices and clinical correlates in the prenatal prediction of lethal lung hypoplasia (LH) in prolonged (>1 week) oligohydramnios. METHODS: Forty-two singleton pregnanc

  14. Influence of Clinical and Pathologic Parameters on Prognosis of Cervical Carcinoma in China

    Institute of Scientific and Technical Information of China (English)

    LUPing; LIANGQiudong; ZHENGQuanqing

    2003-01-01

    Objective: To explore the influence of clinical and pathologic parameters on the prognosis of squamous cell carcinoma and adenocarcinoma. Methods: 702 cases of cervical carcinoma were retrospec-tively studied. Cox regression model was informed in multi-variable analysis. Results: The retrospective analysis showed that 630 out of 702 cases of cervical carcinoma were squamous cell carcinoma, cumulative rate 89.4% and 72 case were adenocarcinoma, cumulative rate 10.6% respectively. The 5-year survival rate was lower for patients with adenocarcinoma than for patients with squamous cell carcinoma (58.3% vs 57.3%), but there was no significant difference. Cox regression model showed that the variable into equation for squamous cell carcinoma included tumor grade of differentiation, lymph node metastasis and FIGO stage, but only lymph node metastasis and FIGO stage for adenocarcinoma. Conclusion: FIGO stage and lymph node metastasis was independent parameter evaluating prognosis of cervical carcinoma.

  15. Clinical Exposure Boost Predictions by Integrating Cytochrome P450 3A4-Humanized Mouse Studies With PBPK Modeling.

    Science.gov (United States)

    Zhang, Jin; Heimbach, Tycho; Scheer, Nico; Barve, Avantika; Li, Wenkui; Lin, Wen; He, Handan

    2016-04-01

    NVS123 is a poorly water-soluble protease 56 inhibitor in clinical development. Data from in vitro hepatocyte studies suggested that NVS123 is mainly metabolized by CYP3A4. As a consequence of limited solubility, NVS123 therapeutic plasma exposures could not be achieved even with high doses and optimized formulations. One approach to overcome NVS123 developability issues was to increase plasma exposure by coadministrating it with an inhibitor of CYP3A4 such as ritonavir. A clinical boost effect was predicted by using physiologically based pharmacokinetic (PBPK) modeling. However, initial boost predictions lacked sufficient confidence because a key parameter, fraction of drug metabolized by CYP3A4 (fmCYP3A4), could not be estimated with accuracy on account of disconnects between in vitro and in vivo preclinical data. To accurately estimate fmCYP3A4 in human, an in vivo boost effect study was conducted using CYP3A4-humanized mouse model which showed a 33- to 56-fold exposure boost effect. Using a top-down approach, human fmCYP3A4 for NVS123 was estimated to be very high and included in the human PBPK modeling to support subsequent clinical study design. The combined use of the in vivo boost study in CYP3A4-humanized mouse model mice along with PBPK modeling accurately predicted the clinical outcome and identified a significant NVS123 exposure boost (∼42-fold increase) with ritonavir.

  16. Predicting the Activity Coefficients of Free-Solvent for Concentrated Globular Protein Solutions Using Independently Determined Physical Parameters

    OpenAIRE

    McBride, Devin W; Rodgers, Victor G. J.

    2013-01-01

    The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent...

  17. Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2014-01-01

    , research has primarily focused on one-step-ahead flow predictions for identifying, estimating, and evaluating greybox models. For control purposes, however, stochastic predictions are required for longer forecast horizons and for the prediction of runoff volumes, rather than flows. This article therefore...

  18. Combining Physical and Biologic Parameters to Predict Radiation-Induced Lung Toxicity in Patients With Non-Small-Cell Lung Cancer Treated With Definitive Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Stenmark, Matthew H. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Cai Xuwei [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shanghai Cancer Hospital, Fudan University, Shanghai (China); Shedden, Kerby [Department of Biostatistics, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Hayman, James A. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Yuan Shuanghu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Radiation Oncology, Shangdong Cancer Hospital, Jinan (China); Ritter, Timothy [Veterans Affairs Medical Center, Ann Arbor, Michigan (United States); Ten Haken, Randall K.; Lawrence, Theodore S. [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Kong Fengming, E-mail: fengkong@med.umich.edu [Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan (United States); Veterans Affairs Medical Center, Ann Arbor, Michigan (United States)

    2012-10-01

    Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1{beta}), IL-6, IL-8, tumor necrosis factor alpha (TNF-{alpha}), and transforming growth factor beta1 (TGF-{beta}1) to ascertain their value in predicting radiation-induced lung toxicity (RILT), both individually and in combination with physical dosimetric parameters. Methods and Materials: Treatments of patients receiving definitive conventionally fractionated radiation therapy (RT) on clinical trial for inoperable stages I-III lung cancer were prospectively evaluated. Circulating cytokine levels were measured prior to and at weeks 2 and 4 during RT. The primary endpoint was symptomatic RILT, defined as grade 2 and higher radiation pneumonitis or symptomatic pulmonary fibrosis. Minimum follow-up was 18 months. Results: Of 58 eligible patients, 10 (17.2%) patients developed RILT. Lower pretreatment IL-8 levels were significantly correlated with development of RILT, while radiation-induced elevations of TGF-ss1 were weakly correlated with RILT. Significant correlations were not found for any of the remaining 3 cytokines or for any clinical or dosimetric parameters. Using receiver operator characteristic curves for predictive risk assessment modeling, we found both individual cytokines and dosimetric parameters were poor independent predictors of RILT. However, combining IL-8, TGF-ss1, and mean lung dose into a single model yielded an improved predictive ability (P<.001) compared to either variable alone. Conclusions: Combining inflammatory cytokines with physical dosimetric factors may provide a more accurate model for RILT prediction. Future study with a larger number of cases and events is needed to validate such findings.

  19. Association between magnesium-deficient status and anthropometric and clinical-nutritional parameters in posmenopausal women

    Directory of Open Access Journals (Sweden)

    Beatriz López-González

    2014-03-01

    Full Text Available Background: During menopause occurs weight gain and bone loss occurs due to the hormone decline during this period and other factors such as nutrition. Magnesium deficiency suggests a risk factor for obesity and osteoporosis. Objective: To evaluate the clinical and nutritional magnesium status in a population of postmenopausal women, assessing intake and serum levels of magnesium in the study population and correlation with anthropometric parameters such as body mass index (BMI and body fat, and biochemical parameters associated. Subjects and Method: The study involved 78 healthy women aged 44-76, with postmenopausal status, from the province of Grenade, Spain. The sample was divided into two age groups: group 1, aged 58. Anthropometric parameters were recorded and nutritional intake was assessed by 72-hour recall, getting the RDAs through Nutriber® program. To assess the biochemical parameters was performed a blood sample was taken. Magnesium was analyzed by flame atomic absorption spectrophotometry (FAAS in erythrocyte and plasma wet-mineralized samples. Results: Our results show that 37.85% of the total subjects have an overweight status. Magnesium intake found in our population is insufficient in 36% of women, while plasma magnesium deficiency corresponds to 23% of the population and 72% of women have deficient levels of magnesium in erythrocyte. Positive correlations were found between magnesium intake and dietary intake of calcium, of phosphorus, and with prealbumin plasma levels, as well as with a lower waist / hip ratio. Magnesium levels in erythrocyte were correlated with lower triglycerides and urea values. Conclusion: It is important to control and monitor the nutritional status of magnesium in postmenopausal -women to prevent nutritional alterations and possible clinical and chronic degenerative diseases associated with magnesium deficiency and with menopause.

  20. Ethics and epistemology of accurate prediction in clinical research.

    Science.gov (United States)

    Hey, Spencer Phillips

    2015-07-01

    All major research ethics policies assert that the ethical review of clinical trial protocols should include a systematic assessment of risks and benefits. But despite this policy, protocols do not typically contain explicit probability statements about the likely risks or benefits involved in the proposed research. In this essay, I articulate a range of ethical and epistemic advantages that explicit forecasting would offer to the health research enterprise. I then consider how some particular confidence levels may come into conflict with the principles of ethical research.

  1. On the Effect of Unit-Cell Parameters in Predicting the Elastic Response of Wood-Plastic Composites

    Directory of Open Access Journals (Sweden)

    Fatemeh Alavi

    2013-01-01

    Full Text Available This paper presents a study on the effect of unit-cell geometrical parameters in predicting elastic properties of a typical wood plastic composite (WPC. The ultimate goal was obtaining the optimal values of representative volume element (RVE parameters to accurately predict the mechanical behavior of the WPC. For each unit cell, defined by a given combination of the above geometrical parameters, finite element simulation in ABAQUS was carried out, and the corresponding stress-strain curve was obtained. A uniaxial test according to ASTM D638-02a type V was performed on the composite specimen. Modulus of elasticity was determined using hyperbolic tangent function, and the results were compared to the sets of finite element analyses. Main effects of RVE parameters and their interactions were demonstrated and discussed, specially regarding the inclusion of two adjacent wood particles within one unit cell of the material. Regression analysis was performed to mathematically model the RVE parameter effects and their interactions over the modulus of elasticity response. The model was finally employed in an optimization analysis to arrive at an optimal set of RVE parameters that minimizes the difference between the predicted and experimental moduli of elasticity.

  2. Comprehensive reference ranges for hematology and clinical chemistry laboratory parameters derived from normal Nigerian adults.

    Directory of Open Access Journals (Sweden)

    Timzing Miri-Dashe

    Full Text Available BACKGROUND: Interpretation of laboratory test results with appropriate diagnostic accuracy requires reference or cutoff values. This study is a comprehensive determination of reference values for hematology and clinical chemistry in apparently healthy voluntary non-remunerated blood donors and pregnant women. METHODS AND FINDINGS: Consented clients were clinically screened and counseled before testing for HIV, Hepatitis B, Hepatitis C and Syphilis. Standard national blood donors' questionnaire was administered to consented blood donors. Blood from qualified volunteers was used for measurement of complete hematology and chemistry parameters. Blood samples were analyzed from a total of 383 participants, 124 (32.4% males, 125 (32.6% non-pregnant females and 134 pregnant females (35.2% with a mean age of 31 years. Our results showed that the red blood cells count (RBC, Hemoglobin (HB and Hematocrit (HCT had significant gender difference (p = 0.000 but not for total white blood count (p>0.05 which was only significantly higher in pregnant verses non-pregnant women (p = 0.000. Hemoglobin and Hematocrit values were lower in pregnancy (P = 0.000. Platelets were significantly higher in females than men (p = 0.001 but lower in pregnant women (p =  .001 with marked difference in gestational period. For clinical chemistry parameters, there was no significant difference for sodium, potassium and chloride (p>0.05 but gender difference exists for Bicarbonate (HCO3, Urea nitrogen, Creatinine as well as the lipids (p0.05. CONCLUSIONS: Hematological and Clinical Chemistry reference ranges established in this study showed significant gender differences. Pregnant women also differed from non-pregnant females and during pregnancy. This is the first of such comprehensive study to establish reference values among adult Nigerians and difference observed underscore the need to establish reference values for different populations.

  3. Locus heterogeneity for Waardenburg syndrome is predictive of clinical subtypes

    Energy Technology Data Exchange (ETDEWEB)

    Farrer, L.A.; Hoth, C. [Boston Univ. School of Medicine, MA (United States); Arnos, K.S. [Galludet Univ., Washington, DC (United States); Asher, J.H. Jr.; Friedman, T.B. [Michigan State Univ., East Lansing, MI (United States); Grundfast, K.M.; Lalwani, A.K. [National Institute on Deafness and Other Communication Disorders, Bethesda, MD (United States); Greenberg, J. [Univ. of Cape Town (South Africa); Diehl, S.R. [and others

    1994-10-01

    Waardenburg syndrome (WS) is a dominantly inherited and clinically variable syndrome of deafness, pigmentary changes, and distinctive facial features. Clinically, WS type I (WS1) is differentiated from WS type II (WS2) by the high frequency of dystopia canthorum in the family. In some families, WS is caused by mutations in the PAX3 gene on chromosome 2q. We have typed microsatellite markers within and flanking PAX3 in 41 WS1 kindreds and 26 WS2 kindreds in order to estimate the proportion of families with probable mutations in PAX3 and to study the relationship between phenotypic and genotypic heterogeneity. Evaluation of heterogeneity in location scores obtained by multilocus analysis indicated that WS is linked to PAX3 in 60% of all WS families and in 100% of WS1 families. None of the WS2 families were linked. In those families in which equivocal lod scores (between -2 and +1) were found, PAX3 mutations have been identified in 5 of the 15 WS1 families but in none of the 4 WS2 families. Although preliminary studies do not suggest any association between the phenotype and the molecular pathology in 20 families with known PAX3 mutations and in four patients with chromosomal abnormalities in the vicinity of PAX3, the presence of dystopia in multiple family members is a reliable indicator for identifying families likely to have a defect in PAX3. 59 refs., 3 figs., 5 tabs.

  4. Applying dynamic parameters to predict hemodynamic response to volume expansion in spontaneously breathing patients with septic shock.

    Science.gov (United States)

    Lanspa, Michael J; Grissom, Colin K; Hirshberg, Eliotte L; Jones, Jason P; Brown, Samuel M

    2013-02-01

    Volume expansion is a mainstay of therapy in septic shock, although its effect is difficult to predict using conventional measurements. Dynamic parameters, which vary with respiratory changes, appear to predict hemodynamic response to fluid challenge in mechanically ventilated, paralyzed patients. Whether they predict response in patients who are free from mechanical ventilation is unknown. We hypothesized that dynamic parameters would be predictive in patients not receiving mechanical ventilation. This is a prospective, observational, pilot study. Patients with early septic shock and who were not receiving mechanical ventilation received 10-mL/kg volume expansion (VE) at their treating physician's discretion after initial resuscitation in the emergency department. We used transthoracic echocardiography to measure vena cava collapsibility index and aortic velocity variation before VE. We used a pulse contour analysis device to measure stroke volume variation (SVV). Cardiac index was measured immediately before and after VE using transthoracic echocardiography. Hemodynamic response was defined as an increase in cardiac index 15% or greater. Fourteen patients received VE, five of whom demonstrated a hemodynamic response. Vena cava collapsibility index and SVV were predictive (area under the curve = 0.83, 0.92, respectively). Optimal thresholds were calculated: vena cava collapsibility index, 15% or greater (positive predictive value, 62%; negative predictive value, 100%; P = 0.03); SVV, 17% or greater (positive predictive value 100%, negative predictive value 82%, P = 0.03). Aortic velocity variation was not predictive. Vena cava collapsibility index and SVV predict hemodynamic response to fluid challenge patients with septic shock who are not mechanically ventilated. Optimal thresholds differ from those described in mechanically ventilated patients.

  5. Doppler ultrasound in kidney diseases: a key parameter in clinical long-term follow-up.

    Science.gov (United States)

    Spatola, Leonardo; Andrulli, Simeone

    2016-12-01

    Doppler ultrasound has been extensively used in detecting reno-vascular diseases, showing to be a non-invasive, safe, low cost and repeatable tool. The Renal Resistive Index (RRI) [(peak systolic velocity - end diastolic velocity)/peak systolic velocity] is a semi-quantitative index derived by Doppler evaluation of renal vascular bed. Normally RRI is in the range of 0.47-0.70, it increases with aging and, usually, it shows a difference between the two kidneys less than 5-8 %. RRI is an important prognostic marker in chronic kidney diseases (CKD), both in diabetic and non-diabetic kidney diseases, because, in longitudinal prospective studies, it significantly correlated with hemodynamic (ABPM, SBP, DBP, pulse pressure) and histopathological parameters (glomerular sclerosis, arteriolosclerosis, interstitial fibrosis/tubular atrophy, interstitial infiltration). In acute kidney injury (AKI) RI is a valid tool in differentiating between pre-renal and renal failure and in predicting renal response to vaso-active agents. In addition a RRI >0.74 can predict the onset of AKI in septic patients. Renal Resistive Index is a useful marker in allograft diseases because it has been widely showed a correlation with histological lesions during worsening of renal function, both in acute rejection and in chronic allograft nephropathy. Recent studies suggest its role in the risk of new onset diabetes after transplantation and it could be one of the parameters to evaluate to shift or withdrawal immunological and/or hypertensive therapy.

  6. Planning and predictability of clinical outcomes in esthetic rehabilitation.

    Science.gov (United States)

    Kurbad, A

    2015-01-01

    In esthetic rehabilitation, it is a challenge to meet the needs of patients with growing expectations. Creating predictable results is the key to success. This can be accomplished by performing a comprehensive esthetic diagnosis, elaborating treatment proposals that satisfy today's esthetic standards, and using modern computer-assisted methods. The diagnostic wax-up and mock-up are effective tools that allow patients to visualize treatment proposals without invasive procedures. Once the patient has approved the proposals, they provide the basis for the fabrication of the final restoration. The use of modern ceramic materials makes it possible to achieve a good esthetic outcome, even in restorations with extremely thin layer thicknesses. Esthetic cementation is the final step of restorative treatment.

  7. How electrodiagnosis predicts clinical outcome of focal peripheral nerve lesions.

    Science.gov (United States)

    Robinson, Lawrence R

    2015-09-01

    This article reviews the electrodiagnostic (EDX) prognostic factors for focal traumatic and nontraumatic peripheral nerve injuries. Referring physicians and patients often benefit from general and nerve-specific prognostic information from the EDX consultant. Knowing the probable outcome from a nerve injury allows the referring physician to choose the best treatment options for his/her patients. Nerve injuries are variable in their mechanism, location, and pathophysiology. The general effects of the injuries on nerve and muscle are well known, but more research is needed for nerve-specific information. Several factors currently known to influence prognosis include: nature of the nerve trauma, amount of axon loss, recruitment in muscles supplied by the nerve, the extent of demyelination, and the distance to reinnervate functional muscles. This article reviews these general concepts and also nerve-specific EDX measures that predict outcome after focal neuropathies.

  8. Women's age and embryo developmental speed accurately predict clinical pregnancy after single vitrified-warmed blastocyst transfer.

    Science.gov (United States)

    Kato, Keiichi; Ueno, Satoshi; Yabuuchi, Akiko; Uchiyama, Kazuo; Okuno, Takashi; Kobayashi, Tamotsu; Segawa, Tomoya; Teramoto, Shokichi

    2014-10-01

    The aim of this study was to establish a simple, objective blastocyst grading system using women's age and embryo developmental speed to predict clinical pregnancy after single vitrified-warmed blastocyst transfer. A 6-year retrospective cohort study was conducted in a private infertility centre. A total of 7341 single vitrified-armed blastocyst transfer cycles were included, divided into those carried out between 2006 and 2011 (6046 cycles) and 2012 (1295 cycles). Clinical pregnancy rate, ongoing pregnancy rate and delivery rates were stratified by women's age (149 h) as embryo developmental speed. In all the age groups, clinical pregnancy rate, ongoing pregnancy rate and delivery rates decreased as the embryo developmental speed decreased (P pregnancy rates observed in the 2006-2011 cohort. Subsequently, the novel grading score was validated in the 2012 cohort (1295 cycles), finding an excellent association. In conclusion, we established a novel blastocyst grading system using women's age and embryo developmental speed as objective parameters.

  9. Baseline {sup 18}F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Hatt, Mathieu; Visvikis, Dimitris; Cheze-le Rest, Catherine [CHU Morvan, LaTIM, INSERM U650, Brest (France); Pradier, Olivier [CHU Morvan, LaTIM, INSERM U650, Brest (France); CHU Morvan, Department of Radiotherapy, Brest (France)

    2011-09-15

    The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[{sup 18}F]fluoro-D-glucose ({sup 18}F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies. Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the {sup 18}F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUV{sub mean}, and total lesion glycolysis (TLG = TV x SUV{sub mean}). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients. Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL. TLG is a promising predictive factor of

  10. Should I Pack My Umbrella? Clinical versus Statistical Prediction of Mental Health Decisions

    Science.gov (United States)

    Aegisdottir, Stefania; Spengler, Paul M.; White, Michael J.

    2006-01-01

    In this rejoinder, the authors respond to the insightful commentary of Strohmer and Arm, Chwalisz, and Hilton, Harris, and Rice about the meta-analysis on statistical versus clinical prediction techniques for mental health judgments. The authors address issues including the availability of statistical prediction techniques for real-life psychology…

  11. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

    Directory of Open Access Journals (Sweden)

    J. Prakash Maran

    2013-09-01

    Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

  12. Systematic review of clinical prediction tools and prognostic factors in aneurysmal subarachnoid hemorrhage

    Directory of Open Access Journals (Sweden)

    Benjamin W. Y. Lo

    2015-01-01

    Conclusions: Systematic reviews for clinical prognostic factors and clinical prediction tools in aneurysmal SAH face a number of methodological challenges. These include within and between study patient heterogeneity, regional variations in treatment protocols, patient referral biases, and differences in treatment, and prognosis viewpoints across different cultures.

  13. Critical appraisal of clinical prediction rules that aim to optimize treatment selection for musculoskeletal conditions

    NARCIS (Netherlands)

    T.R. Stanton (Tasha); M.J. Hancock (Mark J.); C. Maher (Chris); B.W. Koes (Bart)

    2010-01-01

    textabstractBackground. Clinical prediction rules (CPRs) for treatment selection in musculoskeletal conditions have become increasingly popular. Purpose. The purposes of this review are: (1) to critically appraise studies evaluating CPRs and (2) to consider the clinical utility and stage of developm

  14. Association of real-time sonoelastography findings with clinical parameters in lateral epicondylitis.

    Science.gov (United States)

    Kocyigit, Figen; Kuyucu, Ersin; Kocyigit, Ali; Herek, Duygu Tuncer; Savkin, Raziye; Aslan, Ummuhan Bas; Karabulut, Nevzat

    2016-01-01

    The objective of this study was to investigate the role of real-time sonoelastography (RTSE) in patients with lateral epicondylitis (LE) and whether it is associated with clinical parameters. Seventeen patients with unilateral LE were enrolled in the study. The healthy elbows of the participants constituted the control group. Using B-mode ultrasound, color Doppler ultrasound, and RTSE, we prospectively examined 34 common extensor tendon elbows of 17 patients. Both color scales and strain ratio were used for evaluating RTSE images. Two radiologists evaluated the RTSE images separately. Elbow pain was scored on a 100-mm visual analog scale (VAS). Symptom duration and the presence of nocturnal pain were questioned. Quick disabilities of arm shoulder and hand (DASH) Questionnaire was applied to assess the pain, function, and disability. Nottingham health profile (NHP) was used to determine and quantify perceived health problems. Both color scales and strain ratios of the affected tendon portions were significantly different from that of healthy tendons (p elbows as a feasible and practical supplementary method with substantial interobserver agreement. RTSE was superior to B-mode ultrasound and color Doppler ultrasound in discriminating tendons with LE. Strain ratio of the medial portion of the tendon is associated moderately with nocturnal pain and symptom duration. No other associations were present between RTSE findings and clinical or functional parameters.

  15. Predicting pressure ulcers: cases missed using a new clinical prediction rule.

    NARCIS (Netherlands)

    Schoonhoven, L.; Grobbee, D.E.; Bousema, M.T.; Buskens, E.

    2005-01-01

    AIM: The aim of this paper is to report a study describing patients with pressure ulcers that were incorrectly classified as 'not at risk' by the prediction rule and comparing them with patients who were correctly classified as 'not at risk'. BACKGROUND: Patients admitted to hospital are at risk of

  16. Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research

    Science.gov (United States)

    Dietrich, Johannes W.; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H.; Midgley, John E. M.; Hoermann, Rudolf

    2016-01-01

    Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary–thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel’s TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples. PMID:27375554

  17. Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research.

    Science.gov (United States)

    Dietrich, Johannes W; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H; Midgley, John E M; Hoermann, Rudolf

    2016-01-01

    Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary-thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel's TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples.

  18. A novel method for the measurement of linear body segment parameters during clinical gait analysis.

    Science.gov (United States)

    Geil, Mark D

    2013-09-01

    Clinical gait analysis is a valuable tool for the understanding of motion disorders and treatment outcomes. Most standard models used in gait analysis rely on predefined sets of body segment parameters that must be measured on each individual. Traditionally, these parameters are measured using calipers and tape measures. The process can be time consuming and is prone to several sources of error. This investigation explored a novel method for rapid recording of linear body segment parameters using magnetic-field based digital calipers commonly used for a different purpose in prosthetics and orthotics. The digital method was found to be comparable to traditional in all linear measures and data capture was significantly faster with the digital method, with mean time savings for 10 measurements of 2.5 min. Digital calipers only record linear distances, and were less accurate when diameters were used to approximate limb circumferences. Experience in measuring BSPs is important, as an experienced measurer was significantly faster than a graduate student and showed less difference between methods. Comparing measurement of adults vs. children showed greater differences with adults, and some method-dependence. If the hardware is available, digital caliper measurement of linear BSPs is accurate and rapid.

  19. PREDICTING OUTCOME AND SEVERITY IN ACUTE ORGANOPHOSPHOROUS POISONING WITH CLINICAL SCORING AND SERUM CHOLINESTERASE LEVELS

    Directory of Open Access Journals (Sweden)

    Basavaraj R

    2014-11-01

    Full Text Available BACKGROUND AND OBJECTIVES: Organophosphorus compound poisoning is the most common medico toxic emergency in India the increase in pesticide use in agriculture has paralleled the increase in the use of these products for deliberate self-warm. Respiratory failure is the most common complication of OP poisoning leading to death. Early recognition and prompt ventilator support may improve survival. Owing to limited availability of resources, all OP poisoning patients are not managed in ICUs in Indian setup. It is therefore important that clinical features and criteria to predict the need for ventilator support be identified at initial examination. Hence this study was undertaken to assess the severity of organophosphorus compound poisoning both clinically by using Peradeniya scoring and by estimating serum choline esterase levels. METHODS: Cross sectional study was done at basaveswar teaching and general hospital attached to MR Medical College. Cases with history of exposure to organophosphorus compound within previous 24 hours were chosen after applying inclusion and exclusion criteria. Patients were evaluated for Peradeniya OP poisoning scale and serum cholinesterase levels for assessment of severity of poisoning. Serum cholinesterase levels and Peradeniya OP poisoning scale were studied to predict the need for ventilator support. The results were analyzed using Chi-square test. STATISTICAL ANALYSIS: It was done using pearson’s chi square test. RESULTS: In this study requirement of ventilator support was seen in 36% of patients. Mortality in our study was 18%. Only 15.6% of patients with mild grade of poisoning according to Peradeniya OP poisoning scale required ventilator support, whereas 84.4% did not require ventilator support. Most of patients with moderate (70.6% and severe poisoning (100% according to Peradeniya OP poisoning scale required ventilator support. 93.7% of patients with serum cholinesterase levels more than 50% did not require

  20. Evaluation of Clinical and Ultrasonographic Parameters in Psoriatic Arthritis Patients Treated with Adalimumab: A Retrospective Study

    Directory of Open Access Journals (Sweden)

    M. Teoli

    2012-01-01

    Full Text Available Objectives. The aim of this study was to evaluate clinical and US-PD parameters in PsA during adalimumab treatment. Methods. A retrospective study has been conducted in forty patients affected by moderate-to-severe peripheral PsA. Clinical, laboratory, and US-PD evaluations were performed at baseline, after 4, 12, and 24 weeks of treatment. They included erythrocyte sedimentation rate (ESR, C-reactive protein (CRP, visual analogue scale (VAS, Health Assessment Questionnaire (HAQ modified for Spondyloarthritis, Psoriasis Area Severity Index (PASI score, the 28-joint Disease Activity Score (DAS 28, and US-PD assessment. US-PD findings were scored according to a semiquantitative scale (ranging 0–3 for synovial proliferation (SP, joint effusion (SE, bone erosions (BE, and PD. Results. Data obtained for clinical, laboratory findings and US-PD evaluation showed statistical significant improvement in all the measures performed except for BE. A significant parallel decrease in SE, SP, and PD values were demonstrated. Conclusion. This study demonstrated that US-PD is a valid technique in monitoring the response to adalimumab in moderate-to-severe PsA.

  1. Clinical Impact of Sagittal Spinopelvic Parameters on Disc Degeneration in Young Adults.

    Science.gov (United States)

    Oh, Young-Min; Eun, Jong-Pil

    2015-10-01

    The sagittal balance plays an important role in the determination of shear and compressive forces applied on the anterior (vertebral bodies and intervertebral discs) and posterior (facet joints) elements of the lumbar vertebral column. Many studies have also examined the effect of structural changes in the disc on the biomechanical characteristics of the spinal segment. Nevertheless, the relationship between sagittal balance and the degree of disc degeneration has not been extensively explored. Thus, here we investigated the relationships between various sagittal spinopelvic parameters and the degree of disc degeneration in young adults.A total of 278 young adult male patients were included in this study (age range: 18-24 years old). Multiple sagittal spinopelvic parameters, including pelvic incidence (PI), sacral slope (SS), pelvic tilt (PT), lumbar lordosis (LL), sacral inclination (SI), lumbosacral angle (LSA), and sacral table angle (STA), were measured from standing lateral lumbosacral radiographs. The degree of intervertebral disc degeneration was classified using a modified Pfirrmann scale. To assess the pain intensity of each patient, the visual analogue scale (VAS) score for low back pain (LBP) was obtained from all the patients. Finally, the relationships between these spinopelvic parameters and the degree of disc degeneration in young adults were analyzed. Also, we performed multiple logistic regression study.Out of all the spinopelvic parameters measured in this study, a low STA and a low SI were the only significant risk factors that were associated with disc degeneration in young adults. It means that patients with disc degeneration tend to have more severe sacral kyphosis and vertical sacrum.We found that patients with disc degeneration showed a lower SI and lower STA compared with patients without disc degeneration in young adults. Therefore, we suggest that the patients with disc degeneration tend to have more vertical sacrum, more sacral kyphosis

  2. Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Cobaugh Christian W

    2004-08-01

    Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

  3. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech.

    Science.gov (United States)

    Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Strand, Edythe A; Machulda, Mary M; Senjem, Matthew L; Gunter, Jeffrey L; Lowe, Val J; Jack, Clifford R; Josephs, Keith A

    2016-01-01

    Beta-amyloid (Aβ) deposition can be observed in primary progressive aphasia (PPA) and progressive apraxia of speech (PAOS). While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified) and PAOS (n = 42) subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+) status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+) status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+) status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified PPA subjects.

  4. Clinical and MRI models predicting amyloid deposition in progressive aphasia and apraxia of speech

    Directory of Open Access Journals (Sweden)

    Jennifer L. Whitwell

    2016-01-01

    Full Text Available Beta-amyloid (Aβ deposition can be observed in primary progressive aphasia (PPA and progressive apraxia of speech (PAOS. While it is typically associated with logopenic PPA, there are exceptions that make predicting Aβ status challenging based on clinical diagnosis alone. We aimed to determine whether MRI regional volumes or clinical data could help predict Aβ deposition. One hundred and thirty-nine PPA (n = 97; 15 agrammatic, 53 logopenic, 13 semantic and 16 unclassified and PAOS (n = 42 subjects were prospectively recruited into a cross-sectional study and underwent speech/language assessments, 3.0 T MRI and C11-Pittsburgh Compound B PET. The presence of Aβ was determined using a 1.5 SUVR cut-point. Atlas-based parcellation was used to calculate gray matter volumes of 42 regions-of-interest across the brain. Penalized binary logistic regression was utilized to determine what combination of MRI regions, and what combination of speech and language tests, best predicts Aβ (+ status. The optimal MRI model and optimal clinical model both performed comparably in their ability to accurately classify subjects according to Aβ status. MRI accurately classified 81% of subjects using 14 regions. Small left superior temporal and inferior parietal volumes and large left Broca's area volumes were particularly predictive of Aβ (+ status. Clinical scores accurately classified 83% of subjects using 12 tests. Phonological errors and repetition deficits, and absence of agrammatism and motor speech deficits were particularly predictive of Aβ (+ status. In comparison, clinical diagnosis was able to accurately classify 89% of subjects. However, the MRI model performed well in predicting Aβ deposition in unclassified PPA. Clinical diagnosis provides optimum prediction of Aβ status at the group level, although regional MRI measurements and speech and language testing also performed well and could have advantages in predicting Aβ status in unclassified

  5. A clinical tool to predict failed response to therapy in children with severe pneumonia.

    Science.gov (United States)

    Mamtani, Manju; Patel, Archana; Hibberd, Patricia L; Tuan, Tran Anh; Jeena, Prakash; Chisaka, Noel; Hassan, Mumtaz; Radovan, Irene Maulen; Thea, Donald M; Qazi, Shamim; Kulkarni, Hemant

    2009-04-01

    Severe pneumonia in children under 5 years of age continues to be an important clinical entity with treatment failure rates as high as 20%. Where severe pneumonias are common, predictive tools for treatment failure like chest radiography and pulse oximetry are not available or affordable. Thus, there is a need for development of simple, accurate and inexpensive clinical tools for prediction of treatment failure. Using clinical, chest radiographic and pulse oximetry data from 1702 children recruited in the Amoxicillin Penicillin Pneumonia International Study (APPIS) trial we developed and validated a simple clinical tool. For development, a randomly derived development sample (n = 889) was used. The tool which was based on the results of multivariate logistic regression models was validated on a separate sample of 813 children. The derived clinical tool in its final form contained three clinical predictors: age of child, excess age-specific respiratory rate at baseline and at 24 hr of hospitalization. This tool had a 70% and 66% predictive accuracy in the development and validation samples, respectively. The tool is presented as an easy-to-use nomogram. It is possible to predict the likelihood of treatment failure in children with severe pneumonia based on clinical features that are simple and inexpensive to measure.

  6. Optimal parameters for clinical implementation of breast cancer patient setup using Varian DTS software.

    Science.gov (United States)

    Ng, Sook Kien; Zygmanski, Piotr; Jeung, Andrew; Mostafavi, Hassan; Hesser, Juergen; Bellon, Jennifer R; Wong, Julia S; Lyatskaya, Yulia

    2012-05-10

    Digital tomosynthesis (DTS) was evaluated as an alternative to cone-beam computed tomography (CBCT) for patient setup. DTS is preferable when there are constraints with setup time, gantry-couch clearance, and imaging dose using CBCT. This study characterizes DTS data acquisition and registration parameters for the setup of breast cancer patients using nonclinical Varian DTS software. DTS images were reconstructed from CBCT projections acquired on phantoms and patients with surgical clips in the target volume. A shift-and-add algorithm was used for DTS volume reconstructions, while automated cross-correlation matches were performed within Varian DTS software. Triangulation on two short DTS arcs separated by various angular spread was done to improve 3D registration accuracy. Software performance was evaluated on two phantoms and ten breast cancer patients using the registration result as an accuracy measure; investigated parameters included arc lengths, arc orientations, angular separation between two arcs, reconstruction slice spacing, and number of arcs. The shifts determined from DTS-to-CT registration were compared to the shifts based on CBCT-to-CT registration. The difference between these shifts was used to evaluate the software accuracy. After findings were quantified, optimal parameters for the clinical use of DTS technique were determined. It was determined that at least two arcs were necessary for accurate 3D registration for patient setup. Registration accuracy of 2 mm was achieved when the reconstruction arc length was > 5° for clips with HU ≥ 1000; larger arc length (≥ 8°) was required for very low HU clips. An optimal arc separation was found to be ≥ 20° and optimal arc length was 10°. Registration accuracy did not depend on DTS slice spacing. DTS image reconstruction took 10-30 seconds and registration took less than 20 seconds. The performance of Varian DTS software was found suitable for the accurate setup of breast cancer patients

  7. Relationship of periodontal clinical parameters with bacterial composition in human dental plaque.

    Science.gov (United States)

    Fujinaka, Hidetake; Takeshita, Toru; Sato, Hirayuki; Yamamoto, Tetsuji; Nakamura, Junji; Hase, Tadashi; Yamashita, Yoshihisa

    2013-06-01

    More than 600 bacterial species have been identified in the oral cavity, but only a limited number of species show a strong association with periodontitis. The purpose of the present study was to provide a comprehensive outline of the microbiota in dental plaque related to periodontal status. Dental plaque from 90 subjects was sampled, and the subjects were clustered based on bacterial composition using the terminal restriction fragment length polymorphism of 16S rRNA genes. Here, we evaluated (1) periodontal clinical parameters between clusters; (2) the correlation of subgingival bacterial composition with supragingival bacterial composition; and (3) the association between bacterial interspecies in dental plaque using a graphical Gaussian model. Cluster 1 (C1) having high prevalence of pathogenic bacteria in subgingival plaque showed increasing values of the parameters. The values of the parameters in Cluster 2a (C2a) having high prevalence of non-pathogenic bacteria were markedly lower than those in C1. A cluster having low prevalence of non-pathogenic bacteria in supragingival plaque showed increasing values of the parameters. The bacterial patterns between subgingival plaque and supragingival plaque were significantly correlated. Chief pathogens, such as Porphyromonas gingivalis, formed a network with other pathogenic species in C1, whereas a network of non-pathogenic species, such as Rothia sp. and Lautropia sp., tended to compete with a network of pathogenic species in C2a. Periodontal status relates to non-pathogenic species as well as to pathogenic species, suggesting that the bacterial interspecies connection affects dental plaque virulence.

  8. Vmax estimate from three-parameter critical velocity models: validity and impact on 800 m running performance prediction.

    Science.gov (United States)

    Bosquet, Laurent; Duchene, Antoine; Lecot, François; Dupont, Grégory; Leger, Luc

    2006-05-01

    The purpose of this study was to evaluate the validity of maximal velocity (Vmax) estimated from three-parameter systems models, and to compare the predictive value of two- and three-parameter models for the 800 m. Seventeen trained male subjects (VO2max=66.54+/-7.29 ml min(-1) kg(-1)) performed five randomly ordered constant velocity tests (CVT), a maximal velocity test (mean velocity over the last 10 m portion of a 40 m sprint) and a 800 m time trial (V 800 m). Five systems models (two three-parameter and three two-parameter) were used to compute V max (three-parameter models), critical velocity (CV), anaerobic running capacity (ARC) and V800m from times to exhaustion during CVT. Vmax estimates were significantly lower than (0.19Critical velocity (CV) alone explained 40-62% of the variance in V800m. Combining CV with other parameters of each model to produce a calculated V800m resulted in a clear improvement of this relationship (0.83parameter models had a better association (0.93parameter models (0.83parameter models appear to have a better predictive value for short duration events such as the 800 m, the fact the Vmax is not associated with the ability it is supposed to reflect suggests that they are more empirical than systems models.

  9. Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

    Science.gov (United States)

    Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

    2015-01-01

    Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

  10. Evaluation of the Cerebral State Index in Cats under Isoflurane Anaesthesia: Dose-Effect Relationship and Prediction of Clinical Signs

    Directory of Open Access Journals (Sweden)

    Joana R. Sousa

    2014-01-01

    Full Text Available The performance of the cerebral state index (CSI in reflecting different levels of isoflurane anaesthesia was evaluated in ten cats subjected to four end-tidal isoflurane concentrations (EtIso, each maintained for 15 minutes (0.8%, 1.2%, 1.6%, or 2.0% EtIso. The CSI, hemodynamic data, ocular reflexes, and eye position were recorded for each EtIso concentration. Pharmacodynamic analysis of CSI with EtIso was performed, as well as prediction probability analysis with a clinical scale based on the eye reflexes. The CSI values showed great variability. Between all parameters, burst suppression ratio showed the better fitting with the sigmoidal concentration-effect model (R2=0.93 followed by CSI (R2=0.82 and electromyographic activity (R2=0.79. EtIso was the variable with better prediction of the clinical scale of anaesthesia (prediction probability value of 0.94. Although the CSI values decrease with increasing isoflurane concentrations, the huge variability in CSI values may be a strong limitation for its use in cats and it seems to be no better than EtIso as a predictor of clinical signs.

  11. Strategies for automatic online treatment plan reoptimization using clinical treatment planning system: A planning parameters study

    Energy Technology Data Exchange (ETDEWEB)

    Li, Taoran; Wu, Qiuwen; Zhang, You; Vergalasova, Irina; Lee, W. Robert; Yin, Fang-Fang; Wu, Q. Jackie [Duke Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710 (United States)

    2013-11-15

    Purpose: Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (∼2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning).Methods: Simulated treatments of 10 patients were reoptimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency.Results: All online reoptimized plans were finished within ∼2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the

  12. Predicting Out-of-Office Blood Pressure in the Clinic (PROOF-BP)

    Science.gov (United States)

    Stevens, Richard; Gill, Paramjit; Martin, Una; Godwin, Marshall; Hanley, Janet; Heneghan, Carl; Hobbs, F.D. Richard; Mant, Jonathan; McKinstry, Brian; Myers, Martin; Nunan, David; Ward, Alison; Williams, Bryan; McManus, Richard J.

    2016-01-01

    Patients often have lower (white coat effect) or higher (masked effect) ambulatory/home blood pressure readings compared with clinic measurements, resulting in misdiagnosis of hypertension. The present study assessed whether blood pressure and patient characteristics from a single clinic visit can accurately predict the difference between ambulatory/home and clinic blood pressure readings (the home–clinic difference). A linear regression model predicting the home–clinic blood pressure difference was derived in 2 data sets measuring automated clinic and ambulatory/home blood pressure (n=991) using candidate predictors identified from a literature review. The model was validated in 4 further data sets (n=1172) using area under the receiver operator characteristic curve analysis. A masked effect was associated with male sex, a positive clinic blood pressure change (difference between consecutive measurements during a single visit), and a diagnosis of hypertension. Increasing age, clinic blood pressure level, and pulse pressure were associated with a white coat effect. The model showed good calibration across data sets (Pearson correlation, 0.48–0.80) and performed well-predicting ambulatory hypertension (area under the receiver operator characteristic curve, 0.75; 95% confidence interval, 0.72–0.79 [systolic]; 0.87; 0.85–0.89 [diastolic]). Used as a triaging tool for ambulatory monitoring, the model improved classification of a patient’s blood pressure status compared with other guideline recommended approaches (93% [92% to 95%] classified correctly; United States, 73% [70% to 75%]; Canada, 74% [71% to 77%]; United Kingdom, 78% [76% to 81%]). This study demonstrates that patient characteristics from a single clinic visit can accurately predict a patient’s ambulatory blood pressure. Usage of this prediction tool for triaging of ambulatory monitoring could result in more accurate diagnosis of hypertension and hence more appropriate treatment. PMID:27001299

  13. Autoreactive T Cells in Human Smokers Is Predictive of Clinical Outcome

    Directory of Open Access Journals (Sweden)

    Chuang eXu

    2012-08-01

    Full Text Available Cross-sectional studies have suggested a role for activation of adaptive immunity in smokers with emphysema, but the clinical application of these findings has not been explored. Here we examined the utility of detecting autoreactive T cells as a screening tool for emphysema in an at-risk population of smokers. We followed 156 former and current (ever-smokers for two years to assess whether peripheral blood CD4 T cell cytokine responses to lung elastin fragments (EFs could discriminate between those with and without emphysema, and to evaluate the relevance of autoreactive T cells to predict changes during follow-up in lung physiological parameters. Volunteers underwent baseline complete phenotypic assessment with pulmonary function tests, quantitative chest CT, yearly six minutes walk distance (6MWD testing, and annual measurement of CD4 T cell cytokine responses to EFs. The areas under the receiver operating characteristic curve to predict emphysema for interferon gamma (IFN-γ, and interleukin 6 (IL-6 responses to EFs were 0.81 (95% CI of 0.74 to 0.88 and 0.79 (95% CI of 0.72 to 0.86 respectively. We developed a dual cytokine enzyme-linked immunocell spot assay, the γ-6 Spot, using CD4 T cell IFN-γ and IL-6 responses and found that it discriminated emphysema with 90% sensitivity. After adjusting for potential confounders, the presence of autoreactive T cells was predictive of a decrease in 6MWD over two years (decline in 6MWD, -19 meters (m per fold change in IFN-γ; P=0.026, and -26 m per fold change in IL-6; P=0.003. These findings collectively suggest that the EF specific autoreactive CD4 T cell assay, γ-6 Spot, could provide a non-invasive diagnostic tool with potential application to large-scale screening to discriminate emphysema in ever-smokers, and predict early relevant physiological outcomes in those at risk.

  14. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

    Science.gov (United States)

    Gao, Hui; Korn, Joshua M; Ferretti, Stéphane; Monahan, John E; Wang, Youzhen; Singh, Mallika; Zhang, Chao; Schnell, Christian; Yang, Guizhi; Zhang, Yun; Balbin, O Alejandro; Barbe, Stéphanie; Cai, Hongbo; Casey, Fergal; Chatterjee, Susmita; Chiang, Derek Y; Chuai, Shannon; Cogan, Shawn M; Collins, Scott D; Dammassa, Ernesta; Ebel, Nicolas; Embry, Millicent; Green, John; Kauffmann, Audrey; Kowal, Colleen; Leary, Rebecca J; Lehar, Joseph; Liang, Ying; Loo, Alice; Lorenzana, Edward; Robert McDonald, E; McLaughlin, Margaret E; Merkin, Jason; Meyer, Ronald; Naylor, Tara L; Patawaran, Montesa; Reddy, Anupama; Röelli, Claudia; Ruddy, David A; Salangsang, Fernando; Santacroce, Francesca; Singh, Angad P; Tang, Yan; Tinetto, Walter; Tobler, Sonja; Velazquez, Roberto; Venkatesan, Kavitha; Von Arx, Fabian; Wang, Hui Qin; Wang, Zongyao; Wiesmann, Marion; Wyss, Daniel; Xu, Fiona; Bitter, Hans; Atadja, Peter; Lees, Emma; Hofmann, Francesco; Li, En; Keen, Nicholas; Cozens, Robert; Jensen, Michael Rugaard; Pryer, Nancy K; Williams, Juliet A; Sellers, William R

    2015-11-01

    Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

  15. A clinical diagnostic model for predicting influenza among young adult military personnel with febrile respiratory illness in Singapore.

    Directory of Open Access Journals (Sweden)

    Vernon J Lee

    Full Text Available INTRODUCTION: Influenza infections present with wide-ranging clinical features. We aim to compare the differences in presentation between influenza and non-influenza cases among those with febrile respiratory illness (FRI to determine predictors of influenza infection. METHODS: Personnel with FRI (defined as fever ≥ 37.5 °C, with cough or sore throat were recruited from the sentinel surveillance system in the Singapore military. Nasal washes were collected, and tested using the Resplex II and additional PCR assays for etiological determination. Interviewer-administered questionnaires collected information on patient demographics and clinical features. Univariate comparison of the various parameters was conducted, with statistically significant parameters entered into a multivariate logistic regression model. The final multivariate model for influenza versus non-influenza cases was used to build a predictive probability clinical diagnostic model. RESULTS: 821 out of 2858 subjects recruited from 11 May 2009 to 25 Jun 2010 had influenza, of which 434 (52.9% had 2009 influenza A (H1N1, 58 (7.1% seasonal influenza A (H3N2 and 269 (32.8% influenza B. Influenza-positive cases were significantly more likely to present with running nose, chills and rigors, ocular symptoms and higher temperature, and less likely with sore throat, photophobia, injected pharynx, and nausea/vomiting. Our clinical diagnostic model had a sensitivity of 65% (95% CI: 58%, 72%, specificity of 69% (95% CI: 62%, 75%, and overall accuracy of 68% (95% CI: 64%, 71%, performing significantly better than conventional influenza-like illness (ILI criteria. CONCLUSIONS: Use of a clinical diagnostic model may help predict influenza better than the conventional ILI definition among young adults with FRI.

  16. Usefulness of fecal lactoferrin in predicting and monitoring the clinical severity of infectious diarrhea

    Institute of Scientific and Technical Information of China (English)

    Chien-Chang Chen; Chee-Jen Chang; Tzou-Yien Lin; Ming-Wei Lai; Hsun-Chin Chao; Man-Shan Kong

    2011-01-01

    AIM: To explore the value of fecal lactoferrin in predicting and monitoring the clinical severity of infectious diarrhea.``METHODS: Patients with acute infectious diarrhea ranging from 3 mo to 10 years in age were enrolled, and one to three stool samples from each subject were collected. Certain parameters, including white blood cells/differential count, C-reactive protein, fecal mucus, fecal pus cells, duration of fever, vomiting, diarrhea and severity (indicated by Clark and Vesikari scores), were recorded and analyzed. Fecal lactoferrin was determined by enzyme-linked immunosorbent assay and compared in different pathogen and disease activity. Generalized estimating equations (GEE) were also used for analysis.``RESULTS: Data included 226 evaluations for 117 individuals across three differenttime points. Fecal lactoferrin was higher in patients with Salmonella (11.17 )j,g/g ± 2.73 μg/g) or Campyhbacter (10.32 μg/g ± 2.94 μig/g) infections and lower in patients with rotavirus (2.82 μg/g ± 1.27 μg/g) or norovirus (3.16 μg/g ± 1.18 |ag/g) infections. Concentrations of fecal lactoferrin were significantly elevated in patients with severe (11.32 μg/g ± 3.29 μag/g) or moderate (3.77 μg/g ± 2.08 μg/g) disease activity compared with subjects with mild (1.51 yig/g ± 1.36 μg/g) disease activity (P < 0.05). GEE analysis suggests that this marker could be used to monitor the severity and course of gastrointestinal infections and may provide information for disease management.``CONCLUSION: Fecal lactoferrin increased during bacterial infection and with greater disease severity and may be a good marker for predicting and monitoring intestinal inflammation in children with infectious diarrhea.

  17. Research on predicting prosodic parameters for Chinese synthesis by data mining approach

    Institute of Scientific and Technical Information of China (English)

    WANG Wei; CAI Lianhong

    2003-01-01

    Prosodic control is an important part of speech synthesis system. Prosodic pa-rameters choice right or wrong influences the quality of synthetic speech directly. At present,text to speech system has less effective describe to reflect data relationships in the corpus. Anew research approach - data mining technology to discover those relationships by associationrules modeling is presented. And a new algorithm for generating association rules of prosodicparameters including pitch parameters and duration parameters from corpus is developed. Theoutput rules improve the correctness of syllable choice in text to speech system.

  18. Optimal search strategies for identifying sound clinical prediction studies in EMBASE

    Directory of Open Access Journals (Sweden)

    Haynes R Brian

    2005-04-01

    Full Text Available Abstract Background Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges" for retrieval of empirically tested clinical prediction guides from EMBASE. Methods An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. Results 163 clinical prediction studies were identified, of which 69 (42.3% passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1% was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8% was found in a 2-term strategy, but with a

  19. Prediction of Concrete Strength Using Microwave Based Accelerated Curing Parameters by Neural Network

    Directory of Open Access Journals (Sweden)

    S. Ramasundaram

    2013-02-01

    Full Text Available Prediction of compressive strength of concrete is very useful for economic constructions. The compressive strength can be estimated after 28 days of casting the specimen cubes or may be predictedbased on the quantum and quality of ingredients used in making the concrete. When the first one requires a 28-day time, the second one does have problem of accuracy. Hence, a hybrid model is proposed in which the concrete cube is cured using the microwave based accelerated curing procedure and the early strength is used to predict the 28-day strength. Feed-forward neural network model was used to predict compressive strength of the concrete after the microwave curing to ascertain the predictability of neural network models. The results indicate that the neural network models have a good scope for further study and implementations.

  20. A nomogram to predict Gleason sum upgrading of clinically diagnosed localized prostate cancer among Chinese patients

    Institute of Scientific and Technical Information of China (English)

    Jin-You Wang; Yao Zhu; Chao-Fu Wang; Shi-Lin Zhang; Bo Dai; Ding-Wei Ye

    2014-01-01

    Although several models have been developed to predict the probability of Gleason sum upgrading between biopsy and radical prostatectomy specimens, most of these models are restricted to prostate-specific antigen screening-detected prostate cancer. This study aimed to build a nomogram for the prediction of Gleason sum upgrading in clinical y diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and were subsequently treated with radical prostatectomy. Of al included patients, 220 (81.8%) were referred with clinical symptoms. The prostate-specific antigen level, primary and secondary biopsy Gleason scores, and clinical T category were used in a multivariate logistic regression model to predict the probability of Gleason sum upgrading. The developed nomogram was validated internally. Gleason sum upgrading was observed in 90 (33.5%) patients. Our nomogram showed a bootstrap-corrected concordance index of 0.789 and good calibration using 4 readily available variables. The nomogram also demonstrated satisfactory statistical performance for predicting significant upgrading. External validation of the nomogram published by Chun et al. in our cohort showed a marked discordance between the observed and predicted probabilities of Gleason sum upgrading. In summary, a new nomogram to predict Gleason sum upgrading in clinically diagnosed prostate cancer was developed, and it demonstrated good statistical performance upon internal validation.

  1. Discussion and improvement of the refined COSMO-SAC parameters for solubility predictions: part 2

    OpenAIRE

    Bouillot, Baptiste; Teychené, Sébastien; Biscans, Béatrice

    2013-01-01

    Solubility of drugs is a key piece of information for the pharmaceutical industry. Despite its importance, particularly at the beginning of a new drug process development, this thermodynamic property of the solid-liquid equilibria (SLE) can hardly be predicted for a given molecule in a given solvent. In our recent works, some thermodynamic models (UNIFAC and its modifications, COSMO-SAC and its refinements, NRTL-SAC) were investigated and compared for solubility prediction. The main drawbacks...

  2. Applying Theoretical Approach for Predicting the Selective Calcium Channel Blockers Pharmacological Parameter by Biopartitioning Micellar Chromatography

    Institute of Scientific and Technical Information of China (English)

    WANG Su-Min; YANG Geng-Liang; LI Zhi-Wei; LIU Hai-Yan; GUO Hui-Juan

    2006-01-01

    The usefulness of biopartitioning micellar chromatography (BMC) for predicting oral drug acute toxicity and apparent bioavailability was demonstrated. A logarithmic model (an LD50 model) and the second order polynomial models (apparent bioavailability model) have been obtained using the retention data of the selective calcium channel blockers to predict pharmacological properties of compounds. The use of BMC is simple, reproducible and can provide key information about the acute toxicity and transport properties of new compounds during the drug discovery process.

  3. PREDICTION OF CHEMICAL REACTIVITY PARAMETERS AND PHYSICAL PROPERTIES OF ORGANIC COMPOUNDS FROM MOLECULAR STRUCTURE USING SPARC

    Science.gov (United States)

    The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) has been under development for several years to estimate physical properties and chemical reactivity parameters of organic compounds strictly from molecular structure. SPARC uses computational algorithms...

  4. Two-parameter Failure Model Improves Time-independent and Time-dependent Failure Predictions

    Energy Technology Data Exchange (ETDEWEB)

    Huddleston, R L

    2004-01-27

    A new analytical model for predicting failure under a generalized, triaxial stress state was developed by the author and initially reported in 1984. The model was validated for predicting failure under elevated-temperature creep-rupture conditions. Biaxial data for three alloy steels, Types 304 and 316 stainless steels and Inconel 600, demonstrated two to three orders of magnitude reduction in the scatter of predicted versus observed creep-rupture times as compared to the classical failure models of Mises, Tresca, and Rankine. In 1990, the new model was incorporated into American Society of Mechanical Engineers (ASME) Code Case N47-29 for design of components operating under creep-rupture conditions. The current report provides additional validation of the model for predicting failure under time-independent conditions and also outlines a methodology for predicting failure under cyclic, time-dependent, creep-fatigue conditions. The later extension of the methodology may have the potential to improve failure predictions there as well. These results are relevant to most design applications, but they have special relevance to high-performance design applications such as components for high-pressure equipment, nuclear reactors, and jet engines.

  5. Correlation between serum adiponectin and clinical characteristics, biochemical parameters in Indian women with polycystic ovary syndrome

    Science.gov (United States)

    Ramanand, Sunita J.; Ramanand, Jaiprakash B.; Ghongane, Balasaheb B.; Patwardhan, Milind H.; Patwardhan, Varsha M.; Ghanghas, Ravi; Halasawadekar, Nimish R.; Patil, Praveenkumar

    2014-01-01

    Background: Polycystic ovary syndrome (PCOS) is a common disorder. PCOS women are at a high risk for insulin resistance and metabolic syndrome (MS). Adiponectin is positively related to insulin sensitivity. It has a preventive role in atherogenesis and MS. The present work was conducted to study the correlation between serum adiponectin levels and clinical characteristics and biochemical parameters in PCOS patients. Materials and Methods: A prospective study in 49 newly diagnosed (as per Rotterdam criteria) Indian PCOS women was conducted. PCOS women were clinically examined and investigated for biochemical parameters. Results: The mean serum adiponectin was 12 ± 9.4 μg/mL (range 0.47-45). Hypoadiponectinemia (serum adiponectin <4 μg/mL) was present in 22% patients. Age and adiponectin correlated significantly and inversely (r = −0.42, P = 0.027). Overweight/obese patients had lower mean adiponectin levels than normal weight (11.62 ± 9.5 vs 13.58 ± 9.5, P = 0.56). It was significantly lower in patients with acanthosis nigricans (AN) as compared with those without AN (8.4 ± 5.9 vs 15 ± 11, P = 0.038). Hirsute patients showed lower mean adiponectin levels than nonhirsute (10 ± 7.3 vs 13 ± 10, P = 0.57). A positive, insignificant correlation was observed between serum adiponectin and cholesterol, low-density lipoprotein, follicle stimulating hormone (FSH), thyroid stimulating hormone, levels. A negative insignificant correlation existed between serum adiponectin and luteinizing hormone (LH), LH: FSH ratio, prolactin, dehydroepiandrosterone, testosterone, triglyceride, high-density lipoprotein, fasting blood glucose, fasting insulin, and Homeostasis Model Assessment. Conclusion: Hypoadiponectinemia is present in one-fifth of women with PCOS. Adiponectin levels decrease as age advances. Low levels of adiponectin possibly contributes to the development of dermal manifestation (AN) of insulin resistance. PMID:24741521

  6. Correlation between serum adiponectin and clinical characteristics, biochemical parameters in Indian women with polycystic ovary syndrome

    Directory of Open Access Journals (Sweden)

    Sunita J Ramanand

    2014-01-01

    Full Text Available Background: Polycystic ovary syndrome (PCOS is a common disorder. PCOS women are at a high risk for insulin resistance and metabolic syndrome (MS. Adiponectin is positively related to insulin sensitivity. It has a preventive role in atherogenesis and MS. The present work was conducted to study the correlation between serum adiponectin levels and clinical characteristics and biochemical parameters in PCOS patients. Materials and Methods: A prospective study in 49 newly diagnosed (as per Rotterdam criteria Indian PCOS women was conducted. PCOS women were clinically examined and investigated for biochemical parameters. Results : The mean serum adiponectin was 12 ± 9.4 μg/mL (range 0.47-45. Hypoadiponectinemia (serum adiponectin <4 μg/mL was present in 22% patients. Age and adiponectin correlated significantly and inversely (r = −0.42, P = 0.027. Overweight/obese patients had lower mean adiponectin levels than normal weight (11.62 ± 9.5 vs 13.58 ± 9.5, P = 0.56. It was significantly lower in patients with acanthosis nigricans (AN as compared with those without AN (8.4 ± 5.9 vs 15 ± 11, P = 0.038. Hirsute patients showed lower mean adiponectin levels than nonhirsute (10 ± 7.3 vs 13 ± 10, P = 0.57. A positive, insignificant correlation was observed between serum adiponectin and cholesterol, low-density lipoprotein, follicle stimulating hormone (FSH, thyroid stimulating hormone, levels. A negative insignificant correlation existed between serum adiponectin and luteinizing hormone (LH, LH: FSH ratio, prolactin, dehydroepiandrosterone, testosterone, triglyceride, high-density lipoprotein, fasting blood glucose, fasting insulin, and Homeostasis Model Assessment. Conclusion: Hypoadiponectinemia is present in one-fifth of women with PCOS. Adiponectin levels decrease as age advances. Low levels of adiponectin possibly contributes to the development of dermal manifestation (AN of insulin resistance.

  7. Predictive capacity of risk assessment scales and clinical judgment for pressure ulcers: a meta-analysis.

    Science.gov (United States)

    García-Fernández, Francisco Pedro; Pancorbo-Hidalgo, Pedro L; Agreda, J Javier Soldevilla

    2014-01-01

    A systematic review with meta-analysis was completed to determine the capacity of risk assessment scales and nurses' clinical judgment to predict pressure ulcer (PU) development. Electronic databases were searched for prospective studies on the validity and predictive capacity of PUs risk assessment scales published between 1962 and 2010 in English, Spanish, Portuguese, Korean, German, and Greek. We excluded gray literature sources, integrative review articles, and retrospective or cross-sectional studies. The methodological quality of the studies was assessed according to the guidelines of the Critical Appraisal Skills Program. Predictive capacity was measured as relative risk (RR) with 95% confidence intervals. When 2 or more valid original studies were found, a meta-analysis was conducted using a random-effect model and sensitivity analysis. We identified 57 studies, including 31 that included a validation study. We also retrieved 4 studies that tested clinical judgment as a risk prediction factor. Meta-analysis produced the following pooled predictive capacity indicators: Braden (RR = 4.26); Norton (RR = 3.69); Waterlow (RR = 2.66); Cubbin-Jackson (RR = 8.63); EMINA (RR = 6.17); Pressure Sore Predictor Scale (RR = 21.4); and clinical judgment (RR = 1.89). Pooled analysis of 11 studies found adequate risk prediction capacity in various clinical settings; the Braden, Norton, EMINA (mEntal state, Mobility, Incontinence, Nutrition, Activity), Waterlow, and Cubbin-Jackson scales showed the highest predictive capacity. The clinical judgment of nurses was found to achieve inadequate predictive capacity when used alone, and should be used in combination with a validated scale.

  8. Colon cancer: association of histopathological parameters and patients' survival with clinical presentation.

    Science.gov (United States)

    Alexiusdottir, Kristin K; Snaebjornsson, Petur; Tryggvadottir, Laufey; Jonasson, Larus; Olafsdottir, Elinborg J; Björnsson, Einar Stefan; Möller, Pall Helgi; Jonasson, Jon G

    2013-10-01

    Available data correlating symptoms of colon cancer patients with the severity of the disease are very limited. In a population-based setting, we correlated information on symptoms of colon cancer patients with several pathological tumor parameters and survival. Information on all patients diagnosed with colon cancer in Iceland in 1995-2004 for this retrospective, population-based study was obtained from the Icelandic Cancer Registry. Information on symptoms of patients and blood hemoglobin was collected from patients' files. Pathological parameters were obtained from a previously performed standardized tumor review. A total of 768 patients entered this study; the median age was 73 years. Tumors in patients presenting at diagnosis with visible blood in stools were significantly more likely to be of lower grade, having pushing border, conspicuous peritumoral lymphocytic infiltration, and lower frequency of vessel invasion. Patients with abdominal pain and anemia were significantly more likely to have vessel invasion. Logistic regression showed that visible blood in stools was significantly associated with protecting pathological factors (OR range 0.38-0.83, p < 0.05). Tumors in patients presenting with abdominal pain were strongly associated with infiltrative margin and scarce peritumoral lymphocytic infiltration (OR = 1.95; 2.18 respectively, p < 0.05). Changes in bowel habits were strongly associated with vessel invasion (OR = 2.03, p < 0.05). Cox regression showed that blood in stools predicted survival (HR = 0.54). In conclusion, visible blood in stools correlates significantly with all the beneficial pathological parameters analyzed and with better survival of patients. Anemia, general symptoms, changes in bowel habits, acute symptoms, and abdominal pain correlate with more aggressive tumor characteristics and adverse outcome for patients.

  9. Temperature insensitive prediction of glucose concentration in turbid medium using multivariable calibration based on external parameter orthogonalization

    Science.gov (United States)

    Han, Tongshuai; Zhang, Ziyang; Sun, Cuiying; Guo, Chao; Sun, Di; Liu, Jin

    2016-10-01

    The measurement accuracy of non-invasive blood glucose concentration (BGC) sensing with near-infrared spectroscopy is easily affected by the temperature variation in tissue because it would induce an unacceptable spectrum variation and the consequent prediction deviation. We use a multivariable correction method based on external parameter orthogonalization (EPO) to calibrate the spectral data recorded at different temperature values to reduce the spectral variation. The tested medium is a kind of tissue phantom, the Intralipid aqueous solution. The calibration uses a projection matrix to get the orthogonal spectral space to the variable of external parameter, i.e. temperature, and then the useful spectral information relative to glucose concentration has been reserved. Even more, training the projection matrix can be separated to building the calibration matrix for the prediction of glucose concentration as it only uses the representative samples' data with temperature variation. The method presents a lower complexity than modeling a robust prediction matrix, which can be built from comprehensive spectral data involved the all variables both of BGC and temperature. In our test, the calibrated spectra with the same glucose concentration but different temperature values show a significantly improved repeatability. And then the glucose concentration prediction results show a lower root mean squared error of prediction (RMSEP) than that using the robust calibration model, which has considered the two variables. We also discuss the rationality of the representative samples chosen by EPO. This research may be referenced to the temperature calibration for in vivo BGC sensing.

  10. Application of Hansen Solubility Parameters to predict drug-nail interactions, which can assist the design of nail medicines.

    Science.gov (United States)

    Hossin, B; Rizi, K; Murdan, S

    2016-05-01

    We hypothesised that Hansen Solubility Parameters (HSPs) can be used to predict drug-nail affinities. Our aims were to: (i) determine the HSPs (δD, δP, δH) of the nail plate, the hoof membrane (a model for the nail plate), and of the drugs terbinafine HCl, amorolfine HCl, ciclopirox olamine and efinaconazole, by measuring their swelling/solubility in organic liquids, (ii) predict nail-drug interactions by comparing drug and nail HSPs, and (iii) evaluate the accuracy of these predictions using literature reports of experimentally-determined affinities of these drugs for keratin, the main constituent of the nail plate and hoof. Many solvents caused no change in the mass of nail plates, a few solvents deswelled the nail, while others swelled the nail to varying extents. Fingernail and toenail HSPs were almost the same, while hoof HSPs were similar, except for a slightly lower δP. High nail-terbinafine HCl, nail-amorolfine HCl and nail-ciclopirox olamine affinities, and low nail-efinaconazole affinities were then predicted, and found to accurately match experimental reports of these drugs' affinities to keratin. We therefore propose that drug and nail Hansen Solubility Parameters may be used to predict drug-nail interactions, and that these results can assist in the design of drugs for the treatment of nail diseases, such as onychomycosis and psoriasis. To our knowledge, this is the first report of the application of HSPs in ungual research.

  11. R-Peak Time: An Electrocardiographic Parameter with Multiple Clinical Applications.

    Science.gov (United States)

    Pérez-Riera, Andrés Ricardo; de Abreu, Luiz Carlos; Barbosa-Barros, Raimundo; Nikus, Kjell C; Baranchuk, Adrian

    2016-01-01

    In the 12-lead electrocardiogram (ECG), the time from the onset of the QRS complex (Q or R wave) to the apex or peak of R or to R' (when present), using indirect or semidirect surface unipolar precordial leads, bipolar limb leads or unipolar limb leads, is called ventricular activation time (VAT), R wave peak time (RWPT), R-peak time or intrinsicoid deflection (ID). The R-peak time in a specific ECG lead is the interval from the earliest onset of the QRS complex, preferably determined from multiple simultaneously recorded leads, to the peak (maximum) of the R wave or R' if present. Irrespective of the relative height of the R and R' waves, the R-peak time is measured to the second peak. The parameter corresponds to the time of the electrical activation occurring from the endocardium to the epicardium as reflected by the recording electrode located at a variable distance on the body surface, depending on the lead type: a unipolar precordial lead, a bipolar or unipolar limb lead. In normal conditions, the R-peak time for the thinner-walled right ventricle is measured from lead V1 or V2 and its upper limit of normal is 35 ms. The R-peak time for the left ventricle (LV) is measured from leads V5 to V6 and 45 ms is considered the upper limit of normal. In this manuscript, we review the clinical applications of this parameter.

  12. Immunological Parameters Associated With Vitiligo Treatments: A Literature Review Based on Clinical Studies

    Directory of Open Access Journals (Sweden)

    Ana Cláudia Guimarães Abreu

    2015-01-01

    Full Text Available Vitiligo, a depigmentary disorder, caused by the loss of melanocytes, affects approximately 1% of the world population, irrespective of skin type, with a serious psychological impact on the patient quality of life. So far, the origin of vitiligo has not been traced and the pathogenesis is complex, involving the interplay of a multitude of variables. Although there is no treatment that ensures the complete cure of the disorder, there are some pharmacological, phototherapy, and surgical therapies available. A series of variables can affect treatment outcome, such as individual characteristics, emotional issues, type of vitiligo, stability of the lesions, and immunological status. The present literature review identified the main immunological parameters associated with treatments for vitiligo. Cytotoxic CD8+ T lymphocytes are the main cell type involved in treatment success, as fewer cells in skin lesions are associated with better results. Other parameters such as cytokines and regulatory T cells may also be involved. Further clinical scientific studies are needed to elucidate the complex mechanisms underlying vitiligo and its treatments, in order to expand the range of therapeutic approaches for each individual case.

  13. Performance of DFT+U method for prediction of structural and thermodynamic parameters of monazite-type ceramics.

    Science.gov (United States)

    Blanca Romero, Ariadna; Kowalski, Piotr M; Beridze, George; Schlenz, Hartmut; Bosbach, Dirk

    2014-07-05

    We performed a density functional theory (DFT) study of the monazite-type ceramics using DFT+U method, where the Hubbard U parameters are derived ab initio, with the main goal in testing the predictive power of this computational method for modeling of f-electron materials that are of interest in nuclear waste management. We show that DFT+U approach with PBEsol as the exchange-correlation functional significantly improves description of structures and thermodynamic parameters of lanthanide-bearing oxides and monazites over commonly used standard DFT (PBE) approach. We found that it is essential to use the Hubbard U parameter derived for a given element and a given structure to reproduce the structural parameters of the measured materials. We obtained exceptionally good description of the structural parameters with U parameter derived using the linear response approach of Cococcioni and de Gironcoli (Phys. Rev. B 2005, 71, 035105). This shows that affordable methods, such as DFT+U with a clever choice of exchange-correlation functional and the Hubbard U parameter can lead to a good description of f-electron materials.

  14. Corelations between radiological score with clinical and laboratory parameters in rheumatoid arthritis

    Directory of Open Access Journals (Sweden)

    Mihaela Chicu

    2016-12-01

    Full Text Available Staging in rheumatoid arthritis (RA and evaluating the effectiveness of drug treatment involves the determination of radiological scores (for narrowing and erosions, this being the most specific changes and most commonly found in RA.Matherials and methods: Our study was condacted over a period of 12 months in Medical Rehabilitation Clinic of „Sf. Spiridon” Iasi Hospital, on a group of 40 women patients with RA in various stages of evolution. X-ray examination was done on hands and feet at the beginning and the end of the study period. There were computed radiographic Sharp scores for narrowing and erosions and the total score. Erosions were examined for 16 joints in each hand. For narrowing five joints were evaluated. For accuracy, radiological examination was done on mammography film. Rezults:After calculating Sharp scores - Van der Heide version - I compared them with the levels of clinical (HAQ, NAT, NAD, DAS28, bone densitometry and laboratory (ESR, CRP, rheumatoid factor, IL-1β parameters.Conclusions: The values of radiological scores for narrowing and erosions are directly correlate with DAS28, HAQ, rheumatoid factors levels and IgG values, and indirectly correlated with IL-1β levels.

  15. Predictive indices of empirical clinical diagnosis of malaria among under-five febrile children attending paediatric outpatient clinic

    Directory of Open Access Journals (Sweden)

    Hassan A Elechi

    2015-01-01

    Full Text Available Background: Malaria has remained an important public health problem in Nigeria with children under 5 years of age bearing the greatest burden. Accurate and prompt diagnosis of malaria is an important element in the fight against the scourge. Due to the several limitations of microscopy, diagnosis of malaria has continued to be made based on clinical ground against several World Health Organization (WHO recommendations. Thus, we aim to assess the performance of empirical clinical diagnosis among febrile children under 5 years of age in a busy pediatric outpatient clinic. Materials and Methods: The study was a cross-sectional study. Children aged <5 years with fever or 72 h history of fever were recruited. Children on antimalarial prophylaxis or on treatment for malaria were excluded. Relevant information was obtained from the caregiver and clinical note of the child using interviewer administered questionnaire. Two thick and two thin films were made, stained, and read for each recruited child. Data was analysed using SPSS version 16. Results: Of the 433 children studied, 98 (22.6% were empirically diagnosed as having malaria and antimalarial drug prescribed. Twenty-three (23.5% of these children were confirmed by microscopy to have malaria parasitemia, while 75 (76.5% were negative for malaria parasitemia. Empirical clinical diagnosis show poor predictive indices with sensitivity of 19.2%, specificity of 76.0%, positive predictive value of 23.5% and negative predictive value of 71%. Conclusion and Recommendations: Empirical clinical diagnosis of malaria among the under-five children with symptoms suggestive of acute malaria is highly not reliable and hence the need to strengthen parasitological diagnosis.

  16. Significance of uncertainties derived from settling tank model structure and parameters on predicting WWTP performance - A global sensitivity analysis study

    DEFF Research Database (Denmark)

    Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen;

    2011-01-01

    uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...... parameters for calibration is limited. The other SST model is a state-of-the-art, second-order, convection-dispersion tool (Plósz et al., 2007). The sensitivity results obtained from the four scenarios consistently indicate that the settler models and their parameters are among the most significant sources...

  17. Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry.

    Directory of Open Access Journals (Sweden)

    Jochen Frenzel

    Full Text Available BACKGROUND: Peptide patterns of bronchoalveolar lavage fluid (BALF were assumed to reflect the complex pathology of acute lung injury (ALI/acute respiratory distress syndrome (ARDS better than clinical and inflammatory parameters and may be superior for outcome prediction. METHODOLOGY/PRINCIPAL FINDINGS: A training group of patients suffering from ALI/ARDS was compiled from equal numbers of survivors and nonsurvivors. Clinical history, ventilation parameters, Murray's lung injury severity score (Murray's LISS and interleukins in BALF were gathered. In addition, samples of bronchoalveolar lavage fluid were analyzed by means of hydrophobic chromatography and MALDI-ToF mass spectrometry (MALDI-ToF MS. Receiver operating characteristic (ROC analysis for each clinical and cytokine parameter revealed interleukin-6>interleukin-8>diabetes mellitus>Murray's LISS as the best outcome predictors. Outcome predicted on the basis of BALF levels of interleukin-6 resulted in 79.4% accuracy, 82.7% sensitivity and 76.1% specificity (area under the ROC curve, AUC, 0.853. Both clinical parameters and cytokines as well as peptide patterns determined by MALDI-ToF MS were analyzed by classification and regression tree (CART analysis and support vector machine (SVM algorithms. CART analysis including Murray's LISS, interleukin-6 and interleukin-8 in combination was correct in 78.0%. MALDI-ToF MS of BALF peptides did not reveal a single identifiable biomarker for ARDS. However, classification of patients was successfully achieved based on the entire peptide pattern analyzed using SVM. This method resulted in 90% accuracy, 93.3% sensitivity and 86.7% specificity following a 10-fold cross validation (AUC = 0.953. Subsequent validation of the optimized SVM algorithm with a test group of patients with unknown prognosis yielded 87.5% accuracy, 83.3% sensitivity and 90.0% specificity. CONCLUSIONS/SIGNIFICANCE: MALDI-ToF MS peptide patterns of BALF, evaluated by appropriate

  18. Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk

    DEFF Research Database (Denmark)

    Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik

    2014-01-01

    Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date, res...... in this analysis. In conclusion, further research must focus on the development of model structures that allow the proper separation of dry and wet weather uncertainties and simulate runoff uncertainties depending on the rainfall input.......Probabilistic runoff forecasts generated by stochastic greybox models can be notably useful for the improvement of the decision-making process in real-time control setups for urban drainage systems because the prediction risk relationships in these systems are often highly nonlinear. To date......, research has primarily focused on one-step-ahead flow predictions for identifying, estimating, and evaluating greybox models. For control purposes, however, stochastic predictions are required for longer forecast horizons and for the prediction of runoff volumes, rather than flows. This article therefore...

  19. Spatial extrapolation of light use efficiency model parameters to predict gross primary production

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2011-12-01

    Full Text Available To capture the spatial and temporal variability of the gross primary production as a key component of the global carbon cycle, the light use efficiency modeling approach in combination with remote sensing data has shown to be well suited. Typically, the model parameters, such as the maximum light use efficiency, are either set to a universal constant or to land class dependent values stored in look-up tables. In this study, we employ the machine learning technique support vector regression to explicitly relate the model parameters of a light use efficiency model calibrated at several FLUXNET sites to site-specific characteristics obtained by meteorological measurements, ecological estimations and remote sensing data. A feature selection algorithm extracts the relevant site characteristics in a cross-validation, and leads to an individual set of characteristic attributes for each parameter. With this set of attributes, the model parameters can be estimated at sites where a parameter calibration is not possible due to the absence of eddy covariance flux measurement data. This will finally allow a spatially continuous model application. The performance of the spatial extrapolation scheme is evaluated with a cross-validation approach, which shows the methodology to be well suited to recapture the variability of gross primary production across the study sites.

  20. Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

    DEFF Research Database (Denmark)

    Swanton, C.; Larkin, J.M.; Gerlinger, M.

    2010-01-01

    RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection...... inhibitor. Through the analysis of tumour tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumour-derived genomic data with personalised tumour-derived shRNA and high throughput si......, reducing ineffective therapy in drug resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate...

  1. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

    Science.gov (United States)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

  2. Predicting neutrino parameters from SO(3) family symmetry and quark-lepton unification

    Energy Technology Data Exchange (ETDEWEB)

    King, Stephen F. [School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ (United Kingdom)

    2005-08-01

    We show how the neutrino mixing angles and oscillation phase can be predicted from tri-bimaximal neutrino mixing, corrected by charged lepton mixing angles which are related to quark mixing angles via quark-lepton unification. The tri-bimaximal neutrino mixing can naturally originate from the see-saw mechanism via constrained sequential dominance (CSD), where CSD can result from the vacuum alignment of a non-abelian family symmetry such as SO(3). We construct a realistic model of quark and lepton masses and mixings based on SO(3) family symmetry with quark-lepton unification based on the Pati-Salam gauge group. The atmospheric angle is predicted to be approximately maximal {theta}{sub 23} = 45{sup 0}, corrected by the quark mixing angle {theta}{sub 23}{sup CKM} {approx} 2.4 deg., with the correction controlled by an undetermined phase in the quark sector. The solar angle is predicted by the tri-bimaximal complementarity relation: {theta}{sub 12}+(1/2{sup 1/2})({theta}{sub C}/3)cos ({delta}-{pi}) {approx} 35.26 deg., where {theta}{sub C} is the Cabibbo angle and {delta} is the neutrino oscillation phase. The reactor angle is predicted to be {theta}{sub 13} {approx} (1/2{sup 1/2})({theta}{sub C}/3) {approx} 3.06 deg. The MNS neutrino oscillation phase {delta} is predicted in terms of the solar angle to be cos ({delta}-{pi}) {approx} (35.26 deg.-{theta}{sub 12}{sup 0})/3.06 deg. These predictions can all be tested by future high precision neutrino oscillation experiments, thereby probing the nature of high energy quark-lepton unification.

  3. Simple measurements for prediction of drug release from polymer matrices - Solubility parameters and intrinsic viscosity

    DEFF Research Database (Denmark)

    Madsen, Claus G; Skov, Anders; Baldursdottir, Stefania;

    2015-01-01

    (dl-lactide-co-glycolide) (PLGA) were cast with bovine serum albumin (BSA) as a model drug using different solvents (acetone, dichloromethane, ethanol and water). The amount of released protein from the different matrices was correlated with the Hildebrand and Hansen solubility parameters of the solvents, and the intrinsic......PURPOSE: This study describes how protein release from polymer matrices correlate with simple measurements on the intrinsic viscosity of the polymer solutions used for casting the matrices and calculations of the solubility parameters of polymers and solvents used. METHOD: Matrices of poly...... from PLGA matrices varied depending on the solvent used for casting. The maximum amount of released BSA decreased with higher intrinsic viscosity, and increased with solubility parameter difference between the solvent and polymer used. The solvent used also had an effect on the matrix microstructure...

  4. Predicting effects of diffusion welding parameters on welded joint properties by artificial neural network

    Institute of Scientific and Technical Information of China (English)

    刘黎明; 祝美丽; 牛济泰; 张忠典

    2001-01-01

    The static model for metal matrix composites in diffusion welding was established by means of artificial neural network method. The model presents the relationship between weld joint properties and welding parameters such as welding temperature, welding pressure and welding time. Through simulating the diffusion welding process of SiCw/6061Al composite, the effects of welding parameters on the strength of welded joint was studied and optimal technical parameters was obtained. It is proved that this method has good fault-tolerant ability and versatility and can overcome the shortage of the general experiment. The established static model is in good agreement with the actual welding process, so it becomes a new path for studying the weldability of new material.

  5. Clinical algorithm for improved prediction of ambulation and patient stratification after incomplete spinal cord injury.

    Science.gov (United States)

    Zörner, Björn; Blanckenhorn, Wolf U; Dietz, Volker; Curt, Armin

    2010-01-01

    The extent of ambulatory recovery after motor incomplete spinal cord injury (miSCI) differs considerably amongst affected persons. This makes individual outcome prediction difficult and leads to increased within-group variation in clinical trials. The aims of this study on subjects with miSCI were: (1) to rank the strongest single predictors and predictor combinations of later walking capacity; (2) to develop a reliable algorithm for clinical prediction; and (3) to identify subgroups with only limited recovery of walking function. Correlation and logistic regression analyses were performed on a dataset of 90 subjects with tetra- or paraparesis, recruited in a prospective European multicenter study. Eleven measures obtained in the subacute injury period, including clinical examination, tibial somatosensory evoked potentials (tSSEP), and demographic factors, were related to ambulatory outcome (WISCI II, 6minWT) 6 months after injury. The lower extremity motor score (LEMS) alone and in combination was identified as most predictive for later walking capacity in miSCI. Ambulatory outcome of subjects with tetraparesis was correctly predicted for 92% (WISCI II) or 100% (6minWT) of the cases when LEMS was combined with either tSSEP or the ASIA Impairment Scale, respectively. For individuals with paraparesis, prediction was less distinct, mainly due to low prediction rates for individuals with poor walking outcome. A clinical algorithm was generated that allowed for the identification of a subgroup composed of individuals with tetraparesis and poor ambulatory recovery. These data provide evidence that a combination of predictors enables a reliable prediction of walking function and early patient stratification for clinical trials in miSCI.

  6. DYNAMIC PRODUCTION PREDICTION AND PARAMETER IDENTIFICATION FOR GAS WELL WITH VERTICAL FRACTURE

    Institute of Scientific and Technical Information of China (English)

    郭大立; 刘慈群; 赵金洲

    2002-01-01

    In order to devoid the hard work and factitious error in selecting charts while analyzing and interpreting hydraulic fracturing fracture parameters, on the basis of the nonDarcy flow factor, this paper put out the non-Darcy flow mathematical model of real gas in the formation and fracture, established the production history automatic matching model to identify fracture parameters, and offered the numerical solutions of those models, which took the variation of fracture conductivity in production process. These results offered a precise and reliable method to understand formation, analyze and evaluate the fracturing treatment quality of gas well.

  7. Prediction of the droughts in Indonesia with environmental parameters; Prediccion de sequias en Indonesia mediante la utilizacion de parametros medioambientales

    Energy Technology Data Exchange (ETDEWEB)

    Wasser, C.V.H.J.

    1994-12-31

    Every 5 or 10 years, the global climate is modified by the ``El Nino-Southern Oscillation`` (ENSO) oceanic-atmospheric system. This paper shows how data related with this system, combined with other environmental parameters, can be used to predict the intensity of the dry season in Indonesia. Foreseeing a possible short-fall of rain is very important for a country where the mayor part of its 190 million inhabitants depend on rice as their prime food source. (Author)

  8. The Predictive Value of Ultrasound Learning Curves Across Simulated and Clinical Settings

    DEFF Research Database (Denmark)

    Madsen, Mette E; Nørgaard, Lone N; Tabor, Ann

    2017-01-01

    OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation-based tra......OBJECTIVES: The aim of the study was to explore whether learning curves on a virtual-reality (VR) sonographic simulator can be used to predict subsequent learning curves on a physical mannequin and learning curves during clinical training. METHODS: Twenty midwives completed a simulation...

  9. GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes

    Directory of Open Access Journals (Sweden)

    Fine Howard A

    2010-07-01

    Full Text Available Abstract Background Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.

  10. Three-tiered risk stratification model to predict progression in Barrett's esophagus using epigenetic and clinical features.

    Directory of Open Access Journals (Sweden)

    Fumiaki Sato

    Full Text Available Barrett's esophagus predisposes to esophageal adenocarcinoma. However, the value of endoscopic surveillance in Barrett's esophagus has been debated because of the low incidence of esophageal adenocarcinoma in Barrett's esophagus. Moreover, high inter-observer and sampling-dependent variation in the histologic staging of dysplasia make clinical risk assessment problematic. In this study, we developed a 3-tiered risk stratification strategy, based on systematically selected epigenetic and clinical parameters, to improve Barrett's esophagus surveillance efficiency.We defined high-grade dysplasia as endpoint of progression, and Barrett's esophagus progressor patients as Barrett's esophagus patients with either no dysplasia or low-grade dysplasia who later developed high-grade dysplasia or esophageal adenocarcinoma. We analyzed 4 epigenetic and 3 clinical parameters in 118 Barrett's esophagus tissues obtained from 35 progressor and 27 non-progressor Barrett's esophagus patients from Baltimore Veterans Affairs Maryland Health Care Systems and Mayo Clinic. Based on 2-year and 4-year prediction models using linear discriminant analysis (area under the receiver-operator characteristic (ROC curve: 0.8386 and 0.7910, respectively, Barrett's esophagus specimens were stratified into high-risk (HR, intermediate-risk (IR, or low-risk (LR groups. This 3-tiered stratification method retained both the high specificity of the 2-year model and the high sensitivity of the 4-year model. Progression-free survivals differed significantly among the 3 risk groups, with p = 0.0022 (HR vs. IR and p<0.0001 (HR or IR vs. LR. Incremental value analyses demonstrated that the number of methylated genes contributed most influentially to prediction accuracy.This 3-tiered risk stratification strategy has the potential to exert a profound impact on Barrett's esophagus surveillance accuracy and efficiency.

  11. The detonation parameters of high energy density explosive predicted with a new revised VLW EOS

    Energy Technology Data Exchange (ETDEWEB)

    Xinping, L.; Xiaohua, J. [Southwest Institut of Chemical Mat. Chengdu Sichuan (China); Xiong, W. [Xian Modern Chemistry Research Institute (China)

    1996-12-31

    Some new target explosive compounds whose detonation performance significantly exceeds that of HMX have been predicted with the new revised VLM equation of state, which includes up to the sixth viral term. The two different hypotheses have been used in the calculation; solid carbon exists in detonation products as graphite or as diamond. (authors) 10 refs.

  12. Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters

    NARCIS (Netherlands)

    Adadi, Roi; Volkmer, Benjamin; Milo, Ron; Heinemann, Matthias; Shlomi, Tomer

    2012-01-01

    Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal bio

  13. Semen molecular and cellular features: these parameters can reliably predict subsequent ART outcome in a goat model

    Directory of Open Access Journals (Sweden)

    Mereu Paolo

    2009-11-01

    Full Text Available Abstract Currently, the assessment of sperm function in a raw or processed semen sample is not able to reliably predict sperm ability to withstand freezing and thawing procedures and in vivo fertility and/or assisted reproductive biotechnologies (ART outcome. The aim of the present study was to investigate which parameters among a battery of analyses could predict subsequent spermatozoa in vitro fertilization ability and hence blastocyst output in a goat model. Ejaculates were obtained by artificial vagina from 3 adult goats (Capra hircus aged 2 years (A, B and C. In order to assess the predictive value of viability, computer assisted sperm analyzer (CASA motility parameters and ATP intracellular concentration before and after thawing and of DNA integrity after thawing on subsequent embryo output after an in vitro fertility test, a logistic regression analysis was used. Individual differences in semen parameters were evident for semen viability after thawing and DNA integrity. Results of IVF test showed that spermatozoa collected from A and B lead to higher cleavage rates (0

  14. Influence of precision of emission characteristic parameters on model prediction error of VOCs/formaldehyde from dry building material.

    Directory of Open Access Journals (Sweden)

    Wenjuan Wei

    Full Text Available Mass transfer models are useful in predicting the emissions of volatile organic compounds (VOCs and formaldehyde from building materials in indoor environments. They are also useful for human exposure evaluation and in sustainable building design. The measurement errors in the emission characteristic parameters in these mass transfer models, i.e., the initial emittable concentration (C 0, the diffusion coefficient (D, and the partition coefficient (K, can result in errors in predicting indoor VOC and formaldehyde concentrations. These errors have not yet been quantitatively well analyzed in the literature. This paper addresses this by using modelling to assess these errors for some typical building conditions. The error in C 0, as measured in environmental chambers and applied to a reference living room in Beijing, has the largest influence on the model prediction error in indoor VOC and formaldehyde concentration, while the error in K has the least effect. A correlation between the errors in D, K, and C 0 and the error in the indoor VOC and formaldehyde concentration prediction is then derived for engineering applications. In addition, the influence of temperature on the model prediction of emissions is investigated. It shows the impact of temperature fluctuations on the prediction errors in indoor VOC and formaldehyde concentrations to be less than 7% at 23±0.5°C and less than 30% at 23±2°C.

  15. Comparison of four clinical scores for the predicting lower limb deep venous thrombosis in Chinese patients

    Institute of Scientific and Technical Information of China (English)

    Li Zhua; Min Liu; Xiaojuan Guo; Jianguo Wang; Youmin Guo; Chen Wang; Hongxia Ma; Yulin Guo

    2008-01-01

    To evaluate Wells, Kahn, St.Andr é and Constans scores for the prediction of deep venous thrombosis in Chinese patients.Methods:One hundred and seventy-two patients, prospectively, blinded referred for evaluation with four clinical-score systems for suspected deep venous thrombosis, were examined by ultrasonography.Sensitivity, specificity, positive predictive value, nega- tive predictive value and receiver operation curves were calculated for four clinical scores.The difference between areas of the ROC curve for each of the scores was compared with others and reference line.Results:Forty-six of 172 patients had deep venous throm- bosis proven by sonography.The sensitivity, specificity, positive predictive value and negative predictive value for Wells score was 91.3%, 27.4% and 74.2% respectively, for Constans score; 95.7%, 34.9%, 34.9% and 95.7% respectively.Area under ROV curve of Constans with the reference line.Conclusion:Based on the results of our study, the sensitivity, negative prediction value and area under ROC Considering the aim of the clinical assessment, Constans score and Wells score are more efficient for Chinese hospitalized patients.

  16. Analysis and prediction of the alpha-function parameters used in cubic equations of state

    DEFF Research Database (Denmark)

    Privata, Romain; Viscontea, Maxime; Zazoua-Khames, Anis

    2015-01-01

    The performance of two generalized alpha functions (Soave and generalized Twu functions requiring the acentric factor as input parameter) and two parameterizable alpha functions (Mathias-Copeman and Twu) incorporated in cubic equations of state (Redlich-Kwong and Peng-Robinson) are evaluated...

  17. A Multi-Center Prospective Derivation and Validation of a Clinical Prediction Tool for Severe Clostridium difficile Infection.

    LENUS (Irish Health Repository)

    Na, Xi

    2015-04-23

    Prediction of severe clinical outcomes in Clostridium difficile infection (CDI) is important to inform management decisions for optimum patient care. Currently, treatment recommendations for CDI vary based on disease severity but validated methods to predict severe disease are lacking. The aim of the study was to derive and validate a clinical prediction tool for severe outcomes in CDI.

  18. Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization.

    Science.gov (United States)

    Nigsch, Florian; Bender, Andreas; van Buuren, Bernd; Tissen, Jos; Nigsch, Eduard; Mitchell, John B O

    2006-01-01

    We have applied the k-nearest neighbor (kNN) modeling technique to the prediction of melting points. A data set of 4119 diverse organic molecules (data set 1) and an additional set of 277 drugs (data set 2) were used to compare performance in different regions of chemical space, and we investigated the influence of the number of nearest neighbors using different types of molecular descriptors. To compute the prediction on the basis of the melting temperatures of the nearest neighbors, we used four different methods (arithmetic and geometric average, inverse distance weighting, and exponential weighting), of which the exponential weighting scheme yielded the best results. We assessed our model via a 25-fold Monte Carlo cross-validation (with approximately 30% of the total data as a test set) and optimized it using a genetic algorithm. Predictions for drugs based on drugs (separate training and test sets each taken from data set 2) were found to be considerably better [root-mean-squared error (RMSE)=46.3 degrees C, r2=0.30] than those based on nondrugs (prediction of data set 2 based on the training set from data set 1, RMSE=50.3 degrees C, r2=0.20). The optimized model yields an average RMSE as low as 46.2 degrees C (r2=0.49) for data set 1, and an average RMSE of 42.2 degrees C (r2=0.42) for data set 2. It is shown that the kNN method inherently introduces a systematic error in melting point prediction. Much of the remaining error can be attributed to the lack of information about interactions in the liquid state, which are not well-captured by molecular descriptors.

  19. Generation and mid-IR measurement of a gas-phase to predict security parameters of aviation jet fuel.

    Science.gov (United States)

    Gómez-Carracedo, M P; Andrade, J M; Calviño, M A; Prada, D; Fernández, E; Muniategui, S

    2003-07-27

    The worldwide use of kerosene as aviation jet fuel makes its safety considerations of most importance not only for aircraft security but for the workers' health (chronic and/or acute exposure). As most kerosene risks come from its vapours, this work focuses on predicting seven characteristics (flash point, freezing point, % of aromatics and four distillation points) which assess its potential hazards. Two experimental devices were implemented in order to, first, generate a kerosene vapour phase and, then, to measure its mid-IR spectrum. All the working conditions required to generate the gas phase were optimised either in a univariate or a multivariate (SIMPLEX) approach. Next, multivariate prediction models were deployed using partial least squares regression and it was found that both the average prediction errors and precision parameters were satisfactory, almost always well below the reference figures.

  20. Improving prediction of vapor-liquid equilibrium with modified HVOS-PR-UNIFAC model by revision of group interaction parameters

    Institute of Scientific and Technical Information of China (English)

    XUE Rong-shu; LI Yan-jiao; WANG Xiao-qing; TAN Shi-yu; WEI Shun-an

    2007-01-01

    Quantitative description of vapor-liquid equilibrium is very useful for designing separation processes. In this study, we combined the Peng-Robinson equation and the Huron-Vidal-Orbey-Sandler mixing rule into a modified UNIFAC model for the improvement of predicting vapor-liquid equilibria. The predictions of vapor-liquid equilibria for 62 systems including alcoholalkane, alcohol-benzene, and amine-water systems demonstrate that the revised parameters remarkably improve the prediction accuracy for many systems. Especially for amine-water system, the mean deviation of components decreases from 0.094 to 0.021, and the mean deviation of pressure from 22.45% to 4.41%.

  1. Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase

    Science.gov (United States)

    Szaleniec, Maciej; Witko, Małgorzata; Tadeusiewicz, Ryszard; Goclon, Jakub

    2006-03-01

    Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches.

  2. An Integrated Hydrologic Bayesian Multi-Model Combination Framework: Confronting Input, parameter and model structural uncertainty in Hydrologic Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Ajami, N K; Duan, Q; Sorooshian, S

    2006-05-05

    This paper presents a new technique--Integrated Bayesian Uncertainty Estimator (IBUNE) to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. The uncertainties from the input (forcing) data--mainly the precipitation observations and from the model parameters are reduced through a Monte Carlo Markov Chain (MCMC) scheme named Shuffled Complex Evolution Metropolis (SCEM) algorithm which has been extended to include a precipitation error model. Afterwards, the Bayesian Model Averaging (BMA) scheme is employed to further improve the prediction skill and uncertainty estimation using multiple model output. A series of case studies using three rainfall-runoff models to predict the streamflow in the Leaf River basin, Mississippi are used to examine the necessity and usefulness of this technique. The results suggests that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and their associated uncertainty bounds which does not consistently capture and represent the real-world behavior of the watershed.

  3. Prediction of Flow Regimes and Thermal Hydraulic Parameters in Two-Phase Natural Circulation by RELAP5 and TRACE Codes

    Directory of Open Access Journals (Sweden)

    Viet-Anh Phung

    2015-01-01

    Full Text Available In earlier study we have demonstrated that RELAP5 can predict flow instability parameters (flow rate, oscillation period, temperature, and pressure in single channel tests in CIRCUS-IV facility. The main goals of this work are to (i validate RELAP5 and TRACE capabilities in prediction of two-phase flow instability and flow regimes and (ii assess the effect of improvement in flow regime identification on code predictions. Most of the results of RELAP5 and TRACE calculation are in reasonable agreement with experimental data from CIRCUS-IV. However, both codes misidentified instantaneous flow regimes which were observed in the test with high speed camera. One of the reasons for the incorrect identification of the flow regimes is the small tube flow regime transition model in RELAP5 and the combined bubbly-slug flow regime in TRACE. We found that calculation results are sensitive to flow regime boundaries of RELAP5 which were modified in order to match the experimental data on flow regimes. Although the flow regime became closer to the experimental one, other predicted thermal hydraulic parameters showed larger discrepancy with the experimental data than with the base case calculations where flow regimes were misidentified.

  4. Prediction and Optimization Approaches for Modeling and Selection of Optimum Machining Parameters in CNC down Milling Operation

    Directory of Open Access Journals (Sweden)

    Asaad A. Abdullah

    2014-04-01

    Full Text Available In this study, we suggested intelligent approach to predict and optimize the cutting parameters when down milling of 45# steel material with cutting tool PTHK- (Ø10*20C*10D*75L -4F-1.0R under dry condition. The experiments were performed statistically according to four factors with three levels in Taguchi experimental design method. Adaptive Neuro-fuzzy inference system is utilized to establish the relationship between the inputs and output parameter exploiting the Taguchi orthogonal array L27. The Particle Swarm Optimized-Adaptive Neuro-Fuzzy Inference System (PSOANFIS is suggested to select the best cutting parameters providing the lower surface through from the experimental data using ANFIS models to predict objective functions. The PSOANFIS optimization approach that improves the surface quality from 0.212 to 0.202, as well as the cutting time is also reduced from 7.5 to 4.78 sec according to machining parameters before and after optimization process. From these results, it can be readily achieved that the advanced study is trusted and suitable for solving other problems encountered in metal cutting operations and the same surface roughness.

  5. Accuracy of clinical prediction rules in peptic ulcer perforation: an observational study

    DEFF Research Database (Denmark)

    Buck, David Levarett; Vester-Andersen, Morten; Møller, Morten Hylander

    2012-01-01

    and breastfeeding women, non-surgically treated patients, patients with malignant ulcers, and patients with perforation of other organs were excluded. Primary outcome measure: 30-day mortality rate. Statistical analysis: the ability of four clinical prediction rules to distinguish survivors from non......-survivors (discrimination ability) was evaluated by the area under the receiver operating characteristic curve (AUC), positive predictive values (PPVs), negative predictive values (NPVs), and adjusted relative risks. Results. Median age (range) was 70 years (25-92 years), 51% of the patients were females, and 73...

  6. Soil parameters are key factors to predict metal bioavailability to snails based on chemical extractant data

    Energy Technology Data Exchange (ETDEWEB)

    Pauget, B.; Gimbert, F., E-mail: frederic.gimbert@univ-fcomte.fr; Scheifler, R.; Coeurdassier, M.; Vaufleury, A. de

    2012-08-01

    Although soil characteristics modulate metal mobility and bioavailability to organisms, they are often ignored in the risk assessment of metal transfer. This paper aims to determine the ability of chemical methods to assess and predict cadmium (Cd), lead (Pb) and zinc (Zn) environmental bioavailability to the land snail Cantareus aspersus. Snails were exposed in the laboratory for 28 days to 17 soils from around a former smelter. The soils were selected for their range of pH, organic matter, clay content, and Cd, Pb and Zn concentrations. The influence of soil properties on environmental availability (estimated using HF-HClO{sub 4}, EDTA, CaCl{sub 2}, NH{sub 4}NO{sub 3}, NaNO{sub 3}, free ion activity and total dissolved metal concentration in soil solution) and on environmental bioavailability (modelled using accumulation kinetics) was identified. Among the seven chemical methods, only the EDTA and the total soil concentration can be used to assess Cd and Pb environmental bioavailability to snails (r Superscript-Two {sub adj} = 0.67 and 0.77, respectively). For Zn, none of the chemical methods were suitable. Taking into account the influence of the soil characteristics (pH and CEC) allows a better prediction of Cd and Pb environmental bioavailability (r Superscript-Two {sub adj} = 0.82 and 0.83, respectively). Even though alone none of the chemical methods tested could assess Zn environmental bioavailability to snails, the addition of pH, iron and aluminium oxides allowed the variation of assimilation fluxes to be predicted. A conceptual and practical method to use soil characteristics for risk assessment is proposed based on these results. We conclude that as yet there is no universal chemical method to predict metal environmental bioavailability to snails, and that the soil factors having the greatest impact depend on the metal considered. - Highlights: Black-Right-Pointing-Pointer New approach to identify chemical methods able to predict metal bioavailability

  7. Better estimation of protein-DNA interaction parameters improve prediction of functional sites

    Directory of Open Access Journals (Sweden)

    O'Flanagan Ruadhan A

    2008-12-01

    Full Text Available Abstract Background Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment method designed to produce many such sequences. Results We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP, and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites. Conclusion We think that training binding site detection

  8. Discrepancy in clinical versus radiological parameters describing deformity due to brace treatment for moderate idiopathic scoliosis

    Directory of Open Access Journals (Sweden)

    Szulc Andrzej

    2007-12-01

    Full Text Available Abstract Background The shape of the torso in patients with idiopathic scoliosis is considered to reflect the shape of the vertebral column, however the direct correlation between parameters describing clinical deformity and those characterizing radiological curvature was reported to be weak. It is not clear if the management proposed for scoliosis (physiotherapy, brace, surgery affects equally the shape of the axial skeleton and the surface of the body. The aim of the study was to compare clinical deformity of (1 idiopathic scoliosis girls being under brace treatment for radiological curves of 25 to 40 degrees and (2 non treated scoliotic girls matched for age and Cobb angle. Methods Cross-sectional study of 24 girls wearing the brace versus 26 girls without brace treatment, matched for age and Cobb angle. Hypothesis: Patients wearing the brace for more than 6 months, when comparing to patients without brace, may present different external morphology of the trunk, in spite of having similar Cobb angle. Material. Inclusion criteria: girls, idiopathic scoliosis, growing age (10–16 years, Cobb angle minimum 25°, maximum 40°. The braced group consisted of girls wearing a TLSO brace (Cheneau for more than 6 months with minimum of 16 hours per day. The non-braced group consisted of girls first seen for their spinal deformity, previously not treated. The groups presented similar curve pattern. Methods. Scoliometer exam: angle of trunk rotation at three levels of the spine: upper thoracic, main thoracic, lumbar or thoracolumbar. The maximal angle was noted at each level and the sum of three levels was calculated. Posterior trunk symmetry index (POTSI and Hump Sum were measured using surface topography. Results Cobb angle was 34.9° ± 4.8° in braced and 32.7° ± 4.9° in un-braced patients (difference not significant. The age was 14.1 ± 1.6 years in braced patients and 13.1 ± 1.9 years in un-braced group (p = 0.046. The value of angle of trunk

  9. Relationship between psychiatric status, self-reported outcome measures, and clinical parameters in axial spondyloarthritis.

    Science.gov (United States)

    Kilic, Gamze; Kilic, Erkan; Ozgocmen, Salih

    2014-12-01

    This article aims to compare the risks of depression and anxiety in patients with ankylosing spondylitis (AS) and nonradiographic axial spondyloarthritis (nr-axSpA) and investigate the relationship among self-reported outcome measures, clinical parameters, and physical variables of patients with axSpA. Patients with axSpA were recruited from Erciyes Spondyloarthritis Cohort. The patients met Assessment of Spondyloarthritis International Society classification criteria for axial SpA and were assessed in a cross-sectional study design for visual analog scale (VAS) pain, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Ankylosing Spondylitis Quality of Life questionnaire (ASQoL), and Ankylosing Spondylitis Disease Activity Score-C-reactive protein (ASDAS-CRP). Psychological status was evaluated using the hospital anxiety and depression scale (HADS). Multivariate logistic regression analysis was applied to determine the associations between psychological variables and clinical parameters after adjusting for confounding variables. Of the 316 patients (142 nr-axSpA, 174 AS), 139 (44%) had high risk for depression (HADS-D score ≥ 7) and 71 (22.5%) for anxiety (HADS-A score ≥ 10). HADS-D and HADS-A scores were similar between patients with AS and nr-axSpA. Patients with high risk for depression and anxiety had higher scores in BASDAI, BASFI, and ASDAS-CRP, and also poorer scores in VAS pain and ASQoL. Multivariate logistic regression analysis showed that the ASDAS-CRP, ASQoL, BASDAI, as well as educational level were factors associated with the risk of depression whereas the ASQoL and educational level were factors associated with the risk of anxiety. Patients with nr-axSpA and AS have similar burden of psychological distress. The quality of life (ASQoL) and educational level were factors associated with the risk of both depression and anxiety whereas disease activity (BASDAI and ASDAS-CRP) was the

  10. Correlations between IL6 and the main clinical and biological parameters in rheumatoid arthritis

    Directory of Open Access Journals (Sweden)

    Mihaela Chicu

    2016-09-01

    Full Text Available Introduction: Cytokines are a family of complex peptide with hormone-like activity. They are soluble proteins without enzymatic activity and serves as the main intracellular mediators. Many cytokines achieves its effects by binding to special receptors membrane, and their adjustment is via soluble receptors. Cytokines are characterized by pleiotropism, overlapping and mutual adjustment. Proinflammatory cytokine involved in major rheumatoid arthritis are TNF, IL1α, IL1β, IL8.The biological effects of IL6 overlap in large part over those of TNF. If TNF is involved in induction of apoptosis or programmed cell death, IL6 is specifically associated with angiogenic factors activation and the occurrence of neovascularity to the synovium; favors articular cartilage degradation by increasing the release of MMP, decreasing PG, recruit osteoclasts, apoptosis of osteoblasts, release of degradative enzymes and the inflammatory mediators - iNOS, COX2 - TNF, IL6, IL8.Material and methods: Based on these data we proposed and realized – for the first time in Romania – the measurement of IL6 levels and the correlation with values of DAS28 score, HAQ, ESR, CRP, Hb and the immunological parameters too. The study was conducted on a group of 80 sick diagnosed with RA in various stages of evolution, under treatment with disease-modifying medication , type Methotrexate, Arava.Conclusions: Levels of IL-6 correlate a direct manner with those of acute phase reactants ,ESR, CRP and indirect values of Hb, IgG; the clinical parameters (number of tender and swollen joints, DAS28, HAQ are not influenced by values IL6.

  11. Prediction of operational parameters effect on coal flotation using artificial neural network

    Institute of Scientific and Technical Information of China (English)

    E. Jorjani; Sh. Mesroghli; S. Chehreh Chelgani

    2008-01-01

    Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

  12. Genetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows

    OpenAIRE

    Kandel, Purna Bhadra; Vanrobays, Marie-Laure; Vanlierde, Amélie; Dehareng, Frédéric; Froidmont, Eric; Pierre DARDENNE; Lewis, E.; Buckley, F.; Deighton, MH; McParland, S.; Gengler, Nicolas; Soyeurt, Hélène

    2013-01-01

    Genetic selection of low methane (CH4) emitting animals is additive and permanent but the difficulties associated with individual CH4 measurement result in a paucity of records required to estimate genetic variability of CH4 traits. Recently, it was shown that direct quantification of CH4 emissions by mid-infrared spectroscopy (MIR) from milk. The CH4 prediction equation was developed using 452 SF6 CH4 measurements with associated milk spectra and the calibration equation wa...

  13. Prediction of Concrete Strength Using Microwave Based Accelerated Curing Parameters by Neural Network

    OpenAIRE

    Ramasundaram, S; T.R. Neelakantan; R. Vinoth

    2013-01-01

    Prediction of compressive strength of concrete is very useful for economic constructions. The compressive strength can be estimated after 28 days of casting the specimen cubes or may be predictedbased on the quantum and quality of ingredients used in making the concrete. When the first one requires a 28-day time, the second one does have problem of accuracy. Hence, a hybrid model is proposed in which the concrete cube is cured using the microwave based accelerated curing procedure and the ear...

  14. Association between markers of cardiovascular risk and clinical parameters of periodontitis

    Directory of Open Access Journals (Sweden)

    José Eduardo Gomes Domingues

    Full Text Available INTRODUCTION: Periodontal disease is an inflammatory response to bacteria that reside in the gum tissue and can have systemic repercussion. OBJECTIVE: The aim of this study was to assess the relationship between periodontitis and markers of cardiovascular risk. MATERIAL AND METHOD: Ninety selected patients were assigned into two groups in accordance with their levels of probing pocket depth (PPD and Clinical Attachment Level (CAL: control group, n= 45 (< 4 sites with PPD ≥ 4.0 mm and CAL ≥ 3.0 mm and case group, n= 45 (≥ 30% of sites with PPD ≥ 4.0 mm and CAL ≥3.0 mm. Plasma concentrations of C-reactive protein, high sensitive CRP, high-density lipoproteins (HDL-c and electronegative low density lipoproteins (LDL were assessed in all participants. Data from medical history and socioeconomic level were also collected from patients. RESULT: Plasma levels of HDL-c were lower in subjects with periodontal disease (p = 0.016 and were inversely associated with the number of sites with PPD ≥ 3 mm (rho= -0.325 and number of sites with PPD ≥ 3 mm and CAL ≥ 3 mm (rho= -0.216. These associations remained significant after adjustments for dental plaque and smoking using Univariate Analysis of Covariance (p < 0.05. Adjusted odds ratio between periodontal disease and levels of HDL-c was 0.94 (CI95% 0.88-0.99 after adjusting for age, smoking and dental plaque. Other investigated markers of cardiovascular risk were not related to periodontal disease. CONCLUSION: Clinical parameters of periodontitis were inversely associated with plasma concentrations of HDL-c.

  15. Computational prediction of the spectroscopic parameters of methanediol, an elusive molecule for interstellar detection

    Energy Technology Data Exchange (ETDEWEB)

    Barrientos, Carmen; Redondo, Pilar; Largo, Antonio [Departamento de Química Física y Química Inorgánica, Facultad de Ciencias, Universidad de Valladolid, Campus Miguel Delibes, Paseo de Belén 7, E-47011 Valladolid (Spain); Martínez, Henar, E-mail: alargo@qf.uva.es [Departamento de Química Orgánica, Escuela de Ingenierías Industriales, Universidad de Valladolid, Campus Esgueva, Paseo del Cauce 59, E-47011 Valladolid (Spain)

    2014-04-01

    The molecular structure of methanediol has been investigated by means of quantum chemical calculations. Two conformers, corresponding to C{sub 2} and C {sub s} symmetries, respectively, were considered. The C{sub 2} conformer is found to lie about 1.7 (at 298 K) or 2.3 (at 0 K) kcal mol{sup –1} below the C {sub s} conformer. Predictions for their rotational constants, vibrational frequencies, IR intensities, and dipole moments have been provided. The lowest-lying isomer has a very low dipole moment, around 0.03 D, whereas the C {sub s} conformer has a relatively high dipole moment, namely, 2.7 D. The barrier for the C {sub s} →C{sub 2} process is predicted to be around 0.7-1 kcal mol{sup –1}. Based on the energetic results the proportion of the C{sub s} conformer is likely to be negligible under low temperature conditions, such as in the interstellar medium. Therefore, it is predicted that detection by radioastronomy of methanediol would be rather unlikely.

  16. Laboratory nutritional parameters can predict one-year mortality in elderly patients with intertrochanteric fracture

    OpenAIRE

    Jun Lu

    2014-01-01

    "Objectives: The purpose of this study was to investigate the contributing value of nutrition related blood parameters to one-year mortality following intertrochanteric fracture surgery in a Chinese population over the age of 65. Methods: The nutritional status was evaluated by using admission serum albumin level and total lymphocyte count (TLC). One hundred and seventy-four intertrochanteric fracture patients were entered to this study for nutritional status assessment. Gender differences...

  17. Prediction of the effective parameters of the nanofluids using the generalized stochastic perturbation method

    Science.gov (United States)

    Kamiński, Marcin; Ossowski, Rafał Leszek

    2014-01-01

    The paper presents the results concerning a new problem of homogenization of the fluids filled with a random volume fraction of nanoparticles. We use a variety of probabilistic and statistical methods applied for numerical determination of the effective physical properties of different fluids filled with nanoparticles. The new probabilistic approach in the form of a higher order stochastic perturbation method is employed here, which is based on a higher order Taylor expansion of input random quantities and the resulting homogenized parameters using a general order series with random coefficients; it is contrasted with the Monte Carlo simulation and analytical symbolic integration. All computer methods are used to determine up to the fourth probabilistic moments and coefficients for effective specific heat, viscosity, heat conductivity and mass density for some nanofluids of modern technological importance. The volume fraction of the nanoparticles is treated in this study as the input Gaussian parameter truncated to the positive values and uniquely defined by the expectation, where its coefficient of variation is an additional parameter in our analysis. Computational experiments are performed here using computer algebra system MAPLE and they demonstrate a very good agreement of the probabilistic characteristics computed using analytical, perturbation and simulation methods.

  18. Lymphocytic Thyroiditis - is cytological grading significant? A correlation of grades with clinical, biochemical, ultrasonographic and radionuclide parameters

    Directory of Open Access Journals (Sweden)

    Bhatia Alka

    2007-01-01

    Full Text Available Background: Clinical, biochemical, ultrasonographic, radionuclide and cytomorphological observations in Lymphocytic thyroiditis (LT, to define the cytological grading criteria on smears and correlation of grades with above parameters. Methods: This prospective study was conducted on 76 patients attending the Fine needle aspiration cytology clinic of a tertiary care institute in North India. The various parameters like patients′ clinical presentation, thyroid antimicrosomal antibodies, hormonal profiles, radionuclide thyroid scan and thyroid ultrasound were studied. Fine needle aspiration of thyroid gland and grading of thyroiditis was done on smears. The grades were correlated with above parameters and the correlation indices were evaluated statistically. Results: Most of the patients were females (70, 92.11% who presented with a diffuse goiter (68, 89.47%. Hypothyroid features (56, 73.68% and elevated TSH (75, 98.68% were common, but radioiodide uptake was low or normal in majority of patients. Thyroid antimicrosomal antibody was elevated in 46/70 (65.71% patients. Cytomorphology in fine needle aspirates was diagnostic of lymphocytic thyroiditis in 75 (98.68% patients. Most of them had grade I/II disease by cytology. No correlation was observed between grades of cytomorphology and clinical, biochemical, ultrasonographic and radionuclide parameters. Conclusion: Despite the availability of several tests for diagnosis of LT, FNAC remains the gold standard. The grades of thyroiditis at cytology however do not correlate with clinical, biochemical, radionuclide and ultrasonographic parameters.

  19. Lymphocytic Thyroiditis – is cytological grading significant? A correlation of grades with clinical, biochemical, ltrasonographic and radionuclide parameters

    Directory of Open Access Journals (Sweden)

    Dash Radharaman

    2007-01-01

    Full Text Available Abstract Background Clinical, biochemical, ultrasonographic, radionuclide and cytomorphological observations in Lymphocytic thyroiditis (LT, to define the cytological grading criteria on smears and correlation of grades with above parameters. Methods This prospective study was conducted on 76 patients attending the Fine needle aspiration cytology clinic of a tertiary care institute in North India. The various parameters like patients' clinical presentation, thyroid antimicrosomal antibodies, hormonal profiles, radionuclide thyroid scan and thyroid ultrasound were studied. Fine needle aspiration of thyroid gland and grading of thyroiditis was done on smears. The grades were correlated with above parameters and the correlation indices were evaluated statistically. Results Most of the patients were females (70, 92.11% who presented with a diffuse goiter (68, 89.47%. Hypothyroid features (56, 73.68% and elevated TSH (75, 98.68% were common, but radioiodide uptake was low or normal in majority of patients. Thyroid antimicrosomal antibody was elevated in 46/70 (65.71% patients. Cytomorphology in fine needle aspirates was diagnostic of lymphocytic thyroiditis in 75 (98.68% patients. Most of them had grade I/II disease by cytology. No correlation was observed between grades of cytomorphology and clinical, biochemical, ultrasonographic and radionuclide parameters. Conclusion Despite the availability of several tests for diagnosis of LT, FNAC remains the gold standard. The grades of thyroiditis at cytology however do not correlate with clinical, biochemical, radionuclide and ultrasonographic parameters.

  20. Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study

    Directory of Open Access Journals (Sweden)

    Laura Schummers

    2016-09-01

    Full Text Available Abstract Background Compelled by the intuitive appeal of predicting each individual patient’s risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Methods Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225 were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke’s r2 for each model. Results Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4. Area

  1. Predictive Validity of DSM-IV Oppositional Defiant and Conduct Disorders in Clinically Referred Preschoolers

    Science.gov (United States)

    Keenan, Kate; Boeldt, Debra; Chen, Diane; Coyne, Claire; Donald, Radiah; Duax, Jeanne; Hart, Katherine; Perrott, Jennifer; Strickland, Jennifer; Danis, Barbara; Hill, Carri; Davis, Shante; Kampani, Smita; Humphries, Marisha

    2011-01-01

    Background: Diagnostic validity of oppositional defiant and conduct disorders (ODD and CD) for preschoolers has been questioned based on concerns regarding the ability to differentiate normative, transient disruptive behavior from clinical symptoms. Data on concurrent validity have accumulated, but predictive validity is limited. Predictive…

  2. Sensitivity, specificity and predictive value of blood cultures from cattle clinically suspected of bacterial endocarditis

    DEFF Research Database (Denmark)

    Houe, Hans; Eriksen, L.; Jungersen, Gregers;

    1993-01-01

    This study investigated the number of blood culture-positive cattle among 215 animals clinically suspected of having bacterial endocarditis. For animals that were necropsied, the sensitivity, specificity and predictive value of the diagnosis of endocarditis were calculated on the basis...

  3. Clinical picture and risk prediction of short-term mortality in cardiogenic shock

    DEFF Research Database (Denmark)

    Harjola, Veli-Pekka; Lassus, Johan; Sionis, Alessandro

    2015-01-01

    AIMS: The aim of this study was to investigate the clinical picture and outcome of cardiogenic shock and to develop a risk prediction score for short-term mortality. METHODS AND RESULTS: The CardShock study was a multicentre, prospective, observational study conducted between 2010 and 2012. Patie...

  4. Clinical prediction rules for invasive candidiasis in the ICU: ready for prime time?

    Science.gov (United States)

    Ostrosky-Zeichner, Luis

    2011-01-01

    Invasive candidiasis is a major source of morbidity and mortality in critically ill patients. The creation and validation of clinical prediction rules to identify patients at high risk has given clinicians access to advanced management strategies, such as targeted prophylaxis, pre-emptive therapy, and protocolized empirical therapy.

  5. Clinical prediction of fall risk and white matter abnormalities: a diffusion tensor imaging study

    Science.gov (United States)

    The Tinetti scale is a simple clinical tool designed to predict risk of falling by focusing on gait and stance impairment in elderly persons. Gait impairment is also associated with white matter (WM) abnormalities. Objective: To test the hypothesis that elderly subjects at risk for falling, as deter...

  6. Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks.

    Science.gov (United States)

    Flores, Dora-Luz; Gómez, Claudia; Cervantes, David; Abaroa, Alberto; Castro, Carlos; Castañeda-Martínez, Rubén A

    2017-01-01

    Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume, length and width of the heart, while the output was the digoxin concentration in dilution necessary to obtain a desired physiological response. ANNs were trained, validated and tested with the dataset of the in vivo experiment results. To select the optimal network, predictions for all the dataset for each configuration of ANNs were made, a maximum 5% relative error for the digoxin concentration was set and the diagnostic accuracy of the predictions made was evaluated. The double-layer perceptron had a barely higher performance than the single-layer perceptron; therefore, both had a good predictive ability. The double-layer perceptron was able to obtain the most accurate predictions of digoxin concentration required in the hearts of T. stultorum using MLP-ANNs.

  7. Prediction of Driving Safety in Individuals with Homonymous Hemianopia and Quadrantanopia from Clinical Neuroimaging

    Directory of Open Access Journals (Sweden)

    Michael S. Vaphiades

    2014-01-01

    Full Text Available Background. This study aimed to determine whether it is possible to predict driving safety of individuals with homonymous hemianopia or quadrantanopia based upon a clinical review of neuroimages that are routinely available in clinical practice. Methods. Two experienced neuroophthalmologists viewed a summary report of the CT/MRI scans of 16 participants with homonymous hemianopic or quadrantanopic field defects which indicated the site and extent of the lesion and they made predictions regarding whether participants would be safe/unsafe to drive. Driving safety was independently defined at the time of the study using state-recorded motor vehicle crashes (all crashes and at-fault for the previous 5 years and ratings of driving safety determined through a standardized on-road driving assessment by a certified driving rehabilitation specialist. Results. The ability to predict driving safety was highly variable regardless of the driving safety measure, ranging from 31% to 63% (kappa levels ranged from −0.29 to 0.04. The level of agreement between the neuroophthalmologists was only fair (kappa = 0.28. Conclusions. Clinical evaluation of summary reports of currently available neuroimages by neuroophthalmologists is not predictive of driving safety. Future research should be directed at identifying and/or developing alternative tests or strategies to better enable clinicians to make these predictions.

  8. Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

    Science.gov (United States)

    Huang, Lei; Jin, Yan; Gao, Yaozong; Thung, Kim-Han; Shen, Dinggang

    2016-10-01

    Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.

  9. Association between Bone Mineral Density and Clinical Parameters in Traumatic Brain Injury Patients

    Directory of Open Access Journals (Sweden)

    Murat Ersöz,

    2016-04-01

    Full Text Available Objective: Determine the association between the bone mineral density and traumatic brain injury (TBI. Materials and Methods: Twenty-two patients with TBI included to the study. Dual energy X-ray absorptiometry measurements which determines the femur neck and L1-4 vertebrate T scores in patients was performed via Lunar Prodigy DPX system. Clinical parameters such as types of involvements (plegia, upper-lower extremity spasticity values, presence of heterotypic ossification, ambulation levels were determined and their relations with femur neck and L1-4 vertebrate T scores were examined with Mann-Whitney U Test. Results: In the comparison of sub groups of type of plegia (tetraplegic/hemi-paraplegic, lower extremity spasticity values [Ascworth score 0/1-2-3-4, presence of heterotopic ossification no statistically significant (p>0.05 difference was found in the femur neck and L1-4 vertebrate T scores. On the other hand, in the subgroups determined according to ambulatory levels of the patients (confined to bed-wheelchair/ ambulated (orthesis-hand support-independent] significant difference was observed in the femur neck T scores (p=0.044. Femur neck T scores were significantly high in ambulated patients (p=0.044. Conclusion: In TBI cases ambulation level is a factor which significantly affect bone mineral density. It is necessary to ambulate patients with potential as soon as possible and to plan alternative approaches in patient could not be ambulated.

  10. Variations of clinical biochemical parameters of laying hens and broiler chickens fed aflatoxin-containing feed.

    Science.gov (United States)

    Fernandez, A; Verde, M T; Gascon, M; Ramos, J; Gomez, J; Luco, D F; Chavez, G

    1994-03-01

    Two groups of 32 laying hens (Hyssex Brown) and two groups of 32 23-day-old (Hybro) broiler chickens were fed 2.5 and 5 parts/10(6) of aflatoxin in their diet for 4, 8, 16 and 32 days; 16 hens and 32 chicks were maintained as control groups (0 parts/10(6)). After the intoxication period, a clearance period was established of 1, 2, 4 and 8 days. Relative weights of liver and kidneys significantly increased in intoxicated hens, but not in broiler chickens. Histological lesions in both types of bird consisted of hepatic cell vacuolation with fatty infiltration. There was a significant decrease (Phens, cholesterol levels were not significantly (P> 0.05) different from control values, but triglyceride levels decreased (PAST) serum levels remained normal, whereas alanino aminotransferase (ALT) activity decreased in both intoxicated groups. The activity of serum lactic dehydrogenase (LDH) and gammaglutamil transferase (GGT) increased significantly. In intoxicated broiler chickens, aflatoxins did not alter (P> 0.05) the biochemical parameters studied, except that the serum calcium concentration was lower in the 5 parts/10(6) group. These data indicated that in intoxicated laying hens, a severe clinical biochemical alteration was produced, and that this together with the hepatic lesions observed in hens and broilers may aid disease diagnosis.

  11. Physiological, morphological, and immunochemical parameters used for the characterization of clinical and environmental isolates of Acanthamoeba.

    Science.gov (United States)

    Becker-Finco, A; Costa, A O; Silva, S K; Ramada, J S; Furst, C; Stinghen, A E; De Figueiredo, B C; De Moura, J; Alvarenga, L M

    2013-03-01

    The factors that characterize Acanthamoeba strains as harmless or potentially pathogenic have not been elucidated. Analysing the in vitro and in vivo parameters of Acanthamoeba samples, including heat tolerance at temperatures close to that of the human body, cytopathic effects, and their ability to cause infections in animals, has been proposed to identify their pathogenic potential. Another promising criterion for differentiating strains is the analysis of their biochemical and immunochemical properties. In this study, a comparative evaluation between clinical and environmental Acanthamoeba isolates was performed on the basis of physiological, morphological, and immunochemical criteria. Crude antigens were used to characterize the protein profiles by electrophoresis and immunize mice to produce polyclonal and monoclonal antibodies. The antibodies were characterized using ELISA, Western blotting, and immunofluorescence techniques. The results obtained with polyclonal antibodies suggest the presence of specific proteins for each studied isolate and co-reactive immunochemical profiles among conserved components. Ten monoclonal antibody clones were obtained; mAb3 recognized 3 out of 4 samples studied. The results of this study may help standardize criteria for identifying and characterizing Acanthamoeba strains. Taken together, our results support the view that a set of features may help differentiate Acanthamoeba species and isolates.

  12. Clinical evaluation of an ionic tooth brush on oral hygiene status, gingival status, and microbial parameter

    Directory of Open Access Journals (Sweden)

    Deshmukh J

    2006-01-01

    Full Text Available It has long been recognised that the presence of dental plaque leads to gingivitis and periodontal disease, as well as dental caries. Today tooth brushing is the most widely accepted method of removing plaque. Hence this present clinical study was undertaken to evaluate the effectiveness of an ionic toothbrush on oral hygiene status. For this study, 20 dental students in the age group of 18-20 years were included. All the subjects after undergoing dental prophylaxis were then provided with ionic toothbrushes, either active (equipped with lithium battery or inactive (without lithium battery. Plaque index and gingival bleeding index were examined at 7th, 14th, and 21st day. Microbial assessment was done for detection of colony forming units (CFU from the plaque samples which were collected on 0 day and 21st day, both before brushing and after brushing. Results shown a significant reduction in all the parameters and the reduction was more significant in active and inactive ionic toothbrush users. It was concluded that both active and inactive ionic toothbrushes reduced the plaque index and gingival bleeding index scores significantly and active ionic tooth brushes were more effective as compared to inactive ionic toothbrushes. There was no soft tissue trauma following the use of both type of toothbrushes, which showed that ionic toothbrushes were equally safe for regular long-term use.

  13. A study of practical parameters and their relative importance as perceived by various stakeholders in clinical trials.

    Science.gov (United States)

    Pant, R; Joshi, Y

    2011-01-01

    A contract research organization (CRO) is a company which conducts a Good Clinical Practice (GCP) in clinical trial. There are literally hundreds of CROs worldwide employing a workforce of nearly 100,000 professionals. The project proposes the study of practical parameters and their relative importance as perceived by the various stakeholders in clinical trials. The survey was conducted in Bangalore and New Delhi. Primary market data was obtained by primary market research which included 80 clinical trial stakeholders by having a preliminary communication with them, followed by administering a questionnaire along with prior permission. There were 15 Sponsors/ CROs, 27 Investigators /Monitors and 38 Ethics committee members involved in the study. It was shown from the study that a clinical investigator involved in a clinical trial is responsible for ensuring that an investigation is conducted according to the signed investigator statement, the investigational plan, and applicable regulations; for protecting the rights, safety, and welfare of the subjects under the investigator's care; and for the control of drugs under investigation. It was also shown from the study that the sponsors of a clinical trial carry the ultimate responsibility for the initiation, management and financing of the clinical trial. The study has identified a specific training need at the level of the individual stakeholder to perform a particular job function and to identify the actual practical parameters in the Indian context important for the conduction of clinical trials (GCP) with respect to the different stakeholders, to determine the relative importance of these parameters as perceived by various stakeholders involved in clinical trials, and to identify the relative contributions of different stakeholders to the success/ satisfactory conduct of a clinical trial.

  14. Different diagnostic values of imaging parameters to predict pseudoprogression in glioblastoma subgroups stratified by MGMT promoter methylation

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Ra Gyoung [Catholic Kwandong University International St. Mary' s Hospital, Department of Radiology, Catholic Kwandong University College of Medicine, Incheon (Korea, Republic of); Kim, Ho Sung; Shim, Woo Hyun; Kim, Sang Joon [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Paik, Wooyul [Dankook Unversity Hospital, Department of Radiology, Cheonan-si, Chungcheongnam-do (Korea, Republic of); Kim, Jeong Hoon [University of Ulsan College of Medicine, Asan Medical Center, Department of Neurosurgery, Seoul (Korea, Republic of)

    2017-01-15

    The aim of this study was to determine whether diffusion and perfusion imaging parameters demonstrate different diagnostic values for predicting pseudoprogression between glioblastoma subgroups stratified by O{sup 6}-mythylguanine-DNA methyltransferase (MGMT) promoter methylation status. We enrolled seventy-five glioblastoma patients that had presented with enlarged contrast-enhanced lesions on magnetic resonance imaging (MRI) one month after completing concurrent chemoradiotherapy and undergoing MGMT promoter methylation testing. The imaging parameters included 10 or 90 % histogram cutoffs of apparent diffusion coefficient (ADC10), normalized cerebral blood volume (nCBV90), and initial area under the time signal-intensity curve (IAUC90). The results of the areas under the receiver operating characteristic curve (AUCs) with cross-validation were compared between MGMT methylation and unmethylation groups. MR imaging parameters demonstrated a trend toward higher accuracy in the MGMT promoter methylation group than in the unmethylation group (cross-validated AUCs = 0.70-0.95 and 0.56-0.87, respectively). The combination of MGMT methylation status with imaging parameters improved the AUCs from 0.70 to 0.75-0.90 for both readers in comparison with MGMT methylation status alone. The probability of pseudoprogression was highest (95.7 %) when nCBV90 was below 4.02 in the MGMT promoter methylation group. MR imaging parameters could be stronger predictors of pseudoprogression in glioblastoma patients with the methylated MGMT promoter than in patients with the unmethylated MGMT promoter. (orig.)

  15. The effect of differential growth rates across plants on spectral predictions of physiological parameters.

    Directory of Open Access Journals (Sweden)

    Tal Rapaport

    Full Text Available Leaves of various ages and positions in a plant's canopy can present distinct physiological, morphological and anatomical characteristics, leading to complexities in selecting a single leaf for spectral representation of an entire plant. A fortiori, as growth rates between canopies differ, spectral-based comparisons across multiple plants--often based on leaves' position but not age--becomes an even more challenging mission. This study explores the effect of differential growth rates on the reflectance variability between leaves of different canopies, and its implication on physiological predictions made by widely-used spectral indices. Two distinct irrigation treatments were applied for one month, in order to trigger the formation of different growth rates between two groups of grapevines. Throughout the experiment, the plants were physiologically and morphologically monitored, while leaves from every part of their canopies were spectrally and histologically sampled. As the control vines were constantly developing new leaves, the water deficit plants were experiencing growth inhibition, resulting in leaves of different age at similar nodal position across the treatments. This modification of the age-position correlation was characterized by a near infrared reflectance difference between younger and older leaves, which was found to be exponentially correlated (R(2 = 0.98 to the age-dependent area of intercellular air spaces within the spongy parenchyma. Overall, the foliage of the control plant became more spectrally variable, creating complications for intra- and inter-treatment leaf-based comparisons. Of the derived indices, the Structure-Insensitive Pigment Index (SIPI was found indifferent to the age-position effect, allowing the treatments to be compared at any nodal position, while a Normalized Difference Vegetation Index (NDVI-based stomatal conductance prediction was substantially affected by differential growth rates. As various

  16. Future methane, hydroxyl, and their uncertainties: key climate and emission parameters for future predictions

    Directory of Open Access Journals (Sweden)

    C. D. Holmes

    2013-01-01

    Full Text Available Accurate prediction of future methane abundances following a climate scenario requires understanding the lifetime changes driven by anthropogenic emissions, meteorological factors, and chemistry-climate feedbacks. Uncertainty in any of these influences or the underlying processes implies uncertainty in future abundance and radiative forcing. We simulate methane lifetime in three chemical transport models (CTMs – UCI CTM, GEOS-Chem, and Oslo CTM3 – over the period 1997–2009 and compare the models' year-to-year variability against constraints from global methyl chloroform observations. Using sensitivity tests, we find that temperature, water vapor, stratospheric ozone column, biomass burning and lightning NOx are the dominant sources of interannual changes in methane lifetime in all three models. We also evaluate each model's response to forcings that have impacts on decadal time scales, such as methane feedback, and anthropogenic emissions. In general, these different CTMs show similar sensitivities to the driving variables. We construct a parametric model that reproduces most of the interannual variability of each CTM and use it to predict methane lifetime from 1980 through 2100 following a specified emissions and climate scenario (RCP 8.5. The parametric model propagates uncertainties through all steps and provides a foundation for predicting methane abundances in any climate scenario. Our sensitivity tests also enable a new estimate of the methane global warming potential (GWP, accounting for stratospheric ozone effects, including those mediated by water vapor. We estimate the 100-yr GWP to be 32, which is 25% larger than past assessments.

  17. Application of the L-M optimized algorithm to predicting blast vibration parameters

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yi-feng; YAO Dao-ping; XIE Zhai-zhao; YANG Jiang-feng; YE You-quan

    2008-01-01

    @@ Currently, the regression empirical formula with vibration peak as the safety index of single blasting vibration is widely applied in blasting engineering circles throughout the world. Due to complex blasting mechanism and blasting medium environment as well as plural influencing factors, it is difficult that all these factors can be taken into consideration by using one regression empirical formula. In addition, due to the inherent limitations of regression analysis (e.g., requiring good data distribution and large amount of samples), the vibration prediction by empirical formula is not ideal (LI, 1997; CHEN, 2001; ZHANG, 2001).

  18. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection.

    Science.gov (United States)

    Haas, Y de; Windig, J J; Calus, M P L; Dijkstra, J; Haan, M de; Bannink, A; Veerkamp, R F

    2011-12-01

    Mitigation of enteric methane (CH₄) emission in ruminants has become an important area of research because accumulation of CH₄ is linked to global warming. Nutritional and microbial opportunities to reduce CH₄ emissions have been extensively researched, but little is known about using natural variation to breed animals with lower CH₄ yield. Measuring CH₄ emission rates directly from animals is difficult and hinders direct selection on reduced CH₄ emission. However, improvements can be made through selection on associated traits (e.g., residual feed intake, RFI) or through selection on CH₄ predicted from feed intake and diet composition. The objective was to establish phenotypic and genetic variation in predicted CH₄ output, and to determine the potential of genetics to reduce methane emissions in dairy cattle. Experimental data were used and records on daily feed intake, weekly body weights, and weekly milk production were available from 548 heifers. Residual feed intake (MJ/d) is the difference between net energy intake and calculated net energy requirements for maintenance as a function of body weight and for fat- and protein-corrected milk production. Predicted methane emission (PME; g/d) is 6% of gross energy intake (Intergovernmental Panel on Climate Change methodology) corrected for energy content of methane (55.65 kJ/g). The estimated heritabilities for PME and RFI were 0.35 and 0.40, respectively. The positive genetic correlation between RFI and PME indicated that cows with lower RFI have lower PME (estimates ranging from 0.18 to 0.84). Hence, it is possible to decrease the methane production of a cow by selecting more-efficient cows, and the genetic variation suggests that reductions in the order of 11 to 26% in 10 yr are theoretically possible, and could be even higher in a genomic selection program. However, several uncertainties are discussed; for example, the lack of true methane measurements (and the key assumption that methane

  19. Practical Guidance for Implementing Predictive Biomarkers into Early Phase Clinical Studies

    Directory of Open Access Journals (Sweden)

    Matthew J. Marton

    2013-01-01

    Full Text Available The recent U.S. Food and Drug Administration (FDA coapprovals of several therapeutic compounds and their companion diagnostic devices (FDA News Release, 2011, 2013 to identify patients who would benefit from treatment have led to considerable interest in incorporating predictive biomarkers in clinical studies. Yet, the translation of predictive biomarkers poses unique technical, logistic, and regulatory challenges that need to be addressed by a multidisciplinary team including discovery scientists, clinicians, biomarker experts, regulatory personnel, and assay developers. These issues can be placed into four broad categories: sample collection, assay validation, sample analysis, and regulatory requirements. In this paper, we provide a primer for drug development teams who are eager to implement a predictive patient segmentation marker into an early clinical trial in a way that facilitates subsequent development of a companion diagnostic. Using examples of nucleic acid-based assays, we briefly review common issues encountered when translating a biomarker to the clinic but focus primarily on key practical issues that should be considered by clinical teams when planning to use a biomarker to balance arms of a study or to determine eligibility for a clinical study.

  20. Role of Transition Zone Index in the Prediction of Clinical Benign Prostatic Hyperplasia

    Directory of Open Access Journals (Sweden)

    Muhammet Güzelsoy

    2016-12-01

    Full Text Available Objective The objective of this study was to determine the role of the transition zone (TZ index (TZI in the prediction of clinical benign prostatic hyperplasia (BPH in patients who underwent transurethral prostatectomy (TUR-P and to analyze the correlation between the amount of resected tissue and TZ volume (TZV. Materials and Methods Twenty-six male clinical BPH patients with obstructive complaints and 17 male benign prostate enlargement (BPE patients without any complaints were included in the study. Both the groups were over the age of 50. Clinical BPH patients underwent complete TUR-P. Statistical analysis was done with SPSS. Sensitivity, specificity, positive and negative predictive values of TZI-as a method of assessing clinical BPH-were measured. Results There was a statistically significant difference in prostate volume, uroflowmetry patterns, prostate-specific antigen (PSA, International prostate symptom score (IPSS, TZV and TZI between the two groups. There was a correlation between TZV and the amount of resected tissue (r=0.97; p0.40 has a high level of sensitivity and specificity in the prediction of clinical BPH among patients who undergo TUR-P due to obstructive symptoms and reported as BPH. There is a strong correlation between the amount of resected tissue and TZV. TZI is a valuable tool in diagnosis, and TZV gives valuable information about the patient to the surgeon.

  1. Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis.

    Science.gov (United States)

    Hunt, Michael A; Bennell, Kim L

    2011-08-01

    Knee joint loading, as measured by the knee adduction moment (KAM), has been implicated in the pathogenesis of knee osteoarthritis (OA). Given that the KAM can only currently be accurately measured in the laboratory setting with sophisticated and expensive equipment, its utility in the clinical setting is limited. This study aimed to determine the ability of a combination of four clinical measures to predict KAM values. Three-dimensional motion analysis was used to calculate the peak KAM at a self-selected walking speed in 47 consecutive individuals with medial compartment knee OA and varus malalignment. Clinical predictors included: body mass; tibial angle measured using an inclinometer; walking speed; and visually observed trunk lean toward the affected limb during the stance phase of walking. Multiple linear regression was performed to predict KAM magnitudes using the four clinical measures. A regression model including body mass (41% explained variance), tibial angle (17% explained variance), and walking speed (9% explained variance) explained a total of 67% of variance in the peak KAM. Our study demonstrates that a set of measures easily obtained in the clinical setting (body mass, tibial alignment, and walking speed) can help predict the KAM in people with medial knee OA. Identifying those patients who are more likely to experience high medial knee loads could assist clinicians in deciding whether load-modifying interventions may be appropriate for patients, whilst repeated assessment of joint load could provide a mechanism to monitor disease progression or success of treatment.

  2. Analyzing the effects of geological and parameter uncertainty on prediction of groundwater head and travel time

    DEFF Research Database (Denmark)

    He, X.; Sonneborg, T.O.; Jørgensen, F.

    2013-01-01

    in three scenarios involving simulation of groundwater head distribution and travel time. The first scenario implied 100 stochastic geological models all assigning the same hydraulic parameters for the same geological units. In the second scenario the same 100 geological models were subjected to model...... have made it possible to consider this factor in groundwater modeling. In this study we have applied the multiple-point geostatistical method (MPS) integrated in the Stanford Geostatistical Modeling Software (SGeMS) for exploring the impact of geological uncertainty on groundwater flow patterns...

  3. Strange quark polarization of the nucleon: a parameter-independent prediction of the chiral potential model.

    Science.gov (United States)

    Chen, X B; Chen, X S; Wang, F

    2001-07-02

    We perform a one-loop calculation of the strange quark polarization (Deltas) of the nucleon in an SU(3) chiral potential model. We find that if the intermediate quark excited states are summed over in a proper way, i.e., summed up to a given energy instead of given radial and orbital quantum numbers, Deltas turns out to be almost independent of all the model parameters: quark masses and potential strengths. The contribution from the quark-antiquark pair creation and annihilation " Z" diagrams is found to be significant. Our numerical results agree quite reasonably with experiments and lattice QCD calculations.

  4. A practical approach to parameter estimation applied to model predicting heart rate regulation

    DEFF Research Database (Denmark)

    Olufsen, Mette; Ottesen, Johnny T.

    2013-01-01

    baroreceptor feedback regulation of heart rate during head-up tilt. The three methods include: structured analysis of the correlation matrix, analysis via singular value decomposition followed by QR factorization, and identification of the subspace closest to the one spanned by eigenvectors of the model...... Hessian. Results showed that all three methods facilitate identification of a parameter subset. The “best” subset was obtained using the structured correlation method, though this method was also the most computationally intensive. Subsets obtained using the other two methods were easier to compute...

  5. Application of Hansen solubility parameters for understanding and prediction of drug distribution in microspheres.

    Science.gov (United States)

    Vay, Kerstin; Scheler, Stefan; Friess, Wolfgang

    2011-09-15

    In an emulsion solvent extraction/evaporation process for the preparation of microspheres the employed solvents have a tremendous influence on the characteristics of the resulting particles. Nevertheless the solvent selection is often based on empirical data rather than on calculated values. The purpose of this investigation was to use the concept of solubility parameters for interpretation and improved understanding of solvent effects in the process of microparticle preparation. Partial solubility parameters of 3-{2-[4-(6-Fluor-1,2-benzisoxazol-3-yl)piperidino]ethyl}-2-methyl-6,7,8,9-tetrahydro-4H-pyrido[1,2-a]pyrimidin-4-on, which was used as a model drug, were determined experimentally using an extended Hansen regression model. Poly(lactide-co-glycolide) microparticles were prepared with an emulsion solvent removal process employing methylene chloride and its mixtures with benzyl alcohol and n-butanol. It could be shown, that the encapsulation efficiency was influenced by the change of the solvent composition during the extraction process. Furthermore the solvent selection had an essential influence on the morphological state of the drug and it could be shown and explained, that by a decrease of the dissolving power a completely amorphous product was obtained.

  6. Tsunami hazard warning and risk prediction based on inaccurate earthquake source parameters

    Directory of Open Access Journals (Sweden)

    K. Goda

    2015-12-01

    Full Text Available This study investigates the issues related to underestimation of the earthquake source parameters in the context of tsunami early warning and tsunami risk assessment. The magnitude of a very large event may be underestimated significantly during the early stage of the disaster, resulting in the issuance of incorrect tsunami warnings. Tsunamigenic events in the Tohoku region of Japan, where the 2011 tsunami occurred, are focused on as a case study to illustrate the significance of the problems. The effects of biases in the estimated earthquake magnitude on tsunami loss are investigated using a rigorous probabilistic tsunami loss calculation tool that can be applied to a range of earthquake magnitudes by accounting for uncertainties of earthquake source parameters (e.g. geometry, mean slip, and spatial slip distribution. The quantitative tsunami loss results provide with valuable insights regarding the importance of deriving accurate seismic information as well as the potential biases of the anticipated tsunami consequences. Finally, usefulness of rigorous tsunami risk assessment is discussed in defining critical hazard scenarios based on the potential consequences due to tsunami disasters.

  7. Novel Parameter Predicting Grade 2 Rectal Bleeding After Iodine-125 Prostate Brachytherapy Combined With External Beam Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Shiraishi, Yutaka, E-mail: shiraishi@rad.med.keio.ac.jp [Department of Radiology, Keio University School of Medicine, Tokyo (Japan); Hanada, Takashi; Ohashi, Toshio [Department of Radiology, Keio University School of Medicine, Tokyo (Japan); Yorozu, Atsunori; Toya, Kazuhito [Department of Radiology, National Hospital Organization Tokyo Medical Center, Tokyo (Japan); Saito, Shiro [Department of Urology, National Hospital Organization Tokyo Medical Center, Tokyo (Japan); Shigematsu, Naoyuki [Department of Radiology, Keio University School of Medicine, Tokyo (Japan)

    2013-09-01

    Purpose: To propose a novel parameter predicting rectal bleeding on the basis of generalized equivalent uniform doses (gEUD) after {sup 125}I prostate brachytherapy combined with external beam radiation therapy and to assess the predictive value of this parameter. Methods and Materials: To account for differences among radiation treatment modalities and fractionation schedules, rectal dose–volume histograms (DVHs) of 369 patients with localized prostate cancer undergoing combined therapy retrieved from corresponding treatment planning systems were converted to equivalent dose-based DVHs. The gEUDs for the rectum were calculated from these converted DVHs. The total gEUD (gEUD{sub sum}) was determined by a summation of the brachytherapy and external-beam radiation therapy components. Results: Thirty-eight patients (10.3%) developed grade 2+ rectal bleeding. The grade 2+ rectal bleeding rate increased as the gEUD{sub sum} increased: 2.0% (2 of 102 patients) for <70 Gy, 10.3% (15 of 145 patients) for 70-80 Gy, 15.8% (12 of 76 patients) for 80-90 Gy, and 19.6% (9 of 46 patients) for >90 Gy (P=.002). Multivariate analysis identified age (P=.024) and gEUD{sub sum} (P=.000) as risk factors for grade 2+ rectal bleeding. Conclusions: Our results demonstrate gEUD to be a potential predictive factor for grade 2+ late rectal bleeding after combined therapy for prostate cancer.

  8. Prediction and quantifying parameter importance in simultaneous anaerobic sulfide and nitrate removal process using artificial neural network.

    Science.gov (United States)

    Cai, Jing; Zheng, Ping; Qaisar, Mahmood; Luo, Tao

    2015-06-01

    The present investigation deals with the prediction of the performance of simultaneous anaerobic sulfide and nitrate removal in an upflow anaerobic sludge bed (UASB) reactor through an artificial neural network (ANN). Influent sulfide concentration, influent nitrate concentration, S/N mole ratio, pH, and hydraulic retention time (HRT) for 144 days' steady-state condition were the inputs of the model; whereas output parameters were sulfide removal percentage, nitrate removal percentage, sulfate production percentage, and nitrogen production percentage. The prediction performance was evaluated by calculating root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R (2)) values. Generally, the ANN model exhibited good prediction of the simultaneous sulfide and nitrate removal process. The effect of five input parameters to the performance of the reactor was quantified and compared using the connection weights method, Garson's algorithm method, and partial derivatives (PaD) method. The results showed that HRT markedly affects the performance of the reactor.

  9. Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

    Directory of Open Access Journals (Sweden)

    Johannes Rahlf

    2015-11-01

    Full Text Available Image-based point clouds obtained using aerial photogrammetry share many characteristics with point clouds obtained by airborne laser scanning (ALS. Two approaches have been used to predict forest parameters from ALS: the area-based approach (ABA and the individual tree crown (ITC approach. In this article, we apply the semi-ITC approach, a variety of the ITC approach, on an image-based point cloud to predict forest parameters and compare the performance to the ABA. Norwegian National Forest Inventory sample plots on a site in southeastern Norway were used as the reference data. Tree crown objects were delineated using a watershed segmentation algorithm, and explanatory variables were calculated for each tree crown segment. A multivariate kNN model for timber volume, stem density, basal area and quadratic mean diameter with the semi-ITC approach produced RMSEs of 30%, 46%, 25%, 26%, respectively. The corresponding measures for the ABA were 30%, 51%, 26%, 35%, respectively. Univariate kNN models resulted in timber volume RMSEs of 25% for the semi-ITC approach and 22% for the ABA. A non-linear logistic regression model with the ABA produced an RMSE of 23%. Both approaches predicted timber volume with comparable precision and accuracy at the plot level. The multivariate kNN model was slightly more precise with the semi-ITC approach, while biases were larger

  10. Interleukin-1β level in peri-implant crevicular fluid and its correlation with the clinical and radiographic parameters

    Directory of Open Access Journals (Sweden)

    Aniruddha M Kajale

    2014-01-01

    Full Text Available Background and Objectives : Assessing only the clinical and radiographic parameters for evaluation of dental implants may not be enough as they often reflect extensive inflammatory changes in the periodontal tissues. As peri-implant crevicular fluid (PICF can give us a more prompt and objective measure of the disease activity, the purpose of this case series is to assess the peri-implant health status of single tooth dental implants not only clinically and radiographically but also biochemically. Materials and Methods: Thirteen patients were subjected to dental implants at single edentulous sites using a conventional surgical approach. At baseline, 6 months, and 12 months after implant placement, the clinical and radiographic parameters were recorded. Additionally, IL-1β in PICF was estimated using the ELISA kit at 6 th and 12 th month. Results: The clinical and radiographic parameters differed significantly around the implants at different time intervals with IL-1β levels showing highly significant differences between 6 months (31.79 ± 12.26 pg/μl and 12 months (113.09 ± 51.11 pg/μl. However, Spearman′s correlation coefficient showed no correlation with the clinical and radiographic parameters. Interpretation and Conclusion: Assessment of the various parameters confirmed that all the implants had a healthy peri-implant status. Although the levels of IL-1β in PICF were elevated at the 12 th month, they were well within the healthy range as observed by previous studies. This indicates that IL-1β, a biochemical marker, can be used as an adjunct to clinical and radiographic parameters in the assessment of EARLY inflammatory changes around implants.

  11. PERSPECTIVES OF FACTORIAL ANALYSIS IN STUDYING ASSOCIATIONS BETWEEN IMMUNE SYSTEM PARAMETERS AND CLINICAL CHARACTERISTICS IN GASTRIC CANCER

    Directory of Open Access Journals (Sweden)

    I. G. Solovyeva

    2010-01-01

    Full Text Available  When studying functional features of immune system, a lot of quantitative and functional parameters are determined. A multifactorial analysis allows of detecting interdependent immunological parameters and defining them as significant factors. In present study, four factors are revealed, which are associated with certain clinical characteristics of gastric cancer (tumor invasion depth, lymph node status and distant metastases, tumor stage, histological type. The data obtained are of interest, with regard of systemic approach to functional studies of immune functions.

  12. Prediction of qualitative parameters of slab steel ingot using numerical modelling

    Directory of Open Access Journals (Sweden)

    M. Tkadlečková

    2016-07-01

    Full Text Available The paper describes the verification of casting and solidification of heavy slab ingot weighing 40 t from tool steel by means of numerical modelling with use of a finite element method. The pre-processing, processing and post-processing phases of numerical modelling are outlined. Also, the problems with determination of the thermodynamic properties of materials and with determination of the heat transfer between the individual parts of the casting system are discussed. The final porosity, macrosegregation and the risk of cracks were predicted. The results allowed us to use the slab ingot instead of the conventional heavy steel ingot and to improve the ratio, the chamfer and the external shape of the wall of the new design of the slab ingot.

  13. How far are rheological parameters from amplitude sweep tests predictable using common physicochemical soil properties?

    Science.gov (United States)

    Stoppe, N.; Horn, R.

    2017-01-01

    A basic understanding of soil behavior on the mesoscale resp. macroscale (i.e. soil aggregates resp. bulk soil) requires knowledge of the processes at the microscale (i.e. particle scale), therefore rheological investigations of natural soils receive growing attention. In the present research homogenized and sieved (account. Although the influence of the individual factors varies depending on soil texture, the physicochemical features significantly affecting soil micro structure were identified. Based on the determined statistical relationships between rheological and physicochemical parameters, pedotransfer functions (PTF) have been developed, which allow a mathematical estimation of the rheological target value integral z. Thus, stabilizing factors are: soil organic matter, concentration of Ca2+, content of CaCO3 and pedogenic iron oxides; whereas the concentration of Na+ and water content represent structurally unfavorable factors.

  14. Parameter Identification for a New Circuit Model Aimed to Predict Body Water Volume

    Directory of Open Access Journals (Sweden)

    GHEORGHE, A.-G.

    2012-11-01

    Full Text Available Intracellular and extracellular water volumes in the human body have been computed using a sequence of models starting with a linear first order RC circuit (Cole model and finishing with the De Lorenzo model. This last model employs a fractional order impedance whose parameters are identified using the frequency characteristics of the impedance module and phase, the latter being not unique. While the Cole model has a two octaves frequency validity range, the De Lorenzo model can be used for three decades. A new linear RC model, valid for a three decades frequency range, is proposed. This circuit can be viewed as an extension of the Cole model for a larger frequency interval, unlike similar models proposed by the same authors.

  15. Body mass index and other anthropometric parameters in patients with diffuse large B-cell lymphoma: physiopathological significance and predictive value in the immunochemotherapy era.

    Science.gov (United States)

    Sarkozy, Clémentine; Camus, Vincent; Tilly, Hervé; Salles, Gilles; Jardin, Fabrice

    2015-07-01

    Diffuse large B-cell lymphoma (DLBCL) is the most common form of aggressive non-Hodgkin lymphoma, accounting for 30-40% of newly diagnosed cases. Obesity is a well-defined risk factor for DLBCL. However, the impact of body mass index (BMI) on DLBCL prognosis is controversial. Recent studies suggest that skeletal muscle wasting (sarcopenia) or loss of fat mass can be detected by computed tomography (CT) images and is useful for predicting the clinical outcome in several types of cancer including DLBCL. Several hypotheses have been proposed to explain the differences in DLBCL outcome according to BMI or weight that include tolerance to treatment, inflammatory background and chemotherapy or rituximab metabolism. In this review, we summarize the available literature, addressing the impact and physiopathological relevance of simple anthropometric tools including BMI and tissue distribution measurements. We also discuss their relationship with other nutritional parameters and their potential role in the management of patients with DLBCL.

  16. Blood coagulation parameters and platelet indices: changes in normal and preeclamptic pregnancies and predictive values for preeclampsia.

    Directory of Open Access Journals (Sweden)

    Lei Han

    Full Text Available Preeclampsia (PE is an obstetric disorder with high morbidity and mortality rates but without clear pathogeny. The dysfunction of the blood coagulation-fibrinolysis system is a salient characteristic of PE that varies in severity, and necessitates different treatments. Therefore, it is necessary to find suitable predictors for the onset and severity of PE.We aimed to evaluate blood coagulation parameters and platelet indices as potential predictors for the onset and severity of PE.Blood samples from 3 groups of subjects, normal pregnant women (n = 79, mild preeclampsia (mPE (n = 53 and severe preeclampsia (sPE (n = 42, were collected during early and late pregnancy. The levels of coagulative parameters and platelet indices were measured and compared among the groups. The receiver-operating characteristic (ROC curves of these indices were generated, and the area under the curve (AUC was calculated. The predictive values of the selected potential parameters were examined in binary regression analysis.During late pregnancy in the normal pregnancy group, the activated partial thromboplastin time (APTT, prothrombin time (PT, thrombin time (TT and platelet count decreased, while the fibrinogen level and mean platelet volume (MPV increased compared to early pregnancy (p<0.05. However, the PE patients presented with increased APTT, TT, MPV and D-dimer (DD during the third trimester. In the analysis of subjects with and without PE, TT showed the largest AUC (0.743 and high predictive value. In PE patients with different severities, MPV showed the largest AUC (0.671 and ideal predictive efficiency.Normal pregnancy causes a maternal physiological hypercoagulable state in late pregnancy. PE may trigger complex disorders in the endogenous coagulative pathways and consume platelets and FIB, subsequently activating thrombopoiesis and fibrinolysis. Thrombin time and MPV may serve as early monitoring markers for the onset and severity of PE

  17. Support vector machine to predict diesel engine performance and emission parameters fueled with nano-particles additive to diesel fuel

    Science.gov (United States)

    Ghanbari, M.; Najafi, G.; Ghobadian, B.; Mamat, R.; Noor, M. M.; Moosavian, A.

    2015-12-01

    This paper studies the use of adaptive Support Vector Machine (SVM) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For SVM modelling, different values for radial basis function (RBF) kernel width and penalty parameters (C) were considered and the optimum values were then found. The results demonstrate that SVM is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nanostructure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve complete combustion of the fuel and reduce the exhaust emissions significantly.

  18. Finite element lumbar spine facet contact parameter predictions are affected by the cartilage thickness distribution and initial joint gap size.

    Science.gov (United States)

    Woldtvedt, Daniel J; Womack, Wesley; Gadomski, Benjamin C; Schuldt, Dieter; Puttlitz, Christian M

    2011-06-01

    Current finite element modeling techniques utilize geometrically inaccurate cartilage distribution representations in the lumbar spine. We hypothesize that this shortcoming severely limits the predictive fidelity of these simulations. Specifically, it is unclear how these anatomically inaccurate cartilage representations alter range of motion and facet contact predictions. In the current study, cadaveric vertebrae were serially sectioned, and images were taken of each slice in order to identify the osteochondral interface and the articulating surface. A series of custom-written algorithms were utilized in order to quantify each facet joint's three-dimensional cartilage distribution using a previously developed methodology. These vertebrae-dependent thickness cartilage distributions were implemented on an L1 through L5 lumbar spine finite element model. Moments were applied in three principal planes of motion, and range of motion and facet contact predictions from the variable thickness and constant thickness distribution models were determined. Initial facet gap thickness dimensions were also parameterized. The data indicate that the mean and maximum cartilage thickness increased inferiorly from L1 to L5, with an overall mean thickness value of 0.57 mm. Cartilage distribution and initial facet joint gap thickness had little influence on the lumbar range of motion in any direction, whereas the mean contact pressure, total contact force, and total contact area predictions were altered considerably. The data indicate that range of motion predictions alone are insufficient to establish model validation intended to predict mechanical contact parameters. These data also emphasize the need for the careful consideration of the initial facet joint gap thickness with respect to the spinal condition being studied.

  19. Review of clinical studies on dendritic cell-based vaccination of patients with malignant melanoma: assessment of correlation between clinical response and vaccine parameters

    DEFF Research Database (Denmark)

    Engell-Noerregaard, Lotte; Hansen, Troels Holz; Andersen, Mads Hald

    2009-01-01

    During the past years numerous clinical trials have been carried out to assess the ability of dendritic cell (DC) based immunotherapy to induce clinically relevant immune responses in patients with malignant diseases. A broad range of cancer types have been targeted including malignant melanoma...... which in the disseminated stage have a very poor prognosis and only limited treatment options with moderate effectiveness. Herein we describe the results of a focused search of recently published clinical studies on dendritic cell vaccination in melanoma and review different vaccine parameters which...

  20. Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares

    OpenAIRE

    2008-01-01

    The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction andestimation of genetic parameters and breeding values in soybean progenies. F2:3 and F4:5 progenies were evaluated in the2005/06 growing season and the F2:4 and F4:6 generations derived thereof were evaluated in 2006/07. These progenies wereoriginated from two semi-early experimental lines that differ in grain yield. The experiments were conducted in a lattice designand plots consisted of a 2 m row,...

  1. Estimation and prediction of parameters and breeding values in soybean using REML/BLUP and Least Squares

    OpenAIRE

    2008-01-01

    The aim of this study was to compare REML/BLUP and Least Square procedures in the prediction and estimation of genetic parameters and breeding values in soybean progenies. F(2:3) and F(4:5) progenies were evaluated in the 2005/06 growing season and the F(2:4) and F(4:6) generations derived thereof were evaluated in 2006/07. These progenies were originated from two semi-early, experimental lines that differ in grain yield. The experiments were conducted in a lattice design and plots consisted ...

  2. Pre-stimulation parameters predicting live birth after IVF in the long GnRH agonist protocol

    DEFF Research Database (Denmark)

    Pettersson, Göran; Andersen, Anders Nyboe; Broberg, Per;

    2010-01-01

    This retrospective study aimed to identify novel pre-stimulation parameters associated with live birth in IVF and to develop a model for prediction of the chances of live birth at an early phase of the treatment cycle. Data were collected from a randomized trial in couples with unexplained...... infertility, tubal factor, mild male factor or other reason for infertility. All women (n=731) had undergone an IVF cycle (no intracytoplasmic sperm injection) after stimulation with human menopausal gonadotrophin or follicle-stimulating hormone following the long gonadotrophin-releasing hormone agonist...

  3. Departure of some parameter-dependent spectral statistics of irregular quantum graphs from random matrix theory predictions.

    Science.gov (United States)

    Hul, Oleh; Seba, Petr; Sirko, Leszek

    2009-06-01

    Parameter-dependent statistical properties of spectra of totally connected irregular quantum graphs with Neumann boundary conditions are studied. The autocorrelation functions of level velocities c(x) and c[over ](omega,x) as well as the distributions of level curvatures and avoided crossing gaps are calculated. The numerical results are compared with the predictions of random matrix theory for Gaussian orthogonal ensemble (GOE) and for coupled GOE matrices. The application of coupled GOE matrices was justified by studying localization phenomena in graphs' wave functions Psi(x) using the inverse participation ratio and the amplitude distribution P(Psi(x)) .

  4. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    Science.gov (United States)

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-10-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99) and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria.

  5. Predicting counseling psychologists attitudes and clinical judgments with respect to older adults.

    Science.gov (United States)

    Tomko, Jody K; Munley, Patrick H

    2013-01-01

    The purpose of this study was to examine age, gender, training and experience in aging issues, fear of death, and multicultural competence in predicting counseling psychologists' global attitudes toward older adults and specific clinical judgments concerning a case vignette of an older client. A national sample of 364 practicing counseling psychologists participated in the study. Participants completed a demographic measure, Polizzi's refined version of the Aging Semantic Differential (Polizzi, 2003 ), a survey of professional bias based on a clinical vignette of a 70-year-old woman (James & Haley, 1995), the Collett-Lester Fear of Death Scale 3.0 (Lester, & Abdel-Khalek, 2003), the Multicultural Counseling Knowledge and Awareness Scale (MCKAS; Ponterotto, Gretchen, Utsey, Rieger, & Austin, 2002), and a Training and Experience Questionnaire. Hierarchical multiple regression analyses were used to investigate the extent to which the selected variables predicted more favorable attitudes toward older adults and less professional bias toward an older client beyond prediction by age and gender. Results revealed that older age and higher total scores on the MCKAS predicted less professional bias in clinical judgments. Gender was a significant predictor of global attitudes toward older adults. Findings suggest that multicultural knowledge, awareness, and skills are important in working with older adults.

  6. Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model

    Science.gov (United States)

    Yamamoto, Kimiyo N.; Ishii, Masatsugu; Inoue, Yoshihiro; Hirokawa, Fumitoshi; MacArthur, Ben D.; Nakamura, Akira; Haeno, Hiroshi; Uchiyama, Kazuhisa

    2016-01-01

    Although the capacity of the liver to recover its size after resection has enabled extensive liver resection, post-hepatectomy liver failure remains one of the most lethal complications of liver resection. Therefore, it is clinically important to discover reliable predictive factors after resection. In this study, we established a novel mathematical framework which described post-hepatectomy liver regeneration in each patient by incorporating quantitative clinical data. Using the model fitting to the liver volumes in series of computed tomography of 123 patients, we estimated liver regeneration rates. From the estimation, we found patients were divided into two groups: i) patients restored the liver to its original size (Group 1, n = 99); and ii) patients experienced a significant reduction in size (Group 2, n = 24). From discriminant analysis in 103 patients with full clinical variables, the prognosis of patients in terms of liver recovery was successfully predicted in 85–90% of patients. We further validated the accuracy of our model prediction using a validation cohort (prediction = 84–87%, n = 39). Our interdisciplinary approach provides qualitative and quantitative insights into the dynamics of liver regeneration. A key strength is to provide better prediction in patients who had been judged as acceptable for resection by current pragmatic criteria. PMID:27694914

  7. Improvement of a clinical prediction rule for clinical trials on prophylaxis for invasive candidiasis in the intensive care unit.

    Science.gov (United States)

    Ostrosky-Zeichner, Luis; Pappas, Peter G; Shoham, Shmuel; Reboli, Annette; Barron, Michelle A; Sims, Charles; Wood, Craig; Sobel, Jack D

    2011-01-01

    We created a clinical prediction rule to identify patients at risk of invasive candidiasis (IC) in the intensive care unit (ICU) (Eur J Clin Microbiol Infect Dis 2007; 26:271). The rule applies to <10% of patients in ICUs. We sought to create a more inclusive rule for clinical trials. Retrospective review of patients admitted to ICU ≥ 4 days, collecting risk factors and outcomes. Variations of the rule based on introduction of mechanical ventilation and risk factors were assessed. We reviewed 597 patients with a mean APACHE II score of 14.4, mean ICU stay of 12.5 days and mean ventilation time of 10.7 days. A variation of the rule requiring mechanical ventilation AND central venous catheter AND broad spectrum antibiotics on days 1-3 AND an additional risk factor applied to 18% of patients, maintaining the incidence of IC at 10%. Modification of our original rule resulted in a more inclusive rule for studies.

  8. Integration of umbilical venous and arterial Doppler flow parameters for prediction of adverse perinatal outcome

    Directory of Open Access Journals (Sweden)

    Hebbar Shripad

    2015-01-01

    Full Text Available Background: Quantification of umbilical vein (UV blood flow rate and umbilical artery Doppler indices might be valuable in assessing fetuses at increased risk of perinatal complications as they receive their supply of oxygen and nutrients through this vessel. Previous studies have indicated that UV blood volume flow rate to umbilical artery pulsatility index (UAPI ratio (venous arterial index [VAI] evaluates both venous and arterial arm of fetal umbilical circulation and hence, can be adopted as a screening tool in management of high risk pregnancy. Objectives: To compare umbilical VAI with adverse perinatal outcome and also to evaluate its efficacy with other flow indices in determining perinatal outcome. Materials and Methods: Various Doppler indices such as normalized blood flow rate in UV (nUV, ml/kg estimated fetal weight/min, VAI (nUV/UAPI, umbilical artery resistance index (RI, UAPI, and systolic diastolic ratio were determined in 103 pregnant women within 2 weeks of the delivery. A risk score was devised using APGAR at 5 min, birth weight, preterm delivery, fetal distress, Neonatal Intensive Care Unit (NICU care, and perinatal death and this score was correlated with antenatal Doppler findings. Results: Subjects with low VAI were found to have a greater association with intrauterine growth restricted fetuses (28.5% and low liquor (35.7%, preterm deliveries (46.4%, lower mean birth weight (2.25 kg, higher NICU admission rates (32.1%. The unfavorable score was noticed in 25.2% of the neonates. They had lower VAI (156 vs. 241, UV diameter (6.2 mm vs. 7.8 mm, UV velocity (16.2 vs. 17.8, nUV (163.7 vs. 206.4, and higher PI (1.3 vs. 0.9. A cut-off of VAI of 105 ml/kg/min had sensitivity of 86.7% and a specificity of 93.5% for predicting poor perinatal outcome. Conclusion: VAI with a cut-off of 105 ml/kg/min can be used as an additional tool along with the other conventional Doppler indices in order to predict adverse fetal outcome.

  9. Prediction of the date, magnitude and affected area of impending strong earthquakes using integration of multi precursors earthquake parameters

    Directory of Open Access Journals (Sweden)

    M. R. Saradjian

    2011-04-01

    Full Text Available Usually a precursor alone might not be useful as an accurate, precise, and stand-alone criteria for the earthquake parameters prediction. Therefore it is more appropriate to exploit parameters extracted from a variety of individual precursors so that their simultaneous integration would reduce the parameters's uncertainty.

    In our previous studies, five strong earthquakes which happened in the Samoa Islands, Sichuan (China, L'Aquila (Italy, Borujerd (Iran and Zarand (Iran have been analyzed to locate unusual variations in the time series of the different earthquake precursors. In this study, we have attempted to estimate earthquake parameters using the detected anomalies in the mentioned case studies.

    Using remote sensing observations, this study examines variations of electron and ion density, electron temperature, total electron content (TEC, electric and magnetic fields and land surface temperature (LST several days before the studied earthquakes. Regarding the ionospheric precursors, the geomagnetic indices Dst and Kp were used to distinguish pre-earthquake disturbed states from the other anomalies related to the geomagnetic activities.

    The inter-quartile range of data was utilized to construct their upper and lower bound to detect disturbed states outsides the bounds which might be associated with impending earthquakes.

    When the disturbed state associated with an impending earthquake is detected, based on the type of precursor, the number of days relative to the earthquake day is estimated. Then regarding the deviation value of the precursor from the undisturbed state the magnitude of the impending earthquake is estimated. The radius of the affected area is calculated using the estimated magnitude and Dobrovolsky formula.

    In order to assess final earthquake parameters (i.e. date, magnitude and radius of the affected area for each case study, the earthquake

  10. Interaction between anxiety, depression, quality of life and clinical parameters in chronic tension-type headache.

    Science.gov (United States)

    Peñacoba-Puente, Cecilia; Fernández-de-Las-Peñas, César; González-Gutierrez, Jose L; Miangolarra-Page, Juan C; Pareja, Juan A

    2008-10-01

    Our aim was to investigate the mediating or moderating role of anxiety and depression in the relationship between headache clinical parameters and quality of life in Chronic Tension-Type Headache (CTTH). Twenty-five patients diagnosed with CTTH according to the criteria of the International Headache Society were studied. A headache diary was kept for 4 weeks in order to substantiate the diagnosis and record the pain history. Quality of life was assessed by means of the Medical Outcome Study (MOS) 36-Item Short-Form (SF-36) questionnaire. The Beck Depression Inventory (BDI-II) was used to assess depression, and the Trait Anxiety Scale (TA) from the State-Trait Anxiety Inventory was administered in order to assess anxiety. Moderating and mediating analyses were conducted with ordinary least squares multiple regression analysis using the SPSS General Linear Model procedure. Anxiety mediated the effect between headache frequency and quality of life, but not the effect of either headache intensity or duration. Anxiety totally mediated the effects of headache frequency on vitality, social functioning and mental health. On the other hand, depression modulated the effect in the mental health domain. The effect in the mental health domain was a function of the interaction between headache duration and depression (beta=-0.34, p<0.05), after controlling for age, gender, the main effects of headache duration, and depression. We did not find anxiety to be a moderating factor between intensity, frequency or duration of headache and perceived quality of life. Anxiety exerts a mediating effect, conditioning the relationship between headache frequency and some quality of life domains; depression seems to play an inherent role in the reduced quality of life of these patients, that is, it has a moderating effect.

  11. Association between Clinical and Doppler Echocardiographic Parameters with Sudden Death in Hemodialysis Patients

    Science.gov (United States)

    Barberato, Silvio Henrique; Bucharles, Sérgio Gardano Elias; Barberato, Marcia Ferreira Alves; Pecoits-Filho, Roberto

    2016-01-01

    Background: Sudden cardiac death (SCD) is the leading cause of death in maintenance hemodialysis (HD) patients, but there is little information about underlying risk factors. Objectives: Evaluate the association between clinical and echocardiographic variables with SCD on HD patients. Methods: Retrospective nested case-control study on chronic HD patients who were prospectively followed. The primary endpoint was SCD. Variables were compared by Student t test, Mann-Whitney or Chi-Square, and independent predictors of SCD were evidenced by multivariate logistic regression. Results: We followed 153 patients (50 ± 15 years, 58% men) for 23 ± 14 months and observed 35 deaths, 17 of which were SCD events. When compared to the control group (matched for gender, age, and body mass index) there were no differences regarding time on dialysis, traditional biochemical parameters, blood pressure, smoking, use of cardiovascular protective drugs, ejection fraction, left ventricular dimensions, and diastolic function indices. On the other hand, in the SCD group, we found a higher prevalence of previous heart failure, acute myocardial infarction and diabetes, greater left ventricular mass index, greater left atrial size and lower global myocardial performance. After multivariate logistic regression analysis, diabetes (OR = 2.6; CI = 1.3-7.5; p = 0.023) and left ventricular mass index ≥ 101 g/m2.7 (OR = 1.04; CI = 1.01-1.08; p = 0.028) showed independent association with SCD events. Conclusions: HD patients with diabetes mellitus and left ventricular hypertrophy appear to have the highest risk of SCD. Preventive and therapeutic strategies should be encouraged in addressing these risk factors to minimize the occurrence of SCD in HD patients. PMID:27411094

  12. Correlation between nutritional status and clinical parameters among Thalassaemic patients – A study of West Bengal

    Directory of Open Access Journals (Sweden)

    A. Jana

    2016-08-01

    Full Text Available Thalassaemia is the most common monogenic, autosomal recessive hereditary disorder. The severe forms of thalassaemia are associated with chronic transfusion dependent haemolytic anaemia. Normal growth is impeded due to nutritional deficiency, chronic anaemia as well as iron overload. The aim of this study is to focus the nutritional health status of transfusion dependent thalassaemia patients. This is a cross-sectional analysis of the records of the patients registered at Day Care unit of a City Hospital, Kolkata, India. Clinical history of each patient is collected from registered book of the hospital and body weights and height of the patients are taken from day care unit before starting the transfusion. Laboratory parameters like Pretransfusion Haemoglobin (Hb and Periodic Serum Ferritin are noted in respect of each patient. Z score for height, weight and Body Mass Index (BMI is also taken into consideration using WHO reference. Statistical analysis was carried out using Microsoft excel and SPSS16 Software. Out of 117 Bengali speaking patients 84 were from different Hindu caste families and the rest 33 were from the Muslim community. The mean age of studied patients’ population was 10.77 years (range 4 -20years. Major patients (81.1% suffer high level (>1000ng/ml of serum ferritin level due to not proper management of Pretransfusion haemoglobin and as well as not taken regular chelation. About two third (65.8% of studied population are noted to be short stature, 18.8% are thin and 23.9% are very thin (BMI Z score <-3. as well as regular chelation therapy is the central aspects to improve their proper growth Only 3 children are overweight. Height Z scores is significantly co related with mean serum ferritin level. Management of the disease is very important to control the nutritional health status of thalassaemic children. Proper knowledge of iron free food, optimum transfusion as well as regular chelation therapy is the central aspects to

  13. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    Directory of Open Access Journals (Sweden)

    Thomas R O'Brien

    Full Text Available BACKGROUND: Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables. METHODS: HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC. RESULTS: Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study. CONCLUSION: A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

  14. Ability of clinicopathologic variables and clinical examination findings to predict race elimination in endurance horses.

    Science.gov (United States)

    Fielding, C Langdon; Meier, Chloe A; Fellers, Greg K; Magdesian, K Gary

    2017-01-01

    OBJECTIVE To compare results of point-of-care laboratory testing with standard veterinary clinical examination findings at a single time point during endurance competition to identify horses at risk for elimination. ANIMALS 101 endurance horses participating in the 2013 Western States 160-km (100-mile) endurance ride. PROCEDURES At the 58-km checkpoint, blood samples were collected from all horses. Samples were analyzed for pH, Pco2, base excess, anion gap, PCV, and whole blood concentrations of sodium, potassium, chloride, total carbon dioxide, BUN, glucose, and bicarbonate. Corrected electrolyte and PCV values were calculated on the basis of plasma total protein concentration. Immediately following the blood sample collection, each horse underwent a clinical examination. In addition to standard examination variables, an adjusted heart rate was calculated on the basis of the variable interval between entry into the checkpoint and heart rate recording. A combination of stepwise logistic regression, classification and regression tree analysis, and generalized additive models was used to identify variables that were associated with overall elimination or each of 3 other elimination categories (metabolic elimination, lameness elimination, and elimination for other reasons). RESULTS Corrected whole blood potassium concentration and adjusted heart rate were predictive for overall elimination. Breed, plasma total protein concentration, and attitude were predictive for elimination due to metabolic causes. Whole blood chloride concentration and corrected PCV were predictive for elimination due to lameness. Corrected PCV was predictive for elimination due to other causes. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that for horses in endurance competition, a combination of breed and clinical examination and laboratory variables provided the best prediction of overall elimination.

  15. Islet Oxygen Consumption Rate (OCR Dose Predicts Insulin Independence in Clinical Islet Autotransplantation.

    Directory of Open Access Journals (Sweden)

    Klearchos K Papas

    Full Text Available Reliable in vitro islet quality assessment assays that can be performed routinely, prospectively, and are able to predict clinical transplant outcomes are needed. In this paper we present data on the utility of an assay based on cellular oxygen consumption rate (OCR in predicting clinical islet autotransplant (IAT insulin independence (II. IAT is an attractive model for evaluating characterization assays regarding their utility in predicting II due to an absence of confounding factors such as immune rejection and immunosuppressant toxicity.Membrane integrity staining (FDA/PI, OCR normalized to DNA (OCR/DNA, islet equivalent (IE and OCR (viable IE normalized to recipient body weight (IE dose and OCR dose, and OCR/DNA normalized to islet size index (ISI were used to characterize autoislet preparations (n = 35. Correlation between pre-IAT islet product characteristics and II was determined using receiver operating characteristic analysis.Preparations that resulted in II had significantly higher OCR dose and IE dose (p<0.001. These islet characterization methods were highly correlated with II at 6-12 months post-IAT (area-under-the-curve (AUC = 0.94 for IE dose and 0.96 for OCR dose. FDA/PI (AUC = 0.49 and OCR/DNA (AUC = 0.58 did not correlate with II. OCR/DNA/ISI may have some utility in predicting outcome (AUC = 0.72.Commonly used assays to determine whether a clinical islet preparation is of high quality prior to transplantation are greatly lacking in sensitivity and specificity. While IE dose is highly predictive, it does not take into account islet cell quality. OCR dose, which takes into consideration both islet cell quality and quantity, may enable a more accurate and prospective evaluation of clinical islet preparations.

  16. EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

    CERN Document Server

    Bennett, Casey; Selove, Rebecca

    2012-01-01

    Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the high...

  17. Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

    DEFF Research Database (Denmark)

    Giorgio, Antonio; Battaglini, Marco; Rocca, Maria Assunta

    2013-01-01

    : In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed......OBJECTIVES: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). METHODS: In this multicenter, retrospective study, clinical and MRI data at onset...... in 26% of patients. The converting group, despite a greater baseline lesion load compared with the nonconverting group (7 ± 8.1 cm(3) vs 4.6 ± 6.7 cm(3), p brain voxels occupied by lesions). High lesion frequency was found...

  18. A better parameter in predicting insulin resistance: Obesity plus elevated alanine aminotransferase

    Institute of Scientific and Technical Information of China (English)

    Ping-Hao Chen; Jong-Dar Chen; Yu-Cheng Lin

    2009-01-01

    AIM: To investigate the association of obesity and elevated alanine aminotransferase with insulin resistance and compare these factors with metabolic syndrome.METHODS: We enrolled a total of 1308 male workers aged from 22 to 63 years. Data was extracted from the workers’ periodic health check-ups in hospitals. All cases were from the community of northern Taiwan.This was a cross-sectional observational study from July to September in 2004. We grouped all cases into four groups, based on the quartile of homeostasis model assessment. The top fourth quartile group was defined as the group with insulin resistance. We performed multivariate logistic regression analysis for the odds ratio of the risk factors for insulin resistance.RESULTS: Compared with metabolic syndrome, the coexistence of both factors had a 4.3-fold (95% CI: 2.7-6.8) increased risk, which was more than metabolic syndrome with a 3.6-fold (95% CI: 2.6-5.0) increased risk. The two factors had a synergistic effect. The synergistic index of obesity and elevated alanine aminotransferase (ALT) was 2.1 (95% CI: 1.01-4.3).CONCLUSION: Obesity and elevated ALT are associatedwith insulin resistance. The effects are synergistic.Coexistence of them is better than metabolic syndrome in predicting insulin resistance.

  19. Microbial functional diversity enhances predictive models linking environmental parameters to ecosystem properties.

    Science.gov (United States)

    Powell, Jeff R; Welsh, Allana; Hallin, Sara

    2015-07-01

    Microorganisms drive biogeochemical processes, but linking these processes to real changes in microbial communities under field conditions is not trivial. Here, we present a model-based approach to estimate independent contributions of microbial community shifts to ecosystem properties. The approach was tested empirically, using denitrification potential as our model process, in a spatial survey of arable land encompassing a range of edaphic conditions and two agricultural production systems. Soil nitrate was the most important single predictor of denitrification potential (the change in Akaike's information criterion, corrected for sample size, ΔAIC(c) = 20.29); however, the inclusion of biotic variables (particularly the evenness and size of denitrifier communities [ΔAIC(c) = 12.02], and the abundance of one denitrifier genotype [ΔAIC(c) = 18.04]) had a substantial effect on model precision, comparable to the inclusion of abiotic variables (biotic R2 = 0.28, abiotic R2 = 0.50, biotic + abiotic R2 = 0.76). This approach provides a valuable tool for explicitly linking microbial communities to ecosystem functioning. By making this link, we have demonstrated that including aspects of microbial community structure and diversity in biogeochemical models can improve predictions of nutrient cycling in ecosystems and enhance our understanding of ecosystem functionality.

  20. Parameters affecting the accuracy of oxide thickness prediction in thin metal-oxide-semiconductor structures

    Science.gov (United States)

    Mohaidat, J. M.; Ahmad-Bitar, Riyad N.

    2004-01-01

    On the basis of the solution of the time dependent Schrödinger equation within the framework of the effective mass theory, a complete quantum mechanical electron tunneling through a biased square potential model with abrupt interfaces was deduced. Barriers of 3 eV height and widths up to 140 Å were investigated. Current density-voltage ( J- V) curves were computed for Al/SiO 2/ n+Si structure. The computed J- V curves exhibited oscillations at applied voltages above (Fowler-Nordheim tunneling) and below (direct tunneling) 3 V. For oxide thickness estimation, the position of the oscillation extrema from this quantum mechanical model were fitted to a wave interference formula and showed excellent agreement for oxide layer widths less than 50 Å. However, a systematic deviation appeared for layers larger than 50 Å. We show that the electron energy distribution at the injection layer and the electron effective mass on layers other than the oxide layer are important parameters for accurate oxide thickness estimation.

  1. Methamphetamine use parameters do not predict neuropsychological impairment in currently abstinent dependent adults.

    Science.gov (United States)

    Cherner, Mariana; Suarez, Paola; Casey, Corinna; Deiss, Robert; Letendre, Scott; Marcotte, Thomas; Vaida, Florin; Atkinson, J Hampton; Grant, Igor; Heaton, Robert K

    2010-01-15

    Methamphetamine (meth) abuse is increasingly of public health concern and has been associated with neurocognitive dysfunction. Some previous studies have been hampered by background differences between meth users and comparison subjects, as well as unknown HIV and hepatitis C (HCV) status, which can also affect brain functioning. We compared the neurocognitive functioning of 54 meth dependent (METH+) study participants who had been abstinent for an average of 129 days, to that of 46 demographically comparable control subjects (METH-) with similar level of education and reading ability. All participants were free of HIV and HCV infection. The METH+ group exhibited higher rates of neuropsychological impairment in most areas tested. Among meth users, neuropsychologically normal (n=32) and impaired (n=22) subjects did not differ with respect to self-reported age at first use, total years of use, route of consumption, or length of abstinence. Those with motor impairment had significantly greater meth use in the past year, but impairment in cognitive domains was unrelated to meth exposure. The apparent lack of correspondence between substance use parameters and cognitive impairment suggests the need for further study of individual differences in vulnerability to the neurotoxic effects of methamphetamine.

  2. Density functional theoretical (DFT) study for the prediction of spectroscopic parameters of ClCCCN

    CERN Document Server

    Varadwaj, P R

    2005-01-01

    DFT and RHF level calculations in conjunction with three different of basis sets have been used to investigate the variations in the bond lengths, dipole moment and rotational constants, IR frequencies, IR intensities and rotational invariants of ClCCCN. The nuclear quadrupole constants of chlorine and nitrogen of ClCCCN have been calculated on the experimental r$_{s}$ structure as well as on the B3PW91/6-311++g(d,p) optimized geometry and are found to be within the scale length of the experimental uncertainty. Linear regression analysis between the B3LYP/6-311++g(d,p) level calculated and experimental B$_{o}$ values have been achieved for ClCCCN and BrCCCN. The slopes and intercepts obtained from the regression analysis were used to calculate the reasonable values of rotational constants of all the rare isotopic species of ClCCCN and BrCCCN as well having standard deviations $\\pm$0.048 MHz and $\\pm$0.015 MHz respectively. All the spectroscopic parameters obtained from DFT calculation shows satisfactory agree...

  3. BAYESIAN PREDICTION OF GENETIC PARAMETERS IN Eucalyptus globulus CLONES UNDER WATER SUPPLY CONDITIONS

    Directory of Open Access Journals (Sweden)

    Freddy Mora

    2013-06-01

    Full Text Available http://dx.doi.org/10.5902/198050989297A Bayesian analysis of genetic parameters for growth traits at twelve months after planting was carried out in twenty nine Eucalyptus globulus clones in southern Chile. Two different environmental conditions were considered: 1 Non-irrigation and; 2 Plants were irrigated with a localized irrigation system. The Bayesian approach was performed using Gibbs sampling algorithm in a clone-environment interaction model. Inheritability values ​​were high in the water supply condition (posterior mode: H2=0.41, 0.36 and 0.39 for height, diameter and sectional area, respectively, while in the environment without irrigation, the inheritabilities were significantly lower, which was confirmed by the Bayesian credible intervals (95% probability. The posterior mode of the genetic correlation between sites was positive and high for all traits (r=0.7, 0.65 and 0.8, for height, diameter and sectional area, respectively and according to the credible interval, it was statistically different from zero, indicating a non-significant interaction.

  4. Effect of seasonal variation on adult clinical laboratory parameters in Rwanda, Zambia, and Uganda: implications for HIV biomedical prevention trials.

    Directory of Open Access Journals (Sweden)

    Eugene Ruzagira

    Full Text Available To investigate the effect of seasonal variation on adult clinical laboratory parameters in Rwanda, Zambia, and Uganda and determine its implications for HIV prevention and other clinical trials.Volunteers in a cross-sectional study to establish laboratory reference intervals were asked to return for a seasonal visit after the local season had changed from dry to rainy or vice versa. Volunteers had to be clinically healthy, not pregnant and negative for HIV, Hepatitis B and C, and syphilis infection at both visits. At each visit, blood was taken for measurement of hemoglobin, haematocrit, mean corpuscular volume, red blood cells, platelets, total white blood cells (WBC, neutrophils, lymphocytes, monocytes, eosinophils, basophils, CD4/CD8 T cells, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, direct bilirubin, total bilirubin, total immunoglobulin gamma, total protein, creatinine, total amylase, creatine phosphokinase and lactate dehydrogenase (LDH. Consensus dry season reference intervals were applied to rainy season values (and vice versa and the proportion of 'out-of-range' values determined. Percentage differences between dry and rainy season parameter mean values were estimated.In this cohort of 903 volunteers, less than 10.0% of consensus parameter (except LDH values in one season were "out-of-range" in the other. Twenty-two (22 percent of rainy season LDH values fell outside of the consensus dry season interval with the higher values observed in the rainy season. Variability between consensus seasonal means ranged from 0.0% (total WBC, neutrophils, monocytes, basophils, and direct bilirubin to 40.0% (eosinophils. Within sites, the largest seasonal variations were observed for monocytes (Masaka, 11.5%, LDH (Lusaka, 21.7%, and basophils (Kigali, 22.2%.Seasonality had minimal impact on adult clinical laboratory parameter values in Rwanda, Zambia, and Uganda. Seasonal variation may not be an important factor in the

  5. Some uses of predictive probability of success in clinical drug development

    Directory of Open Access Journals (Sweden)

    Mauro Gasparini

    2013-03-01

    Full Text Available Predictive probability of success is a (subjective Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations imposed in the world of pharmaceutical development.Within a single trial, predictive probability of success can be identified with expected power, i.e. the evaluation of the success probability of the trial. Success means, for example, obtaining a significant result of a standard superiority test.Across trials, predictive probability of success can be the probability of a successful completion of an entire part of clinical development, for example a successful phase III development in the presence of phase II data.Calculations of predictive probability of success in the presence of normal data with known variance will be illustrated, both for within-trial and across-trial predictions.

  6. De novo sequencing of circulating miRNAs identifies novel markers predicting clinical outcome of locally advanced breast cancer

    Directory of Open Access Journals (Sweden)

    Wu Xiwei

    2012-03-01

    Full Text Available Abstract Background MicroRNAs (miRNAs have been recently detected in the circulation of cancer patients, where they are associated with clinical parameters. Discovery profiling of circulating small RNAs has not been reported in breast cancer (BC, and was carried out in this study to identify blood-based small RNA markers of BC clinical outcome. Methods The pre-treatment sera of 42 stage II-III locally advanced and inflammatory BC patients who received neoadjuvant chemotherapy (NCT followed by surgical tumor resection were analyzed for marker identification by deep sequencing all circulating small RNAs. An independent validation cohort of 26 stage II-III BC patients was used to assess the power of identified miRNA markers. Results More than 800 miRNA species were detected in the circulation, and observed patterns showed association with histopathological profiles of BC. Groups of circulating miRNAs differentially associated with ER/PR/HER2 status and inflammatory BC were identified. The relative levels of selected miRNAs measured by PCR showed consistency with their abundance determined by deep sequencing. Two circulating miRNAs, miR-375 and miR-122, exhibited strong correlations with clinical outcomes, including NCT response and relapse with metastatic disease. In the validation cohort, higher levels of circulating miR-122 specifically predicted metastatic recurrence in stage II-III BC patients. Conclusions Our study indicates that certain miRNAs can serve as potential blood-based biomarkers for NCT response, and that miR-122 prevalence in the circulation predicts BC metastasis in early-stage patients. These results may allow optimized chemotherapy treatments and preventive anti-metastasis interventions in future clinical applications.

  7. On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV

    Directory of Open Access Journals (Sweden)

    Paul S. Addison

    2015-01-01

    Full Text Available DPOP (ΔPOP or Delta-POP is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method. We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements.

  8. Association between clinical parameters and the presence of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis in patients with progressive periodontal lesions

    Directory of Open Access Journals (Sweden)

    Rakić Mia

    2010-01-01

    Full Text Available Background/Aim. Periodontitis is a chronic inflammatory disease of periodontal tissues with consequential is bone loss as a result of host immunological reactions caused by periopathogens. The aim of the study was to investigate if there is a correlation between clinical parameters and the presence of two most aggressive periopathogens (Aggregatibacter actinomycetemcomitans - Aa and Porphyromonas gingivalis - Pg in patients with progressive periodontal lesions. Methods. A total of 34 systemic healthy people, 23 to 70 years old, were included in the study. The patients were clinically and radiologically examined, and after that, the representative pocket with greatest pocket depth was chosen and the sample was collected from that place. The measured clinic parameters were: gingival index, index of gingival bleeding, pocket depth and plaque indices. The multiplex Polymerase Chain Reaction (PCR method was used for detection of periopathogens. After obtaining results, appropriate statistical tests were used to correlate the clinical and microbiological results. Results. Aa and Pg were detected in the same percentage of samples. Aa and Pg were detected in 35.29% samples alone, and in 29.41% both were detected. The values of measured clinical parameters did not show a statistical significance between the groups. In analysis of correlations among clinical parameters inside the groups, a statistical significance was found only between gingival and plaque index in the group with Aa. Conclusion. Clinical course of periodontitis in the developed stage does not differ in relation to the presence of different periopathogens as the major inductors of immunologically guided destructive processes.

  9. Integration of noninvasive prenatal prediction of fetal blood group into clinical prenatal care

    DEFF Research Database (Denmark)

    Clausen, Frederik Banch

    2014-01-01

    of the fetus and newborn to fetuses of immunized women. Prediction of the fetal RhD type has been very successful and is now integrated into clinical practice to assist in the management of the pregnancies of RhD immunized women. In addition, noninvasive prediction of the fetal RhD type can be applied to guide......Incompatibility of red blood cell blood group antigens between a pregnant woman and her fetus can cause maternal immunization and, consequently, hemolytic disease of the fetus and newborn. Noninvasive prenatal testing of cell-free fetal DNA can be used to assess the risk of hemolytic disease...

  10. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    Science.gov (United States)

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

  11. Prediction of changes in important physical parameters during composting of separated animal slurry solid fractions.

    Science.gov (United States)

    Chowdhury, Md Albarune; de Neergaard, Andreas; Jensen, Lars Stoumann

    2014-01-01

    Solid-liquid separation of animal slurry, with solid fractions used for composting, has gained interest recently. However, efficient composting of separated animal slurry solid fractions (SSFs) requires a better understanding of the process dynamics in terms of important physical parameters and their interacting physical relationships in the composting matrix. Here we monitored moisture content, bulk density, particle density and air-filled porosity (AFP) during composting of SSF collected from four commercially available solid-liquid separators. Composting was performed in laboratory-scale reactors for 30 days (d) under forced aeration and measurements were conducted on the solid samples at the beginning of composting and at 10-d intervals during composting. The results suggest that differences in initial physical properties of SSF influence the development of compost maximum temperatures (40-70 degreeC). Depending on SSF, total wet mass and volume losses (expressed as % of initial value) were up to 37% and 34%, respectively. After 30 d of composting, relative losses of total solids varied from 17.9% to 21.7% and of volatile solids (VS) from 21.3% to 27.5%, depending on SSF. VS losses in all composts showed different dynamics as described by the first-order kinetic equation. The estimated component particle density of 1441 kg m-3 for VS and 2625 kg m-3 for fixed solids can be used to improve estimates of AFP for SSF within the range tested. The linear relationship between wet bulk density and AFP reported by previous researchers held true for SSF.

  12. Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk.

    Science.gov (United States)

    McParland, S; Kennedy, E; Lewis, E; Moore, S G; McCarthy, B; O'Donovan, M; Berry, D P

    2015-02-01

    Energy balance (EB) and energy intake (EI) are heritable traits of economic importance. Despite this, neither trait is explicitly included in national dairy cow breeding goals due to a lack of routinely available data from which to compute reliable breeding values. Mid-infrared (MIR) spectrometry, which is performed during routine milk recording, is an accurate predictor of both EB and EI. The objective of this study was to estimate genetic parameters of EB and EI predicted using MIR spectrometry. Measured EI and EB were available for 1,102 Irish Holstein-Friesian cows based on actual feed intake and energy sink data. A subset of these data (1,270 test-day records) was used to develop equations to predict EI, EB, and daily change in body condition score (ΔBCS) and body weight (ΔBW) using the MIR spectrum with or without milk yield also as a predictor variable. Accuracy of cross-validation of the prediction equations was 0.75, 0.73, 0.77, and 0.70 for EI, EB, ΔBCS, and ΔBW, respectively. Prediction equations were applied to additional spectral data, yielding up to 94,653 records of MIR-predicted EI, EB, ΔBCS, and ΔBW available for variance component estimation. Variance components were estimated using repeatability animal linear mixed models. Heritabilities of MIR-predicted EI, EB, ΔBCS, and ΔBW were 0.20, 0.10, 0.07, and 0.06, respectively; heritability estimates of the respective measured traits were 0.35, 0.16, 0.07, and 0.08, respectively. The genetic correlation between measured and MIR-predicted EI was 0.84 and between measured and MIR-predicted EB was 0.54, indicating that selection based on MIR-predicted EI or EB would improve true EI or EB. Genetic and phenotypic associations between EI and both the milk production and body-change traits were generally in agreement, regardless of whether measured EI or MIR-predicted EI was considered. Higher-yielding animals of higher body weight had greater EI. Predicted EB was negatively genetically correlated

  13. Prediction of Clinically Relevant Safety Signals of Nephrotoxicity through Plasma Metabolite Profiling

    Directory of Open Access Journals (Sweden)

    W. B. Mattes

    2013-01-01

    Full Text Available Addressing safety concerns such as drug-induced kidney injury (DIKI early in the drug pharmaceutical development process ensures both patient safety and efficient clinical development. We describe a unique adjunct to standard safety assessment wherein the metabolite profile of treated animals is compared with the MetaMap Tox metabolomics database in order to predict the potential for a wide variety of adverse events, including DIKI. To examine this approach, a study of five compounds (phenytoin, cyclosporin A, doxorubicin, captopril, and lisinopril was initiated by the Technology Evaluation Consortium under the auspices of the Drug Safety Executive Council (DSEC. The metabolite profiles for rats treated with these compounds matched established reference patterns in the MetaMap Tox metabolomics database indicative of each compound’s well-described clinical toxicities. For example, the DIKI associated with cyclosporine A and doxorubicin was correctly predicted by metabolite profiling, while no evidence for DIKI was found for phenytoin, consistent with its clinical picture. In some cases the clinical toxicity (hepatotoxicity, not generally seen in animal studies, was detected with MetaMap Tox. Thus metabolite profiling coupled with the MetaMap Tox metabolomics database offers a unique and powerful approach for augmenting safety assessment and avoiding clinical adverse events such as DIKI.

  14. External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

    Science.gov (United States)

    Hickey, Graeme L; Blackstone, Eugene H

    2016-08-01

    Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting.

  15. The north Jutland county diabetic retinopathy study (NCDRS) 2. Non-ophthalmic parameters and clinically significant macular oedema

    DEFF Research Database (Denmark)

    Knudsen, Lars Loumann; Lervang, Hans-Henrik; Lundbye-Christensen, Søren

    2007-01-01

    Background: The influence of non-ophthalmic parameters on the prevalence of clinically significant macular oedema has not been unambiguously established. The present study was initiated with the aim of clarification. Methods: This cross-sectional study comprised 656 type 1 and 328 type 2 diabetic...

  16. The effect of medicated, sugar-free chewing gum on plaque and clinical parameters of gingival inflammation: a systematic review

    NARCIS (Netherlands)

    Keukenmeester, R.S.; Slot, D.E.; Putt, M.S.; van der Weijden, G.A.

    2014-01-01

    Objective This study aimed to systematically review the present literature to establish the clinical effect of medicated, sugar-free chewing gum on plaque indices and parameters of gingival inflammation. Materials and methods MEDLINE-PubMed, Cochrane CENTRAL and EMBASE databases were searched up to

  17. Rhythmic 24-hour variations of frequently used clinical biochemical parameters in healthy young males - The Bispebjerg study of diurnal variations

    DEFF Research Database (Denmark)

    Sennels, Henriette P; Jørgensen, Henrik L; Gøtze, Jens Peter;

    2012-01-01

    Purpose. To evaluate the influence of time of day on the circulating concentrations of 14 frequently used clinical biochemical parameters in the Bispebjerg study of diurnal variations. Materials and methods. Venous blood samples were obtained under controlled environmental, activities and food...

  18. The Diagnostic Accuracy of Clinical and External Pelvimetry in Prediction of Dystocia in Nulliparous Women

    Directory of Open Access Journals (Sweden)

    R Alijahan

    2011-08-01

    Full Text Available Introduction: Clinical pelvimetry is very uncomfortable for the patient and is associated with subjective error, while external pelvimetry is a simple and acceptable method for patients. The objective of this study was to compare the diagnostic accuracy of clinical and external pelvimetry in prediction of dystocia in nulliparous women. Methods: In this study between December 2008 and January 2009, 447 nulliparous women with a single pregnancy in vertex presentation and gestational age 38-42 weeks referring to the Ommolbanin hospital of Mashhad were included. External pelvic dimensions were assessed at the time of admission and clinical pelvimetry was performed by another examiner. These measurements were not available to the clinician in charge of the delivery. Dystocia was defined as caesarean section and vacuum or forceps delivery for abnormal progress of labor ( active uterine contractions, arrest of cervical dilatation or cervical dilatation less than 1 cm /h in the active phase for 2 hours, prolongation of second stage beyond 2 hours or fetal head descent less than 1cm/h. Statistical tests included Fisher exact test and Chi- square test. Results: The highest sensitivity obtained from clinical pelvimetry was 33.3% and related to diagonal conjugate less than 11.5 cm. The sensitivity of external pelvic dimensions was higher than clinical pelvimetry that was highest for the Michaelis transverse diameter(60.72%. Conclusion: External pelvimetry in comparison to clinical pelvimetry is a better method for identifying dystocia in nulliparous women and can replace clinical pelvimetry in antenatal care programs.

  19. How do alternative root water uptake models affect the inverse estimation of soil hydraulic parameters and the prediction of evapotranspiration?

    Science.gov (United States)

    Gayler, Sebastian; Salima-Sultana, Daisy; Selle, Benny; Ingwersen, Joachim; Wizemann, Hans-Dieter; Högy, Petra; Streck, Thilo

    2016-04-01

    Soil water extraction by roots affects the dynamics and distribution of soil moisture and controls transpiration, which influences soil-vegetation-atmosphere feedback processes. Consequently, root water uptake requires close attention when predicting water fluxes across the land surface, e.g., in agricultural crop models or in land surface schemes of weather and climate models. The key parameters for a successful simultaneous simulation of soil moisture dynamics and evapotranspiration in Richards equation-based models are the soil hydraulic parameters, which describe the shapes of the soil water retention curve and the soil hydraulic conductivity curve. As measurements of these parameters are expensive and their estimation from basic soil data via pedotransfer functions is rather inaccurate, the values of the soil hydraulic parameters are frequently inversely estimated by fitting the model to measured time series of soil water content and evapotranspiration. It is common to simulate root water uptake and transpiration by simple stress functions, which describe from which soil layer water is absorbed by roots and predict when total crop transpiration is decreased in case of soil water limitations. As for most of the biogeophysical processes simulated in crop and land surface models, there exist several alternative functional relationships for simulating root water uptake and there is no clear reason for preferring one process representation over another. The error associated with alternative representations of root water uptake, however, contributes to structural model uncertainty and the choice of the root water uptake model may have a significant impact on the values of the soil hydraulic parameters estimated inversely. In this study, we use the agroecosystem model system Expert-N to simulate soil moisture dynamics and evapotranspiration at three agricultural field sites located in two contrasting regions in Southwest Germany (Kraichgau, Swabian Alb). The Richards

  20. Predicting the solubility of sulfamethoxypyridazine in individual solvents. II: Relationship between solute-solvent interaction terms and partial solubility parameters.

    Science.gov (United States)

    Martin, A; Bustamante, P; Escalera, B; Sellés, E

    1989-08-01

    In the first paper in the series, an expanded system of parameters was devised to account for orientation and induction effects, and the term Wh was introduced to replace delta 1h delta 2h of the extended Hansen solubility approach. In the present report, a new term, Kh = Wh/delta 1h delta 2h is observed to take on values larger or smaller than unity depending on whether the hydrogen bonded solute-solvent interaction is larger or smaller than predicted by the term delta 1h delta 2h. The acidic delta a and basic delta b solubility parameters are used to represent two parameters, sigma and tau, suggested by Small in his study of proton donor-acceptor properties. The Small equation, including a heat of mixing term for hydrogen bonded species, is shown to be capable of semiquantitative evaluation. A partial molar heat delta H2h of hydrogen bonding is calculated using delta h and Wh terms; delta H2h is found to be correlated with the logarithm of the residual activity coefficient, In alpha R, a term representing strong solute-solvent interaction. The terms Wh, delta H2h, and In alpha 2R may be used to test the deviation from the geometric mean assumed in regular solution theory, and to replace the hydrogen bonding terms of the extended Hansen three-parameter model. The solubility of sulfamethoxypyridazine in 30 solvents is used to test the semiempirical solubility equations. The results are interpreted in terms of partial solubility parameters and the proton donor-acceptor properties of the solvents.

  1. HPA axis in major depression: cortisol, clinical symptomatology and genetic variation predict cognition.

    Science.gov (United States)

    Keller, J; Gomez, R; Williams, G; Lembke, A; Lazzeroni, L; Murphy, G M; Schatzberg, A F

    2016-08-16

    The hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the pathophysiology of a variety of mood and cognitive disorders. Neuroendocrine studies have demonstrated HPA axis overactivity in major depression, a relationship of HPA axis activity to cognitive performance and a potential role of HPA axis genetic variation in cognition. The present study investigated the simultaneous roles HPA axis activity, clinical symptomatology and HPA genetic variation play in cognitive performance. Patients with major depression with psychotic major depression (PMD) and with nonpsychotic major depression (NPMD) and healthy controls (HC) were studied. All participants underwent a diagnostic interview and psychiatric ratings, a comprehensive neuropsychological battery, overnight hourly blood sampling for cortisol and genetic assessment. Cognitive performance differed as a function of depression subtype. Across all subjects, cognitive performance was negatively correlated with higher cortisol, and PMD patients had higher cortisol than did NPMDs and HCs. Cortisol, clinical symptoms and variation in genes, NR3C1 (glucocorticoid receptor; GR) and NR3C2 (mineralocorticoid receptor; MR) that encode for GRs and MRs, predicted cognitive performance. Beyond the effects of cortisol, demographics and clinical symptoms, NR3C1 variation predicted attention and working memory, whereas NR3C2 polymorphisms predicted memory performance. These findings parallel the distribution of GR and MR in primate brain and their putative roles in specific cognitive tasks. HPA axis genetic variation and activity were important predictors of cognition across the entire sample of depressed subjects and HR. GR and MR genetic variation predicted unique cognitive functions, beyond the influence of cortisol and clinical symptoms. GR genetic variation was implicated in attention and working memory, whereas MR was implicated in verbal memory.Molecular Psychiatry advance online publication, 16 August 2016; doi

  2. Evaluating clinical abdominal scoring system in predict- ing the necessity of laparotomy in blunt abdominal trauma

    Directory of Open Access Journals (Sweden)

    Erfantalab-Avini Peyman

    2011-06-01

    Full Text Available 【Abstract】 Objectives: Trauma is among the lead- ing causes of death. Medical management of blunt abdomi- nal trauma (BAT relies on judging patients for whom lap- arotomy is mandatory. This study aimed to determine BAT patients’ signs, as well as paraclinical data, and to clarify the accuracy, sensitivity, specificity, positive and negative predictive value of clinical abdominal scoring system (CASS, a new scoring system based on clinical signs, in predicting whether a BAT patient needs laparotomy or not. Methods: Totally 400 patients suspected of BAT that arrived at the emergency department of two university hos- pitals in Tehran from March 20, 2007 to March 19, 2009 were included in this study. They were evaluated for age, sex, type of trauma, systolic blood pressure, Glasgow coma scale (GCS, pulse rate, time of presentation after trauma, abdomi- nal clinical findings, respiratory rate, temperature, hemoglo- bin (Hb concentration, focused abdominal sonography in trauma (FAST and CASS. Results: Our measurements showed that CASS had an accuracy of 94%, sensitivity of 100%, specificity of 88%, positive predictive value of 90% and negative predictive value of 100% in determining the necessity of laparotomy in BAT patients. Moreover, in our analysis, systolic blood pressure, GCS, pulse rate, Hb concentration, time of presen- tation after trauma, abdominal clinical findings and FAST were also shown to be helpful in confirming the need for laparotomy (P<0.05. Conclusion: CASS is a promising scoring system in rapid detection of the need for laparotomy as well as in minimizing auxiliary expense for further evaluation in BAT patients, thus to promote the cost-benefit ratio and accu- racy of diagnosis. Key words: Abdominal injuries; Laparotomy; Patients; Wounds, nonpenetrating

  3. Evaluating clinical abdominal scoring system in predicting the necessity of laparotomy in blunt abdominal trauma

    Institute of Scientific and Technical Information of China (English)

    Peyman Erfantalab-Avini; Nima Hafezi-Nejad; Mojtaba Chardoli; Vafa Rahimi-Movaghar

    2011-01-01

    Objectives: Trauma is among the leading causes of death. Medical management of blunt abdominal trauma (BAT) relies on judging patients for whom laparotomy is mandatory. This study aimed to determine BAT patients' signs, as well as paraclinical data, and to clarify the accuracy, sensitivity, specificity, positive and negative predictive value of clinical abdominal scoring system (CASS), a new scoring system based on clinical signs, in predicting whether a BAT patient needs laparotomy or not.Methods: Totally 400 patients suspected of BAT that arrived at the emergency department of two university hospitals in Tehran from March 20, 2007 to March 19, 2009 were included in this study. They were evaluated for age, sex,type of trauma, systolic blood pressure, Glasgow coma scale (GCS), pulse rate, time of presentation after trauma, abdominal clinical findings, respiratory rate, temperature, hemoglobin (Hb) concentration, focused abdominal sonography in trauma (FAST) and CASS.Results: Our measurements showed that CASS had an accuracy of 94%, sensitivity of 100%, specificity of 88%,positive predictive value of 90% and negative predictive value of 100% in determining the necessity of laparotomy in BAT patients. Moreover, in our analysis, systolic blood pressure, GCS, pulse rate, Hb concentration, time of presentation after trauma, abdominal clinical findings and FAST were also shown to be helpful in confirming the need for laparotomy (P<0.05).Conclusion: CASS is a promising scoring system in rapid detection of the need for laparotomy as well as in minimizing auxiliary expense for further evaluation in BAT patients, thus to promote the cost-benefit ratio and accuracy of diagnosis.

  4. Predictive Power of the Baseline QRS Complex Duration for Clinical Response to Cardiac Resynchronisation Therapy

    Directory of Open Access Journals (Sweden)

    Ali Kazemisaeid

    2011-02-01

    Full Text Available Background: Determination of predictors of response to cardiac resynchronisation therapy (CRT in patients with moderate to severe heart failure accompanied by a ventricular dyssynchrony can play a major role in improving candidate selection for CRT.Objectives: We evaluated whether the baseline QRS duration could be used to discriminate responders from non-responders to CRT.Methods: Eighty three consecutive patients with moderate to severe heart failure and with successful implantation of a CRT device at our centre were included in the study. QRS durations were measured on 12-lead surface electrocardiogram before and 6 months after implantation of the CRT device, using the widest QRS complex in leads II, V1 and V6. Clinical response to CRT was defined as an improvement of ≥1 grade in NYHA class.Results: Optimal cut-off value to discriminate baseline QRS duration for predicting clinical response to CRT was identified at 152 ms, yielding a sensitivity of 73.3%, a specificity of 56.5% as well as positive and negative predictive values of 81.5% and 44.8%, respectively. The discriminatory pow- er of the baseline QRS duration for response to CRT assessed by the ROC curve was 0.6402 (95% CI: 0.4976 – 0.7829. Baseline QRS duration ≥ 152 ms could effectively predict clinical response to CRT after adjusting for covariates (OR = 3.743, p = 0.017.Conclusion: Baseline QRS duration can effectively predict clinical response to CRT and optimal cut-off value to discriminate baseline QRS duration for response to CRT is 152 ms.

  5. A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia

    OpenAIRE

    Gürsoy, Tuğba; Hayran, Mutlu; Derin, Hatice; Ovalı, Fahri

    2015-01-01

    A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia Tugba Gursoy, MD1 Mutlu Hayran, MD2 Hatice Derin, MD3 Fahri Ovali, MD3 1Department of Neonatology, School of Medicine, KOC University, Istanbul, Turkey 2Department of Preventive Oncology, Faculty of Medicine, Hacettepe University, Ankara, Turkey 3Department of Pediatrics, Zeynep Kamil Maternity and Children’s Research and Training Hospital, Istanbul, Turkey 4Department of Neonatology,...

  6. Clinical Features That Predict the Need for Operative Intervention in Gluteus Medius Tears

    OpenAIRE

    Chandrasekaran, Sivashankar; Vemula, S. Pavan; Gui, Chengcheng; Suarez-Ahedo, Carlos; Lodhia, Parth; Domb, Benjamin G.

    2015-01-01

    Background: Gluteus medius tears are a common cause of lateral hip pain. Operative intervention is usually prescribed for patients with pain despite physical therapy and/or peritrochanteric injections. Purpose: To identify clinical features that predict operative intervention in gluteus medius tears. Study Design: Case control study; Level of evidence, 3. Methods: A matched-pair controlled study was conducted on patients who underwent endoscopic gluteus medius repairs from June 2008 to August...

  7. HPA Axis in Major Depression: Cortisol, Clinical Symptomatology, and Genetic Variation Predict Cognition

    Science.gov (United States)

    Keller, Jennifer; Gomez, Rowena; Williams, Gordon; Lembke, Anna; Lazzeroni, Laura; Murphy, Greer M.; Schatzberg, Alan F.

    2016-01-01

    The Hypothalamic Pituitary Adrenal (HPA) axis has been implicated in the pathophysiology of a variety of mood and cognitive disorders. Neuroendocrine studies have demonstrated HPA axis overactivity in major depression, a relationship of HPA axis activity to cognitive performance, and a potential role of HPA axis genetic variation in cognition. The present study investigated the simultaneous roles HPA axis activity, clinical symptomatology, and HPA genetic variation play in cognitive performance. Patients with major depression with psychosis (PMD) and without psychosis (NPMD) and healthy controls (HC) were studied. All participants underwent a diagnostic interview and psychiatric ratings, a comprehensive neuropsychological battery, overnight hourly blood sampling for cortisol, and genetic assessment. Cognitive performance differed as a function of depression subtype. Across all subjects, cognitive performance was negatively correlated with higher cortisol, and PMD patients had higher cortisol than did NPMDs and HCs. Cortisol, clinical symptoms, and variation in genes, NR3C1 (glucocorticoid receptor - GR) and NR3C2 (minercorticoid receptor – MR) that encode for glucocorticoid and mineralcorticoid receptors, predicted cognitive performance. Beyond the effects of cortisol, demographics, and clinical symptoms, NR3C1 variation predicted attention and working memory, whereas NR3C2 polymorphisms predicted memory performance. These findings parallel the distribution of GR and MR in primate brain and their putative roles in specific cognitive tasks. HPA axis genetic variation and activity were important predictors of cognition across the entire sample of depressed subjects and healthy controls. GR and MR genetic variation predicted unique cognitive functions, beyond the influence of cortisol and clinical symptoms. GR genetic variation was implicated in attention and working memory, whereas MR was implicated in verbal memory. PMID:27528460

  8. Some uses of predictive probability of success in clinical drug development

    OpenAIRE

    Mauro Gasparini; Lilla Di Scala; Frank Bretz; Amy Racine-Poon

    2013-01-01

    Predictive probability of success is a (subjective) Bayesian evaluation of the prob- ability of a future successful event in a given state of information. In the context of pharmaceutical clinical drug development, successful events relate to the accrual of positive evidence on the therapy which is being developed, like demonstration of su- perior efficacy or ascertainment of safety. Positive evidence will usually be obtained via standard frequentist tools, according to the regulations impose...

  9. Biomechanical evaluation of predictive parameters of progression in adolescent isthmic spondylolisthesis: a computer modeling and simulation study

    Directory of Open Access Journals (Sweden)

    Sevrain Amandine

    2012-01-01

    Full Text Available Abstract Background Pelvic incidence, sacral slope and slip percentage have been shown to be important predicting factors for assessing the risk of progression of low- and high-grade spondylolisthesis. Biomechanical factors, which affect the stress distribution and the mechanisms involved in the vertebral slippage, may also influence the risk of progression, but they are still not well known. The objective was to biomechanically evaluate how geometric sacral parameters influence shear and normal stress at the lumbosacral junction in spondylolisthesis. Methods A finite element model of a low-grade L5-S1 spondylolisthesis was constructed, including the morphology of the spine, pelvis and rib cage based on measurements from biplanar radiographs of a patient. Variations provided on this model aimed to study the effects on low grade spondylolisthesis as well as reproduce high grade spondylolisthesis. Normal and shear stresses at the lumbosacral junction were analyzed under various pelvic incidences, sacral slopes and slip percentages. Their influence on progression risk was statistically analyzed using a one-way analysis of variance. Results Stresses were mainly concentrated on the growth plate of S1, on the intervertebral disc of L5-S1, and ahead the sacral dome for low grade spondylolisthesis. For high grade spondylolisthesis, more important compression and shear stresses were seen in the anterior part of the growth plate and disc as compared to the lateral and posterior areas. Stress magnitudes over this area increased with slip percentage, sacral slope and pelvic incidence. Strong correlations were found between pelvic incidence and the resulting compression and shear stresses in the growth plate and intervertebral disc at the L5-S1 junction. Conclusions Progression of the slippage is mostly affected by a movement and an increase of stresses at the lumbosacral junction in accordance with spino-pelvic parameters. The statistical results provide

  10. Evaluation of several FDG PET parameters for prediction of soft tissue tumour grade at primary diagnosis and recurrence

    Energy Technology Data Exchange (ETDEWEB)

    Fendler, Wolfgang P. [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Department of Nuclear Medicine, Munich (Germany); Chalkidis, Rebecca P.; Ilhan, Harun [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Knoesel, Thomas [Ludwig-Maximilians-University of Munich, Institute of Pathology, Munich (Germany); Herrmann, Ken [Julius-Maximilians-University of Wuerzburg, Department of Nuclear Medicine, Wuerzburg (Germany); Issels, Rolf D.; Lindner, Lars H. [Ludwig-Maximilians-University of Munich, Department of Internal Medicine III, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Bartenstein, Peter [Ludwig-Maximilians-University of Munich, Department of Nuclear Medicine, Munich (Germany); Ludwig-Maximilians-University of Munich, Comprehensive Cancer Center, Munich (Germany); Cyran, Clemens C. [Ludwig-Maximilians-University of Munich, Department of Clinical Radiology, Munich (Germany); Hacker, Marcus [Vienna General Hospital, Department of Nuclear Medicine, Vienna (Austria)

    2015-08-15

    This study evaluates the diagnostic accuracy of SUV-based parameters derived from [{sup 18} F]-2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) in order to optimize non-invasive prediction of soft tissue tumour (STT) grade. One hundred and twenty-nine lesions from 123 patients who underwent FDG-PET for primary staging (n = 79) or assessment of recurrence (n = 44) of STT were analyzed retrospectively. Histopathology was the reference standard for tumour grading. Absolute values and tumour-to-liver ratios of several standardized uptake value (SUV) parameters were correlated with tumour grading. At primary diagnosis SUV{sub max}, SUV{sub peak}, SUV{sub max}/SUV{sub liver} and SUV{sub peak}/SUV{sub liver} showed good correlation with tumour grade. SUV{sub peak} (area under the receiver-operating-characteristic, AUC-ROC: 0.82) and SUV{sub peak}/SUV{sub liver} (AUC-ROC: 0.82) separated best between low grade (WHO intermediate, grade 1 sarcoma, and low risk gastrointestinal stromal tumours, GISTs) and high grade (grade 2/3 sarcoma and intermediate/high risk GISTs) lesions: optimal threshold for SUV{sub peak}/SUV{sub liver} was 2.4, which resulted in a sensitivity of 79 % and a specificity of 81 %. At disease recurrence, the AUC-ROC was <0.75 for each parameter. A tumour SUV{sub peak} of at least 2.4 fold mean liver uptake predicts high grade histopathology with good diagnostic accuracy at primary staging. At disease recurrence, FDG-PET does not reliably separate high and low grade lesions. (orig.)

  11. Value of three-dimensional strain parameters for predicting left ventricular remodeling after ST-elevation myocardial infarction.

    Science.gov (United States)

    Xu, Lin; Huang, Xiaomin; Ma, Jun; Huang, Jiangming; Fan, Yongwang; Li, Huidi; Qiu, Jian; Zhang, Heye; Huang, Wenhua

    2017-02-01

    This study was to evaluate the value of multi-directional strain parameters derived from three-dimensional (3D) speckle tracking echocardiography (STE) for predicting left ventricular (LV) remodeling after ST-elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention (PCI) compared with that of two-dimensional (2D) global longitudinal strain (GLS). A total of 110 patients (mean age, 54 ± 9 years) after STEMI treated with primary PCI were enrolled in our study. At baseline (within 24 h after PCI), standard 2D echocardiography, 2D STE and 3D STE were performed to acquire the conventional echocardiographic parameters and strain parameters. At 3-month follow-up, standard 2D echocardiography was repeated to all the patients to determine LV remodeling, which was defined as a 20% increase in LV end-diastolic volume. At 3-month follow-up, LV remodeling occurred in 26 patients (24%). Compared with patients without LV remodeling, patients with remodeling had significantly reduced 2D GLS (-12.5 ± 3.2% vs -15.0 ± 3.1%, p remodeling. However, receiver-operating characteristic curve analysis showed that the area under the curve of 3D GLS (0.82) for predicting LV remodeling was significantly higher than that of 2D GLS (0.72, p = 0.034), 3D GAS (0.68, p remodeling and 3D GLS is the most powerful predictor among them.

  12. Correlation between endogenous polyamines in human cardiac tissues and clinical parameters in patients with heart failure.

    Science.gov (United States)

    Meana, Clara; Rubín, José Manuel; Bordallo, Carmen; Suárez, Lorena; Bordallo, Javier; Sánchez, Manuel

    2016-02-01

    Polyamines contribute to several physiological and pathological processes, including cardiac hypertrophy in experimental animals. This involves an increase in ornithine decarboxylase (ODC) activity and intracellular polyamines associated with cyclic adenosine monophosphate (cAMP) increases. The aim of the study was to establish the role of these in the human heart in living patients. For this, polyamines (by high performance liquid chromatography) and the activity of ODC and N(1)-acetylpolyamine oxidases (APAO) were determined in the right atrial appendage of 17 patients undergoing extracorporeal circulation to correlate with clinical parameters. There existed enzymatic activity associated with the homeostasis of polyamines. Left atria size was positively associated with ODC (r = 0.661, P = 0.027) and negatively with APAO-N(1) -acetylspermine (r = -0.769, P = 0.026), suggesting that increased levels of polyamines are associated with left atrial hemodynamic overload. Left ventricular ejection fraction (LVEF) and heart rate were positively associated with spermidine (r = 0.690, P = 0.003; r = 0.590, P = 0.021) and negatively with N(1)-acetylspermidine (r = -0.554, P = 0.032; r = -0.644, P = 0.018). LVEF was negatively correlated with cAMP levels (r = -0.835, P = 0.001) and with cAMP/ODC (r = -0.794, P = 0.011), cAMP/spermidine (r = -0.813, P = 0.001) and cAMP/spermine (r = -0.747, P = 0.003) ratios. Abnormal LVEF patients showed decreased ODC activity and spermidine, and increased N(1) -acetylspermidine, and cAMP. Spermine decreased in congestive heart failure patients. The trace amine isoamylamine negatively correlated with septal wall thickness (r = -0.634, P = 0.008) and was increased in cardiac heart failure. The results indicated that modifications in polyamine homeostasis might be associated with cardiac function and remodelling. Increased cAMP might have a deleterious effect on function. Further studies should confirm these findings and the involvement of

  13. ANALYSIS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE WITH CLINICAL PARAMETERS, ECG AND ECHO

    Directory of Open Access Journals (Sweden)

    Satish

    2014-10-01

    Full Text Available BACK GROUND: Chronic obstructive pulmonary disease (COPD is a leading cause of morbidity and mortality in countries of high, middle, and low income. Estimates from WHO’s Global Burden of Disease and Risk Factors project show that in 2001, COPD was the fifth leading cause of death in high-income countries, accounting for 3.8% of total deaths, and it was the sixth leading cause of death in nations of low and middle income, accounting for 4·9% of total deaths. OBJECTIVES: 1. To study clinical parameters of chronic obstructive pulmonary disease. 2. To find out Electrocardiographic changes of chronic obstructive pulmonary disease. 3. To confirm with echocardiogram the presence of pulmonary hypertension, tricuspid regurgitation and right heart failure and analyze the incidence of right heart failure and pulmonary hypertension. MATERIALS AND METHODS: Single center hospital based cross sectional study. Patients diagnosed as COPD based on following steps will be included in the study. The patients with cough, sputum production, dyspnoea (wheeze was chosen (sputum AFB negative will be confirmed. Pulmonary function test was done to pick up patients with reduced FEV9 mm, as this is the one of the indication for life long oxygen therapy as per American Thoracic Society (ATS. Out of 72 patients, 12 had coronary artery disease (CAHD as this increases the incidence of cor-pulmonale. CARDIOVASCULAR COMPLICATIONS: Out of 72 patients, 24% developed pulmonary hypertension, 22% developed tricuspid regurgitation, 34% had p-pulmonale, 18% had p-wave amplitude in lead-II + lead-III + lead a VF >9 mm, this is important because this is one of the indication for life long oxygen therapy. 18% had concomitant coronary artery disease (CAHD, this observation is important because systemic inflammation plays enhanced role in atherosclerosis, diabetes mellitus, tumour necrosis factor is increased in COPD patients. CONCLUSION: Pulmonary hypertension was the most common

  14. Genetic parameters for milk mineral content and acidity predicted by mid-infrared spectroscopy in Holstein-Friesian cows.

    Science.gov (United States)

    Toffanin, V; Penasa, M; McParland, S; Berry, D P; Cassandro, M; De Marchi, M

    2015-05-01

    The aim of the present study was to estimate genetic parameters for calcium (Ca), phosphorus (P) and titratable acidity (TA) in bovine milk predicted by mid-IR spectroscopy (MIRS). Data consisted of 2458 Italian Holstein-Friesian cows sampled once in 220 farms. Information per sample on protein and fat percentage, pH and somatic cell count, as well as test-day milk yield, was also available. (Co)variance components were estimated using univariate and bivariate animal linear mixed models. Fixed effects considered in the analyses were herd of sampling, parity, lactation stage and a two-way interaction between parity and lactation stage; an additive genetic and residual term were included in the models as random effects. Estimates of heritability for Ca, P and TA were 0.10, 0.12 and 0.26, respectively. Positive moderate to strong phenotypic correlations (0.33 to 0.82) existed between Ca, P and TA, whereas phenotypic weak to moderate correlations (0.00 to 0.45) existed between these traits with both milk quality and yield. Moderate to strong genetic correlations (0.28 to 0.92) existed between Ca, P and TA, and between these predicted traits with both fat and protein percentage (0.35 to 0.91). The existence of heritable genetic variation for Ca, P and TA, coupled with the potential to predict these components for routine cow milk testing, imply that genetic gain in these traits is indeed possible.

  15. Uncertainty quantification for acoustic nonlinearity parameter in Lamb wave-based prediction of barely visible impact damage in composites

    Science.gov (United States)

    Hong, Ming; Mao, Zhu; Todd, Michael D.; Su, Zhongqing

    2017-01-01

    Nonlinear features extracted from Lamb wave signals (e.g., second harmonic generation) are demonstrably sensitive to microscopic damage, such as fatigue and material thermal degradation. While a majority of the existing studies in this context is focused on detecting undersized damage in metallic materials, the present study is aimed at expanding such a detection philosophy to the domain of composites, by linking the relative acoustic nonlinearity parameter (RANP) - a prominent nonlinear signal feature of Lamb waves - to barely visible impact damage (BVID) in composites. Nevertheless, considering immense uncertainties inevitably embedded in acquired signals (due to instrumentation, environment, operation, computation/estimation, etc.) which can adversely obfuscate nonlinear features, it is necessary to quantify the uncertainty of the RANP (i.e., its statistics) in order to enhance decision-making associated with its use as a detection feature. A probabilistic model is established to numerically evaluate the statistical distribution of the RANP. Using piezoelectric wafers, Lamb waves are acquired and processed to produce histograms of RANP estimates in both the healthy and damaged conditions of a CF/EP laminate, to which the model is compared, with good agreement observed between the model-predicted and experimentally-obtained statistic distributions of the RANP. With the model, BVID in the laminate is predicted. The model is further made use of to quantify the level of confidence in damage prediction results based on the concept of a receiver operating characteristic, enabling the practitioners to better understand the obtained results in the presence of uncertainties.

  16. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    Energy Technology Data Exchange (ETDEWEB)

    Fagedet, Dorothee, E-mail: DFagedet@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de medecine interne, Pole Pluridisciplinaire de Medecine (France); Thony, Frederic, E-mail: FThony@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Timsit, Jean-Francois, E-mail: JFTimsit@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de reanimation, Pole Medecine Aiguee Communautaire (France); Rodiere, Mathieu, E-mail: MRodiere@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Monnin-Bares, Valerie, E-mail: v-monnin@chu-montpellier.fr [CHRU Arnaud de Villeneuve, Imagerie Medicale Thoracique Cardiovasculaire (France); Ferretti, Gilbert R., E-mail: GFerretti@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Vesin, Aurelien; Moro-Sibilot, Denis, E-mail: DMoro.pneumo@chu-grenoble.fr [University Grenoble 1 e Albert Bonniot Institute, Inserm U823 (France)

    2013-02-15

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  17. Modeling and Predicting AD Progression by Regression Analysis of Sequential Clinical Data

    KAUST Repository

    Xie, Qing

    2016-02-23

    Alzheimer\\'s Disease (AD) is currently attracting much attention in elders\\' care. As the increasing availability of massive clinical diagnosis data, especially the medical images of brain scan, it is highly significant to precisely identify and predict the potential AD\\'s progression based on the knowledge in the diagnosis data. In this paper, we follow a novel sequential learning framework to model the disease progression for AD patients\\' care. Different from the conventional approaches using only initial or static diagnosis data to model the disease progression for different durations, we design a score-involved approach and make use of the sequential diagnosis information in different disease stages to jointly simulate the disease progression. The actual clinical scores are utilized in progress to make the prediction more pertinent and reliable. We examined our approach by extensive experiments on the clinical data provided by the Alzheimer\\'s Disease Neuroimaging Initiative (ADNI). The results indicate that the proposed approach is more effective to simulate and predict the disease progression compared with the existing methods.

  18. Predictive factors of rapidly progressive-interstitial lung disease in patients with clinically amyopathic dermatomyositis.

    Science.gov (United States)

    Xu, Y; Yang, C S; Li, Y J; Liu, X D; Wang, J N; Zhao, Q; Xiao, W G; Yang, P T

    2016-01-01

    Clinically amyopathic dermatomyositis (CADM) is a unique subset of dermatomyositis, showing a high incidence of lung involvements. The aim of this study is to identify risk factors, other than melanoma differentiation-associated protein (MDA)-5, for developing rapidly progressive-interstitial lung disease (RP-ILD) in patients with CADM. Forty CADM patients, in whom 11 patients developed RP-ILD, were enrolled. Clinical features and laboratory findings were compared between the patients with and without RP-ILD. We found that skin ulceration, CRP, serum ferritin, anti-MDA5 Ab, and lymphocytopenia were significantly associated with ILD. Multivariate logistic regression analysis indicated that anti-MDA5 Ab(+), elevated CRP, and decreased counts of lymphocyte were independent risk factors for RP-ILD, which can provide a precise predict for RP-ILD in CADM patients. When anti-MDA5 Ab(+) was removed from the multivariate regression model, using skin ulcerations, elevated serum ferritin and decreased counts of lymphocyte can also precisely predict RP-ILD. Except for MDA-5, more commonly available clinical characteristics, such as skin ulcerations, serum ferritin, and count of lymphocyte may also help to predict prognosis in CADM.

  19. Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes.

    Science.gov (United States)

    Perea, J Darío; Langner, Stefan; Salvador, Michael; Kontos, Janos; Jarvas, Gabor; Winkler, Florian; Machui, Florian; Görling, Andreas; Dallos, Andras; Ameri, Tayebeh; Brabec, Christoph J

    2016-05-19

    The solubility of organic semiconductors in environmentally benign solvents is an important prerequisite for the widespread adoption of organic electronic appliances. Solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP). We report a numerical approach to determine the HSP of fullerenes using a mathematical tool based on artificial neural networks (ANN). ANN transforms the molecular surface charge density distribution (σ-profile) as determined by density functional theory (DFT) calculations within the framework of a continuum solvation model into solubility parameters. We validate our model with experimentally determined HSP of the fullerenes C60, PC61BM, bisPC61BM, ICMA, ICBA, and PC71BM and through comparison with previously reported molecular dynamics calculations. Most excitingly, the ANN is able to correctly predict the dispersive contributions to the solubility parameters of the fullerenes although no explicit information on the van der Waals forces is present in the σ-profile. The presented theoretical DFT calculation in combination with the ANN mathematical tool can be easily extended to other π-conjugated, electronic material classes and offers a fast and reliable toolbox for future pathways that may include the design of green ink formulations for solution-processed optoelectronic devices.

  20. Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints

    Directory of Open Access Journals (Sweden)

    K. Anand

    2015-09-01

    Full Text Available The present study focuses on friction welding process parameter optimization using a hybrid technique of ANN and different optimization algorithms. This optimization techniques are not only for the effective process modelling, but also to illustrate the correlation between the input and output responses of the friction welding of Incoloy 800H. In addition the focus is also to obtain optimal strength and hardness of joints with minimum burn off length. ANN based approaches could model this welding process of INCOLOY 800H in both forward and reverse directions efficiently, which are required for the automation of the same. Five different training algorithms were used to train ANN for both forward and reverse mapping and ANN tuned force approach was used for optimization. The paper makes a robust comparison of the performances of the five algorithms employing standard statistical indices. The results showed that GANN with 4-9-3 for forward and 4-7-3 for reverse mapping arrangement could outperform the other four approaches in most of the cases but not in all. Experiments on tensile strength (TS, microhardness (H and burn off length (BOL of the joints were performed with optimised parameter. It is concluded that this ANN model with genetic algorithm may provide good ability to predict the friction welding process parameters to weld Incoloy 800H.

  1. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

    Directory of Open Access Journals (Sweden)

    Aurea Lima

    2015-06-01

    Full Text Available Background: Methotrexate (MTX is widely used for rheumatoid arthritis (RA treatment. Single nucleotide polymorphisms (SNPs could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

  2. Prediction of peak pressure from clinical and radiological measurements in patients with diabetes

    Directory of Open Access Journals (Sweden)

    Nieman Fred HM

    2008-12-01

    Full Text Available Abstract Background Various structural and functional factors of foot function have been associated with high local plantar pressures. The therapist focuses on these features which are thought to be responsible for plantar ulceration in patients with diabetes. Risk assessment of the diabetic foot would be made easier if locally elevated plantar pressure could be indicated with a minimum set of clinical measures. Methods Ninety three patients were evaluated through vascular, orthopaedic, neurological and radiological assessment. A pressure platform was used to quantify the barefoot peak pressure for six forefoot regions: big toe (BT and metatarsals one (MT-1 to five (MT-5. Stepwise regression modelling was performed to determine which set of the clinical and radiological measures explained most variability in local barefoot plantar peak pressure in each of the six forefoot regions. Comprehensive models were computed with independent variables from the clinical and radiological measurements. The difference between the actual plantar pressure and the predicted value was examined through Bland-Altman analysis. Results Forefoot pressures were significant higher in patients with neuropathy, compared to patients without neuropathy for the whole forefoot, the MT-1 region and the MT-5 region (respectively 138 kPa, 173 kPa and 88 kPa higher: mean difference. The clinical models explained up to 39 percent of the variance in local peak pressures. Callus formation and toe deformity were identified as relevant clinical predictors for all forefoot regions. Regression models with radiological variables explained about 26 percent of the variance in local peak pressures. For most regions the combination of clinical and radiological variables resulted in a higher explained variance. The Bland and Altman analysis showed a major discrepancy between the predicted and the actual peak pressure values. Conclusion At best, clinical and radiological measurements could

  3. Association Between a Composite Score of Pain Sensitivity and Clinical Parameters in Low-Back Pain

    DEFF Research Database (Denmark)

    O'Neill, Søren; Manniche, Claus; Graven-Nielsen, Thomas;

    2014-01-01

    A limited number of quantitative sensory pain tests (QST) were selected on the basis of ease of application and interpretation in a clinical setting. QST results were summarized as a composite score on a scale of zero to four which was deemed to facilitate clinical interpretation. The QST set...

  4. INFLUENCE OF PHYSIOTHERAPY ON CLINICAL AND IMMUNOLOGICAL PARAMETERS IN CHILDREN WITH JUVENILE RHEUMATOID ARTHRITIS

    OpenAIRE

    T.L. Nastausheva; L.T. Dmitrieva

    2008-01-01

    Clinical and immunological status has been evaluated in 85 children with juvenile rheumatoid arthritis (RA) before and after physiotherapeutic procedures: electrophoresis with dimexid and magnetotherapy. The control group of 31 children did not follow physiotherapeutic procedures. The following results were fixed: clinical indices and immunological status of children with juvenile rheumatoid arthritis have been changed in a larger degree in case of magnetotherapy.

  5. A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction

    Science.gov (United States)

    Rahmim, Arman; Schmidtlein, C. Ross; Jackson, Andrew; Sheikhbahaei, Sara; Marcus, Charles; Ashrafinia, Saeed; Soltani, Madjid; Subramaniam, Rathan M.

    2016-01-01

    Oncologic PET images provide valuable information that can enable enhanced prognosis of disease. Nonetheless, such information is simplified significantly in routine clinical assessment to meet workflow constraints. Examples of typical FDG PET metrics include: (i) SUVmax, (2) total lesion glycolysis (TLG), and (3) metabolic tumor volume (MTV). We have derived and implemented a novel metric for tumor quantification, inspired in essence by a model of generalized equivalent uniform dose as used in radiation therapy. The proposed metric, denoted generalized effective total uptake (gETU), is attractive as it encompasses the abovementioned commonly invoked metrics, and generalizes them, for both homogeneous and heterogeneous tumors, using a single parameter a. We evaluated this new metric for improved overall survival (OS) prediction on two different baseline FDG PET/CT datasets: (a) 113 patients with squamous cell cancer of the oropharynx, and (b) 72 patients with locally advanced pancreatic adenocarcinoma. Kaplan-Meier survival analysis was performed, where the subjects were subdivided into two groups using the median threshold, from which the hazard ratios (HR) were computed in Cox proportional hazards regression. For the oropharyngeal cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak produced HR values of 1.86, 3.02, 1.34, 1.36 and 1.62, while the proposed gETU metric for a  = 0.25 (greater emphasis on volume information) enabled significantly enhanced OS prediction with HR  =  3.94. For the pancreatic cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak resulted in HR values of 1.05, 1.25, 1.42, 1.45 and 1.52, while gETU at a  = 3.2 (greater emphasis on SUV information) arrived at an improved HR value of 1.61. Overall, the proposed methodology allows placement of differing degrees of emphasis on tumor volume versus uptake for different types of tumors to enable enhanced clinical outcome prediction.

  6. Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

    Science.gov (United States)

    Vukicevic, Arso M; Stojadinovic, Miroslav; Radovic, Milos; Djordjevic, Milena; Cirkovic, Bojana Andjelkovic; Pejovic, Tomislav; Jovicic, Gordana; Filipovic, Nenad

    2016-08-01

    Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems.

  7. Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

    Science.gov (United States)

    Benson, M

    2016-03-01

    Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide